{"type":"user_prompt","text":"Applique à immo-ka la passe de design « éditorial sharp » déjà livrée sur lou-ka\n(commit 872a202 du repo lou-ka), pour en faire la plus belle fiche possible.\nUtilise Playwright pour vérifier le rendu desktop ET smartphone.\n\nCONTEXTE\n- Tu es sur M4M64a, repo de prod : ~/apps/immo-ka (React+Vite frontend, backend\n  Python, PM2). Source de vérité = ce repo ; remote origin = spbgit (gitsrv),\n  PAS GitHub.\n- Les deux apps partagent le design system Groupe KA (ka/tokens.css : --ink,\n  --surface, --line, --r-card/--r-ctl/--r-pill, --shadow-off-soft, --accent +\n  --accent-soft/--accent-deep, --font-display/--font-mono). Immo-ka a son\n  propre accent — utilise TOUJOURS les variables, jamais le orange #ff6a00 de\n  lou-ka en dur.\n\nRÉFÉRENCE À RÉCUPÉRER (SSH inter-nœuds bloqué, mais gitsrv est accessible)\n  git clone --depth 5 gitsrv:srv/git/lou-ka.git /tmp/lou-ka-ref\n  → lire la section finale de frontend/src/styles.css :\n  « PASSE DE DESIGN v3 — éditorial sharp » (ajoutée par 872a202, ~111 lignes).\n\nCE QUE FAIT CETTE PASSE (à transposer aux sélecteurs d'immo-ka, pas à copier\naveuglément) :\n1. Blocs de la fiche = cartes : fond --surface, bordure 1,5 px --ink, rayon\n   --r-card, ombre --shadow-off-soft, padding 18/20 (15 en mobile ≤640px).\n   EXCLURE la galerie photos et le bloc héro prix (ils restent posés sur le\n   papier).\n2. Titres de section (h2) : display flex + carré accent 9px (::before, bordure\n   encre + ombre dure 2px 2px 0 var(--ink)) + filet éditorial (::after, 1,5px\n   --line, flex 1).\n3. Tuiles KPI : fond blanc, bordure 1,5 px --ink, rayon --r-ctl, ombre dure\n   3px 3px 0 var(--accent-soft) ; valeurs en --font-display.\n4. Tableaux : zébrure tbody tr:nth-child(even) → --surface-2, survol →\n   --accent-soft, en-têtes th en --font-mono 10px uppercase + border-bottom\n   1,5 px --ink.\n5. Barres de progression/proximité : piste --surface-2 bordée 1px --line,\n   remplissage en dégradé linear-gradient(90deg, var(--accent),\n   var(--accent-deep)).\n6. Pastilles d'état : inline-flex, --font-mono 10.5px uppercase, bordure\n   1,5 px currentColor, point d'état 6px en ::before (background currentColor).\n7. Notes de bas de bloc (.fine ou équivalent) : border-top 1px dashed --line.\n8. Listes d'items riches (comparables, historiques) : mini-cartes — bordure\n   --line, liseré gauche 3px var(--accent), rayon --r-ctl, fond --surface-2.\n\nMÉTHODE\n1. Repérer d'abord les classes réelles d'immo-ka (page fiche + accueil) :\n   lire les composants et styles.css, faire des captures Playwright AVANT\n   (desktop 1440×900 + mobile 390×844, fullPage + par bloc).\n2. Ajouter la passe en SECTION FINALE de styles.css (commentaire d'en-tête\n   « PASSE DE DESIGN — éditorial sharp »), en adaptant les sélecteurs.\n3. RÈGLE ABSOLUE Groupe Ka : ordre DOM = ordre visuel sur tous les\n   breakpoints. AUCUNE propriété `order`, ni column-reverse, ni\n   display:contents. Valider avec node frontend/scripts/check-order.mjs <uid>\n   (le script existe dans immo-ka).\n4. Vérifier avec Playwright APRÈS : desktop + mobile, zéro débordement\n   horizontal (scrollWidth - clientWidth === 0), lisibilité des blocs un par\n   un. Itérer si un bloc rend mal.\n5. Livrer : cd frontend && npm run build, pm2 restart <processus immo-ka>,\n   healthcheck (port local + site public), puis git add/commit/push origin\n   main. Message de commit : feat(design): passe « éditorial sharp » sur la\n   fiche — cartes, KPI, tableaux, pastilles.","ts":1787604944858}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNEwSB5JdWMxMavCtVFb","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2324,"cache_read_input_tokens":39201,"cache_creation":{"ephemeral_5m_input_tokens":2324,"ephemeral_1h_input_tokens":0},"output_tokens":7,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f5c4ed21-46bb-4b96-ab32-dc4c3c4a420b"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNEwSB5JdWMxMavCtVFb","type":"message","role":"assistant","content":[{"type":"text","text":"J'ai la référence lou-ka (section v3, ~111 lignes). Maintenant j'explore la fiche immo-ka pour repérer les classes réelles."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2324,"cache_read_input_tokens":39201,"cache_creation":{"ephemeral_5m_input_tokens":2324,"ephemeral_1h_input_tokens":0},"output_tokens":7,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"1891e26d-ad0f-4ded-a952-5445ece02eb6"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNEwSB5JdWMxMavCtVFb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SPW7jVD64mAW6i7USQyxac","name":"Bash","input":{"command":"pm2 describe immo-ka-web 2>/dev/null | grep -E \"script|cwd|args\" ; grep -rn \"PORT\\|port\" /Users/simon-pierreboucher/apps/immo-ka/run.py 2>/dev/null | head -5","description":"Find immo-ka web port"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2324,"cache_read_input_tokens":39201,"cache_creation":{"ephemeral_5m_input_tokens":2324,"ephemeral_1h_input_tokens":0},"output_tokens":7,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f0158792-735a-4eb5-8bb0-ccd94e18e462"}}
{"type":"permission_request","requestId":"5a9f19f24430a703","tool":"Bash","input":{"command":"pm2 describe immo-ka-web 2>/dev/null | grep -E \"script|cwd|args\" ; grep -rn \"PORT\\|port\" /Users/simon-pierreboucher/apps/immo-ka/run.py 2>/dev/null | head -5","description":"Find immo-ka web port"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNEwSB5JdWMxMavCtVFb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0154PyRJuH5HFpcqnjZTG8Zk","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/pages/Listing.tsx"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2324,"cache_read_input_tokens":39201,"cache_creation":{"ephemeral_5m_input_tokens":2324,"ephemeral_1h_input_tokens":0},"output_tokens":7,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"0f07bba3-c5f6-4c44-95c1-1f75274a0bab"}}
{"type":"permission_resolved","requestId":"5a9f19f24430a703","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SPW7jVD64mAW6i7USQyxac","type":"tool_result","content":"│ script path       │ /Users/simon-pierreboucher/apps/immo-ka/.venv/bin/python   │\n│ script args       │ run.py serve 8096                                          │\n│ interpreter args  │ N/A                                                        │\n│ script id         │ 9                                                          │\n│ exec cwd          │ /Users/simon-pierreboucher/apps/immo-ka                    │\n/Users/simon-pierreboucher/apps/immo-ka/run.py:10:    python run.py serve [port]          # démarre l'API + le frontend (défaut 8090)\n/Users/simon-pierreboucher/apps/immo-ka/run.py:16:from __future__ import annotations\n/Users/simon-pierreboucher/apps/immo-ka/run.py:18:import os\n/Users/simon-pierreboucher/apps/immo-ka/run.py:19:import sys\n/Users/simon-pierreboucher/apps/immo-ka/run.py:20:from pathlib import Path","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"5c873ace-8793-4711-bc37-d53ecbb0eaa6","timestamp":"2026-08-24T20:57:28.758Z","tool_use_result":{"stdout":"│ script path       │ /Users/simon-pierreboucher/apps/immo-ka/.venv/bin/python   │\n│ script args       │ run.py serve 8096                                          │\n│ interpreter args  │ N/A                                                        │\n│ script id         │ 9                                                          │\n│ exec cwd          │ /Users/simon-pierreboucher/apps/immo-ka                    │\n/Users/simon-pierreboucher/apps/immo-ka/run.py:10:    python run.py serve [port]          # démarre l'API + le frontend (défaut 8090)\n/Users/simon-pierreboucher/apps/immo-ka/run.py:16:from __future__ import annotations\n/Users/simon-pierreboucher/apps/immo-ka/run.py:18:import os\n/Users/simon-pierreboucher/apps/immo-ka/run.py:19:import sys\n/Users/simon-pierreboucher/apps/immo-ka/run.py:20:from pathlib import Path","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0154PyRJuH5HFpcqnjZTG8Zk","type":"tool_result","content":"1\t// -----------------------------------------------------------------------------\n2\t// Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n3\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n4\t// pages/Listing.tsx : fiche complète d'une propriété\n5\t//   galerie + lightbox · specs · caractéristiques (details) · pièces ·\n6\t//   inclusions · description · historique de prix · courtier · mini-carte\n7\t// -----------------------------------------------------------------------------\n8\timport { Suspense, lazy, useEffect, useRef, useState } from \"react\";\n9\timport { Link, useParams } from \"react-router-dom\";\n10\timport {\n11\t  Listing, Room, fetchListing, fetchSources, fmtArea, fmtDate, fmtPrice,\n12\t  registerSourceNames, sourceName,\n13\t} from \"../api\";\n14\t\n15\tconst PropertyMap = lazy(() => import(\"../components/PropertyMap\"));\n16\timport QuartierBlock from \"../components/QuartierBlock\";\n17\timport RisqueInondation from \"../components/RisqueInondation\";\n18\timport QualiteAir from \"../components/QualiteAir\";\n19\timport EssenceProche from \"../components/EssenceProche\";\n20\timport HydroEstimation from \"../components/HydroEstimation\";\n21\timport CommercesProches from \"../components/CommercesProches\";\n22\timport { Ico } from \"../components/Icons\";\n23\timport { TypeFallback } from \"../components/PropertyImg\";\n24\t\n25\t// --- Lightbox : pincement pour zoomer + panoramique + balayage entre photos ---\n26\tfunction ZoomImg({ src, onSwipe }: { src: string; onSwipe: (dir: 1 | -1) => void }) {\n27\t  const [t, setT] = useState({ scale: 1, x: 0, y: 0 });\n28\t  const pointers = useRef(new Map<number, { x: number; y: number }>());\n29\t  const start = useRef({ scale: 1, x: 0, y: 0, dist: 0, cx: 0, cy: 0, t: 0 });\n30\t  const lastTap = useRef(0);\n31\t\n32\t  // repartir à zéro quand on change de photo\n33\t  useEffect(() => { setT({ scale: 1, x: 0, y: 0 }); }, [src]);\n34\t\n35\t  const dist = () => {\n36\t    const p = [...pointers.current.values()];\n37\t    return p.length < 2 ? 0 : Math.hypot(p[0].x - p[1].x, p[0].y - p[1].y);\n38\t  };\n39\t  const center = () => {\n40\t    const p = [...pointers.current.values()];\n41\t    return p.length < 2\n42\t      ? p[0] ?? { x: 0, y: 0 }\n43\t      : { x: (p[0].x + p[1].x) / 2, y: (p[0].y + p[1].y) / 2 };\n44\t  };\n45\t\n46\t  const onDown = (e: React.PointerEvent) => {\n47\t    (e.target as HTMLElement).setPointerCapture(e.pointerId);\n48\t    pointers.current.set(e.pointerId, { x: e.clientX, y: e.clientY });\n49\t    const c = center();\n50\t    start.current = { scale: t.scale, x: t.x, y: t.y, dist: dist(), cx: c.x, cy: c.y, t: Date.now() };\n51\t  };\n52\t  const onMove = (e: React.PointerEvent) => {\n53\t    if (!pointers.current.has(e.pointerId)) return;\n54\t    pointers.current.set(e.pointerId, { x: e.clientX, y: e.clientY });\n55\t    const s = start.current;\n56\t    if (pointers.current.size >= 2 && s.dist > 0) {\n57\t      // pincement : zoom autour du centre des deux doigts\n58\t      const scale = Math.min(4, Math.max(1, (dist() / s.dist) * s.scale));\n59\t      const c = center();\n60\t      setT({ scale, x: s.x + (c.x - s.cx), y: s.y + (c.y - s.cy) });\n61\t    } else if (pointers.current.size === 1 && t.scale > 1) {\n62\t      // panoramique une fois zoomé\n63\t      const p = pointers.current.get(e.pointerId)!;\n64\t      setT({ scale: t.scale, x: s.x + (p.x - s.cx), y: s.y + (p.y - s.cy) });\n65\t    }\n66\t  };\n67\t  const onUp = (e: React.PointerEvent) => {\n68\t    const p = pointers.current.get(e.pointerId);\n69\t    pointers.current.delete(e.pointerId);\n70\t    const s = start.current;\n71\t    if (pointers.current.size === 0 && p) {\n72\t      const dx = p.x - s.cx, dy = p.y - s.cy, dt = Date.now() - s.t;\n73\t      if (t.scale <= 1.05 && Math.abs(dx) > 56 && Math.abs(dx) > Math.abs(dy) * 1.5) {\n74\t        onSwipe(dx < 0 ? 1 : -1);                       // balayage → photo suivante\n75\t      } else if (dt < 260 && Math.abs(dx) < 8 && Math.abs(dy) < 8) {\n76\t        const now = Date.now();\n77\t        if (now - lastTap.current < 320)                 // double-tap : zoom ×2.4\n78\t          setT(t.scale > 1 ? { scale: 1, x: 0, y: 0 } : { scale: 2.4, x: 0, y: 0 });\n79\t        lastTap.current = now;\n80\t      }\n81\t      if (t.scale <= 1.02) setT({ scale: 1, x: 0, y: 0 });\n82\t    }\n83\t  };\n84\t\n85\t  return (\n86\t    <img\n87\t      src={src} alt=\"\" draggable={false}\n88\t      style={{\n89\t        transform: `translate(${t.x}px, ${t.y}px) scale(${t.scale})`,\n90\t        transition: pointers.current.size ? \"none\" : \"transform 0.15s ease\",\n91\t        touchAction: \"none\", cursor: t.scale > 1 ? \"grab\" : \"zoom-out\",\n92\t      }}\n93\t      onClick={(e) => e.stopPropagation()}\n94\t      onPointerDown={onDown} onPointerMove={onMove}\n95\t      onPointerUp={onUp} onPointerCancel={onUp}\n96\t    />\n97\t  );\n98\t}\n99\t\n100\t// --- Galerie : balayage natif (scroll-snap) + vignettes + plein écran --------\n101\tfunction Galerie({ images, captions, titre, type }:\n102\t  { images: string[]; captions?: string[]; titre: string; type?: string }) {\n103\t  const [idx, setIdx] = useState(0);\n104\t  const [zoom, setZoom] = useState(false);\n105\t  const [dead, setDead] = useState<Set<string>>(new Set());\n106\t  const track = useRef<HTMLDivElement>(null);\n107\t\n108\t  // images qui ne chargent pas : retirées de la galerie à la volée (jamais\n109\t  // d'icône d'image cassée) ; légendes gardées alignées\n110\t  const alive = images\n111\t    .map((u, i) => ({ u, cap: captions && captions.length === images.length ? captions[i] : \"\" }))\n112\t    .filter(({ u }) => !dead.has(u));\n113\t  const markDead = (u: string) => setDead((d) => new Set(d).add(u));\n114\t\n115\t  const onScroll = () => {\n116\t    const el = track.current;\n117\t    if (el) setIdx(Math.round(el.scrollLeft / el.clientWidth));\n118\t  };\n119\t  const goto = (i: number) =>\n120\t    track.current?.scrollTo({ left: i * track.current.clientWidth, behavior: \"smooth\" });\n121\t\n122\t  useEffect(() => {\n123\t    if (!zoom) return;\n124\t    const onKey = (e: KeyboardEvent) => {\n125\t      if (e.key === \"Escape\") setZoom(false);\n126\t      if (e.key === \"ArrowLeft\") setIdx((i) => Math.max(0, i - 1));\n127\t      if (e.key === \"ArrowRight\") setIdx((i) => Math.min(alive.length - 1, i + 1));\n128\t    };\n129\t    window.addEventListener(\"keydown\", onKey);\n130\t    // figer l'arrière-plan pendant le plein écran (mobile)\n131\t    document.body.style.overflow = \"hidden\";\n132\t    return () => {\n133\t      window.removeEventListener(\"keydown\", onKey);\n134\t      document.body.style.overflow = \"\";\n135\t    };\n136\t  }, [zoom, alive.length]);\n137\t\n138\t  if (alive.length === 0)\n139\t    return <div className=\"carousel\"><div className=\"carousel-empty\"><TypeFallback type={type} /></div></div>;\n140\t\n141\t  const cur = Math.min(idx, alive.length - 1);\n142\t  const swipe = (dir: 1 | -1) =>\n143\t    setIdx((i) => Math.min(alive.length - 1, Math.max(0, i + dir)));\n144\t\n145\t  return (\n146\t    <>\n147\t      <div className=\"carousel\">\n148\t        <div className=\"carousel-track\" ref={track} onScroll={onScroll}>\n149\t          {alive.map(({ u }, i) => (\n150\t            <img key={u} src={u} loading={i <= 1 ? \"eager\" : \"lazy\"} decoding=\"async\"\n151\t              alt={`${titre} — photo ${i + 1} de ${alive.length}`}\n152\t              onError={() => markDead(u)} onClick={() => setZoom(true)} />\n153\t          ))}\n154\t        </div>\n155\t        {alive[cur]?.cap && <span className=\"carousel-caption\">{alive[cur].cap}</span>}\n156\t        <span className=\"carousel-count\" aria-live=\"polite\">{cur + 1}/{alive.length}</span>\n157\t        {cur > 0 && <button className=\"carousel-nav prev\" aria-label=\"Photo précédente\" onClick={() => goto(cur - 1)}>‹</button>}\n158\t        {cur < alive.length - 1 && <button className=\"carousel-nav next\" aria-label=\"Photo suivante\" onClick={() => goto(cur + 1)}>›</button>}\n159\t      </div>\n160\t      {alive.length > 1 && (\n161\t        <div className=\"thumbs\">\n162\t          {alive.map(({ u }, i) => (\n163\t            <button key={u} className={i === cur ? \"on\" : \"\"} onClick={() => goto(i)} aria-label={`Photo ${i + 1}`}>\n164\t              <img src={u} alt=\"\" loading=\"lazy\" decoding=\"async\" onError={() => markDead(u)} />\n165\t            </button>\n166\t          ))}\n167\t        </div>\n168\t      )}\n169\t      {zoom && (\n170\t        <div className=\"lightbox\" onClick={() => setZoom(false)} role=\"dialog\" aria-label=\"Photo agrandie\">\n171\t          <button className=\"lb-close\" aria-label=\"Fermer\" onClick={() => setZoom(false)}>✕</button>\n172\t          {cur > 0 && <button className=\"lb-nav prev\" aria-label=\"Précédente\" onClick={(e) => { e.stopPropagation(); setIdx(cur - 1); }}>‹</button>}\n173\t          <ZoomImg src={alive[cur].u} onSwipe={swipe} />\n174\t          {cur < alive.length - 1 && <button className=\"lb-nav next\" aria-label=\"Suivante\" onClick={(e) => { e.stopPropagation(); setIdx(cur + 1); }}>›</button>}\n175\t          <span className=\"lb-count\">\n176\t            {alive[cur]?.cap ? `${alive[cur].cap} · ` : \"\"}{cur + 1} / {alive.length}\n177\t          </span>\n178\t        </div>\n179\t      )}\n180\t    </>\n181\t  );\n182\t}\n183\t\n184\t// clés techniques de `details` jamais montrées dans « Caractéristiques »\n185\tconst DETAIL_HIDDEN = new Set([\n186\t  \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n187\t  \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n188\t  \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n189\t]);\n190\t\n191\t// icône de chaque tuile « spec » (Icons.tsx)\n192\tconst SPEC_ICONS: Record<string, string> = {\n193\t  \"Type\": \"home\", \"Chambres\": \"bed\", \"Salles de bain\": \"bath\",\n194\t  \"Salles d'eau\": \"drop\", \"Superficie\": \"area\", \"Terrain\": \"land\",\n195\t  \"Année\": \"calendar\", \"MLS / Centris\": \"tag\",\n196\t};\n197\t\n198\texport default function ListingPage() {\n199\t  const { uid } = useParams<{ uid: string }>();\n200\t  const [l, setL] = useState<Listing | null>(null);\n201\t  const [error, setError] = useState<string | null>(null);\n202\t\n203\t  useEffect(() => {\n204\t    fetchSources().then((r) => registerSourceNames(r.sources)).catch(() => {});\n205\t    if (!uid) return;\n206\t    setL(null); setError(null);\n207\t    fetchListing(uid).then(setL).catch((e) => setError(String(e)));\n208\t    window.scrollTo(0, 0);\n209\t  }, [uid]);\n210\t\n211\t  if (error)\n212\t    return (\n213\t      <div className=\"notice container\">\n214\t        <div className=\"big\"><Ico name=\"alert\" size={44} /></div>\n215\t        <h2>Propriété introuvable</h2>\n216\t        <p>{error}</p>\n217\t        <Link className=\"btn btn-primary\" to=\"/\">Retour aux propriétés</Link>\n218\t      </div>\n219\t    );\n220\t\n221\t  if (!l)\n222\t    return (\n223\t      <div className=\"container detail\">\n224\t        <div className=\"fiche\" aria-busy=\"true\">\n225\t          <div className=\"skel\"><div className=\"sk-img\" /></div>\n226\t          <div className=\"skel\"><div className=\"sk-line\" /><div className=\"sk-line\" /><div className=\"sk-line short\" /></div>\n227\t        </div>\n228\t      </div>\n229\t    );\n230\t\n231\t  const specs: { k: string; v: string }[] = [];\n232\t  if (l.property_type) specs.push({ k: \"Type\", v: l.property_type });\n233\t  if (l.bedrooms != null) specs.push({ k: \"Chambres\", v: String(l.bedrooms) });\n234\t  if (l.bathrooms != null) specs.push({ k: \"Salles de bain\", v: String(l.bathrooms) });\n235\t  if (l.powder_rooms != null) specs.push({ k: \"Salles d'eau\", v: String(l.powder_rooms) });\n236\t  if (l.area_sqft != null) specs.push({ k: \"Superficie\", v: fmtArea(l.area_sqft)! });\n237\t  if (l.lot_sqft != null) specs.push({ k: \"Terrain\", v: fmtArea(l.lot_sqft)! });\n238\t  if (l.year_built != null) specs.push({ k: \"Année\", v: String(l.year_built) });\n239\t  if (l.mls) specs.push({ k: \"MLS / Centris\", v: l.mls });\n240\t\n241\t  const rooms: Room[] = Array.isArray(l.details?.pieces) ? (l.details!.pieces as Room[]) : [];\n242\t  const detEntries = Object.entries(l.details ?? {})\n243\t    .filter(([k, v]) => !DETAIL_HIDDEN.has(k) && (typeof v === \"string\" || typeof v === \"number\") && String(v).trim());\n244\t\n245\t  const hist = (l.price_history ?? []).filter((h) => h.price != null);\n246\t  const baisse = hist.length >= 2 && hist[0].price !== hist[1].price\n247\t    ? { de: hist[1].price!, a: hist[0].price! } : null;\n248\t  const updated = l.updated_at ? fmtDate(l.updated_at) : null;\n249\t\n250\t  return (\n251\t    <div className=\"container detail\">\n252\t      <nav className=\"crumbs\" aria-label=\"Fil d'Ariane\">\n253\t        <Link to=\"/\">Propriétés</Link> ›\n254\t        {l.city && <span>{l.city}</span>} ›\n255\t        <span>{l.address || l.title}</span>\n256\t      </nav>\n257\t\n258\t      <div className=\"fiche\">\n259\t        {/* -------- colonne gauche : galerie, prix/synthèse, description, ------\n260\t             -------- caractéristiques, pièces — ordre du DOM = ordre visuel ---- */}\n261\t        <div className=\"f-col\">\n262\t          <section className=\"f-bloc f-galerie\" aria-label=\"Photos\">\n263\t            <Galerie\n264\t              images={l.images ?? []}\n265\t              captions={Array.isArray(l.details?.photo_captions)\n266\t                ? (l.details!.photo_captions as string[]) : undefined}\n267\t              titre={l.address || l.title}\n268\t              type={l.property_type}\n269\t            />\n270\t          </section>\n271\t\n272\t          <section className=\"f-bloc f-hero\">\n273\t            <div className=\"price-kicker\">\n274\t              {l.details?.transaction === \"location\" ? \"Loyer mensuel\" : \"Prix demandé\"}\n275\t            </div>\n276\t            <div className=\"price-row\">\n277\t              <div className=\"price\">\n278\t                {fmtPrice(l.price, l.price_label)}\n279\t                {l.details?.transaction === \"location\" && <span className=\"per-month\"> /mois</span>}\n280\t              </div>\n281\t              {l.property_type && (\n282\t                <span className=\"type-chip\">\n283\t                  <Ico name={SPEC_ICONS[\"Type\"]} size={13} /> {l.property_type}\n284\t                  {l.details?.transaction === \"location\" ? \" · location\" : \"\"}\n285\t                  {l.details?.price_from ? \" · à partir de\" : \"\"}\n286\t                </span>\n287\t              )}\n288\t            </div>\n289\t            {l.price != null && l.area_sqft != null && l.area_sqft > 200 && (\n290\t              <div className=\"price-sub\">{Math.round(l.price / l.area_sqft).toLocaleString(\"fr-CA\")} $ / pi² habitable</div>\n291\t            )}\n292\t            <h1>{l.address || l.title}</h1>\n293\t            <div className=\"loc\"><Ico name=\"pin\" size={13} /> {[l.sector, l.city, l.region].filter(Boolean).join(\" · \")}</div>\n294\t\n295\t            <div className=\"spec-list\">\n296\t              {specs.map((s) => (\n297\t                <div className=\"spec-row\" key={s.k}>\n298\t                  <span className=\"spec-badge\"><Ico name={SPEC_ICONS[s.k] ?? \"tag\"} size={15} /></span>\n299\t                  <span className=\"spec-k\">{s.k}</span>\n300\t                  <b className=\"spec-v\">{s.v}</b>\n301\t                </div>\n302\t              ))}\n303\t            </div>\n304\t\n305\t            {baisse && (\n306\t              <div className={`prix-histo ${baisse.a < baisse.de ? \"down\" : \"\"}`}>\n307\t                <Ico name={baisse.a < baisse.de ? \"trenddown\" : \"trendup\"} size={16} /> Prix passé de {fmtPrice(baisse.de)} à <b>{fmtPrice(baisse.a)}</b>\n308\t              </div>\n309\t            )}\n310\t\n311\t            {l.vraiprix && l.vraiprix.value != null && (\n312\t              <div className=\"vraiprix\">\n313\t                <div className=\"vp-head\">\n314\t                  <span className=\"vp-logo\">Vrai‑Prix</span>\n315\t                  {l.vraiprix.confidence && (\n316\t                    <span className={`vp-conf vp-conf-${l.vraiprix.confidence}`}>\n317\t                      confiance {l.vraiprix.confidence}\n318\t                    </span>\n319\t                  )}\n320\t                </div>\n321\t                <div className=\"vp-k\">Valeur marchande estimée</div>\n322\t                <div className=\"vp-value\">{fmtPrice(l.vraiprix.value)}</div>\n323\t                {l.vraiprix.low != null && l.vraiprix.high != null && (() => {\n324\t                  const lo = l.vraiprix!.low as number, hi = l.vraiprix!.high as number;\n325\t                  const span = Math.max(1, hi - lo);\n326\t                  const pos = (v: number) => `${Math.max(2, Math.min(98, ((v - lo) / span) * 100))}%`;\n327\t                  return (\n328\t                    <div className=\"vp-gauge\">\n329\t                      <div className=\"vp-gauge-track\">\n330\t                        <span className=\"vp-gauge-band\" />\n331\t                        <span className=\"vp-gauge-est\" style={{ left: pos(l.vraiprix!.value as number) }} />\n332\t                        {l.price != null && l.price >= lo * 0.7 && l.price <= hi * 1.3 && (\n333\t                          <span className=\"vp-gauge-ask\" style={{ left: pos(l.price) }} />\n334\t                        )}\n335\t                      </div>\n336\t                      <div className=\"vp-gauge-ends\">\n337\t                        <span>{fmtPrice(lo)}</span>\n338\t                        <span>{fmtPrice(hi)}</span>\n339\t                      </div>\n340\t                      {l.price != null && l.price >= lo * 0.7 && l.price <= hi * 1.3 && (\n341\t                        <div className=\"vp-gauge-legend\">\n342\t                          <span><i className=\"vp-dot-est\" /> estimation</span>\n343\t                          <span><i className=\"vp-dot-ask\" /> prix demandé</span>\n344\t                        </div>\n345\t                      )}\n346\t                    </div>\n347\t                  );\n348\t                })()}\n349\t                {l.price != null && (\n350\t                  (() => {\n351\t                    const diff = l.price - (l.vraiprix!.value as number);\n352\t                    const pct = Math.round((diff / (l.vraiprix!.value as number)) * 100);\n353\t                    const cls = diff > 0 ? \"over\" : \"under\";\n354\t                    const txt = diff > 0\n355\t                      ? `Prix demandé ${pct}% au-dessus de l'estimation`\n356\t                      : `Prix demandé ${Math.abs(pct)}% sous l'estimation`;\n357\t                    if (Math.abs(pct) > 60) return null;   // prix non comparable (loyer, terrain…)\n358\t                    return Math.abs(pct) >= 1\n359\t                      ? <div className={`vp-delta vp-${cls}`}>{diff > 0 ? \"▲\" : \"▼\"} {txt}</div>\n360\t                      : <div className=\"vp-delta vp-fair\">≈ Prix aligné sur l'estimation</div>;\n361\t                  })()\n362\t                )}\n363\t                <a className=\"vp-link\" href={l.vraiprix.url} target=\"_blank\" rel=\"noopener noreferrer\">\n364\t                  Voir l'analyse détaillée ↗\n365\t                </a>\n366\t              </div>\n367\t            )}\n368\t\n369\t            {(l.broker_name || l.broker_phone) && (\n370\t              <div className=\"broker\">\n371\t                <div className=\"broker-k\">Courtier</div>\n372\t                {l.broker_name && <div className=\"broker-name\">{l.broker_name}</div>}\n373\t                {l.broker_phone && <a className=\"broker-tel\" href={`tel:${l.broker_phone.replace(/\\s/g, \"\")}`}><Ico name=\"phone\" size={14} /> {l.broker_phone}</a>}\n374\t              </div>\n375\t            )}\n376\t\n377\t            <a className=\"cta\" href={l.url} target=\"_blank\" rel=\"noopener noreferrer\">\n378\t              Voir l'annonce chez {sourceName(l.source)} <Ico name=\"external\" size={15} />\n379\t            </a>\n380\t            <div className=\"fine\">\n381\t              Agrégé par Immo-Ka — {sourceName(l.source)}{updated ? ` · synchronisé le ${updated}` : \"\"}.\n382\t            </div>\n383\t          </section>\n384\t\n385\t\n386\t          {l.description && (\n387\t            <section className=\"f-bloc f-desc\" id=\"description\">\n388\t              <h2>Description</h2>\n389\t              <p className=\"desc-text\">{l.description}</p>\n390\t            </section>\n391\t          )}\n392\t\n393\t          {detEntries.length > 0 && (\n394\t            <section className=\"f-bloc\" id=\"caracteristiques\">\n395\t              <h2>Caractéristiques</h2>\n396\t              <div className=\"dtable\">\n397\t                {detEntries.map(([k, v]) => (\n398\t                  <div className=\"drow\" key={k}>\n399\t                    <span>{k}</span>\n400\t                    {/^https?:\\/\\//.test(String(v))\n401\t                      ? <b><a href={String(v)} target=\"_blank\" rel=\"noopener noreferrer\">Ouvrir ↗</a></b>\n402\t                      : <b>{String(v)}</b>}\n403\t                  </div>\n404\t                ))}\n405\t              </div>\n406\t            </section>\n407\t          )}\n408\t\n409\t          {rooms.length > 0 && (\n410\t            <section className=\"f-bloc\" id=\"pieces\">\n411\t              <h2>Pièces</h2>\n412\t              <div className=\"rooms-wrap\">\n413\t                <table className=\"rooms\">\n414\t                  <thead><tr><th>Pièce</th><th>Niveau</th><th>Dimensions</th><th>Revêtement</th></tr></thead>\n415\t                  <tbody>\n416\t                    {rooms.map((r, i) => (\n417\t                      <tr key={i}>\n418\t                        <td>{r.nom || \"—\"}</td><td>{r.niveau || \"—\"}</td>\n419\t                        <td>{r.dimensions || \"—\"}</td><td>{r.revetement || \"—\"}</td>\n420\t                      </tr>\n421\t                    ))}\n422\t                  </tbody>\n423\t                </table>\n424\t              </div>\n425\t            </section>\n426\t          )}\n427\t\n428\t        </div>\n429\t\n430\t        {/* -------- colonne droite (desktop) : inclusions, carte ------------- */}\n431\t        <div className=\"f-col\">\n432\t          {l.features && l.features.length > 0 && (\n433\t            <section className=\"f-bloc\" id=\"inclusions\">\n434\t              <h2>Inclusions</h2>\n435\t              <div className=\"amenity-row\">\n436\t                {l.features.map((f, i) => <span className=\"amenity\" key={i}><Ico name=\"check\" size={13} /> {f}</span>)}\n437\t              </div>\n438\t            </section>\n439\t          )}\n440\t\n441\t          {l.lat != null && l.lng != null && (\n442\t            <section className=\"f-bloc\" id=\"carte\">\n443\t              <h2>Emplacement</h2>\n444\t              <Suspense fallback={<div className=\"lmap3d lmap3d-skel map-loading\">Chargement de la carte…</div>}>\n445\t                <PropertyMap\n446\t                  uid={l.uid} lat={l.lat} lng={l.lng} price={l.price}\n447\t                  propertyType={l.property_type} address={l.address || l.title}\n448\t                  city={l.city} image={l.images?.[0]}\n449\t                  deal={l.price != null && l.vraiprix?.value != null\n450\t                        && l.price <= l.vraiprix.value * 0.95}\n451\t                />\n452\t              </Suspense>\n453\t            </section>\n454\t          )}\n455\t        </div>\n456\t      </div>\n457\t\n458\t      {/* quartier : pleine largeur, APRÈS les infos de propriété (ordre mobile correct) */}\n459\t      <RisqueInondation lat={l.lat} lng={l.lng} />\n460\t\n461\t      <QualiteAir lat={l.lat} lng={l.lng} />\n462\t\n463\t      <CommercesProches lat={l.lat} lng={l.lng} />\n464\t\n465\t      <EssenceProche lat={l.lat} lng={l.lng} />\n466\t\n467\t      <HydroEstimation adresse={l.address || l.title} uid={l.uid} lat={l.lat} lng={l.lng} />\n468\t\n469\t      {l.quartier && <QuartierBlock q={l.quartier} />}\n470\t\n471\t      <div className=\"fine f-foot\">\n472\t        Les prix et disponibilités sont ceux affichés par la source — chaque fiche renvoie à\n473\t        l'annonce originale de l'agence.\n474\t      </div>\n475\t\n476\t      <div className=\"cta-sticky\">\n477\t        <span className=\"cta-sticky-prix\">{fmtPrice(l.price, l.price_label)}</span>\n478\t        <a className=\"cta\" href={l.url} target=\"_blank\" rel=\"noopener noreferrer\">\n479\t          Voir chez {sourceName(l.source)} ↗\n480\t        </a>\n481\t      </div>\n482\t    </div>\n483\t  );\n484\t}\n485\t"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"92d1ea89-d8db-4fff-af1a-97d4db03d15a","timestamp":"2026-08-24T20:57:28.760Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/pages/Listing.tsx","content":"// -----------------------------------------------------------------------------\n// Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// pages/Listing.tsx : fiche complète d'une propriété\n//   galerie + lightbox · specs · caractéristiques (details) · pièces ·\n//   inclusions · description · historique de prix · courtier · mini-carte\n// -----------------------------------------------------------------------------\nimport { Suspense, lazy, useEffect, useRef, useState } from \"react\";\nimport { Link, useParams } from \"react-router-dom\";\nimport {\n  Listing, Room, fetchListing, fetchSources, fmtArea, fmtDate, fmtPrice,\n  registerSourceNames, sourceName,\n} from \"../api\";\n\nconst PropertyMap = lazy(() => import(\"../components/PropertyMap\"));\nimport QuartierBlock from \"../components/QuartierBlock\";\nimport RisqueInondation from \"../components/RisqueInondation\";\nimport QualiteAir from \"../components/QualiteAir\";\nimport EssenceProche from \"../components/EssenceProche\";\nimport HydroEstimation from \"../components/HydroEstimation\";\nimport CommercesProches from \"../components/CommercesProches\";\nimport { Ico } from \"../components/Icons\";\nimport { TypeFallback } from \"../components/PropertyImg\";\n\n// --- Lightbox : pincement pour zoomer + panoramique + balayage entre photos ---\nfunction ZoomImg({ src, onSwipe }: { src: string; onSwipe: (dir: 1 | -1) => void }) {\n  const [t, setT] = useState({ scale: 1, x: 0, y: 0 });\n  const pointers = useRef(new Map<number, { x: number; y: number }>());\n  const start = useRef({ scale: 1, x: 0, y: 0, dist: 0, cx: 0, cy: 0, t: 0 });\n  const lastTap = useRef(0);\n\n  // repartir à zéro quand on change de photo\n  useEffect(() => { setT({ scale: 1, x: 0, y: 0 }); }, [src]);\n\n  const dist = () => {\n    const p = [...pointers.current.values()];\n    return p.length < 2 ? 0 : Math.hypot(p[0].x - p[1].x, p[0].y - p[1].y);\n  };\n  const center = () => {\n    const p = [...pointers.current.values()];\n    return p.length < 2\n      ? p[0] ?? { x: 0, y: 0 }\n      : { x: (p[0].x + p[1].x) / 2, y: (p[0].y + p[1].y) / 2 };\n  };\n\n  const onDown = (e: React.PointerEvent) => {\n    (e.target as HTMLElement).setPointerCapture(e.pointerId);\n    pointers.current.set(e.pointerId, { x: e.clientX, y: e.clientY });\n    const c = center();\n    start.current = { scale: t.scale, x: t.x, y: t.y, dist: dist(), cx: c.x, cy: c.y, t: Date.now() };\n  };\n  const onMove = (e: React.PointerEvent) => {\n    if (!pointers.current.has(e.pointerId)) return;\n    pointers.current.set(e.pointerId, { x: e.clientX, y: e.clientY });\n    const s = start.current;\n    if (pointers.current.size >= 2 && s.dist > 0) {\n      // pincement : zoom autour du centre des deux doigts\n      const scale = Math.min(4, Math.max(1, (dist() / s.dist) * s.scale));\n      const c = center();\n      setT({ scale, x: s.x + (c.x - s.cx), y: s.y + (c.y - s.cy) });\n    } else if (pointers.current.size === 1 && t.scale > 1) {\n      // panoramique une fois zoomé\n      const p = pointers.current.get(e.pointerId)!;\n      setT({ scale: t.scale, x: s.x + (p.x - s.cx), y: s.y + (p.y - s.cy) });\n    }\n  };\n  const onUp = (e: React.PointerEvent) => {\n    const p = pointers.current.get(e.pointerId);\n    pointers.current.delete(e.pointerId);\n    const s = start.current;\n    if (pointers.current.size === 0 && p) {\n      const dx = p.x - s.cx, dy = p.y - s.cy, dt = Date.now() - s.t;\n      if (t.scale <= 1.05 && Math.abs(dx) > 56 && Math.abs(dx) > Math.abs(dy) * 1.5) {\n        onSwipe(dx < 0 ? 1 : -1);                       // balayage → photo suivante\n      } else if (dt < 260 && Math.abs(dx) < 8 && Math.abs(dy) < 8) {\n        const now = Date.now();\n        if (now - lastTap.current < 320)                 // double-tap : zoom ×2.4\n          setT(t.scale > 1 ? { scale: 1, x: 0, y: 0 } : { scale: 2.4, x: 0, y: 0 });\n        lastTap.current = now;\n      }\n      if (t.scale <= 1.02) setT({ scale: 1, x: 0, y: 0 });\n    }\n  };\n\n  return (\n    <img\n      src={src} alt=\"\" draggable={false}\n      style={{\n        transform: `translate(${t.x}px, ${t.y}px) scale(${t.scale})`,\n        transition: pointers.current.size ? \"none\" : \"transform 0.15s ease\",\n        touchAction: \"none\", cursor: t.scale > 1 ? \"grab\" : \"zoom-out\",\n      }}\n      onClick={(e) => e.stopPropagation()}\n      onPointerDown={onDown} onPointerMove={onMove}\n      onPointerUp={onUp} onPointerCancel={onUp}\n    />\n  );\n}\n\n// --- Galerie : balayage natif (scroll-snap) + vignettes + plein écran --------\nfunction Galerie({ images, captions, titre, type }:\n  { images: string[]; captions?: string[]; titre: string; type?: string }) {\n  const [idx, setIdx] = useState(0);\n  const [zoom, setZoom] = useState(false);\n  const [dead, setDead] = useState<Set<string>>(new Set());\n  const track = useRef<HTMLDivElement>(null);\n\n  // images qui ne chargent pas : retirées de la galerie à la volée (jamais\n  // d'icône d'image cassée) ; légendes gardées alignées\n  const alive = images\n    .map((u, i) => ({ u, cap: captions && captions.length === images.length ? captions[i] : \"\" }))\n    .filter(({ u }) => !dead.has(u));\n  const markDead = (u: string) => setDead((d) => new Set(d).add(u));\n\n  const onScroll = () => {\n    const el = track.current;\n    if (el) setIdx(Math.round(el.scrollLeft / el.clientWidth));\n  };\n  const goto = (i: number) =>\n    track.current?.scrollTo({ left: i * track.current.clientWidth, behavior: \"smooth\" });\n\n  useEffect(() => {\n    if (!zoom) return;\n    const onKey = (e: KeyboardEvent) => {\n      if (e.key === \"Escape\") setZoom(false);\n      if (e.key === \"ArrowLeft\") setIdx((i) => Math.max(0, i - 1));\n      if (e.key === \"ArrowRight\") setIdx((i) => Math.min(alive.length - 1, i + 1));\n    };\n    window.addEventListener(\"keydown\", onKey);\n    // figer l'arrière-plan pendant le plein écran (mobile)\n    document.body.style.overflow = \"hidden\";\n    return () => {\n      window.removeEventListener(\"keydown\", onKey);\n      document.body.style.overflow = \"\";\n    };\n  }, [zoom, alive.length]);\n\n  if (alive.length === 0)\n    return <div className=\"carousel\"><div className=\"carousel-empty\"><TypeFallback type={type} /></div></div>;\n\n  const cur = Math.min(idx, alive.length - 1);\n  const swipe = (dir: 1 | -1) =>\n    setIdx((i) => Math.min(alive.length - 1, Math.max(0, i + dir)));\n\n  return (\n    <>\n      <div className=\"carousel\">\n        <div className=\"carousel-track\" ref={track} onScroll={onScroll}>\n          {alive.map(({ u }, i) => (\n            <img key={u} src={u} loading={i <= 1 ? \"eager\" : \"lazy\"} decoding=\"async\"\n              alt={`${titre} — photo ${i + 1} de ${alive.length}`}\n              onError={() => markDead(u)} onClick={() => setZoom(true)} />\n          ))}\n        </div>\n        {alive[cur]?.cap && <span className=\"carousel-caption\">{alive[cur].cap}</span>}\n        <span className=\"carousel-count\" aria-live=\"polite\">{cur + 1}/{alive.length}</span>\n        {cur > 0 && <button className=\"carousel-nav prev\" aria-label=\"Photo précédente\" onClick={() => goto(cur - 1)}>‹</button>}\n        {cur < alive.length - 1 && <button className=\"carousel-nav next\" aria-label=\"Photo suivante\" onClick={() => goto(cur + 1)}>›</button>}\n      </div>\n      {alive.length > 1 && (\n        <div className=\"thumbs\">\n          {alive.map(({ u }, i) => (\n            <button key={u} className={i === cur ? \"on\" : \"\"} onClick={() => goto(i)} aria-label={`Photo ${i + 1}`}>\n              <img src={u} alt=\"\" loading=\"lazy\" decoding=\"async\" onError={() => markDead(u)} />\n            </button>\n          ))}\n        </div>\n      )}\n      {zoom && (\n        <div className=\"lightbox\" onClick={() => setZoom(false)} role=\"dialog\" aria-label=\"Photo agrandie\">\n          <button className=\"lb-close\" aria-label=\"Fermer\" onClick={() => setZoom(false)}>✕</button>\n          {cur > 0 && <button className=\"lb-nav prev\" aria-label=\"Précédente\" onClick={(e) => { e.stopPropagation(); setIdx(cur - 1); }}>‹</button>}\n          <ZoomImg src={alive[cur].u} onSwipe={swipe} />\n          {cur < alive.length - 1 && <button className=\"lb-nav next\" aria-label=\"Suivante\" onClick={(e) => { e.stopPropagation(); setIdx(cur + 1); }}>›</button>}\n          <span className=\"lb-count\">\n            {alive[cur]?.cap ? `${alive[cur].cap} · ` : \"\"}{cur + 1} / {alive.length}\n          </span>\n        </div>\n      )}\n    </>\n  );\n}\n\n// clés techniques de `details` jamais montrées dans « Caractéristiques »\nconst DETAIL_HIDDEN = new Set([\n  \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n  \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n  \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n]);\n\n// icône de chaque tuile « spec » (Icons.tsx)\nconst SPEC_ICONS: Record<string, string> = {\n  \"Type\": \"home\", \"Chambres\": \"bed\", \"Salles de bain\": \"bath\",\n  \"Salles d'eau\": \"drop\", \"Superficie\": \"area\", \"Terrain\": \"land\",\n  \"Année\": \"calendar\", \"MLS / Centris\": \"tag\",\n};\n\nexport default function ListingPage() {\n  const { uid } = useParams<{ uid: string }>();\n  const [l, setL] = useState<Listing | null>(null);\n  const [error, setError] = useState<string | null>(null);\n\n  useEffect(() => {\n    fetchSources().then((r) => registerSourceNames(r.sources)).catch(() => {});\n    if (!uid) return;\n    setL(null); setError(null);\n    fetchListing(uid).then(setL).catch((e) => setError(String(e)));\n    window.scrollTo(0, 0);\n  }, [uid]);\n\n  if (error)\n    return (\n      <div className=\"notice container\">\n        <div className=\"big\"><Ico name=\"alert\" size={44} /></div>\n        <h2>Propriété introuvable</h2>\n        <p>{error}</p>\n        <Link className=\"btn btn-primary\" to=\"/\">Retour aux propriétés</Link>\n      </div>\n    );\n\n  if (!l)\n    return (\n      <div className=\"container detail\">\n        <div className=\"fiche\" aria-busy=\"true\">\n          <div className=\"skel\"><div className=\"sk-img\" /></div>\n          <div className=\"skel\"><div className=\"sk-line\" /><div className=\"sk-line\" /><div className=\"sk-line short\" /></div>\n        </div>\n      </div>\n    );\n\n  const specs: { k: string; v: string }[] = [];\n  if (l.property_type) specs.push({ k: \"Type\", v: l.property_type });\n  if (l.bedrooms != null) specs.push({ k: \"Chambres\", v: String(l.bedrooms) });\n  if (l.bathrooms != null) specs.push({ k: \"Salles de bain\", v: String(l.bathrooms) });\n  if (l.powder_rooms != null) specs.push({ k: \"Salles d'eau\", v: String(l.powder_rooms) });\n  if (l.area_sqft != null) specs.push({ k: \"Superficie\", v: fmtArea(l.area_sqft)! });\n  if (l.lot_sqft != null) specs.push({ k: \"Terrain\", v: fmtArea(l.lot_sqft)! });\n  if (l.year_built != null) specs.push({ k: \"Année\", v: String(l.year_built) });\n  if (l.mls) specs.push({ k: \"MLS / Centris\", v: l.mls });\n\n  const rooms: Room[] = Array.isArray(l.details?.pieces) ? (l.details!.pieces as Room[]) : [];\n  const detEntries = Object.entries(l.details ?? {})\n    .filter(([k, v]) => !DETAIL_HIDDEN.has(k) && (typeof v === \"string\" || typeof v === \"number\") && String(v).trim());\n\n  const hist = (l.price_history ?? []).filter((h) => h.price != null);\n  const baisse = hist.length >= 2 && hist[0].price !== hist[1].price\n    ? { de: hist[1].price!, a: hist[0].price! } : null;\n  const updated = l.updated_at ? fmtDate(l.updated_at) : null;\n\n  return (\n    <div className=\"container detail\">\n      <nav className=\"crumbs\" aria-label=\"Fil d'Ariane\">\n        <Link to=\"/\">Propriétés</Link> ›\n        {l.city && <span>{l.city}</span>} ›\n        <span>{l.address || l.title}</span>\n      </nav>\n\n      <div className=\"fiche\">\n        {/* -------- colonne gauche : galerie, prix/synthèse, description, ------\n             -------- caractéristiques, pièces — ordre du DOM = ordre visuel ---- */}\n        <div className=\"f-col\">\n          <section className=\"f-bloc f-galerie\" aria-label=\"Photos\">\n            <Galerie\n              images={l.images ?? []}\n              captions={Array.isArray(l.details?.photo_captions)\n                ? (l.details!.photo_captions as string[]) : undefined}\n              titre={l.address || l.title}\n              type={l.property_type}\n            />\n          </section>\n\n          <section className=\"f-bloc f-hero\">\n            <div className=\"price-kicker\">\n              {l.details?.transaction === \"location\" ? \"Loyer mensuel\" : \"Prix demandé\"}\n            </div>\n            <div className=\"price-row\">\n              <div className=\"price\">\n                {fmtPrice(l.price, l.price_label)}\n                {l.details?.transaction === \"location\" && <span className=\"per-month\"> /mois</span>}\n              </div>\n              {l.property_type && (\n                <span className=\"type-chip\">\n                  <Ico name={SPEC_ICONS[\"Type\"]} size={13} /> {l.property_type}\n                  {l.details?.transaction === \"location\" ? \" · location\" : \"\"}\n                  {l.details?.price_from ? \" · à partir de\" : \"\"}\n                </span>\n              )}\n            </div>\n            {l.price != null && l.area_sqft != null && l.area_sqft > 200 && (\n              <div className=\"price-sub\">{Math.round(l.price / l.area_sqft).toLocaleString(\"fr-CA\")} $ / pi² habitable</div>\n            )}\n            <h1>{l.address || l.title}</h1>\n            <div className=\"loc\"><Ico name=\"pin\" size={13} /> {[l.sector, l.city, l.region].filter(Boolean).join(\" · \")}</div>\n\n            <div className=\"spec-list\">\n              {specs.map((s) => (\n                <div className=\"spec-row\" key={s.k}>\n                  <span className=\"spec-badge\"><Ico name={SPEC_ICONS[s.k] ?? \"tag\"} size={15} /></span>\n                  <span className=\"spec-k\">{s.k}</span>\n                  <b className=\"spec-v\">{s.v}</b>\n                </div>\n              ))}\n            </div>\n\n            {baisse && (\n              <div className={`prix-histo ${baisse.a < baisse.de ? \"down\" : \"\"}`}>\n                <Ico name={baisse.a < baisse.de ? \"trenddown\" : \"trendup\"} size={16} /> Prix passé de {fmtPrice(baisse.de)} à <b>{fmtPrice(baisse.a)}</b>\n              </div>\n            )}\n\n            {l.vraiprix && l.vraiprix.value != null && (\n              <div className=\"vraiprix\">\n                <div className=\"vp-head\">\n                  <span className=\"vp-logo\">Vrai‑Prix</span>\n                  {l.vraiprix.confidence && (\n                    <span className={`vp-conf vp-conf-${l.vraiprix.confidence}`}>\n                      confiance {l.vraiprix.confidence}\n                    </span>\n                  )}\n                </div>\n                <div className=\"vp-k\">Valeur marchande estimée</div>\n                <div className=\"vp-value\">{fmtPrice(l.vraiprix.value)}</div>\n                {l.vraiprix.low != null && l.vraiprix.high != null && (() => {\n                  const lo = l.vraiprix!.low as number, hi = l.vraiprix!.high as number;\n                  const span = Math.max(1, hi - lo);\n                  const pos = (v: number) => `${Math.max(2, Math.min(98, ((v - lo) / span) * 100))}%`;\n                  return (\n                    <div className=\"vp-gauge\">\n                      <div className=\"vp-gauge-track\">\n                        <span className=\"vp-gauge-band\" />\n                        <span className=\"vp-gauge-est\" style={{ left: pos(l.vraiprix!.value as number) }} />\n                        {l.price != null && l.price >= lo * 0.7 && l.price <= hi * 1.3 && (\n                          <span className=\"vp-gauge-ask\" style={{ left: pos(l.price) }} />\n                        )}\n                      </div>\n                      <div className=\"vp-gauge-ends\">\n                        <span>{fmtPrice(lo)}</span>\n                        <span>{fmtPrice(hi)}</span>\n                      </div>\n                      {l.price != null && l.price >= lo * 0.7 && l.price <= hi * 1.3 && (\n                        <div className=\"vp-gauge-legend\">\n                          <span><i className=\"vp-dot-est\" /> estimation</span>\n                          <span><i className=\"vp-dot-ask\" /> prix demandé</span>\n                        </div>\n                      )}\n                    </div>\n                  );\n                })()}\n                {l.price != null && (\n                  (() => {\n                    const diff = l.price - (l.vraiprix!.value as number);\n                    const pct = Math.round((diff / (l.vraiprix!.value as number)) * 100);\n                    const cls = diff > 0 ? \"over\" : \"under\";\n                    const txt = diff > 0\n                      ? `Prix demandé ${pct}% au-dessus de l'estimation`\n                      : `Prix demandé ${Math.abs(pct)}% sous l'estimation`;\n                    if (Math.abs(pct) > 60) return null;   // prix non comparable (loyer, terrain…)\n                    return Math.abs(pct) >= 1\n                      ? <div className={`vp-delta vp-${cls}`}>{diff > 0 ? \"▲\" : \"▼\"} {txt}</div>\n                      : <div className=\"vp-delta vp-fair\">≈ Prix aligné sur l'estimation</div>;\n                  })()\n                )}\n                <a className=\"vp-link\" href={l.vraiprix.url} target=\"_blank\" rel=\"noopener noreferrer\">\n                  Voir l'analyse détaillée ↗\n                </a>\n              </div>\n            )}\n\n            {(l.broker_name || l.broker_phone) && (\n              <div className=\"broker\">\n                <div className=\"broker-k\">Courtier</div>\n                {l.broker_name && <div className=\"broker-name\">{l.broker_name}</div>}\n                {l.broker_phone && <a className=\"broker-tel\" href={`tel:${l.broker_phone.replace(/\\s/g, \"\")}`}><Ico name=\"phone\" size={14} /> {l.broker_phone}</a>}\n              </div>\n            )}\n\n            <a className=\"cta\" href={l.url} target=\"_blank\" rel=\"noopener noreferrer\">\n              Voir l'annonce chez {sourceName(l.source)} <Ico name=\"external\" size={15} />\n            </a>\n            <div className=\"fine\">\n              Agrégé par Immo-Ka — {sourceName(l.source)}{updated ? ` · synchronisé le ${updated}` : \"\"}.\n            </div>\n          </section>\n\n\n          {l.description && (\n            <section className=\"f-bloc f-desc\" id=\"description\">\n              <h2>Description</h2>\n              <p className=\"desc-text\">{l.description}</p>\n            </section>\n          )}\n\n          {detEntries.length > 0 && (\n            <section className=\"f-bloc\" id=\"caracteristiques\">\n              <h2>Caractéristiques</h2>\n              <div className=\"dtable\">\n                {detEntries.map(([k, v]) => (\n                  <div className=\"drow\" key={k}>\n                    <span>{k}</span>\n                    {/^https?:\\/\\//.test(String(v))\n                      ? <b><a href={String(v)} target=\"_blank\" rel=\"noopener noreferrer\">Ouvrir ↗</a></b>\n                      : <b>{String(v)}</b>}\n                  </div>\n                ))}\n              </div>\n            </section>\n          )}\n\n          {rooms.length > 0 && (\n            <section className=\"f-bloc\" id=\"pieces\">\n              <h2>Pièces</h2>\n              <div className=\"rooms-wrap\">\n                <table className=\"rooms\">\n                  <thead><tr><th>Pièce</th><th>Niveau</th><th>Dimensions</th><th>Revêtement</th></tr></thead>\n                  <tbody>\n                    {rooms.map((r, i) => (\n                      <tr key={i}>\n                        <td>{r.nom || \"—\"}</td><td>{r.niveau || \"—\"}</td>\n                        <td>{r.dimensions || \"—\"}</td><td>{r.revetement || \"—\"}</td>\n                      </tr>\n                    ))}\n                  </tbody>\n                </table>\n              </div>\n            </section>\n          )}\n\n        </div>\n\n        {/* -------- colonne droite (desktop) : inclusions, carte ------------- */}\n        <div className=\"f-col\">\n          {l.features && l.features.length > 0 && (\n            <section className=\"f-bloc\" id=\"inclusions\">\n              <h2>Inclusions</h2>\n              <div className=\"amenity-row\">\n                {l.features.map((f, i) => <span className=\"amenity\" key={i}><Ico name=\"check\" size={13} /> {f}</span>)}\n              </div>\n            </section>\n          )}\n\n          {l.lat != null && l.lng != null && (\n            <section className=\"f-bloc\" id=\"carte\">\n              <h2>Emplacement</h2>\n              <Suspense fallback={<div className=\"lmap3d lmap3d-skel map-loading\">Chargement de la carte…</div>}>\n                <PropertyMap\n                  uid={l.uid} lat={l.lat} lng={l.lng} price={l.price}\n                  propertyType={l.property_type} address={l.address || l.title}\n                  city={l.city} image={l.images?.[0]}\n                  deal={l.price != null && l.vraiprix?.value != null\n                        && l.price <= l.vraiprix.value * 0.95}\n                />\n              </Suspense>\n            </section>\n          )}\n        </div>\n      </div>\n\n      {/* quartier : pleine largeur, APRÈS les infos de propriété (ordre mobile correct) */}\n      <RisqueInondation lat={l.lat} lng={l.lng} />\n\n      <QualiteAir lat={l.lat} lng={l.lng} />\n\n      <CommercesProches lat={l.lat} lng={l.lng} />\n\n      <EssenceProche lat={l.lat} lng={l.lng} />\n\n      <HydroEstimation adresse={l.address || l.title} uid={l.uid} lat={l.lat} lng={l.lng} />\n\n      {l.quartier && <QuartierBlock q={l.quartier} />}\n\n      <div className=\"fine f-foot\">\n        Les prix et disponibilités sont ceux affichés par la source — chaque fiche renvoie à\n        l'annonce originale de l'agence.\n      </div>\n\n      <div className=\"cta-sticky\">\n        <span className=\"cta-sticky-prix\">{fmtPrice(l.price, l.price_label)}</span>\n        <a className=\"cta\" href={l.url} target=\"_blank\" rel=\"noopener noreferrer\">\n          Voir chez {sourceName(l.source)} ↗\n        </a>\n      </div>\n    </div>\n  );\n}\n","numLines":485,"startLine":1,"totalLines":485}}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SycAdpiWyJpYmv8PvDRNST","type":"tool_result","content":"27:.mono { font-family: var(--font-mono); }\n28:.kicker {\n33:.kicker::before { content: \"\"; width: 22px; height: 2px; background: var(--green); }\n35:.container { max-width: 1240px; margin: 0 auto; padding: 0 24px; }\n36:@media (max-width: 640px) { .container { padding: 0 16px; } }\n38:/* ================= Header ================= */\n39:.header {\n46:.header:has(.mobile-menu.open) { z-index: var(--z-modal, 900); }\n47:.header-inner { display: flex; align-items: center; gap: 20px; height: 64px; }\n48:.brand { font-family: var(--font-display); font-weight: 700; font-size: 26px; letter-spacing: -0.04em; display: flex; align-items: center; line-height: 1; }\n49:.brand .ka { background: var(--ink); color: var(--lime); padding: 2px 7px 4px; border-radius: 6px; margin-left: 3px; transform: rotate(-2deg); transition: transform 0.2s ease; }\n50:.brand:hover .ka { transform: rotate(0deg); }\n51:.brand-tag { font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-3); letter-spacing: 0.08em; text-transform: uppercase; margin-left: 12px; }\n52:@media (max-width: 860px) { .brand-tag { display: none; } }\n53:.nav { margin-left: auto; display: flex; gap: 4px; }\n54:.nav a { padding: 9px 16px; border-radius: 999px; font-weight: 600; font-size: 14px; color: var(--ink-2); border: 1.5px solid transparent; transition: all 0.15s ease; min-height: 40px; display: inline-flex; align-items: center; }\n55:.nav a:hover { border-color: var(--ink); color: var(--ink); }\n56:.nav a.active { background: var(--ink); color: var(--lime); }\n58:/* ================= Ticker ================= */\n59:.ticker { background: var(--ink); color: var(--lime); overflow: hidden; font-family: var(--font-mono); font-size: 11.5px; letter-spacing: 0.1em; text-transform: uppercase; padding: 7px 0; white-space: nowrap; border-bottom: 1px solid rgba(226, 55, 68, 0.25); }\n60:.ticker-track { display: inline-flex; gap: 0; animation: ticker 48s linear infinite; will-change: transform; }\n61:.ticker span { padding: 0 26px; position: relative; }\n62:.ticker span::after { content: \"◆\"; position: absolute; right: -6px; opacity: 0.5; font-size: 8px; top: 3px; }\n64:@media (prefers-reduced-motion: reduce) { .ticker-track { animation: none; } * { transition-duration: 0.01ms !important; } }\n66:/* ================= Hero ================= */\n67:.hero { padding: 58px 0 22px; }\n68:@media (max-width: 640px) { .hero { padding: 34px 0 14px; } }\n69:.hero h1 { font-size: clamp(38px, 6.4vw, 78px); font-weight: 700; line-height: 0.98; text-transform: uppercase; letter-spacing: -0.035em; max-width: 940px; margin-top: 14px; }\n70:.hero h1 .outline { color: transparent; -webkit-text-stroke: 2px var(--ink); }\n71:.hero h1 .hl { background: var(--lime); color: #fff; padding: 0 10px; border-radius: 8px; display: inline-block; transform: rotate(-1deg); }\n72:.hero p.lede { color: var(--ink-2); font-size: 16.5px; max-width: 680px; margin: 20px 0 0; }\n73:.stat-row { display: flex; flex-wrap: wrap; gap: 10px; margin-top: 26px; }\n74:.stat-chip { background: var(--surface); border: 1.5px solid var(--ink); border-radius: 999px; padding: 8px 16px; font-family: var(--font-mono); font-size: 12px; color: var(--ink-2); display: flex; gap: 8px; align-items: center; box-shadow: 3px 3px 0 rgba(26, 18, 20, 0.12); }\n75:.stat-chip b { color: var(--ink); font-weight: 700; }\n76:.stat-chip .pulse { width: 8px; height: 8px; border-radius: 50%; background: var(--green); box-shadow: 0 0 0 4px var(--lime-soft); animation: pulse 2.4s ease infinite; }\n79:/* ================= Filter bar ================= */\n80:.filterbar { background: var(--surface); border: 2px solid var(--ink); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); padding: 14px; margin: 30px 0 6px; display: flex; flex-direction: column; gap: 0; min-width: 0; }\n81:.f-primary { display: flex; gap: 10px; align-items: stretch; flex-wrap: wrap; min-width: 0; }\n82:.f-search { flex: 1 1 240px; min-width: 0; display: flex; align-items: center; gap: 9px; border: 1.5px solid var(--ink); border-radius: 9px; background: var(--surface-2); padding: 0 13px; min-height: 52px; color: var(--ink-2); transition: box-shadow 0.15s ease; }\n83:.f-search:focus-within { box-shadow: 3px 3px 0 var(--lime); background: var(--surface); }\n84:.f-search input { border: none; background: none; outline: none; flex: 1; min-width: 0; font-size: 15px; color: var(--ink); font-family: inherit; }\n85:.f-clear { border: none; background: var(--line); color: var(--ink-2); border-radius: 50%; width: 20px; height: 20px; font-size: 10px; cursor: pointer; flex: none; display: grid; place-items: center; }\n86:.f-ctl { flex: 0 1 auto; min-width: 0; display: flex; flex-direction: column; justify-content: center; gap: 2px; border: 1.5px solid var(--ink); border-radius: 9px; background: var(--surface); padding: 7px 12px 6px; min-height: 52px; cursor: pointer; transition: box-shadow 0.15s ease; }\n87:.f-ctl:focus-within { box-shadow: 3px 3px 0 var(--lime); }\n88:.f-ctl > span { font-family: var(--font-mono); font-size: 9px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); }\n89:.f-ctl select { border: none; background: transparent; outline: none; font-family: var(--font-display); font-weight: 700; font-size: 14.5px; color: var(--ink); cursor: pointer; appearance: none; -webkit-appearance: none; padding-right: 16px; min-width: 0; max-width: 180px; text-overflow: ellipsis; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='9' height='5'%3E%3Cpath d='M0 0l4.5 5L9 0z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right center; }\n90:.range-pair { display: flex; align-items: center; gap: 4px; }\n91:.range-pair select { max-width: 92px; }\n92:.range-sep { color: var(--ink-3); font-family: var(--font-mono); font-size: 11px; }\n93:.f-more { flex: none; align-self: stretch; border: 1.5px solid var(--ink); border-radius: 9px; background: var(--surface); color: var(--ink); padding: 0 18px; cursor: pointer; font-family: var(--font-display); font-weight: 700; font-size: 14px; min-height: 52px; transition: all 0.13s ease; }\n94:.f-more:hover { background: var(--lime-soft); }\n95:.f-more.on { background: var(--ink); color: var(--lime); box-shadow: 3px 3px 0 rgba(26, 18, 20,0.22); }\n96:.f-adv { display: grid; grid-template-columns: repeat(auto-fit, minmax(230px, 1fr)); gap: 16px 22px; border-top: 1.5px dashed var(--line); margin-top: 14px; padding-top: 14px; min-width: 0; animation: adv-in 0.18s ease; }\n98:.f-group { display: flex; flex-direction: column; gap: 7px; min-width: 0; }\n99:.f-group > label { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); }\n100:.f-group-end { justify-content: flex-end; }\n101:.f-group .btn:disabled { opacity: 0.4; cursor: default; }\n102:.f-native { border: 1.5px solid var(--line); background: var(--surface-2); border-radius: var(--r-ctl); padding: 10px 30px 10px 12px; font-size: 14px; color: var(--ink); outline: none; font-family: inherit; min-height: 42px; width: 100%; min-width: 0; appearance: none; -webkit-appearance: none; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='10' height='6'%3E%3Cpath d='M0 0l5 6 5-6z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right 12px center; }\n103:.f-native:focus { border-color: var(--ink); box-shadow: 3px 3px 0 var(--lime); }\n104:.seg { display: inline-flex; flex-wrap: wrap; row-gap: 6px; }\n105:.seg button { border: 1.5px solid var(--ink); background: var(--surface); color: var(--ink-2); padding: 8px 13px; font-family: var(--font-display); font-weight: 600; font-size: 13px; cursor: pointer; margin-left: -1.5px; white-space: nowrap; min-height: 38px; transition: all 0.12s ease; }\n106:.seg button:first-child { border-radius: 8px 0 0 8px; margin-left: 0; }\n107:.seg button:last-child { border-radius: 0 8px 8px 0; }\n108:.seg button:hover { background: var(--lime-soft); color: var(--ink); }\n109:.seg button.on { background: var(--ink); color: var(--lime); position: relative; z-index: 1; }\n111:.pills { display: flex; flex-wrap: wrap; gap: 8px; margin: 10px 0 2px; }\n112:.pill { display: inline-flex; align-items: center; gap: 7px; border: 1.5px solid var(--ink); background: var(--lime-soft); color: var(--ink); border-radius: 999px; padding: 6px 13px; font-size: 12.5px; font-weight: 600; font-family: var(--font-mono); cursor: pointer; transition: all 0.13s ease; }\n113:.pill:hover { background: var(--lime); color: #fff; }\n114:.pill-x { font-size: 10px; opacity: 0.65; }\n115:.pill-clear { background: var(--surface); border-color: var(--danger); color: var(--danger); }\n116:.pill-clear:hover { background: var(--danger); color: #fff; }\n117:.link-btn { background: none; border: none; padding: 0; color: var(--green); font: inherit; text-decoration: underline; cursor: pointer; }\n119:.btn { border: 1.5px solid var(--ink); border-radius: var(--r-ctl); padding: 11px 20px; font-weight: 700; font-size: 14px; cursor: pointer; min-height: 44px; transition: transform 0.12s ease, box-shadow 0.12s ease, background 0.15s ease; font-family: var(--font-display); letter-spacing: 0.01em; }\n120:.btn:active { transform: translate(2px, 2px); box-shadow: none !important; }\n121:.btn-primary { background: var(--ink); color: var(--lime); box-shadow: 4px 4px 0 rgba(26, 18, 20,0.25); }\n122:.btn-primary:hover { background: var(--green-deep); }\n123:.btn-ghost { background: transparent; color: var(--ink); }\n124:.btn-ghost:hover { background: var(--lime); color: #fff; box-shadow: 4px 4px 0 rgba(26, 18, 20,0.2); }\n126:/* ================= Chips ================= */\n127:.chips { display: flex; gap: 8px; margin: 16px 0 4px; overflow-x: auto; padding-bottom: 6px; scrollbar-width: none; }\n128:.chips::-webkit-scrollbar { display: none; }\n129:.chip { border: 1.5px solid var(--ink); background: var(--surface); color: var(--ink); border-radius: 999px; padding: 8px 18px; font-size: 13.5px; font-weight: 600; cursor: pointer; font-family: var(--font-display); white-space: nowrap; min-height: 40px; transition: all 0.13s ease; }\n130:.chip:hover { background: var(--lime-soft); transform: translateY(-1px); }\n131:.chip.on { background: var(--ink); color: var(--lime); box-shadow: 3px 3px 0 rgba(26, 18, 20,0.2); }\n133:/* ================= Results ================= */\n134:.results-head { display: flex; align-items: baseline; gap: 14px; margin: 28px 0 18px; flex-wrap: wrap; }\n135:.results-head h2 { font-size: 22px; text-transform: uppercase; letter-spacing: -0.02em; }\n136:.results-head > span { font-family: var(--font-mono); color: var(--ink-3); font-size: 12px; letter-spacing: 0.06em; }\n137:.results-tools { display: flex; align-items: center; gap: 12px; margin-left: auto; }\n138:.sort-ctl select { border: 1.5px solid var(--line-strong); background: var(--surface); border-radius: var(--r-ctl); padding: 7px 28px 7px 12px; font-family: var(--font-mono); font-size: 12px; color: var(--ink); cursor: pointer; appearance: none; -webkit-appearance: none; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='10' height='6'%3E%3Cpath d='M0 0l5 6 5-6z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right 10px center; }\n139:.view-toggle { display: inline-flex; border: 1.5px solid var(--line-strong); border-radius: var(--r-ctl); overflow: hidden; background: var(--surface); box-shadow: var(--shadow-flat); }\n140:.view-toggle button { border: 0; background: transparent; padding: 7px 14px; cursor: pointer; font-family: var(--font-mono); font-size: 12px; color: var(--ink-2); }\n141:.view-toggle button + button { border-left: 1.5px solid var(--line-strong); }\n142:.view-toggle button.on { background: var(--ink); color: var(--lime); }\n144:.grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(290px, 1fr)); gap: 22px; padding-bottom: 20px; }\n145:@media (max-width: 640px) { .grid { grid-template-columns: 1fr; gap: 16px; } }\n146:.more-wrap { display: flex; justify-content: center; padding: 8px 0 70px; }\n148:.card { background: var(--surface); border: 1.5px solid var(--line-strong); border-radius: var(--r-card); overflow: hidden; display: flex; flex-direction: column; box-shadow: var(--shadow-flat); transition: transform 0.16s ease, box-shadow 0.16s ease; }\n149:.card:hover { transform: translate(-3px, -3px); box-shadow: var(--shadow-off); }\n150:.card:focus-visible { outline: 3px solid var(--lime); outline-offset: 2px; }\n151:.card-img { position: relative; aspect-ratio: 16/10.5; background: repeating-linear-gradient(45deg, #eceae3 0 12px, #f3f1ea 12px 24px); overflow: hidden; }\n152:.card-img img { width: 100%; height: 100%; object-fit: cover; transition: transform 0.4s ease; }\n153:.card:hover .card-img img { transform: scale(1.05); }\n154:.card-img .noimg { display: flex; align-items: center; justify-content: center; height: 100%; color: var(--ink-3); font-size: 34px; }\n155:.badge { position: absolute; top: 12px; left: 12px; border-radius: 6px; padding: 4px 10px; font-family: var(--font-mono); font-size: 11.5px; font-weight: 700; letter-spacing: 0.04em; background: rgba(255, 255, 255, 0.95); color: var(--ink); border: 1px solid var(--ink); }\n156:.badge.type { background: var(--ink); color: var(--lime); border-color: var(--ink); }\n157:.badge.right { left: auto; right: 12px; background: rgba(255,255,255,0.92); border-color: transparent; }\n158:.card-body { padding: 16px 18px; display: flex; flex-direction: column; gap: 6px; flex: 1; }\n159:.card-price { font-family: var(--font-display); font-weight: 700; font-size: 21px; letter-spacing: -0.02em; }\n160:.card-title { font-weight: 600; font-size: 14.5px; color: var(--ink); overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }\n161:.card-meta { color: var(--ink-3); font-size: 12.5px; display: flex; gap: 7px; flex-wrap: wrap; align-items: center; font-family: var(--font-mono); }\n162:.card-meta .sep { width: 4px; height: 4px; background: var(--lime); border: 1px solid var(--ink); border-radius: 1px; transform: rotate(45deg); }\n163:.card-specs { display: flex; gap: 12px; font-size: 12.5px; color: var(--ink-2); font-family: var(--font-mono); }\n164:.card-foot { margin-top: auto; padding-top: 11px; border-top: 1.5px dashed var(--line); display: flex; justify-content: space-between; align-items: center; gap: 8px; }\n165:.source-tag { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.08em; color: var(--green-deep); background: var(--lime-soft); border: 1px solid var(--green); border-radius: 4px; padding: 3px 8px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; max-width: 62%; }\n166:.avail { font-size: 11px; color: var(--ink-3); font-weight: 500; text-align: right; font-family: var(--font-mono); }\n168:/* ================= Skeletons ================= */\n170:.skel { border-radius: var(--r-card); border: 1.5px solid var(--line); overflow: hidden; background: var(--surface); }\n171:.skel .sk-img, .skel .sk-line { background: linear-gradient(90deg, #eeece5 25%, #f7f5ef 50%, #eeece5 75%); background-size: 800px 100%; animation: shimmer 1.4s infinite linear; }\n172:.skel .sk-img { aspect-ratio: 16/10.5; }","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"8f6fc540-dea1-44f0-9b67-e91d9436d0ba","timestamp":"2026-08-24T20:57:39.192Z","tool_use_result":{"stdout":"27:.mono { font-family: var(--font-mono); }\n28:.kicker {\n33:.kicker::before { content: \"\"; width: 22px; height: 2px; background: var(--green); }\n35:.container { max-width: 1240px; margin: 0 auto; padding: 0 24px; }\n36:@media (max-width: 640px) { .container { padding: 0 16px; } }\n38:/* ================= Header ================= */\n39:.header {\n46:.header:has(.mobile-menu.open) { z-index: var(--z-modal, 900); }\n47:.header-inner { display: flex; align-items: center; gap: 20px; height: 64px; }\n48:.brand { font-family: var(--font-display); font-weight: 700; font-size: 26px; letter-spacing: -0.04em; display: flex; align-items: center; line-height: 1; }\n49:.brand .ka { background: var(--ink); color: var(--lime); padding: 2px 7px 4px; border-radius: 6px; margin-left: 3px; transform: rotate(-2deg); transition: transform 0.2s ease; }\n50:.brand:hover .ka { transform: rotate(0deg); }\n51:.brand-tag { font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-3); letter-spacing: 0.08em; text-transform: uppercase; margin-left: 12px; }\n52:@media (max-width: 860px) { .brand-tag { display: none; } }\n53:.nav { margin-left: auto; display: flex; gap: 4px; }\n54:.nav a { padding: 9px 16px; border-radius: 999px; font-weight: 600; font-size: 14px; color: var(--ink-2); border: 1.5px solid transparent; transition: all 0.15s ease; min-height: 40px; display: inline-flex; align-items: center; }\n55:.nav a:hover { border-color: var(--ink); color: var(--ink); }\n56:.nav a.active { background: var(--ink); color: var(--lime); }\n58:/* ================= Ticker ================= */\n59:.ticker { background: var(--ink); color: var(--lime); overflow: hidden; font-family: var(--font-mono); font-size: 11.5px; letter-spacing: 0.1em; text-transform: uppercase; padding: 7px 0; white-space: nowrap; border-bottom: 1px solid rgba(226, 55, 68, 0.25); }\n60:.ticker-track { display: inline-flex; gap: 0; animation: ticker 48s linear infinite; will-change: transform; }\n61:.ticker span { padding: 0 26px; position: relative; }\n62:.ticker span::after { content: \"◆\"; position: absolute; right: -6px; opacity: 0.5; font-size: 8px; top: 3px; }\n64:@media (prefers-reduced-motion: reduce) { .ticker-track { animation: none; } * { transition-duration: 0.01ms !important; } }\n66:/* ================= Hero ================= */\n67:.hero { padding: 58px 0 22px; }\n68:@media (max-width: 640px) { .hero { padding: 34px 0 14px; } }\n69:.hero h1 { font-size: clamp(38px, 6.4vw, 78px); font-weight: 700; line-height: 0.98; text-transform: uppercase; letter-spacing: -0.035em; max-width: 940px; margin-top: 14px; }\n70:.hero h1 .outline { color: transparent; -webkit-text-stroke: 2px var(--ink); }\n71:.hero h1 .hl { background: var(--lime); color: #fff; padding: 0 10px; border-radius: 8px; display: inline-block; transform: rotate(-1deg); }\n72:.hero p.lede { color: var(--ink-2); font-size: 16.5px; max-width: 680px; margin: 20px 0 0; }\n73:.stat-row { display: flex; flex-wrap: wrap; gap: 10px; margin-top: 26px; }\n74:.stat-chip { background: var(--surface); border: 1.5px solid var(--ink); border-radius: 999px; padding: 8px 16px; font-family: var(--font-mono); font-size: 12px; color: var(--ink-2); display: flex; gap: 8px; align-items: center; box-shadow: 3px 3px 0 rgba(26, 18, 20, 0.12); }\n75:.stat-chip b { color: var(--ink); font-weight: 700; }\n76:.stat-chip .pulse { width: 8px; height: 8px; border-radius: 50%; background: var(--green); box-shadow: 0 0 0 4px var(--lime-soft); animation: pulse 2.4s ease infinite; }\n79:/* ================= Filter bar ================= */\n80:.filterbar { background: var(--surface); border: 2px solid var(--ink); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); padding: 14px; margin: 30px 0 6px; display: flex; flex-direction: column; gap: 0; min-width: 0; }\n81:.f-primary { display: flex; gap: 10px; align-items: stretch; flex-wrap: wrap; min-width: 0; }\n82:.f-search { flex: 1 1 240px; min-width: 0; display: flex; align-items: center; gap: 9px; border: 1.5px solid var(--ink); border-radius: 9px; background: var(--surface-2); padding: 0 13px; min-height: 52px; color: var(--ink-2); transition: box-shadow 0.15s ease; }\n83:.f-search:focus-within { box-shadow: 3px 3px 0 var(--lime); background: var(--surface); }\n84:.f-search input { border: none; background: none; outline: none; flex: 1; min-width: 0; font-size: 15px; color: var(--ink); font-family: inherit; }\n85:.f-clear { border: none; background: var(--line); color: var(--ink-2); border-radius: 50%; width: 20px; height: 20px; font-size: 10px; cursor: pointer; flex: none; display: grid; place-items: center; }\n86:.f-ctl { flex: 0 1 auto; min-width: 0; display: flex; flex-direction: column; justify-content: center; gap: 2px; border: 1.5px solid var(--ink); border-radius: 9px; background: var(--surface); padding: 7px 12px 6px; min-height: 52px; cursor: pointer; transition: box-shadow 0.15s ease; }\n87:.f-ctl:focus-within { box-shadow: 3px 3px 0 var(--lime); }\n88:.f-ctl > span { font-family: var(--font-mono); font-size: 9px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); }\n89:.f-ctl select { border: none; background: transparent; outline: none; font-family: var(--font-display); font-weight: 700; font-size: 14.5px; color: var(--ink); cursor: pointer; appearance: none; -webkit-appearance: none; padding-right: 16px; min-width: 0; max-width: 180px; text-overflow: ellipsis; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='9' height='5'%3E%3Cpath d='M0 0l4.5 5L9 0z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right center; }\n90:.range-pair { display: flex; align-items: center; gap: 4px; }\n91:.range-pair select { max-width: 92px; }\n92:.range-sep { color: var(--ink-3); font-family: var(--font-mono); font-size: 11px; }\n93:.f-more { flex: none; align-self: stretch; border: 1.5px solid var(--ink); border-radius: 9px; background: var(--surface); color: var(--ink); padding: 0 18px; cursor: pointer; font-family: var(--font-display); font-weight: 700; font-size: 14px; min-height: 52px; transition: all 0.13s ease; }\n94:.f-more:hover { background: var(--lime-soft); }\n95:.f-more.on { background: var(--ink); color: var(--lime); box-shadow: 3px 3px 0 rgba(26, 18, 20,0.22); }\n96:.f-adv { display: grid; grid-template-columns: repeat(auto-fit, minmax(230px, 1fr)); gap: 16px 22px; border-top: 1.5px dashed var(--line); margin-top: 14px; padding-top: 14px; min-width: 0; animation: adv-in 0.18s ease; }\n98:.f-group { display: flex; flex-direction: column; gap: 7px; min-width: 0; }\n99:.f-group > label { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); }\n100:.f-group-end { justify-content: flex-end; }\n101:.f-group .btn:disabled { opacity: 0.4; cursor: default; }\n102:.f-native { border: 1.5px solid var(--line); background: var(--surface-2); border-radius: var(--r-ctl); padding: 10px 30px 10px 12px; font-size: 14px; color: var(--ink); outline: none; font-family: inherit; min-height: 42px; width: 100%; min-width: 0; appearance: none; -webkit-appearance: none; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='10' height='6'%3E%3Cpath d='M0 0l5 6 5-6z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right 12px center; }\n103:.f-native:focus { border-color: var(--ink); box-shadow: 3px 3px 0 var(--lime); }\n104:.seg { display: inline-flex; flex-wrap: wrap; row-gap: 6px; }\n105:.seg button { border: 1.5px solid var(--ink); background: var(--surface); color: var(--ink-2); padding: 8px 13px; font-family: var(--font-display); font-weight: 600; font-size: 13px; cursor: pointer; margin-left: -1.5px; white-space: nowrap; min-height: 38px; transition: all 0.12s ease; }\n106:.seg button:first-child { border-radius: 8px 0 0 8px; margin-left: 0; }\n107:.seg button:last-child { border-radius: 0 8px 8px 0; }\n108:.seg button:hover { background: var(--lime-soft); color: var(--ink); }\n109:.seg button.on { background: var(--ink); color: var(--lime); position: relative; z-index: 1; }\n111:.pills { display: flex; flex-wrap: wrap; gap: 8px; margin: 10px 0 2px; }\n112:.pill { display: inline-flex; align-items: center; gap: 7px; border: 1.5px solid var(--ink); background: var(--lime-soft); color: var(--ink); border-radius: 999px; padding: 6px 13px; font-size: 12.5px; font-weight: 600; font-family: var(--font-mono); cursor: pointer; transition: all 0.13s ease; }\n113:.pill:hover { background: var(--lime); color: #fff; }\n114:.pill-x { font-size: 10px; opacity: 0.65; }\n115:.pill-clear { background: var(--surface); border-color: var(--danger); color: var(--danger); }\n116:.pill-clear:hover { background: var(--danger); color: #fff; }\n117:.link-btn { background: none; border: none; padding: 0; color: var(--green); font: inherit; text-decoration: underline; cursor: pointer; }\n119:.btn { border: 1.5px solid var(--ink); border-radius: var(--r-ctl); padding: 11px 20px; font-weight: 700; font-size: 14px; cursor: pointer; min-height: 44px; transition: transform 0.12s ease, box-shadow 0.12s ease, background 0.15s ease; font-family: var(--font-display); letter-spacing: 0.01em; }\n120:.btn:active { transform: translate(2px, 2px); box-shadow: none !important; }\n121:.btn-primary { background: var(--ink); color: var(--lime); box-shadow: 4px 4px 0 rgba(26, 18, 20,0.25); }\n122:.btn-primary:hover { background: var(--green-deep); }\n123:.btn-ghost { background: transparent; color: var(--ink); }\n124:.btn-ghost:hover { background: var(--lime); color: #fff; box-shadow: 4px 4px 0 rgba(26, 18, 20,0.2); }\n126:/* ================= Chips ================= */\n127:.chips { display: flex; gap: 8px; margin: 16px 0 4px; overflow-x: auto; padding-bottom: 6px; scrollbar-width: none; }\n128:.chips::-webkit-scrollbar { display: none; }\n129:.chip { border: 1.5px solid var(--ink); background: var(--surface); color: var(--ink); border-radius: 999px; padding: 8px 18px; font-size: 13.5px; font-weight: 600; cursor: pointer; font-family: var(--font-display); white-space: nowrap; min-height: 40px; transition: all 0.13s ease; }\n130:.chip:hover { background: var(--lime-soft); transform: translateY(-1px); }\n131:.chip.on { background: var(--ink); color: var(--lime); box-shadow: 3px 3px 0 rgba(26, 18, 20,0.2); }\n133:/* ================= Results ================= */\n134:.results-head { display: flex; align-items: baseline; gap: 14px; margin: 28px 0 18px; flex-wrap: wrap; }\n135:.results-head h2 { font-size: 22px; text-transform: uppercase; letter-spacing: -0.02em; }\n136:.results-head > span { font-family: var(--font-mono); color: var(--ink-3); font-size: 12px; letter-spacing: 0.06em; }\n137:.results-tools { display: flex; align-items: center; gap: 12px; margin-left: auto; }\n138:.sort-ctl select { border: 1.5px solid var(--line-strong); background: var(--surface); border-radius: var(--r-ctl); padding: 7px 28px 7px 12px; font-family: var(--font-mono); font-size: 12px; color: var(--ink); cursor: pointer; appearance: none; -webkit-appearance: none; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='10' height='6'%3E%3Cpath d='M0 0l5 6 5-6z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right 10px center; }\n139:.view-toggle { display: inline-flex; border: 1.5px solid var(--line-strong); border-radius: var(--r-ctl); overflow: hidden; background: var(--surface); box-shadow: var(--shadow-flat); }\n140:.view-toggle button { border: 0; background: transparent; padding: 7px 14px; cursor: pointer; font-family: var(--font-mono); font-size: 12px; color: var(--ink-2); }\n141:.view-toggle button + button { border-left: 1.5px solid var(--line-strong); }\n142:.view-toggle button.on { background: var(--ink); color: var(--lime); }\n144:.grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(290px, 1fr)); gap: 22px; padding-bottom: 20px; }\n145:@media (max-width: 640px) { .grid { grid-template-columns: 1fr; gap: 16px; } }\n146:.more-wrap { display: flex; justify-content: center; padding: 8px 0 70px; }\n148:.card { background: var(--surface); border: 1.5px solid var(--line-strong); border-radius: var(--r-card); overflow: hidden; display: flex; flex-direction: column; box-shadow: var(--shadow-flat); transition: transform 0.16s ease, box-shadow 0.16s ease; }\n149:.card:hover { transform: translate(-3px, -3px); box-shadow: var(--shadow-off); }\n150:.card:focus-visible { outline: 3px solid var(--lime); outline-offset: 2px; }\n151:.card-img { position: relative; aspect-ratio: 16/10.5; background: repeating-linear-gradient(45deg, #eceae3 0 12px, #f3f1ea 12px 24px); overflow: hidden; }\n152:.card-img img { width: 100%; height: 100%; object-fit: cover; transition: transform 0.4s ease; }\n153:.card:hover .card-img img { transform: scale(1.05); }\n154:.card-img .noimg { display: flex; align-items: center; justify-content: center; height: 100%; color: var(--ink-3); font-size: 34px; }\n155:.badge { position: absolute; top: 12px; left: 12px; border-radius: 6px; padding: 4px 10px; font-family: var(--font-mono); font-size: 11.5px; font-weight: 700; letter-spacing: 0.04em; background: rgba(255, 255, 255, 0.95); color: var(--ink); border: 1px solid var(--ink); }\n156:.badge.type { background: var(--ink); color: var(--lime); border-color: var(--ink); }\n157:.badge.right { left: auto; right: 12px; background: rgba(255,255,255,0.92); border-color: transparent; }\n158:.card-body { padding: 16px 18px; display: flex; flex-direction: column; gap: 6px; flex: 1; }\n159:.card-price { font-family: var(--font-display); font-weight: 700; font-size: 21px; letter-spacing: -0.02em; }\n160:.card-title { font-weight: 600; font-size: 14.5px; color: var(--ink); overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }\n161:.card-meta { color: var(--ink-3); font-size: 12.5px; display: flex; gap: 7px; flex-wrap: wrap; align-items: center; font-family: var(--font-mono); }\n162:.card-meta .sep { width: 4px; height: 4px; background: var(--lime); border: 1px solid var(--ink); border-radius: 1px; transform: rotate(45deg); }\n163:.card-specs { display: flex; gap: 12px; font-size: 12.5px; color: var(--ink-2); font-family: var(--font-mono); }\n164:.card-foot { margin-top: auto; padding-top: 11px; border-top: 1.5px dashed var(--line); display: flex; justify-content: space-between; align-items: center; gap: 8px; }\n165:.source-tag { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.08em; color: var(--green-deep); background: var(--lime-soft); border: 1px solid var(--green); border-radius: 4px; padding: 3px 8px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; max-width: 62%; }\n166:.avail { font-size: 11px; color: var(--ink-3); font-weight: 500; text-align: right; font-family: var(--font-mono); }\n168:/* ================= Skeletons ================= */\n170:.skel { border-radius: var(--r-card); border: 1.5px solid var(--line); overflow: hidden; background: var(--surface); }\n171:.skel .sk-img, .skel .sk-line { background: linear-gradient(90deg, #eeece5 25%, #f7f5ef 50%, #eeece5 75%); background-size: 800px 100%; animation: shimmer 1.4s infinite linear; }\n172:.skel .sk-img { aspect-ratio: 16/10.5; }","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNF8ibMexWxYZjBTXYKu","type":"message","role":"assistant","content":[{"type":"text","text":"J'ai le portrait complet. Je vérifie maintenant l'accent d'immo-ka, le composant QuartierBlock, le script check-order, et je récupère un uid de fiche pour les captures."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5579,"cache_read_input_tokens":65348,"cache_creation":{"ephemeral_5m_input_tokens":5579,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"3b581cc4-bcb6-4354-808e-d02e9d08a5ca"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNF8ibMexWxYZjBTXYKu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014tyHMSyxJw4kkMFo27gzK8","name":"Bash","input":{"command":"grep -rn \"accent\" /Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css | head ; grep -n \"accent\" /Users/simon-pierreboucher/apps/immo-ka/frontend/src/ka/tokens.css | grep -v \"^\\s*/\\*\" | sed -n '1,20p' ; grep -rn \"accent\\|#e23744\\|#e2374\" /Users/simon-pierreboucher/apps/immo-ka/frontend/index.html /Users/simon-pierreboucher/apps/immo-ka/frontend/src/main.tsx 2>/dev/null | head","description":"Find immo-ka accent override"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5579,"cache_read_input_tokens":65348,"cache_creation":{"ephemeral_5m_input_tokens":5579,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"75e00ef5-df14-4181-b8df-9f10f1ef684f"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014tyHMSyxJw4kkMFo27gzK8","type":"tool_result","content":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:8:    · SEUL l'accent de marque est surchargé : CERISE Immo-Ka.\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:12:  --accent: #e23744;\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:13:  --accent-soft: #fbe0e2;\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:14:  --accent-deep: #a8232e;\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:15:  --on-accent: #ffffff;\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:17:  --lime: var(--accent);\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:18:  --lime-soft: var(--accent-soft);\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:397:  --ka-accent: var(--accent);\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:398:  --ka-on-accent: var(--on-accent);\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:429:  background: var(--accent); border: 1px solid rgba(26, 18, 20, 0.25);\n6:           fichier PUIS surcharge uniquement son accent (voir accents.css).\n11:      surlignés d'accent inclinés (néo-brutalisme raffiné)\n34:  --accent: #d9f26b;        /* défaut : lime Groupe KA */\n35:  --accent-soft: #f0f9d2;\n36:  --accent-deep: #123f2e;\n37:  --on-accent: var(--ink);  /* couleur du texte posé SUR l'accent */\n40:  --lime: var(--accent);\n41:  --lime-soft: var(--accent-soft);\n114:::selection { background: var(--accent); color: var(--on-accent); }\n142:  display: inline-block; background: var(--accent); color: var(--on-accent);\n160:.btn-primary { background: var(--ink); color: var(--accent); }\n161:.btn-primary:hover { background: var(--accent-deep); }\n162:.btn-accent { background: var(--accent); color: var(--on-accent); }\n183:.chip-accent { background: var(--accent); color: var(--on-accent); }\n184:.chip-soft { background: var(--accent-soft); border-color: var(--line); }\n193:  outline: none; box-shadow: 3px 3px 0 var(--accent); border-color: var(--ink);\n211:  display: inline-block; background: var(--ink); color: var(--accent);\n222:.ka-footer .wordmark .ka { color: var(--accent); }\n224:.ka-footer .notice { max-width: 640px; margin-top: var(--sp-4); border-left: 2px solid var(--accent); padding-left: var(--sp-4); }\n228:.ka-footer .sites a:hover { color: var(--accent); text-decoration: underline; text-underline-offset: 4px; }","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"6c6db3ef-d76f-4d8c-ba9f-3d9f5ee429c8","timestamp":"2026-08-24T20:59:08.146Z","tool_use_result":{"stdout":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:8:    · SEUL l'accent de marque est surchargé : CERISE Immo-Ka.\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:12:  --accent: #e23744;\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:13:  --accent-soft: #fbe0e2;\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:14:  --accent-deep: #a8232e;\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:15:  --on-accent: #ffffff;\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:17:  --lime: var(--accent);\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:18:  --lime-soft: var(--accent-soft);\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:397:  --ka-accent: var(--accent);\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:398:  --ka-on-accent: var(--on-accent);\n/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css:429:  background: var(--accent); border: 1px solid rgba(26, 18, 20, 0.25);\n6:           fichier PUIS surcharge uniquement son accent (voir accents.css).\n11:      surlignés d'accent inclinés (néo-brutalisme raffiné)\n34:  --accent: #d9f26b;        /* défaut : lime Groupe KA */\n35:  --accent-soft: #f0f9d2;\n36:  --accent-deep: #123f2e;\n37:  --on-accent: var(--ink);  /* couleur du texte posé SUR l'accent */\n40:  --lime: var(--accent);\n41:  --lime-soft: var(--accent-soft);\n114:::selection { background: var(--accent); color: var(--on-accent); }\n142:  display: inline-block; background: var(--accent); color: var(--on-accent);\n160:.btn-primary { background: var(--ink); color: var(--accent); }\n161:.btn-primary:hover { background: var(--accent-deep); }\n162:.btn-accent { background: var(--accent); color: var(--on-accent); }\n183:.chip-accent { background: var(--accent); color: var(--on-accent); }\n184:.chip-soft { background: var(--accent-soft); border-color: var(--line); }\n193:  outline: none; box-shadow: 3px 3px 0 var(--accent); border-color: var(--ink);\n211:  display: inline-block; background: var(--ink); color: var(--accent);\n222:.ka-footer .wordmark .ka { color: var(--accent); }\n224:.ka-footer .notice { max-width: 640px; margin-top: var(--sp-4); border-left: 2px solid var(--accent); padding-left: var(--sp-4); }\n228:.ka-footer .sites a:hover { color: var(--accent); text-decoration: underline; text-underline-offset: 4px; }","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNF8ibMexWxYZjBTXYKu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016WFG46DVLwcACWx5W5YAGd","name":"Bash","input":{"command":"head -30 /Users/simon-pierreboucher/apps/immo-ka/frontend/src/components/QuartierBlock.tsx | grep -n \"section\\|className\" ; head -40 /Users/simon-pierreboucher/apps/immo-ka/frontend/scripts/check-order.mjs ; curl -s \"http://localhost:8096/api/listings?limit=3\" | python3 -c \"import json,sys; d=json.load(sys.stdin); items=d.get('listings') or d.get('items') or d.get('results') or d; print(json.dumps(items if isinstance(items,list) else list(d.keys()), default=str)[:600])\"","description":"Check QuartierBlock root, check-order script, sample uid"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5579,"cache_read_input_tokens":65348,"cache_creation":{"ephemeral_5m_input_tokens":5579,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"23d71169-2962-4b04-a0a4-75ec3b044440"}}
{"type":"permission_request","requestId":"f4587d4d6397c60e","tool":"Bash","input":{"command":"head -30 /Users/simon-pierreboucher/apps/immo-ka/frontend/src/components/QuartierBlock.tsx | grep -n \"section\\|className\" ; head -40 /Users/simon-pierreboucher/apps/immo-ka/frontend/scripts/check-order.mjs ; curl -s \"http://localhost:8096/api/listings?limit=3\" | python3 -c \"import json,sys; d=json.load(sys.stdin); items=d.get('listings') or d.get('items') or d.get('results') or d; print(json.dumps(items if isinstance(items,list) else list(d.keys()), default=str)[:600])\"","description":"Check QuartierBlock root, check-order script, sample uid"}}
{"type":"permission_resolved","requestId":"f4587d4d6397c60e","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016WFG46DVLwcACWx5W5YAGd","type":"tool_result","content":"4:// components/QuartierBlock.tsx : section « Le quartier » de la fiche —\n// Validation ordre des sections — fiche Immo-Ka (ordre DOM = ordre visuel)\nimport { chromium, devices } from \"playwright\";\n\nconst BASE = process.env.BASE || \"http://localhost:18096\";\nconst UID = process.argv[2];\nconst URL = `${BASE}/propriete/${encodeURIComponent(UID)}`;\nconst SEL = [\".f-galerie\", \".f-hero\", \".f-desc\", \"#caracteristiques\", \"#pieces\", \"#inclusions\", \"#carte\", \".quartier\"];\n\nasync function check(name, ctxOpts) {\n  const browser = await chromium.launch();\n  const ctx = await browser.newContext(ctxOpts);\n  const page = await ctx.newPage();\n  await page.goto(URL, { waitUntil: \"networkidle\" });\n  await page.waitForSelector(\".f-galerie\", { timeout: 15000 });\n  await page.waitForTimeout(1200);\n  const data = await page.evaluate((sel) => {\n    const out = [];\n    for (const s of sel) {\n      const el = document.querySelector(s);\n      if (!el) { out.push({ s, missing: true }); continue; }\n      const r = el.getBoundingClientRect();\n      out.push({ s, hidden: r.height === 0 && r.width === 0, top: Math.round(r.top + window.scrollY), left: Math.round(r.left), order: getComputedStyle(el).order });\n    }\n    return { out, scrollY: window.scrollY };\n  }, SEL);\n  console.log(`\\n=== ${name} ===  scrollY: ${data.scrollY}`);\n  for (const b of data.out)\n    console.log(b.missing ? `${b.s.padEnd(18)} (non rendue)` : b.hidden ? `${b.s.padEnd(18)} (vide/masquée)` :\n      `${b.s.padEnd(18)} top=${String(b.top).padStart(6)}  left=${String(b.left).padStart(4)}  order=${b.order}`);\n  await browser.close();\n  return data;\n}\n\nconst mob = await check(\"iPhone 14 (mobile)\", { ...devices[\"iPhone 14\"] });\nawait check(\"Desktop 1440px\", { viewport: { width: 1440, height: 900 } });\nconst vis = mob.out.filter(b => !b.missing && !b.hidden);\nconst sorted = vis.every((b, i) => i === 0 || b.top >= vis[i - 1].top);\nconst ok = sorted && mob.scrollY === 0 && vis[0].s === \".f-galerie\" && vis.every(b => b.order === \"0\");\nconsole.log(`\\nMOBILE: ordre ${sorted ? \"CROISSANT ✓\" : \"DÉSORDONNÉ ✗\"} · scrollY=${mob.scrollY} · 1re=${vis[0].s} · sans order=${vis.every(b => b.order === \"0\")}`);\nconsole.log(ok ? \"VALIDATION OK\" : \"VALIDATION ÉCHEC\");\n[{\"uid\": \"si_ag_jga:25467828\", \"source\": \"si_ag_jga\", \"external_id\": \"25467828\", \"url\": \"https://jgacommercial.com/propriete/25467828\", \"title\": \"Rue R\\u00e9mi-Duquette\", \"address\": \"Rue R\\u00e9mi-Duquette\", \"sector\": \"\", \"city\": \"Mirabel\", \"region\": \"15\", \"property_type\": \"Terrain\", \"price\": 35.0, \"price_label\": \"35 $\", \"bedrooms\": null, \"bathrooms\": null, \"powder_rooms\": null, \"area_sqft\": 6867.0, \"lot_sqft\": 105158.84, \"year_built\": null, \"mls\": \"25467828\", \"status\": \"a-vendre\", \"broker_name\": \"JGA \\u00c9quipe Commerciale (Royal LePage)\", \"broker_phone\": \"(514) 953-9940\", \"description\": \"Op","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"72b9fe4f-826e-4059-b08a-24c15121f225","timestamp":"2026-08-24T20:59:23.426Z","tool_use_result":{"stdout":"4:// components/QuartierBlock.tsx : section « Le quartier » de la fiche —\n// Validation ordre des sections — fiche Immo-Ka (ordre DOM = ordre visuel)\nimport { chromium, devices } from \"playwright\";\n\nconst BASE = process.env.BASE || \"http://localhost:18096\";\nconst UID = process.argv[2];\nconst URL = `${BASE}/propriete/${encodeURIComponent(UID)}`;\nconst SEL = [\".f-galerie\", \".f-hero\", \".f-desc\", \"#caracteristiques\", \"#pieces\", \"#inclusions\", \"#carte\", \".quartier\"];\n\nasync function check(name, ctxOpts) {\n  const browser = await chromium.launch();\n  const ctx = await browser.newContext(ctxOpts);\n  const page = await ctx.newPage();\n  await page.goto(URL, { waitUntil: \"networkidle\" });\n  await page.waitForSelector(\".f-galerie\", { timeout: 15000 });\n  await page.waitForTimeout(1200);\n  const data = await page.evaluate((sel) => {\n    const out = [];\n    for (const s of sel) {\n      const el = document.querySelector(s);\n      if (!el) { out.push({ s, missing: true }); continue; }\n      const r = el.getBoundingClientRect();\n      out.push({ s, hidden: r.height === 0 && r.width === 0, top: Math.round(r.top + window.scrollY), left: Math.round(r.left), order: getComputedStyle(el).order });\n    }\n    return { out, scrollY: window.scrollY };\n  }, SEL);\n  console.log(`\\n=== ${name} ===  scrollY: ${data.scrollY}`);\n  for (const b of data.out)\n    console.log(b.missing ? `${b.s.padEnd(18)} (non rendue)` : b.hidden ? `${b.s.padEnd(18)} (vide/masquée)` :\n      `${b.s.padEnd(18)} top=${String(b.top).padStart(6)}  left=${String(b.left).padStart(4)}  order=${b.order}`);\n  await browser.close();\n  return data;\n}\n\nconst mob = await check(\"iPhone 14 (mobile)\", { ...devices[\"iPhone 14\"] });\nawait check(\"Desktop 1440px\", { viewport: { width: 1440, height: 900 } });\nconst vis = mob.out.filter(b => !b.missing && !b.hidden);\nconst sorted = vis.every((b, i) => i === 0 || b.top >= vis[i - 1].top);\nconst ok = sorted && mob.scrollY === 0 && vis[0].s === \".f-galerie\" && vis.every(b => b.order === \"0\");\nconsole.log(`\\nMOBILE: ordre ${sorted ? \"CROISSANT ✓\" : \"DÉSORDONNÉ ✗\"} · scrollY=${mob.scrollY} · 1re=${vis[0].s} · sans order=${vis.every(b => b.order === \"0\")}`);\nconsole.log(ok ? \"VALIDATION OK\" : \"VALIDATION ÉCHEC\");\n[{\"uid\": \"si_ag_jga:25467828\", \"source\": \"si_ag_jga\", \"external_id\": \"25467828\", \"url\": \"https://jgacommercial.com/propriete/25467828\", \"title\": \"Rue R\\u00e9mi-Duquette\", \"address\": \"Rue R\\u00e9mi-Duquette\", \"sector\": \"\", \"city\": \"Mirabel\", \"region\": \"15\", \"property_type\": \"Terrain\", \"price\": 35.0, \"price_label\": \"35 $\", \"bedrooms\": null, \"bathrooms\": null, \"powder_rooms\": null, \"area_sqft\": 6867.0, \"lot_sqft\": 105158.84, \"year_built\": null, \"mls\": \"25467828\", \"status\": \"a-vendre\", \"broker_name\": \"JGA \\u00c9quipe Commerciale (Royal LePage)\", \"broker_phone\": \"(514) 953-9940\", \"description\": \"Op","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"9eaae46f-d685-4c95-bcd6-35630837c8bd","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFBTMf3nDCc1L3A9eFz","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4220,"cache_read_input_tokens":70927,"cache_creation":{"ephemeral_5m_input_tokens":4220,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"421f14e7-7e8d-44dd-bdd7-785121e8f582"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFBTMf3nDCc1L3A9eFz","type":"message","role":"assistant","content":[{"type":"text","text":"Accent immo-ka = cerise `#e23744` via `--accent`. Le script check-order existe et Playwright est dispo. Je cherche une fiche riche (photos, pièces, quartier) et je prends les captures AVANT."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4220,"cache_read_input_tokens":70927,"cache_creation":{"ephemeral_5m_input_tokens":4220,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"077d8827-4d2a-4ae2-a368-bd5a2eb559f4"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFBTMf3nDCc1L3A9eFz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01ADtq66m44VPNvCGYvjjxqn","name":"Bash","input":{"command":"curl -s \"http://localhost:8096/api/listings?limit=60\" | python3 -c \"\nimport json,sys\nd=json.load(sys.stdin)\nitems=d.get('listings') or d.get('items') or d\nbest=None\nfor l in items:\n    score=(len(l.get('images') or [])>=5)*2+(l.get('bedrooms') is not None)+(l.get('description') is not None)+(l.get('lat') is not None)*2\n    if best is None or score>best[0]: best=(score,l['uid'],l.get('city'),len(l.get('images') or []))\nprint(best)\"","description":"Pick a rich listing uid"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4220,"cache_read_input_tokens":70927,"cache_creation":{"ephemeral_5m_input_tokens":4220,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"0e5dd36b-384d-4814-b216-a764c9f5a9b2"}}
{"type":"permission_request","requestId":"1088c98f40189ea2","tool":"Bash","input":{"command":"curl -s \"http://localhost:8096/api/listings?limit=60\" | python3 -c \"\nimport json,sys\nd=json.load(sys.stdin)\nitems=d.get('listings') or d.get('items') or d\nbest=None\nfor l in items:\n    score=(len(l.get('images') or [])>=5)*2+(l.get('bedrooms') is not None)+(l.get('description') is not None)+(l.get('lat') is not None)*2\n    if best is None or score>best[0]: best=(score,l['uid'],l.get('city'),len(l.get('images') or []))\nprint(best)\"","description":"Pick a rich listing uid"}}
{"type":"permission_resolved","requestId":"1088c98f40189ea2","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ADtq66m44VPNvCGYvjjxqn","type":"tool_result","content":"(6, 'domicilia:17516934', 'Nominingue', 13)","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"299b9819-b63e-499d-bb64-2d686a7fda32","timestamp":"2026-08-24T20:59:42.277Z","tool_use_result":{"stdout":"(6, 'domicilia:17516934', 'Nominingue', 13)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"aa38b467-d410-41dd-8ebd-00484d02cde3","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFCqsX5FmgpQxSrZd92","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UoS4JcYXpTmS8s3Pkf9E1m","name":"Bash","input":{"command":"mkdir -p /tmp/immo-shots && cd /Users/simon-pierreboucher/apps/immo-ka/frontend && cat > /tmp/immo-shots/shot.mjs <<'EOF'\nimport { chromium, devices } from \"playwright\";\nconst BASE = process.env.BASE || \"http://localhost:8096\";\nconst UID = process.argv[2];\nconst TAG = process.argv[3] || \"avant\";\nconst URL = `${BASE}/propriete/${encodeURIComponent(UID)}`;\nconst browser = await chromium.launch();\nfor (const [name, opts] of [[\"desktop\", { viewport: { width: 1440, height: 900 } }], [\"mobile\", { ...devices[\"iPhone 14\"] }]]) {\n  const ctx = await browser.newContext(opts);\n  const page = await ctx.newPage();\n  await page.goto(URL, { waitUntil: \"networkidle\" });\n  await page.waitForSelector(\".f-hero\", { timeout: 15000 });\n  await page.waitForTimeout(2500);\n  const overflow = await page.evaluate(() => document.documentElement.scrollWidth - document.documentElement.clientWidth);\n  console.log(`${name}: overflow horizontal = ${overflow}px`);\n  await page.screenshot({ path: `/tmp/immo-shots/${TAG}-${name}-full.png`, fullPage: true });\n  for (const sel of [\".f-hero\", \"#caracteristiques\", \"#pieces\", \"#inclusions\", \".f-zi\", \".f-air\", \".f-commerces\", \".f-gaz\", \".f-hydro\", \".quartier\"]) {\n    const el = page.locator(sel).first();\n    if (await el.count()) { try { await el.scrollIntoViewIfNeeded(); await el.screenshot({ path: `/tmp/immo-shots/${TAG}-${name}-${sel.replace(/[.#]/g, \"\")}.png` }); } catch {} }\n  }\n  await ctx.close();\n}\nawait browser.close();\nEOF\nnode /tmp/immo-shots/shot.mjs \"domicilia:17516934\" avant","description":"Take Playwright BEFORE screenshots desktop+mobile","timeout":180000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":644,"cache_read_input_tokens":75147,"cache_creation":{"ephemeral_5m_input_tokens":644,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"1e1e3014-4cfc-4020-86e4-49c0733775e0"}}
{"type":"permission_request","requestId":"6ce7a99751ea6168","tool":"Bash","input":{"command":"mkdir -p /tmp/immo-shots && cd /Users/simon-pierreboucher/apps/immo-ka/frontend && cat > /tmp/immo-shots/shot.mjs <<'EOF'\nimport { chromium, devices } from \"playwright\";\nconst BASE = process.env.BASE || \"http://localhost:8096\";\nconst UID = process.argv[2];\nconst TAG = process.argv[3] || \"avant\";\nconst URL = `${BASE}/propriete/${encodeURIComponent(UID)}`;\nconst browser = await chromium.launch();\nfor (const [name, opts] of [[\"desktop\", { viewport: { width: 1440, height: 900 } }], [\"mobile\", { ...devices[\"iPhone 14\"] }]]) {\n  const ctx = await browser.newContext(opts);\n  const page = await ctx.newPage();\n  await page.goto(URL, { waitUntil: \"networkidle\" });\n  await page.waitForSelector(\".f-hero\", { timeout: 15000 });\n  await page.waitForTimeout(2500);\n  const overflow = await page.evaluate(() => document.documentElement.scrollWidth - document.documentElement.clientWidth);\n  console.log(`${name}: overflow horizontal = ${overflow}px`);\n  await page.screenshot({ path: `/tmp/immo-shots/${TAG}-${name}-full.png`, fullPage: true });\n  for (const sel of [\".f-hero\", \"#caracteristiques\", \"#pieces\", \"#inclusions\", \".f-zi\", \".f-air\", \".f-commerces\", \".f-gaz\", \".f-hydro\", \".quartier\"]) {\n    const el = page.locator(sel).first();\n    if (await el.count()) { try { await el.scrollIntoViewIfNeeded(); await el.screenshot({ path: `/tmp/immo-shots/${TAG}-${name}-${sel.replace(/[.#]/g, \"\")}.png` }); } catch {} }\n  }\n  await ctx.close();\n}\nawait browser.close();\nEOF\nnode /tmp/immo-shots/shot.mjs \"domicilia:17516934\" avant","timeout":180000,"description":"Take Playwright BEFORE screenshots desktop+mobile"}}
{"type":"permission_resolved","requestId":"6ce7a99751ea6168","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nnode:internal/modules/package_json_reader:301\n  throw new ERR_MODULE_NOT_FOUND(packageName, fileURLToPath(base), null);\n        ^\n\nError [ERR_MODULE_NOT_FOUND]: Cannot find package 'playwright' imported from /private/tmp/immo-shots/shot.mjs\n    at Object.getPackageJSONURL (node:internal/modules/package_json_reader:301:9)\n    at packageResolve (node:internal/modules/esm/resolve:764:81)\n    at moduleResolve (node:internal/modules/esm/resolve:855:18)\n    at defaultResolve (node:internal/modules/esm/resolve:988:11)\n    at #cachedDefaultResolve (node:internal/modules/esm/loader:697:20)\n    at #resolveAndMaybeBlockOnLoaderThread (node:internal/modules/esm/loader:714:38)\n    at ModuleLoader.resolveSync (node:internal/modules/esm/loader:746:52)\n    at #resolve (node:internal/modules/esm/loader:679:17)\n    at ModuleLoader.getOrCreateModuleJob (node:internal/modules/esm/loader:599:35)\n    at ModuleJob.syncLink (node:internal/modules/esm/module_job:162:33) {\n  code: 'ERR_MODULE_NOT_FOUND'\n}\n\nNode.js v25.9.0","is_error":true,"tool_use_id":"toolu_01UoS4JcYXpTmS8s3Pkf9E1m"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f87a7101-233d-48d6-967c-1b0931ccb17e","timestamp":"2026-08-24T21:00:00.645Z","tool_use_result":"Error: Exit code 1\nnode:internal/modules/package_json_reader:301\n  throw new ERR_MODULE_NOT_FOUND(packageName, fileURLToPath(base), null);\n        ^\n\nError [ERR_MODULE_NOT_FOUND]: Cannot find package 'playwright' imported from /private/tmp/immo-shots/shot.mjs\n    at Object.getPackageJSONURL (node:internal/modules/package_json_reader:301:9)\n    at packageResolve (node:internal/modules/esm/resolve:764:81)\n    at moduleResolve (node:internal/modules/esm/resolve:855:18)\n    at defaultResolve (node:internal/modules/esm/resolve:988:11)\n    at #cachedDefaultResolve (node:internal/modules/esm/loader:697:20)\n    at #resolveAndMaybeBlockOnLoaderThread (node:internal/modules/esm/loader:714:38)\n    at ModuleLoader.resolveSync (node:internal/modules/esm/loader:746:52)\n    at #resolve (node:internal/modules/esm/loader:679:17)\n    at ModuleLoader.getOrCreateModuleJob (node:internal/modules/esm/loader:599:35)\n    at ModuleJob.syncLink (node:internal/modules/esm/module_job:162:33) {\n  code: 'ERR_MODULE_NOT_FOUND'\n}\n\nNode.js v25.9.0"}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"85ebc746-fcc8-4efc-9e8c-90eeab8d3e8e","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFECdU2G6v2XH8PkX8k","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1335,"cache_read_input_tokens":75791,"cache_creation":{"ephemeral_5m_input_tokens":1335,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"d802b83c-1771-461f-80c6-c27e300d505c"}}
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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"69b79e13-3236-47a0-9301-9403702a4f43","timestamp":"2026-08-24T21:01:31.704Z","tool_use_result":{"type":"image","file":{"base64":"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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"be6c3456-fe9b-42b6-9186-ae195ff58ef1","timestamp":"2026-08-24T21:01:31.895Z","tool_use_result":{"type":"image","file":{"base64":"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flex-direction: column; gap: 8px; }\n592\t.q-bar { display: flex; align-items: center; gap: 10px; font-size: 13px; }\n593\t.q-bar-label { flex: 0 0 150px; color: var(--ink-2); }\n594\t.q-bar-label { flex-basis: 120px; font-size: 12px; }\n595\t.q-bar-track {\n596\t  flex: 1; height: 10px; background: var(--surface-2);\n597\t  border: 1px solid var(--line-strong); border-radius: 999px; overflow: hidden;\n598\t}\n599\t.q-bar-fill { display: block; height: 100%; background: var(--green); border-radius: 999px; }\n600\t.q-bar-num { flex: 0 0 30px; text-align: right; font-family: var(--font-mono);\n601\t  font-size: 11.5px; font-weight: 700; }\n602\t.q-badges { display: flex; flex-wrap: wrap; gap: 8px; margin-top: 16px; }\n603\t.q-badge {\n604\t  display: inline-flex; align-items: center; gap: 6px;\n605\t  border: 1.5px solid var(--line-strong); border-radius: 999px;\n606\t  padding: 7px 13px; font-size: 12.5px; background: var(--surface);\n607\t}\n608\t.q-badge.cool { background: var(--lime-soft); border-color: var(--green); color: var(--green-deep); }\n609\t.q-badge.hot { background: var(--amber-soft); border-color: var(--amber); color: #8a5a12; }\n610\t.quartier .fine { margin-top: 10px; }\n611\t.q-cell { padding: 9px 11px; }\n612\t.q-val { font-size: 16px; }\n613\t.q-bar { gap: 8px; font-size: 12px; }\n614\t.q-bar-track { height: 8px; }\n615\t.q-badge-sub { display: block; font-size: 10.5px; color: var(--ink-3); font-weight: 400; margin-left: 4px; }\n616\t\n617\t/* --- Vrai-Prix : estimation de valeur ---------------------------------- */\n618\t.vraiprix { border: 2px solid var(--ink); background: var(--surface-2);\n619\t  box-shadow: var(--shadow-off-soft); padding: 14px; margin: 14px 0; }\n620\t.vp-head { display: flex; align-items: center; justify-content: space-between; gap: 8px; }\n621\t.vp-logo { font-family: var(--font-display); font-weight: 700; color: var(--green-deep);"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f6c3eb9a-a5eb-4b79-96db-8c7dfab43c6b","timestamp":"2026-08-24T21:01:41.736Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css","content":".quartier { margin-top: 34px; }\n.quartier h2 { font-size: 24px; letter-spacing: -0.02em; }\n.q-sub { color: var(--ink-3); font-size: 13px; margin: 4px 0 16px; }\n.q-grid {\n  display: grid; grid-template-columns: repeat(3, 1fr); gap: 10px;\n}\n.q-grid { grid-template-columns: repeat(2, 1fr); }\n.q-cell {\n  background: var(--surface); border: 1.5px solid var(--line-strong);\n  border-radius: var(--r-card); padding: 12px 14px; box-shadow: var(--shadow-flat);\n}\n.q-val { font-family: var(--font-display); font-weight: 700; font-size: 19px; letter-spacing: -0.02em; }\n.q-label { font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-3);\n  text-transform: uppercase; letter-spacing: 0.06em; margin-top: 3px; }\n.q-prox { margin-top: 18px; display: flex; flex-direction: column; gap: 8px; }\n.q-bar { display: flex; align-items: center; gap: 10px; font-size: 13px; }\n.q-bar-label { flex: 0 0 150px; color: var(--ink-2); }\n.q-bar-label { flex-basis: 120px; font-size: 12px; }\n.q-bar-track {\n  flex: 1; height: 10px; background: var(--surface-2);\n  border: 1px solid var(--line-strong); border-radius: 999px; overflow: hidden;\n}\n.q-bar-fill { display: block; height: 100%; background: var(--green); border-radius: 999px; }\n.q-bar-num { flex: 0 0 30px; text-align: right; font-family: var(--font-mono);\n  font-size: 11.5px; font-weight: 700; }\n.q-badges { display: flex; flex-wrap: wrap; gap: 8px; margin-top: 16px; }\n.q-badge {\n  display: inline-flex; align-items: center; gap: 6px;\n  border: 1.5px solid var(--line-strong); border-radius: 999px;\n  padding: 7px 13px; font-size: 12.5px; background: var(--surface);\n}\n.q-badge.cool { background: var(--lime-soft); border-color: var(--green); color: var(--green-deep); }\n.q-badge.hot { background: var(--amber-soft); border-color: var(--amber); color: #8a5a12; }\n.quartier .fine { margin-top: 10px; }\n.q-cell { padding: 9px 11px; }\n.q-val { font-size: 16px; }\n.q-bar { gap: 8px; font-size: 12px; }\n.q-bar-track { height: 8px; }\n.q-badge-sub { display: block; font-size: 10.5px; color: var(--ink-3); font-weight: 400; margin-left: 4px; }\n\n/* --- Vrai-Prix : estimation de valeur ---------------------------------- */\n.vraiprix { border: 2px solid var(--ink); background: var(--surface-2);\n  box-shadow: var(--shadow-off-soft); padding: 14px; margin: 14px 0; }\n.vp-head { display: flex; align-items: center; justify-content: space-between; gap: 8px; }\n.vp-logo { font-family: var(--font-display); font-weight: 700; color: var(--green-deep);","numLines":45,"startLine":577,"totalLines":955}}}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFLw2YyFaUm1U3R2jfd","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014ZigfhQrTsCVkZzJgRYHuV","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css","offset":852,"limit":103},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1574,"cache_read_input_tokens":80649,"cache_creation":{"ephemeral_5m_input_tokens":1574,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"35e5da61-15d5-44c9-bf1e-4dfe3ec8b46e"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014ZigfhQrTsCVkZzJgRYHuV","type":"tool_result","content":"852\t.q-meter i { display: block; height: 100%; background: var(--green); }\n853\t\n854\t\n855\t/* ---- Criminalité détaillée SPVM (bloc quartier) ---- */\n856\t.q-crime { width: 100%; }\n857\t.q-crime-cats { list-style: none; margin: 8px 0 0; padding: 0; }\n858\t.q-crime-cats li { display: flex; align-items: center; gap: 8px;\n859\t                   padding: 3px 0; font-size: 12.5px; }\n860\t.q-crime-nom { flex: 0 0 46%; color: var(--ink-2, #555); overflow: hidden;\n861\t               text-overflow: ellipsis; white-space: nowrap; }\n862\t.q-crime-barre { flex: 1 1 auto; height: 10px; background: var(--surface-2, #f0ede8);\n863\t                 border-radius: 5px; overflow: hidden; }\n864\t.q-crime-barre i { display: block; height: 100%; border-radius: 5px;\n865\t                   background: var(--accent, #e23744); opacity: 0.55; }\n866\t.q-crime-n { flex: 0 0 52px; text-align: right; font-weight: 600;\n867\t             color: var(--ink, #222); white-space: nowrap; }\n868\t.q-crime-n small { font-weight: 400; color: var(--ink-3, #888); }\n869\t.q-crime-src { margin-top: 8px; }\n870\t\n871\t\n872\t/* ---- Risque d'inondation (fiche) — BDZI gouv. du Québec ---- */\n873\t.f-zi .zi-head { display: flex; align-items: center; gap: 10px; }\n874\t.zi-badge { display: inline-block; padding: 3px 10px; border-radius: 999px;\n875\t            font-size: 12.5px; font-weight: 700; }\n876\t.zi-eleve { background: #fde8e8; color: #a12622; }\n877\t.zi-modere { background: #fdf3e0; color: #8a5a00; }\n878\t.zi-present { background: #fdf3e0; color: #8a5a00; }\n879\t.zi-ok { background: #e7f4ea; color: #1e6b34; }\n880\t.zi-nc { background: var(--surface-2, #f0ede8); color: var(--ink-3, #888); }\n881\t.zi-liste { margin: 8px 0 0; padding: 0; list-style: none; font-size: 13px; }\n882\t.zi-liste li { padding: 4px 0; border-top: 1px solid var(--line, #e6e4df); }\n883\t\n884\t\n885\t/* ---- tuiles KPI + tableau compacts (partagés air/essence) ---- */\n886\t.rdl-kpis { display: flex; flex-wrap: wrap; gap: 10px; margin: 2px 0 12px; }\n887\t.rdl-kpi { flex: 1 1 90px; min-width: 90px; background: var(--surface-2, #f7f5f1);\n888\t           border-radius: 10px; padding: 10px 12px; }\n889\t.rdl-kpi-v { display: block; font-size: 20px; font-weight: 700;\n890\t             letter-spacing: -0.02em; color: var(--ink, #222); }\n891\t.rdl-kpi-l { display: block; font-size: 11px; line-height: 1.35;\n892\t             color: var(--ink-3, #888); margin-top: 2px; }\n893\t.rdl-table { width: 100%; border-collapse: collapse; font-size: 13px; }\n894\t.rdl-table td { padding: 5px 8px 5px 0; border-top: 1px solid var(--line, #e6e4df);\n895\t                vertical-align: top; }\n896\t.rdl-table .rdl-addr { max-width: 46%; }\n897\t.rdl-table .rdl-prix { font-weight: 600; white-space: nowrap; }\n898\t.rdl-table .rdl-date { white-space: nowrap; color: var(--ink-2, #555); }\n899\t.rdl-table .rdl-dist { color: var(--ink-3, #888); white-space: nowrap; text-align: right; }\n900\t.rdl-cap { caption-side: top; text-align: left; font-size: 13px; font-weight: 700;\n901\t           color: var(--ink, #222); padding: 6px 0; }\n902\t\n903\t/* ---- Qualité de l'air (RSQAQ) + Essence à proximité ---- */\n904\t.air-liste { list-style: none; margin: 10px 0 0; padding: 0; }\n905\t.air-liste li { display: flex; align-items: center; gap: 8px;\n906\t                padding: 4px 0; font-size: 12.5px; }\n907\t.air-nom { flex: 0 0 44%; color: var(--ink-2); overflow: hidden;\n908\t           text-overflow: ellipsis; white-space: nowrap; }\n909\t.air-barre { flex: 1 1 auto; height: 10px; background: var(--surface-2, #f0ede8);\n910\t             border-radius: 5px; overflow: hidden; }\n911\t.air-barre i { display: block; height: 100%; border-radius: 5px;\n912\t               background: #4d9e64; }\n913\t.air-barre i.air-sur { background: #c96a1f; }\n914\t.air-val { flex: 0 0 34%; text-align: right; font-weight: 600;\n915\t           color: var(--ink); white-space: nowrap; overflow: hidden;\n916\t           text-overflow: ellipsis; }\n917\t.air-val small { font-weight: 400; color: var(--ink-3); }\n918\t.air-val .air-ref { display: block; font-size: 10px; }\n919\t.gaz-head th { text-align: left; font-size: 11px; color: var(--ink-3);\n920\t               font-weight: 600; padding: 2px 8px 2px 0; }\n921\t.gaz-best { display: inline-block; margin-left: 6px; padding: 1px 7px;\n922\t            border-radius: 999px; background: #e7f4ea; color: #1e6b34;\n923\t            font-size: 10.5px; font-weight: 700; }\n924\t.gaz-adr { display: block; font-size: 11px; color: var(--ink-3); }\n925\t\n926\t\n927\t/* ---- Commerces et transport (fiche) ---- */\n928\t.cm-grille { list-style: none; margin: 6px 0 0; padding: 0;\n929\t             display: grid; grid-template-columns: 1fr 1fr; gap: 4px 18px; }\n930\t@media (max-width: 560px) { .cm-grille { grid-template-columns: 1fr; } }\n931\t.cm-item { display: flex; align-items: center; gap: 9px; padding: 5px 0;\n932\t           border-bottom: 1px solid var(--line, #eeece7); min-width: 0; }\n933\t.cm-ico { flex: 0 0 28px; }\n934\t.cm-txt { flex: 1 1 auto; min-width: 0; display: flex; flex-direction: column; }\n935\t.cm-nom { font-size: 13px; font-weight: 600; color: var(--ink, #222);\n936\t          white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }\n937\t.cm-poi { font-size: 11px; color: var(--ink-3, #8a877f);\n938\t          white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }\n939\t.cm-dist { flex: 0 0 auto; font-size: 13px; font-weight: 700;\n940\t           color: var(--ink-2, #4c4a45); white-space: nowrap; }\n941\t\n942\t\n943\t/* ---- Coût d'électricité (Hydro-Québec) ---- */\n944\t.hydro-intro { margin: 2px 0 12px; color: var(--ink-2, #4c4a45); }\n945\t.hydro-btn { appearance: none; border: 0; border-radius: 10px; cursor: pointer;\n946\t             background: var(--accent, #2b7a3b); color: #fff; font-weight: 700;\n947\t             font-size: 14px; padding: 11px 18px; }\n948\t.hydro-btn:disabled { opacity: 0.6; cursor: progress; }\n949\t.hydro-montant { font-size: 26px; font-weight: 800; letter-spacing: -0.02em;\n950\t                 color: var(--navy, #1c1b18); display: flex; align-items: baseline;\n951\t                 gap: 10px; flex-wrap: wrap; }\n952\t.hydro-montant small { font-size: 14px; font-weight: 500; color: var(--ink-3); }\n953\t.hydro-an { font-size: 14px; font-weight: 600; color: var(--ink-2, #4c4a45); }\n954\t.hydro-vide { color: var(--ink-3, #8a877f); }"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"bb57e0f6-4707-4d1b-b057-5b741133d260","timestamp":"2026-08-24T21:01:42.623Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css","content":".q-meter i { display: block; height: 100%; background: var(--green); }\n\n\n/* ---- Criminalité détaillée SPVM (bloc quartier) ---- */\n.q-crime { width: 100%; }\n.q-crime-cats { list-style: none; margin: 8px 0 0; padding: 0; }\n.q-crime-cats li { display: flex; align-items: center; gap: 8px;\n                   padding: 3px 0; font-size: 12.5px; }\n.q-crime-nom { flex: 0 0 46%; color: var(--ink-2, #555); overflow: hidden;\n               text-overflow: ellipsis; white-space: nowrap; }\n.q-crime-barre { flex: 1 1 auto; height: 10px; background: var(--surface-2, #f0ede8);\n                 border-radius: 5px; overflow: hidden; }\n.q-crime-barre i { display: block; height: 100%; border-radius: 5px;\n                   background: var(--accent, #e23744); opacity: 0.55; }\n.q-crime-n { flex: 0 0 52px; text-align: right; font-weight: 600;\n             color: var(--ink, #222); white-space: nowrap; }\n.q-crime-n small { font-weight: 400; color: var(--ink-3, #888); }\n.q-crime-src { margin-top: 8px; }\n\n\n/* ---- Risque d'inondation (fiche) — BDZI gouv. du Québec ---- */\n.f-zi .zi-head { display: flex; align-items: center; gap: 10px; }\n.zi-badge { display: inline-block; padding: 3px 10px; border-radius: 999px;\n            font-size: 12.5px; font-weight: 700; }\n.zi-eleve { background: #fde8e8; color: #a12622; }\n.zi-modere { background: #fdf3e0; color: #8a5a00; }\n.zi-present { background: #fdf3e0; color: #8a5a00; }\n.zi-ok { background: #e7f4ea; color: #1e6b34; }\n.zi-nc { background: var(--surface-2, #f0ede8); color: var(--ink-3, #888); }\n.zi-liste { margin: 8px 0 0; padding: 0; list-style: none; font-size: 13px; }\n.zi-liste li { padding: 4px 0; border-top: 1px solid var(--line, #e6e4df); }\n\n\n/* ---- tuiles KPI + tableau compacts (partagés air/essence) ---- */\n.rdl-kpis { display: flex; flex-wrap: wrap; gap: 10px; margin: 2px 0 12px; }\n.rdl-kpi { flex: 1 1 90px; min-width: 90px; background: var(--surface-2, #f7f5f1);\n           border-radius: 10px; padding: 10px 12px; }\n.rdl-kpi-v { display: block; font-size: 20px; font-weight: 700;\n             letter-spacing: -0.02em; color: var(--ink, #222); }\n.rdl-kpi-l { display: block; font-size: 11px; line-height: 1.35;\n             color: var(--ink-3, #888); margin-top: 2px; }\n.rdl-table { width: 100%; border-collapse: collapse; font-size: 13px; }\n.rdl-table td { padding: 5px 8px 5px 0; border-top: 1px solid var(--line, #e6e4df);\n                vertical-align: top; }\n.rdl-table .rdl-addr { max-width: 46%; }\n.rdl-table .rdl-prix { font-weight: 600; white-space: nowrap; }\n.rdl-table .rdl-date { white-space: nowrap; color: var(--ink-2, #555); }\n.rdl-table .rdl-dist { color: var(--ink-3, #888); white-space: nowrap; text-align: right; }\n.rdl-cap { caption-side: top; text-align: left; font-size: 13px; font-weight: 700;\n           color: var(--ink, #222); padding: 6px 0; }\n\n/* ---- Qualité de l'air (RSQAQ) + Essence à proximité ---- */\n.air-liste { list-style: none; margin: 10px 0 0; padding: 0; }\n.air-liste li { display: flex; align-items: center; gap: 8px;\n                padding: 4px 0; font-size: 12.5px; }\n.air-nom { flex: 0 0 44%; color: var(--ink-2); overflow: hidden;\n           text-overflow: ellipsis; white-space: nowrap; }\n.air-barre { flex: 1 1 auto; height: 10px; background: var(--surface-2, #f0ede8);\n             border-radius: 5px; overflow: hidden; }\n.air-barre i { display: block; height: 100%; border-radius: 5px;\n               background: #4d9e64; }\n.air-barre i.air-sur { background: #c96a1f; }\n.air-val { flex: 0 0 34%; text-align: right; font-weight: 600;\n           color: var(--ink); white-space: nowrap; overflow: hidden;\n           text-overflow: ellipsis; }\n.air-val small { font-weight: 400; color: var(--ink-3); }\n.air-val .air-ref { display: block; font-size: 10px; }\n.gaz-head th { text-align: left; font-size: 11px; color: var(--ink-3);\n               font-weight: 600; padding: 2px 8px 2px 0; }\n.gaz-best { display: inline-block; margin-left: 6px; padding: 1px 7px;\n            border-radius: 999px; background: #e7f4ea; color: #1e6b34;\n            font-size: 10.5px; font-weight: 700; }\n.gaz-adr { display: block; font-size: 11px; color: var(--ink-3); }\n\n\n/* ---- Commerces et transport (fiche) ---- */\n.cm-grille { list-style: none; margin: 6px 0 0; padding: 0;\n             display: grid; grid-template-columns: 1fr 1fr; gap: 4px 18px; }\n@media (max-width: 560px) { .cm-grille { grid-template-columns: 1fr; } }\n.cm-item { display: flex; align-items: center; gap: 9px; padding: 5px 0;\n           border-bottom: 1px solid var(--line, #eeece7); min-width: 0; }\n.cm-ico { flex: 0 0 28px; }\n.cm-txt { flex: 1 1 auto; min-width: 0; display: flex; flex-direction: column; }\n.cm-nom { font-size: 13px; font-weight: 600; color: var(--ink, #222);\n          white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }\n.cm-poi { font-size: 11px; color: var(--ink-3, #8a877f);\n          white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }\n.cm-dist { flex: 0 0 auto; font-size: 13px; font-weight: 700;\n           color: var(--ink-2, #4c4a45); white-space: nowrap; }\n\n\n/* ---- Coût d'électricité (Hydro-Québec) ---- */\n.hydro-intro { margin: 2px 0 12px; color: var(--ink-2, #4c4a45); }\n.hydro-btn { appearance: none; 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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFMj3kUTc4aA4cddMfZ","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4889,"cache_read_input_tokens":82223,"cache_creation":{"ephemeral_5m_input_tokens":4889,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"4e1191e9-616b-4b6d-9eb4-b397f35c0ef6"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFMj3kUTc4aA4cddMfZ","type":"message","role":"assistant","content":[{"type":"text","text":"Tout est repéré. Je vérifie un dernier détail — l'espacement des blocs annexes pleine largeur sous `.detail` — puis j'écris la passe."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4889,"cache_read_input_tokens":82223,"cache_creation":{"ephemeral_5m_input_tokens":4889,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"94268679-4ba4-416e-8d40-d11a70218867"}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BiyD5t52tgbLu4VohLMhkc","type":"tool_result","content":"182:.detail { padding: 30px 0 90px; }\n183:.crumbs { font-family: var(--font-mono); font-size: 11.5px; letter-spacing: 0.06em; text-transform: uppercase; color: var(--ink-3); margin-bottom: 20px; display: flex; gap: 8px; align-items: center; flex-wrap: wrap; }\n184:.crumbs a { border-bottom: 1.5px solid transparent; }\n185:.crumbs a:hover { color: var(--green); border-color: var(--green); }\n265:.desc-text { color: var(--ink-2); font-size: 14.5px; white-space: pre-line; line-height: 1.6; margin: 0; }\n280:.amenity-row { display: flex; flex-wrap: wrap; gap: 7px; }\n281:.amenity { background: var(--lime-soft); color: var(--green-deep); font-size: 12px; font-weight: 600; border: 1px solid var(--green); border-radius: 999px; padding: 5px 12px; }\n293:@media (max-width: 899px) { .detail { padding-bottom: 84px; } }\n294:.f-foot { margin-top: 26px; }\n354:.amenity .ico { color: var(--green); margin-right: 2px; }","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"48b3bad7-6232-4bd7-bbc8-b65be8592915","timestamp":"2026-08-24T21:02:22.076Z","tool_use_result":{"stdout":"182:.detail { padding: 30px 0 90px; }\n183:.crumbs { font-family: var(--font-mono); font-size: 11.5px; letter-spacing: 0.06em; text-transform: uppercase; color: var(--ink-3); margin-bottom: 20px; display: flex; gap: 8px; align-items: center; flex-wrap: wrap; }\n184:.crumbs a { border-bottom: 1.5px solid transparent; }\n185:.crumbs a:hover { color: var(--green); border-color: var(--green); }\n265:.desc-text { color: var(--ink-2); font-size: 14.5px; white-space: pre-line; line-height: 1.6; margin: 0; }\n280:.amenity-row { display: flex; flex-wrap: wrap; gap: 7px; }\n281:.amenity { background: var(--lime-soft); color: var(--green-deep); font-size: 12px; font-weight: 600; border: 1px solid var(--green); border-radius: 999px; padding: 5px 12px; }\n293:@media (max-width: 899px) { .detail { padding-bottom: 84px; } }\n294:.f-foot { margin-top: 26px; }\n354:.amenity .ico { color: var(--green); margin-right: 2px; }","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019nd8W3knXY8KPNzndkqmmb","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"d4b3502b-2937-4999-b648-e6175fca3a17","timestamp":"2026-08-24T21:02:34.626Z","tool_use_result":{"oldTodos":[{"content":"Lire les styles fiche existants d'immo-ka (f-bloc, rdl, q-*, dtable, rooms)","status":"completed","activeForm":"Lecture des styles fiche immo-ka"},{"content":"Captures Playwright AVANT (desktop 1440×900 + mobile 390×844)","status":"in_progress","activeForm":"Captures Playwright AVANT"},{"content":"Écrire la passe « éditorial sharp » en section finale de styles.css","status":"pending","activeForm":"Écriture de la passe éditorial sharp"},{"content":"Build + captures Playwright APRÈS + check débordement + check-order.mjs","status":"pending","activeForm":"Vérification Playwright après build"},{"content":"pm2 restart + healthcheck + commit/push origin main","status":"pending","activeForm":"Déploiement et commit"}],"newTodos":[{"content":"Lire les styles fiche existants d'immo-ka (f-bloc, rdl, q-*, dtable, rooms)","status":"completed","activeForm":"Lecture des styles fiche immo-ka"},{"content":"Captures Playwright AVANT (desktop 1440×900 + mobile 390×844)","status":"completed","activeForm":"Captures Playwright AVANT"},{"content":"Écrire la passe « éditorial sharp » en section finale de styles.css","status":"in_progress","activeForm":"Écriture de la passe éditorial sharp"},{"content":"Build + captures Playwright APRÈS + check débordement + check-order.mjs","status":"pending","activeForm":"Vérification Playwright après build"},{"content":"pm2 restart + healthcheck + commit/push origin main","status":"pending","activeForm":"Déploiement et commit"}],"verificationNudgeNeeded":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFRZe5A8Kd76yKwaEVn","type":"message","role":"assistant","content":[{"type":"text","text":"J'écris la passe en section finale de styles.css, adaptée aux sélecteurs d'immo-ka."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":901,"cache_read_input_tokens":90607,"cache_creation":{"ephemeral_5m_input_tokens":901,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"35cd41c0-84d4-4a79-95f6-4f9ae600ac26"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFRZe5A8Kd76yKwaEVn","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01B4oTgBGqEyHxdLQ6iJ9u6T","name":"Bash","input":{"command":"cat >> /Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css <<'EOF'\n\n\n/* ============================================================================\n   PASSE DE DESIGN — « éditorial sharp » appliqué à toute la fiche\n   Blocs = cartes encre (bordure 1,5 px + ombre décalée douce), titres à\n   marqueur accent + filet, tuiles KPI blanches à ombre dure, tableaux zébrés,\n   barres en dégradé, pastilles unifiées (mono, point d'état, liseré).\n   Aucune propriété `order` (règle Groupe Ka : ordre DOM = ordre visuel).\n   ========================================================================== */\n\n/* --- Blocs de la fiche : cartes posées sur le papier --- */\n.f-bloc:not(.f-galerie):not(.f-hero),\n.quartier {\n  background: var(--surface);\n  border: 1.5px solid var(--ink);\n  border-radius: var(--r-card);\n  padding: 18px 20px 16px;\n  box-shadow: var(--shadow-off-soft);\n}\n@media (max-width: 640px) {\n  .f-bloc:not(.f-galerie):not(.f-hero),\n  .quartier { padding: 15px 15px 13px; 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border-radius: var(--r-ctl); }\n.q-val { font-size: 20px; }\n\n/* --- Tableaux : en-têtes mono, zébrure, survol accent --- */\n.rdl-table td { padding: 7px 8px 7px 0; }\n.rdl-table tbody tr:nth-child(even) td,\n.rdl-table > tr:nth-child(even) td { background: var(--surface-2); }\n.rdl-table tr:hover td { background: var(--accent-soft); }\n.gaz-head th {\n  font-family: var(--font-mono); font-size: 10px; font-weight: 700;\n  text-transform: uppercase; letter-spacing: 0.08em;\n  border-bottom: 1.5px solid var(--ink);\n}\n.rdl-cap { font-family: var(--font-display); }\ntable.rooms tbody tr:hover td { background: var(--accent-soft); }\n\n/* --- Barres : pistes bordées + remplissage en dégradé --- */\n.q-bar-track, .q-crime-barre, .air-barre {\n  background: var(--surface-2);\n  border: 1px solid var(--line);\n  height: 12px;\n}\n.q-bar-fill {\n  background: linear-gradient(90deg, var(--accent), var(--accent-deep));\n}\n.q-crime-barre i {\n  background: linear-gradient(90deg, #f0808a, var(--accent));\n  opacity: 0.9;\n}\n.air-barre i { background: linear-gradient(90deg, #6fbc85, #2e7d4a); }\n.air-barre i.air-sur { background: linear-gradient(90deg, #e08a48, #c96a1f); }\n\n/* --- Pastilles d'état unifiées (zi-badge & cie) : mono + point d'état --- */\n.zi-badge {\n  display: inline-flex; align-items: center; gap: 6px;\n  font-family: var(--font-mono); font-size: 10.5px; font-weight: 700;\n  text-transform: uppercase; letter-spacing: 0.06em;\n  padding: 4px 10px; border: 1.5px solid currentColor;\n}\n.zi-badge::before {\n  content: \"\"; width: 6px; height: 6px; border-radius: 50%;\n  background: currentColor;\n}\n.gaz-best { border: 1px solid currentColor; }\n\n/* --- Listes riches (zones d'inondation) : mini-cartes à liseré accent --- */\n.zi-liste { display: flex; flex-direction: column; gap: 8px; }\n.zi-liste li {\n  border: 1px solid var(--line); border-left: 3px solid var(--accent);\n  border-top: 1px solid var(--line);\n  border-radius: var(--r-ctl); padding: 9px 12px;\n  background: var(--surface-2);\n}\n\n/* --- Notes de bas de bloc : filet pointillé --- */\n.f-bloc .fine, .quartier .fine {\n  margin-top: 12px; padding-top: 9px;\n  border-top: 1px dashed var(--line);\n}\nEOF\necho ok","description":"Append éditorial sharp design pass to styles.css"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":901,"cache_read_input_tokens":90607,"cache_creation":{"ephemeral_5m_input_tokens":901,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"81a256c9-8f3a-401c-88cc-0c2f1f09ec7e"}}
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b7IxdGMCc7hj7LjBObfYl4gxP5c0+b/vy8GiqiCwOBEkwKIYQCEAJAtgNQlSwwkz0reWzuRx6vF9ft7JTHSjvWafv44jEn+Ye7Dv/+/BS0862c2SqLjlnX7Zyu584NmxuFmZ9+MJ4dY//suVMb56+/7hrg4u6la6+9cWNjg//Z7rjj9osXL54+dXpqEy8AUPkXyJnnhzjaVx+6brNY1RIm7RAbml7mFB15/aYjdOfe8KQ93brv/cmLwk1b2l2zN5I4JEmCZib7voF7Vu4KNdqiarAR2x2nZ56XuDD4by+0MjWfPzq12T/41OwVT7eqJHR+pd119iUECJxIkgAZJIMgQFLiKQlcxYM34+aNvOMw797PMXOjgpTZLuwfdbU87FS/GsYLh+3iUbu0ZFF0fmu66eRiwfDE+9J2Sx/f7B9xzezo6FJmRgQgCRihg2wtSuGFykxJ/FexHRERcXiwNOYFAyr/AhN1eso3WvtjbK+yxmzHZaG6QdmaldnOYvOe3Vq77XOz+Xw+39zcftlT/cNPdE8+1C19e+zJYvHH9/LEI3VSM3ZmNpOP2uZY5W8v5togpACE+6CH40VT8rcHuqnXmYj9gQef3HzEGY+T7z1qf3dRd68Fskgrc3I2bLDAGCdWoio//KQurbzZcbienrLv7V6HLQdztPaq+UT18Zw2sm2qPPx05XR/z5EPGsd6TcmFo2ltbyxCYqfXo07OlvtHNmBxWRr420/6jAvkgz7qQ//wV37l4Y985HK5vLh78REPf8Rdd901tWk+m3PZjTfe+OIv9uLjOEriv4QkwLb4FwCVf4nQHc94yt5wz87OxjjmmZOzS5eW6eYWs43+tnUsV5U6X/TlcOjn3fbpnY2t+cnaH9vbPvakxdbxjdlKsT7MjNbPatTZ9s71x0p3cuRY7R+6Ue9Zq6VDKjWczqQUCTYqDzquaH76vWf/4tLF5dHRRpezWXd8sbVV1C5NGSVzcG0bWye72fGoVWUelCiRlE4sCrcsuGHeMr3o4kKLp+y1vaFZ6qqUWYouJUgSJd3Vzvj6YxxMyBzrLXHdcV8csgud7Dw5zl+qGxL3MwZ2T+w8/hm3PnY2O37ixN7e3nK13NrcevwTHj+O487Ozn1n77v2mmt3d3e7ruPFACTxL5HEfyGg8i8xNNjc7mdd6ft6cT/LbHP/4vDkJ1+65vRw4pS62p+971LX68QJ1ivfuucy9zR6c1GWRwwJOe1sahh8+lQ5d3Hc3DzRa3Ywds1bOxsuxQervpaoIaNatVpH1Khe/0Ub0XTPLn2dprZarUrt6sYiccvUol+OE0dHB1ubXdTjtXRbx44dHUbtZ1vHTpxcdDPNz3az31h2s/miTaOHo1Det7ee2jika4zzUJ3P+1nMVRd96WdtGOo1J4913cZqPWZsFM3suh6nqU211oiyd3Dw6g+/UchcFgG89id/wusBcONbvw3/kq7reBHYlsS/T2byogEq/yLzqIf3z7hvQnHqeL1wcbr3/N65e318R2fPDst1qTFF1HP3DHefzfWoRz50fvp4ve32aRqmnZNl0aji4LC778J4x13D2XNHmxvt2utjc6tNY9x9x3jiVHS9orlWHy5jufKJndrX7uKBx+ZhsprH2l1zZnZ8wfFTs+1N72yOe/u6cCmn0Xt7ftrT9mezS7XjunXkGBf2h2lqh/s+drybb7iLko2NY+OFcxJ1e3OWybjUapwuHRWFZt10+pQv7g6loyt9qWVj1ubzaWjlvvvq0dDP+l7KrvSrMWYRr/KQT5AEAAKgmLFNl/YvtTYBIDAIDIDAAEgSL4Ktre2N2dw2/1WAyr/AwGocaoz2bBhzXA175w9K685c1y/mWVRNzudtc0fDWhfPTkcHh399X+lrvtRLbE2mK+uN7bjt9uV1M9365MOdLS9X690Ls6c/5Wj72OzkmVgeDYf7NcTYvLkFbhcvcvLkdOmct07AxMFBnjwxjsORlRfOx4V7qTWGpqhlQdkK/uLpq5tvXpw5Hrt75bGP3FzdfvCkx43HZgzLWov6frh4T7vr6bjkwfLo+htX157c2DweFy6uTm31+4fby8O63h6Plj5Ri6cYxtVwMNvYma2PvHd3qfM4f98wjNPBcl06aIEBAYABWI7rc8+4dTa2KoVkAIF5wYSMASHAmAe4eN/Z9Q3Xnzh2wjb/JYDKv0CIo4Op64eq/arSzaabbqo727WvoVrd2vKgu/Ouafu4Lhy2Yztx/vzR3fe2kyc2/vRPLhw/WWY90xBnzw87x2e1mzXGa68tw8Gw0dfVcn3H08dxZFqzd6ldf2PXMlsqx+i6aXt7dna3FbFzvDs8WN1zrzO1sVkuXlyX3sM6t7bLvNYT21sPf/CcmHa2F7uH/MZvn486LOpsezbf2phtbvn8peXFvaOLF9nZ0cFeXuzi0n1H6Ty2Xa67RaETB/vrcX+PNpzfPcyJsbV+Np7fY3uxWGzVnR2p5t5u6WNje6Ocv9TSRgCAADh75x3XR7+eeWs2p1RsAAkbQGCQsJFwgrCRADLBlEomEhKYcbr97rs3NrdmtbPNfz6g8i8yXUy7+wd/83fLcdAtN26cuaacOj3M5/35C1NodeODumuvW4wtrzutvtNyvfOM29cX71VSb3/aelhnFM+38nDP80XXRu6+82ix8HKZZUYbs3Sh4us3VOrU1pp103y7LGb99k7OF7TUMEw1cmM7ds/r2us4c91svRzbsh/G6fgZ7rnnwvXXz2rXEcO1W3F0tC5Vm2favLQHP2R1sO+8mHja2p5OnuzCKnU6faLb3Jo946mx0eUHvu/7PP3OP3nqM3739rPsXsxTZzYbh+Owwv2d9xwcjdx8un/Iw2e33bZ/fPvg9AkdHM5KpI0EIGi4jNMU3dPuufPk1vbQppCA9TjWKBGRzlC0bCXKOI3zftYy513XMsc2HdvcduZ9ly7ubGy2zHGaFLr22MmF4mi1nG11/AcQ/xKg8i9whO49d+6ei5fsxXXXdrc8SKkRz5aHy4O98enPGLpu9aCbpxMnNEwcLukjXu5l5s5YLX3nnVzYzVrUz2N50E5e4zPXzM/eo91L66g+OtDo3NlhWJcs0+nTznWtXexsFjsXveSdv/qr81NOsa3VoYf9PPuMlLrrb+6P3TDfvXhQ69GpY/P1Mts4Pv4J62MnZ9s73V13r+S88YbxcU/QPXdPreWTn7QM6d57Vg95WMm2ceJU3e4XD3vNzVd62bc8cWzjq77zp5721PW41E0P1nWn93cvDUfLMWJ/WpfTOzt759e3PunwpgctHdPfPm5ZY24jAQZsCtJivnfh0j/c9rTlsD6+ub09X6S9GgdBLdX2MI3zvl+u112t1504ddeFczeePHN+/9L5/UuPufnB1x47+aQ7b9ucL6Zstlfj8OI3Pnj7zOnN2Zz/GOZfAlT+JUb7lw73L65e5iW7+WzY3NJ6xaXdo8NVhroXf/HSWr3nrnXX5emTcdfdpdtSZtvcbMdPjdffsrV7YXZ4xMF+Pu6+g0u3TrffcbCz2e+cxq3NomvjPBvSEZM0Mo1jv+Des8LjxiKf9PeHFy6tj53w0dR14Vrj4sX2Eq9QbrguDw52N46tV6t1XcTmvJstpodNPP0Zq739Aev0KdeSR0fV9mMfjce+r32zjx2fDvcO9/f6/andu3v23Nk/uXDxl578lIPdPXZ22j/8zfR3f8XBYVmOh4udvO701nWPmU4eb3fe3s6d08mT3dOePDt2MhSykQAsgJPXXnd+GN7ylV9znKa+1pC4zCBAwiAAQct8iQc/PJ2SBFNrU7bXf5lXbJmCWoqkvTZuXH/9ousBSbwAtvkPAlT+Zd7c1knaYhFHh/zhH+7vnAgnbZLKemODRzx8Y2M7D9cqpZ/17fTJeuutox3nznmaVt0i5v0UbXbN9ZsH++PycHzq0w7KHbr++o7mc+f2rruh2zmm1ZKjISWdOzuuD1s/K8O4eMRjyn33TYtN7rpreOjDu6c9vrvuZm1tTXc8oxuXG/ddWl28pLEdPuoxG1nKi7/4xou/+Pjnfz6S3c7OqsTs9Mn2sFu2Ijde5sXW/Wx9MKzcNg8Odu+6c2/nBI/7m+Ww/7NSf/012495VL/0qpH3PB2F5t3xcb91N46zY6tjp/PBj6jnLswu7g7JZFfJAAgQAPN+ds3DHn7H3XdmkwGb52DAPJMKkADYEH3d3jg+SZlZS7m4Wg7Deuv4CYd2D/Z4gbSxWPSl2uY/AlB5oWxq0R135siwfMJKqrWrj/+H1azO7r5reOVX6a49vfjDPz7Y2iwXz2Wtw8MeVpN27rw3N/TUpx0d7E+v9IrHthYFvHvQ7+1PXa/jJ/vadMOZ7d398ZaN2XL/KDbb4YFPX+Ou896l8SVfZjasWK1WD3+4Tp8rq6NpsdWth/rIl2i3PT2e8rRps0xd5KVLQygidOuThgc9NO6Z2nyriuHSxcNhLC/+sr7vDpYDZbb7tKe5mx8dO5a4P7ZRn3LramNr4zGP2VkeTKeObd1xK2fPtu2d2db2+IiXjNV6Y2dxXRfHn3TX7z7pqRtPe0bcecf64tlVv+jGcb1zUpmIZzIAY5vuu/22rWEMhMAAkowxSFxhSzLPwfY4tpO3PKhG2d292O8dbJo8PHfkBAkMYBAABoFB+8GxG2/c2dqxzb8bUHmhJKbml3yJ03/8V7d3VTfd3B756HnJeMLjVq/86rOt2fyv/mR19z25mnJa+vjxmHXTfNYfHkx33zPec8dgl9li/ehHnnj8E2J7i+VWyxabJ9qjHjM+4e8u5dRueHA9eY26juWKwFPbn3ebbfT+peWdt5flssuhv/vsekpfuDDM5u3E1uz8vZOO5cW91d4l1gfDQx7V7d7X7r6Tv74r+y7291o/97FV+fs/X9E2j5Z721vTvXdNjrzDcezYIA2jcrWuW309POJwf5nS2bPDFLXr49EP2y7Dya2Njd/7sydeund+x9PabNbWQ/ZdjDmuBp78BKYJSSYBDHDvnXecHj0QG/2sdj02gBNEBE4MQITbJAUSNoAADavlvbc9Y+uaa9Z33X1ithidW5s7OEHYCEqlNQQIGwmgtdvvuGP28EfMamebfx+g8sJJbcp77hppi/vOru65Nx/0oMWjXmp27/lpc8MPfcjsxus2/+5vj/7qb/YIHewbl7/4i4O77hzHgf1L02w+Pvnx8xuuna6/fuO224aHP2Trrjvb7fctT55qL/YScbDvyB7y3rOXDteaps2zdy+Ob++05eJP/ugZ4zou7efR/t7qINZD6xcqU1lHRnJpr803tFhrb9cH+209+PBeFN49P052N4tbbuzvuKMtNo/2Lk6Hu4pw7dnc0LjO2kebcu/C0WpXj3n0iYMV193krbjlCX/X1F245fjOtScevH1yb2f7UffcOzzp6U+779y6dq3O2N3rsg0zzUNhDAAhNcxqnaoXDvZjJy5duhgRXSl97cZpOlgtt+aLWorhaL06sbW9GoZhmhZ9P2VmZonYni26aX3u3Nmb5hvnDvYCptYA7Frr1Nq9uxdObu0Yj1PbmM0O16sSsdHPtlT2D/Znx0/y7wZUXji76+Mf/m7viMNuHrvnPbXl2XPDgx8y295ZnT2/PjrKl3ml+S0Pnn7mpw6bp9tuy8ODfPAttev91Mfrrd/21Ej+9V9dIvaf+uRcbHYPekhx5lP+ZtY/Zj4N3eRcr4e/f3wcHk3XX7+67842ntx70MNUKuPRdHC+HezlNDCMdH089tGLCxen5TI3Nzm22a0OclwSpUAeXpzU5dhw6+fRX7h3a310aViNsxn3nV0f35lnc9flzk4pLaZVnNiu119XVkd7Pd3Tb13feIMf9WIv/rRb71t3K+889Kd/48duvPb63X2/3MueqP1Nj//7P908tl/1MDEcLk+gBECAcUHRd4d7h3/8xL/va7cc1rZrKcc2tuZdP7TptrP39LU7vrl18WD/lR71Yth//fQnn9zeOVguEbZf8SGPWlxzemNzc7hw6Z7z5556312z2l06Orywv/eqj37xa4+f/M2//YuTWzsS+8ujY5vbLdvhavXSD3rYg266ue9n/EcAKi+UDahTvfvOtUoX9uZ8/tCX2LjrzuHxf1duuCnXR+PuIteH45u+2ead900X723XPmT2qBf3yeNx6prMLu65I++6m/NnV5cutGPHR8buxJl6662rfkbE7m1PP5rNy9GB10vfPa7TbvD0p+0dXcwbb5zVmcd1njzdg6+5odx0c7nrjuWZ013pWMzL1jG3lrOqjevKvfcMB+enWx68uZjPdjaZWuxeSIdPHJ/fcHrzhhv1t/+wf7hXtqJfHo2YaWgXz49dlOuudTuY3XUb+5tPevJTjlarE/uHT1wvd37/d590/Jr50//yzhPze1aXlhcv1Fd+hfHUmbx0aUk2ECRgAHauufbS+rY3fKlXABb9zLakdAJdqbuHB5I2ZnNw2kXx4DPXTdlCCkXLtpfT/NSp7a3te5bLB113/aNuukWwGoajYX1icwt479d5k7FNgCSBpLSXbWrHtk9tbNrm3w2ovFCSMvPMye72+/pVsqhxy83dqZPxV38+eO2HzI7dvlred8/huTvHRz5aD3nYxrwsr79Zhwd++tPXpcvu4vroYHbq5MZLvtjmE/5hb7Vfbjy9ONobxjGf/oz9aVjOaj08TIXSPtxXa+676djmTK1m0/n72nXXdPfcN5461W9s1OWyXnf9xu//9p5L3vigutioZ67tusX0qFvmJ2ZbY8/G3BcurMZl2bnu0ou9tO65q+zt9a/1OsMzntbe+E3K9dfn2TunS4fZzvat+K6L64OLE47Zlu+6a33+vmF16LP3PvF3/ujPb7ppO+gPjnb3dv30u2577GNPbSz0jHtvn2bLe5+xSAf3EwBbG5vdwx9x7333tDbtmssMFTAuJ7eB/TTPZEDqbMCKfmfnmn6xWA7rneuuuzi7sLdadbXOu51Z5n4mYCzmCMyzLDa3jm/v2OY/AlD5F1jS6eun6y+Wey+25XK89RlHhxeXp06W6bCbS22/3nE+SvL0pw5nbu6vu6kXbXsxv/ax23/1F8ORytH+6lGPmi1my2uv7e44mgrDmdP1yU85vO3W1fHTdeyUU2ZSek8Nt1ju1dWqzWchPK5zKrHR1fGIe++a/vyP9l7tNU6cOTP8+R9fuO/u2N7pdrb7u24byqFOn+7PXzw6WsZ6T4eHyzvubXrJ+lIvH3/2Jwd/8TeraXe2OOF7L7Trrm83Xa9Dl4MSL/nIjaf9/fS0p0xlM8+dHY9vLM5fPEjcmO28RJzYyqHlYqds9Rvrw/HoqD35aas7b9+45sQswjyn5rx4z93z1do2z8MAiOdmAATD4T3nWrMQKqVsKaZh7K/ZPrZ9jBfKNv9BgMq/QNNE6aeXeenF7/3h2mO3M2e7q3ff20E+4+zqoTfN//ZJ69m2Zip33Lq8/uZ6191DeHjpl12cOOm/+av7HvaQzdOnu1/6lf2DC2zO06k2lQfdfOzipdjbPTx9ehO5CKao1So5jexdnNrULpxlsREXLrTFrA7mnlunw914/D/snTw9Ixn2230HY7mZbt32pFtujEXMLh2u21B6zY5tcO7S8oe/b1rUeJlXnP/Dk6YXf3Dcez7++Pd8843d3tHy7Dkvj3ipl9i8cH4aL7ZZV+eLnJUyDDJe77eN091THre86+l+zdfeuPEm/e1faPdCXnc619kUYJAA28C9d925c7CanJuzeVdqOoUAY4GNJGPbJQqX2ako2IBtSYScKbAR3H37Hb5Zx7d3MpPnRxL/cYDKv8ARzOqc4sc8fPGEJw/XXD898pbpGb82Pu7vj/rcmM+mhz9mfPwT2sGltn2se8rjxxJ172B9+63dwx5089O62aKcPjxYDusxQttb85Pbm6Vs/P25i0xtFv31p7tLh97fn6qijakspWc4dFSayu6FdMZqPS126u7uVEeGpfqZzpzcHnJ4hZd/mTvuvX1v/95hxG3rlhu6p959B1A4PbC/PFrvnW/eZGp5/FjdvzRuH2eWWwcHywv3jdM6pjFOXDPe/Aj9w9+V6x/ap6eJ2loM6/HURt9l3b+vu+66pMs7z8bYLIrD62VkEwIMhJSQR0dd7ZfDamhtaFMtxQZRFGnXKGNOQiXKpdWRAJh1/f7hfleqpEU/W4/jehy35ospWyim1o73iwt7u8e3dwRI/CcDKv+SCN13fijdbul3jo6GpzxxfelcdBtx8tq6t5dPvXV9sawtl1pFHhzmYt52tr1VH3Zm8VKv+6q+tHtxd/cf+tr189ja2VpNzvXe0X7b2z/YXHTzzq33k+86WMxivXZUTp+sU42djbrK3N0dN+Z9P9Pu3rReTrOFWmuHy/VbvV33uMdP114fj3nxa3/mV+5ZDV4dbXqtJz91cFttzPcPlu3CWc/77miq5++tp093T3nadN8Frjk9zrbz4IhHP3Z+/FRce1Jbr8MtD9563N8eHNuevd3bH7vj3LmnP21N7Q8O/NpvXLaO6dJ5/davr3a2cbK53aqYJiRsAENAdN2lS/u/8bi/Plgezfv+odfecLReH61X+8ujWddfPNw/vX3s5R72KOMn3HHb/vLommPHzxw7cfFgbzUOXenuu3Th+hOnnnH23q6UtFvmjSfPvMLDHxVdBxjEfzqg8i8yUfPee8ZrjrfVwTpX42HdOnVmsb5xfzwcV+NsbzeKyjTm7iVPY66XftgjOXHj3Xv7D9qcb1778Jv3x717Tt915zNc3D/1tt2z+3sv+1Iv+eBHPKxw8eDgvkc8tPbdTXvLeRfrxcZGm/b2di9s9HH+3NgmwdSWG+O0mobcvnZjtfZC3eu89qu941u/4U/8/Pf8xV//zfJc1azY89XhHgfdIx7xctvbm7sXLtw13XPrrXd5h93zzqM26zuP61PXTVMytrj3bPu7v1vf8dD+sY/tn/D4PbttbNQ3eo1H3X7+2Ml3jJ/60TuecfvBqW7a3dexzXL6VAH1szx/QdtbaZv72Qa2z1xz6Wj5Oo99aYVKhBAgMUyT7YsH+9edODXvOtCxh28sh/XWYqMr5cGnr91bHtYoDz59zdZi8Yjrbkw7JMD2paprz1wHRAQvOtv8WwCVF8FDbjo2TP1Drn3NR7zzLcePnzpz/Man3f133//L37Bab73xW8aTn9D94k8cnb62REGiX+jiPo9/3J0Pmj/5+od3F46WMW4cO6F7z+Y9F3Zvv+eS7Xt34pEPe4kL5/7k3nu47+LRy73yQ179FV/q+muuCd7k27/zS3/vj38lW5Y+hjy48dpXfdDDT/3hX/18fzTPqb7Kyz36xR99w61PevmzO3f/5V/edcfd08Me9OCjo2l5mMeOHXvwQ/Ye8ohjL//gV93pTx6tV3/xuN980p1/OK1jmHTmxlW3sXHXPUeXLrSt7fqMJw2lj394/N6jb3nF133Jl94/Ojx+8uhLv/k3b33KwTu+46N2L04Z3P6MXK/86EfHzvFayrSx3QMbW20+lw0ASAK2N7e6Rz7y3Ln7sjVjDGAICTh2zam91nadQCw2QpvnM42FYuPY2kRsL6P0Uci0pIgJHz92YsqWmUi8iOyu6wqyzQNJ/EuAyr/E+Bl3rB73D9OLPfil3+BVX315cHDm+us2jh+77vd+s7vh8X/5t5sbc17ldeLOu8aDi6XUOHWaGx5Wlpf0kq/zhL/4+9zc6C/s7z/9nmFpnX/GbO9w9aCHc8tj/uGGR/1De/qke8rdF3zn7/zlox6+uvbahz/t9uG2c3976WBVO+0c35pfv/OyL//Kf/34Pz5337RY6Eh7GyfunG+uf/HXnnbv4V/cfXsMyzredNvu3qh45NLT3z/hvqfc/ivPeOQzHnbdK7/Cy7/+5qkTj1xsnNra/tO/v+/S7nTsRJR5d9fTp8UGbfQ0+cSZ2Uu++Lto3JxvHH/8Ux7X8jeGdfzKr9175ppYT6tnPGO86SHzO273xnx8iZee7e8xrNd3PD1KCdsSz5L40vlz9XBpGwDEMxnbzCQJjAHcI8A8W5bYuO76zcUGcO89d9XDo90LF20QAptnE5jnQ2AytHP99ce3j9nmWWz+JUDlXxKK/fvixmMvtT7cePodl5bLc3eevafU4cM+/IbD9sRf/GUODqdXfA3dd67+5Z+5TWyfinvv8A031Q1fd23fbXXHXuoR7WVffHbunq22d3r34I4Xe7HdshiH3aOLTx9ueYif/rT9++7k3O0nHvLyrzjOLr34Qx7WL0+99IudfpmXmbXp+FPveNqNmzl71EseaHfedRGzwbOXfpm9c2df7M+GS4d7+y/1kg+98xnnj21z7vCgtXH/Uv+UZ9x23cmdv/zbP/ibpz6jzNZnjnX9ju65vZw/P0WfG5vcd9fQ1j5YLt/4zTfOX/yWw/Mv2W1cfNLT7z5/7zq6shzGw/16y3U3POPW2w/Ps3FNjX76+38YN+fr7R02Nj2OlgAA28C999y1sXsAzLt+Vru0AbChRAFj0pYkgUmnFBKZljAofefTn65HPPzSvfduHSwnfGq+WSNsjCVJ4UxJthWBDdhGKAqZ6ZQkc9cdd9YHd1uLDdu8yIDKv0BTG9/nXR6ZwxPvu/fLthYPP3PT0GXcfXvec98T//Ifyt/9w/4NZ2Z33do99NG68Yb2x3/ov//r6ejA99yzeo9Xf9n3+ZC3vnhpb9ZvZann77ttKKePb8W0vKvVk23ceZ1HPePi7uP/aOfWCzeff/VHvvz+3o2b8+vf7JV33uQl77jxmtObx66twWs9ar3/6nWc1tEOto6f3Ng4kRgPd9158Y5XbSd2/MiHPWq9vPh3T7z7b550+0n1h0fDW73Ww17sMQ+/sDp881cdnnLHHU+6dffxT7n74sXbLt03tuT48X6n63SsvuJLPvhR12w/8rrobpydPHXTDdce3XXb6bN/f3Z98eDE1pkynOjGO87eebB5vD3jcUs1+pi9/Kv0j3rsJAwCAyElTHv7m7PFheXhZq3rnEC1FBtJF48OailCfVeHcbINbMzmy2GYWtucz6fWIjQ5T3bze++9Z3M9dl0/jkPCmIkoitU0Ha1XW/PFlK0rZf9gf1Z7YN73U2uX9nY354vN2Xw9jsbHar978cLWYsM2LzKg8i+JUNZ75lvPuP7kbH//tqc88c6z99V26aHbZ06+4ost3uFVXuHWO+/489v+6ran07JbzKdXeJW2uVnqQn929mef9pN/+vd/NZ55yFvPuvK4v/vVo4Pz/awdri8dv/a1XvF1P/zcXUe//wvf1aY6DutnHM3OPETUjdsf9yvnnvEHUbpST548dub0duwfHbXp4taiQW7vXH/m+hePunV48Va1gy7WJ294hf39vcNL9yzX6/XR/smNctud92Q5NZ9vOnUmrtu4brExxNHF3cXOej7rTp3Yvnb75INu2nnUQ2684Zobrzn1iNpF183L6Vd+zzd53BMedfcz7jn7sg+/9rVe4SX//nVe/alPfsZP//ofvvLLvvyi37548Kc333TYWh8VGwnAICi1nt+79Be3PmU9DsthXSJqKS/9kEd0pT7j7D2rcZjVbmdj6+yli8thvbOxeX5/79pjJ55+392Lfja1qWU+9NobXuYhDy9djeZz5y78+j/8VUhdrQ+77sYzO8dbtifddce874SuP3Hqaffelfbxza1ze5c2ZvNZ7e66eG41DLOuu+74yVd6xGOiFP6VgMq/wHb5zT996sHhuXG99aBrj9+w/aav/9iH3HTd5jf+6C/91T/c/Uqv+BfPuHDfuk2LrXryZHm5l8tMHy1zKzi6b/H4e0487XBjtTqxOfrs/vF77n5i1Nb1JzYf/IiDLEf1hmV5zLC6q3U3rLZecjeP0frV/MFnl39ZC5snzxysNu84bBsbcw+67dJ98tGxo0t3j2e3rr3h4Oja+267a1qdv2H3rnFcHV665/Spa6+59mG/8ru/eN297aHLR/R9Odw7vzq8e/fsXx0dnl2tI8mDtQ7uPnvr3U/6q6c4f4ubHvzYN3mzj+i6rYO928899S+Pdv/hT5701I2uPKXePLUL47g+XPEN3/Stb/BGbxQqj3/Sn37Nd73LX/7FxemdCWEADILFqdNHyzte8aGPbJlALSWdW/MNiWu2j106POhq3Zovhmuu218ezbp+f3l0fHPrMTfcPLVWS0nb9nnazdfeePHCubp/8MYv+fKSJM26rq9dhM5sHVuN6835oivl5pOnLx0dzrr+4MTR1mJj1nWPWd28tzw8trE1tbYbPnP6DCDJNi8aoPIvELSXftCrbm4+and5933nDsbNp/3l3U/441t1bv/UXXcf/fBP/MPDHjp/9GPn+wcMq73Scu+sL9ynv3mGz51tGzc+YuumR86OXzccnKP2s+0bh+XFsvGg7thNqzH7jVNb177U+Tti4+RDYuum2DwZcr9948bJh7bxgMWZ7eM3RbYIe9gvdWNcnZ9ie2CjzHa2rzm5XE0edjeO3ZjTuusXY3Jub7rxoa9y7Q2PPH7NQ2pX63xzvt+tVxcox3ozDON6vZ/TMLVpmqbSdRvb1x5N3lp0lPklz+Hkqz+q/eXTnnrnpfUzzj/hT//saW/yJq/3Jm/65rfd9ozm6SUe+6ov8bAP+J3f/gxcEBhAAJw4drzr+0sXzpOJGG3QUTYuKyePDXg/U11XNk4c2t2xrb1skoRGDETfnzp9DXDi5OkLta729rANLXTQWqZjsxezXdu2ai2LE2t7dmxznV7asdjePnV8bC36/szpM32ptvnXACovlHGJ8jdPePxe/uWZ6+vubjecm113clF1+Lov/7Ivc/1jFseOW/zp435hPV7ouu7uJ87PX7ru7PnxYH+/6+fX1e1TZx7UbZ84oDuc36gtqVyMjWuzLg4Hj+tpcerBWy3KxomYLYiC6LeOzY4/aFjuzbauL4sTdbaowerSPavlXllI82NTOTbFopTZ4vgNOZ70/Djrg357RnSH+xePXXfLzjUP6uY7LbNunByGlTau31pcQ8yG4XC5f996tbdaLWeLWZTSbV6n/ljWjezWi5M33XuwW1Qf/JCT9BtHh7sPf9jeS7/ky0bEfffdt3vp0sMe8ojN/rr77qRUY57LsFrGNLXWzDMVnsktgSIwSQMaAAbzTJ7auac/LW2glGIwCHLWX3PzgzoF/xq2+VcCKi+UUDov7D+6VnZvO7vYubA+yMPInc0bHnfr9nDh8DFnbrn1/LrtvFfU+TMe9zsHa7I/PW5c7PqDbrapzetGe5qaVDeOX3coZcznJx+0sXWi5Ti2qds8OT+e3eax2eaJre3jObX9eml+7AbqZrdxcrFzSiikfvPYOJyZjorrZpmfKPMdWf3WtcXjfD6f6qwrovSLnWtK6Te2znTzjYK0Up3tbJ+4xW61356mNXXeLt69qCvRLTaOnTjz0OgWU1M331psHT9x7S3r9cEsRJus8qCHvvitd9x7zz13v/RLv1RrbRynP/zLn0lpGo3AALaB+87eW85d2JD60nWlGAQGIbC5zAjZlmQsJAGyLSHJpbMtKaQpGyBpOhrveepTrn/ow6rCvAhsJP71gMq/yG3nxlc+f/iq3dbi6Xf8vTw2PeLSdGzquS+f/IynuMyOH64OTl3/sO7aA+3eu9g4Xpezab3XzU+Wxc5itlDt7TLfPk2Z142TZbZYHh0Foa6rsy3Nh9Jvdv28jcts1H6xOHZtUvrNE/18OxSQtNU4Pxkq0S9m22dKP/eU3WzRlUU/m9XZvDhrKUZ9P6+zBdEv5n1fytFyOdu5dtb1irpc7m9OU2adpqMa3dbWqfnmSSlCGpNuvjHbvmZz58w0DW5jKfQbp8bsv+Lrv/H1XvPV+q78/m/+2O4df/7ar3pTKdgAgCTD+uLuTRtbF5aHxxYLSqU1gFC2JkkSCuzMFrXLaQypZdpOZ611am1qrau16/pxGI7G9fbGFiJbm9W6ebh/ae/SqeMnsfkXSTwf4l8CVP5FUnQLLWpsX7O4RqrdYueEcjpaHl7zkJeU4ujgkstqauu6cbwbxn6x4+jK4kQ324zZTjOr9bqZ6BYqq5jt1I2twbSjQ1TW62WtnUodh6lQFNHP5v3imEoXUZZHB918q4Rd5qXfSlhsH19sHAv1TWPfdTkNAUQtJTAR1Nl8NluYbG1aDuvSzTe6ri91cnT2VjaFmiVzbPv49vYxibG1yVaZLTZ2jBdFOQ2z2XxsUy3zc3sXvvF7fvjg4h1qF89sv+Sx2tcStiVxv1LL+b1Lf/SUx7dMYJhG28c2t7bmixKxHsej9aqr9XC5XI3jSz74Ycc2Nu+9dPHuC+euPX4ypFnX33H+vnk3Ww7rY5ubFw/2j9YrSZl+iVseeuOZMwP/TuZfAlT+Rc75zjVb6261Xqp289m8TWOpVWXWxilxi41uIUelbpTZTsai21iM48pdV/rF2JimlUmVvm4ci5xqN2uOxXyxHsYkonbCdks6t5yaG4Uyj66v/Ua6RcieymyWWP1G6eY4JY1WP9ugVq8O7Kj9rNQukzZOpe+m1saWiuhKNRSotZ/qZulS9nxj69ix46rzYXIJJJVSWvQtR5Vaa5+SpzabLU4vtmaLnQub2+OwOhpXh+OqpSSDANuC+cmTRwe3v9TND53atJ7GedePbZrVLiJALdt6HCM0q/3henndzomulo3abdZ+0ffHN7eBY/OF7YuHB9cdPxnX3njn+bO2N+fzRT876Mo1x05g858JqPzLNE7TlAHZ9RvLoU3TstBithiSzKmVbj2Ow8HadR4bJxSdcwzV2tXlcj8WW2rTahyGaSyl87BWH33th2kyWWo/pW1bbT0shYapteZaZ6pdFGpK8noCap1tRHS1VJwhd7NeKRPRLbqu72Y96a7rLcaxIfWz2RpPzQpny5YoSrfYUYlZ7amzRk0Yh9HObFbpu35OjlFwWWzOw1bmsvbdsZPX5jTak1tThI0EIAk4efxk13WHl3b79KZke0NK2xioaCEZ7DwZcThNNlK/c2zL9p4T0NYs0LVxzZgtFDedPN6yAe66U2euKZJt/jMBlRdBa+1wtZrGwXSq5eQN1+ycOnn+nvsO7r1vGEd7KN1sXB2N09hMIe2J0DCsQnRlHW6oKGobVw21zHE8moZxzNbPFoClnHCOaY8mipqnbCWYEuRRmYra1RKla86AUqpMCLcspW+IYZrNZ805jVNCAcN6NSF3XU/UvsdETmNItfa2M9t6HIqUNlFrX3IYpmky8ehbrv+zv35cv7VY9HWxcWzn2GIYDkOsDi+BeR6ttTZOrTX+JRKgxDkBSABpgAmAVuv2mWu2ZvOEAMA2/8mAyosgnXaX0zQ7tn39Ix+1MZ7PgyfO7/iz4fDGab41DkuGZa0zqTPpNjUXcK3zvq8qtFaMInNSrX1pLVuh63o5cxywyqwSbkkD5YiKENPYLJVSFJB0nUIRWk/TOE1dqXYGWaLkOM26GiVuf+ITju0cO3nt9eMwrMcBNNkF1lMrpZCyIkqNNApUx8kSjSmTNk11kqQos4Oj5a1//UcPXpzYOHPyjov7lDrlVGsf0mJjR1G4n23g3IVzvue+DTTv+q4UGwBhW8hYEgYhwBgEIITTEiDbgIEh73v60+IhD92YzTNTkpAx/3biXwJUXgTZWhex2N6++Iy/fepffHe05eHU7/e3bN281abR6mRaNkObxonwNPZdTedyte4rXe1FZpQA5ygYx0ZJ3FpLYFhOYEPLJimnEYUkZxgTJWYbZVqDonSBWjbnGCVKRIno1Nbr1bUndm6966m3/t3Fl379tw4RIUXQslnKmMapOaexQUoxDePUvL2x0TLb1CQZhmEMBTA5fvUP/vKa1Z2PfeXX0INfObN1tbTWVIrHAQCDAEmGo/PnblxsHeW4mG+AsIlwayoFDCIbkDYgAAy2M7Obz5xtyuy6DoMgfXKZF8+f3bjhZgFgzL+L+ZcAlX+JINNHq7GLcd2dPDd76W7zeD/b7BSrTFIqvfFkT61NU5MmlbJer6KUUurYaB48TqVE5lDCTtfajcNgMkpINaY25NRsmxLFRCNkT2Oi7EqrpQtKDYNba6UUZ07D0M1m09RyGlJ12LvwRZ/9SfecO/+9v/G3tXaZRhFYIWhOmbZaryKqlNMUZWN2tB5iGGrt+iIcw5Q1QvLG5uaLv9ob/Mlv/aQvDI+62cMwTmPr+84ZkzoMiPsZapSLB/u/98S/Ww7rWsrDr7vp8Xfc+uBrrju3d6ll1lIeet0NJ7d27jh/dvdwvyv15PbO4Wq1vzw6tX1s93B/1nW7hwctU5Kd24vNV3nEY7H5LwRU/kViPbXDFXhymZ26/iFyZhvGaZymZpHZWmZzJg5KM2oZpVC61fKgBqWUxGHCyky3McfB2UxSZpJLa6OQimEYJ0tdUZppGruwg3E4KhGZmtqIsigUAZ6mw1mtLV1K/uXj/vq1XuK6Rz/2JT1Oh0Ob9TPD5AmRGfOuU9RSN+yplOiiOFuqK4XWpqNh7Grtak17mgaGMp9vvtobvsvG5s7q8FClpNjf29vYPj4OzTb3sx3QHTs2Hd37mBtubpldqVvzxexBDzuxuX18sVkUpcTWfOPYfGN+zfVnLy0Ws9nxja2xTWf3drfmiz7ixOZWnjyzHNYhRURBu9P62MnrAIMwVxgkABuJ/1BA5V+mYRjW67EGk4OWtoc2BkKxWh2F6pTpQCanQSUgG/IwBCGapyEi3DxOY9ohSrjWkulpfdQo2CWKIiXa1FJykri0aWqSIsDZFHUWDSLRNKScNSzTpinKxMaJj/20T7vx5gdf+xJvgurUWrZ1m6ZSIuxJjgicfd+3aWrOth773qI6BymYskSqRNd107gKsVjMm6cJeRw2+62WHBwcVpkHkAScPn3NxVJOHx4T2GTmdadPtszrTp8EDJl5KTP6xcntTfBRpuhP7WzZvuHMqbQFWwoDOCM2T53enC+AiOBZxDNJgG3+4wCVf5FtiCgCodUwDtNQxWSJFqVIxZnTOIRUyDa1cVr1fd9HaS2nTMnKQBgrwE5Yr0fJIFk2LVu2VkrXnDkMEypd39okt8xSI0qoFjcCKd3W41BD08R6mLqiLmKxffrUI199ceJUswTjMNgtohhlUWstk5YTa9JKZcjDsE6nsnVhOjPJU2YtpXTDMB7uH/az2bzvM6sz+9lGM62tDeI5CGaLjfOHB22aECC3JuHWAAmQAmNyQiCByQlBA4ExTcKmUqZzZy+0hnhBFts7p0+cwjb/MYDKv0iyvR6HQOM4TXYt0QXLIcEKu61yXGE3Qt08cHGdGuM4IIRL4NbSGVKJmMb1lJRKEcN6CUVlJlLy1DJCzYpQaxM4YGqTGxkigywoE6pKF6UE09RWw5goyY3tUzHbaa21nLraR50Nw1p2rZ1rzvu+Dc42lRJOJsmmaBinVD8Pazm1QL1o05RTC5VhypyWpZaWjFOq9GRKMggAG2B379LRHXdsE12tNYptCRtJANi2JLCNJDD3swEkQIDAUzONZxIYIwmwbZA4uue+e9br6667AZv/CEDlRbBerzNBjlK6nNJerQenrZgVTzlFVJyrcRzGva5UpNZGt5VNrQUp7WlqRaIEUMicSEl1oRyKpijd1Dy1oTSmaUBRa18jRJTiUFFOaaftXNstrGEK1RrqTFmtx1KidF3Q0pPNan2YR9H3hWC9WpWu4ixya209uEbMZn2pdTWs+1LBREEM07BcLkuo1kBRCILVcl1qjYhpvXIbQGAQIAFcuveeWzZ29qb11sY2pWCDAVoChJAwYKIwjUQBAGxCIGxaA/NMQgKDACJoU6aj7wGci5Z3Xbx4eOLE5mxhm383oPJCGYApORzaomROo/HF/SPQ9tYWOe0fTC2b7ExHKTDmcDRlGHVdyXSbxknUqBYtJ0VXQulsrU3jWPpZRMlp8jA2FBIlFGVszeOQOMJWFIRSUdo4qajIKiUi1uulvKzdTNY4jpT5KqX1FLRSaxS3NnVRFot+mKaDg2HW11pqy8k0pima+xKraRqto3GUUyA8tnSUooTW9xt9acN6GKfBEskDCRp00u7RwZ8//ck1okRZDmtgZ2Nj3s1KxNSm9TTWKJeODgU3njpzfn9vOaw35/NxmiQdrpbHNrcefM31gifcedvOYjOd24uNs3u7U2tdKSXKvO/v3b3YlSJpbO3RN96yvbU1jiOzBf8RgMoLJZCYptaGYR05Dqvaz1/6wWcuLVd3nj8cpxaiK5paWiVbCwlKSAraNKTJlqXEelpZUck2tAylSqZr7Vob2pgp7OyjNEUbx65ElQIpZGKcxkb2VSQts0SforU2jQ2JYBrXIERXiiJAbcJqIafruJoiGrbcjoYsjLUUiGG0vFRVP5svV8uuBKRV5v1M0jhOKYqmS+M6M/quRlEpFaedQgCQdoGytbW+9+yNx08uh/VqHE5v7QzTuNHN+lpDkj16DLhm+9j+atmX2qbpumMnWraj9NF6tTVbbHSzeeki9PBrb9g9PNicbe5sbAbaPzqc9/35/b0bT5w+ubn9pDtvq6UcW2wUe1XjzOY25j8EUPmX2Kit8CSU0W0f3fqyJ6592rR/1zpKnYOHIYlozjYMCitkp6cpyJBBaUFEa5BjpsQ0TUjULoQFosCUzZ5mJUTzlNTeDaDKipqgbM7mMdMuuK/VKki1lL5oSrcpW66EpEJSohXcmIXqOI3z2awrHTA105JSanRjtnZ4qNKbCJGwf7SsUTLH1qa+lszs+rkTRzcOk7JJMohnO33Ndfe1dnpjUaNISlvCdtqAUIRs25SIYRpf7OQJALAJSVI6161hd5sbZzY3JE32sZMnjp86iX3tdddNrQEv+2IvAdisguM33FilzOQFs82LBqj8SwQqdT0coqy1v+swvvon/3T7xHXzkzcqx0w3So7j1KYq4cjWJDKFiXCmNa0Ty5Yn1X6cpr7rjKZpFFbIRsouyNTYXEoowGOqehoIRTZCIYSMwLaHcQq15mkoZW0DVg0Vh7oia3Ywjun1ouZqGPpachzbephMmnlXcxoTIsiciqauFkpXS3RFs75X2VgPw7Ba9/1s3Thcr7syZKZynZkCwHYpBSgR199484RbpnhhDFuSbR7AIJDEZcYYSbZ5AU5F4bKI4F8iiX8JUHmhDEir9bplTLW05dKzneMPemmJYRyGZBjHqqEvpSsxtvSwxi1CgKOsWyt2a80K44jShjXEsq2rMzMdNWoNcp0jmaXEZHctIso6s4spVKZprAqHMltVGJkszlKraheELcqstcQu8jTlOKztoymj67qj1kqtbWRWVWrMA1TsaNOkYBwnRTHRpmRa2xQnOprPFsO4LNGViXDb6ups3hNdG4okhO3t7a27776r73vbQCgQgAAwiOfDIAAM4pkM4gUyAALAAGQmgITNs0jYXCFhA5Iu7V7c3lpkJi8YUPmXmejslbJJhFtOzjZQSptaADAMUyI5SxGScY4TrFuEao2ujFPSpswA1+KWk0tQenIKT8jRdZm2s4ab01MKS0VMsp0jqQhNdpBdaGgxjk1Tc0thBEREzRoopuauRFeiKKY2Ma1V65RlPSmnNURXFLWbMjAhr8dV1/e9UKCY1VC69bN5Zh6NI/ZqanVs4zgVj7aFpjZtbmyEdPHixbQBMOa/iMSLyN7eWmxubvJCAZUXSgBaLQ/GYd31Wq3XfVdrF6Oq7CpsMm1niTANR7PbOCApSk7Tcr2uhSKbUAm3zKm1NgzOvvTgYWxph5AUtZgAoRQ5jqOMpBJERKUG2aapZUapWKXWFg4ote+6fpxag4AgW3OtbZpGU5QeB7cyzWqJWruuC2w0jm09DbOurzAOQ4qQSoxj1EAGQZbKNM26Opv189lsGtdcJqm1trm5ubOzY5v/wcZxstM2LxhQ+Zd5Puu7WLdpAi1Xyy5orSkUUraWkC1bI2oZWws3IuxkWuLsSh+1s6dxXNecTDioUbHHaSRKiajOUrsEKyOQ7XFsNaIU2mTnMLYopSXC2NlSNCJymrI5grGtxmEIpU3WbtbNhKLWotoy2zTYDbSahilbKGYlqlgsZhuzyEysAkYhTNhqNi1DaVtmGqe9/YNmKk0SYLuUcnh0OKwHSfxPZdjc2Cil2OYFAyr/Mi2PDqf10byPvtCia+MU0Y1tElmKFyjlKKFShmE0ka0ZU2rkOA4HrKUSpUgKt2lKr1oLqKWGchqGINNTpkvpJg9FKqWbhgGa5RIqpU7ObEOnyJBKZ7eSSSC5RNTSh+QowzB6Pa7X6ypqrRuzvit1dBIKEVLXzaMUp6Zso+u0HLK1WqJ0M0RaOQ61n9Wu1i5KaJwGEJd1pTibbeMo5fDw8Gg1zucbmQ2QlOkIcZlB/PcTXLh46dTJ4xFhmxcAqLxQBkDQnFPaObVmZ2vZHBGwXq7WJRRFIhgwDZiGKelqTMpSOuxpWpPhiHQCM2SUNE12S9c6ZZSIzBYlnB7bUGiJMp3NQoau9ilIT9OqqxU0DpMihmEIjhKXUrcXi9LPpmyNEqWu0sN6qEJqocAyY6IubJXlMG4stmqP8eFqHThKn9M6hmUtFcVqPSzms9ls1tpUStfG1kUKYULaPzi84cYHzWYz/me78647z58/f911103TxAsAVF4UuR6HZS1zRYekHHt1tpOsZbOZqKWNq6llX4JpnNognBOZthQhSeM0Sthktq6bWXjMtFGMq6Padzk5SplaCst2EYkQJdIZkj21ZgH2MKyNbNeIiChRBKheOjyqZK0liSxVEbNuDm5ja7l2mfel1FAoMkd52ts7P9kbXVe7fmxTablYbPT9jGzhoZ9vr4Y8XE9bG3PscRwKmGeS1FoD9nYvzmZzh+68486HPexhXHZ4eLi5ucnzs16vJfV9b5v/TJlZSsEextE2LxhQeRFMiaLj4Pzy4j1GG/NuvZ6G9VEDOcmUArlN43Ti2n77hFFGhcxcl/C4nrpuXmezbC2sWgt2S7tNQK0lap2GFRGdU5Il2zlMQUSJnLKT0mQkCGOwqKjUYgt7SkfpQq79LFsb2iQPbRwEUzkqUSKKam/ncj0UtZDm/cylFk8b/QZ2ttEonXt7uzgjtLlY9Kt7lxfOr8bxYtLN5tvXP2RM2YkAANnAX3/Yx922Onzsl3zez//gD978oAdN0yRpWA/DOFx7zbXTNF3auzTrZ8ZHR0fXXXvdXXff9aZv/KaPfvSjx3GUxPOQZJt/iSTbkmzzAJJsAxEBAOJfAFT+JQLbw8U7Tx88+bE33XC4XN539o4HX3PNMM++66dpKqVubW22lvt7+3efv3W9sV36ebSJbNHPat9Ha8N6mcPSqChaemoT6SiyYhqOap2hyJYTYCRqFEW11NpEm3I2kzQMa6nWWls2SRZtHN2miCi1dw6qM5xpl6Ku25K6dBuHAUyOWg/pqH1f6jwUR2O2ad3XWI/rWqJzZoRiXkrtayji/F3PyCf+8c2zRS2xEeUZ62Xb2DrxoEcKMAjAEtC/41vVu+588DXXvtbrvM7JkycvXbr09Kc//RVf4RXvvOvOzY3Nru+GYTh16tTh4eH+/v5NN9302Mc+9vSZ00CUkMTzI8TzITAPIAQI8RwkAMSLCqi8UALbZTbfnPe+NC22trePHd/fP5jNZ+v98dSpE9my6/v5fHb+/IVTp07cc/b88mA3a19F2iGmo0aUTKtUR6zGgRy7ft5UPE14sJiG7GczCi2dQOaYU6ZxdrUjYhxGRC1V4DbWEplAOoq62lrLaSoRuTqSJ6EpCsNRLX3pZ7NZJUqymBqijdM4DgeI2vV9Lav1UlHoZlm7Dg3DQYlI1S5mxzbmB7PZVtddGMfavBqH5cXz2r5gjAAAScArv9VbvTIAr/UarwUAr/6qrw685Eu8JC9ULZX/TGnzogEqL5QBaf/8vQdNbfvm3/2bJ6HSzRd33HYxnE869wxEtglcS22tzU4/tN8+jU1OmW62YGxjiZbZCkkUS+M45TgogqhSyZzWy8Oo1bZxEIjaVUlTy9YabVIUkqQJMSQR6mpMtonSqauKTiwEdiOnJto45npdlSWUaZWum89RLLaPgYdhPFyuag3ntDxcSzmfLTYXs5Zerse9w7XdL08/9G/GZd/PDre3trvFzrHrV0f7NkIGwDawf7D3xLsfn4HTEs9kjEECgwTGWBIghMRzEpjnZZQITX1Zz9t8aTWQwAAGhIxBCkBDu7Elpxbx2BvPoMKLAKi8UNlyYzF/1Zd69K2/+Tdx7GHzMw8ZVgfr1gw5Dn0/a9kCSuBMtXGVky/dE1FyGhWqpaaboi8lpvXR6JTClJzGtGst8tSaShRnY0wLqRJym6Y2ehpCNUqXmeD1NEUISCvaUNw1yHQto9Y5qyVqbaaWEqXv+j69UChbHh0d9CUyp8Pdizj3L9wz6/uNxcb25naq1CjDOLT1akrvHq4KsltRqf28v/ER07icpmx9N9/eCsUrveSjTp3YGaepqxVAAH9y5x/dOXtGp96Y58Mgns0g/kWylSC1UoZ5TD2O7NetO+IBFEpsO1ItczoYs+XhMI31scNhGdu9L/XgG8y/DKi8UBJj8zd9+gd82Ns9cbVad103TGNOY5ESGQxEtT3r+xI1EwVSAYOxJK+H1XoY5pvbihK4tUlSX2MYxrSdrWW2qXV938832zTZzbZt2wJJdmtTsz1bbCJJspPMKcls4NrPaeO4OppvbNZQrXXKHKeMiCItl4egUvthver6vp/Px/Uqp2E+n4+NJCSmadrZ2hzW62EcnZltlKLvunGaBC0bYpraTddds721DSAASSSH08HW8a2S1TL/NgaEQGkspKnGOIupl5W1tY1VdmuwvAkIGyv92NWDjtXtJ8Yz7unPbexuvtaNb7hzbDuYnnHv+T+69+6D3QXcIIl/CVD5lwj/4u/+8R0XB/VzWG/WkHxp0kYXJ7pWcnqZR924c3zrT//0t/dWB/NFXR261aNx7Rw129Dh0fBKL/3qj3nES//Zb/38hbtubapbJ64dVstnnDt4+ENu2VjMu43t46dP3XzzLXc89fG3/v0fbRw7udg+nkSqdB5bm6apbZ++4cZbHuRsj/uz3x4O9yfHfLFY099yvHbzbdfNC3f8/dZ1D3nwy77yX/z+bz/1jnun9eG12/OTJ3bm/Wwv++uuvbGvhba+/iUffcfTn/oPf/p719z0oO2dU097yhNPbfanN2K+sTXaT/2Hv3/YS77Cgx78iIuX9khnZmbaiWSQ3XV9cy4WC0mYZ5EiM+W0zb+awIRNyiWGrozzmDrkrOMw23ddE5OMksvCLkkxXbp7UlvNxzzq5+s8wTpOnjz5K7/yKzfddMvDH/Hg40/85cXxxwBg/iVA5YUqJQ4PVj/9q1/RFuspjx2N2pqp5HSYM439iY3NC/vnL/7GzTe/0Vt94dd/y0OvH8rO+rbbht3Vhb3D/a5Trdq9tPceb/0hH/c+n/1dX/jxy7PPWE3Uax61UTj90Mfcc8dDrz2xdfyam46Ojt7qXd7jV3/8+2//3R996Vd/7Qe/0pscHK3OH4wPuWbnYPf8fGPz8U9+4uGll+q2j/3cd3z5mc1ycDQ8/WK+4mu98d27j98+deMznvaUX/r5X7npVd/4S7//pz/9G77pzW++5li3/sNbn3Lixod3WyduefFXO7l98hlP/ItbH/dnb/Rhn/V3f/J7v/kD33J8a7Pc8OgbX+yVj+46f/eFJy+H9oyL4y/+8q+80/t/yHt/xMc9/nGPf8jDHz1fbBYxDsM4DIoytXbh0qWz993z0i/12Nlsbh7ARmBeZALARFpWlljPy7CIVols3XrcvJRlksaAzO02bqUXzVvpzcyt5pnp7c5054uyZDTGFVvDncDd99yzubFhPeJvZy8xUw+A+JcAlX+R4vzdd+8dPWNzc3Gwf3jO0ziVaWCx8Dkt7jg/vcEj3+DoaDpo5eZTnDx1/Z133zHLcuPGDctxWsy7+WY3m/VTm+5befv0Qxct7zx/YTHrr19eeOS1L7N982NztX/s2EaRx377QnfN+b3D4e8fd+z0qVmdnb/nrmF58Nd/9bdPedwT3u0RL95Ht7j+sWfOzPZuu2fr2Pb5C/euLx388C/+yLWzsduc7U2luK03j//mH//B27/qS77KSzzifL2GxfHZ4V2//9O/qUt3/+Z5XuZw/WKPuGV6yzf90cff+7qn6iO2xzsONu+aNsejS+42Th/bOn3y2NHBwfd/6We+0iu9/IMe8WJbp64/ce119Iuz996ze+/df/Z7v3nuzie/7Hf9JM9FvGgkMCaaZWUp642yXmjqXFr2y2Fjb6hrmeouzNRuPJgeMbRr03PzHAQAuNiFYjcpM9vyYO893/M9Q7r73nuz3WwfAID5FwCVF8E1O8d2Lx7tbD/Ido4u3dQtFgfre7qY99OFqbU0yr2txeHUakw88tEXyb31+ZfOxcU7z5UIl9K91lu991Of9Lg7b7/jQQ+Nv338k+/bW998/o4nPumvbnj4qzG1lz44uPnBD1teeKWn3n3bbX/97Y94ube65kGvsCPvHeYv/tJvv/lHf/Gph77kffc+OWqcPHby4PrutR92/dn77rntcLZ5+oYTp7Zz7/CaG68dVqsHv9QrbG3pwnp935Nuz7gzi37j2IPe8Jp5vf7F/bQLk1Uo943lhx/zptff88sHf/STf3J7apkPeeSjH/Pwh9zxt3+8s5j1NWazYX99z91P2x+fcHT85IP+/K/+7sy1tzz0QTfPx0uMR1LwryMAGbWUlaWsN8p6I6YuYxpnh2xeyjIZR8ZLDI9okU+Kp9nHd9evl5YAZWDuZwPiMgMYAGXLvb29fsoSOjw8hIAEAPEvACr/AmUbtq596RP9Q3fHG+fXnDjcu9D54Nprbljs3rc56zbPXOhzOBjareerG0+782jQ8q47Nze2cmu+C6vDvWG9nKLELY99hfnJG9/iPR72pMf9w4Xdb7/xIY++eNjmJ8489pVf48477l4PA7P5YnPjaY9/0uu+07tde8tLPOgRL3/7M566cbh3w403xsFt04XbOHndbz/hjhu7vOnRL33jwx5Wjt2Y17/cza/VP/Qxj/7L3/n12XQwDOP82Mmz2tg6d8/NNz50V7Pdp/9Fe8VXvunFbrrrtqefPqaiXLZSW7l2p6ynma5/pJb7tz/57LE4vhpS8+1+tuHMdcv+2I3lxHVtOJi2rx3zbxebszLr0sNqmNI8D/HcxBVKqwHRagxbZVjE1Dlamx0NmxdVcoP5YaYyapSJ6dJy3yFttiGvSSs0mQBs8ULZrl137fXXn7+413f1mmuu1Z3nuJ/5FwCVF8qoRj75/EOesT7Vs6fdVfr4sdnpc+vizYf3i0U/Pzq2vmM1jRvz2VPPnrj9wt56ODh7gfXh8TPbvv7aDVZs1DnivnvvKeRT/uL373va4/sae/c99SHXXfvXf/oPT//DP9b1j3yN1/y6k10enti5+ZGP+Mtf/r2djT/+w/Jd58oxdbM7zl28/ulPLa/zOvSzR7/4Y264UTvzw4P7bt1uh+3eg8f/zV8+9U+OLdd+yKNfUs5lqXedO5j93T+83PU6Pgx/uTo8+6TH7Z1ZXLtzzUOv77taL164J+59Qt5Xn767+9pnzpyY713g/Jb3Tp+49ty9tx8M66G5rfLuJzz5tuGJ5y/ub+8sbr/v3qPlePr6m/YOj8JThHiBBICJtBKrjH0ZFjEslOEyttly3LqYZRSy1U/99npzb2N5bNp8seVD+83+3Mbu3+mpPTG0GwAQ5oURAAbAduZf/fVfH9veetRjHmvMiwyo/AsMOnfujvPLc9WrzNYVhqoLUWaz2c5icdjyYLPNmG59yuqXj/ywa4blerzznt7l2Hqts7vdsdnJmYtzvPB3v3rzmZ3Vfffd98Qn799373jq0a/+Nu/04Jd9xhP/9q9u28uIct+99979N79dz95699Pv6q7ZOfT80rEHr5qedvvt53/5l1/vzd7iwXO9wo31dvXH7jt7XX++VB8+7R/ufdxfbm4snn7X3ZtddrX7q7/8yzMPedAbPvJNdh52y/mz9z7s1C03z4dnPO43p4P91ezkrH/j9XL/SY/765svPuHl3vQNTj/koa8+m7/KI649XHHfxXM+df2dZVttvHTuvoPdi4vesX3swQ++4eEvduPR3qX1wdmDg+Xd5/aEeS4GC3A0ZGUp63lZb8Q4A7Ibpo291q0cDSSHsgLCQ4x3Lc4VYuXhSctnXLM6sVeOyk4Y9+W21XSzLQTmmQTmOZgrJFliXL/+674OcP78OUXhRQZUXqhsudjY2Oborr/9i40ZU8taokbMuq7vurNdP66GrZe48dobr9245vS+pzv3tnbm123uaOV6ZGfR6nCnLq7tuv4RN508ueBwt27f9NDXetXXu/mhj1jV7Ye+wus+5rXeev/C3f1sfv1m/sOFex/12m/+Jh/08PnGTnSziGL77/7yz3/xd/5ysbUt1Wfcef70tTcMsxPn0MZia+fFT94wLMqle88P7fh1N802Nl9mPr7bqz/6xW581VufcevFg2W/Gq85cfzMDS9+cOnCPfeeO7k5/1svzm1c/8lv88abG9u33X7vtJ5O33DLsTZeOHv2+ltu2uj6xdZ2f/2Drt32S7/Miz38xV56e3vz0tm7Lu4fLFeH19xyYueWh0ZU2zyAo2WZyli7o+2y3oipc2Trl8P2+ewGR8oiQ1kBMJj7FYrtsWvnz+ydbZckFcKu8/rk5s2D4aWDCckWgHkBVOWj/tj3/O19tLtCtDqvG8fwPgCIfwGgg73zvGC2u9ns7DNuf+JP/RKZANM67RIlSlE3m7fpka/wMtuv82q75y4ul0cJtmotbZqMS6lR+2Obi1rUiHG9TONsXS0oxmnKaZSi6/q+79O5Wq0WG4txGDMtIam11nXFrdV+XkRbHzgdEcYRESLt1WqYku1jp9LZpnEaVsPUMlNRBJkGl1pms0XXddM0DcMqzbAeZl1RqbZDAlprG4uN6LpxdTSN69L1JoZhICcjSVG0sbEFJbN1Xb37nvuuvf6mxWzxC3/1KzmvdeqzjtPsqM2Osg4IZeDgmcwLJYTAGAMgadhfv9rR+LAQkJJtQDwXgXG2bDm01loD+q4bW3vJ8Rmv/7KPvfOuO5cHuzfddFNrjRcAqLxQEpk+uPCMnYfG6TNnxm6r3viS81l/eHBw/q5b929/4qV1e/jLvtjtT37Sk/7h7258xIt3tQj2jg53jp8oXX+0v3/3k/76IY95ydM33PLHP/99+2fvni02rnvxV5vvnLz3tqce7e0+7CVfPmpXdHTtNafuuuP2Jz7+Hzbms+tuevB8a6eWcrC/f/LUqfPnLqyODm+6+cZVi5/9u9XhkMPUxpaGJDar++CW2blrtp/68i/xihm139iZCSEwCAADmZmZpZTNzW2b2JZtm8sMksjMbK3OFt18007MbLaQxDO5tYSUxAPIGjf2xn6dZcKSQ1kBMJgXjTHmgey61f9Zif31dPOUp9IIIIWRbB5IiijMBDWAiNIMiBcNUHnhDFGe+Ls/d/EJf/Gyr/qqd95x+93b//C6b/9etz/+7y+cvfcffuY7n/HUxz/ksS9799kLP/e1n/Gab/kuD3nlN5ptn3zG0578qJd+1RPbp3//l3/u7sf9yelrr59vH3/C7/3yZl/67WNPufPC673D+24eOz3bPDbfPL5/760Xnvznx1/vbc+ev3D37c+497anvcwrvsrixLXz+WK9Wp+/7975fDabb47LwydfLN/2W3eEp2FYT21cTY0o18+z5+hld/7+xjPrV3ypV/U0kDYPZBD3s93aBMrkeRgkyZl2QwJsLjMIDOIK80xm3NibFke19dGqAQzm38sQ4M3ubza6fxjbqaHdPLQbpzyegBEJSLINIElFCgMQEWpGAID5FwCVF0Hp+tnOie7EDTdsnTi8555hfbS6/W+X995548MevHKLri8RJ44dm+59yv6fHtSHveTm5olzT/ytf/j9S3/4m7/zUo+8pZawOXnjg0+fPjPbOfEr3/j1T3ny0970rd9msVj82S/+ycOPjTMH0C/m1z3k4X/953/2+1/xlW/01u/wKq/6yjksL5y7+OiXfNlhcmbmlMsLt89iXC2PyHX1+tGnhkdsjw861R8t7yxlWxKI5yaem3j+BNgGkHgOAkA8i3gWZVEWwJh/K/HcjIH0HNyVc325N/23U54a2o1ju2nKY8a2eRYBEpeJBxL/AqDyLxEalkfp7HfOcHh+dvxBv/zj3/nI05uRy+te+vWPtNP3fcu0tHnN9ds3P3Q8f+sT/uG33+gd3/oZT3j8we6lzdM3HbvmRrstTl5/6uGP3Tpz/faxE0/9h7962su+woOuP/3UP/mtV/nIT7q0f5jpUydPHR6txmG4/a57j5+59kEPedj+/v6F85tIIUqp43h4/txdldU8j17llvMvccv0kGvqZr+4uOyefNsdL3/ta6XNfz+JZzIIDOLZzLMYBAjszCIkniUdaUuQgF1NB9mVe7tyt/ibYXro7vqVwTyLeU7iRQZUXijDrPKUi3nh1ntft/NyHP7uN379MBaPOfZiubz0p7/6s+uzt+c4pDWNK8YlFNVNpvj7fzh38dxhZts/OFytlout7r5bn5IH51d1cfbCwVqzs7fdmvfc2szq3K1n79297lEvc+ftT7/nGU+bd9z0sAf3fT1/923rYX3xnjsWs3mdb0eJaRrOnbvr+sX0Kg+60E2Pf8Kteupti/39MnDfscUxxay1SeK/iHm2SEdzyiBlAhhAGABzmbElGYElBJYxeNbPDyZaggDZ06IbZi7rZnGZwYDdgUx29Ynd9JChXRtqtnihzL8AqPxLptTJbpm+ePGeZ1zaPX/HX//NidOPuPvEP5w/e8d425333HNhvV6DDncvHN75xL99yj8crIa/vePo7/78T+ebm/uH452P/5MLT334jS/7Ok/7h7+4S0PX1fvOn7tjf9z53V8+VX1Q4lX/+KePYjPzbbtu4+LFw7Y4dstD55fue8Z9Z/qL587X6NdHe4cX79veeATOdngh++7xt13cml+4+ZbuxgfddmJc3nnHznx23W1nn1JLt26DJP4LiGcS/cGJyJOaiiSrORUBNTMNEImcdnRTdNmSbFCy0VLNJBvdQ374b6/5mb/OGrKJIO1599SPee3zL3ayrCZLPJsBQXozvSmwxb9EPIDtNBhJEVwGVF4o4UR1eX68dPbw4PDw0u7523cv3PPEg4PFg3c2zt23f++9TFO2bHfcds8TOt23P5ZVOb7fzXY2Ltxzzw0POnP+jiff94wn3/wKb3DXXXd202pno9dyvXfX4eg8efLEuf29P/rTx734Szyq1v5JT/772++76xl33POYnfXdd5WLh/c+8iGPGvYv/tlv/s38+HWb29stj3PfPXcd9Rc6buluvu7aJ58+tjp/IWdx4va77zl+6lQphf96ZpwfrI+tShYru5y9XDxmOzafvn/Xk/s7u6hYNIny8v0jj/Xzp+6efVo7T8ZDZ6dumG9n5dL5vf4nvjuWq823eTPvH7T7zuqa0+MP/Mw1v3bDuZd+DZYT4rlI4zDdMuVWqNniRebMvuvqxkaiyLY6OEiICNuVF8og58Ne461dN+vx6zdn26/wxm/RxUw73aN3jrWH3PZg6nUPeuj2NctXeLN3WPTlWHan9/So7eOnbrh2uRruPFZvj3bqUS+3s731am/1bhee8ZTNjY26117yyK9w6tQr3XLLubE98eD2rUc9YrG5/bCHPXi1PhouXHzUdYvt62+cz/qNje379g4H9Q++5cE3P/IlNg6Wb/1Wr7Yeh+Pd9HIPOtZt3LUdZarT1oPnN1x78VVe/jUzG4j/ci5T1iHcDYwPnq49Rrde7V8c751O7pMBjJqu5/QOfZvaLvcuz5zF9Kt5HeaZxLTvks5c/83f5Xrt5SqWh553TMkLYLSeHsK/kjPnGxt3XTj/tJ/7xdy9tHj4Q1/8NV61n9o4TZJ0sHeef0k3W1gah0GKflaNMc0uihIxrJaCbjZvmYDFhLO1EhHpomiZOQ39YtNpQwjE5BxbK1Ih0uSw6rrembWrUyKMadmQIiLblJk1NJt1pI2mtAhbEcpsIMM4LkH8l6i13H3Pfddef9OiX/zcE35sfXK/Zm2Rxw83N5bzye38sb3sUSKYop0+PHbDwanJ7ek796w3RsOZi8e2jzbsurszHf/hX7nu5/5uCpcxXUuSMZ8/5RNe/75Xu7EejQ7xbJKmKY/vLt/EBJjnJ0JH6+nFVk95/Zd5zJ133bk82L3hhhv6vn/Ck570hI/9jEfdfl9fyoU23f4mr/n6n/fpm2hsrfJCCQzj6qiUqBLOaTlymaBBA0VgD8sjBIApqAjcEBMWiijj8ggBNAMIOglILCiltDYBwzCAQQIJjKcmqUQkHC4HIUACDNAAgYFSKv+ZBC3TNoB5lqFdfzTdXNo86Q66bN0cIhysAIqWkLcFd2yhWFI3GYS4Y3PJ5tjHvarDhQ94xXve9MXclzN/dsfy2q2Dm45l1eraRV1NDvEcJNp6uiUpoWaLKwTmBbFdI3bH4Ymf++Wvfsd5b+80fLqUrZ/9rR+dde/z+Z+tg4PKC2WQ1DL39/bF/3fGi/mi7/vWGgIwQtNqetje6voODIAMYPHcBAlrrhAAtVxa+HHzcuvRgxem3PrWjwBklC5Ds3gemZ4N7RaBLZ7FAAjM87Ldzed3/+3fXv+U2+LEcb3uq8cddx3ee/aW5l/4hV/7m/d451d8xCMrL5Sk1tr5i5cWiy0wz0Fgnj+BeTaBef4E5jkIzPMnMM8mMM+fwDwHgXn+BObZBOb5CcXFSwfHdzb7vre5n6QpRHiyBCAA8TwMgHgmA0zt2F57lVW8+Fb7/RrnoXA/i+cRofW63Tjm8VCzhcA8m3lBBNlStkV03ezFH5MnjvPbfziN4x133PGKj32xygsVEfsHB/P5xrXXXstVsLvb33vPHQ+65WaezVgGEzbPJDDPn3kgKUWu23ZXbu7qfekOkudPkOnucHhpAQjAvCgkjev1tQ9/6B/ffO2jbjt79Au/OsE8os36P19Nb3L9DR7Hyr9EYABaa1EKAIh/gW3b/N8SEbbHaczMUoJnEs/LPDeBARCYZ7GRJJHeAoF5gRRa7w+vPOapULMFIDAAAvOCSBpbO725dfOnfvSvftgnv8Lh4fbG5qX1+gvuedqD3vMdX+4xj9k/PKy8yBSR+wesVuzsrMiDowNJ63E4sXVsmMb95WFXaomQlPap7eNdrbb5P0eI+4kXmUFgMM8mMFhAy5OmA/P8RWi1mh5yND5KNFtcYRAYzPMwDxARy8PDl3mlVxy/9Su+76u/4eLTnn7HZnnxd/qgT/yQD1mu15IqLzJJbf8gL14s8/mF4fDo6KjvuktHB4vZfBjHC/uXAtVaQ2E86/oTWzv83yOeydzPvAgkbJ6bAZAxs3IXTNCBeW6Spil3DoZXEoC4n4TNCyDuZwCilMO9/Vd6yZd6ie/85rvuvffYfHHNqVPLo6PMlFR5AEm2uUySbR4os7vhOm64DrgpN3VKPMANp67hOdnmMkm2eQCBeT4E5jlIAmxLss0DSLINAJJsS7INSLLN/SQBtgFJtiUBtiXZ5gEk2QYASbYl2ea5iPsJ8UwSAAaBeQAjZJ5JYBAgMl0W5b6N/i+SCkYAGACBQYD3h1dpnoWaCTCXGSHzTAILYQfPQQBgl1IOjw5Desip083e39uLCEm2Q5IkSZIASZIkAZIkSUICkIBxvT48PFBotVpxv8wE/uqv/2qcJiCdgCRJkgBJkiRJkoQkSZIkSZIkSUKSJEmSJEkAIAmQJEmSJEmAJEmSAEmAJEmAJEmSJHGZJEmAJC6TBEiSJEmSJECSJEmAJEASlwkhLhMAQgAIxP0EgEAgEAgAgUAACAAnpcZ6a/YHiUGIZxIIAEVodTi+xLpdJzUTAAgEAkAgEABapwfFMmUhBIAASZKAKIWI9TRNrZVSJAGSKv8S80ySfumXf+knfvIn3uiN3mhne+cnf/onX+WVX+Xaa679ju/6jh//kR//jd/6jQ/+0A9+7/d870/7lE8LBS+AW3OmJMA2YCMhZGwQgIwF0XX8Hydge/b7in17Bslzk7Ret5uOxpcKGYIXTDClX/xkHMuhzPvfv4vkBZLEAwCVf42jo6NSSkT84i//4pd/6Zd/5Vd95V133XX61Onlcnl4cLi1ubVYLICjo6ONjQ0um6ZJUikFmC7tcXG3lDK1JqilAkSQObapRokIILOVUtzaOOu7a6/h/yzbsdX/VVdutxeQPDdBpuf761cxCIN4wVrm5maJZ/zD+777237653/JqVd762lv4kUDVP415vP5G77BG/7BH/7BHbff8bSnPW13d/dLvuhLPutzPuvw6LDv+2uvvfa3f+e3P+gDP+hptz5ttVrNZvOu1sxcrlbXnLnmlptu1u6li0cHzzh774mtnZ/+k9+98eTpRT8DthcbL/uwR/3O3//Vop+txuHS0eHWfH7jqWte/sEPb0dHZWOD/4Nsx6zevtH/nT2H5PmRpv3Vq7XclBoEL5Sk1tje2XnN136da6+/4RkTknjRAJV/jZd8yZf8ju/6jtd4tdf4sA/5sO/8ru98vdd7vb7vX/mVXnl7a/uhD3noarV6z/d4z8w8tnPs9KnTrbX1et33s/l8ERLgrlZTpWu3d97q5V51ylajNOfmbD4v9aVueejUWjpLlHTuLDYmu3Qd/wfZRI2jndmf2IXnL6TVcnzManqQlFII8cJJ4+B7j938rl/+zcs1F+4ZriuFFw2gw/0LvGCllEt7e0m97trrMjMieE6ZGRFcdnh4uLm52VqLCEk8D0+Td/ciorWplIqEjUTm1KZaOwCBQXJrubEoGwv+Z7AtaXd39847b33Igx5USrn7nnuvvf6Wxbz+4F/ffXHnps5pxIvEdszrM3bmv23PIXluEuOUJy+u3gQErMZpGkeBAbDTBpDE/WxLDM2Jtjc27Pbiy6e8/ss85s677loeXLz55ptba7wAQOVFJgnIzIiwbVtSRDhTEcDm5iZQSuEBbHOZJNWq0ycNBcyzCSqY5yAo/O9hJEniRSCUpitHGDDPh03dG17NDimH9frRPnfdTjc2CxDz+TwigNYaRiHbpRRAaFgd/N2lg7vrKfGiAiovMklHy6OudruXdo/tHBunUdJ6va61llLW63VrrZZqbNt2rXUxX5RSeCBbAIjnJp6HxP8SClZDTuMaCWMAY5AAsCTbPJPTs7Jxz6KHFJhnEyi03Fu/6tSOSw3KtDx4+Ydsnjp5kvv92R//Qam1Te3aa689d+5srdX2k5721Ic85KEt/fIv99IXVudvXQ7iRQVU/jWmaZLUWhvHUaGDg8NhWJ85c6a1dnh0mC372Syk1Wo1m82GYei7vpRiW5JtSUj8H2OkGIf2mHbXDacWbZgCFBKKiMy0HRHTNNVaQIBx0epoffqp+3fFdpLcL6CFVsvpUcvxkVJimXR62dxaG6ZJqLUWs40pW7eYXzpaLXZOHBwcLBYbZ66/+dyl/a6fDUObjLMhXkRA5UWWmTvbO8BivuCy2ckZANRSr7/2el4ASbYltdYkcZkkSfyfMFnby3vf8Kb+vk/6nPmnfPSTj/Y6s7G5aWdEmc9m+wcHN5255uLuRSAiQhqm8WVvfom8c/mE9T9s9pvpBKRVemN//XLL6VGSQcZAZtvoZgeXLi7X65AODg8fcvON8/l8Y2vn4vmzq9X6hmvOjOPwoBuvn8/nu5f2+vlG+IJtxIsIqLzIIuLSpUvr9frw6PDE8ROHh4c33njjk578pO3t7euvux4AWmsRIQkA7rjjjnEcH/KQh0i6dOnSsWPHeADbkvjfTrTM0107mlr3lm/Y7Ww++NgmmaXU1qZaO0mzrm7Oera3I5R2SC3b4fLS7nRxNm9iFSTU5fjIo/GlptwIJc/irP38b+44d8uJraEpJHdb9x1NPtz3uT1JUly8tIyQl0v7COniM+64+2iadTUn86IBKv8af/Knf/JTP/1Tr/mar/n4xz9+Pp+/xqu/xp133nnh4gWnP+xDP+zc+XM/9MM/NJvNbrjhhnEYX/ZlXvZ3f+93ET/1Mz/1iIc/4q/+5q9e+iVfejabHRwcdF136vSpV3uVV7Mtif+1jEGzwu399b9w3737j3lN3TcFODOz1TpLG5Bmec8qSsUgbEJl2aZp89UW06USd89jvDQ8at1OSihoBFeIbKGN7T8+LL/3jCWWbZ5JIJ5JXCFJoeg2t8+Q2ZUCgPmXAJV/jdd4tVd/6lOfMk3tlltu2dvbO3ny5K233nrixIlpmn7t13/t9OnTR0dHFy5cuHDhwnK5vOWWWza3NpdHywc/6MGPe/zj2tQODw/vvufupzzlKTfddNPtd9z+aq/yarYl8b+XDfFaN2/9ztPvOJiyHK2IAAQFPCCeKXg2QcKGpLWdBZ0Z8YJLm5wzgHgOduYpsAAjwLxAAmS1vfPH+3j4dVtASPxLAB3uX+AFK6Vc2ttL6nXXXmdbErA8OlpsbNiWtFwuF4sFMI5j13VtmkqtgG1Jq9UqMzc2NoDWWikFAGy31mqt/O9hW9Lu7u6dd976kAc9qJZy1z33XnvdTYvFAgDn6oh/JYOEzf3E8yVA4l/BdkS0qLXWO+68Y320d+ONN2YmLwBQeZFJms6dr+thfzG79bZnnDhxcrk8ql2XmSWi6/rMLCWuOXPNU5/+NOBhD3nofD7nfqUU7iep1sr/WpJa5onjx+6++45M20Yhif9ZLLA9rFcnju9kJi8YUPnXUCkupat1au3S3qVSyrhchrS2z91++872znw+v+bMNVubm0fLJWBbEv8X2e77/prTJ/f39zITwCD+JxGX7WydmM1mvFBA5UVmu5w4DpyAEy/24jynRz78Edzv2muuBQBJ/N9lO0o5efKUbf4Hsw3Y5gUDKv9aNmBAYK6QBNgGJPH/h91a438/oPIik7R76dLRahkKbGxj7ifE/xXpxCiEkZTOvutPnz7N/11A5UVmOLa1dSyKwH2n2YwHsC2J//1sS+J52JbE/1FA5V/l0t7R2bPraTp2/ES58frVapV2a1Pfz2Z9z/9+tiU9+SlPXi6XD33IQ9fr9dbW1jiOpZTFYsH/XUDlRWaIiKfce/eT77njrV/5tYCj5dHe/v7R0dHxY8dvuP5625L438y2pD/5kz95/BMef9111x3bOTZOo+0HP+jBr/96r8//XUDlRSYgykOvvf7a4ydiNgNOnjh58sRJwBiQxP9yEQG867u869SmcRhrra21Ukqt1bYk/o8CKi8629ub24uHbkuWANsAIIn/K2xHRB993/U8gG3+7wIq/0qtq4DAmbZ5HpL4Xy4zeU62Syn83wVUXnQSw1AOl2TmrI+tTf4/sc3/XUDlX0P7h3c949bFfL6zc4zNzf3Dg3EcbIZhiAhJXdft7GyXKPyfYFvS2bNn/+Zv/+b1X+/1+b8LqLzIDCrliXffvr9avvHLv2oRR0dH+/v76ZymCehq7fvZ5sZG6Qv/h6yH9d7eHv+nATrcv8ALVkq5tLeX1Ouuvc7gg8PYP8BufVdOn+L/E9uSdnd377zz1oc++MG2+T8EqLzobG1t5mIORIRtwDYPIImr/hcCKv9aIYwBG7AtCbAtybZtSbYl2ZbE/wmS+L8LqLzoJI6WsbcP5HwWx4/x/4lt/u8CKi8yQ6zWT7vt1q7rbjh9DcePnb9w/ujoaGfnWGvt8PDgzOkze/t76/X6uuuuu/2OOzY3No+Ojs6cOb21uWVbEv87LZfL3/nd33njN3pj/u8Cgn8VablePfHOZ7RMAGQYhvU0jaUU2xFRShGSBJRaQPwvN5/PH/7wh2cm/3cBOty/wAtWSrm0t5fU6669zsBqrb19bC/m2tnm/xPbknZ3d++889aHPvjBtvk/BKi86Gzms5zPuMyZgG1JPIBBYFuSbUn872c7Ivi/C6j8q9i0hq1SkLCRuEwSl4lniwj+95ME2Ob/LqDyopO8f8DuJYwXszhzGkk8f5L4vyIzp2nq+57/u4DKi8wQ43jX+bP37V96qYc8Arj73nv29vZOnjjRWu4f7N9w3fWX9i71/ezw8HC1Xp08cfLM6dO2JfG/U2ZGxF//zV8/8UlPfJd3ehf+7wIqLzIBioJmUS0Jtre2Z11fasHMZ7Ou63Z2dmb9rO+7aZrmszkgif+1JAE333Tz5uYm/6cBlX8Nb21e+5CHXAvuO2Brc5PNTR6g73ug6zr+T5AEnDlz5syZM7Yl8X8UUHnR2dSSx3YMAmfa5oWSxP9Ctm1LAiRlJlBK4f8uoPKvIcjlsijoqksJXiDb/C8kyXZEcD/btVbANv93AZUXndQu7pb9w93D/Y2dnf7GG2wDLZsU2IAkY6GI4H8nScvV8r5775vP58bXXXvdOI4XLly49tpr+b8LqPxrlPRdF88949x9L7fxaOCOu+7c39tHYNK5s73TWktnKB7y4Afzv5BtSavV6jd/6zfvO3tfrfVlX/Zl/+7v/u6VXvGVrr32Wv7vAir/KrXk1F7y5of0fQ9cd821Z06d7rpumqZSCtBaAyTxv5MkYGd75+3e9u329vc2NzbvO3vfQx70kAc/+MH8nwZU/jW8vXXTi70Y0+S+F3Rd13UdUEoBgFor//uVUnZ2dnZ2doATJ04AtgFJ/B8FVF50tqWcz2AmcGvmBZLE/0K2bQO2jYUk2ZZUSuH/LqDyopPUWhwtAfedZjP+P7HN/11A5V/l0t7h2bOrcTxx4qRuvH65WmVrxqEopUzTNJ/PSyn8r9Vae/wTHr+zs3PhwoVSSt/3D3vowy5cuHBx9+KjHvko/u8CKi+yxKV0f/Skx/3Z0574cW/9rj0cHh6eO39uHEfbW1tbBwcHD7r5lmPHjtmWxP9Oe3t7u7u7f/GXf3FwcPDIRzzy1ltvfdzjH/dGb/hG/J8G6HD/Ai9YKeXS3l5Sr7v2OgN7+0f33je1afP4iXrdNTyAbUn8L2dbku1xHG1HxO7u7sbGxubmpm1Ju7u7d95560Mf/GDb/B8CVF50Nttbm4sF4BKAbUm2AUn8L2dbEiCp73suO3PmDJCZkvi/C6j8K7Wu2g7JmbZ5AEn8L2cbsA0AmQlIkiSJ/7uAyotOYj2UoyURrqHNTf6vK6VwP9v83wVUXmSGODp6+pOfeM/uxVd49IvXzc39g4Ojo0Mg08ePHVssFvyfYFvS2bNnf/f3fnfn2E5r7cTxE6/0iq/E/11A5V9Hl5ZHF44OhmmqsLe3d+HihVB0fTefzxeLhW1J/J+wubl5tDzKzJMnT547f47/0wAd7l/gBSulXNrbS+p1115n4PCoXbiYzrq5FadO8P+JbUm7u7t33nnrQx/8YNv8HwJUXnQ2mxtlMa9gybZt7ieJ/1tscz9J/J8GVP6VLNIIsG1L4jLbgDFGkm1J/C8kyTZgG5Bk23ZE8H8XUHnRSRwtdWlPwGKu48f4P0oSz8M2/3cBlReZIdbDXzz+7yy99MMfVY8fu+2O2+ezWa3d9tbWMA7Z8uLubmartWutdV23vb29vbVlWxL/s2VmRPzhH/3hrc+49fDwcHNz89KlS5ubm5LGcbzhhhve+A3fmP+7gMqLTIDz9Pax8wf7mQbms/k0TbXW1lqJkulSikIbG4vVah0hMADY5n+Dhzz4IVtbWwcHB7XWc+fPSSpRgGPHjvF/GlD5V9naetDDHvGgTDYWwDVnzvAAc9je2uJ5SOJ/PEnA9ddff/311/P82JbE/1FA5UUm2B/WK7cIsVqyPLINIIQAY8xzEEL872HbmCvMFbZrrSdPnuT/LqDyIrO9tbm5mM/5/yci+D8NqLzIJD3pyU86ODiotdj8PyFIu5Ty4i/24pL4PwqovMhs33rr017qJV7MxhgQAoxBEkL8n5P2fDb7sz//ixtuuPHUyZNg/i8CKi+yTO/s7Fx77XWoggAwiGdK5yQw/6fYjjLbObZzdHh46uRJ/o8CKi8yiTY1Z/7+X//Rehh2Nre2Njb+/qlP2lpsHN/eedjNDzp9/ERrjf9bbM9KbVOTxP9dQOVfw1ilSjpYHt12790PveGm609fA9x+793Ht3euPXVmmqaI4P8Q2wDiCvN/E1D51wjFOKxf/WVeARUwGAIMwtM0jbVWbACJy2xL4oHstLnqfxKg8q9hLOnw8GBqrdbaWgNsS+KZHFHATtsGaq2tNQM2Yppa13Xz2Yyr/icBKv9KETo4PLy0t7dYzDPtzFJrZluv1xGllFJrHcYB01rrurq9tXV4tOy7zvb+wX6pdWO+WMzntvlfSPzfBFT+NYRay2uvvfbaa2+EBgZAzqbowSBIiDatSp07h3Ecj+0ckwRce+aMwbZtrvqfBKj8a9juuu6pT33qhQsXFvN5P5v1fe/Mre3tpzzlKTs7O4eHh9vb23t7ey/3si/7Hd/5rW/yRm90w/XXD+PIZeZ/PfN/E1D517CN4r777vuFX/zFs+fOPfKRj3za0572Gq/+6ieOH//27/quxz7mMddfd91tt932Yi/2YhHxnd/93TfddNMNNz4oc11K4f8E8X8TUPnXMIbytKc//dTp0w9+8IMv7e095tGPtr2xsfHoRz3q1KlTXdfdeOONrbVrzpy59pprsjVAEv9XmP+bgMq/RkS0cfmWb/7mLbPW2tVuNt9s0yozX+alX7rruvV6PZvNlsvl8ePHv+LLvuyG668fh6OQMC+UbRTC5t9O/BuIfxXxfxNQ+dcQGG9tbYJs216vDkopitjc3MzMvu/TeWx2bJzGhzzkoW0azQslMAoRxdMkYRD/Cub5Mf8uNvcrpQCY/9uAyr+GQYoLF3bX6yEijIWcDQPmWQzCaUk8N3OFeSbJw5AXLpbrr6M1BOZfR1wmnpe4n3gu4gHEswhFAQBjSddddx3i/zag8iIzCGW2S3sH1113AxgAtYsXmUYDNgYbG8DGtgFjY2PA2BiwbWyV2s6eW/7Rn2289Zvm0VKSbWxsABsbwAZACCQkAAlJAoSEhAAhISSBEEggBBISIAkJCUBCQoAAdV3sHEdgSil33XXHNK5DwWXm/yag8iITGEeUUyePXbx4tpSCbVCRSwkFmGcSLzrBya140PWrzQ1ObvPfwDyb0qn9ixJOG29vb9RunplcJv5vAir/esM0HhwdnD516uhoeXB4cO011yyXq9NnTvJvJ0W4NcR/G1NKnLtwYX93/7prr12tV+cvXrjummuPlkeQiCvM/01A5V9DYOeZE6fOnD4DcepEkkmpHE9U+XcxRfx3u+bMtdecPE2pO/iak6cp9cQ0gjD/twGVfw1DifI3T37Cud2LpRRAYBvJNv9uAkmAAVshAMAgMAYJzDMJjDHIThD/PhJCthGSWsv5bPaqL/1ykrhM/N8EVF5khsxUmX3GN33Vz/3mr2xuH2vZeDbx72JJrbVhWAOKUksdxzUGIIJMJEW4TSjAIGykUqvtWd/bBvHvYu4XiuV69fCbH/yEn/pVCfN/GVD511vM5rPNre2NzZaN/yCS1sPwyIc96uUe8+IHy6PzuxfT3t7YNGzM50fLZdrDNK7Wq5d65GPPXjw/TtP1p685Wi1vu/vOi/uX5rP5Xzzu7xazmW3+g0hRS9laLPh/AKj867XMNrUpW2uN/yCS0nlx/9Le0WGJOFwtL1zafelHvZidfe267W4cx+WwXq1XR6vl0Wp1fHvn7MULXa3z2fzwvnsuHRwAU2u2+Q8S8tTalI3/B4DKi0xcYUkREQrC/AcxzLrZuYsXfvI3fgmopdZSnnr7M8A2s74fphEoUX7/L/+slJJOwHaJ0tVqmPW97ZD4DyJFiQiFM23+bwMqLzLbXe1A3/xpn/cVH/epoQDzH0qSJMA2IAkAbEsCbIdknoNtwDb/0WyXUkqd11qxAfN/E1B5kZVSdi/t/vVf//msn0UI819HYP4biMzcPXv+zjvvvPnmBwPi/yag8q/xKq/8avfed984jk7+S5n/FjZSAC/xEi+9tbUFgPi/CKi8yGwfO3Zsc3OT/39qrbb5vwuovMgkXTh/NoKI4HmY/7ME09SOnzgtif+jgMqLLDMl7exsZZrLbEviMkn8X2S7lLh0aX9qrY/g/yig8q/UWp67cH4cR9td15eIo+Vya3Pz5IkT0zRJ4kVgWxKXSeJ/MNtcJv4vAyr/etdde12bppZZS8nMkydOdF03TVPXdRExTZMkALAdEZIyU5LtEoGElK3Ztm2b/8EkSeL/OqDyr2FcSvn7v//7M2fOHNvZ2d3fl9Rau3DhwoMf8pALFy8ul8sbrr9+uVwaSkRE7B8cCObzeWaWUvb29jPbcrU6fuzYbD7vau26zjZX/bcCKv8aNlHKn/7Zn+3t7R07dmwcx67rSpRLe5c2NzdvvOGGv/zrvy6lLBaLUydPTtM0TdNDH/KQv/27v9vc3MzMiLh06dJiY2MaR9ubm5s33XTTG7/Rm65XhxHBVf99gMq/RkjTOLzTO7xDaw2otY7jKKmUslwut7a2XvZlXub2O+54xCMeEdIwDBExm81e4eVfPu2QxnFEKhGKyNZKKYZpXEUEV/23Air/Ggah/eXR6RMnS0RmzudzwPbGxkZmnj59+pprr2vTaHtjYwPIzL7vuWxjYwPANkiyDWQmV/13Ayr/Oi6lXLi0u7u/95iHPtw2IFAEBshM56hQKDJby5Rkm8tsA4Ak25K46n8GoPKvlJlnTpw8ODra298bxqmUsMlswGI2NyzXK+yWuTFfbCwWtrmfJO4niav+xwAq/0qSfv/v/uri3t6xre0n3fb013vFV/m7pzz57nP39V33kg9/1JmTp371j37v+tPX7B0ePOja69/6dd5ovTqKCK76nw2o/OsoM1/x5ofmNG3MFy997Y07G5s3Pfalpza1zGNb23a+/cu96vbm5jRNs62t1kZJ/G9m2zb/1wGVfx0j3Xjd9WqZ+PSpU9maFBJI2Rpw+uRpZwq775pdSuF/M9ulBP/XAZUXWURElIu7uyoFADmbEJ4AgxDgaRCAWC45OuL/BFulVP7vAiovMtvHj5/k/yvb/N8FVF5kknYv7T75yU8uUYz5/0HSOI4PetCDrrv2Ov7vAir/Gp/7eZ/7VV/1VcdPHm+t8R9HvEgM4pkM4vkw/8Ei4uDg4DVe/TV+/Zd/rdRqzP9FQOVfY29/r5/3i8Vimib+gwhG3IwghAEDGCRsJACB0GQLBCFNtngmCRugiA6Z/zARgXThwvnd3d1Tp09j/k8CKv8apRTbtrEB8+8lGPEju8VMsXIeup2IOthHzoomXKEBMNmDfUs/u9imhif7sf3GU8cVMJdWzuOlVnTruL6YU4D5j2HbmbXUiOD/LqDyr2IA26MdUvDvJZjszSiP7heT/bRxdSnbS802QEfZeqlKR5lbUS62aS/bdbV7VDcv0t+uj06WerpsjTbQcMJ2lOtq/3MHF3qJ/1C2+T8NqPwrGUI6XbqV88gZ/LskzBR/tT74y9WBwCDxlHFpYwhhA4RIU6RpZaCIQP8wHKYxAEXYGIrUSeaqfx2g8q8h0fC2yovPZo8blgdTK5L59ypIwiAwABKAQQIwFGHoJcAABCoCAAwIgfnPIf5vAyr/GjZ9qavgcW19Cc9q5f+6iCilSOL/NKDyr2G8PlidjYvDNBbE/wMRsVqubrz+Bkn83wVU/jU+6RM+6dGPfFQoAGP+H5A0juNDHvTgxWIBIP5PAnS4f4EXrJRyaW8vqddde11mRgT/v+3u7t55560PffCDbfN/CFB5kUnKzMzkRSMpImxL4rLMlCQpMyVJAmwDkjKTyyKCy2zzAOnERAQghABsS7ItZGw7IjJTkqTMjAgbiZZZIgAgM23zIpMUEZL4Pwqo/GtERETwopmmaW9/v++7aZokARuLjdV65fRisVitVlObJPV931obx3F7axtomXuXLvV953SpNbMBkmqpXdchhvWQmbbTCZQo6ZRkOxS11uVyubGxsVwux2nc2d45Wh5lZmbubO/s7e+11iJiY2Oj73r+NWzzfxdQ+U9je2//UillPpvv7e8t5ouu685fON913WKxmNq0t7/n9NbWVq11b39/1s9KLU6vVsuj5VGJKLWEopSyWq9m/fz4sWOYo+XRar0uJTBRimCcplrKMI4bi0XXdXsHe7PZbBgHYJqmixcvdn0fEZk5DMM0TQZQf6znqvsBOty/wAtWStnb22su1113vW1J/H9lW9Lu7sW77nzGQx78YNv8HwJU/iW2IwKQxP9jkoCIYvN/D1B5oWx3Xbd/uGwtMxsS/2/ZtdaDg4Nai23+bwEqL1RmLhaL1toTnvB3rTVJmP+nhO2u1uPHj9mWxP8hgA73L/AviYjMnKaJqyAiIoL/W4DKi8IGuq7y/15EZNo2/7cAlX+JpIuXLh0t1yFxmQEQz2YQAAbxf5ZN15XTp07yfw5QeaEi4vDwsGU87GGPzEzSKkUibWwQAJZkG5BkG5Akif9DbEu67+x9586du/aaa8ZpksT/FUDwQklqrXV9D0RE1CLRWgspIiIUoYiQFBERISkiIkKSbf4PkQTM+tne/kHakvg/BKj8SyRla8DZv3/cnb/x24/64Pf71V/55fV6/ZjHPKZEKbWMw3jf2ftOnz599z13lyjXXXvdar26/vrrb7zhxszkfz9JtoGIsC1hm/9bgMq/xOBM4NLTb/2Jz/isd3zD157P50944hPuvOvO+XxeSz12/NgwDL/9O7/92Mc8dmrT45/w+Nls9lLtpW684cbWWkTwv5xtHsD8HwTocP8CL1gp5dLeXrpcd931q2m64757H3TmGkm1Vi6zLWmapv39/RMnTvAAtiXxf8vu7u6dd9760Ac/2Db/hwCVF4EkYF7rw2+4keckCai1njhxguckictsS+IFsC3JtiSeH9uS+PexLYkXwLYkHsA2l0niMtuS+D8KCF4EtoH1sD44OOB+rbVpmlpr3K+1xmWtNdvpBDJTUtqr9YrLbNvOTC6TBEgahmFvfw8AWmvcTxKX2eZ5ZKZtLhvHEQBst9Zsc5ltSTxAZnLZOI6ZKcl2aw0Yx7G1JkmSpNYa/w8AlReBbeDs2XO33XHbsZ1je/v789lsnMaDg4O+7x90y4Nuu/32m2644cLFi5kZEZl58sTJ226/7WEPfdjZc2eXy+UjHv6IJz75SbWUfjabpimktGsp4zj2fZ+Z11933VOe+tSI2N7ePjg82FhsXHftdc+4/Rk33XDjfWfPjtMkMB6G8fixY0fLZYScti3p+uuvv/uee2opEbFar4S2traWq9UwrG+64aanP+PWRz784Xffc8/O9o6d5y9ePH3y5PkLF2w/5MEPfsKTnri5sdkyZ31/7NixS3uXTp04efbcuWEcQI942MOnabz3vvtOnTx14w038H8XoMP9C7xgpZRLe3vpct1110/TFBGr1eripd3tre1SyjSNUvRdd3B4uLFYINbrddd1mK7rDo8O57N5Oler9bGdnbSPjg672kWEbUnTNN1z7z2nTp3uu242mw3DsFgsDg4PgNlsbudqtdre2j48POy67uDwoNYqKaK01haL+TRNme66WkudpmkYh/lsPk4jKKQosV4Pi/l8PQyzvj88Otra3FytVlObZv0s7WkcNzY3V8tl1/fjOESU2Wx2dHS4ubk1jsNyubK9sbEh2D/Y397aXiwWwO7u7p133vrQBz/YNv+HADrcv8ALVkq5tLeXLtddd/3UpnEYu77LlrXW1lqtdZwmCafHabTZ2tzMTNvTNJVSANsRMU4jZj6fZ+Y4joCkWmtE2B6GYbVeHds5tlyt5rPZOI6llNZa13XjNJWIUortcRwlSaq1Hi2Puq7P1mqtEXFweLixsWgt2zRJqrVmZt/3wzCkPZ/NpmmyXWuVdHh0ZOfW5tbR0VHXd9mylJKZrbX5fC5pmqZSSmYCpRQusy1pd3f3zjtvfeiDH2yb/0OAyovs/PkLt9526zCMErPZrLW87ppr9/b31uv1MAyIYRivv+66WuvF3V3B0fKo6/qQgLSPHztWStnf3x+GISIAxGMf/djHPf5xR8vlNE233HzzarXa2tq6ePHi0dHRYmNjuVweLY9uvP6GhzzowX/z9383DOuNjU3g4Q996D333rtar1er1fFjx7Y2t85dOH90dCTR1W6cxvl8fnBw8JAHP+T2O+645vSZlm1vf38ax1tuvuX2O29fr4fWpkc8/BF33X33NI7XX3/93ffck5nbW1vjNL3YYx7zd3//9xGxWq+uu+a6Rz3ykbYl8X8aoMP9C7xgpZRLe3tJve7a62xLmqYpImyXUrhfa62UYluSbdsRsV6vI6LrOtu2I8J2ywSHIiKGYai12gYiQlJmSpLUWiulXLx4cWNjo+/7lolda7UtaZqmWmtmArYVEVI6hSS11koprbWIkARM01RrtS2ptRYRmRkRkmxLmqZJUillGAeglhoRrbXM7LqOy2xL2t3dvfPOWx/64Afb5v8QoPKisIFpmoZhUEjSer0exykzM9tisbGYz8+dP7+1tTVNE5Ct9X0fpeQ0LVer2WyWmevVShG1lL7vhzaM0zTr+739va52SJKG9TpK2djYmKbx7NlzJ06cmM1mU5sOd4+2t7amNj3t6U87derUxmJjtV51XT/r+/V6PZ/P16ul08vVcj6bd13Xsq3X667rSimr1bqrteu65WppexxHm1rrsWM7R0fLo6PDWmuJ0nUdwmuXWrLlcrmMKBuLRdd1/P8AVF4EtoG7773ntttvWyw22jRl5jhNXa3TNM0Xi+PHjq3X61tve4YgM233fb9/sD8Mw9bW9qMe8YgnP/Up0zj1sz4UN99009333LNar08cPz4Mw4WLF2ezWYmQBGxtbj78YQ8/d/7cbXfcdurkqXPnz5eIW26+5dz5c0fL5TCOF3d3u67eeP0NFy5etH3NNdfcfffdR8ujUkop5fprr79w8cLF3YvbW9sRcXh0eMP1N5w/f361XvV9n5mgYRge+fCHr9fD05/x9L7vMddee+3Zc2cXi43VcvmgWx509713HxwcPOoRj7ru2mttS+L/OkCH+xd4wUopl/b20uW6664fx7HrOl5k4zhm5mw2479bZkYED5CZEcG/hm1Ju7u7d95560Mf/GDb/B8CVF5k0zSdPXduapPtkI6Wy77vT588dbRcjtPYd93e3t58Pp/N5uv1qta6tbV99tzZzY3N9bA+cez4nXffZXs2m3Vdtzw62tk5dvLEiXvvuzftUsrO9vbh4VFERGgYhkxLAMePHb/73ntqKX3fS2qZ0zhOrV1/7XX3nr2vq1WKjcViNp9duHDxxInju7uXDg4Pzpw+LTROU9d1+/v7i8VitV51XefMqbX5bBZRpmlaD+tTJ0+uVqv1el1KHcdxNuv39w+AUsrNN91USuH/B6DyIjs4PLjzrjuPHz8+jqNgGMeDg4Ou1nGa7r3v3sV80dXuwsWLEaHQfDYfxvHixYubGxv3nb2vRNnd3Z3P5/v7+1tbW8MwLFer7e2t/YODiDg8OgzF+Qvn+7535v7hQd/1kmZ9X2u1XUrZ29uf2tRak1RrXQ/D7u7u1Katza3lajmbzS5cuLBYLC7uXhzH8eLFXYmLu7s33XTTpb1L+wf7EbGxsTGN42Jj46677z59+vQ4jHfefddivliv13fdc/fW5uYwDLuXLm1tbnZ9Pw7DyZMndrZ3+P8B0OH+BV6wUsqlvb2kXnftdfw3sS2Jf5PMjAj+I9iWtLu7e+edtz70wQ+2zf8hQOVFYQPnzp+77+zZYRxXy+X29vbJkyfPnTs3n82HcSilrNfro+VyMZ/fcP31T3v607e2thbz+TCOx44de+rTnnb82LGu606fOn33PXdLiohxHPu+39zcnKZpvV4Pw3DyxAmkS5cunTxx8u577zlz6tTh0dGpkyfvO3v24u7F6665bjabnb9wfmrTzTfedNfdd197zbUHhweZuV6vb7j++r39/dbafDa7uLt74viJ1Xp1/Njxu++5exiH66+9/tLepd1LuxuLjUc+4pG7uxfPX7gQEadPndo/OJimqes6SavVqrUmqat1c2vrlptuti2J/weAyotsc3Pz5DSt1uv+9OlpatnaxsbG9tb2vffde+L48baxubMzSeq7/uTJk12ti8VGXa+6Um+68ca+6zNb33cbGxtd10kax3Ecx/lstoKQ+q6vtVss5tM4bW5uHtvZOXbs2GKxsbFY3HLTfHNjc2dnG9jY2ACXUo8fO765ubFer0qtXa2l1GM7O6vVemNjIzNPnDixXq+2t7ZrLQcHhzvb233fb29vY4d07Nhx26217a3tUspqtZpa297aaq0tFotZ30+tbW1uApL4/wHQ4f4FXrBSyqW9vXS57rrrp2nKzK7rWmu1VttAa01SKYX/62xL2t3dvfPOWx/64Afb5v8QoPIiOzw6/PO//Mta6/Fjx5bLZcu0cxjGjY3Fy7zUy8z63jaAwDxfkmzzL5FkW5JtSbZ5viRsnh9JtiXZ5jlJAmwDkmzzAkji/w2g8iLb2d55hZd7eTAICMk40yH1XQdI4grxgkjiRSAJkARI4gWReAEkAZJ4fiRxmSSuAqDyopAAYGd7m6v+LwIq/wrOtCSehyQAsM1lkmxLAmwDkmxzP0m2JXGZbQCBeSaBkWRbElf9ZwIqLzIpJF44SdxPEpdJ4jJJPIAk7ieJK8SzCUASV/0nAyovChu4tHfp4u7u6VOnMrPWmulsbTabTdPU9d04jvPZ/MLFi8MwpPPM6dPL5bKr3dbW1tlz5+az2Ww2Ozw6Wq1Ws75P52Kxka1tbm6O4ziM48HB/sZio9QyjROS7VprLSVKWS6XtdatzU2u+k8DVF4EtoH9g4OnPf1pd95112I+L6UcHB7O5/MSsbe/95AHP+Tptz79hutvmM1m99xzT8uGff7Chc3NzWvymn94/D+UUq89c818Mb906dL+wX4ouq47Ojq65ZZbImJvb2+apv39/c3Nzcw0YJdaLl68uLW1vTw6evEXe7GtzU3bkrjqPwGgw/0LvGCllEt7e0m97trrbEuyLQmwLSkzJQG2bZdSbEsCbEvKTElcZiwkictsD+NQS40ISZkZEdzP9u6l3Z3tnVIK/wPYlrS7u3vnnbc+9MEPts3/IUDlX0kSl0kCIoLLJHGZJC6TBEQE9xPiASTN+hn3iwgeQNKJ4ye46r8EUPlXss2LQJJt/iW2JUmyDUiyDUiybVsSz48krvqPA1T+NWxL4kWQmRHBv0QSl0niMklcJkkSL4BtSVz1HwSovChs4MLFC2fPnRuGYbVabW9vnzxx8tz5c7PZbBzHErFar5er5ayf3XzTzc+4/RmHh4cnjp84depUm9rR0dGJ48fvO3f22M7O4eHR0eroQTc/6O577l6v17PZrNa6WCwycxzG48eP3XHXnadOnCqlPOP2Z5w+dXocR9sRYbvWGhGtta2trZtuuNG2JK76jwBUXmSL+WJra2tYr48fP54th2Hdd93W5tY9995z+vTp+WKxOW6WKBE6deLkg25+0N7+Xt91B+tha2sTqdYaURaLea0lM7c2t7a3tls2STvb2wcHhwND1/WL+cL4+LFj43RDa21zY6NlSipRdi/tHh4ebm9vz/qeq/5DATrcv8ALVkq5tLdn6rXXXsd/pmmaaq3869mWxH8+25J2d3fvvPPWhz74wbb5PwSovAhsA+fOnfv7xz/u+LFj6/X6xhtuuOe+e/u+397aPnvu7DVnrgHOnju3Xq2ilJ3t7e2trbvuvrtlO7ZzbLVapfP6666/7fbbd7a3x3FcrVcbi41a6+HRodBLv+RL/fXf/s0wDMePH79w8eJ8Nrvpxpue8rSn9H0/62cnjh9vmbu7uzffdPPTbn16rWXWz/b29ra2tubz+aMf+Siu+ncDKi+yjc2Nxz76MVubm0fLo2M7xyKi72c729vbm1s7OzvrYb25sSFF33c2GxuLjY3NrtaWWWsBbW9t7WzvYPd9v1qvutoBCq3XQ6310Y96tMQwjDdcf31ELBYbXddFqO9npZRs7czpM4v5/BEPe1hE6btuGMfMNp/Nueo/AqDD/Qu8YKWUS3t7Sb3u2uu4LDMjYrlc9n2fma21+Xw+TVOtlctsA8vVajGfZ6akiFiv1weHB4v5ou/7iBincdbPMhOIiGmaSimSbEsCxmnsagfYXq3X2drm5uY4jqUU25nZdV1rrZTCfxXbknZ3d++889aHPvjBtvk/BKi8CGwD995331/9zV8vFvO+n2W2rnaIiNhYbFy8eHE+n0fEDddf/4QnPfGaM9e01vb29wFJtgXjOI7TeP111y+Xy1rrxsbGHXfeubO9vbOzc/bcuXEctja31uv1OE2tTRHl5htvPHHixJOf+pRhPYzTeOb0mcVice99907TNJ/NFZGZL/FiL7a5sWlbElf9OwA63L/AC1ZKubS3ly7XXXd9Zi5Xq9amxXzRdV1rLZ211OVy2XUdEBGllNVq1XVdKWUcx1LKNE3rYdhYLEopmSnJtiTbmSlpGIZa6ziOkvq+Pzw8nFo7eeLEcrnsug6ICC6LiMxsrUky7mrHfyHbknZ3d++889aHPvjBtvk/BKi8yFprds5n88Ojw9ay7/uNxWK5XCrUWkO01lbr9WzW2z44OKi1SsrMWd9P0zQMQ2tNESGVWqZxMtRSIuLS3t729jb2MI6bm5tHy6NLly71fR8Ry9Vq1vfDOExTq7VO41i7rkRk5nK5KiVshmF97NjxEsFV/1ZA5UV2cXf3abc+vda6XB6VKGlfe801Z8+e3draOn/h/PbWtkJ7e3sPfchD9/b2jo6OjG+8/oa77rlna3NzatPu7u5ivjBsLBanTp58+q23RsT29natNTOf+OQnbm9t7+3v1dqtVsvtre2TJ09i333vPQ+65UFPf/rTF4vFweFh3/er1erkiRMHhwcbi43lallKraW8xIu9eOl7rvq3AnS4f4EXrJRyaW8vqdddex3Po2WTFIphGPq+599tmqZSiiSen9ZaKYXLMjMibEviv4RtSbu7u3feeetDH/xg2/wfAlReFDZwtFzu7u4e29m5cPHibNa3li3b5sbmuXNnr7322rPnzkUItFgsSomz586dOXX67nvvuebMmWmazp47d+rkyXGapnGaz+e2JYZxPLZzzJlHq+XO1valvb2uq6215XJ5/Njx9bAex2k9rEsp89m8tRYlBFNrtZTrr7vetiSu+o8AVF5kh4cHT7v1abfcdMvB4cG58+u+7yPC9j333Ve7bnNj4667716tVtvb25ubm5cuXdrc2LzvvvtKKcD5Cxcy82i5nPX9fWfvG4ah6zpgc2Pzrrvv6rru6PDo4u7Fne2ds+fP9l0/ny/uvufuxWKxWq0ycz6fr9frjY2Nvf39+Wy2vbUN2AYkcdW/G6DD/Qu8YKWUS3t7pl577XX8/2Zb0u7u7p133vrQBz/YNv+HAJUXgW1g99Kl2++4fZqmvu9tr1arG66/frlcbm1tX9y92HXdfDY7d/583/Vd343jaDsiHvLgBz/91luxkba3tmwOjw5n/cx4a2Pz3IXzG4vF3v7+rJ8dHh2mcz6bb21ubm5u7e5e3D88ELrmzJla6/kLF44fO37XPXcv5rNrr7lub+/Svffd95Iv8ZIbi4WxEFf9OwDBi8zOUsqxnZ31sJ71/YkTJ7qu7/q+1tJ3fShms/lisei6LhSzvj9+7FitdRqnne2diALMZrNaa9d10zSGpIhaSsvc3t7uum6xWJw5dToiaq0hzebzYzvH+r7f3NxsrS3m882NjRPHj5dSx2mczedbW1sCQIir/n0AHe5f4AUrpVza2zP12muv4/8325J2d3fvvPPWhz74wbb5PwSovAhsAxd3d5/y1KccHB52Xdd1XWtTpo8fO3ZwcHDy5Mkbrr/+Hx73uDNnzqzX6/MXztdSZ7PZ1tbWer0upZw7d+7EiRMHh4c33XDjufPntra2FvP5HXfdddMNN95z7z1Tm2qp4zRubmy21k6fOnXb7bfXWltrfd+XUpar5bXXXHt0dFRKOTo6Amaz2Ys/9sVsS+Kqfzeg8iLb2tp85CMeITRO47nz54/tHOv7LjNnN98yTmNEeciDH3JsZ+fi7u41Z66xs+/7EmWaptl8furkqe2trWEcZrP5YrEoJUqUhz90vrW1vbGxWK/XU2uzvi+lSmxtbm0sNg6XR4v5Ytb34zQ6vdhYTOMUEVObxnG6tHcJkMRV/xGAyossMzc3NiMiM0+eOCmJy5arZe26xXy+sVis1+uTJ070fQ/YlgS0bGdOn7Ztu7V26uRJ25JsZ+axnWOZWWvlstbaOI0bmxs7x3ZKFNsRAQzDUEqd9T1g+8zp0+M4dl3HVf8RgMqLwABcuHDhb//h7689c006d3cvPfQhD1nM5099+tOnaRqG9YNuvuXg6PDixd3FfG5cS12tVqdPn57P5xcvXgRaa8M41FINi/n82LFjZ8+dHYbxumuuOX/hwjhNtjcWixMnTtx39uzh4cHW5lbakkJardcbi8Wlvb2bb7ppb28PyMzt7e3HPOrRtiVx1b8PoMP9C7xgpZRLe3tJve7a64BpmmqtQGuttdb3/TRNtdbMtG3bUCIA27ZrrdM0TdPUdd3UplAA4zT2XZ+ZSOCIaFNLp1BElFKA1lpE2G6ZdmL6Wd+mZrxerxfzxTRNXd/XUvivYlvS7u7unXfe+tAHP9g2/4cAlReFDQzDsB7WrTXbXe2Wq+Vyudra2oqQ0Hw+X63Xgr7vDw4PVuv1xmIhxc729nw+B0opXNZ1Hc+plspzKqVwWcezlb4As34GdF3HVf9xgMqLbP9g/8677spMSX3fHx0dtda6C93+wcG111xz8sTJZ9x+m+Daa689e/bs3v5+3/eSHnzLg6679lrbkviPYFsSV/2HAiovCgk4dfLUqZOneMHOnD7NZTfdcCP3sw1I4j+IJK76jwYELzLb/OtJ4qr/8YDKv4Zt/iWSbHM/SbYBSbZ5TpJs8wCSbEuyDUiyzf0kcdV/AqDyryGJF4EkHkASl0nieUjiOUkCJHGZJK76TwZUXhQ2cHF3d29/r5RyeHg4TdPW1tZyuRzH8dprrjl/4cIwDBsbG0fL5fLo6Nprrl2ulsePHV+vVy2zRPT97PDoMDP7vl8ulxFRax3H8fSpU+cvXIgIYBgGSZmZzuuvvW61Wme2ra3t+87ed+rkyd1LlyLiQTffMp/Pueo/GlB5kfVdN03TfD7f2tw8PDra2txarVabG5uzfjafzSJiY7Fh+8ypU5mZ2Uuazxd931+4eGF7Ntva2jw4OBjH6fix40fLo91Ll2Z933X99taWTYSOpIgCXg/DfL44Ojoax2lzc2O+P6+16/t+HEeu+s8B6HD/Ai9YKeXS3l66XHfd9a21Ugr/QWwDkvhfwrak3d3dO++89aEPfrBt/g8BKi8C28A9997z+Cc+8dSpU22abEcptkMCWmunT53e2tp6wpOeOJ/P57PZcrUahnVE1NpN43jtNdesh+He++59xMMefve99y7m8/39/am1zHzQLbeMw3DfuXMPftCDzp49u1qvutqVUiLi6OhoNpstV8uXeOyL33HXnXffc8/xY8ce9chHbm5sctV/KKDyIpAEXHPmmsViw86+n2VrEaHQhQsXh2E4fvyYFBF62EMeMp/P57P5MAzDOG4sFvsHBxsbG33fr9fr06dOd129+cYbNzc2jSPi4ODwxPHj4zgeP3681u6aM2e6ruv7fpqmrnar9WpjsbF/cJD2jTfcePLEyVJi1s+46j8aoMP9C7xgpZRLe3tJve7a62xLGoYBiIhhHPqur7Vyv6Ojo42NDcC2JGA9rGf97OjoqO/7Witgm8vGaexqh9RaKxG2gYgYp6lERMQ0TaVWQWZmZq3VdmYaain8l7MtaXd39847b33ogx9sm/9DgMqLwDZw7333PuFJTzq2szOMo6SjoyNJD7r55sOjo/vO3re5uTmN09Ra13XTNG4sNk4cP3HbHbc95EEPue/cfce2d85fvHjjDTfcdvvti8XCmYCkzJR0eHS4WCza1DKzdt1111xz9ty5vf297a1thQ4ODm65+Zbd3d0osbW5dfc9d7/Gq746V/2HAnS4f4EXrJRyaW8vXa677vppmmx3XWcbkDSOY0SUUoZhAPq+Pzo6ktR1naSISGeJYhvIzFLKNE2SbNdax3GstQKttVrrNE0RAUREZk7TFBERYbuU0lqzXWsdhqHve/7L2Za0u7t75523PvTBD7bN/yFA5UWWzgsXLkxTWywWpcRqtdrc3Oq77sLFixHRd93e/l5EWa2WJ06cuHjx4ubGJnC0PFosFq1lrWUcp1LK1ubmOI4Hhwfz+Xx5sJymVmvJ9Obmxn1n7ztx/MRyuTTe2ty67+x9tdb5fDEMaynAUvR9P47jMA7b29u1VK76jwBUXmR7e3uPf+ITZ7PeZmOxWK1Xi/ni+LFjU2tHy6NxGFu29Xqw87prrovQk57y5GuvuXZ3dxdpc2Njb39vMV8sFosbrr/+zrvu2r20e+bUmfl8fuHihdV6FVFuuuHGpz79qdecuaZEefozbn3xxz52b3//7LlzW1ubQKaxu747f/78xsbmNI0v/ZIvVTeqQVz17wXocP8CL1gp5dLeXlKvu/Y6/mc4ODzYWGxEBP+1bEva3d29885bH/rgB9vm/xCg8qKwgeVyuX+wD6zW62PbO0fLo1prKWUcx9ZyNuttxnHY2d5ZrlaZWWuJiOVyKanve+Do6Kjve6DWev78hWuuORNRLly8cGxnZ7VaLRYbq9USkFSiKGK5XG5vbQHTNI3TZOeZ02e2Nre46j8aUHmRHS2Xz7j9tvlsLqnv+nvuvTciuq6bpqmUkpmr1UrS+tRw7sK5vuvX6/UNN9ywPDo6f+HCbDa78YYb7jt7tmVrLeez2XK1BI4fP37nXXft7u7WWjc31uM4Hi2PbF/c3T127Nh6vbru2utqKecvXBjGMaSdnZ2NxUZmRgRX/ccBdLh/gReslHJpb8/Ua6+9jv9CmRkR/E9iW9Lu7u6dd9760Ac/2Db/hwCVF4FtYG9/7wlPetJiPl8sFtM0SVoul33fHz92bJqm/YOD7a3tvf29zc3N7a3tvb1L6/WwHtazvjeUUmb9bJrG7e2dS3uXpmmKiOVyWWvtuu6hD37Irbc9IzPPnD5z39n7Dg8Pb77ppnEcDw4PT586fettt25ubK7Wq62NTUXUWpfL5UMe9OD5fM5V/0GAyovObG9tZea5c+c2NjYODg5OnTxlp40UW1tbi/n88OhwGIa+78ZpWmwsalclDcMwm82wa9et1qthGPqui4iNjY2IyMyWbXtr+/DoENz3fUS0TBtJgu2t7VJK3/ezfnbHXXdsb22XUiRx1X8cQIf7F3jBSimX9vZMvfba6/j/zbak3d3dO++89aEPfrBt/g8BKi+CtIF77r3nyU996sZisVqvJeazecvWWtZSTp06dWnv0tHR0UMe/JCLFy+uVqv5fL69vX3nnXeePn364ODg8PDwQbc86M677yxRNhYb99x3zyMf/sj7zt5Xa13MF13XnT13NkopEeM0HRzsb29t33LzzU944hOPHz++tbV15513llr6rr/l5lue8tSnbG9vv8SLvThX/ccBKi8CAXD82PEHP+hBe3t7N990s/F8NmtTm9q0sbHZWuu7Ls6U0ydP9V3XWm5sbNgupWwsNk4cPz5N7djOzrFjOwcHhxsbi9OnT21ubNZagY3FwnixmC/mi5YtoozjYHtjY/NRj3xk3/XDODzqkY+S6PtZ33UPfchD+r4fx7HrOq76DwLocP8CL1gp5dLeXlKvu/Y6XoCpTeM4zfo+ImxLAmy31mqtrbVSCrBcrbpagVorME5TVytg23Zm1lpt27ZdSjEIgNYaUEpprdmutdq2vV6vu66rtfJfwrak3d3dO++89aEPfrBt/g8BKi8KG7jv7H2Pf+ITau02NzZOnDhx2+23PejmB9179t5xGI8dO7Zer0utx48du/2OO2oppZRSykMf8tC//tu/uenGG+++++60Txw/vr+/X2t97GMe+xd/9ZcPedCDz50/d7RcLhaLg4P9rc0tScM47h/s33LjzQpdunTpphtvfPoznlFLmabJeBynzc2NcRiRIuKRD3/4qZOnbEviqn8HQIf7F3jBSimX9vbS5brrrs/M/YP9xXxRawVaa7XWcRxLKaWUzMzMUsp6ve66znZrrdYKrIdBoqud7a7rlstl3/fTNHVdl3ZmRsQ0TbXWkDLTdilF0jAOfdeP49j3/dHRUSmllCJJ0jAOtdSIiAj+S9iWtLu7e+edtz70wQ+2zf8hQOVFdnh05PT+wUGJqLW21kotwMHFg/l8LmljsXH23NmNxcYwDFGilHJ4eLgehu2trdVqVTfrar26/c47brj++tVqVWtdrVfjOPb9bJrGWupquQQiYhzHKLFcLhfzxXq1zsxLe5cWi43l4cGsn/V9f7Q8On7sOFf9xwEqL7JxHC9evHD3vffOZ7P1et33/fHjx+87e58Us76XdN111z396U+fL+bHd46dv3Dh+uuuf8Ztz4hSFvP5elifPHHyxPETBwcHj3/CE8ZxfPCDHnzhwvnzFy/OZ7PZbLa1tfWUpz31+muvO7Zz7Bm3PWNraysidi9d2tnerrWuVqvlarWxWJw+ffrSpUvTNL34i714VytX/QcBdLh/gReslHJpb8/Ua6+9jv9ktiXxP5VtSbu7u3feeetDH/xg2/wfAlReBLaBo+Vyd/di1/WHh4ebm5uHR4fHtneMn/b0p1937XWz2Wy1Wm1ubnZdPTg4HMah7/u+62utl/YunTxxYhiGaWp2giQyHSEpNjYWh4eHUtje2ty8cPHCsWPHxnGaprHWrrUmYdvmzOnTl/YuHRwezmfz06dO2ZbEVf8RgMqLwADs7+/ddscd21tbh4eHW8uto6OjWd+fv3Ch7/vDo8Pb77xjc2ND0npY33vfvYv5IiIiYrFY7O/vd7U7f+F8a63v+9VqNQzDYrGYWtvc2Dhazu+9796+69fr9SMe/vD7zp5drde7ly6Nw1BK6ft+uVweHh1tb23N57Oz586dO3/ulptv4ar/UIAO9y/wgpVSLu3tmXrttdfx32ccx67rMjMi+G9iW9Lu7u6dd9760Ac/2Db/hwCVF4FtYHd394677uy6TtIwDH3fz2azo6OjiMhM233fb21u3X3P3VtbWxFhexyGnZ2d1XqdrS1XK0mtta7raqnGGNvHju2sh2Frc+v8hfNd7U6dOvn0W2+VNJ/PgXEcZ31/0003d10XEYBtSVz1HwoIXmS2SymLxeLw6LDv+2manBkRmxubtuez2ebmZtd1XdcdHR0BXe26vs/0crkcp+m6a68tpWxtbYVisVjUUhDb29u1ViHbgPEwjH3f72xvO721udl3fdpHR0eAbUASV/1HA3S4f4EXrJRyaW/P1GuvvY7/32xL2t3dvfPOWx/64Afb5v8QoPIiMACZKYl/ibEQ/xoG8S+QxFX/aYDKiywieBEI8a8krvpvBlReFDYwDEOtNSLGcYyIzJQUEZkZEVNrJWIYh0zP+r6UkplAy1ZLzUxA0jiOfd8DmRkRklpry9Vya3NrHMdSS5tarTUzFSHIzForME1TRHBZZmamcS21lMJV/25A5UVgAHYvXbrn3nu6rgMuXLiQmfP53Hg+mx8/duyue+6upR4tl0Ct5UE333L+woXlaim0HobFfL5cLTc3NqdpOnP6zPmLF5ZHR9dff/16vb7v7Nlpmq6/7jpJXdcdHh4eLY9KFIUODg62Nrde/MVe/KlPe+pyuWyZt9x088Xdi8MwrNfrftavV+vrr7/+oQ9+iG1JXPVvBehw/wIvWCnl0t5eulx33fXTNA3D0HVdrXUcR2MpsrWu70qUzJzalC27rrNdSpmmKSIk7e/vb21ttda6rhuGodTiNBARmZmZUQoQku1a63K1nM/mF3d3M/PYzg5gqKW0bLXUYRwwhpBsD+Owtbklif9ktiXt7u7eeeetD33wg23zfwhQeZFJMp6mqbU2jGNrUyl1Y7FYr9fL5arrOju3t3d2dy9KUWudpmmaxtlsFiWGYYhSzp0/d+L4idVq3do0n82n1sZxODw8PH7suPHBweH29tbe/t6sn124eHE+m21ubi5Xq9VqZWdrGSFJtXZ91wH9bAbMZjOu+ncDKi+y3Uu7j3vCEx50yy1HR0d333P3xsZGRDl96tRsNrv77rtLKcM43HD9DRcuXBzG9dHR0c72zsHBwcbGxmq1OnbsWCllb39/tV7fc++9wzBce+aa9bA+d/6c0LFjx7a3tp/81Kc86pGPPH/+/MHhwebGpu0bbrjB6alNFy5cGIYhnZubm/v7+13tbr7p5huuvz6doeCqfzdAh/sXeMFKKZf29pJ63bXX8f+bbUm7u7t33nnrQx/8YNv8HwJUXhQ2cHBwcHH3Yqm1RAzjWErpar3r7ruPHTs262d93587f25jsWiZ2Vrai/nCeByGvp/N5/Pd3YtSdF23sbGxf7Bv++SJE0dHR8M47u3tZebx48dtR8Ss71er1XyxmHX9nXffdd011y5XK9uSVutVKWW9Xu9sbwMnT5ycz+dc9e8GVF4ExsDu3qX7zp7d2NiYpmlne+fSpUs7OzvAer1er9fHdo4tl8v9g4Odre3VehUR0zSdv3B+vV4//KEP3720e/7ixa3NzaPlUT/r1+v14eFhiTh/4cJisZAUEcvlchiG2Wy2XC6Xy+XGMGxtbu0f7G9vbW9vbz/9GU/f3NgsUXZ3d0sp586fj4jFfDGfz7nq3w3Q4f4FXrBSyqW9vaRed+11/M9jWxL/JWxL2t3dvfPOWx/64Afb5v8QoPKisIHzFy7cdfddfd93XZeZwzD0fb+1uXXu/Lla63w2jxLYs9nsvrNn+74HaikRZblaHts5FhGHR4cRARwcHEia2iQUETfdeFNEHBzsd123t7+/XC43NjZCESUODg5aa9vb28588IMeXEqxzf0kcdV/BCB4kXW1ttYiYhzH5XJZS93a3No/2K+1juM4tSkzFdFam8/nXde11mqtxs5sbTo4OJimKVsO62E2m3Vdd2x7Z2Njo9a6XC4lHRwejuPYdd18Ntvd3R2ncRqnrnZbW1vTONWusw1IkiSJq/7jAJUXgSRgZ2fnpV/ypXhO1117LS9UZkYEL4JjOzu8aGxLksRV/0GAyovANnDu/Lmn3fr0+WwuabVeL5fL2Wz2oJtvedKTn9TPZjdcd/3h0eGFixf6rt/e2tq9dOmmG29crlZPe/rTT5w4Putn11177d333DOO45nTp4+Wy0t7eyeOH5+maRjH+Wx2eHh4zTXXHB4eHhwclFrBy6PlLTfffHF3F+i6TtLh4WE6T588dfHS7rAealcfdPODrjlzxrYkrvp3AHS4f4EXrJRyaW8vXa677vqDw4Pd3d3t7Z3lcpnOeT8bp2lzc3N/f8+wsVgcHBz0/cx219VhGDcWi5atlCLFarWaz2ZHy2UpZdb3wzgIIcZx6vt+tVr1fd933Wq9iiitTcvlamNjo9a6v7+/WMwzs9ZuvV7NZrP5bL5erxUax3Fzc2tzY4P/ErYl7e7u3nnnrQ998INt838IUHlRSMDW5tbW5tY4jsd2doBxGrvaAVubm8A0TcePHeey9TDsbEetNTMBw/bWFrC9vT0MA7C9vQ0Mw9D3PZdN01RK2d7e5gHGaTpx/DjPY3Nzc5qmWitgWxJX/fsAlReBbeDe++593BMeH1Fam/qun1prrT36kY+86+67t7a2QrG3v7dcLbe3tw8ODrra9bOZM5er5Xq9fsiDHnLv2Xs3NzY3Nzfvueee2WxWax2GITMj4uabbnrCk54EvuG668dpOjo6KqVc2turpRw7dmxzc/POu+48fuz4MAyZObVpHMdMnzp58iVe7MUjgqv+3QAd7l/gBSulXNrbS5frrrt+nMZpnCLi8OhwPpv3fZ+ZXdet12tJs9lsnMZsWWvNTEmSSinjOLZsfde31kopEbFer2utrbVa6zRNUaKWuh7W2bLruoPDw62trXEc1+v19tbW1KYSpWXWUjJzGMdpGi/t7Z05dTqd89m8lMJ/CduSdnd377zz1oc++MG2+T8EqLzIxnE8PDjc3t4+tnNsGIflarlarRfz+Ww+G9bDehhsR8Ryuez6bpqm1WrV933X9aWU/f19RCiilFrL3v5+V+ve/n7f9xGapgm0s719eHi4tbm5Wi4VMZ/NVquVQm1qtdb1em17c3MTOLZzjKv+QwGVF9lyubrrnrvbXXdcc+aau+++e7ladbX2fb+9vX3Pvff0Xb+9vb1arfb29x/64AfvXrq0WCz29vcw+wcHrU2nT53evbT70Ac/9ODw4MLFCyVKa21ra2v/YH8+m29vb1+4eOG+++67+aabbr/jjnEaI0pmbm9tSdra2rr1GbeeOX3msY9+TCnFNiAJkMRV/26ADvcv8IKVUi7t7Zl67bXX8W/VsrWp9X3Pv0M6Q8F/H9uSdnd377zz1oc++MG2+T8EqLwIbANHR0f7B/utNZvWplIql0kap7GrXSnR9/3B4WHf9eCj5bLWWkud2oTpunrtNdfedffdXdeth/XxY8f39vZKKcBs1k/TNE5Tidjb399YbOzv7/ezvqvdzvb21Np6WDtTinEc+r63PY7jzs6xixcv3njjDbN+xlX/PkDlRbYe1hcuXrS9sbGxt7c3jENrubW5WWsdx3FzczMz+3Hc3d2tpW5vb1+4cKHWKikiuq5bXVxtbGzce/Y+Z0Ypi/l899KupGEYTp86tVyuVuuV7b7v777n7tlsth7W4zR2XV2t1wcHB13XDcOwWq+vPXNmb39/mqaTJ06eu3D+5MmTs35mWxJX/VsBOty/wAtWSrm0t2fqtddex/9vtiXt7u7eeeetD33wg23zfwhQeRHYBvYP9i9cvOj0/uFBa9OxnWO1lHGaDg4OZrNZKWVne3u5XI3jYBjHsdYKLJfL66+7bhxHm82NjfvOnS2lZKakWso4jv1stpjPDw8Px2mKiNVqtbFYGEJaD0MpRYA0juPpU6fPnT8XERGxvb1dIg4OD1trN15/w8bGBlf9OwDBiywzx2GcpvHY9vb21vYdd9yxXK2cWUqppWbLEmWcxkwLlVLW6/Wsn9Vap9Yy8/DwIG1Ju5cuDcMQEWmv1mtJO9s7teumaer7XlKtdbVa3X3vPcvlcjabGdo0bWxsRISkYRhKKeM4TlNbr9cRwVX/boAO9y/wgpVSLu3tmXrttdfx/5ttSbu7u3feeetDH/xg2/wfAlReBLaB+86efdqtT9/a3ByGwbYi1quV8ckTJx/9yEdlpiReOAmb5yHJNs9Jkm0eQJJtnockrvr3ASovso2NxYNuvnnWz46WR13XdV2/Xq8kRYTtiOBFIfH8SOJ5SOI5SeKq/wRA5UUhAVubW1ubW8BJTnLV/y1A5UWWmZIA25IAg3hOAiPJNs9DEmDA5n6SuMw2VwgMgIQtifsZsHkgIcRV/z5A5UUWEVwmicvE8yMASbwAAiSehySeRTyTxAMIkLjqPxpQeVHYwHK5HKfRRhAlhmHo+z5bAgq11kKhiGEY+q6bpilKYNKZ6damruu6ri8lsuUwDpkex3GxmPf9zJkRcXB4WEoBd7Wb2iRFiVgP6/lsLqnWmpmttdV6XUpIsVqt+r6LKMd2drjq3weovAgMwKW9vdVqec+9987n87RDsp3OUBwcHmwsNlprrbXZbHbq1Knz58/bjoijo6OIaJnTOD7soQ89ODxcLpfplAJ7GEfbO9vbti9cvFhKbG1u7R8czOczoWEY+r6/5syZO+6689jOMduHh4ellKPl0fb29jROh0dHN99007GdHduSuOrfCqi8CCQB1117LfDgBz2YF8GDbr6Ff43MjAhegFtuvgXIzIjg+ZHEVf8OQOVFYQNHy6Pbbrvt9OnTq9V6MZ+nc70eSonMtH14dNRaO3P6zObGxtFyKUlimqZxHLe3tperlaRSyv7B/rHtnd1LlzY3N0+fOnXbHbcfP3Z8vV5lJqi1aWoN+/Do6Joz14zjcLRcnjpxcv9gv5Qaoa2tre2tba76jwZUXmTr1frCxYuz2Xycxv2D/fV6PZ/PV6vVar265vSZo6OjzNzb3+v7/u577t5YLLqu3720O01TKfXue+7u+/7G6284d/78Yr647+x9m0ebi8Vif39/GIaDg4Otra31eg3UWoGjo6Oj5dF6vb5w8cLxnWMRcfc9d6d9zenT21vbmSkJkMRV/xEAHe5f4AUrpVza2zP12muv4/8325J2d3fvvPPWhz74wbb5PwSovAhsA3t7e8+4/bbTp04dHB6ePHFib3//8PBwZ3t7nKZa6mw22720u7FYDMNYSgEymyKy5TAOEXH9ddfdfc89fd/3Xbd76dJ8Np/adPLEiWlqR0eHteuO7excuHhxuVzOZrNZP9vYWLTWlstlphHTNB0cHGxtbtWuHh4eRsTxY8duvOFG25K46t8HCF5k0zRFxGw2Xy6XwHq9tr1craZpysxhHIDWUiHjqU2l1mEYVuvVYj6fpmkcx4u7F9fr9eHRUYmyHtZ930uxf7BvGIbBZrlcAsMwHBwe1FqHYRyGATEMg+2IiBLr9TozJU3TxFX/QQAd7l/gBSulXNrbM/Xaa6/j/zfbknZ3d++889aHPvjBtvk/BKi8CGwDd99z98Xd3WM7x267/bZTJ0+lc//goNbSWrZp2t7eHsdxmqYoJVtrrd18880lyn1n79ve2r73vnu7rttYbFzav1RKvemGG++4847MXCwWh0eHfT9r09R13cHhwTVnrpnP52fPnr3l5puf9JQnz+fz+WzeWtvZ3j5/4YLt9TDsbG8LPfIRj+i6jqv+IwCVF9nm5matteu6m2666fixY8vl8szp00fLZWutqzWiAF1XbYZhvVhs9F0HHD9+/MTx4/P5HJjP54vFYntra2NjI0IXLl48eeJEKFbD2pkbGxvr9TCbzWzfdOON89n8QTc/qO+7YRjm8/livtja2r64e1HSxmIREV3XcdV/EECH+xd4wUopl/b2knrdtdfxr9EyS0RrLSIkZWZEcNlyuUzn5sYmlw3D0Pc9D5CZEQFM01RKkWQ7M0sp/PexLWl3d/fOO2996IMfbJv/Q4DKi8CZwB133XnrM56xsbFoU7vpxht3L106f+H8YrGY9bPNjc1bb3vGNWfOLBaL++67bzabrddrYD2snZ7P57XWnZ2d8+fPb2xsHBwcpHMYxkc94hEXLl6MiNVqtbO9M7Vp99KlkydOXLx40fCQBz3o8U98wubGZss262enTp66sHvxxPHjd99994Mf9OAbrr/etiSu+o8A6HD/Ai9YKeXS3l66XHfd9QeHB7Zr7VprW5ubq9XKkK31fS9p99Kl7e2tWupqvQpFRNhOOyRJfd8fHh4qYtb3tqPEarXe3NhYrdddrev1ejabjeOYmbVW2y1b3/XDMHR9ly0l1VrX63WtdRzH2WxWSuG/lm1Ju7u7d95560Mf/GDb/B8CBC+yWqqkWspsNts/OEBqbSq1rIfh0t7ebNYD+wcHfde3bEDLVkuRNE3T/sG+IaSDw4PMvHRpL6SDg4NaS2YibKczSlmulsM4drWbpqnv+2mcjpZH6/V6uVzOZrPDw8NaaymFq/5DAZUXgQHY29/767/9m5tvuvn4seNPu/XpEhuLjXPnz4E2Fov1MDzollv29vbGabLzmtNnnnH77TffdJPtO+68YzFfDOMY0tb21s7W9tOe/vTZfDafzU+ePLm/v99au+7aa5/4pCdtbm52Xbe3vzfrZ2dOn+m67vY7b2+tbW1uHR4d3nLzLU97+tNf4sVe/NTJk7YlcdV/EECH+xd4wUopl/b2knrdtdfx/BwcHmxtbvFfwrYk/pvYlrS7u3vnnbc+9MEPts3/IUDlRWEDR0dHF3cvzueLaRqnaaq1O1oeYd9339nrr7tunKbVatn3s2ma7jt739bm5mw2Xw/rk8dP3H3PPcePHzs8OhrHcXtraxgGJCCkWrta62q1vOaaa5bL1d7eXtd121tbu3uXnDmbzdbD0Hfder3e2d45duwYV/3nACovsoPDw9vvuOPM6TOr9WoYhohIG3sYhsViUWu58+67rj1zzdFy2Vq79+zZw8PDWspiPr94aXdq0872zrnz51erVa21tSap1nrffffN53PbtXbDMNx1z12bG5sSd9xxx+bm5tbm1uHRoaSjo6NMHzt2zLYkrvqPBuhw/wIvWCnl0t6eqddeex3/3WxL4r+JbUm7u7t33nnrQx/8YNv8HwJUXgS2gQsXLtxx153z+XwaR8M4jkDLPHH8+Kzvz547d8P1N9x39j7g+uuuu+/s2Vrr0dHRYrG4uLtbS7nu2usQFy5csL1YLFprThsfHh1ub26N03T61CnD4cHhelhLsn14eBilLObzaZquv+76a6+5xrYkrvqPBlReZKXWjY2NEiGp67q+65erpWDW9xHl9KlTtdau60opoFrKYrHYWCyA+Wy2HoZaC6iWMp/PDfPZrJTSWvZdt7GxcXR0FFEionZ1Y3Pj6OgoM6+95hpDZs5ns43FAkDiqv8EQOVFIQHHdnaO7ezwQp08cYLLTp08yfNz7TXX8EKdOX2aF0xc9Z8CqLwobODsubPnzp+/cPFC13Vbm1vnzp975MMfcdvtt7fWrrv2unEaz547d/rUqWEYHvqQh2xubNrmP44krvpPA1ReZMd2jtVStzY3NzY2hmHY2d7e2d654YYbMnMxn/f9bD6f11LSFgIkcdX/EkDlRSEBfd/3fX/ixAkeYGNjg/sd29nhqv+dgMqLzE6QbUAS9zMWAmwDkrifJMC2JJ6TbSQBYBsAJHE/24AkwDYgCTBgS+Kq/zhA5UUmBSCJ5yTEZZJ4fiTxPCRxP0k8D0ncTxL3EyBx1X8ooPKisIGjo6PDo8P5bH54dLS9tdX3/Xq9lqSIbG0Yh2lqtZSdnZ3d3d2Ll3avu+bafjYLabValVK6rhvHURLC6am1kBQR0mq9bq2VUrquCilCME7jOI7bW9vjOO4f7C/mi37Wt9aEjpZHi/lia2uLq/6DAJUX2dFyec+99166dClKlCgnT5y47+zZnZ2ds+fOnTh+fBzH5WrZdf0tN918+523z/rZuXPnrr322qOjo6Ojo8y86cab7rrnrs2Nzcy8ePHiYrGwvbGxsbOzc+999x4dHc36WamlTa2UslqvNhYbB4cH15y5xrbgnuFe4ODwoO/6YRyuvebaR2w9PDMlAZK46t8B0OH+BV6wUsqlvb2kXnftdTynaZpqrcA0TbVW/kPZlsT/JLYl7e7u3nnnrQ998INt838IUHlR2MDR0dHd996zmM9ttra2Son9cwdbm5v7BweZrZS6HtazflZrXQ9rp0uJlhmKzc2N9XqYz+eX9i4t5vNxHJG6Wvf290+dPLm3v9/VzvbO9vZisbj3vvtmfT+fz+89e9/GYpHpvu+naZzNZtPUxmkMxXq9rrVsbm7uHxzMZ/PVannD9TeUUrjq3wqovMhWq9V9Z++rtc5n83Ecx2kchsHOu+65e2tz68KFC/P5vLW2tbV1eHi4s7Ozt7fXdd3O9vYwrO89e99NN9x06dKl3d3diJjPZoq45557Njc2ail33n1Xm6bt7e0br7/hvrP3bWxsbGxsjMN4z97ecrmstdo+ffr0xd2LmxubR8vl0dHhjTfceOnuuw8OD2d9X0q58YYbuerfAdDh/gVesFLKpb09U6+99jr+h7Etif8qtiXt7u7eeeetD33wg23zfwhQeRHYBi5cvHDPvffYhAK8XK22tra6rrt06dLW1tY4jtM0nT51evfS7qzvJV3a29tYbJRaDg8Pu64TXHvNtU95+lN3tndOnTx17vy5Usrmxua58+dqKV3Xt2yttb7vTx4/cevtt910w4133nXnxsbGmdOnz50/v16vS8T29s4tN99sG5BkGwAkcdW/A1B5ERiA9Xoopc5mM0nZ2mKxAJ06eTKkWuvW5tb+wX6tdTabLeaLrc1NpK5W2/NTp9arddfVUsvO9s5iPq+1zvoeNJv1mxubxhFRKH3ft2kyPr6zM5/Pt7a2Zn1fS+37frFY2O77jsskAZK46j8CoMP9C7xgpZRLe3umXnvtdfz/ZlvS7u7unXfe+tAHP9g2/4cAlReBbeDSpUu33vaMzCylZOb1111/7TXX2OZ+BvHCSLLNi8y2JMC2JO4niav+QwGVF9lsPrvh+uuHYYwIifl8DkjifuJfJokXmSQAkMRV/5mAyous1nps51jf91ObaqmAbUBSZk5tsh2KWus0TV3X2QYkHR0dzWazUgqX2TZuU+u6juc0TlMtpWUTysxhGGqth0dHfd/VUhHr1dr29vZ2KYWr/uMAlRfZhQsX777n7kt7e5k5n8/OnD5z7333bm1uXdrb29zcHNbro+XRrJ896JZbHv/EJ25ubm5vba3Wq1MnTj316U87trNTSrnh+hueduvTaq0lyjCO4FMnT63X6+VqNazX11xzTd/199x3z43X3/j0W59+8803Hx0dnT516sLFi3fedddNN944m80uXbq0HoaHP/Shp0+dti2Jq/4jADrcv8ALVkq5tLeXLtddd31mrtarUEiyPU4jqO+6vf29nZ1jzrQtqeu61WqlUFe7cRxLKVyWmX3fr9drSQpN05SZi/linEahtGutXa3L1XJjsbFarebz+TiOfd/bXq6Ws36WmQCiliqJ/1q2Je3u7t55560PffCDbfN/CFB5kQ3j4PS6rWf9LJ2SQF3XzWfzaZrms9nRcrler2rtgMV83lpLuytltVr2XV9KGadREX3XLVfLvutbtvV6bTudoTh/aXfWz7a2ti7u7g7DcHB4uLW1+YzbnrGzc2xrc/Po6GgYh53tnUyvxhWwHoatzc1aK1f9uwGVF9nBweHTn3Fr33V7+3t930eE7ZtuuPHJT32qndecuWZjY2N/f//S3p6krqvDMGwsNq45c81TnvaUhz3kobffcUftuqOjw0c+/BFnz50D5rPZ+QsXbrrxxuVqdfLEiQsXL164ePHBD3rQuXPnlsvlfD6/5eZbbr3ttlrL5sbmOI17e3sPfchD77333uVq+WKPebEnPeVJD3nwQ268/gbbkrjq3wHQ4f4FXrBSyqW9vaRed+11tiXxr5GZEcG/yTRNpZTMlCQJAKZpysyu6yLCNiCJ/xK2Je3u7t55560PffCDbfN/CFB5UdjA3v7e3t7esZ1jh8ujvb29kydOrIchM0sptvuuLyUyc71eb25u7u3vl1IWszmwWq/7vosoy+VSYhjGxWJeaydxx513zmfz+WLepglpHMdxHDc3Nh7y4IfUWoFSCg/QdR33k8RV/0GAyr9EPFNr7dbbnnHDdddvb+/s7e+th3VXunEaa6kK7V7axfR93/f91tb24eHher0+ffp0RFy4eGEcx2PHjh0dHR0eHW5vbe/uXdre2rrmzJmWbRiHWutytXQaMU3T1JptSfyPJP4PAnS4f4EXrJRy6dIlor/mmmt5oVprEZKC/7MMurR36Y7bn/7QBz/YNv+HAJUXynat9XC5BmwDILBBPJuhlAKkLQSAeT6EwOaZBAZAYBuJZ5L4n8ZIrFarEsH/OUDlhcrMjY3FOE7/8A9/m5kSL4gNIGEj8XzZABL/S9lE6PixncyUxP8hgA73L/AviYhxHMdx5P89hWqppRT+bwEqLxL3fdf3Pf/vCabW+D8HqPxLImJvb3//8DAUYMAg/h9SOuez2akTx9Pm/xZAh/sXeMEi4ujo6HA53HTzg6ZxJK0SgAGbZ5KxuEKAsRAYCQMGSeJ/N9da77nnnjYtT586NU2TJP6vACovlKRpmmazuaDrOv7fWywWd9xxz6mTJyXxfwhQ+ZdIaq0BF5729PN/9Te3vOWb/uEf/MHW1taNN9w4DIOkftbffvvtmbm9vb1er48dO3bfffeVUs6cObO9vT0O49HR0Xwxv/GGG23zv5kk24Bt/m8BKv8SgzOB83/9tz/0ru/1Zn/2u+Mw/P0//P1P/tRPvtRLvtTf/8Pf33jjjTs7O0996lO3t7dvufmWpz39acePH3f6jjvvuO666/b29mxvb29/4Pt/YNd1rTX+d5JUSuH/KECH+xd4wUopl/b20uW6664/XC4f/+QnvsSjHh1R1ut113Vd143jaHs2m03TVEqJiGEYSikRcfc9d89ms2M7xyS11rquk8T/cru7u3feeetDH/xg2/wfAlReBJKAzcXi5V/ypQGg6zoum81mXNZ1HZf1fc9lN1x/A/crpfD82LYtCbANSLItyTZCyLYkSfz3sW07Ivg/Cqi8CMwz2ZYE2JbEv8S2JF4A25IkcZkkLpMESOIySfy3si1JEv93AZUXhQ2s1+uWbWOxMU1TrbW1ls5aKpCZEQFkJqCQUGaWUngBbEu6ePHiufPnSilbW1sH+wct2/bW9qVLl06fPr23v4eJiMOjwzOnz5w5cwZYD0OEaqktGyYiImJqUygiYpomoJQCSOI/iKRLly7tH+zfdONNtvm/CKi8yA4OD/b290qUo9VyMV8cHR3OZjPg2M6xo+UyWzPYGVGGcQDGcbzphht3tndsS+I52Zb0uMc/7q//5q9vvunmkydPPv7xj9872HvUIx/19Kc//TGPecxdd901TZPTwzg8/GEPf4PXfwPgwoXzl/YuzefzzGytRcT21vbh0WFrbWtra29vD9jY2Dg8PLzmzDUnjp+wLYl/B9uSDg8PL168eNONNxnzfxGgw/0LvGCllEt7e+ly3XXXt9aMbWNsS1IIU0oxbq0JScrMaZpqrYhSSih4wVprQCkFyMyIAFprh4eHpZSNjY3WGrB/sH/i+Akum6YJiIjWWkQgnAamNpUokgDbEVFK4T/a7u7unXfe+tAHP9g2/4cAlReFBEjCSEJIAmwjWmtAKLislFJLNQacnjxJ4vmxHRG2x3EEImKaJtuStre3bbfWJAEnjp8AgMyUBNiOCAAjCei73jYASAKmaZLEC2Wby2xLAmxzmSTbACBJUimF/6OAyotMUkTwL8lMY0mAJF4o26WUUgrPQ1JE8JwigheZbf4lkrgKgMqLwgZWq9Wl/b2NxaK1Vkrp+369HkrEelhLQhJsbmz2fc+LxnZE/P0//P3f/t3fHts5Np/PL+5efOiDHzqfz/tZ/4QnPqG1Foq9/b0Xe+yLvfRLvTSwt7+3Wq/ms/nUWt91QsM4dF03DEMpJTNLKev1upSysbExn835l+zt7T35KU/e399/ylOe8shHPvLCxQvz2fzMmTPHjh1bLVdPevKTZrPZMAzrYf1Kr/hKD3nwQ2zzfxFQeZH1fQ9c3L0oVGvd2Ng8ONjv+n69XmfmbNYPw1BL7fuef41rr7n25ImTx44d67ru/IXz5y6cO3H8xNSmxXwxm83Gcdza2lqtVgAwn8/39vdWq7Xt2ayfz+ZHyyVHR4oIabVeSbINRMR8NudfsrGxce01154+fbq1tr293Vq7dOnS+fPnZ7PZ0dHRsWPH+r7PzHEcl8sl/3cBOty/wAtWSrm0t5fU6669zrYk/iW2JdnmXyLJtiT+c9iWZJsXTBIvGtuSdnd377zz1oc++MG2+T8EqLwobMD2NE0IDCDJNgBI4t8hMwFJgG0gImzbBoBSSikFyMzMtC3JtiTbXCaJy2wjhHjR2AZsS+Iy25Ik2bbNZaWUUgr/RwGVF4UEAF3X8d/Kdq2Vq/6jAZUXhQ3sH+yfv3Bh1vfptO10RLTMM6dOb25u2pbEfxzbko6Oju47e9/mxuaZM2eAe8/eN41jlGInME1TrV1m297aPnnipG1J/MexLeni7sXd3d2TJ04eO3bMmP+LgMqLbHNjs+96Y9uZGRFS2Nl3PSCJ/1CSgMViceMNN0YEAJw6eXKamgDRWpMiQkBEAJL4DyUJOLZzbGtzKyIAIf4vAiovCgkopZRSAEDIGJCUma01XgDbPCdJtgFAkm2ekyQusw1EhCQAmPWzWWckDALAAMatNf5zSKq12ub/LqDybyOEbLfWJEniBSil8B8nM23zPCRJ4j+Nbf5PAyovChsYhmEYhtlshrRcLmsttdTZbMYL9Xu//3sPefBDSinpHMcRmM/n99xzz/XXXd+yCT3jtmc8+EEPHsZh1s8yc29v77rrrlssFkdHR3/9N39dSrF9w/U3PPzhDwcigqv+EwCVF9k4jXv7e223GUeUo+Xh9tbODdddb1sSz4/t8+fPnz179tLepVOnToXi5MmT29vb//C4f3jiE5+4s7Nz/fXXZ+af/tmf2m6tRQSwHtbXXHPN7u7u7u7uzs7O7qVd4OEPfzj/rWxL4v8oQIf7F3jBSimX9vaSet211/GfzLYkHmC1Ws3nc2C1Ws3nc/5b2Za0u7t75523PvTBD7bN/yFA5UVhA/v7+7t7u6WUWuswDK0127PZvKv19KnTvAC2AUm2bUuSlJmSuMxYyLYk4Gd/7mf/7u//bjabveHrv+Ff/c1f3X333ceOHTtz+szbv93b25bEVf8JgMqLrO/7YzvHpmnquq6r3dSmrnattfl8zgsmicskSQKAiOB+QoAkLnuVV3mVl3iJl5B0/Njxk6dOOr1YLMZxBCRx1X8OoPIvEc/U9/1sNuP5aa1J4t/N9pnTZ86cPsNlx48f5362JfHfzVj8HwRU/iW2FQFIsg0IAPNMkkop/AexzfOQJIn/VpKAUorN/z1A5YXKzNlstrt3eHh0hM3/Y7ZrrRcvXuj7zjb/twCVF8r2bDbb2crbbn3KOE0SmP+nhO1Z3x87dsw2/3tI4l8C6HD/Ai+U7VprrWWaGv+/SWRaQgowCMz/dGqt2eaFAir/klLK0XK5v78fCvP/ncAA5n8H2d7Z3prP55nJCwZUXihJw7De2z88eeq6bA1bEZkJKEJgEM9k7LQkSYBBYJtnkcRV/6UUcf7cfSekvu9t8wIAlRcqIlar9Xyxub21xVX/O+3v752/cOGmG26YWuMFACr/EkmZCezde+/+bXdc//Iv++d//meheLEXe7G+78dxnM/n4zh2Xbe/v/8Pj/uH66+//obrbwCmaepn/X333re5uTlN06VLl66//npJkvq+t81V/8lsR4Tt1lravGBA5V9icGvAvb/3hz/5ru/9xn/9h3/xF39+1913/+7v/26tdX9//yEPecjf/M3fvP3bvf0f/tEfnjlz5m/+9m+cXg9r29tb23fdfdcwDrN+dvLEyfWwbq099jGPfdM3edNpmiRx1X8mSYBAkiReMECH+xd4wUopl/b20uW6666/dOHCH//hH7ziq736bDY7ODw4Ojpq03THnXfedNNNtq+79rpz58+dOX1mtVoZX7p0aWtzKyL29/enNi3mi9lsdnB4sFgsNhYbx44d46r/fLYl3XXXnUeHl26+6abWGi8AoMP9C7xgpZRLe3umXnvtdVz1v41tSXfddefR4e7NN93cWuMFACovAttAZh4c7EukMxTpFAIktdZKKYBt2xEB2JZkjJEE2E57c2Or6zqu+p8EqLzIlsujaVqv1+vFfH7p6LDv+qlNoRjG4eSJE/v7l4Zx3N7aqqXuH+xnZomSTtuhaNlqra21jcViuTyo9bgkrvofAwheZLbBU5syc71e246IYRzHcZQ0jON6PbSWtlerdZtaKbFeD+M4roc1ME1Tm1qJYtvmqv9RgMqLbGNj084Tx+et5TVnrrMTtLmBpHFqJ0+clshM22dOXyOR6cXGlngm20DL3FhsRYir/icBKi8KCZC0vX2staYIpyVAxhhJYJ5NYMBGAjAIDJJCsg3YlsS/hiTbPA/bEWEbAGxLkmSb52FbEpdJAmwDgG1JPCdJtgHbkgBjIe4nCbDNv8RYiOciMFfYliSJ/1pA5UUmKTNLKQBFPJN4JvEcxAsmCZDEv1JrrZTC85AESAIASQAgiechieckCQAk8fxIAiQBgBDPQxL/EiGeL3GFJMC2JP4LAZUXgW3g7rvvvvueuzPz2muvfcpTnrKxsRERly5dOnnyZN/3G4uNS3uX1uv1ar06c/qM0O6l3cx8+MMffvbs2fV6PZ/NV+vVwcFBrfX666/f3d3d29t78IMffPbs2c3Nza3NrbvvuXu9Xtda5/P5er2OiHEcMzMz+75/yEMe8g//8A/nz59/6EMfKunSpUvDONx4w42bG5tPfNITd3Z2Hvawh913332r1QoYhmFjY+PUqVO33Xbb5ubmNE2ZeXh4iOhql5knT558yIMfsn+wX0s9f/78Pffe03XdfDY/Wh7NZrPNjc2j5dHNN938l3/1lw95yEM2NzfvuuuurusODw8fdMuDdi/tHh0dPeiWBz3jtmdEhO2Dg4NhHB50y4MW88V9Z+9rrT384Q/f3d09f/58RDzkIQ/5m7/5m+PHjwPDMMzn877vs+XpM6f/6q/+6sUe+2LrYX3h4oVZP+v7/uDg4JZbbnnQLQ+yLYn/KkDlRSAANjY25vN5a62rXa11Npttbm4C0zQdO3Zse2d7NptduHghIra2tu67776IiAhM13WZObVpNpu11q677jqnV6tVKeXYsWOHh4dAqaW1dubMmVLKfffdd3h0eMtNtzzt1qc95MEPsT0MA3Dttdc+6pGP2t/fBzBRou/7ftYfP358Npu11rqus71YLPb397uu6/v+mjPXrNarrc2t9bBurZVSNjY2hmEoUUopGxsbtVRJFy5e2NramqZpsVgcP358HMf5fF5qOX78eN/3kmaz2dbmlqSu644fP54ta60bGxt33nXnDdffcM0116xWq1LK0fJIUq1VaBiGEydODOvB6Y2Njc3NzaOjo62tra2trfV63XVdV7sTJ06UWk5tn5qm6fDwcBiGWqvTgCT+CwE63L/AC1ZKubS3Z+q1117HVf/b2JZ01113Hh3u3nzTza01XgCg8iKwDWSmbUCSbUCSbUAS97MtCbANSAJscz9JCIxtSbYBSba5TBJgG5BkG5BkW5Ix5lkk2QYkAba5TBJgm+dLEs9mG4EBJAG2JdlGCNmWZFsSYFuS7YiwbRtACNkGJNnmMkm2eQBJtiXZlgTYlgTYliSJ/wi2JfEiACovCgkIiQjbgCRJtiOCB7AdEYDtiOAy25IASba5TCGeH9uSuJ9tSZJsS5LEc7IdEbYl2Y4IAGMMSJIE2JYE2JZkWxL3sw0oBNiWZBuQJInLbEcEYFuSJMC2pIiwDUgCbEvifrYjArAtCcAQPEtmSuIySYBt/msBlReFDayGdZvabDYDbLdstXbTOJZSbKczFLXW9XodEbXWaZqMMRFhG8jMUgqAyJbjNPZdD6TtTOMSJUqs1+tpmrqum6ZpY7ERJcZxKhGZaZvLbEdEy1aiZGatdT2s+75fr9dd7SSVWoSmaUpna63v+mmaxmlczBe2gcxEhKK11nWdpGEYgCjhdJTAGKZpBEqppURO2TJrKcA0TWnP+l7SOI6l1sxs0xQREZFOoXGaSomudoeHhy3bxmKjtWa71DKNU5TIzFDMZjNJ/CewzYsGqLzIlsvlvffd2/ezrqt93+/v7x8/dnz30u58Njder9dSnDh+fPfSru0TJ04eHhwghmFYLBahKKWsVqtxGjO9sbEoUVprU2tgYLla9V0naXtre7Ve7x/sb29tj+Ng+/Do8PDwaGNjMY4jqOvqarXqun6axq7rFouN3Uu7J4+f2NvfBw/jWEuptZ4+dcaZ62G9t7/XWpvP54dHR1sbm6v16tKlS33XD+Mw62fjNEkcP3Z8mqbdS7u1dhuLhe1pmlo2Sa01SRGxtbl137mzfdf3fSdptVpN03T82PFjO8fuvveeYzs7e/t7Ukis1utZ3y9Xq8V83lo7tnNs/2Bf0sHBwWq9yszjx08cHOxHxMZi4+Dw4IbrbpjNZvy3AnS4f4EXrJRyaW8vqddde51tScZC/EtsA5IAwFgIyEwgIviX2JZke5zGWmpE8ELZBiTx/NiWxAuQmZIkcT/btiOC+9mepqnrOtuSuJ9tSYBtScAwDH3ft9ZKKTyAsRDPw7Yk/hPYlnTXXXceHV66+aabWmu8AEDlXyLABmxzmTEvGtvczxiICMA2/xKBbUl91wO2eaEk2bbNC2CbFyAibNvmfpIk2eZ+krqusw3Y5n6SbAMStoG+722XUmzznIx5fmzzn8C2JHOZzQsGVP4ltiUBEcF/EEm8CMSzSeJfIol/K0k8D0k8J0k8D0kAIAEAkgBJ/LeSBIRkG4kXDKi8UJm5WCwuXNw7f/48GMRV/5tYEZf2djfms8zkBQMqL5TtWuuJ49v33nfvOA5IXPW/i72xsbG5uWmbFwzQ4f4FXijbtdZaa2YDcdX/Jga11jKTFwqo/EtKKfv7B3v7+xHBVf/b2N7Z3trc3GwtJV4QoPJCRcRqtTpaDTfd8tA2TWSqFNuAJAO2QYAkSFsSgA0CSwKBQVz1X8pAlHL3XXdI2tjYyExeAKDyQkkahmE+X/RdR9dx1f9CXT+7uLu7ubnJCwZU/iWSWmvAhac+7dxf/vWD3urN/+Zv/hp42Zd52cPDw9vvuH1vb+/kyZObG5unTp36h8f9w6Mf9ejd3d3bbr/tMY9+zNlzZ2+4/obt7e3MlMRV/7VsR4TtzLTNCwZU/iUGZwLn//bvf+g93uct/ux3b7/99ic88Ql/+3d/e9ttt81ms9OnT99xxx2PfOQjb7j+hp/5uZ+5+aabX+e1X+fo6Ogrv/ort7a23vqt3vrRj3r01KZQcNV/rYgABJIk8YIBOty/wAtWSrm0t5cu1113/eFy+fgnP+llX/zF77zzrnEcT506NYxDV7vNzc3lctl1HbBar2qtG4sN4MKFC/P5fHt7m6v+m9iWdNdddx4d7t58082tNV4AoPIikARsLhYv/5IvZfvmm2/mOXVdBwCLxQKwjThz5gxg2zYgCbAdEYBBXPU/BVB5EdgGWrY7Ltzt8DhNESEY2zSr/XoaQupK15yttYiopQLAMA2hqKVgpmxASJm++eQNs9pz1f8YQOVFYADu2zt3adjfXx5szjfv2b2vr93Np274i1v/7qUf/GIXDy894+wdfe0edt2DD4727rpwb4kYpuER1z80nXdduLeWembn5NF6efHg0kOuu+Wu3XsecvoWrvofA6i8yKRozuWw2l5szWq/MVtI2pwtJO1sbJ3YOlYUEl2pG7N5iVqi1FLX4xARfe3W47Ax3yhRShQuM4ir/kcAKi8CAXDNzqnmdnJxfGrTzukt46m1x9zwyGEaAj3szIPSbtn6eX9y4zggaZjGftaduP6YoGWzifm2zY3HrwfEVf/BbEviXw+ovCgkQOjG49fZ5n5CxgKQMfezLWQsSZJtQAhhWxL/TYxt80IYQ0iS+F9IEmBbEiCJFw1QeZFJAiTxAEJcJsT9JAFCXCaJ+0kyFuK/nEFIEi+E+F/q6OgoShwdHm1ubs5mMyAzM7PWav5lQOVFYQMHh4fDMGxubKxWq42Njak1Z/Z9f2nv0sZiQ6Fau93d3Wma+q7r+35qrZTY3trmOQnxX85Y6OzhhTv37gkiM22n7cy0bTudTtuttcdc/7BTm8enbBIvElO7XhL/HWxLespTn3Lx4sWLFy8++tGPPn7ixDSOtdbjx0/UWnkRAJUXgQFYLo9uv+MORYQ0m82Wy+VytbrhuutW6/V6vV6uVjdef/3h0dHR0dFytbrmzJlxHDcWi+2tbduS+G9lW9Kf3fk3P/2k3+jp1sMwTtM4jtM0jeM0TuM0TuM4tdZ2D/e/6K0/+o1f8rXG9bJE4UWQmYqoteO/nG0ue/jDHr5/sL+9vR0RR0dH25tbs9ksIgDxLwN0uH+BF6yUcmlvL6nXXXsdD5CZkiTZlgTYBiRx2TRNkkop/E9iez2uAfNAxphnEsy63uZfpZTCf63lcnnPffdKRMTmxuZytRyG4fix4+thnemtzU1JwM72zp133rE8unTzTTe31ngBgMqLzDZCCIgILpPEZZK4zLakWiv/80ia93P+T1gsFjfdeCNQogAndRKwDWRmKQWwDUjiXwJUXmSSeBFI4qr/El3tuF9rTZIkSREBALZ50QCVF4UNDMNgezabDePYd51tSUBmApIASev1ehzHUus0jpubmxHB/xjradhfHWBa5rH5VlerbRD/PoJSK/+FbEuyLSkzI+IfHvcPtz7j1huvv7F29UEPetByuYyIne2dxWJh/mVA5UVgAC5cvHDbHXf0XZeZW1tbh4eHy9XqhuuuW65Wh4eHU2vgUydOSbp46eLGYiMibrrxxu2tbduS+G+VzlD8ypN/9/v/+mdzmc+4545Per33e/tXeNPD1UGJ4N/MIGVmz0atHf9VJAGSgIgAHvmIR544caKr3dbW1jCNGxubi/m86zpA/MsAHe5f4AUrpVza20uX6667PjNba1ObaqnjNGEDpRah1lo6QyGplFJKaa1FhKSI4H+M/dXBvQfnnZ6m6YZjZ7Zmm+kU/y4GQamdJP5L7O3v7V7alVRKraUcHB7OZ7PZbJbOYRiB7a2txXxxx113njl9emtz684771geXbr5pptba7wAQOVFIQGSuq7rug7ouo4XKiL4n2d7vrU93+IBCv/7bGxsSIqIEqWUsrO9IwkYx3FzY9PpUktEXHvNtX3XAUj8S4DKv0NmRgT/e9humQBYkhD/QSKC/yq11O2tbe7XdR2XzWYzHmBjseAy8S8DKi8KG7i4e/HOu+6azWbANE0njh8/d/789tb2sWM7J46fsC2J/9nWqyNI/qPZ7vqNWiv/TTIzIp78lCf/+Z//+c0337xar175lV754u7F0ydPLxYL8y8DKi+yzY3N66+9rmXWWlprG4uN+XyxWq36rgck8T9eqZ2z8Z8gIvjvIwk4eeLkK7/SK4/TuLW5NU7TYr4opfCiASovCgmYzWaz2Yz/zbqu5/8iScCpU6dOnTrF8xD/MqDyorCBzLQNIDCSbAOS+I9jEM/NWIirXijbgLGQJNuSIoIXAVB5UUhARPC/mW1JPJBB/LewLYn/ZJnJiwaovChsYLlaHh0dbWxsANlSku2u69I5TVMpRdJqtaqlzmaz9bBurdnuahclMjMUETGMg6Su652ZmaWWNrWWabvWEopz588tFotaa0QM44gt6eDwYGd7JzMjSillHIf5bG6MqV23XB7VUru+a611XZdp7MOjw67rIgJwejablVKOlker1WqxWAgZ932/XK5qKaWUzDTOzHEcu9pFBKJEWa1Xfd9nZi01M41Xq1Wtte/6YRi6vmtTQ9RSp2nqum69XteuW69XXdfVWrPl0fIonVsbWy1bZg7j2NW6tbl1eHTY1S7tzBYRfde3TEHLli0RkvquN56mlpldrS0bxnY6SylCiBJlmiZJCg3DeLQ8nM/mfdcrNJ/NedEAlRfZNE27ly4tl8upTV3topTVarmzvbNar7O1Uso4jU6nc2fnmDPXwyAxn82n1pbLo4iIiExP07i1uQVMrUUoM1erVa1VaGNj49yF8xEqUba3t1tr4zjW2p2/cG65XKZzmtr11143juPB4eGs75er5dbm1jAMy9VysViM47SxsQiF7f2Dg82NjVrrweFhiTh27FiJMgzDxd2Lh4eHaWe248eO7x8cdLXWWlfr9WzWL5dLSa3lfDbLzMVisXvp0s7OzjCsa6mHR4cRJZ21lM2NzQu7F687c+3B4cE0Tdvb2xd3L544fuLS3h64tQba3NiYzxe7l3alyEwpnGlbcHh0eP7C+b6fgYdhAHa2d4ZhyMypTbXWNrV0njh+IhQHhwfTNG1ubrbWJF3au9T3vW2bElFqWS6XtXZbm5v7BwcSq9U6ImZ9v7hmwYsG0OH+BV6wUsqlvb10ue6661trEWG7tVZKsR0R4zT2Xc9V/yPZlnTXXXceHe7efNPNrTVeAKDyIjs8Ojx3/txsNjs8Oiql1FKmqZUS111z3Xw+ty3JWIh/B9vcT5JtLpNkG5DEczIWAmxL4jJjIcBYiPvZlsR/IduSuJ+xEP9pbEviXwmovCgkYHtre3tr27YkQJJtSbYBSYAQ/z6SeABJ3E8Sz48Ql0nifkJcJsQDSOK/liQeQIj/TJJ4PsQLBVT+JQJswHZESOIy25JsS7LNVf/z2JZkLrN5wYDKv8S2FEBE8ACSAEmAJK76n0cSEArbSLxgQOWFysz5fH5xd3/30i7mqv9tHFH29ncXsz4zecGAygtlu++748e27rnnjnEchbjqfxXjjcVic2PTNi8YoMP9C7xQtmuttdZsDXHV/yYGaZqmzJTECwZU/iWllIPDw729/Yjgqv9tbO9sb21sbLSWEi8IUHmhJK1Wq4PD1fU33NJac2aUkjYgBOYFkQRpixdMsi2QxFX/CQwl4p577lJoMV9kJi8AUHmhImIYhsViYz6fc9X/Tv1sfuHCxZtu3OAFAyr/EkmtNeDibbddfNwTbn6D1/vbv/6rzc2tBz3oQYdHh7XWNrWu6yJimiZJpZY2tfliXqLcdvttW5tbB4cHx3aOzWaz1pqkWuvUJkklyp133nn8xPEzp89kpiSu+g9lOyJsZ6ZtXjCg8i8xOBM492d/+SPv8p5v8me/e8edd56/cP5nf/5na60R0Vqrtd5zzz0PechDZv3s7nvuXq/Xr/Dyr3B0dPTHf/rH7/4u7/74Jzx+tVrdfsftm5ubOzs79957b0QsFouu6xaLxUu8+EucOX1mtVpFBFf9h6q1RgQgSRIvGKDD/Qu8YKWUS3t76XLdddcfHBz87d//3cu81EsPw7BcLUuUxcaiTS0ijI+Ojo7tHFsuly2bUK01IjJzc3NzmqZpmvYP9uez+Xw+v+3228ZxvPmmm1trx44dG8dxsVhw1X8C25LuuuvOo8Pdm2+6ubXGCwDocP8CL1gp5dLenqnXXnsdl6UJ8X9PptMWlBK2bXOFCAX/O9mWdNdddx4d7t58082tNV4AoPIiMJfZSCGAllkieAFscz9JtnlOtiUBkmxL4r+KbZ6TJJsIBeIySZL438Y2/1ZA5UVhA2PzT//e7c+49/A1X/LMKz729PkLF46ODrva1a4uj5YKbSw2dnd3T58+vbOzwwNI4jlJ4n6S+K9iWxLPybakJ9564Xf+7I6Tx+Zv8waPvO+eu+66+575fIbp+u7hD3t4RPA/m21J/FsBlRdBJsAf/v3Zd/yU3zl9ZuN7f3nxt9/9pn/3d3/7tKc/PbM97KEP29jYuOeee9br9dHy6MYbbnyD138D/uexLWn30u7u7qWu6zIzImqtZ85cI/jEr/jdW+/au/vs/vFjWw87c+mv/+avT586df7C+VDcfNPNi8WC/8FsS7r3vvsuXLxw+tSpw6Ojvusz88SJE5sbG7b5lwCVF4ExsHsw7Byfv+wjTzztrsNxyld+pVd6rdd8LUmAbdsRAbRstjPTdqnF6Yjgf4zlcvWEJz3xxInj2fLchfM72zunT52mxIVLq9d5xZt/+09vv+ve3dd/pce+5y2P7GqxLam1ZjszpzZhJEVErZX/YYZheNrTn3Zpb0/S/v7ecrV6qZd4yc2NDfMvAyovgggBr/SY06/1Mtfees/Be7zRQ7oa95292FoLqZQyTmNE6buu1npp71Lf9Zubm6vVqmVO03TNmTOL+YL/bpKA66+77rprr5XE/TINvM/bvPiP/+qTXuzhp17nFW9ZLQ/uO39xa3NzuVyO0xQRmxsbq/UaOzOHcez77uYbb+Z/DEnAzTfddPNNN7XWSik8QEj8SwAd7l/gBSulXNrbM/Xaa6/jATJTUmZmZiklMwFJgG1JkmzbBiRFhG3+Z5AE2OayiOA5ZSaQTiHbAEJIkm1J/A8myTb3i4i77rrz6HD35ptubq3xAgCVF4FtYJqaIUpp01RCERERpZTMlMT9JAG2AUkAkJn8z2AbkMRzGsdJXXUmrdVahWxLkhQRmQlEBNBa438q2wCSwHZE8KIBKi8KCai15DiNBwez7S3g4OCg1jpN03w+r7Xyv1zX1bywy2wWm4v1ej21trmxwWWHh4ebm5vAufPnFvPF5uYm/0vY5kUDVF4UmcCFpz3jh9/1Qw7uPfuoN329t/qGL/7VX/vVe++7V+h1Xvt1HvWoR2VmRPA/m+3VajWbzSRlZikFIJOIez7s0y59/09qZ/vhP/fdt53e/vs//dOLe5c2Fht7+3vTNEXEi7/Yi8/62ZOe8qRpmt7+7d5+c2PTtiT+x8jMYRxrKZnZ9z0giRcNUHkR2AbOP/XWxYnjN7/iyxzcd25arV/39V4vzHK92trcAiKC/w1+8Vd/+aYbbpymaT2sDw4Ptza3Xuc1X0uw/rsnbLzqy6/+4Un7f/pXD/vAdzteuzqbSRqGoeu6YRg2tza3Nrce85jHDMOwmC8ASfzPYFvS7u7ur/zGr11z5pqTJ07cefddbWov81IvdcvNt6TNvwSovEgEbF93zeHZc3t33n3jy79Unc/Gg73VerWxsbm5uWksxP94kl7/dV736Gg56/soZbVa9X1HJqXUa8/s/cQv2q1/6IMGINTc3NzcPPnY8WMHBwd7e3vXnLlma2uL/2EkAZubm6/32q+b2TY3t2668UbbW1tbgCT+JYAO9y/wgpVSLu3tJfW6a68DLjztGcuLl048+OaNUyeOjo5sG29ubErifwPbknguNlK7tD888alxbHv2qIdNbVotV0jORNhezBfAMAyz2azWalsS/5PYlsTzc9dddx4d7t58082tNV4AoPKisIFs7eRDHwQYpmmaz+eSMK01/lexLQmwLanWCnBse/aKLw1M0yRpc3OTB8hMYGNjIzOnaeJ/KtuAJMB2RJRSeBEAlReJAKQ2NbCkUopt20BE8Dwk2eZ+kmzzIpDEZbZ5TpJsS7LN85BkmxeBJNvcLzMB2digUopt2zyAJMC2JEmS+N/ANi8aoPKiEEBEEDyLJF4oSTyAJF40toGI4HlIAiTxvIxCvAhsRwQvgO2I4F+SmZJs8z+SJNu8yIDKi8IGjpZHwzD0/SyzdbUbxmExX4zjmE6na9dltlrrOIy11ogYpymkdGbL2Wy2Wq/7rjOepqmUIkkoM425bJpaKdHVrpQCHB4dRoQkKSLkdCklMyNimqaIGKexlAISRERErId1KaW1FgqFWmulFNvr9Xo+m7dsERGK2Wy2HtbjOLbWuq7vah2GAVFKEer7/uDwUBARxsMw1FpLKeM4TtPU931E1FL7vgck8T+VJNu8aIDKi6y1dnh4uH+wX0rpu3730u7pU2d2L+3WWqZx2tzcXA9DiVgP61pr3/XL1bLWGhHDMGxubB4cHsxms652y+VyGAdJtXaL+Xz/4KCWUmtdrde1lI2Njc2Nzcw8f+FCiWjZaqm11vUwbG5uOG08jVPf9y2zTdM0TemU4vixYweHB61l33fr9brrulCM07SYz/cP9qdpqrXWWiWdOXXmaHm0t79nM5/Nuq5brlazWb9crkqJa85cs1qvLl3alWI+n0/TKMU0jZJst9Yyfeb0mb7v+T8E0OH+BV6wUsre3l6jXnftdbwIbEsCjIX4H8m2JP4fsC3prrvuPDq8dPNNN7XWeAGAyovEQGYihHihbHOZMc9kEP+lDOJ+tiXxALaNhfjXsC2J/z1sSzKXmRcCqPxLbCMBEcG/kfivJh5AEs9DiH8lSfyvIgmQZBvxQgCVF6q13NjYOH/h0t133w0GcdX/GgYkXdq9sL25yExeMKDyL3Ap5dTJY+fOnVuPo7jqf5+tzfnm5qZtXjBAh/sXeKFs11pLKc7kqv91pGytZfJCAZV/SSnl0qW9/YPDiABz1f8mSuf21ubO9nZm8oIBlRcqIo6WR+uxPfRhj5rGESelYC4zz0vCBhmLZzMIDBhJksBc9Z/MUEu97bZbQwdbW1uZyQsAVF4oSdM4zWZzia7vuOp/oa7vdy/tbm9v84IBlX+JpKk14Ozf/8Ndv/E7D/uA9/6t3/z1G2+86cYbbhyncZqm1tp8Ps+WUaLWeuH8hVtuueWee+4ppSBm/eze++6d9bNSyl1337VYLE6fPv2whz5smiZJCMxV/+Ek2ZYUEbaNbfOCAZV/iYFM4NLTn/ETn/nZb/t6r3Hp0t5tt/3hOE3r9fpoefTQBz90tV5N0zSO48u/3Mv/xV/+xUMf+tA/+7M/e8yjH3Pf2fsyc3Nzc7lc1lq3t7ef/OQnv/zLvfzDHvqwcRwjgqv+M5VSIgIQksQLBuhw/wIvWCnl0t5eulx33fWrabrr3NkHn7mmZU5tqqUCkmqtrbXWWq3Vtu177733+PHjm5ub6/W667rMrLXaljRNUylFElf957Mt6a677jw63L35pptba7wAQOVFIAmY1/rQ664fx1FS3/XGGGAcR0mllJZNCLjxxhszcxiGWmtmAuM4IjCSpmmyzf8MkrquA8ZxtM3/IZK6ruNFA1ReBLaBCxcu7O3v1VoBIZ5FYJ5JYIyFEBgEBvFMBvE/hKRxHI/vHFPExd2LpRTbGEmS0hkRtoUAScb8LyFpGqetzc2Tp07Z5l8CVF5kh4eHN914U62V/0Natnue9nRtbNx44021FP4PadnueNKTT546xYsAqLzoRK31j//kjx/y4Idce+21mSmJ/80ktZYRgSgRf/pnf7pcLi/tXbr5pptvufmWv/v7v3vVV3nVo6Mj2xsbG7PZzDb/S0hqLSOCFw1QeZHZBu69797FYnHttdcCkvhfTmCDLenChfP33Xf20qVLf/7nf/7Kr/TKv/8Hvw/89M/89Is99sVe6qVe6hVf4RVtRwT/S4h/BaDyIgtFa+3N3/TNh2EAIoL/K6SYWnvDN3ijs2fP7uzs3Hbbbev1+kM++ENOnjjZWrvhhhu2NreAiOD/KKDyohMtW9/1i8VimqZSCiCJ//0EmVm77tprr83MRz3qUdzv9V739fh/AKi86Ewozp47e3B4WEp0Xef0tddcW0rhfz/BuQvnDg4O0ym0ubEhabVe1VKvu/Y6QBL/dwGVF52wfWzn2NbWllA6S5TW2jiNgG0hRCm1q5X/hbY3t7c3txVqrWWmpJ3tna7rJPF/HVD515C0f7BvM4xrTKm1RBwtj7a3ttfDuqvdcrnc3trudnb4X0IREkAp5cLFC6UU28MwRInW2sZi4+SJk/zvpAheZEDlRWZ7vV4fP3bcNvczHD9+wvamt0I6duw49jhN/G8Q0nq1ckvgaLk8c+aabA1JPJNhnCb+FwppvVq5NV40QOVFdub0mbN33EEmz4cExlwmBBgA8T+YyNauueba2N669xnPuJgJAvMcJP4XEtna6TPX8KIBKi8KCZjP5zc//OH8H3Xzwx/O/1GS+JcAlReZbUn8m9hG4n8gWxJgG4n/S2xsRfAiACovAmcCT3rSk/7wj//wzJkz69V6Pp+XUra2tkopt91+24u/2Is/47ZnOL21tbW9vX3X3Xct5otpmparJeamG296+Zd/eduS+J9G4jJJ/B8jmRcVUHkRSAKuve7a13z111yulvPZvGUrpUjaWGxsbW1tbm5ef+31W1tbtlu2Bz/owZsbm8M4zGfzvf29YzvHAEn8z2CbyyTZBiTxr2SbyyTxr2EbCRuQxP8MQOVFIAk4fuz48WPHeR7XX3898OAHPZgXzLYk/rvZliSJ+0niX8m2JEncz7Yk/iW2JUkCkPifBKi8CGwDB4cH+/v7kqKE7aOjo+uuuW6xWPAikMT/AJIu7V26uHtxNptJ2trcOjg4mNp04/U3SuJFI+ng8ODS3p4zEVub28ePHeNFIOloeXRxd7erNW3b15w+U0rhP40kXjRA5UWWLVtrxh3dNE2ttaPlkaTM5PkxiP9BDCVivV631tartXFrOU0j5vDwsJRimxdBRCyXy9VqNev7aZrW69Vy2dvmXxIRy6PlMKyzNdst2+6l3Y3Fhm3+E9ju+77rOvMvAyovssVikZkA4N4bi43MvHjxoiQuMxaSZFtSSM3J/xhC6ey7/toz19purXHZNE2Hh4fGvIhMqeXUiZO2AdsHBwctG/8iEyVOHDsBZKaklm1vb8+Y/yARgQEkTW06tnOs6zpeBEDlRXZ4eHh4eHj8xPHDg8ONjY2tra2nnLs1qyUwzdmVOrWW2Uopobjp+PXzOuN/mMOjwzvuuKPruhPHT6zX69lsdvr0af6VWmuX9vZKKV2taddS5vM5L5oLFy9mts3NzWlqszLbWGzwH2cYBylCSmdXOy4T/zKg8iKwDZw9d/bnf+Hnjx07dnB48KCbHvSqr/vqT77n6UfrZUh7y4PMvObYKdBdF+/pSnftiTNOP+Kah2QmAiOJ/1a2I2L34u6Tn/zk/f397Z1t25sbm6/7Oq+bmZJ4EdiOiPMXzj/jttu2trYyE6ilPOqRj7LNCyVpf3//1mfc2lrb2NjIzIi4+aabdrZ3bPPvI2kYhtvuuO3E8RN7+3vjNF13zbU72zu8aAAd7l/gBSulXNrbS+p1116XmbYjArBt+cn33roaV6UUIUnpBEIyhOKWEzdszTb5H8n2crnc2Njg36S1JknSNE1d1/Eiy0zbETGOY9d1kvgPlZlARACAbUl33XXn0eHuzTfd3FrjBQAqLwobWA/rc+fPtzbNZvONxeLo6Oj6jTOX9nY3F1vTNE5Tm8361tpqva615JRtNd154S5B3/fL1fLMqTPz+Zz/GSRtbGzwb1VKAYCu6/jXiAgu6/ue/wQRwb8JUHmR9X1/8sQJ27XUUkpEzGfzxWJeoqzXa2OhWus0TcZOR4lT81PTNE7TNJ/Nu67jfxLbkrjqMqDyopCAUGxubHK/ruu438bGBvfr+54Hms34H0kSV90PqLzIbNsGJAHGQjwP21wmCTDGAJK46r+WbUm8CIDKi0ySJNvYSEJcJsk294sIwLZtQBLiWSTZ5n62JfE8JHGZbUmAbUm2JdmWZNs2V/1HACovChsYx3Gcxlk/K6VkpiTbtlu2rna2gZatTU1S13URwWWZaVuSpGEcSilShJTOEmUYB0kR4bQkScbjejQ4s3bV6Zat7/tpnEopwzB0XTeO42w2q7Vy1QtmmxcNUHmRHS2Xe/t7IW1ubh4dHbVswDRNkjY3Nper5Ww2y/QwrDOzlHrq5MmI2N3d3TvYX8zn0zRJaq1FxMZiYzab7e3tnTp1and3N52LxcY4jpmttXbi2ImDo4PWchiHna3taZqOlsvTp06dv3jhxLETh4cHXdcdLZcnjh8/cfyEbUlc9e8DVF4UEnD82LHjx47ZBo7tHAMkAZLSKSQJsC3JtiSglHLm9Jla6zRNpRRJgG3g2M4xYHtr2zaQmYAkSds720KAJC6zfWznmKSTJ07YlmQbkMRV/zLxQgGVf4kAG8hMSZIASdzPdigA25Ik2ZZkG5j1My7ruo77SeJ+tiUBpRTuJ8RltiXZlgTYBiQBkmxz1QtmW5K5zOYFAyr/EttSABHB8yOJyyRxmSRAEi8CSbxgkgBJACCJB5DEVS+YJCAUtpF4wYDKC5WZ8/n84u7+7qVL2Fz1v01E7O3vLmZ9ZvKCAZUXynbfd8ePbd1z9+3jNAqBuep/Ddne2Fhsbmza5gUDdLh/gRfKdq211pqtAQjMVf9rSNM0ZaYkXjCg8i8ppRweHu7t7SuCq/63sb2zvb2xsWgtJV4QoPJCSVqtV/uHq+tuuKVNk22V4kwkAWBbEmAbCRAABvFMBmxJkrjqv5AhIu69564I5vNFZvICAJUXKiKG9bBYbMznc67636mfzc9fuHjTjRu8YEDlXyKptQbs3nb7hcc/4ZbXf92/+Zu/3tzcfNAtD7rzrjvX6/WDH/Tgg4ODpzz1Kddcc01r7cYbbqy13nX3XYv5YhiGk6dO3nHHHX3fd133oFseZJur/qvYjgjbmWmbFwyo/EsMzgTO/tlf/Mi7vOeb/Nnv3nHHHecvnP/Zn/vZnZ2d/f39N3njN/nFX/rFhz/s4b/2G78262cHBwcPechDxnG03ff9MAyz2ezee++dpukzP/0zx3HMTK76L1FrjQhAkiReMKDyIlApwPVv9Pqv8zu//NhHPurhD37I0fKo7/tpnObz+WKxOHP6zMmTJ1/7tV47SozjKNT3PVBrvbR3aWd7ZxzHaZqAvu+56r+KbUAA5oUCdLh/gReslHJpb8/Ua6+9jn8329xPEmCb5ySJy2zzAJJsS7LN/STZBiTZ5nlIss0LJck2DyAJsC3JNveTZJt/PUm2eQBJtiXxn8m2pLvuuvPocPfmm25urfECAJUXgQGw3VqrtfJvJYnnJIkXQBLPSRIgiQeQxGWSeH4k8S+RxPOQBEjiASTxbyKJ5ySJ/zGAyovCBi7uXrz1Gc+YpikiIqLWOo7jiRMntje37j1732q1uvbaaw8ODtbr9TiOpZSu65bLZa21dt3GYrGxWNx62zMe+fBHHB0tj5ZHy+Wy67qTJ07ceffds77vug44PDy84frrz54/j320XC7m8/l8vlwuu64rpZw9d+76666rpewfHMxmM9tnTp+55957Zv1MoaPlkdPjOJZSuGwYx8c88lHL1fLc+fP7B/unT53OzLPnzkXEzTfedHh4uLe/d+rUqdba3t7e1ubmhd3djcXi1MlTq/Xq4OBgPp/fe9+921vbmXny5MmNxcY9994zn8/39/cXi8X+/v6JEydOHD9+x113XXP6TGYeHB5M0xQRpZT1em0sVGvd39/f3Ny0ferkydV6fd/Zs7NZv14Px3Z2Xuwxj+V/BqDyIpvP5iePH8+pGeyczeb7+3uLfpZt2pjN5l3Xl7Ixm3cRZWur67phGKtiY2MxjtOxzS2kY5tbbRxntbbalYW2drYNO9vb21vbwzisVqtTJ0/NZrPTJ09F6PDw0LCYL7a3tzMzpFrr1ubW/sH+5uam013XLRaL+Wy2WCy6rpum6cKFCydPntze2l6ulkJdV1frdS211nr82PHZbLZara45faZlq7Wkcz6fb21uzWezUAzjcO2Za0qJ2WzW990wDBFx3TXXlRJTa/PZrNZyzZkz4zjt7u4eP3ZssVhERK21q7XWCsyn+WynX63Xtrc2t4xLKYeHh6dPn+66zpmbG5uz2SwzQxHHwvbUploq/wMAOty/wAtWSrm0t5fU6669jqv+t7Et6a677jw63L35pptba7wAQOVFYQOG1Z/+ZbvvrLqKeSaBAYPAIDAIAIyFAARgWwjJ0xSbG4tXf2VqxeZfQ5JtAJBkm/tJss0DSDLGPBdJtrmfJNv8GwjMv5kk/mcAKi+KTGD1J39+3/t8OLbHCSeAQSARQUtqoTWukDBg0kgAGAU2Ti0WXi5Pff6nb7/r25NJKfxrSOJ+kngASTwnIcTzksQDSOLfRvwfAFReFAZo99xHa/2LPbp7+EPbxd2ys50Hh26NYSAKJfLCbmxvaWOh+azdd66cPolN1+W5C9pYqJZcrmJjoY3F8MSnjk940nT3PVz1PwNQ+dewQYqd7djeIiJ2trW50c6ez4uX6i03TrXWG66jVq9WRKFNsbUVJ0/45AmvVpg4eRJnOX1quu1OZ3LV/xhA5V8iQADl2mtUy/D4J63/7nFkAtgAEUi0pBYysZGQANJkEkLCRgJoqcWcqdXrrwMM4qr/RAYQ5oUAKv8S21E7YPHKL3/dD3xru/c+1YpBPJMBENggBObZBIBBYAAktym2tuav8gqASuGq/xwSQEi2ES8EUHmhbNdaD5croLXWv8xLiv8YhrTJ5Kr/NLZLKavVqtZimxcM0OH+BV6oiNjb2z97/kJmI9PmP4SACMRV/5lku+/ryePHt7a2eMEAHe5f4F9SIqbWxnHkqv91hFApJSJ4wYDKi2BqTdJsNuOq/4Vs8y8BKi+CUoqdtsVV/5sYJCIiM3mhgMq/JKTd3UuHy1VIXPW/TdqbG/Pjx45lJi8YUHmhIuLw6HBKHvawR2Zr2ESRsM1ltiXZAGBJNoiQuOq/m02EbrvtGXt7e8eOHWut8QIAlRdKUpta18+AKIX7SeKq/wUM1Nrt7e8eO3aMFwyo/EsktWkC7vnzv3zGT//cLR/5Ibvnz2/vbE/TVGvd3d09c+bMuXPnuq572tOfNp/NT5w4MZvNHvmIRwKZKYmr/jvYjghJYMA2LxhQ+ZeYZzo8f/4Xvuu73+ad3nr3/IVf/bVfedjDHm78B3/4B4959GOe+tSnPvjBD77+uuuf8MQn3H333a/0Sq/06Ec9ehgG25K46r+DbUmAAZDECwbocP8CL1gp5dLeXlKvu/a6EZ8/OLh2c2scR0ld103TdOHihdOnTgMRwVX/w9iWdNdddx4d7t58082tNV4AoPIi69B1W9tA3/dcVmu95sw1XPW/H1B5UdjAMAyttb7vx2kspUREZrbWQsFlLTNCobAdES1bZtZSbSsUimmaJJVSWjaMpFLKMA5910/TdHh0OJ/PZ/1sapOkEmUcx67rMtMg4bQkpGxNIUw6Z/3M9mq1SqekUkoobLdstVQgMxXqu56rXjCg8iIbxuH2O++Y9b2k9TDM+v7YzvHdSxdtSxrGMRTjNJ48cWK5XLbWzpw+c2nv0jRNETEMQ+26Nk2ttdOnTh8eHa5Wq67rju0cu+fee86cuebw8GC1Wm1ubk7TBEg6tnP8/IVz21vbhoOD/dlsNgxD13Wr1Wo2m43jGBG1dteeuWb/8MCZe/t7Xddtbmxe2ru0Hoa+7zOzRAHXWm+64aZaK1e9AIAO9y/wgpVSLu3tpct1112fmZkpCTDGlFJsc5ltwHZE2AZKKbZtIznTNiBJ0tSmEkUSkJkRkc5aama21hQhEYrWGlBKyUzbEWHbdiklMyXZth0RgG0gIjIzM0spmRkRhtamWmpE8P+MnVLcddedR4e7N990c2uNFwCovCgkICIiguckiRdMkiSAUniAPnruFxFAEEBERAT3q7UCQCmF51RK4QUopZRSgIjgshI9/y9JwYsGqLwobODw8PDSpUuSbAMRkU4AA0hKZ4nC/yIiW548eRK4cOGCJNuSjIUk8b+XyJbHjh3b3Ny0zb8EqLzI9vb2Tp46OZ/Nucy2JJ6TbduS+B/PdkSs1+v77rtvNpudOnVqNpvxAJkpif+FbEfEer2+6+67HrL5EF4EQOVFJmnWz578lCdjxml89KMe/bSnPS0z05mZ119//R//8R+/8Ru9sST+N5AE1FoBSV3X3XrrrRcuXDhzzZnlcnnm9JkTJ07wv5MkoNYqxIsGqLzIJI3j+AM/+APjOD74wQ++cOHC9//g97/4Y1/8vnP3XXvm2hd/8Rf/mq/7mld9lVfd2dmxLYn/DWwDxsCv/fqvXbx08eVf7uXvuP2OdL73e763bUn872SbFxlQeZHZrrW+4zu84zAMkh7x8EecPn366Oio7/qW7Zabb3n/933/xWLB87Atif/BhDLzxV7sxR720IfN5/MXf7EXv+uuuwBJ/P8AVF50orX22Mc8lvs9+lGP5gHe7m3fjssk8QCS+N/gVV/lVbnfNWeu4f8ToPIiE7J9+523l1IFU2ullBPHj99z7721llk/m6YJcfLEyXPnz3W1O378+Pnz56c2lShRwqbvulMnT/E/hiTbAKK1dtfddxlKKRHKzI3FxvbW9qW9S1ObShTAkK1Nber72akTJ0sptvkfSZJtXmRA5UVmu+u6a85cw2WZKTSfz6+/7jqMIuwE5rP5qZOnImLWz06fPtNasy0BlCiS+J+k6zrA6b7vr7vuutYSGwHUWrvabW1tCSHVUqY2AU4rVEoBJPE/Vdd1xrxogMqLLErcc889i8XCNvc7PDyMCAAMAg4PDiMCONg/iBCI+xl738b8zxCK5WoJ1FrvueeexWJhm/vZth0RXGZbEvc72D8w5n+qUKxWK8yLCKi8yK45c8199913eHQoCfNMAvMcBAZAAJgHksT/GMahuO6G60op995379HREQLzTALAPJPAPIsk/gczlnTzzTcDSPxLgMqL7MKFC4jFYuE0QogrhBD/C0kaxmF/f1+S0Hwxtw1Isg0IAZL430bSOI6XLl06deoUNv8SoPIiW6/X1157bUTwf0hm3nHHHfP5/PTp0xHB/yGZeeutt546dYoXAVB5kSlk+2d/7meHcQgF4mVe6mV+/hd+/pVf+ZVf4eVfobUWEfyvIqm1JkmS7Z/86Z88tnNsvV6/3uu93tn7zmZmlDi2c2x7e9s2/6tIaq1FBC8aoPIiE2qtPfWpT929tFtr3djYePCDHvyUpz7l+uuvf4WXfwVAEv/bSAIktdae+tSnjuO4sbFx+vTp7/zu73ylV3ylO++88+Ve9uXe5I3fxHZE8L+KJF5kgA73L/CClVIu7e2ly3XXXX/PPfdcc8016/XatqTVanXs2LF777t3e2t7a2uL/52mabr77rtn89npU6dXq9VsNpM0jMPR4dGxY8eWy2VEbGxs8L/QNE133HHHgx/84DvvvGN5dOnmm25urfECAJUXnWitLRYLY8xisQCuv+562+M0CUopmSlJUmsNyZkKAaGICP6nEmqtLRYL25Jms1nf905vbW0BmRkR/J8GVF5kQuk8f+H8arVSaBiGa85ce/bcfRGl1rJara695toLFy/OZ7PNzc1777t3sVhElHEYhnE4fuz48WPHbfM/iSTbAML2HXfdOQzr66697ujo6NLepcV8UWo5ODi45sw1O9s7tvnfQ5JtXmRA5UWWmV3tTp08xQM86OYH8QAbiw0ue/AtD+Z5SOJ/mFqr7cystd58401ctrmxeeb0Ga64hisk8b9KrTWdvGiAyotsPp/feeedXdfZ5l/PmP9hQrFar2b9bLFY3HHnHX3X2+b5MeZ/lVCs1qu+63nRAJUX2fHjx1tr0zQpxP2E+F/LeLFYXHPmGknr1bq1phD3E+J/LeONxcbp06cBJP4lQOVFdvbs2VLK9vY2l9m2zf9mktbD+uzZs6WUiNjY2LANGGP+V5M0DMM9995zw/U3YPMvASovssx25swZSa01oJTC/36b3rz99tvn8/mZM2ck2ZbE/wm2b731Vl40QOVFJ2Xm93zf96zX6xtuuGFzY/Pee+99t3d9N8A2/wtJmqZJEsL2d3/vdz/5yU9+l3d6l79/3N9fd+11r/Par5OZkvhfSNI0TZJ40QCVF50ppcz6WYnysi/zskJPfNITL126dOzYMUAS/wtJAmxHxMu+zMteuHBhHMdHPPwRj3vc44CIsC2J/4Uk8SIDdLh/gReslHJpby9drrvu+nvuuef06dO1Vv4Pmabp7rvvns/nJ06cqLXyf8g0TXfccceDH/zgO++8Y3l06eabbm6t8QIAlRedsL1cLtMJhEIhp2utXFZKmaYpIlbrVa3VdmaGYrFY8D+e7eVqmZklSmYO4zCfzWez2dHRkUKhaNkwpZS0JUDTNM5ms77r+d8PqLzIhIDlarlcLZ1GdLVD2pgvFAIBq9Vye2v74u7F+WwOrIehlrJYLPifShJCErC/v29cS0378PBwa3OqXXf+wvl+NiulTONo6Grt+369XktarpYnjp/su57/kSQhXkRA5UXWsk3TdPLESdtCLZskwFgIsL2xWAA333hzZgKSgNYa/yNJGoYhW7Zs0zSdOX3GtqTMPHPqdGbavvmmm20DkgDbwM72jm1Jmdla438ehYZhaFPjRQNUXmQnjh+/9957eRYBYBAYAIF5JoEBAyD+5xCYKySN03jq1KmNxcZdd98lxBUC86ISmP8pxBVC4zSeOH6CFw1QeVFIwGKx8eAHP5j/ox7y4Ifwf5Qk/iVA5UVhA1NrJULSMAx93/OcbEviAdLOYZDEczL/eQxgEJjnIGGrFhQ4QTw3g/jfzRFFkm1JQGZGBC8CoPIiWw3rP3/y37/io17yqU9+yjiNi8VinEah9Xr9kAc/+NTJU7YlAWQScd/nfsnyF36N7U23NE5jbGMwtjE22BjbJDbYNhhsG2wbDLYTjG0Mtg3GNsYGm8Q2xgYbY4Ntg6Vpudp51MNe8we+NWp1piT+b7GNYj7flHTp0qWf/tmffvVXffWHPexhmcm/BKi8yDbni0fc+OBZ1z34QQ86Wh4t5ouWrev6Yb3e2NwAJHGFBBx7h7feeMWXo+uwDWCMATDPZIwBDIABDBgw5pmMAQMGMADGIJXAGMDmmQwYwJjLLHma+hPHu9kCQ/B/jcBWFEnAxubG677O6546eQqQxL8E0OH+BV6wUsqlvb2kXnftdfyr2Ehc9d/BtiTud9dddx4d7t58082tNV4AoPKisIFxGp94562PvflhT3jiE1rmYrEoEbfcfEspheeSJnT+27776Hf/SBuLbGmcYNtgO8G2IW2DsU1iG+M0xjbGCbYNaYxtjNMQGg4Pr3n1V36xj/nQtlwSwb/MIEXwf5cU/WxD0v7+/h/+0R++5Eu85PXXX582/xKg8iLL9DS1lvmgWx50ae9S13WZCQC2eQBhUDl5srvxeubzzDTYNhgbKMW2wbbBYDvB2MZgbGNsYzC2MTbYGFuaLVcb112nElErEoj/74yEAGzfcMMNx48fB8S/DNDh/gVesFLKpb09U6+99jr+VTKJ4L+AQVz1Qtx1151Hh7s333Rza40XAKi8CGwDh8ujJ9z59Efe+ODz953d2Nicpmmapp2dndam5XIZEZIyE3Ti2LGNzc2LP/lzR3/+V20+m1qbnFN6gmG1Wjz0QQ9/33dzayD+LQyAwEgqBXMVAlsq/WxuW9Jtt99WSrnxhhtt8y8BKi8yRUxTE5Li4PBgmqb1eg0+PDrq+x44PDycz+fDMMz7fmNzc7zv7Pppt06L+ThNY+aYOcJqufRiTgROEP8W4pkEOJOrAAM24n4biw1JvGgAHe5f4AUrpVza2zP12muv41/FRuKq/ya2JXHZXXfdeXS4e/NNN7fWeAGAyovANjBO07m9i9ccP3VwsF9rzcy+77Nla202m02ttWmapmlra6vWirR8wpPWt92RtWS62SmmYdh88C07j3y4syFx1X8so4haO0DScrmUNJ/Pzb8MqLzIjtbLv7/1ya/x4jtnz57d29+PiI2NDWB3d/f666+/66675vP50dHRS7zYi29tbQEXf/DHd3/2F4fNjfU4rlobYP/CxVve651e+rM/edw7Uin8V5IkbP6vEhggau0yMyKe/JQnLxaLRzz8Ebb5lwA63L/AC1ZKubS3l9Trrr3Otu2I4PmxLYnnlDwH44LMs4n/IgZn8n+dJEncz7aku+668+hw9+abbm6t8QIAlReZpLFNBY6OjrpaZ7PZNE2lFCAzo4QQDzCdvzBduqQoYMAGMbTkAQwAGABsHsg8BwOY+9k8kHkgA+aZbG/ccH3dWDhT/F8l44iQCpCZ0zTVWiXxIgAqLwob2D86/POn/MOrPPqlbr/99tWwbtO0sbGxXC5LLaFYr9cv/mIvvrFY2JZNxNmv/Pqjn/1ltrfcmk1ig8HG2JC2wbbBOI2xjbFNgrGNsU1ig22D7QQbY4PtBNsGY5vENgZjFOv9/Vf+2i9+8Nu99XB4oFL4v0hgAM0XW5IODg9++Ed++HVf+3Uf/vCHZyb/EkCH+xd4wUopl/b20uW6664fpwnRlQpk5jCOs74fp8mZEWG71hoR3G+6tDftXlIE2Dw38wDmCnOFuZ95buZ+5lmMeU7mOczPnC6z3jb/p0VERAFaa7u7u1tbW7PZ7M677lwe7t58082tNV4AoPKikICuVu4XEfPZDOi7jhegHtupx3a46r9DKeXUqVNcJv5lQOVFlplI4jnYRgjxvGxsEAAGGQNCxgLzbyb+dawI/p+xDUQELwKg8iKTxHOSJAmwDUgCbNsGJCHxTAKEAEAIEACSbAOAJABjLMk2AAgZcz/b/OvINle9YEDlRWEDmdmy1VJtA5mZmemMiFAYOz21qatd3/e2pzZJEpKUmRGRmca11NaapHRiWmu1VuNQTNOEkATYrqVKsj1NU60VsG276zquehHY5kUDVF5kh0eHZ8+d3drcWq6WQGZmZkS0ljs7OwLEpb29Y9s7m5ubNruXLq7X61JKRJmmcT5f2F6tVxvzxTAOUmS2iFitVttb2+M02t5YbFzau2S767rMNADbm1vA/sFBKTFNU8u85aabF/MFV/3HASovCgnY3tre3tq2Lck2IEmSbUAScObUGUlctrW5ads2IMm2JKC1VkoBAEm2eYBTp04Jcdk0TRFRSsnM06dOS7INSOKq/1BA5V9DkiRAEveTxP0k8QCSJHGZJC6rtfIAkngAIe7XdR0ARAQASOKq/wRAcNVVAFReFAawbZur/leRxIsAqLwoBBASEg9gWxL/5WxL4kVgWxL/CWzzP55tXjRA5UVhA6v1erVezWfz1pqklm1zY3O5XK7Wq1IKYLuWGiWEWjYbCWCapsV8sXtpdz6fd13XWpMk1LKFIp211Nba1CbbW5tb62EdEZkZEUKllJYNaK1lptDm5ubupV2kbC0iFovFOI62Z7PZ0dFRRMxn83GaWpu2t7YPDg8k2Y6IzOz73jZQogzjsJgvVut1SFObaq211CixWq1KqYJhHPquH8YhFIAkhYZhrLVsb23zfwhQ+ZeIZ0rn/sH+4dGhpFCsh3WttWU7ODxoLbuu2rQ22WxsLGb97NLeXikxTS2znTh+YhjH5WrZ932bWqllGMa+66bWgK3NzZZtHMaIGLrhaLkcx9HOWqrxfLY4Wh5K0Vrral0PQyllGIZxGmvtQrINtGyllOVqtZjPx2k8PDrCns/mwzAcLY+kKBHG09QiVGudzxYHh/u5kxd3d+fz2TS1zLa5sRkRu5d257N53/d7+/tnTp0ehmE9DBGaxgnJTtBivqi18r+BAYR5IQAd7l/gBSulXLp0ieivueZa/gcxiKv+JbYl3X33XQf7F2+5+ebWGi8AUHmhbNdaD5drIDNBPIAEYPMsEjaAhM2zCQwYxLOZZxICgxDYXGYQz8EgQMJOEJdJmPuZZxMCmweSsAEQGAQ2CAAkAJv7GQkDBoFBXCbxP59tSav1qpRimxcM0OH+BV6oiLi0t3fu/MXM5Kr/bWz3XT1x4vj21hYvGKDD/Qv8S0rEOE3jOCL+vQziqv8yQki1lIjgBQMqL4KptYiYz+f8u0myzVX/tWzzQgGVF0FESLIN2JYE2AYiArBtG7AdEVwmSBuICMA2MAxDrRWQBAiQWmZIgCTANvezDUQEIJhaM4QUknkmgQGwnZmlFJ6HbUmAwDyTbdu1FINtLrPN/z9A5YUStMyLly4dLY82Fxur9RpYzOeHR0cbi8Xe/n6pNaQIrYdRorUEThw7Nk7TMAxRYmOxcXBwECXms/l6vT44PNza3MzMYRyPbW+vh2Gapq3NzdV63ff9arWKCEm2j+3sAKvV6mi5tC0JmM/ny9WqljK1aXtza29/v+s6SethvZgvwF3XHxwehCIiMrOUAPV9t1yt2tQUgd0yj+3sXLh48eSJ49M0HS2Xtmd9D6q1Xnv6dNr8PwPocP8C/5LVeu1MSS2zqxVIu0S01sZpiohaqzMNIbXMWkratmspwDRNhlpKy6y1ZmtImSlpmqaI6Pt+HMeIaK1Jighn9n0Pmtpke5qmllkiZrNZaw1wZq11nKaIsG1nRMGutQ7DEKXY5n4hAYfLo/lsbluiljqOY9/3wP7BQSllPp+31iTNZzP+/wF0uH+Bf0lEABiEbUCSbUkCAzYS97MtCbANSAJsS7ItMEjCRgJsS7ItCcBGykxAkgBJYLDNZZJsS7ItCcBGsi0JG4n72RYowjYA2JZkG4gI7LQlAZnJ/z+ADvcv8P+DbUk8D9uS+P8NqPy/IYnnRxL/7wHBVVcBEFx1FQDBVVcBEFx1FQDBVVcBEFx1FQDBVVcBEFx1FQDBVVcBEFx1FQDBVVcBEFx1FQDBVVcBUKdp4qr/9wDZyVX/7wH6zM/6NK76fw9QnVeu+n8PqKdPn+aq//eAOk0TV/2/BwRXXQVA5V9Dkm1J3M+2JAAbCbAdkCAAzFX/CwBlY2uDF43taZq6rlut19jjOEoCMnMYR0mZaRtp5azSZCOEuOp/PKBsbG3wIpA0TdM7v+M7vNqrvMpdd99dSrnu2mvX63Uppeu6m2+8cT0Mm4vFVMtDXd5s+5TgTTZPFPT0aT2TzFX/owGVF800TcePH7/l5pvvvueej/7wD7u0t39weHD9ddf91m//ziu94issl8tS6ks85jG/81d/+bff/J3vvHPmo+7de+X5zlzxB8t9SWCu+h8MKBtbG7wISilHR0cPuuWWxzz60U992tP/8I//6KYbb3zKU596/XXX72zvzOazpzz1aVObHvfEJ97x5KfcSz66W/zt+mjlvHVcJ+aq/9kAnb7uNC8a2y0zpKk1SYv5/ODgICKuv/7666699o//5E8Wi0UpxTDimeLIWdFMMlf9Twfo9HWneZFJsi0JyMxSiu1pmlpr8/nctm2BICEk2+aq/wWAyr9GZgK2bUdEZgK11lJKaw2QlLYk7CkzImwDkmwDkmxLsg1Isi3JNmA7ImwDkmwDgCQAsG0j8SySbAOSbAOAJNtcJsm27Yjgfra5TBKX2ZbE/1dA5UUmqe/71pqkUso4jpkpKTP7vrdtOzNLKZmpiHnfr1ariKi1TtMUERExjmMpJTNrreDWspRiu9aamV1XV6t1rVUwtVZKASRlJmC7lFJrBaZpAiS11rqusz1NU60VADKz77qWKZhaq7X2fb9cLgFJQK3VNpCZgO2+78dx5P8roPKisd113Ys/9rEKdbWLiPWwXi5XIc3ms42NjWmcEBcv7m5tbR4eHE7TNJvNtra2jIUkRQRwtDzCXLh48dixY5sbi7Pnzh8/dmw9rDc3N4f1sFgsbr/jjuuvu+7ue+659pprjI8Oj7q+LyVaa/v7B9ecOTO1aWOx2D847PtuHMe+61trZ8+dO3XqJAjbdq2167qj5XJzY+P8hQv33HvvIx72sFLK3ffc03X11MlT6/V6Y2NjmiZEtrzr7rsf9chH3PqMZzzpyU/pus42/88AOn3daf41ulqRWmtd120sFodHR33fr9frUkpmay0lRURrbZqmkydOABd3d685c+bo6GgYx4jI1qbWSglM7brWmsC2pJaZmRExjuNsNmutAZJKKa21zJSUmV2tLTMiMjMiMrO1tpjPW2uGzCyl2AYkZeY0TbPZLDNbayGVWqdpsj1NE1Brba3VWltrrTVJEcH/M4BOX3eaF4HtWso111xTa40SJco0TRFRSjk8PKxdXa/Wx44dm6ap1rpcLvu+szl//vy1115ba93d3Z2mqe/7Wuv+wcHO9vYwDLVWYLlcLlera86cGcex77vMXCwW6/Xadinl4OCw67rVenXy+InlahUR8/ns8PBoGIaNjcXBweHGxsbB4eHJEycODw8jAtHVCozTVEu1HaFauwsXL24sFpJqV3cv7m5sbLzUS77k4eFhreXe+86eOHF8c2Pz7//hHy5cvJiZR0dHkvj/BAhedJJxKaVNk+2u66ZpioiNjQ3baU9tmvX9MAxHy2VrrZTS973taRpns1mppWWrtfZd13V1Pp8DkjY2NkKSBBwtl7V24zhJEVGWy9U4jl3XSTK01qY2gWqttdZxnGqts9nMNnhra2uxmIc0Ta217GrNzK7rZrPZMAx2zuez2WxWS1ksFpk5DGtJNttbW2fPngNvbm621rD5/wfQ6etO86IRHD9xfG9vX1JEjONYSzGM41hrLaVM0ySp77radeMwLFer+Wy2Wq+7rgO6rgOOjo6AzOz7vtY6DEMpRTC1Vmu1Lam1ZnuxWIzjmJmZWUqxvZjP18Owv79fahUAklpmiWitRYSkzJRkGwkbsC2plNJa47KIMKxXK0mGkBSRrdVaa61ARPD/DFB5kaV98viJB93yoFLKfD67dOnScrm64Ybr77jjzpMnTqyH4djOzuHhYdd1s/ksW959zz2Lxfzw8Ojmm2862D/o+q6r3R133nl4dCjpoQ9+SMu89957r7vu2rvvvufaa6/pard76dLGYiHp/IULx44dA0uxsVgg7rvvvuPHjw/D+LjHPz4zuV8FcK2VF8p2KYUH6Le3uZ9tSbZt8/8SoNPXneZfo5QC2JaUmZJKRJSyXq8Xi8V6vbYNSJIEZKYk2xGRmUBrTVKt1TYgqbVWa83W0o4ISZkZEZkZEbO+H4Zhag2ICCAzueo/FKDT153mRZNgu4B5Jkm2AdsRkZmSJNnmfpJsS7ItCZBk2zb3k2QbkGQbkGRbkm3bkiQBtiVx1X80oPKiafh41JninjZ0yDyTJEASEBFcJokHkARI4jLbgCQeQBKXSeIySYAkSdxPElf9JwCCF4Ggma0op0qdbHHV/zVA5UUjSLvZAnPV/zVA5UUnJHHV/0VAcNVVAARXXQVAcNVVAFT+9SQBtgFJtiUBgG1JgG0uk8RltiUBtgFJgG1JPIBtQJJtnpMk25IAsA0gCbDNZZIA25IA25IA24Ak29xPEgDYlrD5fwuo/KvYoGmaWmZESLItKTNtR0RETNMkSZLtiMjMzAQiIjMlSQIyU1JEtEzstEMCIgJorUniMhsJwDZgG5AkCchMSaWUzARsA1LYDYiIqTVAoIicJkVgSwJsAwASdimF/6+Ayr+K1Fq7/vTpru/3Dw7aNJVSWub21lbXdUfL5dHR0cbGhjNX63VX6ziOUcpsNgvp4PBwe2trnKZhGCQtFotpmlarVdd1JaKfzdarlWG9XgPz2axlOtNQSrHdWuu6Dui7zvY4Ta21iJjPZsM4Hh4ebm5uAn3Xtcxhva5dV0qsVuvNjQ3DOI7TNG1tba1WK2CapoiopUQEME0T0nK5tM3/S0DlXyOkcRwf/ehHvcSLv/je/v5ivjh58sRdd99zeHhw7TXX3H3PPZIedMuD/uFxj7vxhht2L10K6TGPefTFixdr1/VdN01tvV5L+vt/+IeHP+xhtdYnPulJj3zkI2xvLBat5TAMt99xx98/7nFv+HqvV2tF3HvvfbNZv7Oz8/gnPPEVX/7l9vb2t7a2MnO1Wq3Wq9tuv/0lX/wlbP/O7/3ejTfc8LCHPuRJT37KIx7x8OXRMkrUUu+4807gMY9+1G2333F4eHjNNWfuuuuuRz7iEX/113/z2Mc+Jlt2XW0tJRn/+E/+1Pnz57uus83/M4BOX3eaf4lgsG/q+p2of7c63O66KCUz5/P5xsbGxd3daZpqrZlZSpE0TdOs79fDUEu5/vrr777nntmsP37s+F133x0Rtdblctl1nWA9DC1zGsdSiqRSiqSjo6Naa61V0jiOihAMw9B1nW1JpZTWGpdtbGzYXsznR8tliRinqeu6EtEyMzMihmHo+75NExJgu5bSWuv6fpombENERMR6vW6Z4v8jQKevO82/RDDYN3X9jurfD0czSFtSZmZmKUWSbUm2AUmZGRG2x2nqarXdWqu1ArYjAhiG4REPf/hDHvIQ7GPHjp09d25/f19iZ2dnHMaW2ff9rO93d3ePHTsmqbU2TZNC89m81joMw2233/74Jz6x77rMjAjbkmzblsRlkmxL4n62JWWmJECSbUCSJP5fAir/KgIcKshArRWwDUgCJAkMtRSDpPlsho1Ua7UNSLIdEcCpkyevPXPm3rP3nT9/fj6bHR0dzWazYzvH1uv11KZaaqllmqb1en3y5MmDg4OdnZ1a64WLFxfzed/3i8Vi1veSIsK2JEASz0kSDyAJqKWYZ5LE/2+ATl93mn+JYLBv6vqdqH+/PpwrzH+MzFwul5Ik2S6l2G6tSZJk23at1XZrTQqw7VJKZtruuq7ve676dwMq/60iYnt7m/vZFiDxALYFSNzPtiTAtm2u+ncDKv/dMpMHMGDznAzYPIBtrvqPAwRXXQVAcNVVAARXXQVA5UVnbHPV/0VA8KIxSArJIK76vwaovGgCBucyFchc9X8NUHkRGKp0vk3nmXrJXPV/DVB5kQkk8QC2BUi2uZ8kHsC2JMA2lwmQANuSbHM/SQBgWxL3sw0AkrifbUmAbS6TxP1sSwJscz9JAGBbEmCbyyTxALYl2eZ+krjMNiAJsM39JNmWZFsSYBsAJAG2AUm2uZ8kwLYkwDaXCZB4Hra5TBJgm/tJAmzzrwdU/jWGYeABaq0ts7XWdZ0kwPY4joAk25JKKeM4Sqq1ctnUWmZGhKRpmmqtEQHYHscRkBQRmWk7ImzXWgFgHEfbEWE7IlprEVFKAWxP02QbQKqltNYys9YqCcjMzLQNlFJaa0hdrVw2DIMkSVwmaWqtlFIiANvjOAKSJEXENE22a62SgJaZrUWE7YhorUmqtQLAMAySIgKYWqulRASXjeOY6VqL7cystUoCptYyMyJs244I25JKKYDtcRyBiCilALbHcQQiopTCvxKg09ed5kVgu9b60Ic8JCKmaQJmff+M22+f9bMzZ04/4xnPWK5Wtk+ePHndtde21qZpktRau/e++2668caj5fKOO+6IiFLKiePHT5w4sbe3t1wub7jhhnvuvff8+fMRsbGxccvNN7fWxmk6f/788WPHdnZ29vf3FXHnnXe21jLz5ptv7rsunTbnzp276cYbL126dNfdd0fEYj6//vrrJc1ms9baXXffvVgsThw/ftvtty+Xy8y87tprjx0/hiklbr/9jmuuucb2rc94hiTgkY94xDRN4zh2XbdarfYPDm64/vr77rvv3PnzEbG5sXHDDTeUUoZhaK2dO3/+xInj89n81mc8Y5om4Nprrz1x/DhwdHR0/sKFG66//vDw8M677oqIiHjIgx9cSpw7f2Gapuuvu+6ee++9cOFCKaXWesvNNy8Wizvvuqvvu2M7x2677bbVem37mjNnTpw4sXvp0nw+39rcPHf+3ObG5jAMd951F9B13S0332z76Ojo7nvukTSbzW6+6aZpmvb298+fP19Ksc2LDKj8a6xWq8xEam06ODzMzHEa1+u1AZB0dHS0u7tbSslMgzMzc7laTdMkCbAdEcvlcpymaZpWq1VmkwS01vYPDgBsZ+7v77fWMhOwDUgaxxH74PBwNptla8vVampNEtBau7S3t7GxGKcpW8vWhmFYr9fYQEQcLZcRUbsupJa5Wq9DksRlBwcHhmkcD4+OJLU2rVarlikJaJmr1Qrp6PAQyZnr9SAF9xuH4eDgwHZmZuZqtZpakwQA6/V6tVpN0zRN02q1cqYk2yEtl8vMbK0NA+v12jzTMI7L5TKkaRzPnz+PWa/XwzgAkmyvVqsoMU0Tl2XmarUCbAO2+dcAdPq607xobK9WK6SQSilAKcX2NE1d10kCbLfWbEsyCEop0zRJqrVyWWstMyNC0jRNpdYSAdiepgmQFBG2M1MR2LVWAJimyXZE2I6I1lpElFIA262lnQCo1OLMltnVKgnIzMy0DdRap9awu67jsnEcJSEJAElTayWilALYnqYJiAggIlprtmutkoDWWmZKAiKitSap1goA4zhKighgaq2WEhGA7dZa2rUU25lZa5UEtNYyUxKXSbItqZTCZeM4AhFRSgFsT9MEREQphX8lQKevO82LwHat9eSJE5m5XK2Ojo4k2QYk2eZ+kngA25IA21wmictsS7LN/SQBgG1J3M82AEgCbEuyLQmwzWWSuJ9tSYBt7icJAGxLAmxzmSQewLYk29xPEvezLQmwzWWSuJ9tSYBtAJAE2AYk2eZ+kgDbkgDb3E8Sz8M2l0niMttcJonLbPOvBJSNrQ1eNLXWG2+8cXt7KyIu7u5GBFf9XwHo9HWnedHYbq0BERERXPV/CFB5kUnqug4AbHPV/yFA5V8jM7nqfw9JvGiAyr9GLQWJq/5XsFsmLxqg8q9hwOaq/3OAyr9GZvJvYv5z2JL41zD/6cS/hfkPJv4VgMq/hm3AtiRJmWk7IgBJgG0kbEmAbUCS+M8h2QYyUxIgCZBkW5JtQBJgG5DEfz7btiUBmRkRgCQusw1IAmwDksR/J6Dyr1FKAfq+b61N07S1uRmlLI+OopTWmu2IAGqtwzAYaq1AZtrmP4Htvu9tz+fzcRhKKav1upQyTVNIhlorMIxjiSilAK01/vN1XVdKyUyg7/vlcllKmaYJkBSSIoZhkFRrBV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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"4b6d6ff0-086b-4726-ace0-8a73c36e9371","timestamp":"2026-08-24T2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Y27nnSk/f/4E918vgNb/A6W7NZtlaXq/H4iRPZGgACjHuEwDyTEOJF4Gyzre2//fVfP/qYT3/Quo34r37uV1/+m7+mM3Xn2PGtzS3+4ySMP/Yzjzl2gpd9yXLbnQ/9y3/40x/98bf8sA+t2RowXtxlHMedrYPl0ZRt1vWYo2HZlQ486/oTWzsGbF4owRCRR0tqxzVnMmLrKU970uMftxrHsA10fd9tbNau6/t+1vdd1+1sb2c6s7XMqbXmNHaEIxzhCEc4whGOcIQjHNEy57Dxjm996+238aM/M/uDP/uJ3XtPvNzLbJai/YODrc3Ni3uX9g8OK/RdP5vPVqvVbbff9nIv+3I8l/UABvEcDALA9DME8Ls/+mOP/87vvXN11L/B63z6p336crmsT3zC46+99tpf+/Vfu/eee46fOHH3PXefPnVqZ+fYE5/0xJtvvHE9DJIAReT+Qe4fIIF5JvFMBuHUYlGOH1P6sW/4+jsv91LjenjE9Tfe9oxbz54/p7N33T5Nres6ICLAmf6rv/nrqbXl0VICCRAASAZsLpNkwIBBCBkwKLPNui4i9g+PHvuYx9x4ww1a7u3OZjNJwDRNdbHx1Cc+4dVe73UODg66rjMAgsk2FhIkDmRIu0jGQEJFE+4kEIBdSrlw4cLHfMRHfuVXfk0tpTzuCY+/ePFiKXU+my1Xy2uvubZEbG5uRoRtQcI1pXZo7Wx4oXLkNlMsFHvZOkkwV5xr48nSnW/jZANAKWWxWBwdHTENtZZy7vz5v/27v7vl5pvn8/nTnv70N32jGyJCmZKAQKPzTOke3S/unIYRX1u6QPe28cbaH2VKpL0V5Y5pvbZ3s01kgEGSpForUl2uVq/8Cq/4Si//ChHRWnv9137dJz31yXv7+9M0RYR5pr84OPhzEAgaDiRIDAILJa5oxB0Sz1RqOdo/Wi6XQBVM04Q9ZNp25oljx9/6zd/i7NmztVZjIQRGgLCRACHEczAIzLNFxMHBwcu+9Et7mrS/e3E+n6UxLkgREaHakQ0JO1vjCpsrJIEknod5bgLD4eFhnUydLdq992VrT1oedgaRdkiZ2ff9jTfc+JSnPuUhD37IYj7nXyKem0HQUJUExPHjRTy4HW/TFKUIhnFcr1ebG5t91z3kQQ+e9R3/NjYSUAHAfW3pRa2azw3AxmKBjtme2jSbzWxP08S/nqRSClABYLVarVdrhZxGCNk2FuJZxL/MKBQR2RLIzPl8vrGxAVQA2LC6w2XM+nLmNAAG8W/Ssq1X643tDR4IKhhgvf7Tv/3LG89c++BTp85ePL+/t7ezc+z0qVO2eZHZjog77rjjx3/ix2+55ZbZbJYtH/2oRz/60Y+2rd3d3WPHjvnw6Oi+++p8PrvuWiTAtiT+9WyvVqvWGgKY9bOu6y5dulQBwJsbGw95MJA2mVyWmZL4V5K0WCy4n20AqFyxHjQMSGxuKIJ/H9sAIEkSAFQwwMHBX/zVXzz4hptOPfKR9+1eWK/W4zRK6vv+mtNnuq7jX0OSbUl33X3X05/+9Fd71VfLzMoVtd5w+kw3mxHa3Nic9zMDOCJKKfzrSQJ2dnYe+tCHApK0u7t77NixzAyDSCkkHsC2bQCQxIvMdkQAwKVLlyoACMhmpFp4TpIkcT/bvGgk8SxQwYD39u962lNPnji5uOWWveVBjYLIdCkxTa3WMqwHxPbWdkTwIlutVvfcc8+DH/xg2xUAwj6/t1tqt5COjo6EIiJCtdRhHPuu2z88KBGbm1vBv0IpBQEA2t3dPXbsWA5jrAdC3lhI4gWzzYtMEgBcunSpckXfue8A287kMkm2JQG2+deQZFsSV0AFAzo4XN53ts5n/fXXNQgJyMyIyExAUkTwryFptVoN47CzvWNcAUDj9JRnPO3aU2euve7aO++6s0SJCGCxmE9TOzg4mM9n1117HS8y25LuuOOO+87e96qv8qq2tbu7e+zYMR8tp4sX1ff19Ckk24Ak/n1s246IS5cuVQDwxqJuLIC0yeQy2/ybSLJtG4gIAKgAoPWag8MWUU8cR+LfwdjpiOCy1lopBahgQIfLpz7piZubm9ft7Nx19t5xGGxqLX3fD8N47NjO9ta2bUn8S4R+83d+88lPfvJjH/PY1Wp18803P+bRj7FdASBLnDh2/Nix44gTx49jDBEBZLa+6wFJvGhe7mVe7uEPe3go1uv1wx72MECSdnd3jx07Zlt22oqQxPPITEn8S2zbLqVwv8yMiEuXLlUAcGsyIcxzsw1I4kUQETyniACACgbicPm3f/XnZ06dvv7FX2J379KwXkeUCPVdv7W1xYtsb3/vGc94xv7+/sHBwSMe/oij5dF8Pn/YQx9mO7jCXg7rYRwRoNbaNE3jOLXW+Nfoands5xhge/fS7j333HP61GkAod3d3WPHjuU4RhrIvguJB7DNi0wSz8+lS5cqV9SKBGDblgRkJiCJF1lmAplZa+WBoIKBODy6++lP29za3nnIg8dxbNkws9lMEv8akoCIuHTp0oULFxDHjh07eeKkcXDFMDz9jtvOnTtL+s577n76059+5913DePAv55tYHd397d/97f/5m//5qlPfSrgtHZ3d48dO5bLFXv7dDVOHEfi38e2JB7g0qVLlSsW81jMDWnTmiXxbyFJklBmSgJsRwRQuWK5Wl640C8W5cRxSuHfapqm1lopJSJsAxEBABUM6Ojor//urx98/Y3Xb28/4567FvP51tYW5sLFC8M41lLtPHXq1Nbmlm2eH9sR8dd/89dPeMITIuLg8GDWz9br9Uu91Eu90iu+UmZqd/fisWPHOThc3ne2zPr+xhvGaRKUUiSN4whERMvsapXECzW1ab1aZ+Y4jpkZEfP5fGNj49KlSxUErLqSZ06lNBwehAKwDUgCjIXGYbDNC6VQRJRS+r4HbJdSAKACwDRNB0dHEYHNv5nAmCsMcubW9lbXdcbVNvCExz9ue3uz1pqZoZCUmYh/D0nTNN12262v+IqvLKiSgMVi/shHPFwK1DmHqU1dNwfjRPwbGaQ/+/O/mKamiAoAUpy7cOG3//JPu1q3FpsX9i4tZrNXfcmXPba93VrjWSRs7ieJf4ltSZgKAC3bztb2G77Ka6yHYWM+70o9XC23NjYiQhIgyTb3k2Q7M3mhBEJgoPJsVnpzNm9TW45TF+Vg/8BQSthuLUsppZTWGjC1qat1c2OTFxFUABAC0nabWktJ4zQhlYhSYhiGUgv2NI4G8DiMoeBFBxUAjEHLo6PM3NraQgIODw4kZeaxY8fuuvvuM6dPnzx5srUmQLKdmbyIoAJAibJarb73+7//rrvv3tnZOXH8+M033/zzv/ALL/VSL3Xu3LnXf93X/YEf/uG3eNM3feu3fvtxXJVSbEviRQeV+7XWXvzFX/zlX/7lQ9rZ3i61vt3bvM3Ozs56vX7QLbe80eu93oNuuQUyJEASLwJzP6gA0Fo7dmznzd70zSGhgk176Zd+eUiIZHrYwx9t2npcq6sGDAAGMIABDGAAMiWVEsYAUAEgIo6OlodHu1XK5RKQsRMbQ+b49Gd0D3mQACcGgYSEhCQJCQkJCYjFfLI3N+ahAIDKA5QSBakUjJ3KMEbGCCRI22CDxLMZBIAkJCAUOHkWqABgZ9/3l/b2mM2WkV3X1TLb3toyIAQ8/MGME+I5GDBgwI6I3UuXsrXZbHZpf39jvogIroAKADYh3XTDDSjIhgIgCgACwPQdL4yBa0922IRObh8jNI6jbQCoADDr+9vuvfujv+ILACFjABjHcRjHWopC09QABBCK1pokY6CrVYquVMQVgrS/7hM+s+8620AFgIg4XB79xp/+oRACI2kYx0c+6CEPvuGm+y6ctz2fzbraTdM0jGPLdsOZa/cODk7sHDtYHt1x3z2Zftodz+hqZxthg32wPCoRNkDlfpm5HgYhBCCYWjt78fzmYuP8pYvDOF5z8nTEupa6Wq9Xw/ou7svMg6NDxN7Bwf7hwThNLZMrjHFmFi6DCgCZeeO113/GB3w4gMAAksZpGsahlippbBMgACKitSbJgN3VLiJKCQyAwBhuuu66O55xpwRQbQMXL12S/OFv8y7YBkBgEJIENggBYJ5J3M8Y2zwHoYtnz1+4eKGUAmh3d/fYsWNHR0f3nT07tUmI/zghbWxsXHvttZf2LlUAkLj2zKkogbnCgI34d5CdERXAVAAY1qv5vGvTiMAAkkoptiWeLxtA4jLxfBhxtDyczeZABYCIMKzW63EcZ7PZOI4bG4u+9JIyEwQGJAG2JUmSlJm2bfP8KaIAQAUASdM4TeN4/NixYRii748Oj0qUw8PD7e3tcRwjAhiGodYqqbU2DENrbWNjo9ZaSuEFMgBUACilXLh48dd+/TdOnzo1tRYR+/v7N1x//bnz5y9dunTttdeG1Pe9pL39/VnfHx0dGcZxnPX9TTfd9Mqv9ErDMEjiBQHt7u4eO3Zsb2931teIyEzAtiIyU3Bpb+/E8eOttYiICNtAZkaEJNuAJF6A5Wq9s3Pi0qVLFQAitFyv7jp39rEPeyTZACKwM/Paa2/ADTyOIyAJqLXaBiTxooAKALa72q2G9TPuuj0UpZSDo6NZ321vbF3c2zXMuv6G02fSBgDb/KtABYASZf9o//Y777j73ntay+Pb249/+lNPHTv+6Ic87C8e//dbi42NxeKma69r4yiJfwVzBWh3d/fYsWOX9i5udJX9w1JLZgIlwnZLR0hArbmxwEb8qxwerY8dO3Hp0qUKALKO1oMWc9uSwDaABGAD6GjJv47slAoAVADYOXac/1RQW2vAd3/vd3/zt37z8WPHW2vcT2AAAsxzExgM4pkMAvNMpZRLly69z3u/zwe+/we21qpt4K//+q//5A/+ZOfkzjRNBsFkN1yRxGALDAJDgNCEKxRpsAWCQA33khBQStm/uP/whz3sA9//AxEVABaLRb/Rb2xttqkJQnq1+XaV7pqG7Si9YnBWabKrdKFNZ0pXpXNtvL70K6chYTtiP9sfL/cNQCllnMaNjU0AqACQzinbhjWYpTPh71cHM8WlnArajrK2O6nZVVo5z43DXFrZd7PupckOkaZKmWkAJGXLzASACgChKKU+ZL5x73q1amOVzjntLJLhYk4hbAQ2gUxmEgKwATACm64UgaGWWmuNEABUAFgul+Ph+o/uuXOYJvFMAgMgMAgMAgMgMAAC80wC80yllNXB6ujoCABqRADv9q7vtpjPZ/O5bR7IIAAMAsA8B/FsBkBggAgtV6tXe5VXBUKh3d3dY8eO8Z/s0qVLFQDW63VmIjCllHRKwkytlRKA013XZeY4jaGQZBsopZRSMrNlw9hWCGMIyXYppes6oBoD62E4PDqY9bPlanXy+Im9/b3Njc3a1Ut7l/qum81mwzCcmJ0AVqtV2rWUWmtrKenYzs4wDvsHByVCkqS0s7XFYoEZp7HrOmPt7u4eO3aMf8nh0aHTpZTVejWbzUPaP9jvuu7YzjFJ/EsuXbpUjQHbvGCSSpSoMbWplFIiJM1nc4Uys5RimxdMknEtUQBJvFDz+Rzo6blf3/fcTxIvVC217u1dioiWTYh/mQ1CgDEgxAtlXEq5cOF8nfVx9r47bRD/WYykOuv7sr2NZBshxH+CWmu9eOng+MlTrbUuwvY0NQkpwJKAzOTfwSYidvcuae/gYKsURblweLCYzxeLBTBNLULTNNmezWb8u+3v79e6ufknX/RlZ4fV/A1e589/53dvvPmmWutivrj3vnv7vi9R0vkyL/0yL/1SLz2OoyT+9Wqtman9/f1s7fDw8PjJk+vVynZmLhaLs+fOnTp5smVzemNjo+97Y4wk2zw/kowxkmxL4n6XLl3SxYsXjx8/zn8a25IuXbpUu6678647dy9dKqWEdHh01Pf91tZWtmZ7GMeI6GqdWpv1s9Vqubm5efb8+c2NjdaaEMJ213Xz+fyuu+86tnNsmqYTx48fHS1Pnz59bGeHK0C7u7vHjh0bx3Fvf2+x2MhsoZCUmYqYpqnrqlBrrZQyDMPm5ubR0ZEiQlLEerVaD+utra2IUiJaa+M0zWez5Wp1bGeH+126dEn7+/st24ULF/u+G4ZxPp9P0wSkc9b3UgzDkM6IKBHrYVjMF621xWLeWss0eBjG+Xw2jtPR8mg+m81ms9YyQodHRyXKtddcM5vNLl26VG2XUjY3N8ZxXCzmEaXrqk1rbWtrs00tIiS11ubzWd/3Xdc5DUQX09SA+byENJ/N+r7PzFk/a9kW84WNnbVWwDhs11K7rttYbBgyW2vZd13fdZf29nYvXVqv1/PZrJQ4OjpqrR0dLZer1XpYS5HZhmEYx2G+mHd9f3B4ME1Ty2b7/MULQES56+67MjMUIallrtfrUso4DOM4tTYh2RbUWqNEOltroNVqbVsiIlprraWEYRiGYRiAiGitgUKR2c6dP7cehtYaohrXWg1Hy6Mzp89k5uHR0TgOG4uNlq2UMk3T0dFRKWVrc2O5Wm0sFkfLZa2llHJ0dDSfz7quWy5Xfd9tbmzY3t7aHqcps0l67KMfAwBHR0dV0rBeHxwczPr+6Oiodt04jrZLlGlqR8tlV7vMNgwjsB6GiDhaHs36PtPL1RLRWo7jGKH9g4O+6y7tXYooBwcHpZQTx09IkgTo0qVL88X86OiolHJ0tMxMO7e3t8dxkgC11rquglqbNjc2Dw8Pjfuu77puGIZa63K1rLXazsytra1hPewfHOxsb6/W61LK8WPHgEuXLtWIuHhx9+DwoERp2QSttbS7Wlfrte1pmrraXdy9uFgs6nXduQvnSykbi43FYn7+woWNxWK9HjKbJCmGYThx/MTR8gjYvbR74vhxjh0DAF26dGlza+vuu+/e3t4KRe26kIB0rteDxMZiY71et9a6rhvGYbFYDMOQLYdxWMwXGxsbF3d3I6Lv+wi1qUVEOjFbW1uAbUmXLl2qEXHp0qXDo8NpGtfr4cSJ4wcHB9vb2wcHh8vVstZ6/Nix3d3dtGutJeLCxYvr1arU2lqTdO0119x39r7W2ubm5jRNrbVjOzvTNB0tlw9/6MNKKVwB2t3dPXbs2DiOmQlMbVrMF601haZp6rs+MyPC9jRNtattauM49n1vOyIkTW2qpUpqrZVSWmu2I6KUIsm2pEuXLtVa6z333nvu/LlZ34/TtFqvr7vmmsPDo9V6FRHjOEbExmKhCEnZGtLu7u5sNiulADfecONdd98FbG9tHRwedl13dHQUEbXWhz/s4UXiCtClS5e2t7cPj47ms1lEtNZatq520zSVUpCAkIZhqLVGBJCZktJ2Zmb2fZ+ZgKTMBCKiZU7TNOv7Ugpw6dKlWko5e+7cxd2LEbFer6dp2tzYWCwW99x779bW1s729sHh4fbW9j333rOxsVFKiYhhvW6ZXdd1tbZMIUmHR4fXXXvdXffcPev7g4MDRQhuvOGGa6+5FgB06dKlzc3NzLRda+V+0zTZjgggIgBJq/W6q7WUYhtIu0RwWdoC28Bqtaq1SloPQ9918/n80qVLNUKX9vbuuvuu9Xq9s7OTmdM0jeM4m8329vZOnz69Xq+naVrMF11XL+3tlVJOnzp97vy5iJimKSJ2trcv7e3ZPn3q9P7Bfq11mqbFYjHrZ3fedefm5uajHvFI27p06dL29vb+wcGs70spLVtXu2maaq22AdutNePW2mw2c3qcpq5WSWkLECVKOoXSDkmSJEmAbUmXLl2qpZSLu7t33nXn9tbWehjms/nR8miappMnTx4cHNRah2HY3Njo+9nZc2cX8znS2XNnH3zLgyPizrvvPH3qdGvt4OBgY7FxcHhgHIrrrr3umjNnbEuSBAC6dOnS5ubmNE2SuGyapojous5YyHZESLINIGVrQCllmqZSCjC1KRRTa6UEppQSEVxmW9KlS5dqKWX30u4TnvSkE8ePt9ZsL5fLa6655iEPejDPSRKXRa1c1nUdAPTRA7VWXhDQ7u7usWPHhmEAJCGcjohaK/8RbEu6dOlSBYC+73lOtgFJgAFbkm1AEmCbyyQBtnl+JAFA5QWQxP0ESIAk7ieJB5DE82NbElC7rrv9jjvOnjt7/NgxpGmcuq6WUgx91y+Xy91Lu9dde+1yubz2mmufcftt8/l8c2Pj0t7eOI6z2Qy45sw1ly5dOjg8qLVKsi0JwPR996BbHgQA2tvb67ru0t7erO9tG9sOhXFE6ftutVr3fdemNpvN9g8OIjSfL9o0jdMUoWmaZrM5YOc4jrV2tiUuXdqbz2cbG5vbW1vApUuXtLu7e+zYMf7T2JZ06dKlyn8C2wAgiSuglhK7u7vnL14YhuHYzjHwOE5AOpfL5fFjx9K+cOHC6VOn+n42TSMoM4dh6Gc95uDwYD6bzWaz1lrf9wcHh5ubG2dOn+G5QM305tbmOE2Z2XV1mlrf96vVemdju0Tpuh647trrAHCtXWuTpL7vS5SI2NrctOn72XJ5BOq6DsTzAu3v76/W66Ojw8ViMU6ToJQiRUScOnmSfxPbACDJtqRLly6F7Y3FApRpoVrrweHh7qXdaRr5t5IkSRIgCQAqEBGzWd91Xd910zSdOXU6M2ez2X1nz67Wq/lsNutn+4cHfde3bF2pxq1ly1ZLBQ/jePrkqXvP3jefzVprs9k8M4dhmM1mi/n88Ojo2muuQVRJR8ujo6Nl30+t9buXLh3b2VmuVlubm3v7e9hCUhwdHU3dlM7Y2ByGITPBwzDM+tkwDOM0RsRytRqGAUj76OgIyMzVagUI6dKlS1vbW6vlKm3bfdfNZjP+o126dKlGxMWLu3fdfdfm5uZ6vV7M5w9+0IP5d7MtiWeBmpknT5zY3NwQmloLaf/goKs1MyMC4XTLJgSUUg6Pjuzc2NjIlrXWqbWu1sw0V9jpUkrf90dHR+v1er5YbCwWQC2l3Hf27NNvffrOzo6kiAiFneM4bm1tRSmHB4eLjcXB/r4ipmkqpYzjeGzn2DSNi8XGufPnThw/cXh0OI7jbDY7PDxs2U4cP3Hq5Kl7771nPQw72zsPfchDjHXp0qXt7e3VaiWp1mq76zr+3WxL4n6XLl2qEXFx9+Idd94ZERGxsVg8+EEP5t9NEmAbkATUlu3kiZPbW9vpzJbz+XycJmxJtiVFxNSmEsU2l2UmEBFAZpZS1sPQdVVI0jiOkrqua611XWcbAGqJcu78ubvvuSczM3Nne3sYx8ycpqnrOmBne+f8hfM7OzvTNAHDMLTWJJ06eergYH+5Wh0/fnwYhmEYhMZptH3s2LG+6w4ODx/1iEdKAgDt7u4eO3ZsmiZJXJaZkjJTUtd1tiUBtgGDQBLPyXY6bWopgG1JgG1Jly5dqrXWu+65+7777pM0tWkxX2SmpGmatre3H/rgh0jiMkmAeCbbkrifpKLC/STxQKDd3d1jx44BwzD0fd9akxQR0zTVWvl3sy3p0qVLtdZ697337O/v11KWq1Uppeu6/f3966699vyFC7aPHzt+eHgYJcZx7Lv+0t6lxWIxTROQmbPZ7OSJE3v7+12ty9VqGIaIeLHHPDYieCDQpUuXtra3hvUQEbZLKUBmdl23Wq9bm7racVnaIWUmEni9HiTms7mkdLaplVKGYej7fj6fcz/bki5duqSDg/2j5fK+s2enaeq6LiKA1tpisXjogx/Cv4ltAJBkW9KlS5dqa3nq5Knt7R0J204j2Z71Pf9WkrifJACoEbF76dLtd9zedV1mbmxsLOZzw9Hh0Ww+29vb67oOu2Ved+21u5cuLY+Otrd3JB0tj2Z933XdweEh0FobxzEiTp44afvCxQtdradPn7lw8cLDH/owhXTp0qXZfL5er4Zh6Lu+djVbIsZx7Lt+GAZEKJC6WtNerZbz2Vyh9XooEaXWYViXUjNTUmbr+15onEah2Wy2Wq2OHz9+ae+S9vf3l6vVufPnbrj+hrvvufv0qVOnT53mP9qlS5dqZh4/dkyi77vrrrl2sbHBfwJj7e3tlVpuu/12SfP5fBiGvu+ztWuuuebCxYuzfrZ/sN9a21hsKNSmaZymWup8Ph/HcWNjcXh41PfdwcFBRCD1XVdK2d/fjyi1lqm1rnbHjx8Pqdruuu6a02fGadw/ONjc2CylgFtri9k8Ssxn8wjN5wvb4HGcaq0lQkKKxWLedd0wjrb7rqu19l2/Xq9r18362TiOfd91XZ3GSXt7e4ijo6Na66mTp/jPsXtpNwDQNLULFy6M48i/m23btm3btg0IVSBCs9lsMZ/vXrq0s7Ozv793eHi0ublZazk4OOi6vpQA5rP5ar0Gr9dDRPR9P+v75WoZUTIT2/jkiZO1Vh7ANgBUScMw7O3vbW5sHC2Xi/l8uVy2bMOwXq29Xg+ZOY6j4dgOBwcHERqnKRRHR4dbW9sHBwd93xsfHR2FdOzY8crzA7p06dJ8Pt8/2JfU1a5l1lKmNkVE3/WtNUkbGxv8W9mWdOnSparQ0XJ57vz5iIiIkIZxrLUCpZRhGGqtD33wQwDANi+UJJ4vqJl5bGdnNuuFkI6Ojvq+K1G6rhuGodbasp09d7ar3WKxmM1m/NtALRG7l3bvuffeWmtELJfLUso0TTdcf8P58+drraWUCxcv1FpPnjhx4w038m8D2t3dPXbsGC8a25L417At6dKlS5V/DUn820AFMnOappZNCLAdEdM0lVLWwzoUXdeVUtbDetbPpmlqrXVdN7XW1QpkZtoSmXZmKaXUKmitSZrNZgBQo8TF3d0LFy8A4ziWUsZxPH7s+H1n75vP58MwzOfzbG1jYzOd0zhtbG4cHR211jJz/+BgMZ+fPHlyb28vItbrddqzvt/e3j44ONjZ3lmulo942MMBQLu7u8eOHbMtiX+laZoiIiJ4wWxLunTpUtRa7773nr/6m78ex5H72QZs2+YFsF1rjQjbXGab52SbK6C21q45febk8RO162wDkiQBkgDbkmxLAmwDkiS11tLuarUtSRLPSRJXQC2l3HPvvffce8/Gxka2pohpmjJza3Nra2vrzrvu3N7eHsdxPaxDUWvd2dk5d/78iePHj46OgOVyubW1tbGxcXBwsLHYuLh7cTabRcQ4jsDW1tZDHvRgANDu7u6xY8eGcShRMjMilstl2rPZLFtD9F2fmcZOR4SkzGyZIZVSjJ0upUzT1DIllVKwjUMxTdN8Ppd06dIlHRwcHBwe3nrbrTffePMN11/Pf45Lly7VqU3XXnPN9vZ2LYX/BLYlAbWWevbcubPnzna1DuO4Xq+vu+66o8PDcZxatojAni8W2JLStj2fzQ6PjiIiMwFJ4ziWUlprpZSd7e3zFy50XWe767qHP/RhAFBbayeOH18sFl2tttfD0Pf9sZ2dcRxr7dbrdSmlqxUpJElTm2qpx4+3kIB0RsQ0tVrK1FqtpUTZ2txCkqilcgVof39/nMbdS5eAYzs7J0+c5D+UbUmXLl2qmbm9td33sxJRa+U/iG1JPAvUiDg8Orr9jts3NjZC0dp07Njxo6PDTNu5Wq83NzedaWjTJEWUaFPb2tpczBcXLl7c3NwYx2m1Xi3mi5YtFK01iZtuvIkHgmq777pTJ0+VErXWTC/m88V8vh7Wy+Wq1m4+n5VSj5ZHi/k80xFqrc1mc6Raaym16zrbpZRaq+1aS2vJcwHt7++vh/XB4eF8Nm9tAklIqrWePnWafyvbkgDbki5duhS2F4tFKCSVUmZ9vx6Go6Mjm38PSVwmCQCqcYmyWMxtL+Yb6/X6+LHj0zSVEvfce28pUUtdD+uu6+0chmGx2FivVy2zr93R8uj4sePrYbAzIiLKMKwjIiKGYTx+/NjmxqZtSUANaT0M+wcHmxubwzjs7e91XWdbEev1ej6bLXM5Ta3v22KxODg8rLVL++jwcF1rtlwP69ba0XI5n8+76mmajpZL213txnGDZwFdunRpY2PjaHk0jtOxnZ1aK/8JLl26VCWth+HChYvDOCzm81or/6FsSwKqcVfrxsbims0z4zSdPXdWio3FYrVeC4B+1rfW1uu1TS0lIhRRS1kPa9vjOJUSx3aOXdq7tFgsxnEcx3Frc2s9rI8fOy6JK6CG4uDg4J5777XvsS1pPp+vNzfXw9B3/aW9S6dPnTo6OhqnKTMzcz6bIYXi4PCg67rMdGbXdcvlcrVeT9O0Wi5by0t7l47tHJPEFaBLly5tb28vVytn1lq7rrNdSuE/iG1Jly5dqqWUs+fO3nHnnYvFwvZqtZrNZo96xCNrrfwHgtpaO336zMkTJ4GptWyt7/tpmoDMjAjbgCTbrbVSyjiOtdZpmrq+EwIkTa1la0jZ2nw+H8ex1NrVyhVQSyn33nvvrc+4dTab1Vpns9lytdze3J7atFwur7/uunvvu+/Yzs4wjJltaq3rulrKweGhpI2NjWEYSpT5fH5p71LXdZl5eHR43TXXXdy9eGzn2INuuYUrQLu7u8eOHds/OOj7vu+61lqtFWitlVIA25JsA5IAwLYkwDYgiRfAtqRLly7VWuudd9911913LeaLkydO3HPvvQ9/6MN2dnZKKVwmCZDEA0jiMkm8KEC7l3a3t7YBSbZtIzC2SymSbNsGgIhordmWVErJTEmAbdsRYRuICO5nW9KlS5dqLfXc+XO333HHiePHpQCmNq1Wq43FAiil7O3vA6dPnVquVk7vXtrd3NwchuGaM2cODw+7vt/b25M0juNisZimaRjHa06f2dvfe/hDH9b3PVeALl26tL29fbRcLubzzJRkWxIApFMIsB0R0zRFREQALTMkSbYlZWZEALYltcyuVkm2JV26dKlGifMXLtx9z92ShmGYzWbANE3XnLnm+uuuKxQeoJTC/UopPEAphQcopfBAULPl8ePHd7a3M7OUIgkpW+v7nhfAdmaWUnjRQZV0cLB/x513CgwbGxvr9Xq9Xs9ms2M7O/fcd9/J48en1mqpFy5e2NzcBE6dPHnPvffWWo/t7KxWq6PlcmtrC3Np79LDH/awjcWGsRAPBNrd3T127Ng0TdM0KVRLnaYpSmS677phGIDMjIhxGkOB1HcdsFqtZrNZa21qUy01M8dx3NnZkcQD2JZ06dIl7e/vD+N439n7+q6fpqnv+2kaM3M2n3e1Gm68/gb+NWzzALYj4tKlSzUzjx87FhF9343jVEoBj+O4sbExTROIfyVJPIAkAKgRcWnv0u7ubmbWWmutUly4eGFne2dq0/Fjx++7775hWHd9v5jPV+v15sbm4eEBUramiMzc2d5ZrZbr9bC9vT0MQ9fVo6NlqcX2OIy1loc+5KFAtT2fzY8fP9HalOm+69J50w03DuMQWnRdXSwW29tbNrWW2WxeSsBWrXUch4iwWcznQN/P5vPZfD4bx2k+T1DX1b2233U9IEmX9i71XX/X3Xddd+11Gxsb/Oe4dOlSYBRaLBZ93/OfIDMBoEoax8nmvrP3DcMwm81bm4DM7PuZ7dV6NZ/Npta6Wm3GcZimZtx1XSgyW9f1tRYplsvl9vb2+fPnSq0lYprasWPHTp44AQAhaRrHaRpXq7VhmkZACpuuq6WUYRhaa6GwWS6Paq2zWR8RQpKArqu2M1tmjuNYaxWUUhHjOHAF6OLuxePHjvOfxrakS5cuVSH+09hGAECVNIzDOIyZObUGDkWpJTOz5TiNEbG5sTmMY2stIuwEQH3frdfDbNa3qR0tjzY2NoTSmS0jIu1a69bmpm0EUCUdHR1d3N1dzOcXd3dLhCKO7ewMw7B/cOBMRZw4Ph0c7NteD0Pf97b7vq+lHBwcHDt2fG9/b71eHzt2rEQx3t/fj4j9/f3rrr1ua3OTK0C7l3aP7Rw7PDrsaldKmaaptWa8sdiQxL+PbUnApUuXaoly4eLFu++5e2Njo+/7g/2DdNo+cfz49dddz7+DbUm2JQHa3d09duzYOI6llPV6LWk2m7XWhmFQqJa6Xq9rrdM0KSKk2tVxGLuum6YpSoRiuVx2fQdypkK2beazWSkFsC3p0qVLtZRy7vy53UuXFvP5fWfP9n1/4vgJO3cvXaq1bm1t7e3tjeNYSrENbG9vr5arza3Nixcvbm1ttdYuXLx44thxYJzGvu/39/dns9mJ4yeuv+4621wB2r20u7O9sx4GbESJApRSIoL/CLYlXbp0qYZi99Kl3d2Le/v7kmqtrbUbb7jx5IkT/AeCms4Tx4/v7OyUiNZaRCxXy1k/y8zWWkSkE1NKATIzIlprEWEbKKXYXg9rTN/3BoEkQ7bWdR1XQK2l3nX33fedvW8xnyPVWvf390spJ46fOHfhvCAibEfEpUuXbrj++tV6vVwuNzY2NhYbFy5eOH78+MHBwTiOac/6PhQRapnbW9v3nr33ZV7ypWutAKBLly5tbGxEhO1Sim1JgG3bgCQgM9fr9WKxSFuAFFJmtmyhKKUAtgHb3C8ibEu6dOlSlXRweHjHnXfUWodhaJl91506efK6a68DJHFZKWVjYwMoEveLiIjgfpIASTwvqJl5/NixxXxeSpE0TZPtUgogiX83SQBQa6333HvPPffeu7GxMY5j13Xz+fzixYtd183n81LKarUahqGUUmtdr9fHjx0/ODyQNE3TOI5Aa+3aa65dLOb7+wdTmw4ODvq+r7Wu1+uIWMznD3vowwBdunRpe3v76OgoItLZd/0wjgJJpRQkbCAz085stdY2tVpryyaplNKmVkoppayHoat1nMbWWi01Mw0RsbFYXLp0qUaJCxcvnD13btbPVqvl2KZrTl9z7333njx50pkXL13a2dqaWjt54sSdd91VSjl18tTUpqOjo2M7x3Z3Ly4WG9M0AjZbW5sXd3drrcvl8obrrj916hTPArp06dJisVgPg4TTEaGIcRi6rlut10CEbHe1MwiMM9N2LbW1ZgyyM6JERGZO05iZW5tb8/ncNiDp0qVLNSL2Dw7OXzi/sViM0zQMw+bG5qW9S33X3XLzLX3f829lW5JtAKjpnM3606dOdbVrmSF1Xbezvd0yu77n30ESIAkAakit5X1nz9ZasafWTp44OU1j3/dPv/XpQq1NmZ4v5uv12vbO9vZ6vS6lLFer2Ww2rAeFju3snDt/fjGfz2bz5fIo0+thferkqWuvuca2JKDadLWeOH58HKfZrAdtbmwcLY9qqdtbW7alANdaF/N5azmbzWrt5vN5f3RYStlYLFrL+Wx+bOdYrcX21tb2er3a2FjMZjOeBSowTtMwjF3XrdcDsHtp9/TJU8eOHeOF2trc5AG2trZ4HrYlAUAAIfV9V0oppazWq67rjPmPIMk2AFQgSkixtbm5HgZJ89ms1nrPvfeUUqapdbUaxnGotYIyG7Cx2FgPwzRNdtZaW8v5bIYkaRwHLrPZ2d6ez+cAUAHby+WRxMXd3cxczOeZXg/r7e3to6Oj+WxWSgWOlstLly7N5/NaSinl8PBwGMdaq4YhW6ulLFfL+Xyxf7C/Xq+7rgvFYj6fz+cAoEuXLi0Wi6Pl0cbGZlcr/9FsS7p06VKUUi7t7d12++2tTfxHsy0JAGpr7eSJE4vFYhjG1WoVEZi0sbuuAzITmFqrpbTWEFubW+v1umV2tU6tAaWUzLQ9TSMwjtOs7zc3N2uttiUBVdLe/v59Z++bz+er1WoYhvl8LlS7WkuZpmk9DIv54tLepVprRNgeT0wXdy+u1+szp88cHh2Ow7i5uTlN43oYImIcR0mZ+ZAHP6TWyhWg3Uu7O9s76/UaCcBGhKK1tlgs+HezLenSpUs1InYvXbr33nu7vlsul1tbW4eHh6dOntrb33vwgx7cdx3/PpIAoGbmiePHtzY3x3EEFovF1No4DCdOnBCs1+uWia2I1qa+n2FHxDiNochMhUB2Oq2I1lqJKKWkMzNDMbVpa3MLUUvEhYsX77n3nlLK4eFhlJjP5qvVqtZ6/Njx/YN929M0RURmHts5tlqvtre2L1y8sF6vF4sFME3TjTfceO78uWmaWmt91+8d7Ie0vb09DEOJeOQjHhkK7V7aPbZzrLVWSsnMzKy18iKwLYnnp7UWEZK436VLl2ot9Z57733Gbc/Y2to6fuz43t6eQo942MNLKQbxAkkyiOdmKKUAtiXZlgRod3f32LFj4zjWWjNzaq2UqKXyH8S2pEuXLtVa6z333nPu/HlAUmau1qtbbrrl2muusS2J/xBQW5uuvebakydO2o4IIO0SAUjiPwpUG0l93/OcbHOZJMA2IIn72ZYE2AYk2ZZkm8sk2eYKqLwAkngASTwnSVwmicskAZK4nySugBoRu5d277vvvpZZoqRTUmY6UxGtte2trWEYW7Y2talNrbWTJ05GicPDQ9uttVrrrO/T3tvfX8znmVkiptbms3kpJTMf9tCHAjUzjx87vlhsCADbwDRNAMJ2V7t02jgzbWd2fe/MUydORoTtcZpKKa21a89cU0ppmaVEm1rXd21qLRMAainl3PlzZ8+eNXS1W61XW5tbD3nwg/kPMeOZoGbm5sbm7KYZptbaMrta+Q9iG5AE1IhYD+sLFy5ubm6AWptay5ZtHMdhGG64/vppapkJXq3WGxuL1Wo9m/Xr9RrJ6ZMnT164cL7ruvUwSMqWwzj0Xdd13TiOD3nwQ2wDQLXddd3W1tbGYnFweKgI2V3pNhYbwzDUWjMzooBq17quH4ZxMV9EBGi9XoNns1nL3FgsMl1KyWxSRKjWyrOA9vb2FLp0aU/izOkzXdfxH8q2pEuXLkVmLuaLKDFNU2uN/yC2bfMsUEuJo+WyRJlv76zW63vP3td3PbjvZ+v1upRoLSME2B6GcWtzc2pTpsHTNNVaNzY2Dw4O5vNZRFy6tNfP+vlsfvLECR4IKmiaJvB6vZrN5kKZGRGSMtO2cRsdofUwRMQwjUdHR5hay9Ra13XDej2MQ60FGKcxs9VSeC6gS5cubWxs7O/vI1rLcRxms/k0Ta1NZ06fqbXyb2Kb+0m6dOlSlbRcrXb3LnVdN47jNE7TNK2HwfbO9k6tlX8TSTwQaG/vUt/PbK+Hda2dM9N2Zqm1tTaOQ0S0lltbW9M0ZnochtrVcRxn/cy4TS2dQIkyjON8PitRDMMwACFtbm31XXfp0qUqxcHh4TAMh0eHmxuby+VyPaxba7XWEiVKTNNUa+26bvfSbt91+/v7Gxub+/v7J0+eGMfxwsWLW5tb4zRKCmm1mkl0XXdwcGgMbGxsAIAuXbq0sbm5PDqKCEX0fVdL5T/apUuXqkIH+/t33n3Xer1ezOdnzpw5feq0bUn8B4KaLY8fP769vS0pIqZpGsdREpC2ADFNTdAySymCiGiZq+VyNp91tVsPQ1erIrCBcZpmfb9eryVFhKS+74Faa73zrrvOnju7WCyOHzt+7vw52yeOn7hw8cJisShRDo8OI2Icx9Za13Vbm5vL1SqkcZrGcbzu2uuWq2WbpsViY/9gv+/7w8PDE8ePHxwettaA66699szpM7Z16dKlzc1NwFBL4X6ttVIKz09mRgT/GpcuXaqllHPnzx8dHa6H4eEPfVitlctKKbwAEcG/FtRpmq45c6blSUmhyMyIyMzMjAhAkm0EBsmZkmwDkpBCsg0AmRkRXGY7MyMiIoBaa737nnvOnjt7w/U33HX3XV3XLZfLzIyIkydOrNZr24v5YrlaHj9+/OzZs5Jaa8eOHRuG4eDgYGdnZ2OxOFouF/P5ufPn5/P5arXa2dnZ3Ni8+967a6m2H/uYx0SEdi/tHts5tl6v+75vrUk6Wi5ns1mJkGQ7nSWK7YhorQFAKaW1BkiyLQmRLSMi7ZAktdZKKZlZa7106ZIODw8vXLw4jMNDH/wQ/tNcunSpjtN40403Zib3sy2J+9mWxAPYlsS/CtRa6r333Xv+/Pmu69fDGliuVhuLxY3X33DfubPbW1v3nT2bmVObrj1z7YXdCztbO5f2LvV9z/1aa5JuvOGGe++9F2l7a+vc+fPb29vjOI7jePz48ZtvvMm2Ll26tL29PY5jKaW1ZgjJdtd1wzDMZrOptTZNh0eHx3aOtda6rmutAYoQtNYODw8XG4ta6zROERERrbVaazozE9P3/aVLl7S/v78ehnPnz0UEME1TZp48ceLw6Ghrc/PCxYsnT5wYhmGcJsHRcjmbzU4cP37u/PlSSilFaBjWERFRjh8/fnCwP06T08vVsuu6iDh18uTpU6d3d3drOrc2N0spQNd1rTU7Z/1sa2u777r5fN51/Wq1LFEUcaI1Q991J0+cLKVIqrVM46QIO/t+FpIiVqvVqVOn7AzFYrEAJOnS3qVQ3HPfvUKSZrPZ1KZpmna2ty/u7m5ubG5tbR7bOca/z6VLl6rtxcZie2urlNraNJvNx3FsbZrN5sd2jtmWBNjmfpJsc5kk21wmyTbPQxJQMaWUa6+5luexs73NZbZ5ANvczzb3s80DSQLbAFB5EUji30oSANQosXvp0sXdi8MwHNvZAcZxkpSZR8uj48eOZfrCxQunTp5aLOZ7e3uLxSLT4NZymiZJGxuL5XK1Wq36vosoEWoth2E4c/r0zs6ObUlAdXpjsVitlrN+Vmttrc1m/f7BwebGRstZREicPnUKNE0Tko2dhtYmsKSI6LquZSulZuY4tojous42zwJV0jiNLXMxX2Smje2HPeSh/EewzRVQgdayq90wDOv12rjrOv6DSOIKqMaL+WKapr7vz5w+PY6jIi5cvBhSy1ZrtclMCaCrnaRxGoXSGYrWWu26aZpKKVubm4dHhyXK1KbWWilVUGrd3NgAaij29vfvvOvOra2tzY3No6OjxWJ+eHQ0DMM0TX3fSwrFMA6bm5tOZ7aIslqv+r6vpewfHFx/3XVnz50bx/G6a6/b39+vtUg6ODwchkHS6VOnNzc2bOvSpUvb29ur1QqY2jSbzaaphTSfz/mPc+nSpVpKufueu/cPDtbr9WKxKKUcHR31ff/QBz+k1sq/j21AElCnNl1/3fUnhzXG2HaJkpnjOA7DUGttmW2aELXUcRpLlFrrehhmfZ9OoZYNCMXUWi0lIqY2OV1rnc1mtgGg1lLvvuee8xfOz/p+GEdJ29vb+/v789lstV5vbm6u1+utzc2Lu7uz2Wyaptba6VOn7rn33tOnTi+XS3ApdW9/r+97oO+6KEVwtFxubmw++EEP4grQ7u7uzs7OOI62+76XxH8o25IuXbpUSymX9i7dfscdtk+fOn3D9dfblmRbEv9RoLbWjh87vrGxUaJwmSRAEv+BoJZSdi/t3nb77RFRa33ogx/S9z3/cSQBgC5durS1tTVNk3FXO0mApGmabEcp2BEBAOM41lojwrZt26UULrNtm8tst2xOt9ZqrX3fX7p0qUbExd3du++5e7VeH9vZycxpmsZx7Pt+b3/v9KnT6/V6mqb5fN7Vure/X0o5dfLUhYsXJE3TVErZ3tre29+zferUqf39/VrrOI6bGxuz2eyOO+/c2tp61CMeaVu7l3aP7RxbLpe1VkktW4kyTVPf92kLbAOSbEeEbUmZKSlt7IiwDUjifpIiIjMNJeLSpUu1lnrh4oXb77hja2vrYQ95aKUCtVYgeIEiAij8CyKCK6C21k4cP7G9tV1K4TnZlsSLwLbtiOB52JYE1Ii4uLt739n7MlMS5vjx4/fce8/O9vZsNt/b3xvHcT6ft9aO7ewcHh0t5vODw8OI2NnePjw6kmR7NpuFdPbcuVMnT+3v7x+tjh75sEfs7OzY5grQpb1L8/l8HEcpQrLd9/1yuVRE13XDMGQ2SdPU+r6zLTRNE1Lf961NIIlSaimxXq1rV7PlOE2bGxu1VsC2pEuXLtUSZXf30jNuv+26a6+9dOnSzvb2OE1912fm9vb2yRMneJF1Wx0vCNTW2smTJ/uui4hjOzslymq9ms8XEdHVyr+JbUk8EFSFdncvHh4eLRbzixcvLhYbmW1vfx+IiPV6vbO9fXh4CGxtbe0fHPRdl5nL1SoiNjc2Effcc8/Ozk62lvZyubz2mmtvuvFG2zyAcY2I7a3t1vLYsWMRUUoFImK1Wg3DcOL4ie2trY2NjdZyY2Oj73tA0mI9dF1Xa53P54v5ouuq04hpahsbC0ASD1BL1W23PePkyVNAZosoYJ5JksCtNSkkZWZEALYjZGPbzohiW+IyZWbLJp7JppRy/sK5urnozp29yzYIzPMhMAAC89wE5jlI4oFsIlRrrdtbW4BBvGgk/lXsWmu9tH904uSpaWpdKWlnNkBSRNjmMkmtNRuFsG3+NRxRLl3arcdPntqazVmU85cuLmbz7e1toLU8Wh52XW9b0jAMx44dB2xL4l9PUq2bW3/yxV9xz9HB6bd9i7/74z/eOX58ebTc3t4+f+F813XbW9sHhwcRMU3T9tbWfWfPvtIrvtIrveIrDcMQEbxoaq2Zqf2Dg3G5PDg8vPHmm/f29rqua62VUkoptiOiZQvF+fPnt7e3I6LWOp/PeR62eX5sR8SlS5d08eLF48eP859s99JujYhxHDOzlAK0zBKBwAC2JRljuq6zLYnnsVwu777n7o3FRsu2Xq+7rqultmxOr9fr6667rkSpkpar5cHhYbamUGvZ1bpYLFarlW1JU5taa13X33Dd9ZJ4TrYlXdy9+Ld/97cPftCDL1y8cPHixWM7x2xnZmYeHh4+ZnjMQx/yUO3u7h47diwzuUwS97MtKTMjArDNCxARvFCXLl2qABARPA9JQCklM3mhMtM2DyAJsG271gpob28v7YOD/a7rpjbN+pkhWyulrNarvuslnT51mn+rg8OD++6977rrrtOlS5e2trYOjw5LKZkuEUBm2kZIElpsLIR4oWzb5jJJgG1JU5tKlIODg2o7Ira3tnkBbGdLXijbtVZJPIAkoKsdkM4qaRiH9XoopZQIIJ2tNUlC8/k8IkopvGC2Jd1+x+133XXX5ubmNE7XXHNNlLjn7nsUOjo6ethDH7axuVEBSfsH++D5bG47M8dxLKWks+u6iOBFs7+/X0q5975718N6a2vr0t6lWuvR8ujS3qWtrS3t7u4eO3aMKwzigTLTtiT+JRHBC3bp0qWKADIzIowxkmwDkkJBYJsXahiH9WqdmV3XSVosFq215XIpaZqm7e1tSTUUy9Xy3PlzUsxnM2CcJjCmZWstZ7P+2jPXSuL5sS3pvvvuu/3222+99dZTp07dd999N9504/Fjx59+69MjYn9//2Vf+mUf9rCHaXd399ixY+v1GiEpFC1bRGSm0xGB6GrHCyXJdmvN9mq16vouWwJd101tCsV6va4AMJvNuF+l2iZAYIwzkxdBREja3t62DQgZd10HrFarqtBqvVoul6UU27YlbW1ulVIAhBD/SpIAAHPp0qWNjQ1JFSPJdmstMyUBtvmPIEmSJKDanvWzWT/jATKztcZ/hJ2dHcB2lZSZrTVJkmwDEVFKsc2/2zRNtVagSjo4PNg/OCilZDYbwfbW9vb2tiT+fWyfv3B+Z3snIrS7u3vs2LHWmiTbSAJAEg9g2zaXSbINSAJscz9JPCdJki5dulQBoJTC/TITsM0DRAT/HlAj4mh5dHBwUGodhyEiTp86HRE8wP7+/u//we8fP348M2utO9s7t99x+/b29vb29sWLF7uukzQMw3w+f9SjHvWMZzzjyU95cle7a6+99ulPfzrizd7kzUop2r20u7O9s1wuAUnGG4sNntM0TWfPnT08PJzP5/PZvNZ6cHBgvLOzY/vo8GgcR0nz+fz48eO7u7tc1lpLZ5vaDTfccHR0pL29vVLK/sF+1/XjNLTWpNjZ3t7c2OTfZ7lc/tTP/FQt9RGPeMRjHv0Y7V7a3d7aXi6XUSJbAoi+72upPIBtXgSSbAOSpjYdHhyWUmaz2Wq1qpiI2Nzc5IWSxItGEpd1tTt+/DgArFarevHCuUuXdu0EgQEASfx7GbABKY6ODnR0cPHo6NA2CMx/PBl3tdbdS3vYICQwz0MSiH87g1sbaymza669lhfM9jQ1SWD+9Qy1lLNnz+pgHPf+4I9WsL7lhhimYRxn85nQ4dHh5ubmNE033HDDzvaOnSD+TSTt7+/XUstdT7/17ku7t1x35hlPfGKpFViuViViNp8P63WJsrO9k+mQwDwHGduWxAtgu5SSmdq9eOHY8RO8YIYpUxKY51EU4l926dKlqii2737G0xlGSZIAbPNMkiRxPwFgnqmJneuu29rYsi2J52FbElAVceHsfZvLYTFf9IsNWgOQeCAbABOF1rApBQximu6+6+7Fwx5eJF4IqM0uzv2joz992hM3Z/Ou1Km1ltnVmplAcy76GTDv+osHezedvmY9jruHByViPY43nji1c/z4MI2LrrfNAwkhroBaoOwca7Oz1584XWvZ6OfpbJk1SjolZeas61u2EqXrulnXz7tZSBFRSjFw/Nii6wFJvCCgixcvHj9+/ML+pfX+XtppC5CwQWAk2wAmQi1zc2t7c7YY2nRwsGfY2NgStgGDeCaXrtvZ3AaAS5cu1Vrr4fJwfeddiyiLfg7YgCXZCUK2HZINMubS7m558In13Xf3eweLfqajC7aBUkpmSrIdiuU03Hfs4JprrweAaHi9e2nePEzTMAxHq1VrbZzGg+VRZk5tGsdRsBqGzDYMo+3jGbff9vTZcj0OwzRNoIiIKHtHhy29HseWeTSsF6XT3sF6GgGgCkrf37e3+4yz90pKe9HPNmbzg9Xy4sHezsZmLeVh19341LvvjIipta6Uh95w0+YN164v7N5+792radpfHpZSXurBj/jLpz5xMZuth2Exmy2H9fXHTt74kIeUKACgS5cube/snL3rDq/XtVSBbYOkYRy7WgHbEeHMiGjZ2sbiumuuv3Dp4nDh4qzWbG1qrUYpEc0pBAjWzq1rr93a2AQuXbpUbQtcymAP04gAMIBCLRuXuTUhT01Svx7vfcbTFaWJvXHYueaaM1s7LZtBPJPNsVICbEsCapRy8fzZ7vzFE/ONfrHBNCERwRXTBFAKNk4kAEOpILqezHvvvmd6yKKvHc+HkQCgNlvjtFoPj7vztnnXn9zaGaZxPY5AOm8+c+00tdvP3bu92NicL3YP92upwHoc7QQt+v66k6fW63VfO9uSeA7CRgKiQLezsyQ3+tms64xLRIRW43psLVCE5n0/6/pFP9ucL0LKbBt9bztEa9Oyr5ubm4AkXhDQxYsXjx8/fnH/0vpg33ZLS4QCAMZpRKpR0pnOUEhC2tzYnHV9ZkMx39gsEbwAtiVdunSpllKW6+XqrrtninnXG2xDC8kgJMlTMw6FbWPs/fHS9oMeXBAvIogUywsXNxrZWhhlzqLMo4zD4Kkp3capTVMlpmHMqUU6zOZqvHjuHGAnLwqogujqhf1Ld148v57GzLzx1Jnrjp+87dy9wzSd2j52dm/XdsvW1+7S4UEtBXjUzQ/e7HsAxIsCdOnSpa3t7fvuuK2MkyRJtiUJTdm6UtOZmWnXUjAWMm3en7n+RvEvsy3p0qVL1XZIMZ8vp31ANsI2oGCVI0gBaO0ESKvvbrj+BvGvAdrf3x+HVd5z39Z8MZtv0CbSRCDhRAKBQdhEII2r5blO19/8IF4EtiVdunSpTrbWwziMf3PfPYuuP7W9szVf3HXxvKSt+WJ/edSy9bUTOlqvJDXnddvHu+3NMVsXxTZCiBcOokjdzvZhG5U5jMM0TZm5qF1OU0GLrie9HtbrYX1ssbF/dDAMw8GwqseOdVEASUL8i0C7uxePHTt+cf/ScHBgPLWWmRERUqYRIQGgdNZSMC6xub0TYHOFQluLTZ4f25IuXbpUI8pqWC/vuXcGXekkAKY0iGcyBiQxpCGE9++dnCXCxjjTd2/MrrvpFvECQLV0eP785pSDvT3bWE9TiUAIJNlumSWKFGObsCNiyoa02S+O1utaoq+dMzlc7h/s72xt25bEc4EKLrVe2L/09LP32p5aq6Wc2NqWtL88EiyH4eT2zsOuu/Gpd98xtnbt8RPDOJ3fv2S8NV+cvbS7nsaTWzuPuPlBXQlAEs8LtHfp0mJr8+xtt3UtEwcyCIxtg0ICSkQ6p5Z9V223zJY567o2tWEcFSrHj11z7fU8D9uSLl26VNOuUerO9uGlS5K4n5EICduASUSUsspEqEi1rrLNj2+fOXna6b5WXgiopZTd3Qvcfe91i41ZvyATCUAiM7NFKQjSSGBCABJA1cULl476+bGdY7Yl8YJAnWwvV27593c8Y3M2m3X9OLUIDdO0PV+c2No5u3ff1NrmfLFcrySFYsq2Hoe+dlO2G3ZODIcH7BzjhYNaJDY3Ltxxx7BaqbWpjulsmeM0KXNntpiGcTmst/v5NI4tcxjHeT9brVdr1uBL88WpnZsASTwXG4krQLu7u8eOHTu/eyGPjgy2EUKSWubUWo2Q1DJDMkgChwKwzXx2/NiJzJQA2ZZku5TSlwrYlnTp0qUaoam14eJuTFOJwgNU6AQ0myoAm2cTQrEeDy5cQgLbRChtTLO70ydPnjzNFVCt2Dt33+ZqoJSdxRZtQoEThARgI0A4kZAwODOdzlCoylhIJYZh6GtHCaZ237kLq+2dedcDQLUd0sWD/afed3eJMkxjLfXE1pakw9XSZjUOxze3hnEc2yTFehym1h5780P6Wm89e8+sdtuLjbsvngNqqX2tF/b3WuaZ7WMPuvlm2wBgrIODg/nG4p6nPkXDOGWGBEQEMLUmiAhgaq2vdWzT2FpX6rzva5RhGjM97/vlsLbdMjfn88PVKjNrV7euv/7EiVMAsL+/r7vuuvPMmWvX0zCOY0i2eTYBYECSbUkAkJnGUgjSGQpAUsssEYAiZrXPbIZaytmz91XRnnHrk4GI4EVhEP8i22mLZzLW4f75cRgRtkH8RxHimWy6rtaz5y9tbW5lZqldZma2UBhsAxI2QESAbf4NJO1d3NOlS5d2dnaA5XK5WCz4z7G3t1frzs5ffvXXX5iG/Zd87IXbb19sbq5Wq2maTp06dXBw0Pf9er2ez+ZTm974Dd/4xMkTwzBI4oUSMuYy2/PZ3LYOl8uLT7/14Gh582Mfuzo6vHTpUkT0fQ8A8/livV7VWvu+39nZQfzbXLp0Sbu7u8eOHbu4vDQN64iICC4zAJkZksGZrTXbCGOwHMqKlWUChAGTnWOu/kjDRAphrj95w97enpb7y8dd/Lu/OvzzjX4jnTw3AWBkhJCyROtoBYc0ultmmSDsAqVRFuvNM+tr7i53OJbhsu+Dlzn2co898eLV6RWrzWMb81iYBEBcZoyMTEa0qqmPVilMszXRKOPkY2N7dOZ2eqN5Yc9MXW/UZ8T4yrNHvOypWUi/fc+lWy8ePWrzsCKCyObEthEojQFliamPqVcWR7qMq9lhi/Fh40336d7d8Zb91avxnCQCcjmdOLX153/xx5uL2fbNL7t76R6UlStkohkrI6ZeYxetAxwt69i6g522OCyrU237hjyzOx2syzhMjwBCkwmeTYYonDt//hVf4eWFf+3p50oAqoBBrZZxq04VB8qswzQ/cJkclpHjQKuE1TQcDIe78/1W3Mcda64xAYB4TplZaydnZgJARS7DvBuOaR5ttsw6OBKQhaUUlzksOJyvnjK/s7hEzhb93ybdwfASAglIACSRLY+f2rn33nvk3Nk5cd/5i0DFyn41LM6pzDFYyuAFCKvQGYPt2Wb3NyUuDdMtY17Tcg4g7Ixad/f3HnvLGew/f/peRAFXAFCWUEkbmRfEKBMhLrMcZV6fNq9Py9wY85qh3TS266bcWPTd3x0t/uHvLgqOuq1HdwdpKshKl8nRTPJMkmUAC4FkstC2FzLmMhFHA61HSMOs3jqrt+LuaHzx/fVLlI3tNtsMUahqB0BFGeu5hhOlnxWBjZxKxxTFZBg3jVmYHeWDv/fPuvv2sqsxteH09m3v9OLjLGRAdg8Sw6w+9XB8MVxLpKA5lAKqTYl4+cVjtoaNv25PWneTmjrqy289NMfxLw5uK6V79OxM3dw8+r3f3/m235q/xRv53kvu5vkLf7T/iFN3v95Duv21Q2AA5TDdYIeUEAAIAGqTt635+sL5dsfR8dvUx+Tpmunk1np9+/reo1PP2PRGPT+PprFrnnXjn/5VZlOEQtNGpzTPQUO7gQcyAKCjvYO/PPe0Jw2X+tnc1ZFJTHXS7EjDbN0WYzR3K3W6NM2Wx//8ntnde1t3HxzcsLW+duf8q9wM5tls9xdWb27PwYBgZT18uPPVr+1qMK588268xEbiFVdYeIFMHGHhSo0Lm/G44XU3XBRDy77Qsh6NPJukcWg3ZM6lBPFAUEFiCrdAKa4QKDEghJRkO3lpfPXN4Ynb/R+lZuVoRDjEswlyaLdwP0FgIQCoAAiC52Sek5ooE2ccHeAinoOk9dBuXE0PlQwCJpNoSgBD5UUVQPOWmcEA4jnYrgfDKxiEhSZzcqbr59y5ol1CELyo0mZe7hQrCJ5DSOuj8cXHdlw0kJ3R6djeHd/1/m+0denO6Gc4gxeJTZmVS5v9n5gAc1koQqXElL5mNb1kDUIlpIhCUnZOv8WHfJK3TuU0girPZIx4/kwE3ul/H0bowFx2tD7KTOFL69ccWhMDSBIC8zhi87GvezTwCCcqFQFGMUEzz01gDMdmv+dyj9nCCSDGo+nF68tsb262LKu6yDwbEdgt05lG8/lsXK9/92grq7CrbSnasD4+HyqO1pAA25Iym9TVOL+Zu+3o+j0ulo3A2PaQj7r+0fPFHDh/9t6/+/t/mM1mx3Z2loeHmXnu3Lmt7e1XepmX/tP9saURNaT1OD14dviWf/97y+uuu/fak/N01NrVOk1tPp+t1weoa/kKW1tb9wx3/vnqjzbmG1ObsIb1cljuT61N4/gyL/mSx44fP3v27DVnzkh67KMeNYzjfNa36VA9QhXczLwN5669YbVzsvXbR9OIoqq2mNaeTZGhIZWZ44U8V/pCIILZ6i/uu73q+nSDosOJC+dtwGBplJSX9pZly+zbrpTulh399h3jbfOb2rrE3S2TCNmT5JZts3/qoj7F9Ha6Zlf7wRNO5+yvL3XrYSUmkDFGEpcZC5VaS12e2WlRF7r77rvPnD5177nz4ziFZJCwAQABJAKDwNjmmUKElLxQmXlsZ/vg6Eh33/mMw+WyRPCvIzAvmpbpTB3uXxiHwQAGQDyAsRDPwQCIf42uq/Xs+d2tre3MVkq1maZJwkagiFrqME3YCEk2oTB2WhLiX2QTEXsX93Tp0qWdnR1gtVqVUrqu4wEODg+3Njf5d9vb26vdzs6ff9lXnZ/Go5d7yaf+7d8+6MEPzrSkEydODONw8eLFxXyRzq7W/f2Dw6PDvusXGwvgNV/9NW+88cbVahURYJ4/2dn3M9s6ODo6uPe+Swf7D3rUoy6cP7darff2Lm1tbm1vbwMbm5tHR0fL5dF8Ph/WwzAMG5ubbZoUOnXy1Hw+t82/RNKlS5d08eLF48ePA9M01tpxP9uSpjbVUkD8+1y6dKlGxHJ5eOnShVLrOI4R0aZWaqmlHi2Ptre2x3EYxykiWjYhJEHXd9tbx+fzBf8S25KAAJAk2c7MEsU2l7WWQMsEJGWmbQnbJcI2LzqothfzjVBwP0m2gWPHTrbWNjZ3BLYlAbYBw3w2tw1Isg0AkmxzmSTbXAEVAcxmc56fruOFkAQAkrifJO4niSug1lJvv+P2O+6445oz19x51x2LxcbR0dHm5uapkycPj452d3cXi0Wt9fz58zfffPNqtVqtViXK/sH+6VOnV+vVarU6c/rM4dHh5ubmPffc0/f9MAxd161Wq1pra+1lX/Zld7Z3bGv30u6xnWMXdy/OZrNpnGqtmZmZOzs70zStVqta6ziOrbXZbBYR4zhGhO3ZbHZwcBARi8Xi6OhoNptN0zSfz+87e9/m5uawHra3t4dhWCwWXdddunRJBwcHBwcHf/XXf3XyxMl+1p87f+76a69H3HvvvQ9/+MP39vaGYRjH8dTJU+thvVquSi0HBwetNUnHdo496EEPaq3t7e3dfsftGxsb0zRtbm5m5onjJ5729Ke93Mu+3Pb2NnDp0iVdunRpPp+31iStVitJpRRJwzDM5/NhGCIiM7uuQ0zjFCXW63Uoaq3pPLZzDMjM3d1dIEp0XddaEzo4PDh54uRsNgMuXbqk/f39cRzvuvsu4MTxEweHB5sbmweHB621cRhX69XNN918cHjQWjtz+szZc2dns1lE7F7cXWwsMlPSxmLj0qVLm1ubtdb9/f2u6zLzEQ9/RCkFsC3p0qVLFZC0mC9KKbXWjcVGrbXv+gsHF66//vppmlo227N+ls75fN53fWZubm1KWswXQN/3O8d2SpTFYrFaraZpmvWzcRpLKYAkANDF3YvHjx3nP4FtSbYlXbp0KYT4l9jmWcwLYZsHyEzbAFAjYv/gYLk8ms/nrbWIaK1FxNRaKLqurtbrjcXGOA6ZWWsNxdRaZosoJWKcxoiwPU5TRMxns6OjI9uKOLazM+tntgGg2l4sFq1NpZQSRVJEKRERk1Appe+6CNXa2SmplBIlhsERihKLbqO1CShRkGqpmxubthGh4FmgShqHYRxHoLUGAJnZ933LtD2O43K1xGxv7yzm86Pl0eHhYd/3y+VysVisVivbgCRJfd8dHB5ERNd1reWxruMK0KW9S7N+tlqvJIWi1jpOk0SJMoxDrTUisiWSM2tXnZ7alJl914/TaFsSSMJGokTp+14SUEqxLenSpUtVME3Tar0SktR13cHh4Xw2k7Qe1n3Xb25uroe1pGmapoMJCVyi2B7HURHZGtB1/TSNpVRXb9QNngvo0qVLOzs7/Cfb29urwzgAtkEAAvNAEjbPJDD/AiGezbakcRzr8nDvKRfPYyP+UxikaRy0OtpdrVb8Z7LpulrvPXuhr51tlXA67ZAAYyHbkiRlpiRJ/CsZJPYPjur21rETJ0/yn+zixYs6dN76Hd97Kdv6pR578c47r7n2umfc9gzbx3aO3XjjjX/zt3/z0i/10vfdd9/e3t7DH/7wl36plx6nMRS8yGzXWi/tXaoap+6RD+uOjk6fuTZW65Ae9tCHbW9trdfD8ePHH/ygB58+deqaM9c88UlPvP7a64CudAjANi8C2wBGFy9eOH78BNDcigovWJLT1ABAokaVxIvm0qVLtZbu/MHFO3bvrqUerY8W/Xw1DhuzxcHywLA131wOq8wWis35xtGw6mtnez2uT2wef8ipW0oEL5RtSUDYXrb1xeWlg/Xh+cPdxBeOdruu7q8PLxzuWj5/cOHO3XsvLi818o4Ld587uHA0LFfTsG7DelrzIgLtXdqbbc7vvXQ2IiRlZkS01mopwJStRJFku2WrUdJOJzCvszPbp3ihbAOSLl26VJtbX7qbT97AfzTbkrgCaolYrVbjNEqqtWttKqUMw1BrV0o5PDwwzPo+Mzc2Nmsp5w4v3nr+jhPznYecujGdQqV2PKen3/r0u+6869jxY7fccst6vT5+7LikirRcLu89e19Xu1LK0fJoZ3u7ZS6Xy+2t7WEcjo6ONjc203njbFZLefL5p//An/7sS5951MNe7e3Ww1oRUaqkdA7rIe2u1u3t7Qc/5MFbW1vr9XqxWJRSAO3u7h47dox/PduAJC5r2XZ3d233/QwsKSKmaZrP5xFleXRU+dcwFgKMJfEAJcqpk6e4bJomICIiAgCO7BoR+wcH6/UqFF3fD8Mw6/upTa0lMJ/P9vb2Jc36/sSJE0JPPfuM47OtExs7UzYQUtf1PKenPvWp586fO3P6zMnTp7K1kydOSqqAnZcu7dWuxlGsVsvNza3W2nq9stnZ2c5McLpLO6R79s9Vc2Kx3bIJQHT9crUcx7GU0lqTdNNNN504eaKUMpvNMjMzEbq4e/H4seO8aGxL4nkcHh6uh3Xf9YBxKCIinfPZfD0MXa3L5VKXLl2az+frYSilhDSOY5SYz+YRwQNI4rJpGm1L4jJBqR3P4/z580DXdds7O4JLly7VCB0tj+69775SSq211rparW68/obFYmEQzy3blDmBuEKKUiXZRmAASbc+49a9vb3Tp09fc801W5tbEaHd3d1jx47xb2IQL5JLly5VALBtWxLPSZJtIQRgGwAk2QYMgCTAtiSuMMa2gYgAqqTW2jiOtdZ0CimEsS1palOtNVu2bKEopdiWlJkRAdiWlJlARKzWqxKllDK1KSLms3lmAkCVtF6v9w/2I6KUMk1T3/etNaBltjbNZ3NJq/VqPpsDq/W6q904DhExny9amyJiuVxKms/nF3cvjtO0tbnpdCllfs08IgBA+/v74zTuXrq0sVhERNd1mVlKjYjWJtu11ChRSlkul0DXdeM4bSwWpZbd3d1SiqS+68dpLKWEwvZ6WEsqpUZoGIatre31alVtz/rZxmLR931mzvrZ1CZJtvu+H4bB2AbT9z1GUkQYY7quy0zAOCIiorUWEbVW2xKSIkLCuBpHxHw+by2nNh0cHpRaQ7JprQGY5WqZrSFtbW7u7e+1bPbmNE2Zub21fbQ8mqbJdsvc3NhYrwc7bbfWtra2jh87DhzlUcV0XddaK8Xz2Uwhp6MESCIzJZVSxmmstdZSjx87boyZz+aKwN7e2pZkbDsiZr0BSZkZJWxLAirQWjs4Opj1s1AM4zAMQ6nFdle79TDM+h5YrpallL6fCabWxnGstWZmV+vpU6d5wWwDQAX6vr/2zLX82xnECyWpjuPIv5d4wSQB0zTVw4Pd3d3ztvlPIzSMa62Odlerlc1/Jtfa1fvOXqil2o5S7EybBxAylmRbEmAbEJhnkiSJF0xoOlzVzc2dk6dO8Z/swoULOmjTvT/980d47yE3791732Jjo7UmMU1tNpvVWvf29h78oAfffc/dXdeVUtbrNVBrnaYpImxLes3XeM1xHHl+bPd9v7e3FwWOQnftXbrlxpu2t7dns9nW1tb29k4pZXNzs9badV1mLhaL66677pprrun7PiIWi8WFCxdsb25u3nTTTZlZSomIiIiIiIiIiIiIUgpgW7sXLxw7foL/ZJcuXapWAKvVOJ9XINMR0Vqb2jTrZ5kpyTYgCbDNc5IkiRcO6vGt7qd//66P+do/e5NXvvFrPvwlfuf3fi8zr7322sPDw/V6PY7jq77Kq25tbXE/SbwI1uv1er2WFBGLxUJSpZS/f9rubfcd/unjz4/NL/mSL1lK6bu+ZcPYBtbrdWYaz/pZKYUXyrakS3t7T3nqU7a3ty9duvTgBz/4xLHjdXU0vNebPHQYx1d67JmNRbdc1ZCmNqVz1s8yc7VeIcZxbK2VUkoptiXxAkiyfc2ZM9ecOcP9Ll26pAsXL544fpzLbCSexTYgybYQIjNt8yKwzWVp91136dKl2tVYrvO3/+wZj3rI6RtOlqfdemvXdRFxy823dF1n2zaAACKCfxPjurU1+9Yf/4ev/t4/f7FHXPPNn/rK99xzTyllmqabbrwJkMS/yZOe8uSIiIjW2jVnzpRSKmZj3m0suo15OX78+Cu90ivV2g3jsFwtV+vVOI6tNeDUyVO1Vl5ki/niaHnU9/1qtWqtlSi6tHdpa3P77554zw3XbJ8+uTg8XErKzFKKJEm2bc9ms4iwzYsmIniA3Uu7unjx4vHjx7nMtiSuMAgMAshM/jVsGwSZ2XXdpUuXareYX3jK0//gG777oa/2ig9609f5kz/+I+AVXv4VdnZ2bEvisojg36SUAhjXbjZ/3G/+/pN+8dcP77rnIW/8WrfcckubWu0q/1a2Jf3pX/zZrJ8Bh0dHD77llmM7x2qbxhte+sWve4lH3/IqLzff2jpx4sR8Pt9YbACS+NeTBLzEi734MAx916+H9ebG5nK51O7uxWPHjnNZa621ZruUIsl2KSUzAUm2JfEisB0RkmxLAi5dulRBwHR4FIu5SulL4QGGYej7HsjMiOBFI0kSIIkroMax7aOf/bW7Pv5zj7/R65Qv/Pi/+Yu/iIhxmg4PD0+fPn361OmnPf1pD33oQx/5iEfalsSLZv9gf5qmvuuBxWIhqYoYnvCU4clP218sbprNH/tiLwZM01RKCcXGxsb1119fawUk8SKwLemOO++87+zZ66655t6zZx/2kIccP3Zcl5ZHW2d3z3/nD2+8wkvP3/R19i5eQGqt1Vo3Fht7+3snTpwoUWxL4t/q0qVL2r148djx44AhW4sI7mc7FC0b/w62a62XLl2qSECOoyKiFGzuJ8k4IrhMgGSbyyTZBoSMJQG2uUySbZ4FqqSptam1wGQTktSyhYL7RURmGmMkGQsZh8LYtpBt26WWEmWapnSGQpLtruuAihjHYW9vr+/7zCyl2B7HsZRSSpmmCai1TtMkCalEtNZq7ZbLI0Tf9bZba6WUcRrns/nmxube/t5qvY5QLXWapmuvuVaSLl26tNhYrNfrTAsy01hS1/XDsOYySTYRGoahlALMZ/Pa1b29PUnz2XxqEyBpc2MzIngely5dqgBGUikSioiWrbUMqZQCZGaJks5SSiklM0sp6XS61ipkHBG2MzMzI4IHsC0JqJKmadq9tDvrZ1NrEbLdpna0PJzPF6dOnOQBNjc2eYC+73kRQc3M+WJ+kpOlFEm2bUfE1CZJtnkONghAvKgMMq6KEFosFjynrut4PiT+tQSUUuuF82eXR4eZyfMl/r1MRFzau6T9S2cPDg6N+c9jSi11uVpLEZIBkMA8k8AgMA9kACSJF4mhhOo0cc0NNzdnTRsyUxKX2QZsRwRgEyFAkqRhGDKTF0GUcuH8OR2sV9NTb81a7g02uu7YsePDOAhFRKllvV5vLDaGYWjZaqn7+/v9rN/f38fccssts9mMF83+/n4t/ezvfvXX7j7YP/56r33rEx5fah2G4czpM4eHh5tbm4vFYlgPFy5cOHb82P7eft/3mVm7eu+99771W731jTfcOE2TJF4ASbZLKZmpS7u7O8eOrTNnETw/6/W61lpK4TnZNhbiXyLp0qVLurS3R+Z6vVaEbQAwFuKyiLCdTiGgtWlrc+vg6LCrnaTMNMZkpiRJPJBoLRezWZSoma1N7dTp05Ik8S853N+/cN99p264YT6brdfrxWLBv+TcnXfVjUV1emNj44lPfOL+wf4rvsIrttZKKTw/tiUJIqKU8ku//Eu33X5b13Unjp/o+s7pzc3N13yN1+z7nmexkSIEVIXW6/XDHvaw9XoNlFL4lxiAV3vVV3vZ5cuePXd2uVzecP0NT3zSE48dOzZNU9/3PIsEGIAK2I4SCu3t70WUxWJeovCCSWqtbW1tdX138tSpWd+P03jzzTfzQkDFSBrH8fDoqERI6mqlkpkSoFKKwDyTeabDo8NhGGzXWm13tdva2pIkMA8gAUAFgFrr5sbm1KaIWA3rtjwqpWC3zJPHTwDimUqttkspmWVjY3Mch7RrrTvbO9xPPAdJ2Lp06dJ6ve5KlCgGgUEIDCDZxgaAKOVob892zGeLfpaZSAIDNs9Lysxhf39+/LguXbq0s729d/6CQtgGIWNAYIMAJGEbimLzxPHD/f02DFKAeSbxvES23N7ZPlivq7Fh5/Qp/jU2d3b41/BqVWf97O/+/u+e9rSnzefzWutisSgRR8vlxsbG7u7uqVOnVqtVZmbmcrm8+eabX+LFXyIzJfEAAvMCSQK0u7t77NixYRgASUBmZmZrrZRiOzNrrYhQREStledhWxIPYJv7Sbp06VKVtFqvjpbLrtZhHLtad7Z3eH4u7V1ar9e162opq/XqxPETe3t7rbUocfrk6dV6tX9wICmkkydOSuKBoErKlsvDo7HWcRrHUpWkLTBIYBAtcz2sbU/TFBHTNO3u7o7DiGBid3d3GIdpmjDGF3xBEpdl5tbWlqQKSDp27BjQd/3++uDccrdEpB1S2kAozmyfjIj1er1YLDJzPp8jhmEQUsj2bDYbx7HUwmWhyMyIsN33/Wq9ql3XPeWpT3nKU55Sa334wx4+ndDT7r0NWA7L45vHVsPqcLU8tX3ixLGXP3/u/OOf9Pitza1Syku8xEsc2zn2tKc/fTabZebmxsbW1tYz7rhtPptLOrazs1gsVqtVOkuUjY0NjC5durS5ubm3v1dr7Wp38ejS+YOLfdfZhDSbzdIZiuuPXdOVDpimqdYK2OYyQ0i2gcyMCEm2uZ+kS5cuVcD2MI6llHEat/vNk9ccl3R4dFhr3d7e5jnVWrlMEpcJAElAKYXLJPFAUCWt1qsSMQxD3/erYTVMQ5vamdNnANsIjCT+TWxLAmo6dzZ2aqlAlJjP5lObskvbkiQBiH8zSQBQMZLm8zn36+kB27b5jyAJqLwAkngA25L417MNADUijpZHy+VyY7ExTiPQWpNUSsWeWpumcdbPNjY2xnFcrlYRAk1trKXr+y4zI2K9Xvdd37IBpZTVapWZs9lsuVoe2zk+n82AanvWz7KlMQgcUSIkUIk+okQoJKlly2wR1c5aqgSQmaWUUgrCBlyi9H2f6a7rbJcIANDe3l6t9fDoMCJaaxFx6uQp/qNdunSpZuZ8Pq+1SrINZKYknh/bEgZAAOK5GcSz2SYiwFWSpK7reBFIAsQLIZ6DJICIUi9cOLu/v2cnzyb+I1mKg4M9He6fPzg8xPznMa6l1v2DQ6dBiGcyCGwAiedkOyL411HLrKZed8P1DSJTEQCQmRHBCzBNU8sUAvMiKKWcPXdWB6vV9PRbs9R7lUytn/X7+/s333Tz057+NGA2m50+dXp/fy/tWT87PDochuGRj3jk1tZWZkriX2I7Ivb392uZzf7+V37tjku7D3/nd3ji3/5NlLJYLNbr9blz51br1e7u7ubm5ubGZqlltVzN5rM777zzmjPXbG1tTdMkiX+J7b7vM1OXdnd3jh2boPKf6NKlS9USmW0cVaskAIiIzOSy1hqiRGmtRQkh26UU25lpu9baspUotjOzlMID2JYE1BJx9sL5pz/jGSWi7/vW2rGdnVLr+Qvnj+8cO1ou1+t13/fTNGVm13UnT5w4e+7c6VOnhmG8tL+3vbU1jmNrravd/sH+Qx/y0JMnTtiWxAOBdi/tHts5tlwtS5Ra6ziOCtkIaq22ucx22iWilDKMY4lI25m11tZaKaVly5az2YznZFvSpUuXai313vvuu+/sfev1ej6fl1IiYr1eR8R8Nl+ulvPZbGdn58LFixuLxe6lS8MwbGxsZGZr7dprrpW44667tre2Wmv7+/vXnLnm4PAAeLHHPDYibHMFaPfS7s72ju3MtB0RmVlKyczMBAxdrbZLKUdHR1ObdrZ3xnEspdiWNLUpFICx0DiOXdfVWrnMtqRLly7VEuXsubNn77tPqNRy/Njx8xfOl1IOj476rjt18mRreXh4OLUGzGb99tb2PXfffWzn2N7eHgLJANher9d93584fuLg8GB/f39q043X33D9ddcDgHZ3d7ePHdtdHkUppZS+1NU4ZGaEMrPvZ6Fo2Q4PDzc3NzOzRIzTNOv7cZpCGvYPaikhjdMESMz6WWttHEeFaq1bm1vApUuXKhHR2s6Tng4G2bkZRdggKTOxO2lDShvJ9lzKdAd0devFHs0LZVsSUGN76/B7f+S+z/6ScuqE14OknEbVCtASUFedSa1MjdaoBVsKQnlwePorv2Drzd/IrRHBZZIA24AkroAKamfPl2PbW2/1ptjY2O38BW1s5O6lcua0utou7sZiHseO5f4+ClrT5obX64Mf/em8cBEQIPEAknggqAKdOtl2Lx3+ym9ik6lavB6IAFSrM8nmluo7JKaGhITkaSonjgMG8UIJnb948eTW9vqv/47WACRsSQBgG5AE2AYkmctszWb9SzyWEvxLLl68qFuf/pRmM58jgXluAvP8CaeXK9KIF8gghmHQ0cHF9Xrt1hD/elIIxL8kIqqk+XwG4j+TpHr+wkWMbUXYRmCei0L8+zhdZ7PN02fO8ELZAAjMv54lnTt3TodtOvvzv3JpGrZf+RUOL1zY2tq+tHep7/vlcnl4eDiO48kTJ1/8xV88IjJTEv9Ktmute3t7NaRL4/ru3YuzYf2MZzzjlgc96B/+4R+OHTvWst1y0y3PuO0ZkmqtrTXANv8akgDAti5euHD8xAn+k126dKlGLcN6PQxDlGJcS22t2Ta2LSkUkgDbiFC0TEGtdZzGWmo6QzFO0zSNi8WitVZLba11XVdrtS0JqELNeXB02HXdNE2bm5vTNLXMaZpam2qpfd+P07i5sblcLYHMtG277/ujo6P5fN5ai4jWmvF6GLK1xWKxXq83Nja2t7a5AqrtrnYbG5sRms1mmVlKCcXGYmPW90fLo9VqtZgt2tQiIiJCkc7Nzc1a6onjJ3gRQQVatnEcIgKU2UoprTVFCICIUGgap6lN4SJaZo7jWEvlRSAJAKrtWT/rjneAJMC2JNuZ2ff91uZWZm4sNiTZlgTYHscRAQjxghl3tQN0ae+S05nZ931rzXaUEMrMUsowDNzPOBTjOPZ9P47jYrEwtp0tMxPxvIRathIlImq2LKXMZrO9vb3Nzc1SytHRUToX88Xu7u6NN97Iczo6Ojp//vy11157dHQ0jmPf95ubm7VWXrCzZ8/WWmtmbm1t/cEf/sEv/8ovv9Zrvta999577vw5SY99zGN/67d/67M/87MXi4VtAJBku5QyTuOP/cSPnT51WtKJEyde6zVfq7UWETwPSZIAXbx4sZTSdd3e3t7GxkbXdXt7exExn893d3dvvPFG7mdb0uHh4e7u7omTJ1bL1fHjx8dxXA/rne0dXrCz587WUivCNmJre8v2MA6nT5/eP9gHTpw8eXR01LJl5s72jiTuF4p+1l/au1RrjYhhGKY2jeMYCmBqk6Ttre1SCldAxSg0TdNytepqba0ty9LG9jQOfdeXKCDbgCQAsI2JCKctS8JElFLCdlXluUAFECUKdmZGRGvt2M4OL0ApxXatdbFY8ABd1/ECCAE1IqZxCsXW5pZtADg8POQKAWAk2S6lHBwclFIOjw77vncaACQZYxCYKyQBtqdpql2tkjY3N++++26FACHjUEgy5gGEbJdSrrvuurNnz166dCkUkngW8VyEWmunT59urenSpUutte3t7VorkJkRMU3TNE0IzANFxNHyaBiG+Wy+s7MDjOMYEZJaa601BOaBjI+OjmqptbU2m8/uvffepz7tqbXW66677rbbbnvsYx57zTXX8PxM03Swf7C9tf1Xf/1XmfmIhz8iM8dp3NrcWiwWPD8H+weprMa11DvuvOMP/ugPFvPFS73kS/3VX/3ViRMnrrnmmsyUhBACbEuyHRHTNP3FX/5FZh4eHj7u8Y/b2tq68YYbX+e1XyczI4L72ZaEALS7uwscO3aM+9mWxPMwFjo8Orx48eKZ02dmsxkvmrPnztZSq3Eo1sN6uVxO07RYLKZpGscxMzc3Nper5bFjx7taeRYjlJn3nb2v67pau/2D/flsNo7jieMn+r7n+YIKSGqt2e67fpom4Pix48ah6Pu+RAC2AQQASDp18lQ6S5T5bIawqaUAtnkASUJAxaRzVmZTmWb9bBgHSbVWACilcJkkLpPUskkqpRQKEBE8gCSeUzoLRZf2Lgktl8tSim1JQGby/EgahmFzc3MYB9uAkLEQL0BmllL6vq9CXdcdHB4ghFprCCGeH9td121ubo67o22EbcCY50ukc3tju7Wm3d3dzDxx4gQwTVOtFbDN8yPp4OBgd3f35MmTGxsbtiUBtnnBzp492/d9bdm2trb+4i//4g/+8A9e8RVe8c677nyd13qdkydP2pbE84gIRN/33/Fd37G9tX3y5MkTJ0683Mu+nG1JPD8RYbtKmqbpmmuuefmXe/mXeemX2dzYNAYk8fzYxkh6rdd8rUuXLl1z5prlaglI4gUwBrS7uwvs7OxIGoah7/tpmmwjQmG7tcZltufz+eHh4e7u7smTJxeLBZCZmQm01gADtkIAJp3z2fzc+XO11Gq7lDKM4/7+HhARpZT5fC5r9AiM45iZtjNzPp+XUtIZEXv7e7ZB6/Xq5MlTR6ulJEHL7GonaZpG24v5QgjQpb1LbWqttVILxjYC8yySAGzA9nK57PveuKsdknEoWrZQcD/bkgDENE7TNG1sbmj30u6xnWNHR0cRAdiWZNs2l9mWpFmPQjgietRgHMccR55JYJ6HbUkbGxuX9i5VoeacLebDeh2KUmu2VmrpascDTHfe1VZrS5O9zmZTt7c2rr/OTp4/SQKGYRjHUah2pZ7dvbB3dLAdswu7FxfzeWZKesiDHxIRtiXlON7zVd+4vP3OdSlDmwY4OjzcftmXfKnP+sS2Xkviucl21y9qrY97/OP6vn/oQx6q3d3dY8eO8a9kEKSNzfMjSRL32720q8ODg8M2rNbrrrF3sD+fza+55sx8NrfNZZJymu76tM87uPPuVV8vXbh4wzu/zUPe+e3Hw31F4fmRsOn6eSn17/7+77Y2t6677jpdunRpc3trmqZZ7ZbrVUTM+hnPY7zz7vXZc1PmNE3zm67fuOF6TxMSL1hEkXR4eLi5uXlp71INableLdermWpErNvQpja11lqT5MzZfL4xn196ylO3X+llN+cbQJJtGCTZyfMjMEQUYDabXbp0KSKqpNU4HK5WpZsfHh0O47iYz6fWImIcR2BjmjZms72/+tvuwTfFjde3YVAEEuYFEdi4WFJrzbYk7e7uHjt2zLYkXjAD4NaQeNFEBPfbvbSro8PDs4eXjtarE93G2fPnNjc2bNIZiqPl0Xw+f/hDH5bL5RO/4Mtu+oj377a33RoCQ4QiwDw/Nv1so5Syt793/vz5a6+5Vru7u8eOHeMFsC2JFyBbUwTPw0ZCEve7dOlSLaXsLQ+X65WGdrRazvqZbYmptWtOn5nNZoBbO/v137q6+96xxNDaCEcHB7e8y9td+2qvPC4PFcEDGAQ2tZuVUp7wxCf0XX/99ddX7BJlYzbv5kURJSJt26UUJK6QZo96hK69pg/N0xNerNaLM6cBRZHEAwgBkiUBx48dr7Vmpi7uXjx+7DgvgG1JvCAG8ULYlgTs7u5WIcC2kLEkAGMsSZJtQLZtHkhSBP+S1lopRVKV1FobxiEUtjMznREREZnZWlvMF5LGcZSEwCA5MyLSiclMhSRhWrZQGEeEpL7rSykAUCUN47hcLiWl3aapZdZSZrNZZq7X6652iP2D/XSGAiilTtMYEbYltcwSYbvWOgyD7Yho2SLizKkzkgCg2i4lpCglchxPnjhZa+V5zGdz/k1sSwICiCiSMJKm1gDbAGCby2zbNrYNGAO2bds2tg3Ytm0bMOZZoNqupRw/dowHkAQAkrhMElcIQAiQxLMIQBL3EwIkAUAFgMyUZFsS/3Ek2ZYEVACICEAS/6EyE5AE1IhYrpbL5bLW2lqbz+a2D48OSylAREQUcGbaBiSth2E+m9tpA7bd1W5q0zRNGxsb0zTZBmqtW5tbtgGg2u66bhjGUgqAVErU2klExDhOEVFrHYYxQra7rpcUIVHTLhEtW+1qqaXr+lqrpGlqESpRAEkAUBFtaq1N0zS21jAt82h5WKLMZnMw5uDwsO86EeM09n2/XK0EkrhsNpsdHh31Xbder4+OLGlrc+vg8KDrut29S9tbW1ubW8a6dOnSxsbGcrUqpUhkZimlRImIdAohMBGRTqcjomXDSGrZhCKCy2xLsi2pZStRgIgopVy6dKlKyszl6kgonZkGrrvmWkmFwgMEQQEopXBZR8cL0NHxAJLqarXa3t4+c+oaCRuEINPGvCAGQGAAxDMZBAaBQWDAEbEe1rr9GU9drgfb4gHEf6zWmpaHu9M02ZZ4FhsJEAA2iGeyASQBYMBGAjBgJB7IBoiImpmlFMC2JNtAKGwb25ZCEmBbEgBkJiCFJC6zkXgutiMCcLqev3gxpMyMEgBmPQySSinTNNmWtLGxWK3WfdcB4zTOZ/PDo6OIAEoEEnZEGE9Ti5AUW5ub4zgeHh3ZBo7t7Ojee26PiIgQAAZwpiMCGwkcirQB7LRLhEGSbey0aym162xjSwIiwrbtzARKKXW9Xhu6WqfWJLU21VJrrev1umVKgEqEpKPlstaS6Vnfj9O4sdjY29+X1Pd9SCdmMx7AtqSQSimAbS0PdwHbkrjCNkjifrYl2RYgYSNhGwRItm3zQkDNTC6zzQPY5gFsAwbbAmwuM2DzL4LKv5L4N4HgvwYE/zUg+K8BwX8NCP5rQPBfA4L/GhD814DaWuO/AMge+S8Aeo/3ejf+C4D6jZ7/AlBPnDjBfwGorTVJvCC2wACYfweoksZxlNRaAyIiSpnGMSJsR60j7qRmZpL5t4JSuvISL/7ir/War7F/sH/82PFZ32fm9dddN5/PN3d2dlq+1nznROkeO9t4wrDsJfNvAnWapptuuvHlX/ZlH/3IRx0cHuxs71y4ePHGG29YLZfX3HzTD3/xl7/qpeFpyofU/neOLjUQ/yagE2dO7OzsPPqRj7z1ttsW83mU2NzYfPjDHva3f/d3pda7b799q3mz1Es5HWZb2+LfBHTm+jOttdV6Pev7zLSddkgA0PV9g8QFFUn8W9gGqm1g1ve2I0KSwTa2pDZNQlWkPdo8ixRS2gLANoAknskgsB0RpRSgZuaZ06e3t7clZeZyuVTEfDarta7X67TbNEmqtUYEIEnSer0exnFjsRjHUVIpJSKA5XJZasUupWRmqWW5XJ09ezYiqqSDw0NAku1SyzRNS9uZU2tAtja1trmxUUqRFCWWR0tgtV5nJgBIhCLtaZq8XpVSgdZa33fTOEkCamZubm4eO7YTUcZxNI6IYT1M9rGdncwEZn3fModhuHTp0rXXXjush7SvOXMGMMbsHxzs7Ozs7u7ubG9LigjbETFN08XVru2I0KlrT21ubkaEpJBaa5IM2KXWzMzMCGEUsVqtZrOZbUCShNOllGEca63TNJVSnCkJSZD2MKzX60FSzcyNjY1TJ08Ow7BarTY2Ni5dunT69Kn1ejg4OFgsFuM49n0Hsn1Qa0RsbW4M47S9tZWZmblcLjelUkrf9+M4Lhbzvb19SaWUCxcvHhwczmYz2zp17anZbDabzaZpCqnWOrUWkkFSa01QShmnaWtrU4pxHEuJrnYXd3czc3NzY7Va11qnaSqlTOMYpUiy3fe97aOjo2EYJFXbmxsbp0+fHsdxahOm1gIqpbTWjo6Otre3sS/u7j7olgcdP7YzjtPe/v7Jkyf+4R8ed/bcuVLKDddf33XdwcFB13WZGaFaO8D2fD47f/7CnXfdFRE6de2p+Xwuqes6QNLh4aHtxWLudNq2a62SWmvTOM5ms5bZWuu6LqTadRcvXpxaC8l2RAC2AduAIkICamZub231fT+fz2wiQjBO0zVnrhmGARjHcXt7e7VadV03TZNxrbXvemB/f3+xWCyXy+VyGRGAbUASIAmwDQA6fd3psbUA24ChRACZKYnLbAOAJMC2JNtItmspknjhoAI3zObn2sT9bAOlFF40tvkXQTWcjHpuGs1zsM1/IKhAs/nPBpUHkCQJsG1bkiTbXCbJtiTbkmzznCTZBiRhGwDbAFC5X2ZO05SZtmvXhZR2a62Ukq0pYpqmWkprLSJsl1IMgG0A21BLAaZpkhQRmTmbzQCgApKmaXr1V3/1zY2NYRjmi/ml3UsnT5289977jh8/tndpr+u65XJ54403juNQSgXGcbj3vrPXXnvNhQsXuq7b2d6OKMMwPO3pT18sFjfddOPR4dHRcrm5ufkHf/iHAFABQynlHx73uL7vNhYbtdZz58/3fe/MO+4o4zj2s75NbblaXrq0d+zYscPDw8w8Wi7PnTu3HoYSYailAEdHR+fOn7948WLXdavVKiIk2QYqABge9chHHj92rNY6temGG64/ODi85eabH/+EJxw7tlOizGazlnnjDTfed/a+G264YTGf7+/vRym1lqOjo2vOnLEBSimX9i4d2zmWmRIXL+4eHh3t7e2VUnTqutOP6BdPHZal60ICBIbMlFRKwV5sbAhq12VrEZGZkqZp6rqu1rpcrY6OjjJTgFRLUcR6vZYkGKfJNlABbGAcBp5Ha21ra+sVX/7lV6uVQkL7+/snT56cpqnrunEcZrP5/v7+H//pnw7DIAlY2YAkLpMEADp13elHdPOnjivzAk3TJMm2bUm2AUm2AUld19nmhYDKi6DrOl4o27xwUHkR2ObfCSpQJP6zQRXcM438Z4MKXGhj8EySeADb3E+SbUlcZluSQTyTbUCSbQCQxBVQga6UiHBmRLRMwLZtSV2taXNZZkZEZnJZLSXtkGzbBmqtQGaWUiTZzkzbAFBba9dde+3pU6fWw1BKuefee7e3t8dhuPOuu7quu/GmG+ez+TRNmXnh4sVrzpx5xjOesVytjh87dv111128dGk+m43jeMedd/Z9/5AHP7jWeuHChSjl2M72ej3cfc89y+UyIgCdvu50ZtqWZHBmrTWkqbWW6cxaq6TWWq01IqZpaq1FRCllmqZSa4mYpqm1FhGSJE3T1HVdRGQmV0DNzJMnT25ubmZrh4eHOzs79509e3R0dOL48WPHj/Vdf3h4eHh0dPzYsbvvuWcYhlOnTu1sbwMXL148duzY2XPnDg8PT506deL48dVqhTg6PNrZ2bn7nnumaZLEFVAWm4ta6zRNy9Vqmiak5XJpW5LNMI6r1WoaR2C1WnFZtja1tlytJA3r9dSaJNur9XoYhtYa0mq1ykxJXAE6fd3pUkotRdLUWmut67phGDKz1tp1XWa21iJimibbtdZa63q97rpuGseWWUrpus72NE211vV6nZmlVvEAUGaL2ZnTp2+66aa+70vE1tbWqZMnd3d3geuuu+6G66+fzWYRcf0N1+/vHwzDcM0111xz5kzLvObMmb7v9w8OSik333TTiRMngOuvu25vf1+SeE6g09edbq211oCIAGx3XQdM09QyaykR0VoDIkJSZtrOzFprRACZaTsibEcEzwtqa+2aM2e2t7fHcexns/VqFRF33HlnZt5y882LxeLue+7Z29s7ceLEtddcc/HixbPnznVdd/NNN0m64847x3GUFBECgySeLyib25stcz0Mq9Vqf29vGIaj5XKaJqBN03K1Wq9WCc5cr9fL1aq1BozjuFqthmHgRQQ6fd1p25kJSDIIIgLITNsRIcl2ZkqKCKC1BkSEJF4UUIGIiAieRymF+0mKCO5Xa+VFZhuogEAR/KdxZtoVSFuZtiXZ5jJJgG3uJ8k2/zZQgcxsrUUpmVkiMpPLMrPWapvLMrPrOv5toLbWbrjhhhuuv761ZjsiTp44cfbcOfDGYuPue+89fuzYhYsXFvPFsZ2dv/m7vxvHURL/WqBT155aLOZd7cZpqrVO07S1ublcrbBns9n+wUHfdWkLopSDgwP+bUAnrz21s709m/Vd7Q4OD0sptRaQMxXCTK3VWp0ZpYR07vz5zORfC2pIBwcHq2VBAiTZ5gFsS7LNZZnJvwFUwPYwjpIy0yDIzFIKkLbAdimFyyTxbwB1mqabbrrp+uuui4gTx4+fPXu2ZW5vbd19zz3b29t7e3u11lOnTv313/zNarWKCP5tQKeuPTWfzRThzMViMYxja23W98vVKiK4rJSyXC5t828G1fZ8Pt/a2mqtrdfrvu9LCaH5YpGtjdO4mC+m1ra2NmutFy/uHh4eRYh/LdDp605LAiQBtiMCsG07IiS11iQBtm3zbwAVyExJtiXZnqbJNhAR0zTZlgRI4t8M6jRND37Qgx7+8IdfvHhxY2Mxn83vuffejcUiIu6+554zZ87cddddN9xww2q1+vt/+Idaq23+DUCnrj016/tSqzOR+r5fLZdd3wPDMJRSbNdah2EYhkES/zZQgVLr8ePHs7X9g4Na69b2tiAiZn3fMrFrravVin8P0OnrTvM8bAOAJMC2JP49oPL8SOIBJPHvBMF/DQj+a0DwXwOCF5kkXgBJvHBQAduAbUCSbS6TZDsibNu2LYn7SbINALYjQhIvCFTbi8UC2NzcGIZxHMeu62xLGsdxPptd2tvr+36xmGOGcay1Clpma63WGhGZWUpZLo/GcbLN8wVlY2sjIsC2W0tnGiKEPU6T7cwESim2W2vjOEqybTszS0S2lpnjOGUmLwhU24vFYj6bTa3VWqdpCikzFXF4dDRN0+lTp4C0s7X5fN5aW6/X21tbEXFweDifz1fr9aVLlyKCFwLKxtZGrbXUAjhTkLYkZ3Z9Z7uUUkuZpgmQdHR0ZDtKyWwliu31etVa8sJBAJKEMpsikGwDSF3XL+ZzsO2IsFMwjuNqtbKNQTIMw8i/CCoARuq6vk0T0NV6tFyWUpxZa83MdAr6rj9aLqdpsn3h4kXxTJL4F0FZbC7m83kpZZomJEnjMNRanE47SglJIRvbkiJiPp9na5IASbwoIJBsgwWCrlaFQIqICEHLBgKcWUtZLBbz+RzITMC2bS6zDdgGJPFAULEl2UQpttfD0HX94eHhzs72crlarlZbm5tTa5Jq3wGYlrmxWPSzmQApJEW0Nq1W6/l8BqxX6+2dnYsXL65WK64Anbr21ObGxmw+BwTmCo/j1HVdZk7TNJ/PQM4cxrGWglRKyUwuy8z1aoWEjSSwjTRN0ziOkgCgAmlHSFKJ0lpLu0RpU4tQRJ2mKRSGxBFKuyulREjYSGqtHS6XIQG2AUm2JUniCtDp605zmW0uk2Q7ImwDkjITkCTJNmAbkMSLCCr3k8T9JNnmMtuSAMA2AEjiXwWC/xoQ/NeA4L8GBP81IPivAcF/DQj+a0DwXwOC/xoQ/NeAfwSqMfeyib3cYAAAAABJRU5ErkJggg==","type":"image/png","originalSize":4922421}}}}
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","media_type":"image/png"}}]}]},"parent_tool_use_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","type":"image/png","originalSize":127306,"dimensions":{"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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"e269aa74-3502-4773-a5cb-401fd1350eba","timestamp":"2026-08-24T21:04:22.756Z","tool_use_result":{"type":"image","file":{"base64":"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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"466707bf-9cd4-41bf-a5ab-ca16971621fe","timestamp":"2026-08-24T21:04:31.403Z","tool_use_result":{"type":"image","file":{"base64":"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","type":"image/png","originalSize":51624,"dimensions":{"originalWidth":1240,"originalHeight":193,"displayWidth":1240,"displayHeight":193}}}}}
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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"d88dfb76-b902-4576-a0ce-acb774f0deb2","timestamp":"2026-08-24T21:04:31.406Z","tool_use_result":{"type":"image","file":{"base64":"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/JidjVMBk7BZsKPv/880lTJm0p2bLXXnsdf+zxgbGOlw1rnsOHDcea4e0jb8/MzCQrli9fvnTp0jVr1syYMaNWzVpd9+yKpV3DGzSxkpKS73/4HgYuXrx4zz33hHqNd2nMOW8q3PT1118jz5/L/+zYoWOPfXrQ864wE8vFubm5JOe999+bNXtWzRo1jzn6GOxAICJwEPjo448QzPc/pj90mL9gPgquWr2qbk5dKrUmd83kKZN/+OGHLaVbunTusv9++9OTrthQKndt7vr16zvt2gl14R8sIpVQF9oCpqEuMgc15ufnL/tz2TfffFMhUaFTp04qrtcZthudgee3vTPWg7mL69kQ2+O3zZ95l7NHpdhEw5+5QP2UNq+UstnvW7O1TaWGcBbOwlk4C2fhLJyFs3AWzv9qzopuzeUqpoIVWwfDpbzw06UlUUUa8nayhdA+QrKFixZi/4jDj3DZLb59+vQ5a8hZSK9atSplfv7F5y+57BJd5ZixYxC8vfLyK00aN8HPTz79ZNQ9o+rVq8efhBz76FjEciedeBIivcfHPY44Tetz9bVXY2fQGYMQiJaxb7Yq/wnM4048jm4Ppnrff/99JHJrjzzmSKy19t6vNwI/lJoydcqA0wcUFhaShAcfehBiX3j2BcSBhgMp/7VMg88a/OFHH9Kt3i++/CIi2/FPjMe6ImVYu3btCSefMH3GdKprgjMBZa+/9vrzKsUCpQAAEABJREFUzjnv408+vvf+ezVAGIW/bdu07XdMv2++/eaa665BjVOnTn3z7TeRvvtuu9eqVQuWYr9zp84UiE6bNu2kU09CFbohsJj5xKNP7NtjX+x/+923EAJ9Bg0cBKT33HePxkJ1tWndhu5qfujhh2657RbOBKvB1159reGXfDM42zkpA3cYY994zNplj4lrc/g7xLbBn3Uv5YeM/slzGv6sWP90o7esG92Y78eOejrFkMlFKeEsnIWzcBbOwlk4C2fhLJz/hZwV3ZprVGZINPhyWIEs5fgly9wYeslssenFxcVEp06dOtykrMysW266RWv/2uuvXXzpxaBWvXp1LPRhIfGzLz5bvGTxUccc9enHn9bJrqPiIl5eF9bxEIxhTQ+Lgfh57tnn4m+3vboZvLCdN+w8LK4qP8A78ogjEeC9/+H7geFuIBwFhwwectABByn/luBTTz+1qKiodevWhx92ONYwsXg49aup5w8/f9xj43SraHoXXXoRolAspR5y0CEogv3fpv925jlnvvPGO507d0aYevSxR8+bNw/RIHRu2qTpF19+8cOPP9x0y03Yx0LrOWeds2HDhpcmvASBRx1xVIMGDSjCJOGbNm166523mjdv3qF9h9q1arsqQh5i+5/QH8uYOXVyjjryKEStb7/z9qLFi0457ZQfv/sRido7sbPnHnuCEr1HV9dFeRA8UxSKRWkE+T/++OOnn3+K0BQZhgwaYvgrta/N2X4hmNFj7X3Ds7mX6p96gmfb/Tmug6Uqwjc39exU7Ghl2KWiPhzrzzrRHheEsxLOwtnSXDgr4WxpK5yFs3AWzqmKGMR4NiWc/wHOaTwrr9u1XkFklI/U4ZZRgn9brmvU7vhbrCWxLAyV8PflV15GIpZAJ30+qWLFisr/nsqxJxy7ctXKyZMn0xqdG24q2py0DTx9IP4+9/xzkyZPws6N199oM8KGwPizzz/DoeOOPe6hBx6iPOPGj7vqmqs8ZVTAB0t/uvjE9yZuKtyEYPiDiR9UrlQZ6fvus+/wi4a//8H7CPn0W4so/+bNm996+y3sX3X5Veee4wXDFw6/sGfvnn8s/AMBZJcuXbDQinAR6Y+NfazvYX2R8+ILL+5/fH/Ew08+9eQrL72yf+/9FyxYACBQA+uWdGsx9+nrrr5u6HlD6efsObM5xg8++gBLuwjm337z7WZNmyEDdOjRqwciW5C5aMRF3NFREdVFgejAMwaiLjr68oSXkYI4/LlnniPJIy4aAZUmvDJh8MDBRrManP/44w+urd1RNa7YPhxbNtUhFfX78ounqoVv9jgSq7xdnd33+IgWO64Zw58xLpRjXfnG2soLZ0OUcDZS7OqEs3AWzoYo4Wyk2NUJZ+EsnA1ROwbnZs2aqSgrfcgAzotrRN4zonrFlutUTrM54SIv5QnDM0W35zJV4htSi9WV6qCfI8PaIHawMIid77//Hun9+/XHQiLlQVDUsGHDpUuXTv16qv4WC7dCpQh6dR6HLVXrbIgDS0pKkO2E407QHPod3e/6G6+HGkZm2r7/4XtE7KVlpf2O60cppVtKierPv/zcc9+eGhRSsPiJWBR5EN29/tbrpNLGjRuxg2VP/KUnY2vVqkVRKK22Y3EYK5NdOnfhpulNm0baDjhlAE/hbf/V119hByo1axo4DeLnR8Y8UqNGjY4dOhpNzBuL11JaWjpt2jTsz547++C+B9OhDes3YGf6jOmFhYVYaOWeY3MO3Cbq35xqLGejHVW0k8QOOoYnl+PP3CfduKkgu2o7xe6osTv8r+GxXKZRNpXVscWFs3BWwlk4C2fhLJyFs3AWzv8wZ204LxJLwOaMLc0wzyjJlbCbJNhx/BdAeYfKHC/4xD/Ko3irKNaQWoPKlStnZWZhRXHmrJm8hZYtW7b7Xrtj/55R9xx6yKEFmwqQ2KZNG62397N1G2Rbvny5YaS2Ii8vz2gbbogKnYynL1+xnHbatWun9axZs2Z2dvaKFSu48lpbpOPvunXrECtqOLTAvWD+Anpnr67uzz//pOJz5s4xmn/hHwtd/9W7SGlQrwGvpdOunbThdlvyRklPT8eCp8FZb8CFxAb1G+i2RojYZ/8+sb7Cq+OgsHyK9iJVSVutBmJ1VNGqVSte0ODctGnTWC/njmfs8zY1LHLZV5VsF91Kf7YHGjt/7D7Pb1fHNdevFDP0t/twOVUbf1WcN/7laCKchbNwFs7CWTgLZ+EsnIXzf4ozF2uobWhl65aINd4wW0U/XGOK8x+dVK5+ENb10pxEqvYzdGrZsiWEv//B+0VFRbq69z54LzjaomV27exmTZthf+LEiVoUVhGnTJ2i/Ffy4C+WEFE2NzcXASFJQMg3e85s8gbFwirHCoa5UQj5KMO7E9/ViT/9/FMQ7roROCRtty67IRuWK5cvWb5i6Qr6N3v6bPw844wzKD9amkxDZir4/DPPI9vKZSuRbdZvs7D/27TfkN51z674O2feHAr2KPMDox849YxTx40fZ7efimtvg7OWQ9+V+WXaL7xZB581eOgFQz/57BNd1mGxpW0vUDfepTF2hp47lPTHv/lz5y+avwhWoDW3hrPe6Nlo29fterlYncgftraxcCu0GuX7M4fJ6+KH+F/Dn1ONIMZoVY6GNgSuPP8rnIWzcI6twkhUwlk4C2fhLJwtDYWzcP5/5+wJNHJzNLEpXArFV14FCQcxmoblFyjTgYwbboY0OkRvi8Xq5WVXXob1NASTjz3x2EMPe89n1q5de9eOuyJn927dkeejTz56acJLhUWFCxctvO6G6+geV3pIksLR0tLSMWPH5OfnL1q86LY7b7PNpu+v4L8LFy7k7aGVada0WXatbOU/F/rV119hZ9bsWaPuGRVwVxGPob977bWX438/5smnnkT0WFxcPPaRsbvvufvtd95OfHhbNmrUqF69etgZefdIipNhS9+j+h56+KGQoC0tKSkZNnwY1h4hYdLkSQhEP/r4o1mzZgUwVQCTXjhsULXdQnshvSL4hx9/GD1mNPQEqCeefOK99997/Y3X8/PyVejukZ4Qvu6I6qJDXbt60fKzzz87ecpkCMRS8IUXXdh93+6Qw2E61myKsvq27gO8y9nm2F3LdiSje9vZYlMMf9Z12U6rC5bvz3qHd2l7IFNhZzYGPhuC0SI2TOEsnIWzcFbCWTgLZ+EsnIXzv4qzJyGnQQ7WtQyD+SOgBheXva9J+Z8J5aUqVKiAaJAXobpRBYeoi7h+0HXgIQfOmTvHYQ8TUraHHnjo+OOOx86SpUuOP/F4ioUqZVUq2FRAOpxy0imjRnpR4pYtW4459pjvf/gexTMzMxFl4W96ejpiuZNOPOmBex+g6hD79dq/F+2npaU99OBD/Y7up+lQ1S+/8vLwC4eThoiE165dm5GRQXoOPW/o9ddcz7FAYdR12sDTEC7iZ+XKlRG45RfkY//UU069Z9Q9yOm9Effdd3rv13vCixPw89PPPh04ZCAURtmcOjmr16wmoz7+4GNaTrz73rvxj+RXqVKFbjCuU7vOu++827RJU9d/1nT3rrsvX+Et0kK3U04+5a477qI3KsHkpQuXag1hb+8DemPn3bfexVprYWHhkLOHfPb5Z5BQsWLFhJNAVK/8ldK333gbbTf+6fEkZMkfS0gB6Im6Vq5aiSKo69QBp955252LlywGbbrNuHat2uvWr0OjoxXGPDimf7/+Ritzf+WceboK3z2d6rtGxounDZ+M9att82futMZQsjUVGXmMkUtvXLKyRjoV3WITy8mshLNFw5AjnIWzcI6VLJyVcBbOwlk4x6nEU4Tzf4ozosjkAmsqJQztdQZSgu4v1UW0xrysTuH7Wg7CnolvTzz7zLPpm5wkGRHXc08/hyiUUhrv0hiRUp/9+9ADpUjEuuJFIy669+57iQKiSuTH4ioiuqKiIkR0T417qnOnzrxGiG3Tus0lF11Cn3vxXoYULOi6XNsTjz/x7pF3161bFymIQnNycl556ZVWLVr5mGO8JCsr69mnnoWqNWrUKCgoQBSK6PGySy4becdIFd2o4AF9Dhg/bnz7tu0hClEoEjt37vzhex+2aNGCsqHsHbfd0aRxE9dfKEZ8uHf3vT947wOKQpEBKTffdDNscfwPsZaVlhkmGMx1YyE4B5bTTj0tOzsb68mIQqtUrjLg5AGvTXgNAFXc/A3quuXmW6guENtSsoWa4/VXXsf6Kposd20uGr1+/fpPP/n0sf2PVZY78S5ke1SZv/EuoaK9qByLVHRSSmfbHn/mwpU1FcSHGEMrLkdFN8P/bWVixyNDAv9rjHdKOEfzKOEsnC1RXDfhbFQhnG0FhLNwNkwWzkYVwtlWQDj/Xc7eVqd+HVqu1Id5bjcaf9s6UfX1GtXjCIy6kYJ4lQt04+JmAMVSW25ubpMmTRArxuJAnpmzZmIVDpGPisOEwCw/Px8xoRMXgsea6UZjep04d+7crEpZuzTahRfR1unHlF32nRus6yI2a9a0GXegWJ/A3/Xr189fML9Vy1bVqlWL5bxq9aoVy1e0adMGASTJ1w3PfT1WfjmcaWf+/PklW0pat2ptdCHt7m50TiXWHRHzz549u56/bTNno96/5GxI/kvO2+DP/0HOsWX/Lmc3xdyVcBbOwlk4C2fhLJyFs3DmtRuZhfP/LGesiHq35vIoUZVLzdamHIg6fxl7tZTdunoRVWczTI0lRYn2irOxcfqxNGMp2/n1oVgasRJsbW2nEc5GfuEsnIWzcBbOwlk4K+EsnIWzcN7ROWMh07y717BHhTGuPsoT+Y4TbjpdK63/cv10FTrK5xm0WL1vm8eV5FXzFGMOQEUbz6ZvJHJDtPmcj+GOBpnYeoWznSiclXAWzsJZOAtn4SychbNwFs47E+cEV0JXb3MxrNU/kdPQxtCPHzKCfpdtnKyBz2gA+5CtmEHHYeE4bWX6fb/WVIHNiEsuY69T0qC5n8Xi4o/OCmfhLJyFs3AWzjxROAtn4SychbNw3gk5e+9ZMswzatJQtIWUTf/lPzkRwySOwGgtvtJtNLbWwTDetjM2W2yTu+xmazca65djEWdnqModhZfSN2fHupEhXDjzssJZOBvpwlk4K+EcBaiEcyhNOAtn4SyclXD+t3H2dnIa5CxfsjyWkRMNo2216JAOvnlOwwz7p2JtYBtvN7ZKgcZW6S/1TFWQ/7TdxdnqeQiHLWe7LO53ozMohs7CWQnnOFzC2SgonLnOwlkJ5zhcwtkoKJy5zsJZCec4XMLZKCicuc7/BGfvGVGXhd18XZXbRltsrYauKq5VdKKWo+1XcfYYZWMTDVON2vVRXkUi/M6sLsX3DQ15SjmSeXMa7UcwuYbCWQnnFInCWTgLZyWc4zbhrISzcBbOwjncEc47GOcEt1YfiEXABSnWSHrjKhrtp8Vqyly+Ad2wVqN0WWBt1GJA5xS4QKOdUm3aLhui639mxpAWC1pXZOQUzkYp4abjZjcAABAASURBVCychbNwFs7CWTgbtgtn4SychfMOzzlNlzcU4uZxEbE4uFpOdAFXRVvIbhhDrJtiBoK/NDlWE53oRiN+OzNf++avEuY5XSuy1xtXw0CkrIZUKRxUOBtC7E042xKEs3AWzko4C+cwp3DWKcJZOAtn4Uzbv4izp63OamPiostBwOvgxQ1Rhn7GT57IueifxmO7mkWsHP1Xv4qJ5zFEcUN4WRu0bb6hieG4fJ+8Sjgr4SychbNwFs7CWTgLZ+EsnIXzzs1Z0edbuG1GJp2u0ej6CJNhlbKax4mG1DzdVtc+ZBA00CeirxXmyuv8/C/PRj/tIjwD52Braz9V7ERnF3R+7jrCWTgLZ+GshLNwFs7CWTgLZ+EsnHdyzjkNclYsXaF/60y0FmxbpY8aoo2/RqlYy8vPoKUZShvNHFuXrTPfuKqKOcdWVm2LtQnYmsSSEc7CWTgLZ+EsnIWzcFbCWTgLZ+G8k3Gu27BuQqfSpqUbdyQbEhPR75YathmijEbVpXgeva81MRAbRXgenW6nlLOVsU++ch24mdouevCXpxu2lMNBFxTONi7hLJyFs3AWzlxVJZyFs3AWznFacVCGqko4C+d/J+eEbVIsWf6T12cwMjb7KBeYSjP+kyujqzMOcc3tnMa+UZzby1EYrW5Yym/pjsVqqBrrOsJZCWfhHLcJZ8Nq4cyNUsJZOMdtwtmwWjhzo5RwFs5xm3A2rP4vc1YUiDrR6FZFQ1jbYH2UZ1PWE7d0tJy6dY2xerthiG+bp0HYxQ0Him1mLlNFCRryXRbc02SDIdZWxtDBcC/hLJyFs3BWwjlahXBWwpnJFM7CWTgLZ+G8k3BO2EIVW2l1WNRLKfxGYa6r8dM+lCqzscMpkCXaVIfNDaiwYTgaOsTTqWCZvxlVGyx4oxpeQhtnYqutVbVrsRtJOAtn4Rz7UzgLZ+EsnIWzcBbO/KdwFs47MuecBjkrl600jDEQOGxNVkPkmd24SNdQWreQcciot5yKdLobjdftKgw/KN8o23CjRr3DX35li3WiS+cqriWEM89j1Kh3hLMSzhYEJZxTJApnJZwtNYSzcBbOPI9Ro94Rzko4WxCUcE6R+B/nXK9RvTRbuVgNKA9ZaGuvhRgsDG1s8wxF+U6sfIq/Yw9pNJSBO5M2wTDKiVtSj4Wo4tpeOxO3zmgGg4NwFs7C2TZWCWfhLJyjhghn4WzrL5yFs3C2RSnhHFXP0P9/nHPyGVFOlufgZLWKBlBDLn/DkmGVRu9EZx04qViVdEG+8VbR2WJBxKrN0fDa9W3WvPZUxO12shVW0dYVzsJZOAtn4SychbNwFs7CWTgL552aM31HVNfHoXCNjfJ846V4or57mIviGvAG5grYLW2gsXlxsSracspCuTUbt8jwBm6dTcywztBKCefoJpxtfYSzLUo4C2e+CWdbH+FsixLOwplvwtnWRzjbooTzf5NzvUb1EoZCbrjpMvynoQel6B3+lwTa3FMRtxvJ0IoXj7Xc1o1bYUjmZY0aU2lrGBiLKLbZYl1NOAtn4SychbNwFs7CWQln4SychXNU8k7CWdGtubZtHIQTbrqkE94cbBjg+Lcy81LKoqzDYpuUjcamb7e93tfguD6GCfpO61QybZVIMr02amsQGT5E0srCj9IKZ+EsnIWzcBbO5RglnIWzcBbOwlk47ySczUdI3XDJldetDxk0uSq8PoO1TnGiATo3mO9w+UZmY99AaajkWivL+mZlG42hNq9Fm2y4lE1DWU7JdRPOwtnIrIQzU144C2fhLJyFs3A2bFHCWTgL5x2Xc8JW0VDLOGQrpMVRPM21tDXWOwZ6FW1yQ2kn3HTIbmc27Lctsityo0E4F8VbhStjFzQcJbZSJ/zOrHAWzsJZOAtn4SychbNwFs7CWTjbJu9UnBWtiNIPrr3ebOL2qrHeEMeXlpZyRrEGpGpdDlEv9XJdXfborU1BpW65WA/Tn3Y1lDEy8wwaDh3l1Wn0KsWmrftbnPPz83Nzc5XFmathVLGVnNFSS5Yu3TE4L1229P2PP1y7bt02c07lz9vP+Z/2Z1j9wScfbdy48b/jz1u2bIHb/CXnRYsX62EoljPUfu+jD/Ly8v4tnNW/atz49/pzOZxz165FN4erC+e/5Ky3beD8zfff/jTtlx3Vn3+bMX3K11/9L/izu3OMGytXrSosLPynOUP/jz775I9FC2V8VuLPwvlfxVnpZ0QNZPyvgS+2bXB5+u77791w280nDTx16EXDHxn3OEYEXfCb774dMvRswzYtavWa1Zv8cUqbqunzql1rVkAxsjZNo2GM1lVR1lwUl2wgM1Bw4bwxnOg0QOxOKs6ID/9cvhw8KfHuB++7/PqrjbIEx7aRK8OL0E9IXrFyJc0UYHvjnbfOv3g46jLs+o9w3rx58xlnD8EpX/3znFeuWgnHm79g/o2330KBDRWEXxVs2rRt/rw9nPWhcvx5/Yb1p5056PcF89VfcYZRr7zxGkl74OGHHnh4NOeMnRdfeemXadOqVq1KlW7YuOH0swbPm//7NnPW/THWn+E2F1x64fIVK4yCnPDsuXNGXH7x55O+xD4uQVasXKEszi9MeHHar79C7X+U839n3MD4Nui8s/5T/qy2adxQ2+TP1Ee4MrGitoHz0y88O+r+e7aZs3fKOP8cgzP5TLVq1dT/N+ePP/vk7AuGGkV++OlHDHp5+fl/y59ffu2VW0bezg9tpz8jjLznwftOP3sw/t370APffv/d3/JndO17Rz9QzR9PSPJTzz199wP3qv8K52m//QqGOP3h58JFizCULV6yJFXZbRs3atWs+dCjD0P4dnI26nL/ndcb6h84DxYVFeGkTIdQdtglIx598gn1D3OePHXKa2++npNd5y854+QLH8PJ1/Pt55/BMGWgMEz7n+Xs/I+dB3dUfxbO/zRnRbfm0m/+XGmsaBuHE17NXHXjte9+MLHLrp2vvOTyE489rri4+PJrr/rg44+oVMmWkg3+NLZhHkm45qbrP/nsU51OeeihW5ctKDtWcM8BGW3mhlG4kWIANaTRNIZeVec3autpCWVtWj5/0pcf4n6s/oozZv3Pu3DY4iWLKeWMU067cOgFtljDd5XVOjZnSD53xPkkGT8P6L3/5RdeUr9evX+CM3YQEdH1xD/NefbcuecOOXvYOUP7HXn03N/n6ZxX3XDtp59/pv6+P28nZyM91p/dMndj3kbw+UvOBQUFOK9TwS1bSkrZLR/YwbRC1SpVLzg3eU0MySCP9G3mjP4Ibqn8+cD9+1w24uK6OTkqtT+3bN7i4gsu7LZnVwj5+rtvL7ziUoMzqT3snPP+ac7qvzJubCndku+HH/+P44baJn9GW3/2xee81H+K86ZNhXwa6O9yBlKsfHID0VmqVa0Gn/lf4FxcvBmXswZnaIiu57Ivj8fWYvAsLCrMy8/7j/hzSUnJg2PH3PfQg/Xr1T//7HOHnnVu3To594y+/6FHx+qZzb/052V/LsN4gi6scRUWFesJvn+aM0wAWJKfnp6OQRJ/1X90fG7YoOHFF4xYvmL5NnN2dqDrDfUPnAe/+vYbGvORXqFChSsvvuz4fsf+05wLNhVcftGllSpV+kvOjnLgY3Rx4g1TBQX/Us7u/9h5cEf1Z+Ecq8Z/kDO2NC1CEzS42MWMndfeeqOkZMs9d4yq7I8C2Hr33K9t6zYPP/5Iz316VKlShRKXLF2CM+KSZUs7ddz1+GP6N2/WfP369ZdcfcWa3DVPPvvUWxPfGXnzbdnZ2Zs2bXrp1Qnf/fg9VtX26b7PgBNPzsrMREXPvfQCTkt77r7Ha2++gTWW7nt1O2fwWVrt73/84f2PP8RSTIP69U8/+dTOu3ZC4qdffP78yy9yy3ft0GHE0At0W7phJLxPt70PP/QwaqFnXnhu/YYNF55/AY7iEAK2devXf/I5QmXnyMP6Hn5oXy3tzXffnvzVlOXLl7dv1/6sgYPr5tTl0HVrwTRk+/PPPzu073DmGYOQjSBPnzkDRi1avAgpJxx7HHTA7PUd99yFUhddeRkAjrzl9ilfT129Zs2uHToqP0Louc++WGqeNGVS2zZtYX7u2txHnnhs5epVHdq2u+zCSzAKQywG1hdfnTD1m69wcdyuTduzBw1p1LARVhhuv3sklLn4qstbt2wFyb/Pn//0C8/12HsfUvj7n358/6MP5s6d27ZNm0MPOmSP3Xan9GtvvqHPfr3Xrlv36RcIThQIHHHY4dwX3XBOBfBff/vNGbNmNm/a7KxBQzTeZ198ftXq1ZcMv5DyIzJ5+vlnH3ngIWoC7XXYoaaHJvn5eXvuvueg006vXq06qfHdjz988PGHc+fNbdOq9WEHHwo30OqBW++evbAPPZ967plJU6cMHHAa/Apwxj/3NODfedOtderU4f4MRC+88vLX332DC6z2bdqdNWgwEHm1/PD9sy89f9mIS7CeM2vO7NYtWl5w7vm1atXCIViR52nluR+me/fasyv44yxL7dKj+z59DzmU5EMH8p8AbOiZDRs0gGdSU9ojhe7869ate+GVl7C6Ur169UGnnsE5Z2ZmIZcevKAtSf51xm+YsEC34mOB489GA+mE11/99ofv0Jv27rY3elOlrCzy/8VLl7z+1ps//fJzekb6QfsfcEL/43DpD8dbu27tuGfGw7fvuuV2HLrw8kvRdq+8/upvM2c8+8RTcJtnXnx+3316kBU//PwT2mXm7Fl1srOPP+ZYpHvtWLgJTYye+NVbb7z+zlvIOfi8s3t033vIGYMIyIeffowiv82cDrVtIFD7ultu3L9X71WrV3386cdVqlSFWFzKaGcjzX+e9nO9evX23bvHkb5DUs9FZyFPQ05cDyHl0QfHkJ/st29PKD/566mY2em6x568h2oHI0NO7H889QvHvzc+AFhcvHf3fU47eQANR3pUdcL7Z7Q/U1eaN29e61atuK9i+2LSlx988hHGwD132/3A/Q9AzICOkJGRgUOLFi8G8x9/+alK5cpH9T0CfVCFsSJspF7frm27swedmez17dpfOvyiypUrk3As8mC9+qdpP0MCmBxy4MFkIB/EqAtjEMPYi6FAtzX1Ea2nPdQvWrL4jbff+vmXn9My0g/s3QfeQs4PPl5X+vbr/IKCdq3b6q7k+J6qb8+Gn4AJ4T2h33E9e+xLg/bjTz352OiHKQ8GmUuvvuKay69s1qSp0S+QE9y4q+tx4+333sUgidHVHoTJlxAcXnzl5VdechmGgp+n/dK0cROYD28k09BBoP9X0J+NltS54OfnDjlr3NPjke3eO0cZcLT/KKs7o9IRl19y6IEH0ckCPwsLCy+84pIzBpy+T7fu+PniKy9D580lJUcc2lePn9pL5y9YAC8dcd4wDDKE/ZdpP+fUrQs3QP7YawjijNMHTjT33jFql0aNSCCqwykYDQ1/69Z1L5xAL7risqsvu6JFs+aU4eobr8PJ+pADD3Iu+waoAAAQAElEQVT8Oa/gzFtcPGvOHHL1oDp/3MbJdNOmgq57dD3tpFPoXgblj6UohQ6CGBIjDJWiQ+inGC3BXI8w/LSrW/CzL7/48JOP/lz+J+zdc7dkZ8msWNEf9DJJVUTIz774AgYN7Hfvuhc6Ah2Ce2OswGw4jRVwreOO6U8SMGhvzMtr3qzZq2+8htMExh/khxsAPs7an0/+csjpA+vVrUdq4MyF0/HCRQtz6uTgdAzaNuffZkwHhD8W/oFSyINhn3SDjS9MeAmNVbVKlb6HHAYFdLPya0dqTQy5aelpAIIxDZ2IDr3z3kRohTPL7l1263fkMY132UX5k7lor2suu/K9j97//scfc+rUAV5yfmykMJTBvMPx/Y+FgaRneAX1A1TG4HPK8ScRKN+lnxt+7rD7H34Qk5vHHHH0YQcf8sRTT6LerKxKp5xwYp/99qe+hpPgsLOHIhtWFHQ2uFalSpWRDbSJxtJlS59+/rkZs2citNtrzz2pRTDy4zIAGfSYj0EJujVq2JBoBJauXrVbp86wtEnjxmQpOunVl17x/scfwFKMEmcMCE4N/MQNB8b5CPMIr7zx2nT/fITTGUZO1AiqlStXOvrwI/XIyccNnIz4uBF7iaz8CwB9GanzGzmN0ML25797/cw34zyohRg5yxHFL6h0uhsXJKjoU3w8vxNdK+M/uT8b50GSZl/XKfZyVGPTi2bCWe3cnBP2Ma6rrbdO0TbP/2NBl06dqoTXQ2TA3t26Y3/Bwj8oEWsgd913D6KIM045NTc397ZRI3HVi8DpjAGn4miL5s1PP2UAQlboet+YB3E1dsShh+OkO+WrqQ8+PJpqzMvPX7x06bvvvden9/4YXj/+7FMM/XQIA8ed946qXav2WQOH1KmdfePttyz44w/ogGuOI/sefoR34XXY/r32wwUc8ug5DN3kuETDpbMbvvs4vyB/Qzgpi4jiq2++nvf778ce3Q+D42Pjx/3y6zQqi+H15dde2aPL7qcPOA3Xczij08qVik4PIBvOCrt16oJsUICyIX3lqlXQE5dNF54/vH27doCDa82mTZoiVMBRLO71P+oYX5kCDL7EEMpgmM7ISB9yxmCIeu7lFx4c+1DfQw875YST5i2Y/9j4J4jGA2Mfmjx1ChQeft6w4s3Ft951h4eiSdNTTzoFQnCNC+FIwSGorcIY8o67R2ZlZZ1x6um4LEbIOmfeXKoUV4cggCt4CMSJUBPQvkX7WG69bdSduCgZOOB0XAbddd/d2sPQcDBBFykuKkK93Mu1z6Dpv/ruG6y5nX7KqbPmzLrp9ltJPmLCkfeOwkkOV3IVK1aEelCYt532eMSKGzduIL9CCi5BsFM1vM1MOzAQTZo6GRyAqGhzETSn1i8qLoIhANuxfQdcyv++YP7I++4m3dAQS5Yte/d9z/0QpX/y+We4miSBaBcsa+g+Av/JC5vM0/y+u7NrZ5NnXn/rTX8sXKg7udEVXf/CfdSD9+Fyp99Rx+DkPfaJx3L9NiIObVq1at2yNf1EA0Ey/PnsgWdC8g233QyfV9FBAbXc//DoH37+kXrT1K+/Gj32ITqKCPzmO27Fxd8pJ5506IEH40SOoB0OMPDU01AQ/RHcEOGUlZahsUY/MqZOnRzMoWSkp8Nt4Hukrdfv7rkLLYJLEMTzdz94H65ivarLsLCfiy7fvVt3TAkhBWIpRkWRu+6/p1bNWiiSk10HaiN84uMGDSxo1nfeexcx1YnHndClc2dMdb3pB7TeOJCXd+vI27GgcfLxJ7Vp2RqhJkIRKoglOO5pJZs3Q3n6CYGv+jMIuCzbpdEufFgjB4MhcDBo1aFt+1EP3EuGwAQN8Mi+RwDgA2Me5GcOZY3aEIWuBFHo7yCDeSWYTJkxuQAPR5dHHyksLHp03OOgRC2Ly2X0U/zE1U/X3fdEL5v44fvaw9Hr09PTqNfjGhHNcfhhfTFQzJv/+xNPP0mGoJdBwrr16yB8rz26QgKuwPS4QV0YQwq6MGI/dGH0ETQKjb3UR3QrqOi0pfKjzVvuvA2RwEknnHjYQYcgcMXlJhnljTZfTYG7DjtnaGFxIY02ip1ZqdGJCeZugBd+8s333/IhiE5RoI1JSfx1opOm5DPk6vAZPbzjKIIKDMIYXTFw0eiKS2eXbd5sse+NYx4dC2fGzE6N6tVH3X8PLuKpFj0UYMoJ3f+WkbfTORu6YboTbYTOjqHPPg/qv/aL/pHSqkXLLyZP0v6MpsdA2mXXTjiK4GHiB+9hjvW4o/t9OXnS917AEEhGW7/yxuvLV6446dgTGu/SGNjh6uikJx53YusWrRBWvfP+xNhxg9TDibhxo13oip+OwhZ4e6MGDelEXLqlFCgQMapwLh8Og6UkyqnPvHB1OAzOvHp8BgrMr/XptR9GxR9++uHe0ffr8erehx5AqaP6HokRBqWog3jDvj/CoJ/SCAOfeer5Z/hpl3YQP+Ashkm6gaeesWHDBoy9mgZcFNPNmN1GCiL5G++4FTNTZ54xGNHjT9N+gedr98ZYAR0wVuzWpQtClDf8QIgGBLg6Jpig2957dVP+3DFOx7t33u20U06Fs5HDIDOdjutk18E8dccOHUbdfy9gGpxXrPSeAUGYqk/ZlAenCbiNV/a8YZj9QaT62ZefO9aaCS540JroRANOOhmdCBcGCAvpELSC4bg6Oum4E5cuW3bLyNvQnT28pWVoLyiMS6MT+h+7qbAQ1zneqruvDM4mqPSiYSPatW1L1w+6Had88xVOowgsMRmHBiJNNpdsxjzdS69NOO6YYxGuozkwSmzMzztnyFnNmjbFIgE9p4NsoPHyaxNO6H88Bm2d7dwzz27apMnoRx5evmIFtcgNt9+C+H/I6YMwkqBFHn9qHNJx4YcIEzsYVWi+Ep5PPub4k/LPT3jRs/TYE5b9+edto+7Y6C+2UydFX9aWYuSEpSp6aQoHxogx5rGxOf75KK1CBZyXAR+JA08Nxj09cpYzbvAByg33vbNAeBmJ/MBL+Q2ndaKBh7Gj/v71M09U1mqbLVBZ47MTXbNyrOW7VNcbxnnQta7rHCuOsq+fdXF+iBc35LvR8dlIEc47J+c017qnWevnRmNlm4jyr9VwbX34IX05CGTG1CCWknD+w7QWCey1b0+aqtx3n32HXTz8zYnvnH7ygF49euL8ul+PXpiXdf1Ju+kzpj949/05/vR8yxYtaTjAQImfc+bOefbx8bhmwokN037TfvsVE4RIf/TJxzGynzlwMOrF4hjmlTHaXnXJ5bjGwj/SHxcfOLWfdNwJhj85cevFnBSG0btvH4mZSwzrZ5wz5JffpuHaGmdZXAFjERKnN+Tstudewy4Z8dFnnyDM0+hc/9yDK7bLL7qUzoLdu3Y7/+LhyIbT+czZM9Eww845D4Ef5qoRYIAYIhZwwJUK5sWbNW1mA8coiTMxdurm5GDV6Lwzz8aZD9XhFI4lUNe/Mti3+z6YasWVENKxtoAacV6pV7fufvv2QugOPpDMPYkAIrbH6gr2cQJDpPfok0/c5y8CIAMIYEHA8e7m7eMR+PWXLp06u+z6G9km4BRSufKNV1+Xnp6OxPHPPo0TatLnOOe4mUjlT0b++PNPd982EtEjfnZs3xHnG1SdXbs21MPl4OUXXoK6DupzAOJGqHfvHXcZ44LeEADAWPIr7Bj+jL+Yycb0fMvmLZCI2YoLLr2QECl/heTgPgeiOs+Hq1Z9fPw4nHEz/Tn+2XNmw/1wpjzkgIPI/RDbu9YkEO9+mB1A5rMHn4kUTIVg8v7FV1/GvK+yRjrSENcN836fB/LwVdcLCFsgAFDhZ5qwhqaRPjLuMUjGyrPn8716QTL5PBdLvemBUffRnbTUm9BbcT2BUz70vvGa6zGjDMkIZeHYcEX0R8/9fG4ogqs0r8Pu3YNmMQzU5DaXjbiYrEPTr1ixkufB2jv8GTGzbgUUIbWVd9+E11Wx/EtA3Ohs4voN60ff8wA6BRJr1qgJrbDUgIbA+iQGb2hOCy85OTlPPvNUn1699Z0XTopZNwxHN1x9nT0IklYwBA7GDcFRxA8zZs4AQExC4SeaAwAROQOgMSpqgRCF5VbyVaxD4nKKfBVHsfYIky8aNhz7OHT1TdfrToQF52rVqt5w1bW0zIiGQFc9/JDDSD2ayMAORkUooHs9Fs+xlEdVQ0LVqlWuv/IakgATcEGMUdEJFxtpTQ9deOC5Z6Kt0YXRKNTWaHRlza3yDcxh5E3X3kDMs2vVhgTdlWi0wU+MNuhKq1avpq6kQtTEBH5C4zOsW7VqtRO9T8mNuyWJ9uHqGPCD4T10dfgMRlcahLE+hg6Cy1AaXfmyoe74NWrUuOJi73ZBjG+4csWwPPIWL+bcZ6/u0B9DAemPYVwPBQjYBp12BlaoOBatNo4i6uZ6UlBNGwZwBC0IdRAq4OfUb7+GHMzsoCEw6XP+2ece1OdAFMEZYejFw6tUrqIL1q5dG25AJ3U4NlydsHutn5ODcZVc3T6FKX/al06UnCd2MJjMXzDf8YeR2IsA5a8d0ZnXcHUIdPw47Y4bb2nbpi0kYOX5iuuuxhrgbp27oBQ6CC9FU0soRT6j+yl8Ztr0X11rch2dAu6HCUEkoutdef012ih0drQywUdL4dyquzkWk19783UNDWPFQ/c+WM2fTKlRvQa8AmNFlj+yYVoQwylGcn06hsNgoViftT/89GNIxukYKcPPOz8tLQ3Rb6sWrar6FXHOyIPOdcG5Q9G5EG7hlE014sSHoKiv31uxLV6y+Nfpv9GyIb+CQoSM1rz5uhsDILWzp/3mTeki5oRWCOcwY+55Ts9euDqa8Porg08bSAJzcupgAkV593PtOvyyizCjhNMTKaMVhjJQg66gMGqNuvVOuvJByH3J1ZejmXCNofynNzGBhfVw7EPO9z9+P/4Rb/569y67n3H2YFoJhBAM+wNOOgXZ9lf7YX0e2Z4c+ziafrfOuw08ZwjGc0wc4MKPWoTYlmzZQi2CMb/Xvr1m+2O+YlelflsUaEvxE5ZecAnWNl+FpYSobt0czKNhX1uKYdllty9Sv9jHX3inIhjBMHJi3KNbuHHqxwRx34MPRc5U44YTDQn0WIP8GDD5uQknayxHGyOJfcZ3tuP6WUWjGmWdB90US3B844jc1GFPqloctg5mXNfxn4ZFKnr9HFuLikY7qdTgGYTzzsk5wW2zETjRa33NS7fBli1bMCRlZlZUFprMihU3+W/CILE99wnudcG17157dl3oz9Fya7GDOf569ephEFmTm4vZuwb16it/ZKejGONwHqJ9XEgtW/6n678XB5EqZiipCFa0MIBCDif7+ZdffvP9d7gExGjF0TtWUMRpBhV17kJhAMb9Lrt2XrbsT+TEKR+nRly+oDrUCwiYQlu4aKFBibJBH8qGaxfKhkO4dMNPzIOu8V+Ki3MwXbIYEoy2AAHap0AFs92UGZqQHKiKgB/CgQUXBJgnRilMJLvWEe1mCAAAEABJREFUPIfecNZZtHgxXdrS0UMPOmTR4kWQQEVAQCvQuWOnZX8ut4XAUlyS4hKTtO3VY9/IYc6ZuZNikzRoMliBKJQMr1+v3m033Fy7Vi2oQeppCVAPDEHPcB7XmlJRVq9w/JMZLhNRFyH6+ddfkA5EOk+njrvSThd/Ne/PFcH7nMj9gkO++2mVYnsjeWaH9u3RNPSvedNmv/tXhLoheItg54+Ff+ACAs1KiXvutnuWfyetsUEy4kmSDJ/HX/J5w59jexNa1ju0YP5+PXpSFIqfuCbAND8fWLluXfwZbmOgoHY5+ICDdBMMPn3g0UccaajKmwZNptXGP1yUExBlDal+pV3oWhA/cVWHLrZw8SLs//7Hgl777IuxhUph1gBdTN95wdU2BuVOHTryeUG9Q25AhtAGQ446/Ajlr3XUrVs3FiDnzNtl8ZIlEKUVgN8iP5THdAYWHGAI5YTMnv4SMakxe94cXEpiQQwDBf7BObEahoYl7BTmYWvSuIny+z4VRPqa8JXakNCmVRstAYcKCgrohdvIjC5MRTCIdeqw659/LlfW9I099OlEeAuY61sue/faD95CktGV0C9oDMEKtvJXz3hLcLwkTeM1PEqx0UAnkqtjeKchlDoRvYuLj65oHZyG6tbJQQ+yxeIvoh0tGVfPf/hjNXk+aMMzoT9GS8WGArCi9XyV4jxYEeNdejr9TU9LS09LTzpbx12rV6/+9bffYh/LO1j3o8cH/vAHf7pGh5CaNWt2bN+Bw9deStjRSekOVfzEVbV29dfeemPkvXdjHQz/sANvUf4kWsWKmXbL4uxc6N+Gk6qVoRJcncYK4gxXd713Xy+ibNnZ2e3atiOwbVu3adSwEXHWIwyVali/AaQtXLxQ91OMMFQKPoPFRuNlkmhcdIo++/V2wjn1nvvsa3PGhoH36MOPxF94NSqdM3cuqtMLCAiQqvoXBjRWeJqH1wzwFn3NQI1OZ204DHolJuAW+guJ8GEogyVKiCWv1qdjrS2dsp996XmU9Szq2QvBJNLr16tP92CvXbsWISjWKlesWuVYV6jUmroT7R92ogV/LIAhmB+hnMgA95j3++96yN2jy+4koUnjxphPofMRV9i3ej8sEnrYF8yHgS1btKCqcTaB2vRiHnJpAKEWb7zLLnRviPJjaVhBdyrRKRKTMnRol0aNGjdqTNNbgFyvbj1Mr0BaZf8JArCF7839fR61CL2bQFlXq7RxS5V/HYjmhqWaEsJm2odusJQu8Ax/9pu7i/455/e5GDmxKo5GwT8aOdEQ5YwbxuBAsTJGMAxTdBlJ5yYMLPptgq4VLSjrekNt0/WzirsKNTyHS1Bx47NjLcSp6HkwVVk3ehFiC7EViwHI9l3ruk5Z82L2+GzrI5x3Ns5pHA0/pkUbRhq2YQoKs2g4i++9V3dlnWPQ+XVBDJR6v25OXZybnWigpfyHKDB8DBl6Nld3xaqVdAKji3ISUjGjIo16WHlDyqj77+W3QbvsqS2cGB598rGTjz8Ra19OdD7D9hXeivSTTqWUB2d0uhF0yTLvCxZnsvc6uv7QbFBe+qen29kXnMebuXIlb+YS53IslL30yoS33n2nebPmA044iR7L1I0a66yY/uTyk3eEs7kWnAufn/AixncQaNSwIa/a3nAIzYSc9fwnV0lg/bp1cZLGCa/JLt7zG/RUoRvMUgcEDPeFpYcceJBOwekq4tNxXdpQDE1fj705KTTLWfbnn2joeuGjXzjaoF49pOAs1dS/Lie1U3m5/sn9GYuZWIgzEKkwTMWFJf3EVZ3rv1mHhGSGT0yhFLmfk3qCSnkPNXlzFrhSNDQhz9T6cCFw5rp16vIU/QgTx4Js+EmSeWa608MJA13qTfBSruFK/zp7yZLFfcKgSFmjm9E08DrtbzoR1pHbqL8aN7Q/0yI5qe1GJ+eUtWEVV0urWaMGxhk4aptWrRcvXtQnfEhJ+YuHuJpfsnRJEDOwwdQNTSMaulm5qq73dhbPELryNhRD6IupBBqONOeV4bSOsXm9YNkyeEX9ukk3bli/PoTDV8mLaGmaiueEV7rIhsu4ufPmvf/RB5whxr06dep4Y3RacpSOKMk4ozhJ4PpjIQshhAq7sAoHsaLiIq650WqOdfoB8/177qfN5JR++XWa7kr60UTOh3xV91/HmknV+/aL4MlnqBPZgxgGYaScNexc3o8wuqo4f6Ylu4B8nRycnnDZiubAUIDZQKy9oIHowTzd3VQ4wDpx50Fcml/q3wugxWIxatac2drMfffu8fV33yDk/uGnHxCodt19T9/ZlsFdK/qxJRWEDlip06bRwg5JIOy6dhzCqj5c3ZspQ42OKnPLOEOcbRE62pwxDtBcBh9JjA4IV8eFOFzdZec+eCA1B1yac4DOGPCV/xQoRpjB4Suj6SiWxLXPaIzGCOaG50c0Ou/pOeGDyo51jn7vow/eeW/in8v/BAfKppuJuhWVwliBno5OjYAZPzFBoIXQWVufjik/zS02bNDwnCFn0ekY67o4HdNaHCdpnLJPOf5E5HH8GAYLfZOmTsnLz8M5AkEdgljSjX/dgQPRmzcUL1taq2bNzDBARQZcUGFhX9deyb85mQ5hVqKstIwUPvfMs194+aW3J76rrx9QHdq6bt263CtwBqE3D5M++prBZff4+fKTbq+fXKUtUSH5NhSU0J7wzvsT333/PUw8YYDSLaKs86BOhLdoS6kurKx+/HnyRZV81lVbyv2BVOVXQbPnzMHgiXGPuwou+Tbm5aUaN/iZl5/OVNy5iU7WbvTqJZU/u3//+tnYbPdQbGBUKcZnu4jLz4PRr4k40YsWWyvXOg/qFO7PTtw1qmNd13FNnGis4lqXgsJ55+SMnTRKss2ONd7gTunNmwZTR3yjidum/msnqCBOt5jloqMbNmyoVbOWYSf+1q5ZC3N4V19yBTcv01+3oXwGRPysXdt7l8wVF1/aslkL7jr61VL3jL4PatAjl8qK6TkaMopWAstpD/ytUd0zZNzDjyo3ybdCOD5qRDVr1MRfyqYT0/13k+DnYQcdgkvq32ZM/2LypFtG3n7dFVfrVwRxn1BxV71cPcX8CfN/N995W7euew05fVATf9X0pIGncq1sd8G5AYkb8zbSbTnKf+oGKbVq1NR5jEa3uyhaE6sx+qd+8yGVKmX3vpeWlXLn0VvtWrWXhzOgvAosijr+V0mgHsmniuAquu20+Wg77c+GHJ2CyU4g6r5XtzPPGLyL/2IVIHJSRP4clxOeluwnwnnHoaVaD2AtD+CVl1zWqnlLro8xBvF64cwIt3jmdevXKWtiCdmQAp9v3aKVTjSEEyL0pqsuvpxLoN4E2vQEDs8fy40f0g3neveL1lb+SyZwMWGMG8b4q6JdFUDQVVV0As/muWHjRn0FgOVEtCw5KjSnF5ZqlfLy8uB+VJC+xkxHt2wpUSnOCnxIJQeDTHqDNNchu1ZtrC0AIB80svxXsnFuyeHLf68V+Splhq/CZ7Sv4hodKw+UeU24XOl601INsQLc/8ijFXNUjJacT+zwpTe0AiT0O+IobhoW5VTcZgxo/ErL5kPM9ZPqXAi6EgYuGm0a+8v4Jw86LamhX1b7CfVfN3pSLPM36lD6za5cQ/gM9o1OpIKQwxtdnxjziKOS5900/54XFXVaUkAXxz5qrFG9OkbLm+64lYYCGi1PPGOAE71IdVOfB7l8zo22Xj32fff9iei/WBft3rUbvZIKgyp98lfXsn79Bs6ES6BOqo8iVNaufmz4Ohxeb/NmzaZ++zUwIlRbumzpq2++fvLxJyEwW7Ro0SEHHKx11jO2ypsvDu4rgYuiOLm6lpwVvn2QPtGs09FZEPTiJ8zh52uSmemHE+QzHJG+ulKh49Xy+8X69eh6+tSz3hiCKD9C+sfHjzvpuBP26bY3XP3Hn3+6bdSd7HSzgXZgGsJCGiu4wpSTztpwGDod01E6HWOj0/H0mTM+n/QlvPr6K6/ZvctuxrjR9+BDD9hvf5yyKc8NV12LPE+/8NykqZMHnz5o1/YdateuPf65p+fNn+9ErwV1axrjhutdJNRA1KR7gfJPcHpAU+GIyv2NZGKFHJEtFKbrB1JYn0Y1540Yohs23Bp/VnGbl26N59D22x++G/f0eLRIj+77oGtTi+jINlYsGgWWwo11Nm2pY8WKtobKOg/iJx85FRv3CosKU40bip2VtHPqy0g6pbrsMtLwonL82d2m6+dyNn29Uf747MaFK4abGSRtOeVzNooo68RqD4aGJnYVhj8L552Zc8L4zc9SevAyrHWjl4977bHn9z/+8NU3X+sUnAkeGfd4k8aN69Wtq3HMmD1Ta/PrjN9otS04NbrBSg7Ohbg+q1atGpYCMJePYR0/9ZqkYqG5tr9a1WqYJ8b0NuXH36Li4grhPOjLr72C6cAR/hdQeBNygTnZ2bPnzNZMF4T3DPOGNNyiZXPvKYu8vHytJ86FldiCLWVr3rQpZcsON1xe06sI16xZM/f3eZgd9J6yu/BihMo//PRjEjLjrKJN4LIZa91q2l0W+vcBnn/Wua1btsK8O+YgdR7aMb7/gQ20cTLAyoA2E2uqsAjpnLMbF11oT23auPFv06frlOkzZ2p6mCudN/93ekOG8h9NMbofbUD6+x8L6F5u/CwqKkLb4VKmatWqUI8+Ser6V67YB0mop9tOi+K3aHqKKdf2ZyDCuZAQgT8QGV3dTRElKv6mbGYCPGDW7Fm6Iu0/uO7B6gc8EzDJT3CJRJ7pRGNRzbnxLo0xp6M/7YhpWvqIhVaGSsHnIRldo7a/QfLmkpK08DsHmgauEZEHZ2VyUXQT3ZvQ0dDKWiAWQ9794D2KBKg/8nd8x44y4I+LHghR4bjx1bff4HJEGdE7sxRqU4+mvoB9DYRLJh00Utf/Br0PZxekNGvaFD91kZmzZkFtmINsQDxvwe/0Xn7PGf74g7uZMW5oi8j/NQ3Hf1IXAxoOoS5oi4CwdrjhijwrM1NZsRypSkzQlXR15KtYdansrZNkT/06eJAbh7757hsVToG1aNZiTe4a3ZrpGelYtNQT/5yMsaO3ls1bQgJRxZZRMaN4c7FeXlPWeU6Fl1mYGNIZ9A7Fh7oicPj1t980OiyCYXkKGfRog5VqdCWs7RhnB+yj/2o/oSqAF4uH3siQ7S2F6fvl/lj0h24XfVGuh3feiSjapNG1oGBTHX/DIQzC+p5tN/qe+hmzZulBAGcfTDogMiT9h555Dg0FtMTK+5qT+jzInYqn6FJggsUo9Ihpv03r3bMX2dWkcRPUSAunrj/pOff3ucb5RaODV5Pn00+UwihKj9DH+jNWShGGjX/2addba2qIIfT+MQ8+Nn4cDOjQrj2yoS3gEvT2LNefGqZ5LuUHsQjLMVYQZ/zFT3o21fVejbNi3bp1VB3ixiVLlzb2T99UCmKJvx5h6BB1BJIPn8EIUxb97B5isOpVq/0admfvxBG+REX3PTgAABAASURBVMpAiiV3oDvx2OOx6g7foLVN7cZz5s7Rb5IkT8O1BydJ/tzCR5efX0DdBDrjdEx3PsM6nI5xxsRcMJ2OaQTgnNeuXTtn3ly4DfJcOuIinWfuvLn79ezVZ7/etf05l6VLlyprxMa+14n8QYbUxuhEnQjz+GhWjGPaeXAmpcc43eiVK3/tJK4f0HGgMK4fLhl+oVYGNuI0WlBQQAWxg58YXvTNMtyfbZ92onGasi5AKQM4z503j1oEoSBWUKnv48RqfECCt0KzJs3g/DNnz9LpsJRuFXbZtZYq93rDSIFpq9es1iMnxj2MnHDycsYN17quo3OTPqWiIIrQuYnPGcVqZcDZhutnl10JGNLsxVgVNz4bnF1rocxJff3slntdlyrFUN7W3w232OpsfxbOOzNn/E0YXPi7j1wrpFZWEI+tZ499Dz+07+hHxjz30gsYZTBZiBWntevWXnPZVVobDFXPv/wSZu8w4iMbgsMD+xxAYlu2aDlj1sw1/jMbe+3RFScGnD5xCY7LceS8+qbrVq4KH3Fk+MoYtX5HHv3si8/j4ganXlzH33znrc+99Dzy4NTyyhuv7d9rP8xE4uyLE/mCPxYQXG5Lq5atcC589/33cH59+vlnHetEqKytbk5drAPcM/p+nCBx7kcQftm1V3797TcOCxHxFxciyHb3g/chG1Z1kO3y66766tuvIXPy11Ovuel6AIHOmFxcvGRxs2bNHG/xoRZOzwhKN4SzvElfs5pQRa9Nkd7Uvz6YNHUKYOLc8PhT43QRb3ytWRMXRmgCN5zXpJ1jjjwK0+dTvpqKCenJX00BtP5HHmNUqqzQVzsGfObQgw6ZPmvGhNdfxZXNL79Oe+OdN3VLtWrREjZi9hSXfZ99+QVOn4m4d0yj6XG1PvqRh9EKuNYZ+8Rjb098J62CF6X0O+poqIeLeLTjlK+nQr1+RxxNahhtx+/RhV/hMg5XD3RXpPbnZv4NvUDkPdG3dAm968+NzhJpaBFniJsSw09ccUKH9z78YOmyZU899wwfDo454ij48Lfff4cAG4bDM5998TllXWG44ZeRu3fdKyM9Y8xj3gsMcek2+tGHs/y4UXPWN8fC5yGZfB5+fuPtNz/rv4NRsZN6tz33wqUkehOkUW+69pYbVqz03ifU9+DD0CNeenUC2gvoHhj70DfffUsPAoEb3DLXf+DH8Gfufp51Rx712ltvwG3yCwpwjfXQI2Pm+/2LjxutWrSA5+OSlK6NAARdVQO56Y5bqKvaPrZq9SrARPNhcHji6SexbIVwEeYffkhfdOQXX3kZGuLQw088uvde3en5KHgaxGpPo4/KxnI26up/9DEwBA62qbAQ3jvm0YcX+I/AASCGo/seegAAceiZF5676sZrV7IHwJQ1UUVMIEp3JfJVbAMHnP7hpx+PeewR6HbXffes9h/uooKHH3rYT7/8jIIIpWDXA2NGY3lB3xPuWtdhyuqMfQ85FF0b3QTX1hhL7/cl0Idqyxk3aOxdvXo1tfXHn33ywccf0gmDv5rPYz7XY45RHUM3vAVDGTI09VcRdVeiF3fb44buv5t8T4Cf0NN6TZs0wSUj3BJzSfAfnDhUePLj183UieDqcCTyGepEfHRFL8Dwi9EVf1X0soBU+nLKpC8mfYmJrS8nT5r4wfuHHXQo0mm0nPzVVO8Z0SWLob8GZYyryjoP8s2NDtE6AxZFX37tVUQLHdt3oLJYzUO4OPaJR9H7MHHwxFNPUr8wmok2KInzKTopegG65MOPP4qVVfo+Taw/N6jf4KJhIyZ++P5Djz4MZ+i6R1cUR5tecsGFmBGmZm3ZvMWHn3wESmiCx54aV8NfMydXr5iRgbFi+YoV/MxLkjGMPPDwaDQx/j049iGsRe/uP6dHHQSHqIPoUp5DhiMMOgJGmAcfGYNK+fhMO30PPQy+gbkJ+O37H31ID+zY1xsILJevWD5n7lx4mn9+eYuPwytXrcTg71GaMWPcM+PhFdAwyOBLI3/Guutee3Yd9cC9mAyFw+jTMSR8MWXStTffMGPmTPgYnY6b+tMcnPOXUydfd8uNyEOn7CVLl9ApG0Eg6kUnKiwqggnT/EhYsXGYnNkYcjG2Y8j1tfLceOy4xyAZJrww4SX0tcMOPsTo+3yQwTbpqym4fkDnhcLf/fg9XT9Qi1SuVAmnUfRTuDSwV61SpeseeybYdyCSYFP4s3FCdOOOovOiRRCOYpBBi7z57tuac5tWrYAIiToeplpgKfhjicK21PZnbi+/3lDhFS3tY9z7edovNO6tWrXqvocexLhHQlKNG6aBoV38MlKfrKmWW++646nnnzHOg9w0tR3Xz/wnhxzTXqE0Y3x2WPhhDEGG5PLPg5yzG73eUJY/K2vGWcVtbtz1szE+l/OXFxHOagfljJQ0O2s5FjrRVSMd4A45fSCiCIzOuPDC9D/OtddfcQ1/bCArM2vggNPQn3GliKvG4eedj0loOnT04Uc+8sRjZw479/Ybb2nftt0NV12HMfqCSy/EAIfACdOTGL+Cu4rZuJwIb77FdsShfTHkPfLk4xjdkGuf7nvjmk95wcZkXGDh3IB/lBPn40ceGEPG61j/+H7H4qSCgSbrtczDDj7Ufx/PMjf1bAft4OyOC7Lrbr0RwxxmSU/of9x+/uso+AYNLx1+EbJdf9tNyIarLtTV23/s59ADD8YyFE5+rv/02uGHHNbHf8sCigw48WScrkDypaeec/1TqXF/iBuNmlx2rkKweebAwbhwx7UOYpgzzxhMLwNQ/lzAKSecBMkYtV8c/yzvCUcednheXt7jTz+JCBCn8GOP7ocrY2VFYrpTudal7a4dOp4z5CysYeLsAge44qJLcc4m+Th08vEnIqr85IvPduvcBVfqD44d41hLglhNuu6Kq3EGHXH5xfS81g1XX0ffpz6q7xH5+fmkXi1fPXrBILZk22V6bee9iCV8hxD51ZDzz7n9hps7+G8EoQ0Tn0POGIT485Fxj2EZhBA5VpDpWMEGdwbFesdxx/SHDuOfexrXZH0POQz+QzogG3TAWe3R8U9oz6T3H/JBLfBnv4kB/4qLL3348UeGXnQB2uvUk05JMLfnBSEZXUn7fI+994HPG46KlYrrr7yWehOQYoIDc+d0A3ab1q0vvuBCnHfhD8iJy8oR/suKiPaj4x4/64LzbrvhZnr7iMFBjyDcbbB8d+hBB6M5OBnXfxYaTO68dxSugG+57kYUQVcFEIRbMFkDsf35iMMORzSI5vNeEtap84jzhlE6NMfV9vMvvwjN4eHQXL9mCcMO+s6b77z18eefYtWCe5rBWfsz1YgBBKtDMGTj6PtRHa6NyBD4pB6OCCCGI7rLg7sKHyEhyuhKR/Y9nDJ4H7BJOJOnTkWo0HnXXffr2fP2USOpVNvWbTBQPD/hJbSI8l+LctUlV9C8QKwT8i3A0qr1ZSMuJgmO/36jKy66LD28T1WlGDfQ1nrsxaLZ2xPfpRdiq+iJp3WrVvAWXNUhFnW8V3F63qL8FxHTaKO70ujfx+g7fLS2GNwI7/oH1hNeen06imCgQNkrb7gWCp81aAhWdVw2uUv7UFK7OoTv3a07fV8Xh4xBGK2GQdg41VFO6PbSa6/cN+ZBeskKDSBY/UClXH/9khgVd4bmbRF7HjRKIRDFMI6uqplg5/ILL7nr/nuuuuFapMD/MYdLryA2zi/4C1e/ZPhFaFBgx0gI7PR22XL8eZ9u3dEdPvvi89vvHqn1pHuYSSz6DjrjyPvuhhufMeC0peEiNtZCMVY89NjYYZeMgKvrMy8J79ih4y4NG4247GJvBa9Z86suuQwYadCmEcYoRf2UfCYYYbrsBuUNv8JfnDfXrluH4BYtiGjzrIFDMO9jc8ZkE+KNK2+4hp4p7XfEUc/4fk7Zjux7BKbABg89m94vdZHvnIpdDip21kZshtMxPIocht4f1vfgQxctXnzNzdcjG3oNPPbA3n1UdHw+zHuH32LM5RmnbKwK3nnPXehEyIlOdFCfAxYtWWKf4HQn4kAoG7S6/+HRN915K8JsDJgYpdGdldXruWtBGYSa/PrhgP32D1vkGjqNuv6bpa674ppq/ottnRRXnLY/qxTXu3x8NlrkmMOPfMYfu5T//CqN+ejUN197g2KLIRjl0AdhKaZ+kO3iC0ZAQ9KEhBv+rFW1dyh/uzZtaeQEWCTiqhLjHl3Wpxo3VPSCWG/8lKrzkxpL/XslYjnYo40BbevHjXKu62wCTvReTcXGDds6N/oejVTnwXI42/6sr5+NzEY2vr+VxITzzsbZ67A5DXJWLlvpRq+MY8/lbjScNXZoHxdhOGXSJZSdE+cq+iCHrSUiDf12O+W/ZxyDQi3/8S0+Thn1GnpiVhIDcUX/7izbIlWue2FkrOBvBvrYGvVf/9t3ubAodpWPt9PqNWvohRlcE1SKRYY62XU0MV0E1+t0so/lbBvFEyF29ZrVEGtciarw6+qVwod/DFW9u/v8NwGWz9n2DZ2IAINazVBV+TdsZ4QP5DjWaUCnYGa9qLAQ14i219GXYG3OmLPgc1f6L/mVgZFURcNhzQ1zIkR+2zjznLCO3vHgWHM/2LBIBc+ktwpvjT9jPRxtRLdWxnLW+yS5YvhqTQMs/dS9ya5x3fr16WlpVcMrFV2W3E/FOYmy+gLWQLC0oq1T1rhB79aGksmmzM1FpRnh89JGqfMvHtFnv96I4hDAVPQ3ZbkNEGHEAHPHOhnA/wnIVo4bep8M0QOIzoAuw4ejVJw5K/Rr/fiTrlG/pwr7b7/3LgKM58c9zYcOrPmnVUjTI6Htz385PmMBypZQvj9TH8HP4087+ZrLrtzNf8Wua51FsJO7dm1Gerp+d7QKRxvqSmnsxTCxnLEaT++dMvy5uLg4M3rDcyxnuDriJX6/sQpPKxiE9ehqjBuYERh47pl33nwbLnmRDcGSMcsL56Rxj0bLreRc/mhA+ytWrjh3xLB7br+LvqXBc8Kd4AzU72zOhi/BBOSkc9NW+jOQJvz3ruECHROy9915d6Xw2Wbln14rsnuY+Q6NFTT8GpxxqKi4qEb1Gq51HtQdRFkbzvhYa6XBJJU/owW910aEJ45UnFFLXn4eWirBPuZOYwWmNiAB9maFb5SIbRHaB3l9OuaqYgxH94F8cqRYzgCLJU14u77qopz0ylmK+VW51xu8E3HF4IfoiZgndVNfbxiNAoWhDE70HAjt5BcUJPzX//wH/dkWFdsi2hxw5icFveEQzi/oy393fFapz4OxIydVl2rcSMWZn5to08+1ll/2n+OsyvVnlWLc0Dn/g5xjCdhUt4ZzrMJbU1Y472Cc6zWq5wWiK5auKEdErA2OP5o70XGcEvkzr3rjtyYrK842NFZWe9ukdDaXzRzQjj4Um99um/LbyXCXWPW2hpidUwnnqG5KOAtn/9DQi4bTxeWOxBkrb0uWLBk+dBiuVn+fP/+e0fc13qUxAr//EX8aVS0qAAAQAElEQVRGlIiF3xfHP2u8Xlj9y/2ZAtGRt9xOq0z/Hc5+SLPh8afGITq669bk50b/X/wZ1/00X7NDjhvDLrlw/1778XsxlIzPccaq7eMs50HhLJyF8z/BuW7Dumm2DZoa7ai4VnHDu4c1Vhsop6C1KYu+uldL439tgQZZFZ0/0JXyern+hvKG8FhXsOsyNm4Ol2xk03W57OXOwlk4C+dyOGtpOxLnww465P4xowefdzbm5rGK2KnjrkPPPOf/lzP355ItJSf0P44+FbMj+TPP8F/j/N2PP9x1393169WjiYb/33GDv/Vqxx43/n85/3f8WTgLZ+EsnFPV9W/k7OWsU7/OymUruX6xZLVE3gyGVbH5jUMGglgtt14Nw85U2YwG4MrobEbTcrhcVePvXwqJ3YSzcI5VQzhTzqIi752x9o3lOwBnLJGtyc2tV69eFf9uWPHnVAaq/5A/YyvYtKlS+NIvQ0jstv2cS0tL8wvyq1erLuPGP+3PhYWFGCtoGV84/3OcjfzGIeGshLNwTqGGcC6fM1ZEI7fmGsQNbfgOX3I1FDWyOf5ms7DNto2JdQtDQ8NjbDvtNubKuNHniWNJ2TrbTmnjjm0/4SyclXAWzsI5SlU42wCFs3AWzsJZOBsKKOG8Y3FGEJrGC+vKOGtbFZ0SW6udzbCwHMupiKF6bOZUwrm0sug3cwyCvNJUEsgDymld2y5l0bPrEs62PsJZOKcqyKsWzko4s0PCWTinKsirFs5KOLNDwlk4pyrIqxbO6p/njPREObXar88m0VqcQU3rqtVS0U0rx3Pqn074eSiSTBmMl5VxsVwBIw8dNd5k64YbL2soE4vCBmdkM6wwDhnmC2fhHCtWOAtn4SychbNwtuUIZ+EsnJVw3hE542/y1RTcHi2IZ+X6GXW7/pcJeDYnLibmpYyPbRjmGYq6VsxtCNQmlPOQMW3Gu/ttPW35irmLgStWVS6fmyOchbNwFs7CWTgLZ+EsnIWzcBbOwtmzV5dXqTc33LSKtiB9iMf3ulW4HJ2HdDWK26W09lxPA1ZsXfRTbzzd5qjDfRX1G94GqVxQp3P3sl/YKJyFs6GkcBbOSjgLZ+EclSachbNwFs7CeWfgjL8JfUwncfN4+QT7gjPHrU3ietBfGGlk00dd9m0GXR03LJWddqLDonYbCqdmWGRL43TszWhOY9/xl+xdfzJApXY44azrFc5KOAtn4SychbNwFs7RROEsnIWzscPr3WE4e3v01lw3Gq/bCtnptnn2TyfFOqxBjVtoyDfkpBJi1G6kawl62TpWScNGWw1Dgb+ljOGFwlkJZ+EsnP9KGeEsnIWzcFbCOYV1wlk4C2f1b+Zct2HdBCXRpkXr3K41AaBl8YZJZWeseUZ1XCCv19BY680tN6rQO3ozgn5DrEGqHJSGdbGZddWpMgtnJZyFs3AWzsJZOAtn4SychXNUSeG8c3JOGPrZynH6tM+Xqo2NF9c16XRbPodiW6gL0pKuDTHWIfgh7geGT9htaac4cRMAdsOoaDNoQww/Fs7CWTgLZ+GshLNwFs7CWTgLZ+EsnCHEkGvopI8adLhm3Bi9xUqIVZcenDVef+zETQPYj9VyXsZPFW1jZbWBsWMYZfzUomIPabi229l/hbNRnXC2qxbOwlk4K+EsnC2VhLNwVsJZOAvnHYhzmsHIgGLA4nD5TwONkW5wsSsy8nNAsU1obFzgXyppgNBqGM4X65E8s62GXR1vLaMi4WzIFM6xApVwjsMinHVO4WyUFc5GceEsnIWzIVM4xwpUwjkOi3DWOf8Jzoo+32KYrSzEXA99lN/WTImal1FH7I5hv/HXQMOL08abx95Igl2XPuREG88NvxfkMLEGDV4w9iity9v2cjLCWTgLZ+EsnIWzcBbOwlk4GwKFsxLOOx/nyMdPDSico0436Bg1OSmicBX9dg1Xgn8Yx1DOyM9r581jHFUWX66bjZvn5G3JnYnzLYcVV8mxJhWEsxLOwlk4C2fhLJyFs3Bm8oWzEs7CeWflHLw1NxVlrbd+oTDXgCtn2G/byRXlNvMPnvJ0nmIX5PJ5iq0Jr5oji900Vi7ZDT+5EytBf07HMJ8D1JKFs5YmnIWzcFbCWTgLZ+EsnIWzcBbOOyvnNPoPp+ywEJkLNVJiWSNx0+SvN8+cs3nu75tnzXXXrXeVS7nc5H+9PzpFhfKVcrVFgQEupVKlyQzKTynzBEVMVG6ZzqBTeTEnlJZk5Ovn+hG5VtUvQtIcl0REBZLa+OPrEJYKRLta1UBtXzHDM0KSShur285BpYEcl9uiTdZ1eEdJuKfnzsg5UatmRptWFdu1Sm/XJqtHN832P+jPbpJbZOhR0V5nyOHZdFleKRcSNG5ZWflC9M8y60vETnRSyg2/wqz7PC/LUwzb6ahiw4phgmG+cBbOwlkJZ+EsnIWzcBbOwvnvc/b0zmmQs2LpCiOfjdWoSbPgmbcsW55708ji32bqcgxiUoyiOMVJsHhGUcyjY6ewiBtU6AbFlGOIUhTKOV5IFpRyg1xOIijoUpzGwCmqIgixHArFtO1RR/RjJEoPwyEyOUSmiDVpl9RUBQGbE8oM/+OEUSLpGSqjwziKzcgiNyku1J9ZKpw554xd22ffcEWFBvW03tvpzwbbKGdzPLI3Xjsv4vD4PG5UogFF/9SU9CyUPTRQ/tjhxhgv+A7Pb4wv9n6sacJZOAtn4ayEs3AWzsJZOAvnv8+5bsO6kUDUriyV3nYb5L/13roHHlGFRWGQURYW9DOQ/PAP10z/ouDMNWInJ7Im5n/41GX2eH8ohgnjEjes1OW1u5ZJnliVXLiD/ITD2zjMyPKEa3m0m1znpGCME3KSxtKu4ySXMYOgzsxLAV0Yu2o1QzO11oEKwtnm7GRl1br4/EqHH6yt3mZ/Nrq3na6sMUhZnbOcDs9vkTf6Z2BstGOXo5Whnh689M0Y9hiUamjQVfMbOVIRS/qzcBbOwlk4C2fhLJyFs3AWzn+Hc71G9RKK4TYWZPmOhqssoNjyX39n3cj7yygK9WoI7iEN8we3Zfq/dWDjR0Sh1EAnvypa4KJDjh8dqVAWMy+QrIKVNJcqCbUN5JM+wW2hgbb+vh+A+Sp5t556LRREOMm4K/h/GA55BivdQsGdomVkgq4nqDrIyYgFxgacyUy6zTYKiWF2Kex0QwKBHk4QDQpnm7MqKsq97Z6CNycGnLfVn3miYkBS5aejLpv0sgcUh20q2hVj+yfPptN1RSpuIwXorWjGIwH2jmGdHuY4mViVlDXqCWdDeeEsnA0NhbNwFs5KOAtn4SycrfwJoxpdh6EiF2Tw2rL0z3WjH3f9GMMvEv5R+mf427fd/0+SfCAtWA9MVkJ/XH8NLYxYPHX8IspJroz5AZtyDCvoV9I9wltk/d+USvUmm8Q/qqJOEgJxXQ2f6vXVdkOzkuaFJQMbyXodhAaHXBUqE0jTdfH28ssnuEGuL1Q4q3I5rx/9WOmy5Vy/v+XPKuzP1A8NLU2lo0J433Pi5rdc/4FyI48bnWOzteWGGAOZrQ/jbGbmcgwy9ihpCFRsXHPZrJ6hnnAWzsJZCWfhLJyjuISzva9ThLMhUAnnnYazl9n4rRh0J/r2JNdaqPWylZWtvWWUKi5yg8BJa+z9C2KJ8EUzoQZ83Y/Vq3TB4LB/rybdKarc8B5Xioh0Of9+UQrXHAOIp7COogLDiIVKUg2jJF1cK+YVDyG7yYW+wDRqoCDGS7Z3snZ9I6u/70b0CpV3w9rL3MhhJ5qX/mgOwrl8zi7WReGT4bjw9/yZdTyjJxt9O1QyMnBEOLOCxiDiWHcscJVc9tiAyTk6EOgqjGFCVxcr3DaBjw5uuClrPIpNV9s2bghn4SychbNwFs5RIcJZOAvnnY2z+RJebgPdjqyiG7cN2+aZc4p/m2mVNQr5AYZbRpU4bLZA0U2e/qN/FBm5fnjmBit4viDvUUaN1Q3zhEE8SfbEKh7bhNoovvzI9An/JJ9XDEj4/9GlmQck29HL4JiLmdotSLfgza6UWubqQ6x+0gpHg5t12aHImqE2SgnnreRc/OsMeGaU1Vb5s7KGCScOtD1euNHJJD4w8SHAFmsU15J5vUZZlWKI5PuGNG6sio5NKjqW2YOLPqor1W8J3+ZxQwln4ZxaK+EsnHmNwlk4K+EsnIXzDso5YUjhiFMh4GbT5b7rL2r5kUxQLogTqGzwAh4vRnGDYCzSGH4eWu8qo0gmqZGXWkbBBjck5OuSZN8spatWOrIKdoOwx9F3nCbN8YLAMhMuFfHfAUsaBqGh4VJKi1UR9agKV4VPXCZ8w4NsTsDcJUpKWZxVUn6YkIQvnLeOc/GM2erv+7OdqNXgvZqLMvo5HwVcNlVmlDJSDMm8o7rWaMWF8FooEYOF8RkorpvDJquMumzbUylMDypsz7ghnIVzrO3CWTgLZ+FsVKqEc3hUOCvhHAq3i9h1/e9zRnrCxg1tnDBQ1kpwNFotcNk8Y2agQfDyGwpV/N+O4ybfahMoqOsJVPf+l0iEX7J02EpdoE4QdwW3WYa3sPrqJcMcHeSQhkpnUvTcYPjgIt34qeMZCnui72LVpJKKOg69+tXVBZPig6jPDQ0MWyRsGBXcTBuQIVFUS8IJHnB0yE7XpfXIIITzm4awlIX6COet50ye+Xf9OZQbGWL0iKA7mM5gjzuKfTvY7sbGjq6RSyYJZezbxFy4kZOrqvt87BPtdkX2MGrYa9ilFdA5t3ncUMJZOAtn4SychbNwFs7CeSfm7NKKKNdA/3TCTYVvTzJ4UXrxb7McP45RXD69K9UNGixg4QQhiqJFMO9PwuU3bzpOMoDx4xYS6AcnDhUJb2F1/bLKYd9E0Xe3+nWRUq4XmbjBM5cueyWP/oyn/8VOFq0FC3S0w5s8gOSbkXDCtURHJVfk/AJBEBUWKAtxBanaJcgcN7AwkMbeQhTl7CcI57/FuXj67G3wZ6WSMJU1HEQ4R0crnq7Lcgm8SLL7sVsduHzFXlnGxxG7n9Ij8rZwFR1NFNu0qrxg7PCn4bhsMzIY+ghnW1XhrISzcBbOwlk4C2fhHKUknLGTphU1lEtYH6tx4yLyLStWebsUKVBRP8Yp08EM3bcZMS0IbNyyUif4XAfD6gdcjtLRlN7zV/GCbP6qoBOuI9ODjsEtr4p9eTKsKHgHjhto44SfIglCOL9Wxwm+hUIMHFLfUNuvN5nm+Le6hjSVCl8zpFMcN8Dt/dcNP+AZcvZSE4rulk044TdCw8QQcxDeKeH8tziXrli5Df5s7OtOaN8fb4xNOqfuxkYGProlzbTqNRK1tk7cpJSKGxR0/nI+acUVMOw1jDIU5iOIrk44C2fhrIRztGrhLJyFs3A2FBDOwtnIr/TnW8g2fdgwO8gajXdpxdahV98oem+qXyT8iwTvDk83+PIkNzzIohfvkq0ZfgmEIjbSh4yhwMdN5nH08eRTgn4Gz/IEcxruLHQ8YBDx3FcOCwAAEABJREFUIHrakG6IDT5nqeMqh4SEbeMt1oXrdVqG44bBYiCOHn30QqqyMD34cKf+mYwB6UVAfsFECIaMTQS3xgrnbeP8d/05uQX1+TuUjcraQ4+98Tx8XNC47IGJd/skLavzq+jwp8cvZSnGBRoDn2IjIM/Pf7rWsOtGp83Udo0bwlk4C2fhLJyFs3AWzsJ55+Xs6pcVOdE382rlXLYmy9Ewe4KgJXjIkO6o9BXwK3Qp0AnrDla13OCP98/PRWIp/CoLZIaGBH+DWz19IZQ/2UaOUvrZwqCNlH7tjae5Lzi821MpR2cia7wlNWLnlgX/Td4vGgTr3qt8ArLKf01rAIMqdf2Vwwhdql2bS5xZwElqu0EjKSf42grnHHqMI5y3mbPSum6dP7t2RzU6lb1jZzb6Of9ppztsjopLNkYH2xZljYb2YKSN1dkS4TvEeQaHTf7ZNcZu2zduCGfhbNqihLNwFs7CWTiXuyOcbd2E87+Xs+Owa3pdtz5mEOEK6Sqd5FN8YTDghQCuUsnXtrr62US9H/52w3erBvX4gU0YclDAo2MqT2aAwM/vqqBqXcaPPYJIx5cWvFiHsNCTimF1SunFvdAwStQAkwFO8JfqJcMD45I5w4oUi/p8HVR4a2syb5mrS5HRNufw3T9epUGAJ5z/Nudt8WdH5+d5FKPJBxf7EB8IdLqKDhCKNbrDZsWMnFp/rVWs2vah2J9GNhUdO2IHDiOFD47cOuGshLNwFs7COU5t4cyVFM7CWTgr4RzdN2NoxVAqq0m4uIApRQQU+bhlwctpvEPeH0e/8lQp/SFJ7/8VEhktm6e1aJ7eopm307J5Ov42qB/VO6Tj/6H3vqrAWlWxy661b7wSRRgIlfyKSKSNXLaGRqqpMJIJbgalI24QX5ECXmIiOzutZTOnalUnjAMNGkgsS0aCARiltEBf1+AxS+WGXxahw74o73iiZs06d96Qff+dGW1ah4aHBqjghttUnJ0qVTyA9euTDmmNd8lo0cxNJALOvtqVDj8k58E7K3ZoS0Ir1KkD2onq1baGs962knOFOrVr33BlZreu5XBOZCPPFVnd9tScqbu4bF4jvXWrnAdHVj76sO3ivA3+zF53ZpdN1Yv4IbuK2J+xnT/K2bWfa081lNiG8Ip0cT026WyWPzv0lLyyBildnO9v+7ghnIWzcBbOwlk4hz+Fs3AWzrEpOzZnbGnGAcMMvkMZ6AXE+v5gR+n3wwQvzqESZGbwVGGQS4V/VaJKlbrPjDVUKf7h51XDr3B0gJGs1JNXcY8u6W1bbZ42vXi691mOSgf1rnxIn6Kfp235c7l/u6ZDsUoi0nyeAEVv3FFOKIrRUyp49U9ggvLzK52z+jkDKx124Ib7xua9+qbmGcpWioWsTnSfKg++zxIooMKjVJW354VuNarXGT3SLSwsWbAI4eLK4VeWzJmLDBmNGlTcr4e7clXBJ18Sv1jOlfbeq/bNV22a/PWay2/Aoezbr0tv3nTpwf3L8vKoRjctvcY5A4tnzimeMYsizGpnn1G570Hr7304/7W3bM6kspP8zgqZRmqXqYyMaif0gwMUvPhqLOeKnTtUPvQA6Ff83Q+pOGd27ljpkD6YjCj67ocKDRtU6r3vluUrCj6dFPqDx7n6wJPTd2lU+MkkKrttnFW0L22NPys2ZFA670uxndAQaNRlD0ZciN0z+cC3NU/S86P2IBg7hjopJrdUdGS0R8lUY9w2jBtKOMcpxneEs3A28tgmK+EsnIWzcBbOUZOFc6z8/1nOadzI2CrtQ9qYMMULU1w/HvBTgijOcUJjksr5ERW7H3jDg49CX+XrsNl7Aa+xBQKxl7Vv9yon9tvw+NMIRJG2fvTjmyZ+tHnuPA6LFHC8jeISJ1goU2EKhceu5kI/Ev5imqNtjW1s3bgajBO+DJYOhTlDnZPhd5CdFXfpDlY0llO50roHHtk8a667qbDg4y/0i4IQT9Y8/8ziH38p+OQLV982a3FmT3B6mcoKNnlCC4v8Cr2X7yayMteNGl38y2/Bi4mcWG9Icg4WRkMtwxYPWtBJz6g+dAiaLP+FV2I5F342ecWyYVsWLSmH86bPJpUs/bPEzwMzIRBmbvp0EudcMPGjdWOfLCso2D7O2+bPQR4j3bc3OabYvV1FhwD9wDffDPixmvD+HPqzYwx2dk6Vop+nGryMjm8YmApLrHwlnIVzak2Es3COla+Es3BOrYlwFs6x8pVw3uE4p9nV2NK1CKMCRWGD/ybX8FMdQVQQ5nEUvzvXSWYgURtffj2M/7w/XikngdWwSgf2TqtXF6ud+a++vfHNiTUvOLvqSf3hAdXPOqPygb1XDDi7xnmDsvbvueHRpza991HdJ0ZXyMleffkNta64sELtmuufenHT+5/UvnJE1j7dIGHt7fdunvO7V4HjVBtyalaP7mkN65fMmbd2zOMls39HjdVHnFOpT68NY5+sdOiBFVu3WNb3eLuB6CHNSgftX/mIgyu2b7N52fK8Z1/a9MkkiE348R5hqVCjRrVzBmbtuVuiWtWin3/d8MiTCMm8GE0lqg0akNWjW1qDesXTpue98iZCL6CodMB+NS88r3Dqt8Xf/4Qq0hs3rD5oQP57H2+ePaf2bddDINaBG7z94qphl5cuWao5p7dtXf28wRXbtS76aZpXMGgM72hZYaEqKXFKt1DDVDq4T+UjDslo22rLnyvynn1506dfum7oEKFplQ7qU+XIQzLatSajCr2A0KsF8qsOOD5z985lJVsKJ03dMHZ8olJW/Vef8sokEtBq/aNPFX//c90nHihdvjL/jYnVhw7e+NwEN3dt9RHnFn313do774cCGW3bVIGQ3Tqp0rJNX05ZP3a8u6mgUh9Y7eXZ9M132bddD3m+mS/AzJLFSyu2b1vt1OMzunRy8wvyP/hk49MvuVu2GJydIC6lflimEhX8tx8pHbG7rhGdbr0/O3YiL66iPTDiJGwgMAYanp+/kYzy2MX5ZrzBzK7RKG50/lQFuQ5UBWXjj7wbw2I5KISzEs7CWTgLZ6tG4SychbNwVsI5NefkW3ONYzo3vZbXQMOzBZ/TUOGfMPikSMEJXqqq8ytuY3qrFmnNm6W3aJ7eqnmFatVQBgFntTNPdzIyCj76zKlSucblwyvt37Ns9RrXXwWGxJKFiyAgUblyWp1sJzMT0irUrJGoXav29Zdj9Q87NYedVfvy4RU7tIX8jFYt6tx+PSlf65Jh1Qaf6lTMwPJgxq4d6o65J61ujneba5UqFbJr1zhnYGbnjqUbNhr4VGgSgtXaN16R3rxZ4Xc/p+fUqX3zNVm9e7jhLadezoqZ2XffUvWYw/EDK35Zvfap88CdTqXKKF7z4vOrn3laeqMGpUjft3udu25GuOWJzciAwohaPc6OSmRleT+rVC7L26Q5lyxY6G7erDk7tWpm33VjZtfdyjZtSm/WpNqgU103+Bwo/m3BSuP8hcQ564BetW+4Ir1Z06Lvf6lQJ7vWzVdn9d5XRVsuyzcqrVlTGAWe2bdcm9m7BzRJb7JLnfvvwNEty1cisq1y7FG1brrSLSnZ/Pt8wlIy/w93w0anQgLo0ps2rnXZBd4qfMkWWAQ5ZFF6k8bZ990ObqUrVpUWFUFI7Zuu9NTMSIeZTtXAzKTA4mKv3ntvy9y7W/GvM9zS0hpnnl5z+Nm+NUnObvIpUEX3OHsfbmFO5TAH3xZ/Zh2e59E9kPcfnZ+n2FVrmbwbkyYqbnDhKUb3dKypL56i79HX0uL9OWqC8dCCHkeM0cqwdHvHDeEsnIWzcFbCWTgLZ+EsnHdezmlakBMXwnJ1FQv92U/v/+wdrcGKJx3wC1JCcIwS3VCV+k+P1fLX3nYPljcrdmwPsXkT3sib8Gb6Lg3TWzYtmT678PPcRN2cqif22/jEM+uffD78pAeJDYTnv/R6/tvv1Rh+LkLZ9NYt/jxhcFqd2g1ef7ZC/boVEPYknMr9j9iyeOnygec5JVuqnNCvxohzIXDtg4+SpaW565affKZbVOR9esQNHuIMqvDRVT9nIAKeledcuGXZnxktW9R9Zmz1M07Z9MVUFURHbqX99kb0WzxrzqqzRoBR9q3XVmjcKK15k9IVK6sed1RZwablA84uy82tdtpJ1c4dVO2Mk1dfeZNiTsk5b/7x59wrb6p1x/XFP/y8+sKrOGfEsYj9SubNXzl4mFtalnP/7Zn+W3+o9deNGu2EnKuf5St83sVblizNaNWy3jNjq0Hhz6ck2xJ5zvbyrDr3IkSw6S2be0adfkrh51OrnXKcqlI57+U31j8w1qlYsd4zj1SoVhWB6KoR1+zyyRuwbs3F13jrvPVyPNesVjX3uttoubXyYQfqdoEQHIKQDQ884las2ODZR/DTqZQV+qKz+QfPzOyRNxbBzIuuBvnaV17oSbv17oKJH2KWocGE8VX6H7nh8WdK8/I1ZxW6li/FczD833WC1+e6rj7k8J6wlf7sppjLsTsIP2oI5N2e5zF6tWvNWhmSDYH2CBW7OSk+eWwbnkoIHwFdNr2nLeUWbdu4IZyVcBbOwlk4C2fhLJyF807MGX/TeA6+oy3nUbXLglotNLj85y9fdfw7P/2Xuzp+sBAS84UgZ/he4LzX3nFCppv/WIxjhZO/wkpjzYuGVjrkACyL5b85ccuatZSZtEoEqoX/SVSgnaLpsyCneOacKvg7a64q3bJlxcota3K9BbqszPTmTZEtrXGjOrd7L/VJb1APRdJbNHVCHPmffllWVOwb4rr6SU2qBf+vWjXRsD5+17r4fLLRY9e0cRAd+iFQRrs22Cmc9DWW8pBn1TW30iuFKvXaBz+LZ8wuW5OLggWfTap+3uD0dq25fP6aJQgpc5OzA/SGIc0Za7zY3/TlV4hCUb7g8ykV99qDdOWcHSzzNmqA3zVHnOM4wY24WLr0o2y/EfCvSpX0xo2wX+OiodozsMqK/BntWqPdi76Y4pUt3rzipMH0hCcWeKms34wJ5fsGYuzgjUrJ+QhvJ71tK69dvpjiWVhcvPzEQVTMDVyZ/u87Fems3Ird94TLVT2xf+U+Pb22zq7tcW7WpOzX6Sp4ADgINXV/cP0PqtKhoGHCVfdt9Odob4nUxcYgQ4iK9lWHTWK5bCJKi9USuD7lD0nGsMXTeUHFxixeUEVHCmMkMgYarrD+S4+e20oKZ15QOAtn4SychbNwFs7CWTj/JWeITaP/0LuPdN3cQl7MQBBkC8zz9vwnRdnbiVy9qqjK9Hcswz/Y1t3zkOP6b7nxBeG/Be9/XLomt9pJ/TM6dUA4VO3Efrk3j9r0wSeByoa1vvZJNZKvtPVEqZA+dhKVsuhI6YaNnjIbNhYiMly5Sre+s7nEf87QUeFbcBwnaWaFihXJdn3vbv57H3ulMjJcFPSrTGRmehxKSih0dIKaHadSprdXUlLmR9Flm0s8gVlZXqDuKx+6iUqkBfMCCb/6oHWRjWJRavWKGaGtv0oAABAASURBVF69W0oUfQZm82bDEYhzIisrVDiPDha897H3cc/0DN2IFTIzAybrN1CeTe9/4v3OSFMVK3oaFW8OYj2XWimMGEmCGwR8vskaWpDF2/fllxYXcy9POIHDBN+wJd39Pa9L+AzL1m+A2vi5xdfHLS5W9D6kwEl8fwtcJslZKfq2jRtwdmNGiq3yZ6srGiMRHdIjQuwIEluLig5qPJvh2EZdscKNQ3y2TPnjoF2RMRzE2m6MqvZgp/Ns77ghnIWzcBbOwlk4C2fhLJx3Vs7Y0ngOGyspUQ4+pT8/6Zejv342ChXKgnelemEEhamQE+Tzs7l+4OrX4HrBRnqbVhXq5qy+9naEHzWGnFZ10ClV+h6U/8EnfkEsf6YFX1khcEyxIFBLJG1x9FuR/AVJJG6ePW/tLaO85qxRPa1ezuYFCxW3KHgdjn6GOECBH1tWr3HXrnOqV1//wCNeKOt9yLRTye8LvDgwsEVtnjXHPeqwzN12zXt+AspVP/P0tGZN8559qXjGHGTIaNsqkVkRC4NZu3fGz81YswWNfO+tsBU7tFO+CRnt2wTmULwFof56b/hVEs9gr1KlMrvuvuGZl5EBO2S6h5VxLl212l23PuErXLYxD5FZxc67bvYULtbNjOVixPyJGjU2PPho6YYNTiKR3nlX36iSkjnz0nZpmLF7p83et2Sc2iNvLMsvWH/vw4FWSEpUcMtKHe2O/mSCG+0Mm2fPhZDM3TuXzPZeblxn5E2lvhAV4C1zHP3oZ4IcoPDbHysf1Dvv1beKJn+FZAApXbUGK8lUgjiHH6dNfqmFVoLJnUIvjIwif8efYzajm2mZRh/mwwHv2HafN26xUNEOb+/zvypuzHKjk16G8rHjo22+MYrZGYzE7Rs3hLNSwlk4C2fhLJyFs3AWzjsrZ/xNsw0wKkgFQh+nP46i5U+6MdqPKlRyQZQKe0KC9xgFtdQdc7d33PVCCYRA6+57uMYZJ2f13rfSAb0Kp3yLQAhli+fMQ6Cz2ftqS79Khx7gJpyNjz8daOi6WimHPiPpasu9lcQAmeuWLvuz6OdfK3bZNXvkjcW//Fb50APSW7VAUISYR4sK/1gQfTEb35pYfdCpdR64s+Djzyvu2j6r1z6FX0xZc/XNDn3YU6lNU76tNnhNVo/u2bffsCV3bbV+h2MxcP19Y0rXrC3+4ZeKe3bJeWjU5pmzqxx6IITmvTkRMotmzlGlpRXq1K7/8pNluWsr1Kgeknc341BZWUb71jUvHb5h/HNluevIyKKvv3fPGZSxR5ecB+4o21SY2W0PTzknhnPem+9VG3RK3dF35X/4aWanDpmewpPXXH2r0vMQys1/+/1qgwZ4Rn3yRcUObbP260F58t56H61Q46zT0xrUr5CTnbVv9y2/L3A3FYBF6aIlaU0b177xirxX396yarWiuQ2aYlARh85/+4NK+/f03tLUsEGiTu3MfbuXzJvv+l9k0Xm0mTUuuWDjk89umvhRVu8eta4YkdeyeaJqlarHHV2am7v8xMGYldCc3dAtyZEStOP69fMv0Ib7f9OfFS9l9ws+KtlH+ZjC+zPvisYIYmiotTKGD8W+y6T7cCqdjYKGP1Nx+xkGbUhZ9ANQJJNSyhnChLNwFs7CWTgLZ+EsnDkZ4Sycy+GMv+bNwVxvWzmjJoeiHn00WYfyoz8KeShEDE11IhIyuuxacbddK+7eKXP3ThktmyN/7qjRmyZ9ldV191qXDsto1aLgzYkbnnhWeWHeN0WTv07UrlXt9JMc/wZOEua6HErSEIpDwko9ZVZfel3Rl1Oy9uhc44Kz0xrWz3vqhfw3gydUKU8Y1lJBpYIluEDzvCee3fjMS4ma1WsOOytrn70KP5u07q4HAzP9rIgkV4+4qmja9Er7dqt67JEly5avueLGMv8B19VX3LDpsy8zmuxSBZHVuvUoiOIoWLZ23drb7sGyZIU62aVr1258bkKos4PEjc++7BYVQ1RF77nQgHPJkqW5N9yxZdnyil13T2+yy4bHnnZdvR4d4bzh8ac3PvNyomaNmhecnbnPXps+m7T2rtFOxEvUxieeyXv2Zc+o88/M6tFt06dfrh31ECAU//jzmhvu3PLniqr9j0AUuvmnaauuuNH15xU2jHt2y5JllQ7aP7NHt9AH9DuTtc8pX8gvEFK6fGWVfodX6rl30Y+/rL7iRmozv5RXcEtoZuV+h6e3bln03Q/rbhlVun5D9SGnVjux3+Y589ZcfgOWnTnnsK38WmiiQLG7ssNlcuVsmz9HBhcVHV/sMciQYIw7vDfysjxPqvzl6MaNMjpv7KhhFKR9fa+/LsjNNEZhvmMLF87CWQln4SychbNwFs7CWTj/Tc5OToOclctW8gN2Pic6K8ATl+xzsAq/EaqDN/9QkOIEsQK9UMbVNVBmyqkUf7Wu/zOtQqJGjbLc3EAm3d+LghXTnUSFssIizU0XL1OR9/2wWrRwPxEraJC8dl2Y7mptuUAVvPqX/gaq0oFEzZqlGzYo/11B3CiC4P2tmKEys8rWbwjKhxy8yqtW9coGD5Eq/+5hfyE3PcP7/ifl9LUK3lGUcCpUqVK6MU9rkkRXpYoqKNgazohF3bw8d0tpDGcd0dWsWbZhvQcxeGI3wOK9LHfLFrew0OAMQ8ry873vpqTinHxnsiekrKREFRWl5JxwElWqlPlmEmfId0tK0NBJOyzOvPUjnEMf2OWrj9Tf9OfYn0508sbumXZirDS7M/NDWm1KNJ4LTyW8HD15WXvjQ0+sNGWNd+XLF84qbhPOSjgLZ+EsnIWzcLZYCWfhXK9RPZOIrUEq1qHSFAW4TIFkqJBMTX7BJREGq5SBYh7+ARhPGqImRKFBNv9Nr44v2C1GcFIYnVZwFcWTvL7kqqYi4aQU6V22dm1ggSfSYSttZJTSoRQ9u+gXpfVrL0/p2nXwJuWEy4/00p3woU5sZUXFXhRKRgX0AuleFBrSCP/v16Sj0OSDrT7VMhdRaKh8hLPy73HdGs5l69Z5r9iN5aw/yLlunfc8b8hZha+oLUMEW1hoc0a6r2FqzqyBvI+vFBaVxxkl8/I4Z8gPphtScw6d1uK8Xf5sbrqULcrOFjtaceH6rxOCjq03VaKyBji7FK+a5+RV0yHKWc7opjNwo2KHVyWchbNwZptwFs6xpYSzEs7CmVVh1CicY0vt2JzTjFp5eV4sVoRft/e2V+xFQgy+REbhSng8XPMLdQoQhJn5f5N6+xLCOsNXCgXpfmVBQMvfY0SHE+G6nJsM5JykOX4JrVJoqUpW51LEmbzPWDeZ0neJBrI4mICAJy3JKlA+jBV9o8KbS8Ni+kfQfJQxaHjhvC2cFatxa/w5voPZI5HdjXkRlWLYUik2Qx9jpNB59C3+XJTzV3NRvCLjJWzlSDNAG3BSjSnCmTbhzPURzsI5VuG/rF04K+EsnIWzcN6hOaelgmvI0lXyleXQDG9PURgT3imqjFtbE1jV9L4e4otiIHwplOjq16L6PN0w1CkL7mtNeK9pdZLhk/+Aq/eeWKa3y3YcHR2FAZ7rROJyne44yXtilRNEf94vJ0wLNAvvWU0ap1SYkawIDoRRor9Sp0USmwgBX4oTvnXW1WopejGPcN4ezqlc+q/8OWUPtNONQ/Zw4waLzEnJ+r3VRl2xYx/fjAfNjeqMdN3/eR5jnOI6p6Kk97l6usj2jRvC2cwjnJVwFs7CWTgL5+gmnJVw3nE5x3x01UjRWXUxN9w8JWrX8hL9n37cgDBIKX6PLH3O0/U/mJl8qSmZESyThXdd+g3rW+ULcVz9IiJfsKIlvjBgo1rcsAIVvDQ1YOEk7aXqVHjbp0sVkRo6g//LDaIal8yOhF5+kaDZXB0u6TVM/zObvrmKbugNbHTLEk6kBRlnqsYN7tT1pfpKBuuabvivzA2UF85bz7lCrZqKbVvpzyo60Ci/p3E5NPrw4irafdzoWMCFUBG7oxlDg4puesjTMrkt9shljCy2vckGY8MBz2bYa1Sqxx21HeOGEs7CWTgLZ+EsnKNqxJqphLNwFs47KGdFgSgdsy23S3KTHC+WSWS0aK6CD0gG6QlHx1quyxflHLpdksIMFWhLEQV9UtJx3CBgchMqWMQLquYRlxvERK4bBjeBPn7GIITT37QM6vB082K34N5RPwBLrp1pZ/BvIU0EB6FVqLoKojInqNThNXNQwUIeA+hwr3IUEdCclQoQOQHnQLXAGGKV8BEJ57/FOb1VS/X3/VmnaGlITDUY2VNZuvvxPsWFpNLHsSaT+L7Wx2FTYop1Zj0EGHJ0tliZhsL2iGZUx5VR2zduCGfhLJyFs3AWzsJZOAvnnZkzZUvAWluowZeK0V9+Q3NGm5YsknEcdn+pfvOOl+IGwr1MyY98BIGSGyzKlfnhlp9TqSCQ8Yq4YfBGFmhk3p8wLgqsC3P47ReEQ1SvF/kk/KVBh5bSlMtaT4Vvq/V1CM1xg3iHtAoeWPRrLgsDJRWEaYEJpLPRfr40J1EWLHSG0wb0JiRHadNIT2q3BD14GUakDusHwnlrOKe3ar4N/mzk5B3SjZsTMjqtwyaijE7Oi/BOrtgQ4FoTUXyzs5EJegiwxyO9ccN5oiGTj1B2dcbAtz3jhnAWzsJZOAtn4SychbNw3sk5p/GvqRpGksYcq2Eq/uJyn1RQ+jFFX77ywxRF61QhyeAdMl5ygunhsiceubXhrhs+DkjRTqgP/lvmJtcT/XoSlD3UWPlrgN5rfly97BY8iFhGEoJKadVRMQdKvm5HB3te5oSjkq/TcZJlHP0ZGOVEWYV4vOVHxw1UDaoJSrqaHd1DWxaK1M9BhjMHwnmrOVds01JFt63xZ6ML8Jy6F/EexSd1jH5odG8uJLZbOuHHiFWKDsxVjTXEic7VGYYbZtJf3fe5fMUGX0NJ2ujn9owbwlk4c1WFs3AWzsJZOAtnJZyjhuzwnCmaTfD1XypJKQ6LyLUsvVGRSvvund6iWbIa5YYHKcX/ScGEq5Ivn/EWxFxfgL8Wp+//VMFbWcmcMKwKVsUoCmGxjaufHCwLHxR03SAmC28z9QFF6LhK31bqV+0EdPwiQdWenm7AJNRUB15uMGHgRm8ZdcO4UCX/E/5DQIiVurBRtHuEESd9AZXqcn3jfIOdIARz/VtqdZML57/knN60cWaPbtvgz7oIT9F9WLGerFP4pruZFqXHFxU3chljjZZsPPgecnANJWOr5jXqHUM3Q46duZyK9LY944ZwFs7CWTgLZ+GshLNwFs47MWdvNdQgq4sptkRrmKEt935nVsy+8Uo3LS0IOrwMHALb/OxlQWpSKxXcZRrkd4O4yCXxQaSkKEhR+u5NzUHR+h+xpi+OkIRg8S0Idfy9CL7g3TYuxS8VHEeFmgV//ZAuoW9HdRzFvMuP18J/ZW7IV2mdg/tRA1uCf4HhAeeAkxeOhpxDTOGDkSENbz/QUDj/Jee0tNq3XpPIytoGf+Zd2uByJrfFAAALDElEQVRrgQ5kal1VdBgyxgVjtLJHB8V6JtdBS0hEv4PMx0GH3aCv4no752BXwfM44QSeMVDao8n2jBvCWTgLZ+EsnIWzcBbOwnln5uyZaWjPgRpqpWqJtBZNawwd4pIZKnjqUWsfyHPDp/0Ca6nSZBZO06W7NF2dh3SjkMmhf1qdIEALDgXC3WQUl3zXq8PuAg0VC8Mfb+WQqxTWHohxQ62SGpJd/o6bCGMnhwVRtFLo39TqlLlGBm2d/3gk8yeqguvmBtxcnSicy+dc4/wzM1o022Z/5j/5CBKqFAjkt1s4Zlu4Uc7JzulEhy2dR6cbZfXAEXtIWT3X6O222vY4qNjYRzsJ62F9LYGs1kPS9owbwlk4C2fhLJyFs3AWzsJ5p+WslDKjbRXdNCYOQufUidVPPrbWFSNUZqZyOCzvj6LbPIO7T5VZgZeUSCTz01/SVW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DFUAG26/56w/8Afs//ABVABtuv+esP/fs//FUAG26/56w/9+z/APFUAG26/wCesP8A37P/AMVQAbbr/nrD/wB+z/8AFUAG26/56w/9+z/8VQAbbr/nrD/37P8A8VQAbbr/AJ6w/wDfs/8AxVABtuv+esP/AH7P/wAVQAbbr/nrD/37P/xVABtuv+esP/fs/wDxVABtuv8AnrD/AN+z/wDFUAG26/56w/8Afs//ABVABtuv+esP/fs//FUAORbgMN8kRXuAhB/nQBLQBg+J/wDl2/4H/SgDXsP+PC3/AOuS/wAhQBMxwpPpQCKU1yIsFmYsxwqqMlvoKRq7IkgnEgV0fcjd6BNJosSSJEu6Rgq+ppmY1p4lGWkUduTQA5ZEcsFYEqcHHagB1ABQAUAFABQAUAFABQAUAFABQAUAFABQBmy67YQicvKw8iURP8h+8emPXoefagC493ChxvDEOsZCfMVJOBnHSgBftVuE3+fFs3bd28Y3en1oAcJo2kMayIXHJUMMj8KAH0AYPif/AJdv+B/0oA17D/jwt/8Arkv8hQBM4yhHtQNbmZeWrXATy5jC65+YDJwfxFSW1cls4TCixlt7bixbGMknJNMNkXZYxIhUkgHHT60zMiezid2ZgfmYMR64GKAHQwiEyYJIdi2PSgCWgAoAKACgAoAKACgAoAKACgAoAKACgAoAw7jw2k88shuXAlZ2Zdg6knB/DcfzoAdH4f23j3DXbNvkWQgoO0m8d/cigCIeGQtv5QuhwW2gwgqqsoU4BPXjr29McUAW7bRIra7S4SQllkZ8lRkgoFwT+ANAGpQBg+J/+Xb/AIH/AEoA17D/AI8Lf/rkv8hQBPQA0qp6p+lA7sUADouKBXDPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaADPsaAFz7GgAoAwfE/8Ay7f8D/pQBr2H/Hhb/wDXJf5CgCcnAJPagCDzm7AUrmnIhyTEsAQOaLicSWmQFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFACbhnGRn0oAWgDB8T/wDLt/wP+lAGvYf8eFv/ANcl/kKAJX+430oGtzKvILuS4hktbgRKgO4MMhs46j86Rck76Fi0E4UC4KM4bqgwCO1Aa21LNysjxgR5zuBIDbSR9aZmQSQ3h8zZKOWBXJ6AEn/AUAMkt7slyszHc+QN33RluP1FADvIuQ5Jcspl3YDH7vPHX6UAWLdJFaXzCSC2Vy2eKAJqACgAoAKACgAoAKACgAoAKACgAoAKACgAoAxFsJv+ElF59kVEXcDJlcEEcMMYbcTwc5GBxQBt0AYPif8A5dv+B/0oA17D/jwt/wDrkv8AIUAT0ARGFc9cUrFczFWNVOc5NFgcmyTI9aZIZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZHrQAZoAKAMHxP/wAu3/A/6UAa9h/x4W//AFyX+QoAnoAjMqf5FK5XKxVkRjgdfemJxaH4HpQIMe1ABgUAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGBQAYHpQAYHpQAYHpQAYHpQAYHpQAUAFAGD4n/wCXb/gf9KANew/48Lf/AK5L/IUASv8Acb6UDW5l3twYJYBnCsSWO5VBwOmT/nikXJ2J7ScXCLIoIG7A5Bzg9QR1FAXuizdytDCXUAkEdaZmRfbGErhk2oHChiCMgg5P5igAku2C/Lt3CXY2ew556j0oAlgmaSSVWA+RsDA7UAVxeSlgCqAGXZnHQc8devH60ATXU7wvCqKCHbBzQBGl277MqE3TFMMDyuCQR+lAEllO9xEWkTYwOMfgD/WgCxQAUAFABQAUAFABQAUAFABQAUAFAGbczXcet2aCRRayhlKBckkKTknt2/WgDSoAwfE//Lt/wP8ApQBr2H/Hhbf9cl/kKAJiMgj1oAgMDZ6A46GlY05kOjhKkZwAOwosJyVtCamQFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAYPif/l2/wCB/wBKANew/wCPC3/65L/IUAT0AIcf3v1oAOvQ0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0AGPc0ALQBg+J/8Al2/4H/SgDXsP+PC2/wCuS/yFAEznCEj0oGtyhPKYlG1N8jnaq5xk/WpNG7ElvKJFSRM4b16/Q0weqLE0qwx73zjIHFMyGi5jMrR5O5WCnjuRmgAe5jRQxzy+wfX/ACKAHRTLKXC5+RtpyO9ADUuUckANkNt+7+v04oAat5EwU84aTyxx3oAkimSZSUzgY6j2zQBJQAUAFABQAUAFABQAUAFABQAUAFABQBmw6r5t+IPs7LE8jxJLuHLIOcjsODg+1AGlQBg+J/8Al2/4H/SgDXsP+PC3/wCuS/yFAEzDKkeooGtyjcWyTqFkDZVtykHBBqTRpMkt4RGFRAQq0xOyRZkjWVCjjKnqM4pmZGbWHJOzBJySpIOfw+tACm3iOeD97fwx6+v60AOSJEZmUfM3U5oAI4ki3bFxuOTzQAz7LDgDZkKdwyScH29KAHxxJECEXAJyaAH0AFABQAUAFABQAUAFABQAUAFABQAUAU49Mtorw3Sq+/czBS5KqzfeIHQE0AXKAMHxP/y7f8D/AKUAa9h/x4W//XJf5CgCegBOfagA59qADn2oAOfagA59qADn2oAOfagA59qADn2oAOfagA59qADn2oAOfQUAHPtQAc+1ABz7UAHPtQAc+1ABz7UAHPtQAc+1ABz7UALz7UAFAGD4n/5dv+B/0oA17D/jwt/+uS/yFAEzHCk+lAIrmV+uT+FI15UNtrtbhVeKQSRkkZH5UE2TV0WiQoySAPU0yALKOrAfU0AJvTJG5eDjr3oAPMTn5l4OOvegBd6/3h1x170AAYNnBBxwcGgBaACgAoAKACgAoAKACgAoAKACgCrcajaWs6wzzrG74wGz3OBz060ARf21pxiMgu4yobbxk5OM8DvwCc0AXY3SWNZI2DIwDKwOQR60AQi9tjOIBKPMLMoXnkjqKALFAGD4n/5dv+B/0oA17D/jwt/+uS/yFAEr/cb6UDW5nXMMszRqkgWLJ81e7D0B7VJo02WIkVCqooUZ6AYoB7E1zD58JTIBzkEjOKoyIGsA7NvfKls4Ax6/40AD2G9HUyYDSFxgfXj9aAHm0wmFYZ83zDuGe+cCgBBaEAAMnEofOzk8k/nz1oAktofIjKbgcsTnHrQBNQAUAFABQAUAFABQAUAFABQAUAU7yze6uIGLr5UO5xGRnc/RSfYc8euPSgDIOg3rxq0s0LzgglwzrvG1hjK4KgbjgLx60ASaho9xPFYW0flOsNs8TSOCFDYUBgAevBI+lAF+y0xbeZppm82bexSQk5CkDj9KANCgDB8T/wDLt/wP+lAGvYf8eFv/ANcl/kKAJ6AGFEP8NA+ZihVU5AoBtsXP+cUCDP8AnFABn6/lQAZ/zigAz9fyoAM/5xQAZ/zigAz9fyoAM/X8qADP+cUAGfr+VABn6/lQAZ+v5UAGf84oAM/5xQAZ+v5UAGfr+VABn/OKADP1/KgAz/nFABmgBaAMHxP/AMu3/A/6UAa9h/x4W/8A1yX+QoAnJwM0AQmfnhaVy+QckwY4IxRcTjYkpkhQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAGD4n/AOXb/gf9KANew/48Lf8A65L/ACFAEr/cb6UDW5j6p9tPlrZqWU5L4bHpjnOfXpSKlfoT6Ybn7On2sYlDY+o7UDV7al65WRoSImYNkYIx60zMrSC8Il8stu8z5c4xtwen6UADfbh5m0Z/eAryPu5OfzAA/GgAm+2fOYd+RLwGxyuO340AXqACgAoAKACgAoAKACgAoAKACgAoAzNTs2ur60CLKuG3STK7AKqnO3AOMkkD6ZoAz9TE8WozTsbmPbIrLOGPlpCE+fPO3Oc8HnOKAOZ+2X1xB5YnlggbaxUO29yARuJJO3Oc49QKylU7Ho0cC5LmqaeRMt7fqQV1G8yO5lz+h4qfaSOp4Gjbr95b0zWJYtThk1KcmEFv3gBHzNx84zjH0GB6VcZ30OHEYOVJc0dUdwCCMjpWhxGD4n/5dv8Agf8ASgDXsP8Ajwt/+uS/yFAE5GRigCIwejfpSsXzipCFOSc0WE5XJKZIUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAcx4yuSfstkD8rkyyD1C4wPzOfwqJuyOvB01Orr01MCGJ55RHGMu2cZOO2awSue3KSirsdJbyxEK6HcVD7RyQD6+lFhRnGWqISAQQRkHqKCnrudD4K1F3jn0yZixtjmInrsPb8OK6Yu6ufO1oezqOJb8T/8ALt/wP+lMyNew/wCPC2/65L/IUAT0AMLx56ii4+VihkY4GKAaaFwPSgQYHpQAYHpQAYHpQAYHpQAYHpQAYHpQAYHpQAYHpQAYHpQAYHpQAYHpQAYHpQAYHpQAYHpQAYHpQAYHpQBynjGEpe2dxj5GR4s+h4YfyP5VnUWh3YCSVVrujJsrj7Jdxz4LbCTgfQisk7O561WHtIOPcu2+qxQ3D3Bt3Mkm1nw/BYdfwP6U1LqYTw8pRUb6IzGwWO0EAngGpOpbFvwZmTxJcSJ9wRsCfxUfzFdEFZHz+JkpVpNG/wCJ/wDl2/4H/SqMDXsP+PC3/wCuS/yFAEr/AHG+lA1uZl3LcJLFHbRqxYFm3egx7j1qS230JLCWWSMGdVWVXKNt6ZFME7rUt3VwLaAyEbsEDHrTMytJqaxmXMRPltg/N255/SgB/wBsYFvkGPN8sc/Xn9KALlABQAUAFABQAUAFABQAUAFABQAUAFABQBU1Owi1Oye2lJUNyrjqjDoRRuOMnF3W5wV7HLpt0ba+Ajk/hf8AgkHqp/p2rCUGj2qOMhNWloxhkjAyXUD13CosdTnFK9ynPeiQ+Ta5Zm43KOn09TWkYdWcGIxsUuWnq+53HhLRW0qxaSddtxNglf7ijoPr3NbHki+J/wDl2/4H/SgCxZ61paWUCtqFsGEaggyDjgUAStrelFSP7Rtuf+mgoBFWXUdIlwJLyzfHTMgOKRpdMfDqmkxYVb60RQc4EgoE2raFj+3NK/6CFr/38FMgT+2tI/5/7X/v4KAEOsaOc5vrTk5PzrzQA7+3NK/6CNt/38FAB/bmlf8AQRtv+/goAP7c0r/oI23/AH8FAB/bmlf9BG2/7+CgA/tzSv8AoI23/fwUAH9uaV/0Ebb/AL+CgA/tzSv+gjbf9/BQAf25pX/QRtv+/goAP7c0r/oI23/fwUAH9uaV/wBBG2/7+CgA/tzSv+gjbf8AfwUAH9uaV/0Ebb/v4KAD+3NK/wCgjbf9/BQAf25pX/QRtv8Av4KAD+3NK/6CNt/38FAEF5qGg30Bhu7qzmjP8LOP09KAMJ9E8Js+5dRSMZ6LOpH6igRo6bH4Y0yTzbe4tDN/z1eUM34Ht+FAzT/tzSv+gjbf9/BQBjeIdUsJ/s/k3kD43Z2uDjpQB0WB6CkMXA9BQAYHoKADA9BQAYHoKAMjVPEmmaTcC3upHMuNxWNN20ds0Bcpf8Jvovrcf9+f/r0CuH/Cb6N63H/fn/69AXD/AITfRvW4/wC/P/16AuH/AAm+i+tx/wB+f/r0BcP+E30X1uP+/P8A9egLh/wm+jetx/35/wDr0BcP+E30X1uP+/P/ANegLh/wm+i+tx/35/8Ar0BcP+E30X1uP+/P/wBegLh/wm+i+tx/35/+vQFw/wCE30b1uP8Avz/9egLh/wAJvovrcf8Afn/69AXD/hN9F9bj/vz/APXoC4f8Jvovrcf9+f8A69AXD/hN9F9bj/vz/wDXoC4f8Jvo3rcf9+f/AK9AXD/hN9G9bj/vz/8AXoC4f8Jvo3rcf9+f/r0BcP8AhN9G9bj/AL8//XoC5q6TrFlrETvZuW8s4dWXay+nFAy8QPQUALQAYPoaADn0oAOnXigBAQwBBBB5BHegDy/xp/yNF39E/wDQRQJmHTEFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAdZ8PyRf3GD1VR+ppDR6AaBhQByhleHXJvLkE9w9zIqRb5BJGNpIbbnaVH0wc9aBC6You3gt7K9mDT2f+mOXZykmV65Pyt98f/qoAfK7/wDCEoWnljySpc7m43twxHOCOCaALng+QyaFGP3mEbaN7bhjA+6fT+RyKBo4rxp/yM939E/9BFAmYdMQUAFABQBaijieAMxVSMoST3JGD+WfyqG3c6YQhKF36fjuO+zRA7SxGcdSCR1/wpczL9jDZv8AK/UiiiSRHbLcdBkcDHU5qm7GMKcZJv8AyJjbwPJ8r7R0IBHPyg8VN2bOjTlLR2/4YimgSOFnVmY7sA5GMe9NPUzqUoxi2n1JngiZUDKiEhcbTy3ykkdfXFK7NpUoNJNW2266a/iMNvCu7LtxngEcYAOPrz+lO7M/Y01e7/LyFNtETjdnbkfKQCfmIzz9BRzMr2MNr/l3eoGKIx8bN23r+C/4mi7D2cOXp/SRHPAsUyKjAhv7x6c96ad0ZVaShJJPcbcGE7PJGOobrz780K/UVV03bkIaoxCgAoAKAOs+H/8Ax/3H0T+ZpDR6AaBi0AHv3oASgAKhlKkAgjBB70AIiLHGscahUUBVUDAAHQUAeYeNP+Rou/on/oIoEzDpiCgAoAKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAOs+H/APx/3H0T+ZpDR6AaBi0AJQAUAFABQBz2veFLbVrv7WJJIpiAH24w2Oh570BYy/8AhAk/5+Zf/HaBWD/hAk/5+Zf/AB2gLB/wgSf8/Mv/AI7QFg/4QJP+fmX/AMdoCwf8IEn/AD8y/wDjtAWD/hAk/wCfmX/x2gLB/wAIEn/PzL/47QFg/wCECT/n5l/8doCwf8IEn/PzL/47QFg/4QJP+fmX/wAdoCwf8IEn/PzL/wCO0BYP+ECT/n5l/wDHaAsH/CBJ/wA/Mv8A47QFg/4QJP8An5l/8doCwf8ACBJ/z8y/+O0BYP8AhAk/5+Zf/HaAsH/CBJ/z8y/+O0BYP+ECT/n5l/8AHaAsH/CBJ/z8y/8AjtAWNjQfDqaO7MjFixBZmIycdBxQM3TQAWPzWNuzckxqST34FMRW15mj0S8eMlGWIkMpwRQBdIGelIYYFAgwKBhgUCDAoGGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAGB6UAUNTdkm0/YxXddBWwcZGx+DQIvx8pzzTAx/EckkX2fy3ZM7s7TjPSgDUsP+Qfbf9cl/kKAKviH/kA3v/XFqALxGcjpn0pAYdnPqe+MOr/KVgcyITnarFnHTqdoz0oITYWl/qFxLGZLVwV3HbtaMMCoIznjrkfhQCbEkOqrcXd1HuCxsdsTMWDfIvAUDoCScg9qA13JDd6hBOyCIyK05DuY2IRfl+YDPTkjH49jQO7NqgoKACgAoAKACgAoAKACgAoAKACgAoAKACgAoAKAM7Vv9dpv/X4v/oD0AaEX3KYGJ4n/AOXb/gf9KANew/5B9t/1yX+QoAq+If8AkA3v/XFqAL560gCgAoAKACgBKACgAoAKACgAoAKACgBaACgAoAKACgAoAKACgAoAztW/12m/9fi/+gPQBoRfcpgYnif/AJdv+B/0oA17D/kH23/XJf5CgCr4h/5AN7/1xagC+e9ICsBcZU56HnnqBx+vJ/KgBQJiYs7hgENz1Pr1oAaVuWUYJUjH8XtQA6VZzKfLOEOO/THX86AGstyQcHBLFhhugweP5UAO2zZ4JB3cknIxn0+lADdtyV6/wbeDzn1oAULcb+c9eoPHX0+lAAFm+zYO7zMjvz1570AKUlJcAvgrwd2MHFACSJcbv3bHHI5PsOfw5oAbsnw33s5XnPbv3+tAFodBQAtABQAUAFABQAUAFABQBnat/rtN/wCvxf8A0B6ANCL7lMDE8T/8u3/A/wClAGvYf8g+2/65L/IUAVfEP/IBvf8Ari1AF89aQFBda01777Et7Ebjdt2Z/i9M9M+1ACLremNffYxewm43bNmf4vTPTPtQBLNqdlBex2ctyi3EuNkZ6nPT+VAC32o2enRq95cJCHOF3dWPsByaAIW1vTFslvDew/Z3bar5zk+mOufagB39sad9g+3fbIvs2dvmZ4z6eufagCWx1C01GIy2c6TIDtJXsfQjqKAKaeINOfWDpqzoZcfe3DaWzjZ/vUAalAC0AFABQAUAFABQAUAFABQAUAFABQBnat/rtN/6/F/9AegDQi+5TAxPE/8Ay7f8D/pQBr2H/IPtv+uS/wAhQBV8Q/8AIBvf+uLUAXz1pAcRb3ljqRt7SGex0+wiuhIkRl3TyMGyOP4cn6mgCtZTtBplpAbqGe4W72tpjwruzvOT/e3Dru6UASTvewXNncXmmXP2ubUBK7Eph8Bgsa89h6+9AGt4jlMWr6VIJ47B9kn+lTjci5AymOmT9R0oAx9LuIYNUtLy7kQwC6uQ150ikkKrhh2Xpj8KAJDNAdYOpbl/sz+1AfNx+7z5WN303d6ANvRZorrxHq11aMsluyQp5ifdZwDnB79qAOSilt4dYRYE8+Mso+yXC4lRvOJKgr3B+bJ7GgD0ygAoAKACgAoAKACgAoAKACgAoAKACgDO1b/Xab/1+L/6A9AGhF9ymBieJ/8Al2/4H/SgDXsP+Qfbf9cl/kKAKviH/kA3v/XFqAL560gIhbwg5EMYP+4KAHeVGJfN8tPMxjdtGfzoAUqrYyoODkZHSgBJI0lXbIiuvowyKAAxRtH5ZjQx9NpUY/KgA8tPL8vYuzGNuBjH0oAVESNAkaqqjoFGBQBD9itxfG98ofaCnl7/APZzmgCegBaACgAoAKACgAoAKACgAoAKACgAoAztW/12m/8AX4v/AKA9AGhF9ymBieJ/+Xb/AIH/AEoA17D/AJB9t/1yX+QoAq+If+QDe/8AXFqALzsEVmY4VQST7UgIFa6kUOscSgjIDMc/jimAhe5U4PkZ+rUhpNiebcf9MP8Ax6gfKw8249YP/HqA5WJ50/rB/wCPf4UBySDz5/WD/wAe/wAKA5JCefN6wf8Aj3+FFx8kiC11GW5EpAhXy5CnJbnHfpSUkzSpQlC3mrk/2mX+9B/49/hTujP2cuwn2mX+9B/49/hRdB7OXYPtUv8Aeg/8e/woug9nLsH2mX+9B/49/hRdB7OXYX7RN/eg/wDHv8KLoOSQfaJvWD/x7/Ci6DkkHnzesH/j3+FFw5JC+dP6wf8Aj3+FAuSQedP6wf8Aj1AcshfNuPWD/wAeoDlYebcf9MP/AB6gOVjlN0wyBAfxamJqwqySrKsc6KN+drISRkduaQirq3+u03/r8X/0B6ANCL7lMDE8T/8ALt/wP+lAGvYf8g+2/wCuS/yFAFXxD/yAb3/ri1AF5gGBUjIPBFICusc0ahUuhsA43ICcfXPNMAMc55NxH+MQ/wAaQ7sPKmP/AC3i/wC/Q/xoC7E8qXJHnxZHbyR/jQF2L5E3/PWP/vyP8aAu+4fZ5v8AnrH/AN+R/jQHM+4n2eX/AJ6xf9+R/jQPmfcht9Oa2EgSVD5jlzuiB5P49KSSRc6s52u9tCb7NL/z0i/78j/GmRzS7h9mk/56Rf8Afkf40BzS7ifZZP8AnpF/35H+NAc0u4v2aT/npF/35H+NAc0u4fZpf+ekX/fkf40BzS7h9nl/56xf9+R/jQHNLuH2eX/nrF/35H+NAcz7i+RN/wA9Y/8AvyP8aBcz7h5E3/PWP/vz/wDXoC77h5Uw/wCW8X/fof40Bdh5M2M+fHj/AK4j/GgLsUJOOBcxj2EQ/wAaBCpE3nB5ZfMdQdoAAAz3xQBV1b/Xab/1+L/6A9AGhF9ymBieJ/8Al2/4H/SgDXsP+Qfbf9cl/kKAKviH/kA3v/XFqAL560gK/wBlBAy3IzjAwOuen50AOeANGibvuEc4oARLYI6tuJx7UAPWLbO8mfvADGKAJKACgAoAKACgAoAKACgAoAKACgAoAKAIPsw3A7yeSfmGaAHGLMCx7iMAcgelADFtgrhtx4OelAEnlf6R5uf4duMUAUtW/wBdpv8A1+L/AOgPQBoRfcpgYnif/l2/4H/SgDXsP+Qfbf8AXJf5CgCr4h/5AN7/ANcWoAt3EhiglkABKKWAPsKQM4YeOr4gH7Jbfm3+NBj7Ri/8J1ff8+lt+bf40B7Rh/wnV9/z6W35t/jQHtGH/CdX3/Ppbfm3+NAe0Yf8J1ff8+lt+bf40B7Rh/wnV9/z6W35t/jQHtGH/CdX3/Ppbfm3+NAe0Yf8J1ff8+lt+bf40B7Rh/wnV9/z6W35t/jQHtGH/CdX3/Ppbfm3+NAe0Yf8J1ff8+lt+bf40B7Rh/wnV9/z6W35t/jQHtGH/CdX3/Ppbfm3+NAe0Yf8J1ff8+lt+bf40B7Rh/wnV9/z6W35t/jQHtGH/CdX3/Ppbfm3+NAe0Yf8J1ff8+lt+bf40B7Rh/wnV9/z6W35t/jQHtGH/CdX3/Ppbfm3+NAe0Yf8J1ff8+lt+bf40B7Rml4f8UXWq6otrNbwohRmyhOePrQVGbbsbOrf67Tf+vxf/QHoNDQi+5TAxPE//Lt/wP8ApQBr2H/IPtv+uS/yFAFXxD/yAb3/AK4tQBZvf+PO4/65t/I0gex4+v3R9KZyi0AFABQAfhmgDf8A7DtZWVYZ3HnfvY8Yc+UdoGRx0JbJ9FoL5UVLvTYbeK3fztxlZE2oMg/KpJzn3oE0SDSYbme8S3kaNbXepLYYMwLkd+mFA+tAcpKvh5JJikd5kDcpYoBhgQPXpz1oHykcmjQFi6XOyMNHGQRuIdgpH1GCx/4DigOUbdaGLa1uZjcgmE8Lt5PCnnB4J3cfSgTiWbfQIGjgLzMWY7jxw67A2Fwcnk9eOhoGokY0GOVQYrhsCEsXKjaWBYYHOQPl/UUByk7aFaMQqyFWLDndkEZbgD1IUAe5oDlRSvtIS2tJblblWCyFQgQ9M4wT2PsaBONivdW1pDYxssrfbMjzI9wZQCCew69MjnGaBNKxRoEFABQAUAb/AIJ/5GFP+uT/AMhSLh8R2urf67Tf+vxf/QHoNzQi+5TAxPE//Lt/wP8ApQBr2H/IPtv+uS/yFAFXxD/yAb3/AK4tQBZvATZzgDJMbfyNIHseSi0udo/0afp/zzb/AAoOazF+yXP/AD7T/wDftv8ACgLMPslz/wA+0/8A37b/AAoCzD7Jc/8APtP/AN+2/wAKAsw+yXP/AD7T/wDftv8ACgLMPslz/wA+03/fpv8ACgLMPslz/wA+0/8A37b/AAoCzD7Jc/8APtN/37b/AAoCzD7Jc/8APtN/36b/AAoCzHmG9MIhMNx5QbcE8tsZ9elMNRn2S5/59pv+/Tf4Ugsw+yXP/PtN/wB+m/woCzD7Jc/8+03/AH6b/CgLMPslz/z7Tf8Aftv8KAsw+yXP/PtN/wB+m/woCzD7Jc/8+0//AH7b/CgLMPslz/z7T/8Aftv8KAsw+yXP/PtP/wB+2/woCzD7Jc/8+0//AH7b/CgLMPslz/z7T/8Aftv8KAszd8G288evo0kMqL5T8shA6UFwWp2Orf67Tf8Ar8X/ANAeg2NCL7lMDE8T/wDLt/wP+lAGvYf8g+2/65L/ACFAFXxFxoN8T08lqAHf2vpmf+Qjaf8Af9f8aQB/bGm/9BG0/wC/6/40AH9sab/0EbT/AL/r/jQAf2xpv/QRtP8Av+v+NAB/bGm/9BG0/wC/6/40AH9sab/0EbT/AL/r/jQAf2xpv/QRtP8Av+v+NAB/bGm/9BG0/wC/6/40AH9sab/0EbT/AL/r/jQAf2xpv/QRtP8Av+v+NAB/bGm/9BG0/wC/6/40AH9sab/0EbT/AL/r/jQAf2xpv/QRtP8Av+v+NAB/bGm/9BG0/wC/6/40AH9sab/0EbT/AL/r/jQAf2xpv/QRtP8Av+v+NAB/bGm/9BG0/wC/6/40AH9sab/0EbT/AL/r/jQAf2xpv/QRtP8Av+v+NAB/bGm/9BG0/wC/6/40AH9sab/0EbT/AL/r/jQAf2xpv/QRtP8Av+v+NAFO+v7O5utNjt7uCZ/tYO2OQMcbH7CgDYi+5TAxPE//AC7f8D/pQBr2H/IPtv8Arkv8hQBOQCMEZFAEL20bHiOP/vkUAJ9lT+5H/wB8CgA+yp/cj/74FAB9lT+5H/3wKAD7Kn9yP/vgUAH2VP7kf/fAoAPsqf3I/wDvgUAH2VP7kf8A3wKAD7Kn9yP/AL4FAB9lT+5H/wB8CgA+yp/cj/74FAB9lT+5H/3wKAD7Kn9yP/vgUAH2VP7kf/fAoAPsqf3I/wDvgUAH2VP7kf8A3wKAD7Kn9yP/AL4FAB9lT+5H/wB8CgA+yp/cj/74FAB9lT+5H/3wKAD7Kn9yP/vgUAH2VP7kf/fAoABbKDkKgPqFAoAmRdq4oAwvE/8Ay7f8D/pQBr2H/IPtv+uS/wAhQA+4YpA7AgHHUnGKAGCBASfmJznJY9enrSABAgxgNxj+I9hj1oABBGMYDcYx8x7dO9AAIIxjAbjGPmPbp3oABBGMYDcY/iPY59aADyIx2b/vo+ufWgA8iP0b/vs+ufWgA8iM9m/76Prn1oADBGc5Dc5/iPc59aAAwRnOQ3Oc/Me/XvQAGCM5yG5zn5j3696AFMCHOQ3Of4j3GPWgAMCHPDc/7R9MetAB5Kejf99H0x60AHkoOzf99H0x60AAgQY4bjH8R7DHrQAggjGMBuMY+Y9unegAEEYxgNxjHzHt070AAgjGMBuMfxHtz60AAgjHZuMfxn1z60AHkR+jf99n1z60AHkR+jf99H1z60ABXy3Rk3ctgjJIwfx/WgCemBg+J/8Al2/4H/SgDXsP+Qfbf9cl/kKAHXRxbuc499239aAHPkKxUZODgUgIS8oIwpI25+7j/P0oATdOd3y44BHHfjNADmaQOQAcbwM7e2P8aAHRGQhvMGDnj6UASUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAEcoyY+M4cH7uf/1fWgCamBg+J/8Al2/4H/SgDXsP+Qfbf9cl/kKAHXRxbuc4992P1oAc7BVZjnABPAyfypAU7TVLO8iaSKbCqFJ8xSnDdD82OD60AWTNErbWkQN6FhmgBqXMLgYfBLMoDcElTg4B69KAIodRtZmCo7Zxk7kIA+UNznpwRQA+5vLe1tDdTSYhGPmUbs56Yx1oAes8L7NsqEuoZRuGSD0OKADz4dobzo9pOM7xjPpQA4yxhmUyICgyw3D5R6n0oAZJcwx+TukGJjtQjkE4J6/QGgBwnhIJEsZAGThhx2oAPPh2F/Nj2rjLbhgZoAjN7bh5k80b4Rl17/dDcDvwR0oAjGpWpEhDtiNWZ/kPAXGfx+YcUAWPPhC7jKgG3dksOnr9KAHgggEHIPQigBaACgAoAKACgAoAjlGTHxn5x2JoAmpgYPif/l2/4H/SgDXsP+Qfbf8AXJf5CgB10cW7nOPxA/nQArglWCnaxBwcZwaQGInhwrDGrXfmSRBEQvCpTaoYAFc8/eJz64oAVvDNuUK+aSdpUMyAsP3aoOfbbn8aAJRoSi5WYTA/MWYNErH77ONpP3eWxnvxQBGPDqrDsW6cHy9gOwf3EX/2T9aAJV0RRYQWbXMnlxTGXKDaTySBntgn9KAKJ8Pyx3dskbB4U2b5XwHAVSoA4z0I/HmgCaLwvAlt5Ly7/ldcmP8AvIEBwSeQBQA+Tw8kguFNxxLkg+UN4JKnlupGV6f/AFqAJn0SN9Ngs/OKiLedwXqWVlPBPH3yfwoAiPh233llYKC24qIxgjdGcfT93/48aAEHhyJFHlSqpUggNCGUkF+q9+Hx7YFADR4ajUgC5OwKBzGN3EewfN6d8UAOl8OxyIy/aXGeny98oeeeR8g/OgAPhuD7O0YlIYrGA2zpsJPrnBJ6ZoA07G1Wys4bZDlY12g+tAFigAoAKACgAoAKAIpRkx8fxjsT/KgCemBg+J/+Xb/gf9KANew/5B9t/wBcl/kKAHXX/Hu//wBb+vFADmyAcDJ7D1pAVLG+S5XDFFlyRsB549jzQJO5coGFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAEUvWP/fHr/SgCemBg+J/+Xb/gf9KANew/5B9t/wBcl/kKAHXX/Hu//wBb+vFADiQMk9KQGbBe2U06SpDIJGQSFthwoIOCccZIH15oJuiYapaGK3k3sBcSeXGChBLZxyO3NA+ZDv7Qh+yC4YOAXMYTblt2SNuB3yKAvoNTVLWR4kRnYyAEYQ/LkkDd6cgjn0oDmRJaXsN5u8neQvOShAYeoPccUDTuMk1O2ieVZDIvlgtkxnDYIB2+vJA/GgV0MOr2aqhd3QNvzuQjbtzuB9DwaA5kWLa6julYx7wUbayupVlOM8g+xBoGncnoAKACgAoAKACgAoAKACgAoAKACgAoAil6x/747n+lAE9MDB8T/wDLt/wP+lAGvYf8g+2/65L/ACFADroZt3GM/gD/ADoAWQKUYPwpBB+lIdr6GPDYwo9o8l3ETbqg+WMBjtBA+brgjqKLoPYz7AdNtCY2F9IrI+7KkYI8wvjH1PX2FF0HsZdiX7PC2ntbvdR72lMxbbkbi27p+nUGi6H7KdtivHpVlE8LJdKNm3d8oyxVi3B/hySQcdqLoXsZdiSz02wtgyvNHMhVVClABhScE+rc9aLoFRkugXNjDc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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"6b2279eb-93e5-438b-a4bb-edd7e7bc177d","timestamp":"2026-08-24T21:04:58.339Z","tool_use_result":{"type":"image","file":{"base64":"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","type":"image/png","originalSize":68783,"dimensions":{"originalWidth":466,"originalHeight":334,"displayWidth":466,"displayHeight":334}}}}}
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font-weight: 700; line-height: 0.98; text-transform: uppercase; letter-spacing: -0.035em; max-width: 940px; margin-top: 14px; }\n.hero h1 .outline { color: transparent; -webkit-text-stroke: 2px var(--ink); }\n.hero h1 .hl { background: var(--lime); color: #fff; padding: 0 10px; border-radius: 8px; display: inline-block; transform: rotate(-1deg); }\n.hero p.lede { color: var(--ink-2); font-size: 16.5px; max-width: 680px; margin: 20px 0 0; }\n.stat-row { display: flex; flex-wrap: wrap; gap: 10px; margin-top: 26px; }\n.stat-chip { background: var(--surface); border: 1.5px solid var(--ink); border-radius: 999px; padding: 8px 16px; font-family: var(--font-mono); font-size: 12px; color: var(--ink-2); display: flex; gap: 8px; align-items: center; box-shadow: 3px 3px 0 rgba(26, 18, 20, 0.12); }\n.stat-chip b { color: var(--ink); font-weight: 700; }\n.stat-chip .pulse { width: 8px; height: 8px; border-radius: 50%; background: var(--green); box-shadow: 0 0 0 4px var(--lime-soft); animation: pulse 2.4s ease infinite; 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color: var(--ink-2); border-radius: 50%; width: 20px; height: 20px; font-size: 10px; cursor: pointer; flex: none; display: grid; place-items: center; }\n.f-ctl { flex: 0 1 auto; min-width: 0; display: flex; flex-direction: column; justify-content: center; gap: 2px; border: 1.5px solid var(--ink); border-radius: 9px; background: var(--surface); padding: 7px 12px 6px; min-height: 52px; cursor: pointer; transition: box-shadow 0.15s ease; }\n.f-ctl:focus-within { box-shadow: 3px 3px 0 var(--lime); }\n.f-ctl > span { font-family: var(--font-mono); font-size: 9px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); }\n.f-ctl select { border: none; background: transparent; outline: none; font-family: var(--font-display); font-weight: 700; font-size: 14.5px; color: var(--ink); cursor: pointer; appearance: none; -webkit-appearance: none; padding-right: 16px; min-width: 0; max-width: 180px; text-overflow: ellipsis; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='9' height='5'%3E%3Cpath d='M0 0l4.5 5L9 0z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right center; }\n.range-pair { display: flex; align-items: center; gap: 4px; }\n.range-pair select { max-width: 92px; }\n.range-sep { color: var(--ink-3); font-family: var(--font-mono); font-size: 11px; }\n.f-more { flex: none; align-self: stretch; border: 1.5px solid var(--ink); border-radius: 9px; background: var(--surface); color: var(--ink); padding: 0 18px; cursor: pointer; font-family: var(--font-display); font-weight: 700; font-size: 14px; min-height: 52px; transition: all 0.13s ease; }\n.f-more:hover { background: var(--lime-soft); }\n.f-more.on { background: var(--ink); color: var(--lime); box-shadow: 3px 3px 0 rgba(26, 18, 20,0.22); }\n.f-adv { display: grid; grid-template-columns: repeat(auto-fit, minmax(230px, 1fr)); gap: 16px 22px; border-top: 1.5px dashed var(--line); margin-top: 14px; padding-top: 14px; min-width: 0; animation: adv-in 0.18s ease; }\n@keyframes adv-in { from { opacity: 0; transform: translateY(-4px); } to { opacity: 1; transform: none; } }\n.f-group { display: flex; flex-direction: column; gap: 7px; min-width: 0; }\n.f-group > label { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); }\n.f-group-end { justify-content: flex-end; }\n.f-group .btn:disabled { opacity: 0.4; cursor: default; }\n.f-native { border: 1.5px solid var(--line); background: var(--surface-2); border-radius: var(--r-ctl); padding: 10px 30px 10px 12px; font-size: 14px; color: var(--ink); outline: none; font-family: inherit; min-height: 42px; width: 100%; min-width: 0; appearance: none; -webkit-appearance: none; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='10' height='6'%3E%3Cpath d='M0 0l5 6 5-6z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right 12px center; }\n.f-native:focus { border-color: var(--ink); box-shadow: 3px 3px 0 var(--lime); }\n.seg { display: inline-flex; flex-wrap: wrap; row-gap: 6px; }\n.seg button { border: 1.5px solid var(--ink); background: var(--surface); color: var(--ink-2); padding: 8px 13px; font-family: var(--font-display); font-weight: 600; font-size: 13px; cursor: pointer; margin-left: -1.5px; white-space: nowrap; min-height: 38px; transition: all 0.12s ease; }\n.seg button:first-child { border-radius: 8px 0 0 8px; margin-left: 0; }\n.seg button:last-child { border-radius: 0 8px 8px 0; }\n.seg button:hover { background: var(--lime-soft); color: var(--ink); }\n.seg button.on { background: var(--ink); color: var(--lime); position: relative; z-index: 1; }\n\n.pills { display: flex; flex-wrap: wrap; gap: 8px; margin: 10px 0 2px; }\n.pill { display: inline-flex; align-items: center; gap: 7px; border: 1.5px solid var(--ink); background: var(--lime-soft); color: var(--ink); border-radius: 999px; padding: 6px 13px; font-size: 12.5px; font-weight: 600; font-family: var(--font-mono); cursor: pointer; transition: all 0.13s ease; }\n.pill:hover { background: var(--lime); color: #fff; }\n.pill-x { font-size: 10px; opacity: 0.65; }\n.pill-clear { background: var(--surface); border-color: var(--danger); color: var(--danger); }\n.pill-clear:hover { background: var(--danger); color: #fff; }\n.link-btn { background: none; border: none; padding: 0; color: var(--green); font: inherit; text-decoration: underline; cursor: pointer; }\n\n.btn { border: 1.5px solid var(--ink); border-radius: var(--r-ctl); padding: 11px 20px; font-weight: 700; font-size: 14px; cursor: pointer; min-height: 44px; transition: transform 0.12s ease, box-shadow 0.12s ease, background 0.15s ease; font-family: var(--font-display); letter-spacing: 0.01em; }\n.btn:active { transform: translate(2px, 2px); box-shadow: none !important; }\n.btn-primary { background: var(--ink); color: var(--lime); box-shadow: 4px 4px 0 rgba(26, 18, 20,0.25); }\n.btn-primary:hover { background: var(--green-deep); }\n.btn-ghost { background: transparent; color: var(--ink); }\n.btn-ghost:hover { background: var(--lime); color: #fff; box-shadow: 4px 4px 0 rgba(26, 18, 20,0.2); }\n\n/* ================= Chips ================= */\n.chips { display: flex; gap: 8px; margin: 16px 0 4px; overflow-x: auto; padding-bottom: 6px; scrollbar-width: none; }\n.chips::-webkit-scrollbar { display: none; }\n.chip { border: 1.5px solid var(--ink); background: var(--surface); color: var(--ink); border-radius: 999px; padding: 8px 18px; font-size: 13.5px; font-weight: 600; cursor: pointer; font-family: var(--font-display); white-space: nowrap; min-height: 40px; transition: all 0.13s ease; }\n.chip:hover { background: var(--lime-soft); transform: translateY(-1px); }\n.chip.on { background: var(--ink); color: var(--lime); box-shadow: 3px 3px 0 rgba(26, 18, 20,0.2); }\n\n/* ================= Results ================= */\n.results-head { display: flex; align-items: baseline; gap: 14px; margin: 28px 0 18px; flex-wrap: wrap; }\n.results-head h2 { font-size: 22px; text-transform: uppercase; letter-spacing: -0.02em; }\n.results-head > span { font-family: var(--font-mono); color: var(--ink-3); font-size: 12px; letter-spacing: 0.06em; }\n.results-tools { display: flex; align-items: center; gap: 12px; margin-left: auto; }\n.sort-ctl select { border: 1.5px solid var(--line-strong); background: var(--surface); border-radius: var(--r-ctl); padding: 7px 28px 7px 12px; font-family: var(--font-mono); font-size: 12px; color: var(--ink); cursor: pointer; appearance: none; -webkit-appearance: none; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='10' height='6'%3E%3Cpath d='M0 0l5 6 5-6z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right 10px center; }\n.view-toggle { display: inline-flex; border: 1.5px solid var(--line-strong); border-radius: var(--r-ctl); overflow: hidden; background: var(--surface); box-shadow: var(--shadow-flat); }\n.view-toggle button { border: 0; background: transparent; padding: 7px 14px; cursor: pointer; font-family: var(--font-mono); font-size: 12px; color: var(--ink-2); }\n.view-toggle button + button { border-left: 1.5px solid var(--line-strong); }\n.view-toggle button.on { background: var(--ink); color: var(--lime); }\n\n.grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(290px, 1fr)); gap: 22px; padding-bottom: 20px; }\n@media (max-width: 640px) { .grid { grid-template-columns: 1fr; gap: 16px; } }\n.more-wrap { display: flex; justify-content: center; padding: 8px 0 70px; }\n\n.card { background: var(--surface); border: 1.5px solid var(--line-strong); border-radius: var(--r-card); overflow: hidden; display: flex; flex-direction: column; box-shadow: var(--shadow-flat); transition: transform 0.16s ease, box-shadow 0.16s ease; }\n.card:hover { transform: translate(-3px, -3px); box-shadow: var(--shadow-off); }\n.card:focus-visible { outline: 3px solid var(--lime); outline-offset: 2px; }\n.card-img { position: relative; aspect-ratio: 16/10.5; background: repeating-linear-gradient(45deg, #eceae3 0 12px, #f3f1ea 12px 24px); overflow: hidden; }\n.card-img img { width: 100%; height: 100%; object-fit: cover; transition: transform 0.4s ease; }\n.card:hover .card-img img { transform: scale(1.05); }\n.card-img .noimg { display: flex; align-items: center; justify-content: center; height: 100%; color: var(--ink-3); font-size: 34px; }\n.badge { position: absolute; top: 12px; left: 12px; border-radius: 6px; padding: 4px 10px; font-family: var(--font-mono); font-size: 11.5px; font-weight: 700; letter-spacing: 0.04em; background: rgba(255, 255, 255, 0.95); color: var(--ink); border: 1px solid var(--ink); }\n.badge.type { background: var(--ink); color: var(--lime); border-color: var(--ink); }\n.badge.right { left: auto; right: 12px; background: rgba(255,255,255,0.92); border-color: transparent; }\n.card-body { padding: 16px 18px; display: flex; flex-direction: column; gap: 6px; flex: 1; }\n.card-price { font-family: var(--font-display); font-weight: 700; font-size: 21px; letter-spacing: -0.02em; }\n.card-title { font-weight: 600; font-size: 14.5px; color: var(--ink); overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }\n.card-meta { color: var(--ink-3); font-size: 12.5px; display: flex; gap: 7px; flex-wrap: wrap; align-items: center; font-family: var(--font-mono); }\n.card-meta .sep { width: 4px; height: 4px; background: var(--lime); border: 1px solid var(--ink); border-radius: 1px; transform: rotate(45deg); }\n.card-specs { display: flex; gap: 12px; font-size: 12.5px; color: var(--ink-2); font-family: var(--font-mono); }\n.card-foot { margin-top: auto; padding-top: 11px; border-top: 1.5px dashed var(--line); display: flex; justify-content: space-between; align-items: center; gap: 8px; }\n.source-tag { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.08em; color: var(--green-deep); background: var(--lime-soft); border: 1px solid var(--green); border-radius: 4px; padding: 3px 8px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; max-width: 62%; }\n.avail { font-size: 11px; color: var(--ink-3); font-weight: 500; text-align: right; font-family: var(--font-mono); }\n\n/* ================= Skeletons ================= */\n@keyframes shimmer { 0% { background-position: -400px 0; } 100% { background-position: 400px 0; } }\n.skel { border-radius: var(--r-card); border: 1.5px solid var(--line); overflow: hidden; background: var(--surface); }\n.skel .sk-img, .skel .sk-line { background: linear-gradient(90deg, #eeece5 25%, #f7f5ef 50%, #eeece5 75%); background-size: 800px 100%; animation: shimmer 1.4s infinite linear; }\n.skel .sk-img { aspect-ratio: 16/10.5; }\n.skel .sk-line { height: 14px; border-radius: 4px; margin: 12px 16px; }\n.skel .sk-line.short { width: 45%; }\n\n/* ================= Empty / error ================= */\n.notice { text-align: center; padding: 72px 24px; color: var(--ink-2); }\n.notice .big { font-size: 44px; margin-bottom: 10px; }\n.notice h2 { text-transform: uppercase; }\n\n/* ================= Detail page ================= */\n.detail { padding: 30px 0 90px; }\n.crumbs { font-family: var(--font-mono); font-size: 11.5px; letter-spacing: 0.06em; text-transform: uppercase; color: var(--ink-3); margin-bottom: 20px; display: flex; gap: 8px; align-items: center; flex-wrap: wrap; }\n.crumbs a { border-bottom: 1.5px solid transparent; }\n.crumbs a:hover { color: var(--green); border-color: var(--green); }\n\n/* Ordre DOM = ordre visuel sur TOUS les breakpoints (standard Groupe Ka\n   « Ordre des sections — pages détail »). Interdit : `order` / `column-reverse`\n   / `display:contents` pour réordonner — un bloc sans `order` retombe à\n   order:0 et passe devant la galerie sur mobile (bug corrigé sur Lou-Ka).\n   Mobile : colonnes empilées ; desktop : grid 2 colonnes (fiche | annexes). */\n.fiche { display: flex; flex-direction: column; gap: 22px; }\n.f-col { display: flex; flex-direction: column; gap: 22px; min-width: 0; }\n@media (min-width: 900px) {\n  .fiche { display: grid; grid-template-columns: 1.6fr 1fr; gap: 30px; align-items: start; }\n  .f-col { gap: 26px; }\n  .f-col:last-child { position: sticky; top: 88px; }\n}\n.f-bloc { min-width: 0; }\n.f-bloc h2 { font-size: 21px; letter-spacing: -0.02em; margin-bottom: 12px; border-left: 4px solid var(--lime); padding-left: 10px; }\n.f-bloc:empty { display: none; }\n\n/* galerie */\n.carousel { position: relative; border-radius: var(--r-card); overflow: hidden; border: 1.5px solid var(--line-strong); background: var(--surface-2); box-shadow: var(--shadow-off-soft); }\n.carousel-track { display: flex; overflow-x: auto; scroll-snap-type: x mandatory; -webkit-overflow-scrolling: touch; scrollbar-width: none; aspect-ratio: 16/11; }\n.carousel-track::-webkit-scrollbar { display: none; }\n.carousel-track img { flex: 0 0 100%; width: 100%; object-fit: cover; scroll-snap-align: center; cursor: zoom-in; }\n.carousel-empty { display: flex; align-items: center; justify-content: center; aspect-ratio: 16/11; font-size: 48px; }\n.carousel-count { position: absolute; right: 12px; bottom: 12px; z-index: 2; background: rgba(26, 18, 20, 0.82); color: var(--lime); font-family: var(--font-mono); font-size: 12px; font-weight: 700; padding: 4px 10px; border-radius: 999px; }\n.carousel-nav { position: absolute; top: 50%; transform: translateY(-50%); z-index: 2; width: 44px; height: 44px; border-radius: 50%; border: 1.5px solid var(--ink); background: rgba(255, 255, 255, 0.92); font-size: 22px; cursor: pointer; display: flex; align-items: center; justify-content: center; line-height: 1; }\n.carousel-nav.prev { left: 10px; } .carousel-nav.next { right: 10px; }\n@media (max-width: 640px) { .carousel-nav { display: none; } }\n.thumbs { display: grid; grid-template-columns: repeat(auto-fill, minmax(84px, 1fr)); gap: 8px; margin-top: 10px; }\n.thumbs button { border: 2px solid var(--line); border-radius: 8px; overflow: hidden; padding: 0; cursor: pointer; aspect-ratio: 4/3; background: #eceae3; transition: border-color 0.12s ease, transform 0.12s ease; }\n.thumbs button:hover { transform: translateY(-2px); }\n.thumbs button.on { border-color: var(--ink); box-shadow: 3px 3px 0 var(--lime); }\n.thumbs img { width: 100%; height: 100%; object-fit: cover; }\n@media (max-width: 640px) { .thumbs { display: flex; overflow-x: auto; scrollbar-width: none; } .thumbs::-webkit-scrollbar { display: none; } .thumbs button { flex: 0 0 84px; } }\n\n/* lightbox */\n.lightbox { position: fixed; inset: 0; background: rgba(16, 18, 16, 0.94); z-index: 100; display: flex; align-items: center; justify-content: center; cursor: zoom-out; padding: max(16px, env(safe-area-inset-top)) 16px; }\n.lightbox img { max-width: 94vw; max-height: 86vh; border-radius: 6px; border: 2px solid var(--lime); object-fit: contain; }\n.lb-close { position: absolute; top: 18px; right: 22px; background: none; border: none; color: #fff; font-size: 30px; cursor: pointer; }\n.lb-nav { position: absolute; top: 50%; transform: translateY(-50%); background: none; border: none; color: var(--lime); font-size: 52px; cursor: pointer; padding: 0 22px; user-select: none; }\n.lb-nav.prev { left: 6px; } .lb-nav.next { right: 6px; }\n.lb-count { position: absolute; bottom: 24px; color: #c9ccc2; font-family: var(--font-mono); font-size: 13px; }\n\n/* hero panneau (colonne droite) */\n.f-hero { background: var(--surface); border: 1.5px solid var(--ink); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); padding: 26px; }\n.price-kicker { font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.14em; color: var(--green); }\n.price-kicker::before { content: \"\"; display: inline-block; width: 16px; height: 2px; background: var(--lime); vertical-align: 3px; margin-right: 7px; }\n.price-row { display: flex; align-items: baseline; gap: 12px; flex-wrap: wrap; }\n.f-hero .price { font-family: var(--font-display); font-size: clamp(30px, 6vw, 40px); font-weight: 700; letter-spacing: -0.03em; }\n.type-chip { display: inline-flex; align-items: center; gap: 5px; padding: 4px 11px; background: var(--lime-soft); color: var(--green-deep); border: 1px solid rgba(179, 32, 43, 0.25); border-radius: 999px; font-size: 12px; font-weight: 600; }\n.price-sub { font-family: var(--font-mono); font-size: 11.5px; color: var(--ink-3); margin-top: 2px; }\n.f-hero h1 { font-size: clamp(20px, 4.5vw, 24px); margin-top: 10px; }\n.f-hero .loc { color: var(--ink-2); font-size: 13.5px; margin-top: 3px; display: flex; align-items: center; gap: 5px; }\n.f-hero .loc .ico { color: var(--lime); }\n\n/* liste de specs — fiche technique premium */\n.spec-list { margin: 18px 0 4px; border-top: 1.5px solid var(--line-strong); }\n.spec-row { display: grid; grid-template-columns: 30px 1fr auto; align-items: center; gap: 11px; padding: 8.5px 0; border-bottom: 1px dashed var(--line); }\n.spec-row:last-child { border-bottom: 1.5px solid var(--line-strong); }\n.spec-badge { display: inline-flex; align-items: center; justify-content: center; width: 30px; height: 30px; border-radius: 9px; background: var(--lime-soft); color: var(--green-deep); }\n.spec-k { font-family: var(--font-mono); font-size: 10.5px; text-transform: uppercase; letter-spacing: 0.08em; color: var(--ink-2); }\n.spec-v { font-family: var(--font-display); font-size: 15.5px; font-weight: 700; text-align: right; }\n.deal-badge { display: inline-flex; align-items: center; gap: 6px; margin: 8px 0 4px; border-radius: 999px; padding: 6px 13px; font-size: 12.5px; font-weight: 600; border: 1.5px solid var(--line-strong); }\n.deal-ok { background: var(--lime-soft); border-color: var(--green); color: var(--green-deep); }\n\n.specs-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(96px, 1fr)); gap: 8px; margin: 18px 0; }\n.spec { border: 1.5px solid var(--line-strong); border-radius: var(--r-ctl); padding: 9px 11px; background: var(--surface-2); }\n.spec b { display: block; font-family: var(--font-display); font-size: 16px; }\n.spec span { font-family: var(--font-mono); font-size: 9.5px; text-transform: uppercase; letter-spacing: 0.08em; color: var(--ink-3); }\n\n.broker { margin: 16px 0; padding: 14px; border: 1.5px dashed var(--line-strong); border-radius: var(--r-ctl); background: var(--surface-2); }\n.broker-k { font-family: var(--font-mono); font-size: 9.5px; text-transform: uppercase; letter-spacing: 0.1em; color: var(--ink-3); }\n.broker-name { font-family: var(--font-display); font-weight: 700; font-size: 15px; margin-top: 3px; }\n.broker-tel { display: inline-block; margin-top: 6px; color: var(--green-deep); font-weight: 600; border-bottom: 1.5px solid var(--lime); }\n\n.cta { display: block; text-align: center; background: var(--ink); color: var(--lime); font-weight: 700; font-family: var(--font-display); border-radius: var(--r-ctl); padding: 15px; border: 1.5px solid var(--ink); min-height: 48px; box-shadow: 4px 4px 0 rgba(26, 18, 20,0.25); transition: all 0.14s ease; margin-top: 6px; }\n.cta:hover { background: var(--lime); color: #fff; }\n.cta:active { transform: translate(2px, 2px); box-shadow: none; }\n.f-hero .fine { font-size: 11.5px; color: var(--ink-3); margin-top: 14px; text-align: center; font-family: var(--font-mono); letter-spacing: 0.02em; }\n\n.desc-text { color: var(--ink-2); font-size: 14.5px; white-space: pre-line; line-height: 1.6; margin: 0; }\n\n/* tableau caractéristiques */\n.dtable { display: grid; grid-template-columns: repeat(auto-fill, minmax(240px, 1fr)); gap: 0 22px; }\n.drow { display: flex; justify-content: space-between; gap: 12px; padding: 8px 0; border-bottom: 1px solid var(--line); font-size: 13.5px; }\n.drow span { color: var(--ink-3); } .drow b { text-align: right; font-family: var(--font-display); }\n\n/* tableau pièces */\n.rooms-wrap { overflow-x: auto; border: 1.5px solid var(--line-strong); border-radius: var(--r-card); }\ntable.rooms { width: 100%; border-collapse: collapse; font-size: 13.5px; min-width: 460px; }\ntable.rooms th { text-align: left; background: var(--ink); color: var(--paper); padding: 9px 12px; font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.06em; }\ntable.rooms td { padding: 8px 12px; border-bottom: 1px solid var(--line); }\ntable.rooms tr:last-child td { border-bottom: none; }\ntable.rooms tr:nth-child(even) td { background: var(--surface-2); }\n\n.amenity-row { display: flex; flex-wrap: wrap; gap: 7px; }\n.amenity { background: var(--lime-soft); color: var(--green-deep); font-size: 12px; font-weight: 600; border: 1px solid var(--green); border-radius: 999px; padding: 5px 12px; }\n\n.prix-histo { margin-top: 14px; padding: 10px 14px; border-radius: var(--r-card); border: 1.5px solid var(--line-strong); font-size: 13.5px; background: var(--surface); }\n.prix-histo.down { background: var(--lime-soft); border-color: var(--green); color: var(--green-deep); }\n\n.mini-map { height: 320px; border: 1.5px solid var(--line-strong); border-radius: var(--r-card); overflow: hidden; }\n.mini-map .mapview { border: none; border-radius: 0; box-shadow: none; height: 100%; }\n\n.cta-sticky { position: fixed; left: 0; right: 0; bottom: 0; z-index: 45; display: flex; align-items: center; gap: 12px; background: rgba(247, 241, 239, 0.94); backdrop-filter: blur(12px); border-top: 2px solid var(--ink); padding: 10px 16px calc(10px + env(safe-area-inset-bottom)); }\n.cta-sticky .cta { flex: 1; margin: 0; }\n.cta-sticky-prix { font-family: var(--font-display); font-weight: 700; font-size: 19px; white-space: nowrap; }\n@media (min-width: 900px) { .cta-sticky { display: none; } }\n@media (max-width: 899px) { .detail { padding-bottom: 84px; } }\n.f-foot { margin-top: 26px; }\n.fine { font-size: 12px; color: var(--ink-3); }\n\n/* ================= Sources / Agences ================= */\n.sources { padding: 44px 0 90px; }\n.sources h1 { font-size: clamp(28px, 4vw, 40px); text-transform: uppercase; margin: 10px 0 6px; }\n.sources .sub { color: var(--ink-2); margin-bottom: 30px; max-width: 720px; }\n.src-wrap { overflow-x: auto; border: 1.5px solid var(--ink); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); background: var(--surface); }\n.src-table { width: 100%; border-collapse: separate; border-spacing: 0; min-width: 760px; }\n.src-table th { text-align: left; font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); padding: 14px 18px; border-bottom: 1.5px solid var(--ink); background: var(--surface-2); }\n.src-table td { padding: 13px 18px; border-bottom: 1px solid var(--line); font-size: 14px; vertical-align: top; }\n.src-table tr:last-child td { border-bottom: none; }\n.src-table tr:hover td { background: var(--surface-2); }\n.src-table a { color: var(--green-deep); font-weight: 600; border-bottom: 1.5px solid var(--lime); }\n.pill.ok { background: var(--lime); color: #fff; border: 1px solid var(--ink); border-radius: 4px; padding: 3px 10px; font-family: var(--font-mono); font-size: 10.5px; font-weight: 700; text-transform: uppercase; }\n.pill.todo { background: var(--amber-soft); color: #8a5a12; border: 1px solid var(--amber); border-radius: 4px; padding: 3px 10px; font-family: var(--font-mono); font-size: 10.5px; font-weight: 700; text-transform: uppercase; }\n.count-pill { font-weight: 700; font-family: var(--font-display); font-size: 16px; border-bottom: 1.5px solid var(--lime); }\n\n/* ================= Stats ================= */\n.stats-page { padding: 44px 0 90px; }\n.stats-title { font-size: clamp(30px, 4.6vw, 46px); text-transform: uppercase; margin: 10px 0 6px; }\n.stats-page .sub { color: var(--ink-2); max-width: 660px; margin-bottom: 30px; }\n.tiles { display: grid; grid-template-columns: repeat(auto-fit, minmax(160px, 1fr)); gap: 14px; margin-bottom: 30px; }\n.tile { background: var(--surface); border: 1.5px solid var(--ink); border-radius: var(--r-card); padding: 18px 20px; box-shadow: 3px 3px 0 rgba(26, 18, 20, 0.1); }\n.tile-v { font-family: var(--font-display); font-weight: 700; font-size: clamp(22px, 3vw, 32px); letter-spacing: -0.03em; line-height: 1.05; }\n.tile-k { font-family: var(--font-mono); font-size: 10.5px; text-transform: uppercase; letter-spacing: 0.1em; color: var(--ink-3); margin-top: 6px; }\n.hero-tile { background: var(--ink); color: var(--lime); border-color: var(--ink); }\n.hero-tile .tile-k { color: rgba(226, 55, 68, 0.7); }\n.viz-card { background: var(--surface); border: 1.5px solid var(--ink); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); padding: 26px 28px 20px; margin-bottom: 22px; min-width: 0; max-width: 100%; overflow-x: auto; }\n/* mobile : une table large ne doit JAMAIS déborder la page (grid blowout) —\n   chaque bloc de la page Stats défile horizontalement en interne au besoin */\n.stats-page section, .stats-page > * { min-width: 0; max-width: 100%; }\n@media (max-width: 640px) { .stats-page .viz-card { padding: 18px 14px 14px; } }\n.viz-card h2 { font-size: 19px; text-transform: uppercase; letter-spacing: -0.01em; }\n.viz-sub { font-family: var(--font-mono); font-size: 11px; letter-spacing: 0.06em; text-transform: uppercase; color: var(--ink-3); margin: 4px 0 20px; }\n.viz-grid { display: grid; grid-template-columns: 1fr 1fr; gap: 22px; }\n@media (max-width: 900px) { .viz-grid { grid-template-columns: 1fr; } }\n.hbars { display: flex; flex-direction: column; gap: 7px; }\n.hbar-row { display: grid; grid-template-columns: minmax(96px, 180px) 1fr auto; gap: 12px; align-items: center; min-height: 26px; }\n.hbar-label { font-size: 13px; font-weight: 600; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }\n.hbar-label a { border-bottom: 1.5px solid var(--lime); }\n.hbar-label a:hover { color: var(--green-deep); }\n.hbar-track { background: var(--surface-2); border-radius: 0 4px 4px 0; height: 18px; overflow: hidden; }\n.hbar-fill { display: block; height: 100%; background: var(--green); border-radius: 0 4px 4px 0; min-width: 2px; transition: background 0.12s ease; }\n.hbar-row:hover .hbar-fill { background: var(--ink); box-shadow: inset 0 0 0 2px var(--lime); }\n.hbar-value { font-family: var(--font-mono); font-size: 11.5px; font-weight: 700; white-space: nowrap; }\n.hbar-value em { font-style: normal; font-weight: 500; color: var(--ink-3); }\n.viz-table { margin-top: 4px; }\n.viz-table table { width: 100%; border-collapse: collapse; font-size: 13px; }\n.viz-table th { text-align: left; font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.08em; color: var(--ink-3); padding: 6px 10px; border-bottom: 1.5px solid var(--ink); }\n.viz-table td { padding: 6px 10px; border-bottom: 1px solid var(--line); }\n.stats-foot { font-family: var(--font-mono); font-size: 11px; color: var(--ink-3); letter-spacing: 0.04em; margin-top: 6px; }\n\n/* ---- Iconographie maison (Icons.tsx) ---- */\n.ico { vertical-align: -3px; flex-shrink: 0; }\n.card-specs span { display: inline-flex; align-items: center; gap: 5px; }\n.card-specs .ico { color: var(--green); }\n.badge.right .ico { vertical-align: -2px; }\n.noimg .ico, .carousel-empty .ico { color: var(--ink-3); }\n.notice .big .ico { color: var(--lime); }\n.amenity .ico { color: var(--green); margin-right: 2px; }\n.q-badge .ico { vertical-align: -3px; margin-right: 2px; }\n.q-bar-label { display: inline-flex; align-items: center; gap: 7px; }\n.q-bar-label .ico { color: var(--green); }\n.mm-ico .ico { vertical-align: -4px; }\n.mv-noimg svg { color: var(--ink-3); }\n.broker-tel .ico { vertical-align: -2.5px; }\n.cta .ico { vertical-align: -2.5px; margin-left: 3px; }\n.prix-histo .ico { vertical-align: -3.5px; }\n.spec { position: relative; }\n.spec-ico { position: absolute; top: 10px; right: 10px; color: var(--lime); opacity: 0.85; }\n\n/* ---- Survalorisation vs Vrai-Prix (jauges divergentes) ---- */\n.vpg-axis { display: grid; grid-template-columns: 220px 1fr 74px 90px; gap: 10px; margin-bottom: 2px; }\n.vpg-axis > div { grid-column: 2; display: flex; justify-content: space-between; font-family: var(--font-mono); font-size: 10px; color: var(--ink-3); }\n.vpg-row { display: grid; grid-template-columns: 220px 1fr 74px 90px; align-items: center; gap: 10px; padding: 7px 0; border-bottom: 1px dashed var(--line); }\n.vpg-row:last-of-type { border-bottom: none; }\n.vpg-name { font-size: 13.5px; font-weight: 600; }\n.vpg-name em { display: block; font-style: normal; font-family: var(--font-mono); font-size: 10px; color: var(--ink-3); }\n.vpg-track { position: relative; height: 18px; background: var(--surface-2); border: 1px solid var(--line); border-radius: 999px; overflow: hidden; }\n.vpg-zero { position: absolute; left: 50%; top: 0; bottom: 0; width: 1.5px; background: var(--line-strong); opacity: 0.5; }\n.vpg-band { position: absolute; top: 3px; bottom: 3px; background: rgba(26, 18, 20, 0.14); border-radius: 999px; }\n.vpg-median { position: absolute; top: 1px; bottom: 1px; width: 4px; margin-left: -2px; border-radius: 2px; }\n.vpg-median.vp-sur { background: var(--lime); }\n.vpg-median.vp-juste { background: #d9a942; }\n.vpg-median.vp-sous { background: #4c8b4f; }\n.vpg-value { font-family: var(--font-display); font-weight: 700; font-size: 15px; text-align: right; }\n.vpg-value.vp-sur { color: var(--green); }\n.vpg-value.vp-juste { color: #a97b1e; }\n.vpg-value.vp-sous { color: #3c7440; }\n.vpg-split { display: flex; height: 10px; border-radius: 999px; overflow: hidden; border: 1px solid var(--line); }\n.vps-sous { background: #7fb283; }\n.vps-juste { background: #d9cfc7; }\n.vps-sur { background: var(--lime); }\n.vpg-sep { border-top: 1.5px solid var(--line-strong); margin: 4px 0; }\n.vpg-legend { display: flex; gap: 16px; margin-top: 10px; font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-2); flex-wrap: wrap; }\n.vpg-legend i { display: inline-block; width: 14px; height: 9px; border-radius: 999px; margin-right: 5px; vertical-align: -1px; }\n@media (max-width: 780px) { .vpg-row { grid-template-columns: 1fr 64px; } .vpg-track { grid-column: 1 / -1; } .vpg-split { grid-column: 1 / -1; } .vpg-axis { display: none; } }\n\n/* ================= Carte (Ka Maps — framework partagé Groupe Ka) =========\n   Jetons du design system appliqués au chrome de la carte, mêmes réglages\n   que Lou-Ka Maps. */\n.mapview .ka-map, .lmap3d .ka-map {\n  --ka-accent: var(--accent);\n  --ka-on-accent: var(--on-accent);\n  --ka-surface: var(--surface);\n  --ka-ink: var(--ink);\n  --ka-line: var(--line-strong);\n  --ka-radius: var(--r-ctl);\n  --ka-shadow: 0 8px 24px rgba(26, 18, 20, 0.14);\n  --ka-font: var(--font-body);\n}\n.mapview { position: relative; overflow: hidden; }\n.mapview .ka-map { position: absolute; inset: 0; }\n\n/* mini-carte 3D de la fiche (Emplacement) — bâtiment de l'annonce en cerise */\n.lmap3d {\n  position: relative; height: 340px; border: 1.5px solid var(--ink);\n  border-radius: var(--r-card); box-shadow: var(--shadow-off-soft);\n  overflow: hidden; background: var(--surface-2);\n}\n.lmap3d-skel {\n  background: linear-gradient(90deg, #eeece5 25%, #f7f5ef 50%, #eeece5 75%);\n  background-size: 800px 100%; animation: shimmer 1.4s infinite linear;\n}\n.lmap3d-legende {\n  position: absolute; left: 10px; bottom: 10px; z-index: 5;\n  display: inline-flex; align-items: center; gap: 6px;\n  padding: 4px 10px; border: 1.5px solid var(--ink);\n  border-radius: 999px; background: var(--surface);\n  font-size: 10.5px; font-weight: 500; color: var(--ink-2);\n  pointer-events: none;\n}\n.lmap3d-legende i {\n  width: 10px; height: 10px; border-radius: 3px;\n  background: var(--accent); border: 1px solid rgba(26, 18, 20, 0.25);\n}\n@media (max-width: 780px) { .lmap3d { height: 280px; } }\n\n/* ================= Carte ================= */\n.map-split { display: grid; grid-template-columns: minmax(300px, 400px) 1fr; gap: 18px; height: calc(100dvh - 200px); min-height: 420px; }\n.map-list { overflow-y: auto; display: flex; flex-direction: column; gap: 14px; padding-right: 4px; scrollbar-width: thin; }\n.map-list .card { margin: 0; flex: 0 0 auto; }\n/* sync liste ↔ carte : fiche sélectionnée depuis une pastille de la carte */\n.map-card { border-radius: var(--r-card); }\n.map-card-sel .card { border-color: var(--accent); box-shadow: 4px 4px 0 var(--accent-soft), 0 0 0 2px var(--accent); }\n.mapwrap { position: relative; width: 100%; height: 100%; }\n.mapview { width: 100%; height: 100%; border: 1.5px solid var(--line-strong); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); overflow: hidden; background: var(--surface-2); }\n.mv-brand { position: absolute; top: 10px; left: 10px; z-index: 3; display: flex; align-items: center; gap: 6px; padding: 5px 11px; background: var(--surface); border: 1.5px solid var(--line-strong); border-radius: 999px; box-shadow: var(--shadow-off-soft); font-family: var(--font-mono); font-size: 11px; letter-spacing: 0.04em; color: var(--ink-2); pointer-events: none; }\n.mv-brand b { color: var(--ink); }\n.mv-brand-dot { width: 8px; height: 8px; border-radius: 50%; background: var(--lime); box-shadow: 0 0 0 3px var(--lime-soft); }\n.mv-legend { position: absolute; bottom: 26px; left: 10px; z-index: 3; display: flex; flex-direction: column; gap: 4px; padding: 8px 11px; background: rgba(255, 255, 255, 0.92); border: 1px solid var(--line); border-radius: var(--r-ctl); font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-2); pointer-events: none; }\n.mv-legend span { display: flex; align-items: center; gap: 7px; }\n.mv-lg-pill { display: inline-block; width: 22px; height: 12px; border-radius: 999px; background: #fff; border: 1.5px solid rgba(26, 18, 20, 0.35); }\n.mv-lg-deal { background: var(--lime); border-color: var(--green); }\n.mv-credit { position: absolute; bottom: 8px; right: 8px; z-index: 3; padding: 4px 10px; background: rgba(255, 255, 255, 0.9); border-radius: 999px; font-family: var(--font-mono); font-size: 10px; letter-spacing: 0.04em; color: var(--ink-2); pointer-events: none; }\n.mv-credit b { color: var(--ink); }\n.mini-map .mv-brand, .mini-map .mv-legend { display: none; }\n.mini-map .mapwrap { height: 100%; }\n\n/* ---- carte propriété (fiche) : marqueur pulsant + anneaux piéton ---- */\n.pm-wrap { position: relative; width: 100%; height: 100%; }\n.pm-marker { display: flex; flex-direction: column; align-items: center; }\n.pm-pill { padding: 5px 13px; background: #fff; border: 1.5px solid var(--line-strong); border-radius: 999px; font-family: var(--font-display); font-weight: 700; font-size: 14px; color: var(--ink); box-shadow: var(--shadow-off-soft); white-space: nowrap; margin-bottom: 4px; }\n.pm-pill-deal { background: var(--lime); border-color: var(--green); color: #fff; }\n.pm-dot { width: 14px; height: 14px; border-radius: 50%; background: var(--lime); border: 2.5px solid #fff; box-shadow: 0 1px 4px rgba(26, 18, 20, 0.4); }\n.pm-pulse { position: absolute; bottom: -8px; width: 30px; height: 30px; border-radius: 50%; background: var(--lime); opacity: 0.35; animation: pm-pulse 2s ease-out infinite; }\n@keyframes pm-pulse { 0% { transform: scale(0.5); opacity: 0.45; } 80% { transform: scale(1.9); opacity: 0; } 100% { opacity: 0; } }\n.pm-rings-legend { position: absolute; bottom: 8px; left: 8px; z-index: 3; display: flex; gap: 12px; padding: 4px 10px; background: rgba(255, 255, 255, 0.9); border-radius: 999px; font-family: var(--font-mono); font-size: 10px; color: var(--green); pointer-events: none; }\n.mini-map .pm-wrap .mv-credit { display: block; }\n.map-loading { display: flex; align-items: center; justify-content: center; color: var(--ink-3); font-family: var(--font-mono); font-size: 13px; }\n@media (max-width: 780px) { .map-split { grid-template-columns: 1fr; height: calc(100dvh - 230px); } .map-list { display: none; } .results-tools { width: 100%; justify-content: space-between; } }\n.maplibregl-popup-content { padding: 0; border-radius: var(--r-card); overflow: hidden; border: 1.5px solid var(--line-strong); box-shadow: var(--shadow-off); font-family: var(--font-body); }\n.maplibregl-popup-close-button { font-size: 18px; padding: 2px 8px; color: var(--paper); z-index: 2; text-shadow: 0 0 4px rgba(26, 18, 20, 0.8); }\n.mv-pop img, .mv-noimg { width: 100%; height: 130px; object-fit: cover; }\n.mv-noimg { display: flex; align-items: center; justify-content: center; font-size: 34px; background: var(--surface-2); }\n.mv-pop-body { padding: 10px 12px 12px; }\n.mv-pop-price { font-family: var(--font-display); font-weight: 700; font-size: 18px; }\n.mv-pop-title { font-size: 13px; color: var(--ink-2); margin-top: 2px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }\n.mv-pop-meta { font-family: var(--font-mono); font-size: 11px; color: var(--ink-2); margin-top: 6px; }\n.mv-pop-src { font-family: var(--font-mono); font-size: 10px; color: var(--ink-3); text-transform: uppercase; letter-spacing: 0.06em; margin-top: 2px; }\n.mv-pop-cta { display: block; margin-top: 10px; padding: 8px 10px; text-align: center; background: var(--ink); color: #fff; border-radius: var(--r-ctl); font-weight: 600; font-size: 13px; }\n.mv-pop-cta:hover { background: var(--green-deep); }\n.mv-pop-deal { display: inline-block; margin-left: 6px; padding: 2px 8px; vertical-align: 3px; background: var(--lime); color: #fff; border-radius: 999px; font-family: var(--font-mono); font-size: 9.5px; text-transform: uppercase; letter-spacing: 0.05em; }\n.maplibregl-ctrl-attrib { font-size: 10px; }\n\n/* ================= Menu mobile ================= */\n.menu-btn { display: none; position: relative; z-index: 60; width: 44px; height: 44px; margin-left: auto; border: 1.5px solid var(--ink); border-radius: var(--r-ctl); background: var(--surface); cursor: pointer; padding: 0; flex-direction: column; align-items: center; justify-content: center; gap: 5px; box-shadow: 3px 3px 0 rgba(26, 18, 20, 0.15); transition: box-shadow 0.15s ease, transform 0.15s ease; }\n.menu-btn:active { transform: translate(2px, 2px); box-shadow: 1px 1px 0 rgba(26, 18, 20,0.15); }\n.menu-btn span { display: block; width: 18px; height: 2px; background: var(--ink); border-radius: 2px; transition: transform 0.25s ease, opacity 0.2s ease; }\n.menu-btn.open span:nth-child(1) { transform: translateY(7px) rotate(45deg); }\n.menu-btn.open span:nth-child(2) { opacity: 0; }\n.menu-btn.open span:nth-child(3) { transform: translateY(-7px) rotate(-45deg); }\n.mobile-menu { display: none; position: absolute; left: 0; right: 0; top: 100%; background: var(--paper); border-bottom: 2px solid var(--ink); box-shadow: 0 18px 30px rgba(26, 18, 20, 0.18); padding: 8px 16px calc(16px + env(safe-area-inset-bottom)); max-height: 0; overflow: hidden; opacity: 0; transition: max-height 0.3s ease, opacity 0.22s ease; }\n.mobile-menu.open { max-height: 480px; opacity: 1; }\n.mm-link { display: flex; align-items: center; gap: 12px; padding: 15px 10px; min-height: 52px; font-family: var(--font-display); font-weight: 700; font-size: 19px; letter-spacing: -0.02em; border-bottom: 1.5px dashed var(--line); opacity: 0; transform: translateY(-8px); transition: opacity 0.25s ease, transform 0.25s ease; }\n.mobile-menu.open .mm-link { opacity: 1; transform: translateY(0); }\n.mm-link.active { color: var(--green); }\n.mm-ico { width: 26px; text-align: center; font-size: 17px; }\n.mm-arrow { margin-left: auto; opacity: 0.25; }\n.mm-link:active { background: var(--lime-soft); border-radius: var(--r-ctl); }\n.mm-foot { padding: 12px 10px 4px; font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-3); letter-spacing: 0.04em; }\n.mm-backdrop { position: fixed; inset: 0; z-index: var(--z-overlay, 800); background: rgba(26, 18, 20, 0.35); backdrop-filter: blur(2px); }\n@media (max-width: 760px) { .menu-btn { display: flex; } .nav { display: none; } .mobile-menu { display: block; } }\n\n/* ================= Footer =================\n   Remplacé par le footer commun Groupe KA (ka/KaFooter.tsx — styles .ka-footer\n   dans ka/tokens.css). */\n\n/* ================= Mobile : feuille de filtres + FAB ================= */\n.sheet-head { display: none; }\n.sheet-apply { display: none; }\n.sheet-backdrop { display: none; }\n.fab { display: none; }\n@media (max-width: 640px) {\n  .header-inner { height: 56px; }\n  .brand { font-size: 21px; }\n  .hero h1 { font-size: clamp(30px, 9.4vw, 44px); }\n  .hero p.lede { font-size: 15px; }\n  .stat-row { flex-wrap: nowrap; overflow-x: auto; scrollbar-width: none; padding-bottom: 6px; margin-right: -16px; padding-right: 16px; }\n  .stat-row::-webkit-scrollbar { display: none; }\n  .stat-chip { flex: 0 0 auto; white-space: nowrap; }\n  .filterbar { display: none; }\n  .filterbar.open .f-primary { flex-direction: column; }\n  .filterbar.open .f-ctl select { max-width: none; width: 100%; }\n  .filterbar.open .f-more { display: none; }\n  .filterbar.open { display: flex; flex-direction: column; gap: 12px; position: fixed; left: 0; right: 0; bottom: 0; z-index: 95; margin: 0; border-radius: 20px 20px 0 0; border-width: 2px 0 0 0; max-height: 82dvh; overflow-y: auto; -webkit-overflow-scrolling: touch; padding: 16px 18px calc(18px + env(safe-area-inset-bottom)); box-shadow: 0 -16px 48px rgba(16, 18, 16, 0.35); animation: sheet-up 0.22s ease; }\n  @keyframes sheet-up { from { transform: translateY(30%); opacity: 0.4; } to { transform: none; opacity: 1; } }\n  .filterbar.open .sheet-head { display: flex; justify-content: space-between; align-items: center; font-family: var(--font-display); font-weight: 700; font-size: 17px; text-transform: uppercase; position: sticky; top: -16px; background: var(--surface); padding: 6px 0 8px; border-bottom: 1.5px solid var(--line); margin-bottom: 2px; z-index: 1; }\n  .sheet-close { border: 1.5px solid var(--ink); background: var(--surface); border-radius: 50%; width: 36px; height: 36px; font-size: 15px; cursor: pointer; line-height: 1; }\n  .filterbar.open .sheet-apply { display: block; width: 100%; }\n  .sheet-backdrop { display: block; position: fixed; inset: 0; z-index: 90; background: rgba(16, 18, 16, 0.45); backdrop-filter: blur(2px); }\n  .fab { display: flex; align-items: center; gap: 6px; position: fixed; left: 50%; transform: translateX(-50%); bottom: calc(18px + env(safe-area-inset-bottom)); z-index: 80; background: var(--ink); color: var(--lime); border: 1.5px solid var(--ink); border-radius: 999px; padding: 13px 24px; font-family: var(--font-display); font-weight: 700; font-size: 15px; cursor: pointer; box-shadow: 0 8px 24px rgba(16, 18, 16, 0.35), 4px 4px 0 rgba(26, 18, 20, 0.25); }\n  .fab:active { transform: translateX(-50%) scale(0.97); }\n  .results-head { margin-top: 20px; }\n  .chips { margin-right: -16px; padding-right: 16px; }\n  .notice { padding: 48px 16px; }\n}\n@media (max-width: 900px) { .f-search input, .f-native, .f-ctl select { font-size: 16px; } }\n\n/* --- Sources : bannières → sous-agences -------------------------------- */\n.franchise-list { display: flex; flex-direction: column; gap: 10px; margin: 18px 0; }\n.franchise { border: 2px solid var(--ink); background: var(--surface); box-shadow: var(--shadow-off-soft); }\n.franchise-head {\n  width: 100%; display: flex; align-items: center; gap: 12px; padding: 14px 16px;\n  background: none; border: 0; cursor: pointer; font-family: var(--font-display);\n  font-size: 16px; font-weight: 600; color: var(--ink); text-align: left;\n}\n.franchise-head:hover { background: var(--surface-2); }\n.fh-caret { color: var(--green); width: 14px; }\n.fh-name { flex: 1; }\n.fh-sub { font-size: 12px; font-weight: 500; color: var(--ink-3); font-family: var(--font-mono); }\n.fh-count {\n  font-family: var(--font-mono); font-weight: 700; background: var(--ink);\n  color: var(--lime); padding: 3px 10px; min-width: 64px; text-align: right;\n}\n.subagency-list { list-style: none; margin: 0; padding: 0 16px 10px 40px; border-top: 1px solid var(--line); }\n.subagency-list li {\n  display: flex; align-items: center; justify-content: space-between; gap: 12px;\n  padding: 8px 0; border-bottom: 1px dotted var(--line); font-size: 14px;\n}\n.subagency-list li:last-child { border-bottom: 0; }\n.sa-name { color: var(--ink-2); }\n\n/* --- Pagination (accueil) ---------------------------------------------- */\n.pager { display: flex; flex-wrap: wrap; align-items: center; justify-content: center;\n  gap: 6px; margin: 32px 0 8px; }\n.pager-btn {\n  font-family: var(--font-mono); font-size: 14px; min-width: 40px; padding: 8px 12px;\n  border: 2px solid var(--ink); background: var(--surface); color: var(--ink);\n  cursor: pointer; box-shadow: var(--shadow-off-soft);\n}\n.pager-btn:hover:not(:disabled) { background: var(--lime-soft); }\n.pager-btn.on { background: var(--ink); color: var(--lime); font-weight: 700; }\n.pager-btn:disabled { opacity: .4; cursor: not-allowed; box-shadow: none; }\n.pager-gap { padding: 0 4px; color: var(--ink-3); }\n.pager-info { width: 100%; text-align: center; margin-top: 8px; font-size: 12px;\n  color: var(--ink-3); font-family: var(--font-mono); }\n\n\n\n/* --- Le quartier (census / proximité / chaleur / crime) — porté de Lou-Ka --- */\n/* --- Section « Le quartier » (fiche) --------------------------------------- */\n.quartier { margin-top: 34px; }\n.quartier h2 { font-size: 24px; letter-spacing: -0.02em; }\n.q-sub { color: var(--ink-3); font-size: 13px; margin: 4px 0 16px; }\n.q-grid {\n  display: grid; grid-template-columns: repeat(3, 1fr); gap: 10px;\n}\n.q-grid { grid-template-columns: repeat(2, 1fr); }\n.q-cell {\n  background: var(--surface); border: 1.5px solid var(--line-strong);\n  border-radius: var(--r-card); padding: 12px 14px; box-shadow: var(--shadow-flat);\n}\n.q-val { font-family: var(--font-display); font-weight: 700; font-size: 19px; letter-spacing: -0.02em; }\n.q-label { font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-3);\n  text-transform: uppercase; letter-spacing: 0.06em; margin-top: 3px; }\n.q-prox { margin-top: 18px; display: flex; flex-direction: column; gap: 8px; }\n.q-bar { display: flex; align-items: center; gap: 10px; font-size: 13px; }\n.q-bar-label { flex: 0 0 150px; color: var(--ink-2); }\n.q-bar-label { flex-basis: 120px; font-size: 12px; }\n.q-bar-track {\n  flex: 1; height: 10px; background: var(--surface-2);\n  border: 1px solid var(--line-strong); border-radius: 999px; overflow: hidden;\n}\n.q-bar-fill { display: block; height: 100%; background: var(--green); border-radius: 999px; }\n.q-bar-num { flex: 0 0 30px; text-align: right; font-family: var(--font-mono);\n  font-size: 11.5px; font-weight: 700; }\n.q-badges { display: flex; flex-wrap: wrap; gap: 8px; margin-top: 16px; }\n.q-badge {\n  display: inline-flex; align-items: center; gap: 6px;\n  border: 1.5px solid var(--line-strong); border-radius: 999px;\n  padding: 7px 13px; font-size: 12.5px; background: var(--surface);\n}\n.q-badge.cool { background: var(--lime-soft); border-color: var(--green); color: var(--green-deep); }\n.q-badge.hot { background: var(--amber-soft); border-color: var(--amber); color: #8a5a12; }\n.quartier .fine { margin-top: 10px; }\n.q-cell { padding: 9px 11px; }\n.q-val { font-size: 16px; }\n.q-bar { gap: 8px; font-size: 12px; }\n.q-bar-track { height: 8px; }\n.q-badge-sub { display: block; font-size: 10.5px; color: var(--ink-3); font-weight: 400; margin-left: 4px; }\n\n/* --- Vrai-Prix : estimation de valeur ---------------------------------- */\n.vraiprix { border: 2px solid var(--ink); background: var(--surface-2);\n  box-shadow: var(--shadow-off-soft); padding: 14px; margin: 14px 0; }\n.vp-head { display: flex; align-items: center; justify-content: space-between; gap: 8px; }\n.vp-logo { font-family: var(--font-display); font-weight: 700; color: var(--green-deep);\n  letter-spacing: -.3px; }\n.vp-conf { font-family: var(--font-mono); font-size: 11px; padding: 2px 7px; border: 1px solid var(--ink); }\n.vp-conf-A { background: var(--lime); color: #fff; } .vp-conf-B { background: var(--lime-soft); }\n.vp-conf-C { background: var(--amber-soft); } .vp-conf-D { background: #f0d9d9; }\n.vp-k { font-size: 11px; color: var(--ink-3); text-transform: uppercase;\n  letter-spacing: .5px; margin-top: 8px; }\n.vp-value { font-family: var(--font-display); font-size: 26px; font-weight: 700;\n  letter-spacing: -1px; color: var(--ink); }\n.vp-range { font-size: 12px; color: var(--ink-2); font-family: var(--font-mono); }\n/* jauge Vrai-Prix : fourchette P10–P90 + marqueurs estimation / prix demandé */\n.vp-gauge { margin-top: 10px; }\n.vp-gauge-track { position: relative; height: 14px; border-radius: 999px; background: var(--surface); border: 1px solid var(--line-strong); overflow: hidden; }\n.vp-gauge-band { position: absolute; inset: 3px 6%; background: linear-gradient(90deg, var(--lime-soft), rgba(226, 55, 68, 0.35), var(--lime-soft)); border-radius: 999px; }\n.vp-gauge-est { position: absolute; top: 0; bottom: 0; width: 4px; margin-left: -2px; background: var(--lime); border-radius: 2px; }\n.vp-gauge-ask { position: absolute; top: 2px; bottom: 2px; width: 4px; margin-left: -2px; background: var(--ink); border-radius: 2px; }\n.vp-gauge-ends { display: flex; justify-content: space-between; font-family: var(--font-mono); font-size: 10px; color: var(--ink-3); margin-top: 4px; }\n.vp-gauge-legend { display: flex; gap: 14px; font-family: var(--font-mono); font-size: 10px; color: var(--ink-2); margin-top: 5px; }\n.vp-dot-est, .vp-dot-ask { display: inline-block; width: 9px; height: 9px; border-radius: 3px; margin-right: 4px; vertical-align: -1px; }\n.vp-dot-est { background: var(--lime); }\n.vp-dot-ask { background: var(--ink); }\n.vp-delta { font-size: 13px; margin-top: 8px; padding: 6px 8px; border-radius: var(--r-ctl); }\n.vp-over { background: #f6e2e2; color: #8a2b2b; }\n.vp-under { background: #e2f0e6; color: #1c5c41; }\n.vp-fair { background: var(--surface); color: var(--ink-2); }\n.vp-link { display: block; text-align: center; margin-top: 10px; font-weight: 700;\n  font-size: 13px; padding: 8px; background: var(--green-deep); color: #fff;\n  text-decoration: none; }\n.vp-link:hover { background: var(--green); }\n\n/* =============================================================================\n   Harmonisation Groupe KA — header (badge + compte KA ID), pages Profil,\n   Contact et légales. Le socle vient de ka/tokens.css.\n============================================================================= */\n\n/* ---- Header : badge « Un service Groupe KA » + zone compte ---- */\n.header-inner .gk-badge { flex: none; }\n@media (max-width: 900px) { .header-inner .gk-badge { display: none; } }\n.header-acct { display: flex; align-items: center; gap: 10px; flex: none; }\n.ka-auth { display: flex; align-items: center; gap: 10px; }\n.ka-login {\n  display: inline-flex; align-items: center; gap: 6px; min-height: 40px;\n  padding: 8px 15px; border: 1.5px solid var(--ink); border-radius: 999px;\n  background: var(--surface); color: var(--ink); cursor: pointer;\n  font-family: var(--font-display); font-weight: 600; font-size: 13.5px;\n  box-shadow: 3px 3px 0 rgba(20, 24, 20, 0.15);\n  transition: transform 0.13s ease, box-shadow 0.13s ease, background 0.15s ease;\n}\n.ka-login b { background: var(--ink); color: var(--accent); border-radius: 5px; padding: 0 6px 1px; transform: rotate(-2deg); transition: transform 0.15s ease; }\n.ka-login:hover { transform: translate(-1px, -1px); box-shadow: 4px 4px 0 rgba(20, 24, 20, 0.2); }\n.ka-login:hover b { transform: rotate(0); }\n.ka-login:active { transform: translate(2px, 2px); box-shadow: none; }\n.ka-signup { font-family: var(--font-mono); font-size: 11px; color: var(--ink-2); text-decoration: underline; text-underline-offset: 3px; white-space: nowrap; }\n.ka-signup:hover { color: var(--accent-deep); }\n@media (max-width: 1100px) { .header-acct .ka-signup { display: none; } }\n.ka-acct {\n  display: inline-flex; align-items: center; gap: 8px; min-height: 40px;\n  padding: 6px 14px; border: 1.5px solid var(--ink); border-radius: 999px;\n  background: var(--surface); font-family: var(--font-display);\n  font-weight: 600; font-size: 13.5px; color: var(--ink);\n  max-width: 160px; overflow: hidden; white-space: nowrap; text-overflow: ellipsis;\n}\n.ka-acct:hover, .ka-acct.active { background: var(--ink); color: var(--accent); }\n.ka-acct-pic { width: 24px; height: 24px; border-radius: 50%; flex: none; border: 1px solid var(--line); }\n@media (max-width: 760px) {\n  .header-acct { margin-left: auto; }\n  .menu-btn { margin-left: 0; }\n  .ka-login { min-height: 44px; }\n}\n@media (max-width: 380px) { .header-acct { display: none; } } /* → menu mobile */\n\n/* ---- Menu mobile : compte + badge groupe ---- */\n.mm-auth { display: flex; align-items: center; gap: 14px; flex-wrap: wrap; padding: 15px 10px; border-bottom: 1.5px dashed var(--line); }\n.mm-auth .ka-login { min-height: 44px; }\n.mm-foot p { margin: 10px 0 0; }\n\n/* ---- Générique : lede (Profil / Contact) ---- */\n.lede { color: var(--ink-2); font-size: 15.5px; max-width: 640px; }\n\n/* ================= Page Profil (KA ID) ================= */\n.profil { padding: 44px 0 90px; }\n.profil h1 { font-size: clamp(28px, 4.6vw, 44px); margin: 10px 0 24px; letter-spacing: -0.03em; }\n.profil .lede { margin-bottom: 22px; }\n.profil .notice { padding: 60px 0; }\n\n/* Carte de membre — encre + cerise */\n.pc {\n  position: relative; overflow: hidden; max-width: 460px;\n  background: var(--ink); color: var(--paper);\n  border: 1.5px solid var(--ink); border-radius: var(--r-card);\n  box-shadow: var(--shadow-off-soft); padding: 22px 26px 0;\n}\n.pc-watermark {\n  position: absolute; right: -18px; bottom: 14px; font-family: var(--font-display);\n  font-weight: 700; font-size: 150px; line-height: 1; letter-spacing: -0.06em;\n  color: rgba(245, 243, 238, 0.05); pointer-events: none; user-select: none;\n}\n.pc-head { display: flex; align-items: baseline; justify-content: space-between; gap: 12px; flex-wrap: wrap; }\n.pc-brand { font-family: var(--font-display); font-weight: 700; font-size: 24px; letter-spacing: -0.03em; }\n.pc-ka { display: inline-block; background: var(--accent); color: var(--on-accent); border-radius: 6px; padding: 0 7px 2px; margin-left: 3px; transform: rotate(-2deg); }\n.pc-label { font-family: var(--font-mono); font-size: 9.5px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.16em; color: rgba(245, 243, 238, 0.5); }\n.pc-id-block { margin: 26px 0 22px; display: flex; flex-direction: column; gap: 4px; }\n.pc-id-label { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.2em; color: var(--accent); }\n.pc-id { font-family: var(--font-mono); font-weight: 700; font-size: clamp(17px, 4.4vw, 22px); letter-spacing: 0.04em; word-break: break-all; }\n.pc-foot { display: flex; align-items: flex-end; justify-content: space-between; gap: 14px; padding-bottom: 18px; }\n.pc-holder { display: flex; flex-direction: column; gap: 3px; min-width: 0; }\n.pc-holder-name { font-family: var(--font-display); font-weight: 600; font-size: 15px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }\n.pc-role-badge { align-self: flex-start; font-family: var(--font-mono); font-size: 9.5px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.1em; background: var(--accent); color: var(--on-accent); border-radius: 999px; padding: 2px 9px; }\n.pc-holder-since { font-family: var(--font-mono); font-size: 10.5px; color: rgba(245, 243, 238, 0.5); letter-spacing: 0.06em; }\n.pc-avatar { width: 52px; height: 52px; border-radius: 50%; border: 2px solid var(--accent); flex: none; object-fit: cover; }\n.pc-strip { display: flex; align-items: flex-end; gap: 5px; margin: 0 -26px; padding: 9px 18px; background: rgba(226, 55, 68, 0.14); border-top: 1px solid rgba(226, 55, 68, 0.4); overflow: hidden; }\n.pc-strip i { display: block; width: 3px; border-radius: 1px; background: var(--accent); opacity: 0.75; }\n.pc-strip i:nth-child(3n) { height: 14px; opacity: 0.4; }\n.pc-strip i:nth-child(3n+1) { height: 9px; }\n.pc-strip i:nth-child(3n+2) { height: 17px; opacity: 0.95; }\n.pc-copy { margin-top: 16px; }\n.pc-copy.ok { background: var(--ink); color: var(--accent); }\n\n.profil-grid {\n  display: grid; grid-template-columns: repeat(auto-fill, minmax(220px, 1fr));\n  gap: 12px; margin-top: 28px; max-width: 720px;\n}\n.pg-item { background: var(--surface); border: 1.5px solid var(--line-strong); border-radius: var(--r-ctl); padding: 12px 14px; display: flex; flex-direction: column; gap: 4px; min-width: 0; }\n.pg-label { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); }\n.pg-value { font-weight: 600; font-size: 14.5px; word-break: break-word; }\n.pg-value.mono { font-family: var(--font-mono); color: var(--accent-deep); }\n.profil-note { max-width: 640px; color: var(--ink-2); font-size: 13.5px; margin-top: 22px; }\n.profil-note a { text-decoration: underline; text-underline-offset: 3px; }\n.profil-actions { display: flex; gap: 10px; flex-wrap: wrap; margin-top: 26px; }\n\n/* Profil hub Groupe KA (lecture seule) */\n.hub-profile { margin-top: 34px; padding-top: 24px; border-top: 1.5px solid var(--line-strong); max-width: 720px; display: flex; flex-direction: column; gap: 14px; }\n.hub-profile h3 { font-size: 18px; }\n.hub-profile .hub-bio { color: var(--ink-2); font-size: 14px; margin: 0; max-width: 560px; }\n.hub-profile .pub-meta { display: flex; gap: 8px; flex-wrap: wrap; }\n.hub-profile .stat-chip { display: inline-flex; align-items: center; gap: 6px; }\n.hub-profile a.stat-chip:hover { background: var(--accent-soft); }\n.hub-hint { color: var(--ink-3); font-size: 12.5px; margin: 0; }\n\n/* ================= Pages légales ================= */\n.legal { padding: 48px 0 80px; max-width: 780px; }\n.legal h1 { font-size: clamp(28px, 4.6vw, 44px); margin: 8px 0 6px; }\n.legal .legal-meta { color: var(--ink-3); font-family: var(--font-mono); font-size: 12px; margin: 0 0 26px; }\n.legal h2 { font-size: 20px; margin: 30px 0 8px; border-left: 4px solid var(--accent); padding-left: 10px; }\n.legal p, .legal li { color: var(--ink-2); font-size: 14.5px; }\n.legal ul { padding-left: 20px; display: flex; flex-direction: column; gap: 6px; }\n.legal a { text-decoration: underline; text-underline-offset: 3px; }\n.legal b { color: var(--ink); }\n\n/* ================= Page Contact (écosystème Groupe KA) ================= */\n.contact { padding: 48px 0 90px; }\n.contact h1 { font-size: clamp(30px, 5vw, 52px); margin: 12px 0 14px; text-transform: uppercase; letter-spacing: -0.03em; }\n.contact-cards { display: grid; grid-template-columns: repeat(auto-fit, minmax(230px, 1fr)); gap: 14px; margin: 28px 0 6px; max-width: 900px; }\n.contact-card {\n  display: flex; flex-direction: column; gap: 8px; min-height: var(--touch, 44px);\n  background: var(--surface); border: 1.5px solid var(--ink);\n  border-radius: var(--r-card); padding: 18px 20px;\n  box-shadow: var(--shadow-off-soft);\n  transition: transform 0.15s ease, box-shadow 0.15s ease;\n}\n.contact-card:hover { transform: translate(-2px, -2px); box-shadow: var(--shadow-off); }\n.contact-role { font-family: var(--font-mono); font-size: 10.5px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.1em; color: var(--green); }\n.contact-mail { font-family: var(--font-display); font-weight: 700; font-size: clamp(14px, 2vw, 17px); word-break: break-all; border-bottom: 2px solid var(--accent); align-self: flex-start; }\n.contact-disclaimer { max-width: 720px; margin: 24px 0 0; padding: 14px 16px; border-left: 3px solid var(--accent); background: var(--accent-soft); color: var(--ink-2); font-size: 13.5px; }\n.contact-disclaimer b { color: var(--ink); }\n.contact-hub { display: flex; gap: 12px; flex-wrap: wrap; margin-top: 26px; }\n.contact-sites { margin-top: 46px; }\n.contact-sites h2 { font-size: 21px; text-transform: uppercase; margin-bottom: 14px; }\n.contact-sites ul { list-style: none; margin: 0; padding: 0; display: grid; grid-template-columns: repeat(auto-fill, minmax(230px, 1fr)); gap: 10px; }\n.contact-sites a { display: flex; flex-direction: column; gap: 3px; min-height: var(--touch, 44px); background: var(--surface); border: 1.5px solid var(--line-strong); border-radius: var(--r-ctl); padding: 12px 14px; transition: transform 0.14s ease, box-shadow 0.14s ease; }\n.contact-sites a:hover { transform: translate(-2px, -2px); box-shadow: 4px 4px 0 var(--ink); }\n.contact-sites b { font-family: var(--font-display); font-size: 15px; }\n.contact-sites span { font-size: 12px; color: var(--ink-3); }\n@media (max-width: 480px) {\n  .header-inner { gap: 10px; }\n  .ka-login { font-size: 12px; padding: 6px 10px; gap: 4px; min-height: 44px; }\n}\n\n/* Bulle KA Agent : sur mobile, remontée au-dessus de la barre CTA collante des\n   fiches (sinon elle chevauche le bouton « Voir chez … »). */\n@media (max-width: 899px) {\n  .kaa-btn {\n    bottom: calc(88px + env(safe-area-inset-bottom, 0px)) !important;\n  }\n}\n\n/* Footer commun KA : zones tactiles ≥ 44 px pour les petits liens (Ka2, Ka4…)\n   — padding compensé par une marge négative : aucun changement visuel. */\n.ka-footer .sites a, .ka-footer .legal a, .ka-footer .contacts a {\n  display: inline-block; padding: 12px 8px; margin: -12px -8px;\n}\n\n/* ================= Qualité des annonces / images ================= */\n/* Image de secours par TYPE DE BIEN (jamais d'icône d'image cassée) */\n.type-fallback {\n  display: flex; flex-direction: column; align-items: center; justify-content: center;\n  gap: 9px; width: 100%; height: 100%; min-height: 120px;\n  background:\n    radial-gradient(circle at 78% 18%, rgba(226, 55, 68, 0.14), transparent 52%),\n    repeating-linear-gradient(45deg, #f3ece9 0 14px, #f7f1ef 14px 28px);\n  color: var(--accent-deep);\n}\n.type-fallback .ico { opacity: 0.75; }\n.type-fallback span {\n  font-family: var(--font-mono); font-size: 10.5px; font-weight: 700;\n  text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3);\n}\n.carousel-empty .type-fallback { aspect-ratio: 16/11; }\n\n/* légende de photo (étiquette de pièce de la source) */\n.carousel-caption {\n  position: absolute; left: 12px; bottom: 12px; z-index: 2; max-width: 60%;\n  background: rgba(26, 18, 20, 0.82); color: #f5f3ee;\n  font-family: var(--font-mono); font-size: 11px; font-weight: 600;\n  padding: 4px 10px; border-radius: 999px;\n  overflow: hidden; text-overflow: ellipsis; white-space: nowrap;\n}\n\n/* mention loyer mensuel (transaction = location) */\n.per-month { font-size: 0.55em; font-weight: 600; color: var(--ink-3); letter-spacing: 0; }\n\n/* lightbox : l'image zoomable ne doit pas déclencher le scroll de la page */\n.lightbox img { user-select: none; -webkit-user-drag: none; }\n\n/* ---- Stats : qualité des données ---- */\n.q-table-wrap { overflow-x: auto; }\n.q-table { width: 100%; border-collapse: collapse; font-size: 13px; min-width: 520px; }\n.q-table th { text-align: left; font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.08em; color: var(--ink-3); padding: 6px 10px; border-bottom: 1.5px solid var(--ink); }\n.q-table td { padding: 6px 10px; border-bottom: 1px solid var(--line); }\n.q-table .num { text-align: right; font-family: var(--font-mono); }\n.q-meter { display: inline-block; width: 74px; height: 9px; background: var(--surface-2); border: 1px solid var(--line-strong); border-radius: 999px; overflow: hidden; vertical-align: -1px; margin-right: 7px; }\n.q-meter i { display: block; height: 100%; background: var(--green); }\n\n\n/* ---- Criminalité détaillée SPVM (bloc quartier) ---- */\n.q-crime { width: 100%; }\n.q-crime-cats { list-style: none; margin: 8px 0 0; padding: 0; }\n.q-crime-cats li { display: flex; align-items: center; gap: 8px;\n                   padding: 3px 0; font-size: 12.5px; }\n.q-crime-nom { flex: 0 0 46%; color: var(--ink-2, #555); overflow: hidden;\n               text-overflow: ellipsis; white-space: nowrap; }\n.q-crime-barre { flex: 1 1 auto; height: 10px; background: var(--surface-2, #f0ede8);\n                 border-radius: 5px; overflow: hidden; }\n.q-crime-barre i { display: block; height: 100%; border-radius: 5px;\n                   background: var(--accent, #e23744); opacity: 0.55; }\n.q-crime-n { flex: 0 0 52px; text-align: right; font-weight: 600;\n             color: var(--ink, #222); white-space: nowrap; }\n.q-crime-n small { font-weight: 400; color: var(--ink-3, #888); }\n.q-crime-src { margin-top: 8px; }\n\n\n/* ---- Risque d'inondation (fiche) — BDZI gouv. du Québec ---- */\n.f-zi .zi-head { display: flex; align-items: center; gap: 10px; }\n.zi-badge { display: inline-block; padding: 3px 10px; border-radius: 999px;\n            font-size: 12.5px; font-weight: 700; }\n.zi-eleve { background: #fde8e8; color: #a12622; }\n.zi-modere { background: #fdf3e0; color: #8a5a00; }\n.zi-present { background: #fdf3e0; color: #8a5a00; }\n.zi-ok { background: #e7f4ea; color: #1e6b34; }\n.zi-nc { background: var(--surface-2, #f0ede8); color: var(--ink-3, #888); }\n.zi-liste { margin: 8px 0 0; padding: 0; list-style: none; font-size: 13px; }\n.zi-liste li { padding: 4px 0; border-top: 1px solid var(--line, #e6e4df); }\n\n\n/* ---- tuiles KPI + tableau compacts (partagés air/essence) ---- */\n.rdl-kpis { display: flex; flex-wrap: wrap; gap: 10px; margin: 2px 0 12px; }\n.rdl-kpi { flex: 1 1 90px; min-width: 90px; background: var(--surface-2, #f7f5f1);\n           border-radius: 10px; padding: 10px 12px; }\n.rdl-kpi-v { display: block; font-size: 20px; font-weight: 700;\n             letter-spacing: -0.02em; color: var(--ink, #222); }\n.rdl-kpi-l { display: block; font-size: 11px; line-height: 1.35;\n             color: var(--ink-3, #888); margin-top: 2px; }\n.rdl-table { width: 100%; border-collapse: collapse; font-size: 13px; }\n.rdl-table td { padding: 5px 8px 5px 0; border-top: 1px solid var(--line, #e6e4df);\n                vertical-align: top; }\n.rdl-table .rdl-addr { max-width: 46%; }\n.rdl-table .rdl-prix { font-weight: 600; white-space: nowrap; }\n.rdl-table .rdl-date { white-space: nowrap; color: var(--ink-2, #555); }\n.rdl-table .rdl-dist { color: var(--ink-3, #888); white-space: nowrap; text-align: right; }\n.rdl-cap { caption-side: top; text-align: left; font-size: 13px; font-weight: 700;\n           color: var(--ink, #222); padding: 6px 0; }\n\n/* ---- Qualité de l'air (RSQAQ) + Essence à proximité ---- */\n.air-liste { list-style: none; margin: 10px 0 0; padding: 0; }\n.air-liste li { display: flex; align-items: center; gap: 8px;\n                padding: 4px 0; font-size: 12.5px; }\n.air-nom { flex: 0 0 44%; color: var(--ink-2); overflow: hidden;\n           text-overflow: ellipsis; white-space: nowrap; }\n.air-barre { flex: 1 1 auto; height: 10px; background: var(--surface-2, #f0ede8);\n             border-radius: 5px; overflow: hidden; }\n.air-barre i { display: block; height: 100%; border-radius: 5px;\n               background: #4d9e64; }\n.air-barre i.air-sur { background: #c96a1f; }\n.air-val { flex: 0 0 34%; text-align: right; font-weight: 600;\n           color: var(--ink); white-space: nowrap; overflow: hidden;\n           text-overflow: ellipsis; }\n.air-val small { font-weight: 400; color: var(--ink-3); }\n.air-val .air-ref { display: block; font-size: 10px; }\n.gaz-head th { text-align: left; font-size: 11px; color: var(--ink-3);\n               font-weight: 600; padding: 2px 8px 2px 0; }\n.gaz-best { display: inline-block; margin-left: 6px; padding: 1px 7px;\n            border-radius: 999px; background: #e7f4ea; color: #1e6b34;\n            font-size: 10.5px; font-weight: 700; }\n.gaz-adr { display: block; font-size: 11px; color: var(--ink-3); }\n\n\n/* ---- Commerces et transport (fiche) ---- */\n.cm-grille { list-style: none; margin: 6px 0 0; padding: 0;\n             display: grid; grid-template-columns: 1fr 1fr; gap: 4px 18px; }\n@media (max-width: 560px) { .cm-grille { grid-template-columns: 1fr; } }\n.cm-item { display: flex; align-items: center; gap: 9px; padding: 5px 0;\n           border-bottom: 1px solid var(--line, #eeece7); min-width: 0; }\n.cm-ico { flex: 0 0 28px; }\n.cm-txt { flex: 1 1 auto; min-width: 0; display: flex; flex-direction: column; }\n.cm-nom { font-size: 13px; font-weight: 600; color: var(--ink, #222);\n          white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }\n.cm-poi { font-size: 11px; color: var(--ink-3, #8a877f);\n          white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }\n.cm-dist { flex: 0 0 auto; font-size: 13px; font-weight: 700;\n           color: var(--ink-2, #4c4a45); white-space: nowrap; }\n\n\n/* ---- Coût d'électricité (Hydro-Québec) ---- */\n.hydro-intro { margin: 2px 0 12px; color: var(--ink-2, #4c4a45); }\n.hydro-btn { appearance: none; border: 0; border-radius: 10px; cursor: pointer;\n             background: var(--accent, #2b7a3b); color: #fff; font-weight: 700;\n             font-size: 14px; padding: 11px 18px; }\n.hydro-btn:disabled { opacity: 0.6; cursor: progress; }\n.hydro-montant { font-size: 26px; font-weight: 800; letter-spacing: -0.02em;\n                 color: var(--navy, #1c1b18); display: flex; align-items: baseline;\n                 gap: 10px; flex-wrap: wrap; }\n.hydro-montant small { font-size: 14px; font-weight: 500; color: var(--ink-3); }\n.hydro-an { font-size: 14px; font-weight: 600; color: var(--ink-2, #4c4a45); }\n.hydro-vide { color: var(--ink-3, #8a877f); }\n\n\n/* ============================================================================\n   PASSE DE DESIGN — « éditorial sharp » appliqué à toute la fiche\n   Blocs = cartes encre (bordure 1,5 px + ombre décalée douce), titres à\n   marqueur accent + filet, tuiles KPI blanches à ombre dure, tableaux zébrés,\n   barres en dégradé, pastilles unifiées (mono, point d'état, liseré).\n   Aucune propriété `order` (règle Groupe Ka : ordre DOM = ordre visuel).\n   ========================================================================== */\n\n/* --- Blocs de la fiche : cartes posées sur le papier --- */\n.f-bloc:not(.f-galerie):not(.f-hero),\n.quartier {\n  background: var(--surface);\n  border: 1.5px solid var(--ink);\n  border-radius: var(--r-card);\n  padding: 18px 20px 16px;\n  box-shadow: var(--shadow-off-soft);\n}\n@media (max-width: 640px) {\n  .f-bloc:not(.f-galerie):not(.f-hero),\n  .quartier { padding: 15px 15px 13px; }\n}\n/* blocs pleine largeur sous la grille : respiration entre les cartes */\n.detail > .f-bloc { margin-top: 22px; }\n\n/* --- Titres de section : carré accent + filet éditorial --- */\n.f-bloc h2, .quartier h2 {\n  display: flex; align-items: center; gap: 10px;\n  font-size: 19px; letter-spacing: -0.015em; margin-bottom: 14px;\n  border-left: 0; padding-left: 0;\n}\n.f-bloc h2::before, .quartier h2::before {\n  content: \"\"; flex: 0 0 9px; width: 9px; height: 9px;\n  background: var(--accent); border: 1.5px solid var(--ink);\n  box-shadow: 2px 2px 0 var(--ink);\n}\n.f-bloc h2::after, .quartier h2::after {\n  content: \"\"; flex: 1 1 auto; height: 1.5px; min-width: 20px;\n  background: var(--line);\n}\n\n/* --- Tuiles KPI : blanches, bord encre, ombre dure accent --- */\n.rdl-kpi {\n  background: var(--surface);\n  border: 1.5px solid var(--ink);\n  border-radius: var(--r-ctl);\n  box-shadow: 3px 3px 0 var(--accent-soft);\n}\n.rdl-kpi-v { font-family: var(--font-display); }\n.q-cell { box-shadow: 3px 3px 0 rgba(20, 24, 20, 0.1); border-radius: var(--r-ctl); }\n.q-val { font-size: 20px; }\n\n/* --- Tableaux : en-têtes mono, zébrure, survol accent --- */\n.rdl-table td { padding: 7px 8px 7px 0; }\n.rdl-table tbody tr:nth-child(even) td,\n.rdl-table > tr:nth-child(even) td { background: var(--surface-2); }\n.rdl-table tr:hover td { background: var(--accent-soft); }\n.gaz-head th {\n  font-family: var(--font-mono); font-size: 10px; font-weight: 700;\n  text-transform: uppercase; letter-spacing: 0.08em;\n  border-bottom: 1.5px solid var(--ink);\n}\n.rdl-cap { font-family: var(--font-display); }\ntable.rooms tbody tr:hover td { background: var(--accent-soft); }\n\n/* --- Barres : pistes bordées + remplissage en dégradé --- */\n.q-bar-track, .q-crime-barre, .air-barre {\n  background: var(--surface-2);\n  border: 1px solid var(--line);\n  height: 12px;\n}\n.q-bar-fill {\n  background: linear-gradient(90deg, var(--accent), var(--accent-deep));\n}\n.q-crime-barre i {\n  background: linear-gradient(90deg, #f0808a, var(--accent));\n  opacity: 0.9;\n}\n.air-barre i { background: linear-gradient(90deg, #6fbc85, #2e7d4a); }\n.air-barre i.air-sur { background: linear-gradient(90deg, #e08a48, #c96a1f); }\n\n/* --- Pastilles d'état unifiées (zi-badge & cie) : mono + point d'état --- */\n.zi-badge {\n  display: inline-flex; align-items: center; gap: 6px;\n  font-family: var(--font-mono); font-size: 10.5px; font-weight: 700;\n  text-transform: uppercase; letter-spacing: 0.06em;\n  padding: 4px 10px; border: 1.5px solid currentColor;\n}\n.zi-badge::before {\n  content: \"\"; width: 6px; height: 6px; border-radius: 50%;\n  background: currentColor;\n}\n.gaz-best { border: 1px solid currentColor; }\n\n/* --- Listes riches (zones d'inondation) : mini-cartes à liseré accent --- */\n.zi-liste { display: flex; flex-direction: column; gap: 8px; }\n.zi-liste li {\n  border: 1px solid var(--line); border-left: 3px solid var(--accent);\n  border-top: 1px solid var(--line);\n  border-radius: var(--r-ctl); padding: 9px 12px;\n  background: var(--surface-2);\n}\n\n/* --- Notes de bas de bloc : filet pointillé --- */\n.f-bloc .fine, .quartier .fine {\n  margin-top: 12px; padding-top: 9px;\n  border-top: 1px dashed var(--line);\n}\n","structuredPatch":[{"oldStart":1056,"oldLines":6,"newStart":1056,"newLines":9,"lines":["   background: var(--surface-2);"," }"," ","+/* --- Inclusions : icône et libellé alignés sur une seule ligne --- */","+.amenity { display: inline-flex; align-items: center; gap: 6px; }","+"," /* --- Notes de bas de bloc : filet pointillé --- */"," .f-bloc .fine, .quartier .fine {","   margin-top: 12px; padding-top: 9px;"]}],"userModified":false,"replaceAll":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"d0fed77a-77ad-4d19-a36a-b7b554be14e0","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFeoV8MobSXf6HBWsxF","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Ao1GQcZrbaKTPktW8dbZE1","name":"Bash","input":{"command":"npm run build 2>&1 | tail -1 && pm2 restart immo-ka-web >/dev/null 2>&1 && sleep 3 && BASE=http://localhost:8096 node scripts/check-order.mjs \"domicilia:17516934\"","description":"Rebuild, restart, run check-order validation","timeout":300000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":420,"cache_read_input_tokens":102184,"cache_creation":{"ephemeral_5m_input_tokens":420,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"3c369b0f-662a-4f67-b145-3f8afba12889"}}
{"type":"permission_request","requestId":"645883eb6f865e29","tool":"Bash","input":{"command":"npm run build 2>&1 | tail -1 && pm2 restart immo-ka-web >/dev/null 2>&1 && sleep 3 && BASE=http://localhost:8096 node scripts/check-order.mjs \"domicilia:17516934\"","timeout":300000,"description":"Rebuild, restart, run check-order validation"}}
{"type":"permission_resolved","requestId":"645883eb6f865e29","allow":true,"always":false}
{"type":"claude","event":{"type":"system","subtype":"task_started","task_id":"bosvfa716","tool_use_id":"toolu_01Ao1GQcZrbaKTPktW8dbZE1","description":"Rebuild, restart, run check-order validation","task_type":"local_bash","uuid":"d1d7c6b1-c5b1-43df-a6dc-927cff079682","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"system","subtype":"task_notification","task_id":"bosvfa716","tool_use_id":"toolu_01Ao1GQcZrbaKTPktW8dbZE1","status":"completed","output_file":"","summary":"Rebuild, restart, run check-order validation","uuid":"ebcc07c4-2433-4326-8a11-6b71d74a6dbd","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ao1GQcZrbaKTPktW8dbZE1","type":"tool_result","content":"✓ built in 2.13s\n\n=== iPhone 14 (mobile) ===  scrollY: 0\n.f-galerie         top=   160  left=   0  order=0\n.f-hero            top=   523  left=   0  order=0\n.f-desc            top=  1639  left=   0  order=0\n#caracteristiques  top=  4068  left=   0  order=0\n#pieces            (non rendue)\n#inclusions        top=  4443  left=   0  order=0\n#carte             top=  4862  left=   0  order=0\n.quartier          top=  6849  left=   0  order=0\n\n=== Desktop 1440px ===  scrollY: 0\n.f-galerie         top=   168  left= 100  order=0\n.f-hero            top=   853  left= 100  order=0\n.f-desc            top=  1974  left= 100  order=0\n#caracteristiques  top=  3440  left= 100  order=0\n#pieces            (non rendue)\n#inclusions        top=   168  left= 875  order=0\n#carte             top=   559  left= 875  order=0\n.quartier          top=  5012  left= 100  order=0\n\nMOBILE: ordre CROISSANT ✓ · scrollY=0 · 1re=.f-galerie · sans order=true\nVALIDATION OK","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"6b832ffb-a01a-4468-8824-f821f74e8a34","timestamp":"2026-08-24T21:06:05.268Z","tool_use_result":{"stdout":"✓ built in 2.13s\n\n=== iPhone 14 (mobile) ===  scrollY: 0\n.f-galerie         top=   160  left=   0  order=0\n.f-hero            top=   523  left=   0  order=0\n.f-desc            top=  1639  left=   0  order=0\n#caracteristiques  top=  4068  left=   0  order=0\n#pieces            (non rendue)\n#inclusions        top=  4443  left=   0  order=0\n#carte             top=  4862  left=   0  order=0\n.quartier          top=  6849  left=   0  order=0\n\n=== Desktop 1440px ===  scrollY: 0\n.f-galerie         top=   168  left= 100  order=0\n.f-hero            top=   853  left= 100  order=0\n.f-desc            top=  1974  left= 100  order=0\n#caracteristiques  top=  3440  left= 100  order=0\n#pieces            (non rendue)\n#inclusions        top=   168  left= 875  order=0\n#carte             top=   559  left= 875  order=0\n.quartier          top=  5012  left= 100  order=0\n\nMOBILE: ordre CROISSANT ✓ · scrollY=0 · 1re=.f-galerie · sans order=true\nVALIDATION OK","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"9c0dc205-a9a6-415e-9e98-6581c5fb6231","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFh7arXkEwJvJxszMe3","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAIS4QIKpwEIERgCKkBGq4lQCHUjEMksRp8ovxGBJw8MlGkGxxJ8xvV3kTA5LP5HBY+WvYfW4zNQk76PX81DRlOjTf/gSEwjPVolOoe0Mg5jbGF1ZGUtZmFibGUtNTgBQgh0aGlua2luZ1okNmZjYzhmODMtMWY5Yy00ZDMzLWJhMDctNGQ0ZGYzN2Q2N2Y1chCpF7oiUB8N4as4sc/CyGIoiAEBqAHB5LLUBrABAhIMhudl01+ogHx4wr5PGgxiI187o2veei1uvoMiMEFu9uICyK4WxXoBjZsWjv4LNs500WrMB6pZiyR2nwPliThgk7yU8SgP7eRfP9Nfiypn2kPeOL0Vl/vyRQtIJQ/8KCgF5bLG5dCGvOn8vPxJ6cCjssE/dge0DuULDSeulDk8kQ4aMnZC1S/z4gIP5idvcHHS+e6FMieAbeVZoyfXd1dxHhKrBTKck0KbhmB4n98z4OlRhZe2LRgB"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":617,"cache_read_input_tokens":102604,"cache_creation":{"ephemeral_5m_input_tokens":617,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"82689ff2-7455-4aef-9d7d-f1cc8dccb6f4"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFh7arXkEwJvJxszMe3","type":"message","role":"assistant","content":[{"type":"text","text":"Validation d'ordre DOM OK (mobile croissant, scrollY 0, aucun `order`). Dernières captures de contrôle."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":617,"cache_read_input_tokens":102604,"cache_creation":{"ephemeral_5m_input_tokens":617,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"e4dc7d57-1835-4a17-8b0f-75610e4a0944"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNFh7arXkEwJvJxszMe3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RbmP4FuhqpD55bpDsx9m6p","name":"Bash","input":{"command":"node scripts/_shot-tmp.mjs \"domicilia:17516934\" final","description":"Final AFTER screenshots + overflow check","timeout":240000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":617,"cache_read_input_tokens":102604,"cache_creation":{"ephemeral_5m_input_tokens":617,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"c01ba996-9ffd-421b-a702-a964c966c2a4"}}
{"type":"permission_request","requestId":"806c8f5fbf16f68e","tool":"Bash","input":{"command":"node scripts/_shot-tmp.mjs \"domicilia:17516934\" final","timeout":240000,"description":"Final AFTER screenshots + overflow check"}}
{"type":"permission_resolved","requestId":"806c8f5fbf16f68e","allow":true,"always":false}
{"type":"claude","event":{"type":"system","subtype":"task_notification","task_id":"bea1xtb0o","tool_use_id":"toolu_01RbmP4FuhqpD55bpDsx9m6p","status":"completed","output_file":"","summary":"Final AFTER screenshots + overflow check","uuid":"4c9fe665-aba1-44dd-af0b-343105edb2dd","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"system","subtype":"task_started","task_id":"bea1xtb0o","tool_use_id":"toolu_01RbmP4FuhqpD55bpDsx9m6p","description":"Final AFTER screenshots + overflow check","task_type":"local_bash","uuid":"0b61bd34-3f91-485e-bba2-15fa09eb9cd9","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RbmP4FuhqpD55bpDsx9m6p","type":"tool_result","content":"desktop: overflow horizontal = 0px\nmobile: overflow horizontal = 0px","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"c2e07fe7-252b-4154-90fd-6d189ca46dec","timestamp":"2026-08-24T21:06:28.850Z","tool_use_result":{"stdout":"desktop: overflow horizontal = 0px\nmobile: overflow horizontal = 0px","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"96394b14-fe0b-4d53-9633-d355219a18d1","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"08ddb2b0-a761-43ba-a68d-bd2e98fc1ff5","timestamp":"2026-08-24T21:06:34.875Z","tool_use_result":{"type":"image","file":{"base64":"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","type":"image/png","originalSize":4909531}}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SdKHoGAjLL4dKnioqdqWkU","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"dc7d32d1-1447-4418-b257-8877babde814","timestamp":"2026-08-24T21:06:58.815Z","tool_use_result":{"oldTodos":[{"content":"Lire les styles fiche existants d'immo-ka (f-bloc, rdl, q-*, dtable, rooms)","status":"completed","activeForm":"Lecture des styles fiche immo-ka"},{"content":"Captures Playwright AVANT (desktop 1440×900 + mobile 390×844)","status":"completed","activeForm":"Captures Playwright AVANT"},{"content":"Écrire la passe « éditorial sharp » en section finale de styles.css","status":"completed","activeForm":"Écriture de la passe éditorial sharp"},{"content":"Build + captures Playwright APRÈS + check débordement + check-order.mjs","status":"in_progress","activeForm":"Vérification Playwright après build"},{"content":"pm2 restart + healthcheck + commit/push origin main","status":"pending","activeForm":"Déploiement et commit"}],"newTodos":[{"content":"Lire les styles fiche existants d'immo-ka (f-bloc, rdl, q-*, dtable, rooms)","status":"completed","activeForm":"Lecture des styles fiche immo-ka"},{"content":"Captures Playwright AVANT (desktop 1440×900 + mobile 390×844)","status":"completed","activeForm":"Captures Playwright AVANT"},{"content":"Écrire la passe « éditorial sharp » en section finale de styles.css","status":"completed","activeForm":"Écriture de la passe éditorial sharp"},{"content":"Build + captures Playwright APRÈS + check débordement + check-order.mjs","status":"completed","activeForm":"Vérification Playwright après build"},{"content":"pm2 restart + healthcheck + commit/push origin main","status":"in_progress","activeForm":"Déploiement et commit"}],"verificationNudgeNeeded":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WByQd7GxgCxGsCY2RpViqY","type":"tool_result","content":"│ script args       │ http --url=www.immo-ka.com 8096                              │\n M frontend/src/styles.css","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"6d62a346-9b22-4035-a1d7-54a78db86e0a","timestamp":"2026-08-24T21:07:04.194Z","tool_use_result":{"stdout":"│ script args       │ http --url=www.immo-ka.com 8096                              │\n M frontend/src/styles.css","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"permission_request","requestId":"16efb6c09a92dad0","tool":"Bash","input":{"command":"curl -s -o /dev/null -w \"local: %{http_code}\\n\" http://localhost:8096/ && curl -s -o /dev/null -w \"public: %{http_code}\\n\" https://www.immo-ka.com/","description":"Healthcheck local and public site"}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KmQ3vefQ8BrtimJ9ptQu3h","type":"tool_result","content":"local: 200\npublic: 200","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f5e7c4ba-a252-4e8a-bd10-15ea5be1a068","timestamp":"2026-08-24T21:07:15.214Z","tool_use_result":{"stdout":"local: 200\npublic: 200","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JDxeGYmNv4QmLsQvPUMGqG","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"e255ea44-592a-4631-b638-a0b920599cc3","timestamp":"2026-08-24T21:15:19.081Z","tool_use_result":{"oldTodos":[{"content":"Lire les styles fiche existants d'immo-ka (f-bloc, rdl, q-*, dtable, rooms)","status":"completed","activeForm":"Lecture des styles fiche immo-ka"},{"content":"Captures Playwright AVANT (desktop 1440×900 + mobile 390×844)","status":"completed","activeForm":"Captures Playwright AVANT"},{"content":"Écrire la passe « éditorial sharp » en section finale de styles.css","status":"completed","activeForm":"Écriture de la passe éditorial sharp"},{"content":"Build + captures Playwright APRÈS + check débordement + check-order.mjs","status":"completed","activeForm":"Vérification Playwright après build"},{"content":"pm2 restart + healthcheck + commit/push origin main","status":"completed","activeForm":"Déploiement et commit"}],"newTodos":[{"content":"Cartographier la fiche web + modules backend (formes de retour exactes)","status":"in_progress","activeForm":"Cartographie fiche + backend"},{"content":"Lire la référence lou-ka pdfgen.py (approche + style)","status":"pending","activeForm":"Lecture référence lou-ka"},{"content":"Écrire immoka/pdffiche.py (canvas, pagination dynamique, sections conditionnelles)","status":"pending","activeForm":"Écriture du générateur PDF"},{"content":"Endpoint PDF + tester fiche riche et fiche pauvre (pdftoppm, taille, temps)","status":"pending","activeForm":"Tests sur annonces réelles"},{"content":"pm2 restart + healthchecks + commit/push origin main","status":"pending","activeForm":"Déploiement et commit"}],"verificationNudgeNeeded":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GJXw3oM3c6V64iRAESVpMB","type":"tool_result","content":"1\t# -----------------------------------------------------------------------------\n2\t# Lou-Ka — Agrégateur de logements à louer (province de Québec)\n3\t# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n4\t# pdfgen.py : génération PDF à l'identité visuelle officielle Lou-Ka\n5\t#   - fiche_pdf(uid)  : fiche de propriété (photos, prix + badge marché, chips,\n6\t#     description structurée, inclusions, quartier, à proximité, QR code)\n7\t#   - rapport_pdf()   : rapport global du marché (totaux, régions, histogramme\n8\t#     des loyers, types, villes, gestionnaires)\n9\t#   Palette identique au site : blanc #ffffff, marine #0b1330, orange #ff6a00,\n10\t#   bleu vif #2258ff, gris pâle #f2f2f2. Polices de base (Helvetica) stylées\n11\t#   par la mise en page — aucune dépendance de police externe.\n12\t# -----------------------------------------------------------------------------\n13\tfrom __future__ import annotations\n14\t\n15\timport io\n16\timport json\n17\timport math\n18\timport time\n19\t\n20\timport qrcode\n21\timport requests\n22\tfrom reportlab.lib.colors import HexColor\n23\tfrom reportlab.lib.pagesizes import letter\n24\tfrom reportlab.lib.utils import ImageReader\n25\tfrom reportlab.pdfgen import canvas as rl_canvas\n26\t\n27\tfrom . import db\n28\t\n29\t# --- palette Lou-Ka (identité officielle) ------------------------------------\n30\tPAPER = HexColor(\"#ffffff\")\n31\tSURFACE = HexColor(\"#ffffff\")\n32\tINK = HexColor(\"#0b1330\")          # bleu marine profond\n33\tINK2 = HexColor(\"#3e4560\")\n34\tINK3 = HexColor(\"#8a90a5\")\n35\tGREEN = HexColor(\"#2258ff\")        # accent secondaire : bleu vif\n36\tGREEN_DEEP = HexColor(\"#0b1330\")\n37\tLIME = HexColor(\"#ff6a00\")         # accent d'action : orange vif\n38\tLIME_SOFT = HexColor(\"#fff1e6\")\n39\tAMBER = HexColor(\"#ff6a00\")\n40\tAMBER_SOFT = HexColor(\"#fff1e6\")\n41\t\n42\tPAGE_W, PAGE_H = letter\n43\tM = 40          # marge\n44\tIMG_TIMEOUT = 6\n45\tUA = \"LouKaBot/1.0 (+https://www.lou-ka.com/bot; contact@spboucher.ai)\"\n46\t\n47\tPOI_LABELS = {\n48\t    \"epicerie\": \"Épicerie\", \"depanneur\": \"Dépanneur\", \"pharmacie\": \"Pharmacie\",\n49\t    \"ecole\": \"École\", \"garderie\": \"Garderie\", \"parc\": \"Parc\", \"bus\": \"Arrêt de bus\",\n50\t    \"metro\": \"Métro\", \"gym\": \"Gym\", \"cafe\": \"Café\", \"clinique\": \"Clinique\",\n51\t    \"hopital\": \"Hôpital\", \"bibliotheque\": \"Bibliothèque\",\n52\t}\n53\tPROX_LABELS = [\n54\t    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n55\t    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n56\t    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n57\t]\n58\t\n59\tNBSP = \" \"\n60\t\n61\t\n62\tdef _fmt_money(v) -> str:\n63\t    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n64\t\n65\t\n66\tdef _fmt_dist(m_: float) -> str:\n67\t    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n68\t            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n69\t\n70\t\n71\tdef _marche(m_: float) -> str:\n72\t    return f\"≈{NBSP}{max(1, round(m_ * 1.3 / 80))}{NBSP}min à pied\"\n73\t\n74\t\n75\tdef _fetch_image(url: str) -> ImageReader | None:\n76\t    try:\n77\t        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n78\t        r.raise_for_status()\n79\t        return ImageReader(io.BytesIO(r.content))\n80\t    except Exception:\n81\t        return None\n82\t\n83\t\n84\tclass _Style:\n85\t    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n86\t\n87\t    def __init__(self, c: rl_canvas.Canvas):\n88\t        self.c = c\n89\t\n90\t    def fond(self):\n91\t        self.c.setFillColor(PAPER)\n92\t        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n93\t\n94\t    def logo(self, x: float, y: float, taille: float = 22):\n95\t        \"\"\"Wordmark Lou·Ka : « Lou » encre + boîte encre avec « Ka » lime.\"\"\"\n96\t        c = self.c\n97\t        c.setFont(\"Helvetica-Bold\", taille)\n98\t        c.setFillColor(INK)\n99\t        c.drawString(x, y, \"Lou\")\n100\t        w = c.stringWidth(\"Lou\", \"Helvetica-Bold\", taille)\n101\t        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n102\t        c.saveState()\n103\t        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n104\t        c.rotate(-3)\n105\t        c.setFillColor(INK)\n106\t        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n107\t        c.setFillColor(LIME)\n108\t        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n109\t        c.restoreState()\n110\t        return x + w + 6 + bw\n111\t\n112\t    def entete(self, titre: str):\n113\t        c = self.c\n114\t        self.logo(M, PAGE_H - M - 16)\n115\t        c.setFont(\"Courier-Bold\", 8)\n116\t        c.setFillColor(INK3)\n117\t        c.drawRightString(PAGE_W - M, PAGE_H - M - 6,\n118\t                          titre.upper())\n119\t        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n120\t                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n121\t        c.setStrokeColor(INK)\n122\t        c.setLineWidth(2)\n123\t        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n124\t        return PAGE_H - M - 40   # y de départ du contenu\n125\t\n126\t    def pied(self, texte: str, page: int | None = None):\n127\t        c = self.c\n128\t        c.setStrokeColor(INK)\n129\t        c.setLineWidth(1)\n130\t        c.line(M, M + 16, PAGE_W - M, M + 16)\n131\t        c.setFont(\"Helvetica\", 6.5)\n132\t        c.setFillColor(INK3)\n133\t        c.drawString(M, M + 6, texte[:150])\n134\t        if page:\n135\t            c.setFont(\"Courier-Bold\", 7)\n136\t            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n137\t\n138\t    def pilule(self, x: float, y: float, texte: str, fg=LIME, bg=INK,\n139\t               stroke=None, taille: float = 8) -> float:\n140\t        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n141\t        c = self.c\n142\t        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n143\t        c.setFillColor(bg)\n144\t        if stroke:\n145\t            c.setStrokeColor(stroke)\n146\t            c.setLineWidth(1.2)\n147\t        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n148\t                    stroke=1 if stroke else 0, fill=1)\n149\t        c.setFillColor(fg)\n150\t        c.setFont(\"Helvetica-Bold\", taille)\n151\t        c.drawString(x + 7, y + 5, texte)\n152\t        return x + w + 6\n153\t\n154\t    def titre_section(self, y: float, texte: str) -> float:\n155\t        c = self.c\n156\t        c.setFont(\"Helvetica-Bold\", 12.5)\n157\t        c.setFillColor(INK)\n158\t        c.drawString(M, y, texte)\n159\t        c.setStrokeColor(LIME)\n160\t        c.setLineWidth(3)\n161\t        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n162\t        c.line(M, y - 4, M + w, y - 4)\n163\t        return y - 18\n164\t\n165\t    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n166\t                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n167\t                   max_lignes: int = 100) -> float:\n168\t        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n169\t        c = self.c\n170\t        c.setFont(\"Helvetica\", taille)\n171\t        c.setFillColor(couleur)\n172\t        mots = texte.split()\n173\t        ligne = \"\"\n174\t        n = 0\n175\t        for mot in mots:\n176\t            essai = (ligne + \" \" + mot).strip()\n177\t            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n178\t                ligne = essai\n179\t            else:\n180\t                c.drawString(x, y, ligne)\n181\t                y -= interligne\n182\t                n += 1\n183\t                ligne = mot\n184\t                if n >= max_lignes - 1:\n185\t                    ligne += \" …\"\n186\t                    break\n187\t        if ligne:\n188\t            c.drawString(x, y, ligne)\n189\t            y -= interligne\n190\t        return y\n191\t\n192\t\n193\t# ---------------------------------------------------------------------------\n194\t# Fiche de propriété\n195\t# ---------------------------------------------------------------------------\n196\t\n197\tdef fiche_pdf(uid: str) -> bytes | None:\n198\t    \"\"\"PDF de la fiche du logement `uid`. None si introuvable.\"\"\"\n199\t    con = db.connect()\n200\t    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n201\t    if row is None:\n202\t        con.close()\n203\t        return None\n204\t    l = dict(row)\n205\t    l[\"amenities\"] = json.loads(l.get(\"amenities\") or \"[]\")\n206\t    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n207\t    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n208\t    l[\"digest\"] = json.loads(l[\"digest\"]) if l.get(\"digest\") else None\n209\t    poi = []\n210\t    quartier = None\n211\t    if l.get(\"lat\") is not None:\n212\t        key = f\"{round(l['lat'], 4)},{round(l['lng'], 4)}\"\n213\t        pr = con.execute(\"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n214\t        poi = json.loads(pr[\"pois\"]) if pr else []\n215\t        from . import quartier as qmod\n216\t        dauid = l.get(\"dauid\")\n217\t        quartier = qmod.fiche_quartier(l[\"lat\"], l[\"lng\"], l.get(\"city\") or \"\",\n218\t                                       dauid if dauid and dauid != \"hors-zone\" else None)\n219\t    reg = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n220\t    src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]), l[\"source\"])\n221\t    con.close()\n222\t\n223\t    buf = io.BytesIO()\n224\t    c = rl_canvas.Canvas(buf, pagesize=letter)\n225\t    c.setTitle(f\"Lou-Ka — {l.get('title') or l.get('address')}\")\n226\t    s = _Style(c)\n227\t    s.fond()\n228\t    y = s.entete(\"Fiche logement\")\n229\t\n230\t    # --- bloc prix + badge marché\n231\t    prix_txt = (_fmt_money(l[\"price\"]) + f\"{NBSP}/ mois\") if l.get(\"price\") \\\n232\t        else (l.get(\"price_label\") or \"Prix sur demande\")\n233\t    c.setFont(\"Helvetica-Bold\", 30)\n234\t    c.setFillColor(INK)\n235\t    c.drawString(M, y - 24, prix_txt)\n236\t    loyer_secteur = (quartier or {}).get(\"demographie\", {}).get(\"loyer_moyen\") \\\n237\t        if quartier else None\n238\t    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n239\t    if l.get(\"price\") and loyer_secteur:\n240\t        delta = (l[\"price\"] - loyer_secteur) / loyer_secteur\n241\t        pct = f\"{'+' if delta > 0 else '−'}{abs(round(delta * 100))}{NBSP}%\"\n242\t        if delta <= -0.15:\n243\t            s.pilule(bx, y - 22, f\"{pct} vs secteur · Bon deal\", GREEN_DEEP, LIME_SOFT, GREEN)\n244\t        elif delta <= 0.10:\n245\t            s.pilule(bx, y - 22, f\"{pct} vs secteur · Dans le marché\", INK2, SURFACE, INK)\n246\t        else:\n247\t            s.pilule(bx, y - 22, f\"{pct} vs secteur · Au-dessus du marché\",\n248\t                     HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER)\n249\t    y -= 44\n250\t\n251\t    # --- titre + adresse\n252\t    c.setFont(\"Helvetica-Bold\", 13)\n253\t    c.setFillColor(INK)\n254\t    c.drawString(M, y, (l.get(\"title\") or l.get(\"address\") or \"\")[:80])\n255\t    y -= 14\n256\t    c.setFont(\"Helvetica\", 9)\n257\t    c.setFillColor(INK2)\n258\t    loc = \" · \".join(x for x in (l.get(\"address\"), l.get(\"sector\"), l.get(\"city\"))\n259\t                     if x and x != l.get(\"title\"))\n260\t    c.drawString(M, y, loc[:110])\n261\t    y -= 20\n262\t\n263\t    # --- chips clés\n264\t    x = M\n265\t    chips = []\n266\t    if l.get(\"unit_type\"):\n267\t        chips.append(l[\"unit_type\"])\n268\t    ad = l.get(\"availability_date\")\n269\t    if ad == \"now\":\n270\t        chips.append(\"Libre maintenant\")\n271\t    elif ad:\n272\t        chips.append(f\"Dispo {ad}\")\n273\t    if l.get(\"area_sqft\"):\n274\t        chips.append(f\"{round(l['area_sqft'])}{NBSP}pi²\")\n275\t    if l.get(\"furnished\"):\n276\t        chips.append(\"Meublé\")\n277\t    if l.get(\"pets\"):\n278\t        chips.append({\"oui\": \"Animaux acceptés\", \"non\": \"Animaux refusés\",\n279\t                      \"conditions\": \"Animaux sous conditions\"}.get(l[\"pets\"], l[\"pets\"]))\n280\t    if l[\"details\"].get(\"floor\") is not None:\n281\t        chips.append(f\"{l['details']['floor']}e étage\")\n282\t    for chip in chips[:7]:\n283\t        x = s.pilule(x, y - 6, chip)\n284\t    y -= 30\n285\t\n286\t    # --- photos : 1 grande + 3 vignettes\n287\t    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n288\t    if imgs:\n289\t        big_h = 170\n290\t        big_w = big_h * 16 / 10.5\n291\t        c.setStrokeColor(INK)\n292\t        c.setLineWidth(1.5)\n293\t        try:\n294\t            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n295\t                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n296\t        except Exception:\n297\t            pass\n298\t        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n299\t        tx = M + big_w + 10\n300\t        th = (big_h - 16) / 3\n301\t        for i, im in enumerate(imgs[1:4]):\n302\t            ty = y - (i + 1) * th - i * 8\n303\t            try:\n304\t                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n305\t                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n306\t                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n307\t            except Exception:\n308\t                pass\n309\t        y -= big_h + 18\n310\t\n311\t    # --- « En bref » (digest) sur bandeau lime\n312\t    dg = l.get(\"digest\")\n313\t    if dg and dg.get(\"en_bref\"):\n314\t        c.setFillColor(LIME_SOFT)\n315\t        c.setStrokeColor(GREEN)\n316\t        c.setLineWidth(1.2)\n317\t        c.roundRect(M, y - 40, PAGE_W - 2 * M, 44, 6, stroke=1, fill=1)\n318\t        s.paragraphe(M + 10, y - 8, \"EN BREF — \" + dg[\"en_bref\"],\n319\t                     PAGE_W - 2 * M - 20, taille=8.5, couleur=GREEN_DEEP,\n320\t                     max_lignes=3)\n321\t        y -= 54\n322\t\n323\t    # --- description (2-3 sections max) | inclusions en colonne droite\n324\t    col_g = PAGE_W * 0.56 - M\n325\t    y_desc = s.titre_section(y, \"Description\")\n326\t    if dg and dg.get(\"sections\"):\n327\t        for sec in dg[\"sections\"][:3]:\n328\t            c.setFont(\"Helvetica-Bold\", 9)\n329\t            c.setFillColor(INK)\n330\t            c.drawString(M, y_desc, sec[\"titre\"][:60])\n331\t            y_desc -= 12\n332\t            y_desc = s.paragraphe(M, y_desc, sec[\"texte\"], col_g, max_lignes=6)\n333\t            y_desc -= 4\n334\t            if y_desc < 150:\n335\t                break\n336\t    elif l.get(\"description\"):\n337\t        y_desc = s.paragraphe(M, y_desc, l[\"description\"], col_g, max_lignes=16)\n338\t\n339\t    xr = M + col_g + 24\n340\t    y_incl = s_titre_droite(c, y, xr, \"Inclusions et commodités\")\n341\t    d = l[\"details\"]\n342\t    badges = []\n343\t    inc = d.get(\"inclusions\") or {}\n344\t    for k, lab in ((\"heating\", \"Chauffage inclus\"), (\"electricity\", \"Électricité incluse\"),\n345\t                   (\"hot_water\", \"Eau chaude incluse\"), (\"internet\", \"Internet inclus\")):\n346\t        if inc.get(k):\n347\t            badges.append(lab)\n348\t    app = d.get(\"appliances\") or {}\n349\t    if app.get(\"dishwasher\"):\n350\t        badges.append(\"Lave-vaisselle\")\n351\t    if app.get(\"washer_dryer\"):\n352\t        badges.append(\"Laveuse-sécheuse\")\n353\t    for k, lab in ((\"ac\", \"Climatisation\"), (\"elevator\", \"Ascenseur\"),\n354\t                   (\"balcony\", \"Balcon\"), (\"pool\", \"Piscine\"), (\"gym\", \"Gym\"),\n355\t                   (\"laundry\", \"Buanderie\"), (\"storage\", \"Rangement\")):\n356\t        if d.get(k):\n357\t            badges.append(lab)\n358\t    if (d.get(\"parking\") or {}).get(\"available\"):\n359\t        badges.append(\"Stationnement\")\n360\t    if l.get(\"furnished\"):\n361\t        badges.append(\"Meublé\")\n362\t    c.setFont(\"Helvetica\", 8.5)\n363\t    for b in badges[:12]:\n364\t        c.setFillColor(GREEN)\n365\t        c.drawString(xr, y_incl, \"✓\")\n366\t        c.setFillColor(INK2)\n367\t        c.drawString(xr + 12, y_incl, b)\n368\t        y_incl -= 12\n369\t    autres = [a for a in l[\"amenities\"]\n370\t              if not any(b.lower() in a.lower() or a.lower() in b.lower() for b in badges)]\n371\t    for a in autres[: max(0, 14 - len(badges))]:\n372\t        c.setFillColor(INK3)\n373\t        c.drawString(xr + 12, y_incl, a[:38])\n374\t        y_incl -= 11\n375\t\n376\t    y = min(y_desc, y_incl) - 8\n377\t\n378\t    # --- pied page 1 + page 2 (quartier / à proximité / références)\n379\t    s.pied(\"Données de l'annonce publiée par le gestionnaire — Lou-Ka est un \"\n380\t           \"agrégateur indépendant ; chaque fiche renvoie à l'annonce originale.\", 1)\n381\t    c.showPage()\n382\t    s.fond()\n383\t    y = s.entete(\"Fiche logement · suite\")\n384\t\n385\t    # Le quartier\n386\t    if quartier:\n387\t        y = s.titre_section(y, \"Le quartier\")\n388\t        demo = quartier.get(\"demographie\") or {}\n389\t        tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n390\t                  (\"Ménages locataires\", f\"{round(demo['pct_locataires'])}{NBSP}%\" if demo.get(\"pct_locataires\") is not None else \"—\"),\n391\t                  (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n392\t                  (\"Âge médian\", f\"{round(demo['age_median'])} ans\" if demo.get(\"age_median\") is not None else \"—\"),\n393\t                  (\"Français à la maison\", f\"{round(demo['pct_francais'])}{NBSP}%\" if demo.get(\"pct_francais\") is not None else \"—\"),\n394\t                  (\"Diplôme universitaire\", f\"{round(demo['pct_univ'])}{NBSP}%\" if demo.get(\"pct_univ\") is not None else \"—\")]\n395\t        tw = (PAGE_W - 2 * M - 20) / 3\n396\t        for i, (lab, val) in enumerate(tuiles):\n397\t            tx = M + (i % 3) * (tw + 10)\n398\t            ty = y - 34 - (i // 3) * 44\n399\t            c.setFillColor(SURFACE)\n400\t            c.setStrokeColor(INK)\n401\t            c.setLineWidth(1.2)\n402\t            c.roundRect(tx, ty, tw, 38, 5, stroke=1, fill=1)\n403\t            c.setFont(\"Helvetica-Bold\", 12)\n404\t            c.setFillColor(INK)\n405\t            c.drawString(tx + 8, ty + 20, val)\n406\t            c.setFont(\"Courier\", 6.5)\n407\t            c.setFillColor(INK3)\n408\t            c.drawString(tx + 8, ty + 8, lab.upper())\n409\t        y -= 34 + 2 * 44 + 6\n410\t\n411\t        prox = quartier.get(\"proximite\") or {}\n412\t        for k, lab in PROX_LABELS:\n413\t            if k not in prox:\n414\t                continue\n415\t            v = max(0.0, min(1.0, prox[k]))\n416\t            c.setFont(\"Helvetica\", 8)\n417\t            c.setFillColor(INK2)\n418\t            c.drawString(M, y, lab)\n419\t            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n420\t            c.setFillColor(SURFACE)\n421\t            c.setStrokeColor(INK)\n422\t            c.setLineWidth(0.8)\n423\t            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n424\t            c.setFillColor(GREEN)\n425\t            c.roundRect(bx0, y - 1, bw_ * v, 7, 3.5, stroke=0, fill=1)\n426\t            c.setFont(\"Courier-Bold\", 7.5)\n427\t            c.setFillColor(INK)\n428\t            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n429\t            y -= 14\n430\t        y -= 4\n431\t        badges_q = []\n432\t        ch = quartier.get(\"chaleur\")\n433\t        if ch:\n434\t            if ch[\"classe\"] <= 3:\n435\t                badges_q.append((\"Îlot de fraîcheur\", GREEN_DEEP, LIME_SOFT, GREEN))\n436\t            elif ch[\"classe\"] >= 7:\n437\t                badges_q.append((f\"Îlot de chaleur (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\"),\n438\t                                 HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER))\n439\t        cr = quartier.get(\"crime\")\n440\t        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n441\t            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"]) / cr[\"indice_canada\"])\n442\t            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n443\t                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} la moyenne canadienne\",\n444\t                             INK2, SURFACE, INK))\n445\t        elif cr and cr.get(\"type\") == \"points\":\n446\t            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m (12 mois)\",\n447\t                             INK2, SURFACE, INK))\n448\t        x = M\n449\t        for txt, fg, bg, stk in badges_q:\n450\t            x = s.pilule(x, y - 8, txt, fg, bg, stk)\n451\t        y -= 30\n452\t\n453\t    # À proximité\n454\t    if poi:\n455\t        y = s.titre_section(y, \"À proximité\")\n456\t        col2 = y\n457\t        for i, p in enumerate(poi[:12]):\n458\t            px = M if i % 2 == 0 else M + (PAGE_W - 2 * M) / 2 + 10\n459\t            if i % 2 == 0 and i > 0:\n460\t                y -= 13\n461\t            py = y\n462\t            c.setFont(\"Helvetica\", 8)\n463\t            c.setFillColor(INK2)\n464\t            lab = POI_LABELS.get(p[\"cat\"], p[\"cat\"])\n465\t            nom_poi = p[\"name\"][:19] + \"…\" if len(p[\"name\"]) > 20 else p[\"name\"]\n466\t            c.drawString(px, py, f\"{lab} — {nom_poi}\")\n467\t            c.setFont(\"Courier-Bold\", 7.5)\n468\t            c.setFillColor(INK)\n469\t            c.drawRightString(px + (PAGE_W - 2 * M) / 2 - 14, py,\n470\t                              f\"{_fmt_dist(p['dist_m'])} · {_marche(p['dist_m'])}\")\n471\t        y -= 26\n472\t\n473\t    # Références : QR + gestionnaire + source\n474\t    y = s.titre_section(y, \"Références\")\n475\t    qr = qrcode.make(f\"https://www.lou-ka.com/logement/{uid}\", box_size=4, border=1)\n476\t    qb = io.BytesIO()\n477\t    qr.save(qb, format=\"PNG\")\n478\t    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n479\t    c.setFont(\"Helvetica-Bold\", 9)\n480\t    c.setFillColor(INK)\n481\t    c.drawString(M + 82, y - 14, f\"Gestionnaire : {src_name}\")\n482\t    c.setFont(\"Helvetica\", 8)\n483\t    c.setFillColor(INK2)\n484\t    c.drawString(M + 82, y - 28, \"Annonce originale :\")\n485\t    c.setFillColor(GREEN)\n486\t    c.drawString(M + 82, y - 40, (l.get(\"url\") or \"\")[:90])\n487\t    c.setFillColor(INK2)\n488\t    c.drawString(M + 82, y - 56, \"Fiche à jour en ligne (scannez le code) :\")\n489\t    c.setFillColor(GREEN)\n490\t    c.drawString(M + 82, y - 68, f\"www.lou-ka.com/logement/{uid}\")\n491\t\n492\t    s.pied(\"Quartier : Statistique Canada (Recensement 2021), INSPQ (CC-BY 4.0)\"\n493\t           + (\", Ville de Montréal (CC-BY 4.0)\" if (quartier or {}).get(\"crime\", {}).get(\"type\") == \"points\" else \"\")\n494\t           + \" · Commodités : OpenStreetMap. Prix et disponibilités : ceux affichés par la source.\", 2)\n495\t    c.save()\n496\t    return buf.getvalue()\n497\t\n498\t\n499\tdef s_titre_droite(c, y, x, texte):\n500\t    c.setFont(\"Helvetica-Bold\", 12.5)\n501\t    c.setFillColor(INK)\n502\t    c.drawString(x, y, texte)\n503\t    c.setStrokeColor(LIME)\n504\t    c.setLineWidth(3)\n505\t    c.line(x, y - 4, x + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n506\t    return y - 18\n507\t\n508\t\n509\t# ---------------------------------------------------------------------------\n510\t# Rapport global du marché\n511\t# ---------------------------------------------------------------------------\n512\t\n513\t_REGIONS = [\n514\t    (\"Québec métro\", {\"Québec\", \"Lévis\", \"Saint-Augustin-de-Desmaures\", \"L'Ancienne-Lorette\", \"Pont-Rouge\", \"Shannon\"}),\n515\t    (\"Outaouais\", {\"Gatineau\", \"Chelsea\", \"Thurso\", \"Perkins\", \"Maniwaki\", \"Val-des-Monts\"}),\n516\t    (\"Estrie / Montérégie-Est\", {\"Sherbrooke\", \"Magog\", \"Orford\", \"East Angus\", \"Waterville\", \"Granby\", \"Waterloo\", \"Bromont\", \"Cowansville\", \"Richmond\"}),\n517\t    (\"Mauricie / Centre-du-Québec\", {\"Trois-Rivières\", \"Bécancour\", \"Shawinigan\", \"Drummondville\", \"Victoriaville\", \"Notre-Dame-du-Bon-Conseil\", \"Saint-Léonard-d'Aston\", \"Louiseville\", \"Nicolet\", \"Wickham\", \"Saint-Narcisse\", \"Saint-Nicéphore\"}),\n518\t    (\"Lanaudière / Laurentides\", {\"Joliette\", \"Saint-Jérôme\", \"Berthierville\", \"Saint-Ambroise-de-Kildare\", \"Saint-Gabriel-de-Brandon\", \"Lachute\", \"Brownsburg-Chatham\", \"Saint-Charles-Borromée\", \"Mirabel\", \"Sainte-Agathe-des-Monts\", \"Sainte-Thérèse\", \"Blainville\", \"Charlemagne\", \"Notre-Dame-des-Prairies\"}),\n519\t    (\"Bas-Saint-Laurent / Gaspésie\", {\"Rimouski\", \"Rivière-du-Loup\", \"Matane\", \"Saint-Ulric\", \"Amqui\", \"Le Bic\", \"New Richmond\", \"Carleton-sur-Mer\", \"Gaspé\"}),\n520\t    (\"Saguenay–Lac-Saint-Jean\", {\"Saguenay\", \"Alma\", \"Chicoutimi\", \"Jonquière\", \"Chambord\", \"La Malbaie\"}),\n521\t    (\"Abitibi-Témiscamingue\", {\"Rouyn-Noranda\", \"Val-d'Or\", \"Amos\", \"Malartic\"}),\n522\t    (\"Côte-Nord\", {\"Sept-Îles\", \"Port-Cartier\", \"Baie-Comeau\", \"Forestville\"}),\n523\t    (\"Chaudière-Appalaches\", {\"Saint-Georges\", \"Sainte-Marie\", \"Thetford Mines\", \"Montmagny\", \"Vallée-Jonction\", \"Scott\", \"Saint-Isidore\", \"La Guadeloupe\", \"Saint-Raphaël\"}),\n524\t]\n525\t\n526\t\n527\tdef _region_de(city: str) -> str:\n528\t    for nom, villes in _REGIONS:\n529\t        if city in villes:\n530\t            return nom\n531\t    return \"Grand Montréal & environs\"\n532\t\n533\t\n534\tdef rapport_pdf() -> bytes:\n535\t    \"\"\"Rapport global du marché locatif Lou-Ka (multi-pages).\n536\t\n537\t    Tous les chiffres viennent de marketstats.compute() — la même source\n538\t    que la page Statistiques du site.\n539\t    \"\"\"\n540\t    from . import marketstats\n541\t    st = marketstats.compute()\n542\t    reg_file = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n543\t    noms = {s[\"id\"]: s[\"name\"] for s in reg_file}\n544\t\n545\t    t = st[\"totals\"]\n546\t    total, moy, med, gps = t[\"total\"], t[\"avg\"] or 0, t[\"median\"] or 0, t[\"gps_pct\"]\n547\t    n_sources = t[\"sources\"]\n548\t\n549\t    buf = io.BytesIO()\n550\t    c = rl_canvas.Canvas(buf, pagesize=letter)\n551\t    c.setTitle(\"Lou-Ka — Rapport du marché locatif\")\n552\t    s = _Style(c)\n553\t\n554\t    # ---- page 1 : couverture + grands chiffres + régions\n555\t    s.fond()\n556\t    s.logo(M, PAGE_H - 130, 42)\n557\t    c.setFont(\"Helvetica-Bold\", 24)\n558\t    c.setFillColor(INK)\n559\t    c.drawString(M, PAGE_H - 185, \"Rapport du marché locatif\")\n560\t    c.setFont(\"Courier-Bold\", 9)\n561\t    c.setFillColor(INK3)\n562\t    c.drawString(M, PAGE_H - 205,\n563\t                 time.strftime(\"PROVINCE DE QUÉBEC · GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n564\t\n565\t    grands = [(f\"{total:,}\".replace(\",\", NBSP), \"annonces actives\"),\n566\t              (str(n_sources), \"gestionnaires connectés\"),\n567\t              (_fmt_money(moy), \"loyer moyen\"),\n568\t              (_fmt_money(med), \"loyer médian\"),\n569\t              (f\"{gps}{NBSP}%\", \"annonces géolocalisées\")]\n570\t    tw = (PAGE_W - 2 * M - 4 * 10) / 5\n571\t    for i, (val, lab) in enumerate(grands):\n572\t        tx = M + i * (tw + 10)\n573\t        ty = PAGE_H - 285\n574\t        c.setFillColor(INK)\n575\t        c.roundRect(tx + 3, ty - 3, tw, 58, 6, stroke=0, fill=1)   # ombre décalée\n576\t        c.setFillColor(SURFACE)\n577\t        c.setStrokeColor(INK)\n578\t        c.setLineWidth(1.5)\n579\t        c.roundRect(tx, ty, tw, 58, 6, stroke=1, fill=1)\n580\t        c.setFont(\"Helvetica-Bold\", 15)\n581\t        c.setFillColor(INK)\n582\t        c.drawCentredString(tx + tw / 2, ty + 32, val)\n583\t        c.setFont(\"Courier\", 6.3)\n584\t        c.setFillColor(INK3)\n585\t        c.drawCentredString(tx + tw / 2, ty + 14, lab.upper())\n586\t\n587\t    y = s.titre_section(PAGE_H - 330, \"Couverture par région\")\n588\t    c.setFont(\"Courier-Bold\", 7)\n589\t    c.setFillColor(INK3)\n590\t    for lab, xoff in ((\"RÉGION\", 0), (\"ANNONCES\", 280), (\"SOURCES\", 360),\n591\t                      (\"LOYER MOYEN\", 430)):\n592\t        c.drawString(M + xoff, y, lab)\n593\t    y -= 4\n594\t    c.setStrokeColor(INK)\n595\t    c.setLineWidth(1)\n596\t    c.line(M, y, PAGE_W - M, y)\n597\t    y -= 14\n598\t    regions = st[\"by_region\"]\n599\t    max_n = max((r[\"count\"] for r in regions), default=1)\n600\t    for r in regions:\n601\t        c.setFont(\"Helvetica-Bold\", 8.5)\n602\t        c.setFillColor(INK)\n603\t        c.drawString(M, y, r[\"key\"])\n604\t        c.setFillColor(LIME)\n605\t        c.setStrokeColor(INK)\n606\t        c.setLineWidth(0.7)\n607\t        bw = 90 * r[\"count\"] / max_n\n608\t        c.roundRect(M + 180, y - 1, max(3, bw), 8, 3, stroke=1, fill=1)\n609\t        c.setFont(\"Helvetica\", 8.5)\n610\t        c.setFillColor(INK2)\n611\t        c.drawRightString(M + 330, y, f\"{r['count']:,}\".replace(\",\", NBSP))\n612\t        c.drawRightString(M + 400, y, str(r[\"sources\"]))\n613\t        c.drawRightString(M + 500, y, _fmt_money(r[\"avg_price\"]) if r[\"avg_price\"] else \"—\")\n614\t        y -= 15\n615\t    s.pied(\"Rapport généré automatiquement à partir des annonces publiques \"\n616\t           \"agrégées par Lou-Ka. Loyers : bornes 300–10 000 $.\", 1)\n617\t    c.showPage()\n618\t\n619\t    # ---- page 2 : histogramme des loyers + par type\n620\t    s.fond()\n621\t    y = s.entete(\"Rapport du marché · loyers\")\n622\t    y = s.titre_section(y, \"Distribution des loyers\")\n623\t    hist = st[\"histogram\"]\n624\t    max_c = max((h[\"count\"] for h in hist), default=1) or 1\n625\t    ch_h, ch_y = 150, y - 170\n626\t    bw = (PAGE_W - 2 * M) / len(hist)\n627\t    for i, h in enumerate(hist):\n628\t        bh = ch_h * h[\"count\"] / max_c\n629\t        bx = M + i * bw\n630\t        borne = h[\"hi\"] is None or h[\"lo\"] == 0\n631\t        c.setFillColor(INK3 if borne else GREEN)\n632\t        c.setStrokeColor(INK)\n633\t        c.setLineWidth(0.6)\n634\t        c.rect(bx + 2, ch_y, bw - 4, max(1, bh), stroke=1, fill=1)\n635\t        if h[\"count\"] and h[\"count\"] > max_c * 0.06:\n636\t            c.setFont(\"Courier-Bold\", 6)\n637\t            c.setFillColor(INK)\n638\t            c.drawCentredString(bx + bw / 2, ch_y + bh + 3, str(h[\"count\"]))\n639\t        c.setFont(\"Helvetica\", 5.6)\n640\t        c.setFillColor(INK3)\n641\t        lab = f\"<{h['hi']}\" if h[\"lo\"] == 0 else (f\"{h['lo']}+\" if h[\"hi\"] is None\n642\t                                                  else str(h[\"lo\"]))\n643\t        c.drawCentredString(bx + bw / 2, ch_y - 9, lab)\n644\t    y = ch_y - 30\n645\t\n646\t    y = s.titre_section(y, \"Par taille de logement\")\n647\t    c.setFont(\"Courier-Bold\", 7)\n648\t    c.setFillColor(INK3)\n649\t    for lab, xoff in ((\"TAILLE\", 0), (\"ANNONCES\", 160), (\"LOYER MOYEN\", 260),\n650\t                      (\"LOYER MIN\", 360)):\n651\t        c.drawString(M + xoff, y, lab)\n652\t    y -= 4\n653\t    c.line(M, y, PAGE_W - M, y)\n654\t    y -= 13\n655\t    for r in st[\"by_type\"][:9]:\n656\t        c.setFont(\"Helvetica-Bold\", 8.5)\n657\t        c.setFillColor(INK)\n658\t        c.drawString(M, y, r[\"key\"])\n659\t        c.setFont(\"Helvetica\", 8.5)\n660\t        c.setFillColor(INK2)\n661\t        c.drawRightString(M + 220, y, str(r[\"count\"]))\n662\t        c.drawRightString(M + 330, y, _fmt_money(r[\"avg_price\"]) if r[\"avg_price\"] else \"—\")\n663\t        c.drawRightString(M + 430, y, _fmt_money(r[\"min_price\"]) if r[\"min_price\"] else \"—\")\n664\t        y -= 13\n665\t    s.pied(\"Lou-Ka — agrégateur indépendant. Chaque annonce renvoie à la \"\n666\t           \"source originale du gestionnaire.\", 2)\n667\t    c.showPage()\n668\t\n669\t    # ---- page 3 : offre, inclusions, prix au pi², baisses de prix\n670\t    s.fond()\n671\t    y = s.entete(\"Rapport du marché · offre\")\n672\t    o = st[\"offre\"]\n673\t    y = s.titre_section(y, \"Inclusions et caractéristiques du parc\")\n674\t    carac = [(\"Chauffage inclus\", o[\"chauffage_pct\"]),\n675\t             (\"Électricité incluse\", o[\"electricite_pct\"]),\n676\t             (\"Eau chaude incluse\", o[\"eau_chaude_pct\"]),\n677\t             (\"Internet inclus\", o[\"internet_pct\"]),\n678\t             (\"Climatisation\", o[\"clim_pct\"]),\n679\t             (\"Stationnement\", o[\"stationnement_pct\"]),\n680\t             (\"Balcon\", o[\"balcon_pct\"]),\n681\t             (\"Meublé\", o[\"furnished_pct\"])]\n682\t    for lab, v in carac:\n683\t        if v is None:\n684\t            continue\n685\t        c.setFont(\"Helvetica\", 8.5)\n686\t        c.setFillColor(INK2)\n687\t        c.drawString(M, y, lab)\n688\t        bx0, bw_ = M + 150, PAGE_W - 2 * M - 200\n689\t        c.setFillColor(SURFACE)\n690\t        c.setStrokeColor(INK)\n691\t        c.setLineWidth(0.8)\n692\t        c.roundRect(bx0, y - 1, bw_, 8, 4, stroke=1, fill=1)\n693\t        c.setFillColor(GREEN)\n694\t        c.roundRect(bx0, y - 1, bw_ * min(1, v / 100), 8, 4, stroke=0, fill=1)\n695\t        c.setFont(\"Courier-Bold\", 8)\n696\t        c.setFillColor(INK)\n697\t        c.drawRightString(PAGE_W - M, y, f\"{v}{NBSP}%\")\n698\t        y -= 15\n699\t    y -= 6\n700\t    c.setFont(\"Helvetica\", 8)\n701\t    c.setFillColor(INK3)\n702\t    c.drawString(M, y, f\"Disponibles maintenant : {o['dispo_now']:,} · à date future : \"\n703\t                       f\"{o['dispo_date']:,} · superficie moyenne : \"\n704\t                       f\"{o['superficie_moyenne'] or '—'} pi² \"\n705\t                       f\"({o['superficie_connue']:,} annonces la publient)\"\n706\t                       .replace(\",\", NBSP))\n707\t    y -= 24\n708\t\n709\t    if o[\"prix_pi2\"]:\n710\t        y = s.titre_section(y, \"Prix au pied carré (loyer / superficie)\")\n711\t        for r in o[\"prix_pi2\"][:6]:\n712\t            c.setFont(\"Helvetica-Bold\", 8.5)\n713\t            c.setFillColor(INK)\n714\t            c.drawString(M, y, r[\"key\"])\n715\t            c.setFont(\"Helvetica\", 8.5)\n716\t            c.setFillColor(INK2)\n717\t            c.drawString(M + 70, y, f\"{r['val']:.2f}\".replace(\".\", \",\") +\n718\t                         f\"{NBSP}$/pi² · {r['count']} annonces\")\n719\t            y -= 13\n720\t        y -= 10\n721\t\n722\t    if st[\"baisses\"]:\n723\t        y = s.titre_section(y, \"Baisses de prix récentes (30 jours)\")\n724\t        for b in st[\"baisses\"][:8]:\n725\t            c.setFont(\"Helvetica\", 8.5)\n726\t            c.setFillColor(INK2)\n727\t            c.drawString(M, y, f\"{(b['title'] or b['uid'])[:46]} — {b['city'] or ''}\")\n728\t            c.setFont(\"Courier-Bold\", 8)\n729\t            c.setFillColor(GREEN)\n730\t            c.drawRightString(PAGE_W - M, y,\n731\t                              f\"{_fmt_money(b['avant'])} → {_fmt_money(b['apres'])} \"\n732\t                              f\"({b['pct']}{NBSP}%)\".replace(\".\", \",\"))\n733\t            y -= 13\n734\t    s.pied(\"Caractéristiques dérivées des annonces publiées ; les inclusions \"\n735\t           \"non mentionnées par une source ne sont pas comptées.\", 3)\n736\t    c.showPage()\n737\t\n738\t    # ---- page 4 : top villes + top gestionnaires\n739\t    s.fond()\n740\t    y = s.entete(\"Rapport du marché · détail\")\n741\t    y = s.titre_section(y, \"Top 20 des villes\")\n742\t    col_w = (PAGE_W - 2 * M) / 2\n743\t    for i, r in enumerate(st[\"by_city\"][:20]):\n744\t        vx = M if i < 10 else M + col_w\n745\t        vy = y - (i % 10) * 13\n746\t        c.setFont(\"Helvetica\", 8.5)\n747\t        c.setFillColor(INK2)\n748\t        c.drawString(vx, vy, f\"{i + 1:>2}. {r['key']}\")\n749\t        c.setFont(\"Courier-Bold\", 8)\n750\t        c.setFillColor(INK)\n751\t        c.drawRightString(vx + col_w - 24, vy, f\"{r['count']:,}\".replace(\",\", NBSP))\n752\t    y -= 10 * 13 + 16\n753\t\n754\t    y = s.titre_section(y, \"Top 20 des gestionnaires\")\n755\t    for i, r in enumerate(st[\"by_source\"][:20]):\n756\t        vx = M if i < 10 else M + col_w\n757\t        vy = y - (i % 10) * 13\n758\t        c.setFont(\"Helvetica\", 8.5)\n759\t        c.setFillColor(INK2)\n760\t        c.drawString(vx, vy, f\"{i + 1:>2}. {noms.get(r['key'], r['key'])[:34]}\")\n761\t        c.setFont(\"Courier-Bold\", 8)\n762\t        c.setFillColor(INK)\n763\t        c.drawRightString(vx + col_w - 24, vy, f\"{r['count']:,}\".replace(\",\", NBSP))\n764\t    y -= 10 * 13 + 20\n765\t\n766\t    c.setFont(\"Helvetica\", 7.5)\n767\t    c.setFillColor(INK3)\n768\t    c.drawString(M, y, \"Sources de données de quartier : Statistique Canada (Recensement 2021, \"\n769\t                       \"licence ouverte), INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), OpenStreetMap.\")\n770\t    s.pied(\"© Lou-Ka — www.lou-ka.com · rapport non contractuel, généré automatiquement.\", 4)\n771\t    c.save()\n772\t    return buf.getvalue()\n773\t"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"4f6d669f-b975-4d2c-b69e-05af7264f6c2","timestamp":"2026-08-24T21:17:21.587Z","tool_use_result":{"type":"text","file":{"filePath":"/tmp/lou-ka-ref/louka/pdfgen.py","content":"# -----------------------------------------------------------------------------\n# Lou-Ka — Agrégateur de logements à louer (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdfgen.py : génération PDF à l'identité visuelle officielle Lou-Ka\n#   - fiche_pdf(uid)  : fiche de propriété (photos, prix + badge marché, chips,\n#     description structurée, inclusions, quartier, à proximité, QR code)\n#   - rapport_pdf()   : rapport global du marché (totaux, régions, histogramme\n#     des loyers, types, villes, gestionnaires)\n#   Palette identique au site : blanc #ffffff, marine #0b1330, orange #ff6a00,\n#   bleu vif #2258ff, gris pâle #f2f2f2. Polices de base (Helvetica) stylées\n#   par la mise en page — aucune dépendance de police externe.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport math\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Lou-Ka (identité officielle) ------------------------------------\nPAPER = HexColor(\"#ffffff\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#0b1330\")          # bleu marine profond\nINK2 = HexColor(\"#3e4560\")\nINK3 = HexColor(\"#8a90a5\")\nGREEN = HexColor(\"#2258ff\")        # accent secondaire : bleu vif\nGREEN_DEEP = HexColor(\"#0b1330\")\nLIME = HexColor(\"#ff6a00\")         # accent d'action : orange vif\nLIME_SOFT = HexColor(\"#fff1e6\")\nAMBER = HexColor(\"#ff6a00\")\nAMBER_SOFT = HexColor(\"#fff1e6\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"LouKaBot/1.0 (+https://www.lou-ka.com/bot; contact@spboucher.ai)\"\n\nPOI_LABELS = {\n    \"epicerie\": \"Épicerie\", \"depanneur\": \"Dépanneur\", \"pharmacie\": \"Pharmacie\",\n    \"ecole\": \"École\", \"garderie\": \"Garderie\", \"parc\": \"Parc\", \"bus\": \"Arrêt de bus\",\n    \"metro\": \"Métro\", \"gym\": \"Gym\", \"cafe\": \"Café\", \"clinique\": \"Clinique\",\n    \"hopital\": \"Hôpital\", \"bibliotheque\": \"Bibliothèque\",\n}\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\nNBSP = \" \"\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _marche(m_: float) -> str:\n    return f\"≈{NBSP}{max(1, round(m_ * 1.3 / 80))}{NBSP}min à pied\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        return ImageReader(io.BytesIO(r.content))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Lou·Ka : « Lou » encre + boîte encre avec « Ka » lime.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Lou\")\n        w = c.stringWidth(\"Lou\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(LIME)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6,\n                          titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=LIME, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M, y, texte)\n        c.setStrokeColor(LIME)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M, y - 4, M + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n\n# ---------------------------------------------------------------------------\n# Fiche de propriété\n# ---------------------------------------------------------------------------\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche du logement `uid`. None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"amenities\"] = json.loads(l.get(\"amenities\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    l[\"digest\"] = json.loads(l[\"digest\"]) if l.get(\"digest\") else None\n    poi = []\n    quartier = None\n    if l.get(\"lat\") is not None:\n        key = f\"{round(l['lat'], 4)},{round(l['lng'], 4)}\"\n        pr = con.execute(\"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n        poi = json.loads(pr[\"pois\"]) if pr else []\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        quartier = qmod.fiche_quartier(l[\"lat\"], l[\"lng\"], l.get(\"city\") or \"\",\n                                       dauid if dauid and dauid != \"hors-zone\" else None)\n    reg = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n    src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]), l[\"source\"])\n    con.close()\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Lou-Ka — {l.get('title') or l.get('address')}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche logement\")\n\n    # --- bloc prix + badge marché\n    prix_txt = (_fmt_money(l[\"price\"]) + f\"{NBSP}/ mois\") if l.get(\"price\") \\\n        else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    loyer_secteur = (quartier or {}).get(\"demographie\", {}).get(\"loyer_moyen\") \\\n        if quartier else None\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    if l.get(\"price\") and loyer_secteur:\n        delta = (l[\"price\"] - loyer_secteur) / loyer_secteur\n        pct = f\"{'+' if delta > 0 else '−'}{abs(round(delta * 100))}{NBSP}%\"\n        if delta <= -0.15:\n            s.pilule(bx, y - 22, f\"{pct} vs secteur · Bon deal\", GREEN_DEEP, LIME_SOFT, GREEN)\n        elif delta <= 0.10:\n            s.pilule(bx, y - 22, f\"{pct} vs secteur · Dans le marché\", INK2, SURFACE, INK)\n        else:\n            s.pilule(bx, y - 22, f\"{pct} vs secteur · Au-dessus du marché\",\n                     HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER)\n    y -= 44\n\n    # --- titre + adresse\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, (l.get(\"title\") or l.get(\"address\") or \"\")[:80])\n    y -= 14\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    loc = \" · \".join(x for x in (l.get(\"address\"), l.get(\"sector\"), l.get(\"city\"))\n                     if x and x != l.get(\"title\"))\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés\n    x = M\n    chips = []\n    if l.get(\"unit_type\"):\n        chips.append(l[\"unit_type\"])\n    ad = l.get(\"availability_date\")\n    if ad == \"now\":\n        chips.append(\"Libre maintenant\")\n    elif ad:\n        chips.append(f\"Dispo {ad}\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft'])}{NBSP}pi²\")\n    if l.get(\"furnished\"):\n        chips.append(\"Meublé\")\n    if l.get(\"pets\"):\n        chips.append({\"oui\": \"Animaux acceptés\", \"non\": \"Animaux refusés\",\n                      \"conditions\": \"Animaux sous conditions\"}.get(l[\"pets\"], l[\"pets\"]))\n    if l[\"details\"].get(\"floor\") is not None:\n        chips.append(f\"{l['details']['floor']}e étage\")\n    for chip in chips[:7]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- « En bref » (digest) sur bandeau lime\n    dg = l.get(\"digest\")\n    if dg and dg.get(\"en_bref\"):\n        c.setFillColor(LIME_SOFT)\n        c.setStrokeColor(GREEN)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 40, PAGE_W - 2 * M, 44, 6, stroke=1, fill=1)\n        s.paragraphe(M + 10, y - 8, \"EN BREF — \" + dg[\"en_bref\"],\n                     PAGE_W - 2 * M - 20, taille=8.5, couleur=GREEN_DEEP,\n                     max_lignes=3)\n        y -= 54\n\n    # --- description (2-3 sections max) | inclusions en colonne droite\n    col_g = PAGE_W * 0.56 - M\n    y_desc = s.titre_section(y, \"Description\")\n    if dg and dg.get(\"sections\"):\n        for sec in dg[\"sections\"][:3]:\n            c.setFont(\"Helvetica-Bold\", 9)\n            c.setFillColor(INK)\n            c.drawString(M, y_desc, sec[\"titre\"][:60])\n            y_desc -= 12\n            y_desc = s.paragraphe(M, y_desc, sec[\"texte\"], col_g, max_lignes=6)\n            y_desc -= 4\n            if y_desc < 150:\n                break\n    elif l.get(\"description\"):\n        y_desc = s.paragraphe(M, y_desc, l[\"description\"], col_g, max_lignes=16)\n\n    xr = M + col_g + 24\n    y_incl = s_titre_droite(c, y, xr, \"Inclusions et commodités\")\n    d = l[\"details\"]\n    badges = []\n    inc = d.get(\"inclusions\") or {}\n    for k, lab in ((\"heating\", \"Chauffage inclus\"), (\"electricity\", \"Électricité incluse\"),\n                   (\"hot_water\", \"Eau chaude incluse\"), (\"internet\", \"Internet inclus\")):\n        if inc.get(k):\n            badges.append(lab)\n    app = d.get(\"appliances\") or {}\n    if app.get(\"dishwasher\"):\n        badges.append(\"Lave-vaisselle\")\n    if app.get(\"washer_dryer\"):\n        badges.append(\"Laveuse-sécheuse\")\n    for k, lab in ((\"ac\", \"Climatisation\"), (\"elevator\", \"Ascenseur\"),\n                   (\"balcony\", \"Balcon\"), (\"pool\", \"Piscine\"), (\"gym\", \"Gym\"),\n                   (\"laundry\", \"Buanderie\"), (\"storage\", \"Rangement\")):\n        if d.get(k):\n            badges.append(lab)\n    if (d.get(\"parking\") or {}).get(\"available\"):\n        badges.append(\"Stationnement\")\n    if l.get(\"furnished\"):\n        badges.append(\"Meublé\")\n    c.setFont(\"Helvetica\", 8.5)\n    for b in badges[:12]:\n        c.setFillColor(GREEN)\n        c.drawString(xr, y_incl, \"✓\")\n        c.setFillColor(INK2)\n        c.drawString(xr + 12, y_incl, b)\n        y_incl -= 12\n    autres = [a for a in l[\"amenities\"]\n              if not any(b.lower() in a.lower() or a.lower() in b.lower() for b in badges)]\n    for a in autres[: max(0, 14 - len(badges))]:\n        c.setFillColor(INK3)\n        c.drawString(xr + 12, y_incl, a[:38])\n        y_incl -= 11\n\n    y = min(y_desc, y_incl) - 8\n\n    # --- pied page 1 + page 2 (quartier / à proximité / références)\n    s.pied(\"Données de l'annonce publiée par le gestionnaire — Lou-Ka est un \"\n           \"agrégateur indépendant ; chaque fiche renvoie à l'annonce originale.\", 1)\n    c.showPage()\n    s.fond()\n    y = s.entete(\"Fiche logement · suite\")\n\n    # Le quartier\n    if quartier:\n        y = s.titre_section(y, \"Le quartier\")\n        demo = quartier.get(\"demographie\") or {}\n        tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                  (\"Ménages locataires\", f\"{round(demo['pct_locataires'])}{NBSP}%\" if demo.get(\"pct_locataires\") is not None else \"—\"),\n                  (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                  (\"Âge médian\", f\"{round(demo['age_median'])} ans\" if demo.get(\"age_median\") is not None else \"—\"),\n                  (\"Français à la maison\", f\"{round(demo['pct_francais'])}{NBSP}%\" if demo.get(\"pct_francais\") is not None else \"—\"),\n                  (\"Diplôme universitaire\", f\"{round(demo['pct_univ'])}{NBSP}%\" if demo.get(\"pct_univ\") is not None else \"—\")]\n        tw = (PAGE_W - 2 * M - 20) / 3\n        for i, (lab, val) in enumerate(tuiles):\n            tx = M + (i % 3) * (tw + 10)\n            ty = y - 34 - (i // 3) * 44\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1.2)\n            c.roundRect(tx, ty, tw, 38, 5, stroke=1, fill=1)\n            c.setFont(\"Helvetica-Bold\", 12)\n            c.setFillColor(INK)\n            c.drawString(tx + 8, ty + 20, val)\n            c.setFont(\"Courier\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(tx + 8, ty + 8, lab.upper())\n        y -= 34 + 2 * 44 + 6\n\n        prox = quartier.get(\"proximite\") or {}\n        for k, lab in PROX_LABELS:\n            if k not in prox:\n                continue\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(GREEN)\n            c.roundRect(bx0, y - 1, bw_ * v, 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        y -= 4\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch[\"classe\"] <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_DEEP, LIME_SOFT, GREEN))\n            elif ch[\"classe\"] >= 7:\n                badges_q.append((f\"Îlot de chaleur (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\"),\n                                 HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"]) / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} la moyenne canadienne\",\n                             INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m (12 mois)\",\n                             INK2, SURFACE, INK))\n        x = M\n        for txt, fg, bg, stk in badges_q:\n            x = s.pilule(x, y - 8, txt, fg, bg, stk)\n        y -= 30\n\n    # À proximité\n    if poi:\n        y = s.titre_section(y, \"À proximité\")\n        col2 = y\n        for i, p in enumerate(poi[:12]):\n            px = M if i % 2 == 0 else M + (PAGE_W - 2 * M) / 2 + 10\n            if i % 2 == 0 and i > 0:\n                y -= 13\n            py = y\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            lab = POI_LABELS.get(p[\"cat\"], p[\"cat\"])\n            nom_poi = p[\"name\"][:19] + \"…\" if len(p[\"name\"]) > 20 else p[\"name\"]\n            c.drawString(px, py, f\"{lab} — {nom_poi}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + (PAGE_W - 2 * M) / 2 - 14, py,\n                              f\"{_fmt_dist(p['dist_m'])} · {_marche(p['dist_m'])}\")\n        y -= 26\n\n    # Références : QR + gestionnaire + source\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.lou-ka.com/logement/{uid}\", box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    c.drawString(M + 82, y - 14, f\"Gestionnaire : {src_name}\")\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, y - 28, \"Annonce originale :\")\n    c.setFillColor(GREEN)\n    c.drawString(M + 82, y - 40, (l.get(\"url\") or \"\")[:90])\n    c.setFillColor(INK2)\n    c.drawString(M + 82, y - 56, \"Fiche à jour en ligne (scannez le code) :\")\n    c.setFillColor(GREEN)\n    c.drawString(M + 82, y - 68, f\"www.lou-ka.com/logement/{uid}\")\n\n    s.pied(\"Quartier : Statistique Canada (Recensement 2021), INSPQ (CC-BY 4.0)\"\n           + (\", Ville de Montréal (CC-BY 4.0)\" if (quartier or {}).get(\"crime\", {}).get(\"type\") == \"points\" else \"\")\n           + \" · Commodités : OpenStreetMap. Prix et disponibilités : ceux affichés par la source.\", 2)\n    c.save()\n    return buf.getvalue()\n\n\ndef s_titre_droite(c, y, x, texte):\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x, y, texte)\n    c.setStrokeColor(LIME)\n    c.setLineWidth(3)\n    c.line(x, y - 4, x + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\n# ---------------------------------------------------------------------------\n# Rapport global du marché\n# ---------------------------------------------------------------------------\n\n_REGIONS = [\n    (\"Québec métro\", {\"Québec\", \"Lévis\", \"Saint-Augustin-de-Desmaures\", \"L'Ancienne-Lorette\", \"Pont-Rouge\", \"Shannon\"}),\n    (\"Outaouais\", {\"Gatineau\", \"Chelsea\", \"Thurso\", \"Perkins\", \"Maniwaki\", \"Val-des-Monts\"}),\n    (\"Estrie / Montérégie-Est\", {\"Sherbrooke\", \"Magog\", \"Orford\", \"East Angus\", \"Waterville\", \"Granby\", \"Waterloo\", \"Bromont\", \"Cowansville\", \"Richmond\"}),\n    (\"Mauricie / Centre-du-Québec\", {\"Trois-Rivières\", \"Bécancour\", \"Shawinigan\", \"Drummondville\", \"Victoriaville\", \"Notre-Dame-du-Bon-Conseil\", \"Saint-Léonard-d'Aston\", \"Louiseville\", \"Nicolet\", \"Wickham\", \"Saint-Narcisse\", \"Saint-Nicéphore\"}),\n    (\"Lanaudière / Laurentides\", {\"Joliette\", \"Saint-Jérôme\", \"Berthierville\", \"Saint-Ambroise-de-Kildare\", \"Saint-Gabriel-de-Brandon\", \"Lachute\", \"Brownsburg-Chatham\", \"Saint-Charles-Borromée\", \"Mirabel\", \"Sainte-Agathe-des-Monts\", \"Sainte-Thérèse\", \"Blainville\", \"Charlemagne\", \"Notre-Dame-des-Prairies\"}),\n    (\"Bas-Saint-Laurent / Gaspésie\", {\"Rimouski\", \"Rivière-du-Loup\", \"Matane\", \"Saint-Ulric\", \"Amqui\", \"Le Bic\", \"New Richmond\", \"Carleton-sur-Mer\", \"Gaspé\"}),\n    (\"Saguenay–Lac-Saint-Jean\", {\"Saguenay\", \"Alma\", \"Chicoutimi\", \"Jonquière\", \"Chambord\", \"La Malbaie\"}),\n    (\"Abitibi-Témiscamingue\", {\"Rouyn-Noranda\", \"Val-d'Or\", \"Amos\", \"Malartic\"}),\n    (\"Côte-Nord\", {\"Sept-Îles\", \"Port-Cartier\", \"Baie-Comeau\", \"Forestville\"}),\n    (\"Chaudière-Appalaches\", {\"Saint-Georges\", \"Sainte-Marie\", \"Thetford Mines\", \"Montmagny\", \"Vallée-Jonction\", \"Scott\", \"Saint-Isidore\", \"La Guadeloupe\", \"Saint-Raphaël\"}),\n]\n\n\ndef _region_de(city: str) -> str:\n    for nom, villes in _REGIONS:\n        if city in villes:\n            return nom\n    return \"Grand Montréal & environs\"\n\n\ndef rapport_pdf() -> bytes:\n    \"\"\"Rapport global du marché locatif Lou-Ka (multi-pages).\n\n    Tous les chiffres viennent de marketstats.compute() — la même source\n    que la page Statistiques du site.\n    \"\"\"\n    from . import marketstats\n    st = marketstats.compute()\n    reg_file = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n    noms = {s[\"id\"]: s[\"name\"] for s in reg_file}\n\n    t = st[\"totals\"]\n    total, moy, med, gps = t[\"total\"], t[\"avg\"] or 0, t[\"median\"] or 0, t[\"gps_pct\"]\n    n_sources = t[\"sources\"]\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(\"Lou-Ka — Rapport du marché locatif\")\n    s = _Style(c)\n\n    # ---- page 1 : couverture + grands chiffres + régions\n    s.fond()\n    s.logo(M, PAGE_H - 130, 42)\n    c.setFont(\"Helvetica-Bold\", 24)\n    c.setFillColor(INK)\n    c.drawString(M, PAGE_H - 185, \"Rapport du marché locatif\")\n    c.setFont(\"Courier-Bold\", 9)\n    c.setFillColor(INK3)\n    c.drawString(M, PAGE_H - 205,\n                 time.strftime(\"PROVINCE DE QUÉBEC · GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n\n    grands = [(f\"{total:,}\".replace(\",\", NBSP), \"annonces actives\"),\n              (str(n_sources), \"gestionnaires connectés\"),\n              (_fmt_money(moy), \"loyer moyen\"),\n              (_fmt_money(med), \"loyer médian\"),\n              (f\"{gps}{NBSP}%\", \"annonces géolocalisées\")]\n    tw = (PAGE_W - 2 * M - 4 * 10) / 5\n    for i, (val, lab) in enumerate(grands):\n        tx = M + i * (tw + 10)\n        ty = PAGE_H - 285\n        c.setFillColor(INK)\n        c.roundRect(tx + 3, ty - 3, tw, 58, 6, stroke=0, fill=1)   # ombre décalée\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.roundRect(tx, ty, tw, 58, 6, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        c.drawCentredString(tx + tw / 2, ty + 32, val)\n        c.setFont(\"Courier\", 6.3)\n        c.setFillColor(INK3)\n        c.drawCentredString(tx + tw / 2, ty + 14, lab.upper())\n\n    y = s.titre_section(PAGE_H - 330, \"Couverture par région\")\n    c.setFont(\"Courier-Bold\", 7)\n    c.setFillColor(INK3)\n    for lab, xoff in ((\"RÉGION\", 0), (\"ANNONCES\", 280), (\"SOURCES\", 360),\n                      (\"LOYER MOYEN\", 430)):\n        c.drawString(M + xoff, y, lab)\n    y -= 4\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.line(M, y, PAGE_W - M, y)\n    y -= 14\n    regions = st[\"by_region\"]\n    max_n = max((r[\"count\"] for r in regions), default=1)\n    for r in regions:\n        c.setFont(\"Helvetica-Bold\", 8.5)\n        c.setFillColor(INK)\n        c.drawString(M, y, r[\"key\"])\n        c.setFillColor(LIME)\n        c.setStrokeColor(INK)\n        c.setLineWidth(0.7)\n        bw = 90 * r[\"count\"] / max_n\n        c.roundRect(M + 180, y - 1, max(3, bw), 8, 3, stroke=1, fill=1)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawRightString(M + 330, y, f\"{r['count']:,}\".replace(\",\", NBSP))\n        c.drawRightString(M + 400, y, str(r[\"sources\"]))\n        c.drawRightString(M + 500, y, _fmt_money(r[\"avg_price\"]) if r[\"avg_price\"] else \"—\")\n        y -= 15\n    s.pied(\"Rapport généré automatiquement à partir des annonces publiques \"\n           \"agrégées par Lou-Ka. Loyers : bornes 300–10 000 $.\", 1)\n    c.showPage()\n\n    # ---- page 2 : histogramme des loyers + par type\n    s.fond()\n    y = s.entete(\"Rapport du marché · loyers\")\n    y = s.titre_section(y, \"Distribution des loyers\")\n    hist = st[\"histogram\"]\n    max_c = max((h[\"count\"] for h in hist), default=1) or 1\n    ch_h, ch_y = 150, y - 170\n    bw = (PAGE_W - 2 * M) / len(hist)\n    for i, h in enumerate(hist):\n        bh = ch_h * h[\"count\"] / max_c\n        bx = M + i * bw\n        borne = h[\"hi\"] is None or h[\"lo\"] == 0\n        c.setFillColor(INK3 if borne else GREEN)\n        c.setStrokeColor(INK)\n        c.setLineWidth(0.6)\n        c.rect(bx + 2, ch_y, bw - 4, max(1, bh), stroke=1, fill=1)\n        if h[\"count\"] and h[\"count\"] > max_c * 0.06:\n            c.setFont(\"Courier-Bold\", 6)\n            c.setFillColor(INK)\n            c.drawCentredString(bx + bw / 2, ch_y + bh + 3, str(h[\"count\"]))\n        c.setFont(\"Helvetica\", 5.6)\n        c.setFillColor(INK3)\n        lab = f\"<{h['hi']}\" if h[\"lo\"] == 0 else (f\"{h['lo']}+\" if h[\"hi\"] is None\n                                                  else str(h[\"lo\"]))\n        c.drawCentredString(bx + bw / 2, ch_y - 9, lab)\n    y = ch_y - 30\n\n    y = s.titre_section(y, \"Par taille de logement\")\n    c.setFont(\"Courier-Bold\", 7)\n    c.setFillColor(INK3)\n    for lab, xoff in ((\"TAILLE\", 0), (\"ANNONCES\", 160), (\"LOYER MOYEN\", 260),\n                      (\"LOYER MIN\", 360)):\n        c.drawString(M + xoff, y, lab)\n    y -= 4\n    c.line(M, y, PAGE_W - M, y)\n    y -= 13\n    for r in st[\"by_type\"][:9]:\n        c.setFont(\"Helvetica-Bold\", 8.5)\n        c.setFillColor(INK)\n        c.drawString(M, y, r[\"key\"])\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawRightString(M + 220, y, str(r[\"count\"]))\n        c.drawRightString(M + 330, y, _fmt_money(r[\"avg_price\"]) if r[\"avg_price\"] else \"—\")\n        c.drawRightString(M + 430, y, _fmt_money(r[\"min_price\"]) if r[\"min_price\"] else \"—\")\n        y -= 13\n    s.pied(\"Lou-Ka — agrégateur indépendant. Chaque annonce renvoie à la \"\n           \"source originale du gestionnaire.\", 2)\n    c.showPage()\n\n    # ---- page 3 : offre, inclusions, prix au pi², baisses de prix\n    s.fond()\n    y = s.entete(\"Rapport du marché · offre\")\n    o = st[\"offre\"]\n    y = s.titre_section(y, \"Inclusions et caractéristiques du parc\")\n    carac = [(\"Chauffage inclus\", o[\"chauffage_pct\"]),\n             (\"Électricité incluse\", o[\"electricite_pct\"]),\n             (\"Eau chaude incluse\", o[\"eau_chaude_pct\"]),\n             (\"Internet inclus\", o[\"internet_pct\"]),\n             (\"Climatisation\", o[\"clim_pct\"]),\n             (\"Stationnement\", o[\"stationnement_pct\"]),\n             (\"Balcon\", o[\"balcon_pct\"]),\n             (\"Meublé\", o[\"furnished_pct\"])]\n    for lab, v in carac:\n        if v is None:\n            continue\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y, lab)\n        bx0, bw_ = M + 150, PAGE_W - 2 * M - 200\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(0.8)\n        c.roundRect(bx0, y - 1, bw_, 8, 4, stroke=1, fill=1)\n        c.setFillColor(GREEN)\n        c.roundRect(bx0, y - 1, bw_ * min(1, v / 100), 8, 4, stroke=0, fill=1)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK)\n        c.drawRightString(PAGE_W - M, y, f\"{v}{NBSP}%\")\n        y -= 15\n    y -= 6\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK3)\n    c.drawString(M, y, f\"Disponibles maintenant : {o['dispo_now']:,} · à date future : \"\n                       f\"{o['dispo_date']:,} · superficie moyenne : \"\n                       f\"{o['superficie_moyenne'] or '—'} pi² \"\n                       f\"({o['superficie_connue']:,} annonces la publient)\"\n                       .replace(\",\", NBSP))\n    y -= 24\n\n    if o[\"prix_pi2\"]:\n        y = s.titre_section(y, \"Prix au pied carré (loyer / superficie)\")\n        for r in o[\"prix_pi2\"][:6]:\n            c.setFont(\"Helvetica-Bold\", 8.5)\n            c.setFillColor(INK)\n            c.drawString(M, y, r[\"key\"])\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M + 70, y, f\"{r['val']:.2f}\".replace(\".\", \",\") +\n                         f\"{NBSP}$/pi² · {r['count']} annonces\")\n            y -= 13\n        y -= 10\n\n    if st[\"baisses\"]:\n        y = s.titre_section(y, \"Baisses de prix récentes (30 jours)\")\n        for b in st[\"baisses\"][:8]:\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{(b['title'] or b['uid'])[:46]} — {b['city'] or ''}\")\n            c.setFont(\"Courier-Bold\", 8)\n            c.setFillColor(GREEN)\n            c.drawRightString(PAGE_W - M, y,\n                              f\"{_fmt_money(b['avant'])} → {_fmt_money(b['apres'])} \"\n                              f\"({b['pct']}{NBSP}%)\".replace(\".\", \",\"))\n            y -= 13\n    s.pied(\"Caractéristiques dérivées des annonces publiées ; les inclusions \"\n           \"non mentionnées par une source ne sont pas comptées.\", 3)\n    c.showPage()\n\n    # ---- page 4 : top villes + top gestionnaires\n    s.fond()\n    y = s.entete(\"Rapport du marché · détail\")\n    y = s.titre_section(y, \"Top 20 des villes\")\n    col_w = (PAGE_W - 2 * M) / 2\n    for i, r in enumerate(st[\"by_city\"][:20]):\n        vx = M if i < 10 else M + col_w\n        vy = y - (i % 10) * 13\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(vx, vy, f\"{i + 1:>2}. {r['key']}\")\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK)\n        c.drawRightString(vx + col_w - 24, vy, f\"{r['count']:,}\".replace(\",\", NBSP))\n    y -= 10 * 13 + 16\n\n    y = s.titre_section(y, \"Top 20 des gestionnaires\")\n    for i, r in enumerate(st[\"by_source\"][:20]):\n        vx = M if i < 10 else M + col_w\n        vy = y - (i % 10) * 13\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(vx, vy, f\"{i + 1:>2}. {noms.get(r['key'], r['key'])[:34]}\")\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK)\n        c.drawRightString(vx + col_w - 24, vy, f\"{r['count']:,}\".replace(\",\", NBSP))\n    y -= 10 * 13 + 20\n\n    c.setFont(\"Helvetica\", 7.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources de données de quartier : Statistique Canada (Recensement 2021, \"\n                       \"licence ouverte), INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), OpenStreetMap.\")\n    s.pied(\"© Lou-Ka — www.lou-ka.com · rapport non contractuel, généré automatiquement.\", 4)\n    c.save()\n    return buf.getvalue()\n","numLines":773,"startLine":1,"totalLines":773}}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SFmDqHs7QXC1pjpALkNgUh","type":"tool_result","content":"1\t# -----------------------------------------------------------------------------\n2\t# Lou-Ka — Agrégateur de logements à louer (province de Québec)\n3\t# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n4\t# pdfgen.py : génération PDF à l'identité visuelle officielle Lou-Ka\n5\t#   - fiche_pdf(uid)  : fiche de propriété complète — tout ce que montre la\n6\t#     fiche web : photos, prix + analyse Lou-Ka (juste valeur, histogramme),\n7\t#     chips, description structurée, inclusions, registre des loyers,\n8\t#     historique TAL, inondation, qualité de l'air, coût d'électricité,\n9\t#     commerces/transport, essence, KA Scores, quartier, à proximité, QR\n10\t#   - rapport_pdf()   : rapport global du marché (totaux, régions, histogramme\n11\t#     des loyers, types, villes, gestionnaires)\n12\t#   Palette identique au site : blanc #ffffff, marine #0b1330, orange #ff6a00,\n13\t#   bleu vif #2258ff, gris pâle #f2f2f2. Polices de base (Helvetica) stylées\n14\t#   par la mise en page — aucune dépendance de police externe.\n15\t# -----------------------------------------------------------------------------\n16\tfrom __future__ import annotations\n17\t\n18\timport io\n19\timport json\n20\timport math\n21\timport time\n22\t\n23\timport qrcode\n24\timport requests\n25\tfrom reportlab.lib.colors import HexColor\n26\tfrom reportlab.lib.pagesizes import letter\n27\tfrom reportlab.lib.utils import ImageReader\n28\tfrom reportlab.pdfgen import canvas as rl_canvas\n29\t\n30\tfrom . import db\n31\t\n32\t# --- palette Lou-Ka (identité officielle) ------------------------------------\n33\tPAPER = HexColor(\"#ffffff\")\n34\tSURFACE = HexColor(\"#ffffff\")\n35\tINK = HexColor(\"#0b1330\")          # bleu marine profond\n36\tINK2 = HexColor(\"#3e4560\")\n37\tINK3 = HexColor(\"#8a90a5\")\n38\tGREEN = HexColor(\"#2258ff\")        # accent secondaire : bleu vif\n39\tGREEN_DEEP = HexColor(\"#0b1330\")\n40\tLIME = HexColor(\"#ff6a00\")         # accent d'action : orange vif\n41\tLIME_SOFT = HexColor(\"#fff1e6\")\n42\tAMBER = HexColor(\"#ff6a00\")\n43\tAMBER_SOFT = HexColor(\"#fff1e6\")\n44\t\n45\tPAGE_W, PAGE_H = letter\n46\tM = 40          # marge\n47\tIMG_TIMEOUT = 6\n48\tUA = \"LouKaBot/1.0 (+https://www.lou-ka.com/bot; contact@spboucher.ai)\"\n49\t\n50\tPOI_LABELS = {\n51\t    \"epicerie\": \"Épicerie\", \"depanneur\": \"Dépanneur\", \"pharmacie\": \"Pharmacie\",\n52\t    \"ecole\": \"École\", \"garderie\": \"Garderie\", \"parc\": \"Parc\", \"bus\": \"Arrêt de bus\",\n53\t    \"metro\": \"Métro\", \"gym\": \"Gym\", \"cafe\": \"Café\", \"clinique\": \"Clinique\",\n54\t    \"hopital\": \"Hôpital\", \"bibliotheque\": \"Bibliothèque\",\n55\t}\n56\tPROX_LABELS = [\n57\t    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n58\t    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n59\t    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n60\t]\n61\t\n62\tNBSP = \" \"\n63\t\n64\t\n65\tdef _fmt_money(v) -> str:\n66\t    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n67\t\n68\t\n69\tdef _fmt_dist(m_: float) -> str:\n70\t    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n71\t            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n72\t\n73\t\n74\tdef _marche(m_: float) -> str:\n75\t    return f\"≈{NBSP}{max(1, round(m_ * 1.3 / 80))}{NBSP}min à pied\"\n76\t\n77\t\n78\tdef _fetch_image(url: str) -> ImageReader | None:\n79\t    try:\n80\t        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n81\t        r.raise_for_status()\n82\t        raw = r.content\n83\t        try:\n84\t            # les photos source dépassent parfois 5 Mo chacune : sans\n85\t            # redimensionnement le PDF grimpe à 30 Mo\n86\t            from PIL import Image\n87\t            im = Image.open(io.BytesIO(raw))\n88\t            im.thumbnail((1200, 1200))\n89\t            buf = io.BytesIO()\n90\t            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)\n91\t            buf.seek(0)\n92\t            return ImageReader(buf)\n93\t        except Exception:\n94\t            return ImageReader(io.BytesIO(raw))\n95\t    except Exception:\n96\t        return None\n97\t\n98\t\n99\tclass _Style:\n100\t    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n101\t\n102\t    def __init__(self, c: rl_canvas.Canvas):\n103\t        self.c = c\n104\t\n105\t    def fond(self):\n106\t        self.c.setFillColor(PAPER)\n107\t        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n108\t\n109\t    def logo(self, x: float, y: float, taille: float = 22):\n110\t        \"\"\"Wordmark Lou·Ka : « Lou » encre + boîte encre avec « Ka » lime.\"\"\"\n111\t        c = self.c\n112\t        c.setFont(\"Helvetica-Bold\", taille)\n113\t        c.setFillColor(INK)\n114\t        c.drawString(x, y, \"Lou\")\n115\t        w = c.stringWidth(\"Lou\", \"Helvetica-Bold\", taille)\n116\t        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n117\t        c.saveState()\n118\t        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n119\t        c.rotate(-3)\n120\t        c.setFillColor(INK)\n121\t        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n122\t        c.setFillColor(LIME)\n123\t        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n124\t        c.restoreState()\n125\t        return x + w + 6 + bw\n126\t\n127\t    def entete(self, titre: str):\n128\t        c = self.c\n129\t        self.logo(M, PAGE_H - M - 16)\n130\t        c.setFont(\"Courier-Bold\", 8)\n131\t        c.setFillColor(INK3)\n132\t        c.drawRightString(PAGE_W - M, PAGE_H - M - 6,\n133\t                          titre.upper())\n134\t        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n135\t                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n136\t        c.setStrokeColor(INK)\n137\t        c.setLineWidth(2)\n138\t        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n139\t        return PAGE_H - M - 40   # y de départ du contenu\n140\t\n141\t    def pied(self, texte: str, page: int | None = None):\n142\t        c = self.c\n143\t        c.setStrokeColor(INK)\n144\t        c.setLineWidth(1)\n145\t        c.line(M, M + 16, PAGE_W - M, M + 16)\n146\t        c.setFont(\"Helvetica\", 6.5)\n147\t        c.setFillColor(INK3)\n148\t        c.drawString(M, M + 6, texte[:150])\n149\t        if page:\n150\t            c.setFont(\"Courier-Bold\", 7)\n151\t            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n152\t\n153\t    def pilule(self, x: float, y: float, texte: str, fg=LIME, bg=INK,\n154\t               stroke=None, taille: float = 8) -> float:\n155\t        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n156\t        c = self.c\n157\t        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n158\t        c.setFillColor(bg)\n159\t        if stroke:\n160\t            c.setStrokeColor(stroke)\n161\t            c.setLineWidth(1.2)\n162\t        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n163\t                    stroke=1 if stroke else 0, fill=1)\n164\t        c.setFillColor(fg)\n165\t        c.setFont(\"Helvetica-Bold\", taille)\n166\t        c.drawString(x + 7, y + 5, texte)\n167\t        return x + w + 6\n168\t\n169\t    def titre_section(self, y: float, texte: str) -> float:\n170\t        c = self.c\n171\t        c.setFont(\"Helvetica-Bold\", 12.5)\n172\t        c.setFillColor(INK)\n173\t        c.drawString(M, y, texte)\n174\t        c.setStrokeColor(LIME)\n175\t        c.setLineWidth(3)\n176\t        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n177\t        c.line(M, y - 4, M + w, y - 4)\n178\t        return y - 18\n179\t\n180\t    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n181\t                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n182\t                   max_lignes: int = 100) -> float:\n183\t        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n184\t        c = self.c\n185\t        c.setFont(\"Helvetica\", taille)\n186\t        c.setFillColor(couleur)\n187\t        mots = texte.split()\n188\t        ligne = \"\"\n189\t        n = 0\n190\t        for mot in mots:\n191\t            essai = (ligne + \" \" + mot).strip()\n192\t            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n193\t                ligne = essai\n194\t            else:\n195\t                c.drawString(x, y, ligne)\n196\t                y -= interligne\n197\t                n += 1\n198\t                ligne = mot\n199\t                if n >= max_lignes - 1:\n200\t                    ligne += \" …\"\n201\t                    break\n202\t        if ligne:\n203\t            c.drawString(x, y, ligne)\n204\t            y -= interligne\n205\t        return y\n206\t\n207\t    def tuile(self, x: float, y: float, w: float, h: float,\n208\t              val: str, lab: str):\n209\t        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n210\t        c = self.c\n211\t        c.setFillColor(INK)\n212\t        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n213\t        c.setFillColor(SURFACE)\n214\t        c.setStrokeColor(INK)\n215\t        c.setLineWidth(1.2)\n216\t        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n217\t        c.setFont(\"Helvetica-Bold\", 12)\n218\t        c.setFillColor(INK)\n219\t        c.drawString(x + 8, y + h - 20, str(val))\n220\t        c.setFont(\"Courier\", 6.2)\n221\t        c.setFillColor(INK3)\n222\t        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n223\t\n224\t\n225\t# ---------------------------------------------------------------------------\n226\t# Fiche de propriété\n227\t# ---------------------------------------------------------------------------\n228\t\n229\t_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n230\t         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n231\t\n232\t\n233\tdef _fmt_mois(d: str | None) -> str:\n234\t    \"\"\"« 2017-11-04 » → « novembre 2017 » (sinon la valeur telle quelle).\"\"\"\n235\t    if not d:\n236\t        return \"—\"\n237\t    try:\n238\t        an, mois = d[:7].split(\"-\")\n239\t        return f\"{_MOIS[int(mois) - 1]} {an}\"\n240\t    except Exception:\n241\t        return d\n242\t\n243\t\n244\t_VERDICTS = {\n245\t    \"sous\": (\"Sous le marché\", GREEN_DEEP, LIME_SOFT, GREEN),\n246\t    \"marche\": (\"Dans le marché\", INK2, SURFACE, INK),\n247\t    \"sur\": (\"Au-dessus du marché\", HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER),\n248\t}\n249\t\n250\t_PIED_FICHE = (\"Données de l'annonce publiée par le gestionnaire — Lou-Ka est \"\n251\t               \"un agrégateur indépendant. Enrichissements (analyse de prix, \"\n252\t               \"registre, TAL, environnement) fournis à titre indicatif, sans \"\n253\t               \"valeur juridique.\")\n254\t\n255\tdef fiche_pdf(uid: str) -> bytes | None:\n256\t    \"\"\"PDF de la fiche du logement `uid` — reflète TOUS les blocs de la fiche\n257\t    web : prix + analyse Lou-Ka, photos, description, inclusions, registre des\n258\t    loyers, historique TAL, risque d'inondation, qualité de l'air, coût\n259\t    d'électricité, commerces/transport, essence, KA Scores, quartier,\n260\t    à proximité, références + QR. None si introuvable.\"\"\"\n261\t    con = db.connect()\n262\t    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n263\t    if row is None:\n264\t        con.close()\n265\t        return None\n266\t    l = dict(row)\n267\t    l[\"amenities\"] = json.loads(l.get(\"amenities\") or \"[]\")\n268\t    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n269\t    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n270\t    l[\"digest\"] = json.loads(l[\"digest\"]) if l.get(\"digest\") else None\n271\t    poi = []\n272\t    quartier = None\n273\t    if l.get(\"lat\") is not None:\n274\t        key = f\"{round(l['lat'], 4)},{round(l['lng'], 4)}\"\n275\t        pr = con.execute(\"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n276\t        poi = json.loads(pr[\"pois\"]) if pr else []\n277\t        from . import quartier as qmod\n278\t        dauid = l.get(\"dauid\")\n279\t        quartier = qmod.fiche_quartier(l[\"lat\"], l[\"lng\"], l.get(\"city\") or \"\",\n280\t                                       dauid if dauid and dauid != \"hors-zone\" else None)\n281\t    reg = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n282\t    src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]), l[\"source\"])\n283\t    kasc = None\n284\t    if l.get(\"coord_key\"):\n285\t        kr = con.execute(\"SELECT walk, transit, bike, calme, services,\"\n286\t                         \" global AS glob FROM kascores WHERE coord_key=?\",\n287\t                         (l[\"coord_key\"],)).fetchone()\n288\t        if kr is not None:\n289\t            kasc = dict(kr)\n290\t    price_history = [dict(r) for r in con.execute(\n291\t        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 6\",\n292\t        (uid,)).fetchall()]\n293\t    con.close()\n294\t\n295\t    # --- enrichissements de la fiche (mêmes modules que les blocs du site) ---\n296\t    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n297\t    geo = lat is not None and lng is not None\n298\t\n299\t    def _safe(fn, *a, **k):\n300\t        try:\n301\t            return fn(*a, **k)\n302\t        except Exception:\n303\t            return None\n304\t\n305\t    from . import air as air_mod\n306\t    from . import commerces as com_mod\n307\t    from . import fairvalue as fv_mod\n308\t    from . import gaz as gaz_mod\n309\t    from . import hydro as hydro_mod\n310\t    from . import inondation as inon_mod\n311\t    from . import rdl as rdl_mod\n312\t    from . import tal as tal_mod\n313\t    fv = _safe(fv_mod.explain, uid)\n314\t    rdl_d = _safe(rdl_mod.nearby, lat, lng) if geo else None\n315\t    tal_d = (_safe(tal_mod.lookup, l[\"address\"], l.get(\"city\"))\n316\t             if l.get(\"address\") else None)\n317\t    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n318\t    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n319\t    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n320\t    com = _safe(com_mod.nearby, lat, lng) if geo else None\n321\t    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n322\t                uid=uid, lat=lat, lng=lng, solve=False)\n323\t\n324\t    buf = io.BytesIO()\n325\t    c = rl_canvas.Canvas(buf, pagesize=letter)\n326\t    c.setTitle(f\"Lou-Ka — {l.get('title') or l.get('address')}\")\n327\t    s = _Style(c)\n328\t    s.fond()\n329\t    y = s.entete(\"Fiche logement\")\n330\t    page = [1]\n331\t\n332\t    def saut_page() -> float:\n333\t        s.pied(_PIED_FICHE, page[0])\n334\t        c.showPage()\n335\t        page[0] += 1\n336\t        s.fond()\n337\t        return s.entete(\"Fiche logement · suite\")\n338\t\n339\t    def place(y_: float, besoin: float) -> float:\n340\t        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n341\t        return saut_page() if (y_ - besoin) < M + 34 else y_\n342\t\n343\t    # --- bloc prix + badge marché (verdict du modèle Lou-Ka en priorité)\n344\t    prix_txt = (_fmt_money(l[\"price\"]) + f\"{NBSP}/ mois\") if l.get(\"price\") \\\n345\t        else (l.get(\"price_label\") or \"Prix sur demande\")\n346\t    c.setFont(\"Helvetica-Bold\", 30)\n347\t    c.setFillColor(INK)\n348\t    c.drawString(M, y - 24, prix_txt)\n349\t    loyer_secteur = (quartier or {}).get(\"demographie\", {}).get(\"loyer_moyen\") \\\n350\t        if quartier else None\n351\t    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n352\t    if l.get(\"price\") and fv and fv.get(\"verdict\") in _VERDICTS:\n353\t        dev = fv.get(\"deviation\") or 0\n354\t        pct = f\"{'+' if dev > 0 else '−'}{abs(round(dev * 100))}{NBSP}%\"\n355\t        lab, fg, bg, stk = _VERDICTS[fv[\"verdict\"]]\n356\t        s.pilule(bx, y - 22, f\"{pct} vs juste valeur · {lab}\", fg, bg, stk)\n357\t    elif l.get(\"price\") and loyer_secteur:\n358\t        delta = (l[\"price\"] - loyer_secteur) / loyer_secteur\n359\t        pct = f\"{'+' if delta > 0 else '−'}{abs(round(delta * 100))}{NBSP}%\"\n360\t        if delta <= -0.15:\n361\t            s.pilule(bx, y - 22, f\"{pct} vs secteur · Bon deal\", GREEN_DEEP, LIME_SOFT, GREEN)\n362\t        elif delta <= 0.10:\n363\t            s.pilule(bx, y - 22, f\"{pct} vs secteur · Dans le marché\", INK2, SURFACE, INK)\n364\t        else:\n365\t            s.pilule(bx, y - 22, f\"{pct} vs secteur · Au-dessus du marché\",\n366\t                     HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER)\n367\t    y -= 44\n368\t\n369\t    # --- titre + adresse\n370\t    c.setFont(\"Helvetica-Bold\", 13)\n371\t    c.setFillColor(INK)\n372\t    c.drawString(M, y, (l.get(\"title\") or l.get(\"address\") or \"\")[:80])\n373\t    y -= 14\n374\t    c.setFont(\"Helvetica\", 9)\n375\t    c.setFillColor(INK2)\n376\t    loc = \" · \".join(x for x in (l.get(\"address\"), l.get(\"sector\"), l.get(\"city\"))\n377\t                     if x and x != l.get(\"title\"))\n378\t    c.drawString(M, y, loc[:110])\n379\t    y -= 20\n380\t\n381\t    # --- chips clés\n382\t    x = M\n383\t    chips = []\n384\t    if l.get(\"unit_type\"):\n385\t        chips.append(l[\"unit_type\"])\n386\t    ad = l.get(\"availability_date\")\n387\t    if ad == \"now\":\n388\t        chips.append(\"Libre maintenant\")\n389\t    elif ad:\n390\t        chips.append(f\"Dispo {ad}\")\n391\t    if l.get(\"area_sqft\"):\n392\t        chips.append(f\"{round(l['area_sqft'])}{NBSP}pi²\")\n393\t    if l.get(\"furnished\"):\n394\t        chips.append(\"Meublé\")\n395\t    if l.get(\"pets\"):\n396\t        chips.append({\"oui\": \"Animaux acceptés\", \"non\": \"Animaux refusés\",\n397\t                      \"conditions\": \"Animaux sous conditions\"}.get(l[\"pets\"], l[\"pets\"]))\n398\t    if l[\"details\"].get(\"floor\") is not None:\n399\t        chips.append(f\"{l['details']['floor']}e étage\")\n400\t    for chip in chips[:7]:\n401\t        x = s.pilule(x, y - 6, chip)\n402\t    y -= 30\n403\t\n404\t    # --- photos : 1 grande + 3 vignettes\n405\t    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n406\t    if imgs:\n407\t        big_h = 170\n408\t        big_w = big_h * 16 / 10.5\n409\t        c.setStrokeColor(INK)\n410\t        c.setLineWidth(1.5)\n411\t        try:\n412\t            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n413\t                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n414\t        except Exception:\n415\t            pass\n416\t        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n417\t        tx = M + big_w + 10\n418\t        th = (big_h - 16) / 3\n419\t        for i, im in enumerate(imgs[1:4]):\n420\t            ty = y - (i + 1) * th - i * 8\n421\t            try:\n422\t                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n423\t                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n424\t                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n425\t            except Exception:\n426\t                pass\n427\t        y -= big_h + 18\n428\t\n429\t    # --- « En bref » (digest) sur bandeau lime\n430\t    dg = l.get(\"digest\")\n431\t    if dg and dg.get(\"en_bref\"):\n432\t        c.setFillColor(LIME_SOFT)\n433\t        c.setStrokeColor(GREEN)\n434\t        c.setLineWidth(1.2)\n435\t        c.roundRect(M, y - 40, PAGE_W - 2 * M, 44, 6, stroke=1, fill=1)\n436\t        s.paragraphe(M + 10, y - 8, \"EN BREF — \" + dg[\"en_bref\"],\n437\t                     PAGE_W - 2 * M - 20, taille=8.5, couleur=GREEN_DEEP,\n438\t                     max_lignes=3)\n439\t        y -= 54\n440\t\n441\t    # --- description (2-3 sections max) | inclusions en colonne droite\n442\t    col_g = PAGE_W * 0.56 - M\n443\t    y_desc = s.titre_section(y, \"Description\")\n444\t    if dg and dg.get(\"sections\"):\n445\t        for sec in dg[\"sections\"][:3]:\n446\t            c.setFont(\"Helvetica-Bold\", 9)\n447\t            c.setFillColor(INK)\n448\t            c.drawString(M, y_desc, sec[\"titre\"][:60])\n449\t            y_desc -= 12\n450\t            y_desc = s.paragraphe(M, y_desc, sec[\"texte\"], col_g, max_lignes=6)\n451\t            y_desc -= 4\n452\t            if y_desc < 150:\n453\t                break\n454\t    elif l.get(\"description\"):\n455\t        y_desc = s.paragraphe(M, y_desc, l[\"description\"], col_g, max_lignes=16)\n456\t\n457\t    xr = M + col_g + 24\n458\t    y_incl = s_titre_droite(c, y, xr, \"Inclusions et commodités\")\n459\t    d = l[\"details\"]\n460\t    badges = []\n461\t    inc = d.get(\"inclusions\") or {}\n462\t    for k, lab in ((\"heating\", \"Chauffage inclus\"), (\"electricity\", \"Électricité incluse\"),\n463\t                   (\"hot_water\", \"Eau chaude incluse\"), (\"internet\", \"Internet inclus\")):\n464\t        if inc.get(k):\n465\t            badges.append(lab)\n466\t    app = d.get(\"appliances\") or {}\n467\t    if app.get(\"dishwasher\"):\n468\t        badges.append(\"Lave-vaisselle\")\n469\t    if app.get(\"washer_dryer\"):\n470\t        badges.append(\"Laveuse-sécheuse\")\n471\t    for k, lab in ((\"ac\", \"Climatisation\"), (\"elevator\", \"Ascenseur\"),\n472\t                   (\"balcony\", \"Balcon\"), (\"pool\", \"Piscine\"), (\"gym\", \"Gym\"),\n473\t                   (\"laundry\", \"Buanderie\"), (\"storage\", \"Rangement\")):\n474\t        if d.get(k):\n475\t            badges.append(lab)\n476\t    if (d.get(\"parking\") or {}).get(\"available\"):\n477\t        badges.append(\"Stationnement\")\n478\t    if l.get(\"furnished\"):\n479\t        badges.append(\"Meublé\")\n480\t    c.setFont(\"Helvetica\", 8.5)\n481\t    for b in badges[:12]:\n482\t        c.setFillColor(GREEN)\n483\t        c.drawString(xr, y_incl, \"✓\")\n484\t        c.setFillColor(INK2)\n485\t        c.drawString(xr + 12, y_incl, b)\n486\t        y_incl -= 12\n487\t    autres = [a for a in l[\"amenities\"]\n488\t              if not any(b.lower() in a.lower() or a.lower() in b.lower() for b in badges)]\n489\t    for a in autres[: max(0, 14 - len(badges))]:\n490\t        c.setFillColor(INK3)\n491\t        c.drawString(xr + 12, y_incl, a[:38])\n492\t        y_incl -= 11\n493\t\n494\t    y = min(y_desc, y_incl) - 16\n495\t\n496\t    # --- Analyse de prix Lou-Ka (juste valeur, fourchette, distribution)\n497\t    if fv and fv.get(\"fv\"):\n498\t        hist = fv.get(\"histogram\") or []\n499\t        y = place(y, 168 if hist else 92)\n500\t        y = s.titre_section(y, \"Analyse de prix Lou-Ka\")\n501\t        jv = _fmt_money(round(fv[\"fv\"]))\n502\t        c.setFont(\"Helvetica-Bold\", 17)\n503\t        c.setFillColor(INK)\n504\t        c.drawString(M, y - 10, jv)\n505\t        c.setFont(\"Helvetica\", 8.5)\n506\t        c.setFillColor(INK2)\n507\t        c.drawString(M + c.stringWidth(jv, \"Helvetica-Bold\", 17) + 8, y - 10,\n508\t                     \"/ mois — juste valeur estimée par le modèle Lou-Ka\")\n509\t        meta = []\n510\t        if fv.get(\"fv_low\") and fv.get(\"fv_high\"):\n511\t            meta.append(f\"fourchette {_fmt_money(round(fv['fv_low']))} – \"\n512\t                        f\"{_fmt_money(round(fv['fv_high']))}\")\n513\t        if fv.get(\"confidence\"):\n514\t            meta.append(f\"confiance {fv['confidence']}\")\n515\t        if fv.get(\"comps\"):\n516\t            meta.append(f\"{fv['comps']} comparables\")\n517\t        if fv.get(\"segment_n\"):\n518\t            meta.append(f\"segment de {fv['segment_n']} annonces\")\n519\t        c.setFont(\"Helvetica\", 8)\n520\t        c.setFillColor(INK3)\n521\t        c.drawString(M, y - 24, \" · \".join(meta)[:115])\n522\t        if l.get(\"price\") and fv.get(\"verdict\") in _VERDICTS:\n523\t            dev = fv.get(\"deviation\") or 0\n524\t            pct = f\"{'+' if dev > 0 else '−'}{abs(round(dev * 100))}{NBSP}%\"\n525\t            lab, fg, bg, stk = _VERDICTS[fv[\"verdict\"]]\n526\t            s.pilule(M, y - 48, f\"{pct} vs juste valeur · {lab}\", fg, bg, stk)\n527\t        y -= 58\n528\t        if hist:\n529\t            ch_h = 40\n530\t            lo, hi = hist[0][\"x0\"], hist[-1][\"x1\"]\n531\t            larg = PAGE_W - 2 * M\n532\t            base = y - ch_h - 6\n533\t            if hi > lo and fv.get(\"fv_low\") and fv.get(\"fv_high\"):\n534\t                bx0 = M + larg * max(0.0, (fv[\"fv_low\"] - lo) / (hi - lo))\n535\t                bx1 = M + larg * min(1.0, (fv[\"fv_high\"] - lo) / (hi - lo))\n536\t                c.setFillColor(LIME_SOFT)\n537\t                c.rect(bx0, base, max(2, bx1 - bx0), ch_h, stroke=0, fill=1)\n538\t            max_n = max((h[\"n\"] for h in hist), default=1) or 1\n539\t            bw = larg / len(hist)\n540\t            c.setFillColor(GREEN)\n541\t            for i, h in enumerate(hist):\n542\t                bh = ch_h * h[\"n\"] / max_n\n543\t                if bh > 0:\n544\t                    c.rect(M + i * bw + 1, base, bw - 2, max(0.6, bh),\n545\t                           stroke=0, fill=1)\n546\t            if hi > lo and l.get(\"price\"):\n547\t                px = M + larg * (min(max(l[\"price\"], lo), hi) - lo) / (hi - lo)\n548\t                c.setStrokeColor(LIME)\n549\t                c.setLineWidth(2)\n550\t                c.line(px, base - 3, px, base + ch_h + 3)\n551\t                c.setFont(\"Courier-Bold\", 6.5)\n552\t                c.setFillColor(LIME)\n553\t                c.drawString(min(max(px - 28, M), PAGE_W - M - 92),\n554\t                             base + ch_h + 6,\n555\t                             f\"CE LOYER · {_fmt_money(l['price'])}\")\n556\t            c.setFont(\"Helvetica\", 6.5)\n557\t            c.setFillColor(INK3)\n558\t            c.drawString(M, base - 9, _fmt_money(lo))\n559\t            c.drawRightString(PAGE_W - M, base - 9,\n560\t                              _fmt_money(hi) + \" — distribution des loyers du \"\n561\t                                               \"segment de référence\")\n562\t            y = base - 20\n563\t        ph = [p for p in price_history if p.get(\"price\")]\n564\t        if len(ph) >= 2 and ph[0][\"price\"] != ph[-1][\"price\"]:\n565\t            c.setFont(\"Helvetica\", 8)\n566\t            c.setFillColor(INK2)\n567\t            chn = \" → \".join(_fmt_money(p[\"price\"]) for p in reversed(ph[:4]))\n568\t            c.drawString(M, y - 2, f\"Historique de prix de l'annonce : {chn}\")\n569\t            y -= 16\n570\t\n571\t    # --- Registre des loyers (déclarations citoyennes à proximité)\n572\t    if rdl_d and rdl_d.get(\"n\"):\n573\t        comp = rdl_d.get(\"items\") or []\n574\t        y = place(y, 128 + min(len(comp), 8) * 12)\n575\t        y = s.titre_section(y, \"Registre des loyers — déclarations citoyennes\")\n576\t        tw = (PAGE_W - 2 * M - 20) / 3\n577\t        med_rec = rdl_d.get(\"median_recent\")\n578\t        tuiles = [(str(rdl_d[\"n\"]), f\"loyers déclarés à <{rdl_d['radius_m']} m\"),\n579\t                  (_fmt_money(rdl_d.get(\"median\")), \"médiane du secteur\"),\n580\t                  (_fmt_money(med_rec) if med_rec else \"—\",\n581\t                   f\"médiane depuis 2023 ({rdl_d.get('n_recent', 0)} décl.)\")]\n582\t        for i, (val, lab) in enumerate(tuiles):\n583\t            s.tuile(M + i * (tw + 10), y - 44, tw, 38, val, lab)\n584\t        y -= 56\n585\t        rooms = rdl_d.get(\"by_rooms\") or {}\n586\t        if rooms:\n587\t            def _rk(k: str) -> float:\n588\t                try:\n589\t                    return float(k)\n590\t                except ValueError:\n591\t                    return 99.0\n592\t            parts = [f\"{k}{NBSP}ch. {_fmt_money(v['median'])} ({v['n']})\"\n593\t                     for k, v in sorted(rooms.items(), key=lambda kv: _rk(kv[0]))]\n594\t            c.setFont(\"Helvetica\", 8)\n595\t            c.setFillColor(INK2)\n596\t            c.drawString(M, y, (\"Médianes par taille : \" + \" · \".join(parts))[:118])\n597\t            y -= 14\n598\t        if comp:\n599\t            c.setFont(\"Courier-Bold\", 7)\n600\t            c.setFillColor(INK3)\n601\t            for lab, xoff in ((\"ADRESSE\", 0), (\"CH.\", 280), (\"DÉBUT DE BAIL\", 320),\n602\t                              (\"DIST.\", 420), (\"LOYER\", 480)):\n603\t                c.drawString(M + xoff, y, lab)\n604\t            y -= 4\n605\t            c.setStrokeColor(INK)\n606\t            c.setLineWidth(1)\n607\t            c.line(M, y, PAGE_W - M, y)\n608\t            y -= 11\n609\t            for it in comp[:8]:\n610\t                c.setFont(\"Helvetica\", 8)\n611\t                c.setFillColor(INK2)\n612\t                c.drawString(M, y, (it.get(\"address\") or \"\")[:52])\n613\t                c.drawString(M + 280, y, str(it[\"rooms\"]) if it.get(\"rooms\") is not None else \"—\")\n614\t                c.drawString(M + 320, y, _fmt_mois(it.get(\"date\")))\n615\t                c.drawString(M + 420, y, _fmt_dist(it[\"dist_m\"]))\n616\t                c.setFont(\"Helvetica-Bold\", 8)\n617\t                c.setFillColor(INK)\n618\t                c.drawString(M + 480, y, _fmt_money(it.get(\"price\")))\n619\t                y -= 12\n620\t        c.setFont(\"Helvetica\", 6.5)\n621\t        c.setFillColor(INK3)\n622\t        c.drawString(M, y - 2, \"Loyers déclarés volontairement par des locataires \"\n623\t                               \"au Registre des loyers (registre-des-loyers.ca) — \"\n624\t                               \"données citoyennes non vérifiées.\")\n625\t        y -= 16\n626\t\n627\t    # --- Historique au TAL (décisions SOQUIJ à l'adresse)\n628\t    if tal_d and tal_d.get(\"status\") in (\"ok\", \"pending\"):\n629\t        decs = (tal_d.get(\"decisions\") or [])[:6]\n630\t        y = place(y, 70 + len(decs) * 24)\n631\t        y = s.titre_section(y, \"Historique au Tribunal administratif du logement\")\n632\t        if tal_d[\"status\"] == \"pending\":\n633\t            c.setFont(\"Helvetica\", 8.5)\n634\t            c.setFillColor(INK2)\n635\t            c.drawString(M, y - 4, \"Adresse en file de vérification auprès des \"\n636\t                                   \"décisions publiées du TAL (résultat sur la \"\n637\t                                   \"fiche en ligne sous peu).\")\n638\t            y -= 18\n639\t        elif not decs:\n640\t            s.pilule(M, y - 12, \"Aucune décision trouvée\", GREEN_DEEP,\n641\t                     LIME_SOFT, GREEN)\n642\t            c.setFont(\"Helvetica\", 8.5)\n643\t            c.setFillColor(INK2)\n644\t            c.drawString(M + 130, y - 7, \"Aucune décision publiée du TAL n'a été \"\n645\t                                         \"repérée à cette adresse.\")\n646\t            y -= 26\n647\t        else:\n648\t            n = tal_d.get(\"n\") or len(decs)\n649\t            cl = tal_d.get(\"contre_locataire\") or 0\n650\t            c.setFont(\"Helvetica-Bold\", 9)\n651\t            c.setFillColor(INK)\n652\t            c.drawString(M, y - 4,\n653\t                         f\"{n} décision{'s' if n > 1 else ''} publiée\"\n654\t                         f\"{'s' if n > 1 else ''} à cette adresse — \"\n655\t                         f\"{cl} à l'initiative du propriétaire.\")\n656\t            y -= 20\n657\t            for dec in decs:\n658\t                qui = {\"locateur\": \"demande du propriétaire\",\n659\t                       \"locataire\": \"demande du locataire\"}.get(\n660\t                    dec.get(\"demandeur\") or \"\", \"demandeur inconnu\")\n661\t                c.setFont(\"Helvetica-Bold\", 8)\n662\t                c.setFillColor(INK)\n663\t                c.drawString(M, y, _fmt_mois(dec.get(\"date\")))\n664\t                c.setFont(\"Helvetica\", 8)\n665\t                c.setFillColor(INK2)\n666\t                suite = qui\n667\t                if dec.get(\"verdict\"):\n668\t                    suite += f\" · demande {dec['verdict']}\"\n669\t                if dec.get(\"tags\"):\n670\t                    suite += \" · \" + \", \".join(dec[\"tags\"][:3])\n671\t                c.drawString(M + 78, y, suite[:88])\n672\t                c.setFont(\"Courier-Bold\", 7)\n673\t                c.setFillColor(GREEN)\n674\t                c.drawRightString(PAGE_W - M, y, dec.get(\"citation\") or \"SOQUIJ\")\n675\t                y -= 12\n676\t            y -= 6\n677\t        c.setFont(\"Helvetica\", 6.5)\n678\t        c.setFillColor(INK3)\n679\t        c.drawString(M, y - 2, \"Décisions publiques diffusées par SOQUIJ, repérées \"\n680\t                               \"par correspondance d'adresse — une décision peut \"\n681\t                               \"concerner un autre logement du même immeuble.\")\n682\t        y -= 16\n683\t\n684\t    # --- Risque d'inondation (BDZI)\n685\t    if inon:\n686\t        zones = (inon.get(\"zones\") or [])[:3]\n687\t        y = place(y, 56 + len(zones) * 12)\n688\t        y = s.titre_section(y, \"Risque d'inondation\")\n689\t        statut = inon.get(\"statut\")\n690\t        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n691\t               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n692\t        if statut == \"en_zone\":\n693\t            s.pilule(M, y - 12, f\"En zone inondable ({sev})\",\n694\t                     HexColor(\"#a12622\"), HexColor(\"#fde8e8\"), HexColor(\"#a12622\"))\n695\t        elif statut == \"a_proximite\":\n696\t            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n697\t            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n698\t                     HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER)\n699\t        elif statut == \"hors_zone\":\n700\t            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\","}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"ae843d1f-30c3-4ae8-a8c4-abfbcac4ebb3","timestamp":"2026-08-24T21:18:37.278Z","tool_use_result":{"type":"text","file":{"filePath":"/tmp/lou-ka-ref/louka/pdfgen.py","content":"# -----------------------------------------------------------------------------\n# Lou-Ka — Agrégateur de logements à louer (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdfgen.py : génération PDF à l'identité visuelle officielle Lou-Ka\n#   - fiche_pdf(uid)  : fiche de propriété complète — tout ce que montre la\n#     fiche web : photos, prix + analyse Lou-Ka (juste valeur, histogramme),\n#     chips, description structurée, inclusions, registre des loyers,\n#     historique TAL, inondation, qualité de l'air, coût d'électricité,\n#     commerces/transport, essence, KA Scores, quartier, à proximité, QR\n#   - rapport_pdf()   : rapport global du marché (totaux, régions, histogramme\n#     des loyers, types, villes, gestionnaires)\n#   Palette identique au site : blanc #ffffff, marine #0b1330, orange #ff6a00,\n#   bleu vif #2258ff, gris pâle #f2f2f2. Polices de base (Helvetica) stylées\n#   par la mise en page — aucune dépendance de police externe.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport math\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Lou-Ka (identité officielle) ------------------------------------\nPAPER = HexColor(\"#ffffff\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#0b1330\")          # bleu marine profond\nINK2 = HexColor(\"#3e4560\")\nINK3 = HexColor(\"#8a90a5\")\nGREEN = HexColor(\"#2258ff\")        # accent secondaire : bleu vif\nGREEN_DEEP = HexColor(\"#0b1330\")\nLIME = HexColor(\"#ff6a00\")         # accent d'action : orange vif\nLIME_SOFT = HexColor(\"#fff1e6\")\nAMBER = HexColor(\"#ff6a00\")\nAMBER_SOFT = HexColor(\"#fff1e6\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"LouKaBot/1.0 (+https://www.lou-ka.com/bot; contact@spboucher.ai)\"\n\nPOI_LABELS = {\n    \"epicerie\": \"Épicerie\", \"depanneur\": \"Dépanneur\", \"pharmacie\": \"Pharmacie\",\n    \"ecole\": \"École\", \"garderie\": \"Garderie\", \"parc\": \"Parc\", \"bus\": \"Arrêt de bus\",\n    \"metro\": \"Métro\", \"gym\": \"Gym\", \"cafe\": \"Café\", \"clinique\": \"Clinique\",\n    \"hopital\": \"Hôpital\", \"bibliotheque\": \"Bibliothèque\",\n}\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\nNBSP = \" \"\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _marche(m_: float) -> str:\n    return f\"≈{NBSP}{max(1, round(m_ * 1.3 / 80))}{NBSP}min à pied\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à 30 Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Lou·Ka : « Lou » encre + boîte encre avec « Ka » lime.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Lou\")\n        w = c.stringWidth(\"Lou\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(LIME)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6,\n                          titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=LIME, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M, y, texte)\n        c.setStrokeColor(LIME)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M, y - 4, M + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\n# ---------------------------------------------------------------------------\n# Fiche de propriété\n# ---------------------------------------------------------------------------\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n\ndef _fmt_mois(d: str | None) -> str:\n    \"\"\"« 2017-11-04 » → « novembre 2017 » (sinon la valeur telle quelle).\"\"\"\n    if not d:\n        return \"—\"\n    try:\n        an, mois = d[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return d\n\n\n_VERDICTS = {\n    \"sous\": (\"Sous le marché\", GREEN_DEEP, LIME_SOFT, GREEN),\n    \"marche\": (\"Dans le marché\", INK2, SURFACE, INK),\n    \"sur\": (\"Au-dessus du marché\", HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER),\n}\n\n_PIED_FICHE = (\"Données de l'annonce publiée par le gestionnaire — Lou-Ka est \"\n               \"un agrégateur indépendant. Enrichissements (analyse de prix, \"\n               \"registre, TAL, environnement) fournis à titre indicatif, sans \"\n               \"valeur juridique.\")\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche du logement `uid` — reflète TOUS les blocs de la fiche\n    web : prix + analyse Lou-Ka, photos, description, inclusions, registre des\n    loyers, historique TAL, risque d'inondation, qualité de l'air, coût\n    d'électricité, commerces/transport, essence, KA Scores, quartier,\n    à proximité, références + QR. None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"amenities\"] = json.loads(l.get(\"amenities\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    l[\"digest\"] = json.loads(l[\"digest\"]) if l.get(\"digest\") else None\n    poi = []\n    quartier = None\n    if l.get(\"lat\") is not None:\n        key = f\"{round(l['lat'], 4)},{round(l['lng'], 4)}\"\n        pr = con.execute(\"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n        poi = json.loads(pr[\"pois\"]) if pr else []\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        quartier = qmod.fiche_quartier(l[\"lat\"], l[\"lng\"], l.get(\"city\") or \"\",\n                                       dauid if dauid and dauid != \"hors-zone\" else None)\n    reg = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n    src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]), l[\"source\"])\n    kasc = None\n    if l.get(\"coord_key\"):\n        kr = con.execute(\"SELECT walk, transit, bike, calme, services,\"\n                         \" global AS glob FROM kascores WHERE coord_key=?\",\n                         (l[\"coord_key\"],)).fetchone()\n        if kr is not None:\n            kasc = dict(kr)\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 6\",\n        (uid,)).fetchall()]\n    con.close()\n\n    # --- enrichissements de la fiche (mêmes modules que les blocs du site) ---\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import fairvalue as fv_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    from . import rdl as rdl_mod\n    from . import tal as tal_mod\n    fv = _safe(fv_mod.explain, uid)\n    rdl_d = _safe(rdl_mod.nearby, lat, lng) if geo else None\n    tal_d = (_safe(tal_mod.lookup, l[\"address\"], l.get(\"city\"))\n             if l.get(\"address\") else None)\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Lou-Ka — {l.get('title') or l.get('address')}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche logement\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche logement · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- bloc prix + badge marché (verdict du modèle Lou-Ka en priorité)\n    prix_txt = (_fmt_money(l[\"price\"]) + f\"{NBSP}/ mois\") if l.get(\"price\") \\\n        else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    loyer_secteur = (quartier or {}).get(\"demographie\", {}).get(\"loyer_moyen\") \\\n        if quartier else None\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    if l.get(\"price\") and fv and fv.get(\"verdict\") in _VERDICTS:\n        dev = fv.get(\"deviation\") or 0\n        pct = f\"{'+' if dev > 0 else '−'}{abs(round(dev * 100))}{NBSP}%\"\n        lab, fg, bg, stk = _VERDICTS[fv[\"verdict\"]]\n        s.pilule(bx, y - 22, f\"{pct} vs juste valeur · {lab}\", fg, bg, stk)\n    elif l.get(\"price\") and loyer_secteur:\n        delta = (l[\"price\"] - loyer_secteur) / loyer_secteur\n        pct = f\"{'+' if delta > 0 else '−'}{abs(round(delta * 100))}{NBSP}%\"\n        if delta <= -0.15:\n            s.pilule(bx, y - 22, f\"{pct} vs secteur · Bon deal\", GREEN_DEEP, LIME_SOFT, GREEN)\n        elif delta <= 0.10:\n            s.pilule(bx, y - 22, f\"{pct} vs secteur · Dans le marché\", INK2, SURFACE, INK)\n        else:\n            s.pilule(bx, y - 22, f\"{pct} vs secteur · Au-dessus du marché\",\n                     HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER)\n    y -= 44\n\n    # --- titre + adresse\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, (l.get(\"title\") or l.get(\"address\") or \"\")[:80])\n    y -= 14\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    loc = \" · \".join(x for x in (l.get(\"address\"), l.get(\"sector\"), l.get(\"city\"))\n                     if x and x != l.get(\"title\"))\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés\n    x = M\n    chips = []\n    if l.get(\"unit_type\"):\n        chips.append(l[\"unit_type\"])\n    ad = l.get(\"availability_date\")\n    if ad == \"now\":\n        chips.append(\"Libre maintenant\")\n    elif ad:\n        chips.append(f\"Dispo {ad}\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft'])}{NBSP}pi²\")\n    if l.get(\"furnished\"):\n        chips.append(\"Meublé\")\n    if l.get(\"pets\"):\n        chips.append({\"oui\": \"Animaux acceptés\", \"non\": \"Animaux refusés\",\n                      \"conditions\": \"Animaux sous conditions\"}.get(l[\"pets\"], l[\"pets\"]))\n    if l[\"details\"].get(\"floor\") is not None:\n        chips.append(f\"{l['details']['floor']}e étage\")\n    for chip in chips[:7]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- « En bref » (digest) sur bandeau lime\n    dg = l.get(\"digest\")\n    if dg and dg.get(\"en_bref\"):\n        c.setFillColor(LIME_SOFT)\n        c.setStrokeColor(GREEN)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 40, PAGE_W - 2 * M, 44, 6, stroke=1, fill=1)\n        s.paragraphe(M + 10, y - 8, \"EN BREF — \" + dg[\"en_bref\"],\n                     PAGE_W - 2 * M - 20, taille=8.5, couleur=GREEN_DEEP,\n                     max_lignes=3)\n        y -= 54\n\n    # --- description (2-3 sections max) | inclusions en colonne droite\n    col_g = PAGE_W * 0.56 - M\n    y_desc = s.titre_section(y, \"Description\")\n    if dg and dg.get(\"sections\"):\n        for sec in dg[\"sections\"][:3]:\n            c.setFont(\"Helvetica-Bold\", 9)\n            c.setFillColor(INK)\n            c.drawString(M, y_desc, sec[\"titre\"][:60])\n            y_desc -= 12\n            y_desc = s.paragraphe(M, y_desc, sec[\"texte\"], col_g, max_lignes=6)\n            y_desc -= 4\n            if y_desc < 150:\n                break\n    elif l.get(\"description\"):\n        y_desc = s.paragraphe(M, y_desc, l[\"description\"], col_g, max_lignes=16)\n\n    xr = M + col_g + 24\n    y_incl = s_titre_droite(c, y, xr, \"Inclusions et commodités\")\n    d = l[\"details\"]\n    badges = []\n    inc = d.get(\"inclusions\") or {}\n    for k, lab in ((\"heating\", \"Chauffage inclus\"), (\"electricity\", \"Électricité incluse\"),\n                   (\"hot_water\", \"Eau chaude incluse\"), (\"internet\", \"Internet inclus\")):\n        if inc.get(k):\n            badges.append(lab)\n    app = d.get(\"appliances\") or {}\n    if app.get(\"dishwasher\"):\n        badges.append(\"Lave-vaisselle\")\n    if app.get(\"washer_dryer\"):\n        badges.append(\"Laveuse-sécheuse\")\n    for k, lab in ((\"ac\", \"Climatisation\"), (\"elevator\", \"Ascenseur\"),\n                   (\"balcony\", \"Balcon\"), (\"pool\", \"Piscine\"), (\"gym\", \"Gym\"),\n                   (\"laundry\", \"Buanderie\"), (\"storage\", \"Rangement\")):\n        if d.get(k):\n            badges.append(lab)\n    if (d.get(\"parking\") or {}).get(\"available\"):\n        badges.append(\"Stationnement\")\n    if l.get(\"furnished\"):\n        badges.append(\"Meublé\")\n    c.setFont(\"Helvetica\", 8.5)\n    for b in badges[:12]:\n        c.setFillColor(GREEN)\n        c.drawString(xr, y_incl, \"✓\")\n        c.setFillColor(INK2)\n        c.drawString(xr + 12, y_incl, b)\n        y_incl -= 12\n    autres = [a for a in l[\"amenities\"]\n              if not any(b.lower() in a.lower() or a.lower() in b.lower() for b in badges)]\n    for a in autres[: max(0, 14 - len(badges))]:\n        c.setFillColor(INK3)\n        c.drawString(xr + 12, y_incl, a[:38])\n        y_incl -= 11\n\n    y = min(y_desc, y_incl) - 16\n\n    # --- Analyse de prix Lou-Ka (juste valeur, fourchette, distribution)\n    if fv and fv.get(\"fv\"):\n        hist = fv.get(\"histogram\") or []\n        y = place(y, 168 if hist else 92)\n        y = s.titre_section(y, \"Analyse de prix Lou-Ka\")\n        jv = _fmt_money(round(fv[\"fv\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, jv)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(jv, \"Helvetica-Bold\", 17) + 8, y - 10,\n                     \"/ mois — juste valeur estimée par le modèle Lou-Ka\")\n        meta = []\n        if fv.get(\"fv_low\") and fv.get(\"fv_high\"):\n            meta.append(f\"fourchette {_fmt_money(round(fv['fv_low']))} – \"\n                        f\"{_fmt_money(round(fv['fv_high']))}\")\n        if fv.get(\"confidence\"):\n            meta.append(f\"confiance {fv['confidence']}\")\n        if fv.get(\"comps\"):\n            meta.append(f\"{fv['comps']} comparables\")\n        if fv.get(\"segment_n\"):\n            meta.append(f\"segment de {fv['segment_n']} annonces\")\n        c.setFont(\"Helvetica\", 8)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        if l.get(\"price\") and fv.get(\"verdict\") in _VERDICTS:\n            dev = fv.get(\"deviation\") or 0\n            pct = f\"{'+' if dev > 0 else '−'}{abs(round(dev * 100))}{NBSP}%\"\n            lab, fg, bg, stk = _VERDICTS[fv[\"verdict\"]]\n            s.pilule(M, y - 48, f\"{pct} vs juste valeur · {lab}\", fg, bg, stk)\n        y -= 58\n        if hist:\n            ch_h = 40\n            lo, hi = hist[0][\"x0\"], hist[-1][\"x1\"]\n            larg = PAGE_W - 2 * M\n            base = y - ch_h - 6\n            if hi > lo and fv.get(\"fv_low\") and fv.get(\"fv_high\"):\n                bx0 = M + larg * max(0.0, (fv[\"fv_low\"] - lo) / (hi - lo))\n                bx1 = M + larg * min(1.0, (fv[\"fv_high\"] - lo) / (hi - lo))\n                c.setFillColor(LIME_SOFT)\n                c.rect(bx0, base, max(2, bx1 - bx0), ch_h, stroke=0, fill=1)\n            max_n = max((h[\"n\"] for h in hist), default=1) or 1\n            bw = larg / len(hist)\n            c.setFillColor(GREEN)\n            for i, h in enumerate(hist):\n                bh = ch_h * h[\"n\"] / max_n\n                if bh > 0:\n                    c.rect(M + i * bw + 1, base, bw - 2, max(0.6, bh),\n                           stroke=0, fill=1)\n            if hi > lo and l.get(\"price\"):\n                px = M + larg * (min(max(l[\"price\"], lo), hi) - lo) / (hi - lo)\n                c.setStrokeColor(LIME)\n                c.setLineWidth(2)\n                c.line(px, base - 3, px, base + ch_h + 3)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(LIME)\n                c.drawString(min(max(px - 28, M), PAGE_W - M - 92),\n                             base + ch_h + 6,\n                             f\"CE LOYER · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 9, _fmt_money(lo))\n            c.drawRightString(PAGE_W - M, base - 9,\n                              _fmt_money(hi) + \" — distribution des loyers du \"\n                                               \"segment de référence\")\n            y = base - 20\n        ph = [p for p in price_history if p.get(\"price\")]\n        if len(ph) >= 2 and ph[0][\"price\"] != ph[-1][\"price\"]:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            chn = \" → \".join(_fmt_money(p[\"price\"]) for p in reversed(ph[:4]))\n            c.drawString(M, y - 2, f\"Historique de prix de l'annonce : {chn}\")\n            y -= 16\n\n    # --- Registre des loyers (déclarations citoyennes à proximité)\n    if rdl_d and rdl_d.get(\"n\"):\n        comp = rdl_d.get(\"items\") or []\n        y = place(y, 128 + min(len(comp), 8) * 12)\n        y = s.titre_section(y, \"Registre des loyers — déclarations citoyennes\")\n        tw = (PAGE_W - 2 * M - 20) / 3\n        med_rec = rdl_d.get(\"median_recent\")\n        tuiles = [(str(rdl_d[\"n\"]), f\"loyers déclarés à <{rdl_d['radius_m']} m\"),\n                  (_fmt_money(rdl_d.get(\"median\")), \"médiane du secteur\"),\n                  (_fmt_money(med_rec) if med_rec else \"—\",\n                   f\"médiane depuis 2023 ({rdl_d.get('n_recent', 0)} décl.)\")]\n        for i, (val, lab) in enumerate(tuiles):\n            s.tuile(M + i * (tw + 10), y - 44, tw, 38, val, lab)\n        y -= 56\n        rooms = rdl_d.get(\"by_rooms\") or {}\n        if rooms:\n            def _rk(k: str) -> float:\n                try:\n                    return float(k)\n                except ValueError:\n                    return 99.0\n            parts = [f\"{k}{NBSP}ch. {_fmt_money(v['median'])} ({v['n']})\"\n                     for k, v in sorted(rooms.items(), key=lambda kv: _rk(kv[0]))]\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, (\"Médianes par taille : \" + \" · \".join(parts))[:118])\n            y -= 14\n        if comp:\n            c.setFont(\"Courier-Bold\", 7)\n            c.setFillColor(INK3)\n            for lab, xoff in ((\"ADRESSE\", 0), (\"CH.\", 280), (\"DÉBUT DE BAIL\", 320),\n                              (\"DIST.\", 420), (\"LOYER\", 480)):\n                c.drawString(M + xoff, y, lab)\n            y -= 4\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.line(M, y, PAGE_W - M, y)\n            y -= 11\n            for it in comp[:8]:\n                c.setFont(\"Helvetica\", 8)\n                c.setFillColor(INK2)\n                c.drawString(M, y, (it.get(\"address\") or \"\")[:52])\n                c.drawString(M + 280, y, str(it[\"rooms\"]) if it.get(\"rooms\") is not None else \"—\")\n                c.drawString(M + 320, y, _fmt_mois(it.get(\"date\")))\n                c.drawString(M + 420, y, _fmt_dist(it[\"dist_m\"]))\n                c.setFont(\"Helvetica-Bold\", 8)\n                c.setFillColor(INK)\n                c.drawString(M + 480, y, _fmt_money(it.get(\"price\")))\n                y -= 12\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Loyers déclarés volontairement par des locataires \"\n                               \"au Registre des loyers (registre-des-loyers.ca) — \"\n                               \"données citoyennes non vérifiées.\")\n        y -= 16\n\n    # --- Historique au TAL (décisions SOQUIJ à l'adresse)\n    if tal_d and tal_d.get(\"status\") in (\"ok\", \"pending\"):\n        decs = (tal_d.get(\"decisions\") or [])[:6]\n        y = place(y, 70 + len(decs) * 24)\n        y = s.titre_section(y, \"Historique au Tribunal administratif du logement\")\n        if tal_d[\"status\"] == \"pending\":\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 4, \"Adresse en file de vérification auprès des \"\n                                   \"décisions publiées du TAL (résultat sur la \"\n                                   \"fiche en ligne sous peu).\")\n            y -= 18\n        elif not decs:\n            s.pilule(M, y - 12, \"Aucune décision trouvée\", GREEN_DEEP,\n                     LIME_SOFT, GREEN)\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M + 130, y - 7, \"Aucune décision publiée du TAL n'a été \"\n                                         \"repérée à cette adresse.\")\n            y -= 26\n        else:\n            n = tal_d.get(\"n\") or len(decs)\n            cl = tal_d.get(\"contre_locataire\") or 0\n            c.setFont(\"Helvetica-Bold\", 9)\n            c.setFillColor(INK)\n            c.drawString(M, y - 4,\n                         f\"{n} décision{'s' if n > 1 else ''} publiée\"\n                         f\"{'s' if n > 1 else ''} à cette adresse — \"\n                         f\"{cl} à l'initiative du propriétaire.\")\n            y -= 20\n            for dec in decs:\n                qui = {\"locateur\": \"demande du propriétaire\",\n                       \"locataire\": \"demande du locataire\"}.get(\n                    dec.get(\"demandeur\") or \"\", \"demandeur inconnu\")\n                c.setFont(\"Helvetica-Bold\", 8)\n                c.setFillColor(INK)\n                c.drawString(M, y, _fmt_mois(dec.get(\"date\")))\n                c.setFont(\"Helvetica\", 8)\n                c.setFillColor(INK2)\n                suite = qui\n                if dec.get(\"verdict\"):\n                    suite += f\" · demande {dec['verdict']}\"\n                if dec.get(\"tags\"):\n                    suite += \" · \" + \", \".join(dec[\"tags\"][:3])\n                c.drawString(M + 78, y, suite[:88])\n                c.setFont(\"Courier-Bold\", 7)\n                c.setFillColor(GREEN)\n                c.drawRightString(PAGE_W - M, y, dec.get(\"citation\") or \"SOQUIJ\")\n                y -= 12\n            y -= 6\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Décisions publiques diffusées par SOQUIJ, repérées \"\n                               \"par correspondance d'adresse — une décision peut \"\n                               \"concerner un autre logement du même immeuble.\")\n        y -= 16\n\n    # --- Risque d'inondation (BDZI)\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\",\n                     HexColor(\"#a12622\"), HexColor(\"#fde8e8\"), HexColor(\"#a12622\"))\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",","numLines":700,"startLine":1,"totalLines":1202}}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"1ead4f5f-10c5-4beb-ba3b-823ac8b9b362","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNGeXhxr6UtX1BiAE6eB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017Y8i2ALysUt1XxeGqXebFf","name":"Read","input":{"file_path":"/tmp/lou-ka-ref/louka/pdfgen.py","offset":700,"limit":330},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":14573,"cache_read_input_tokens":137637,"cache_creation":{"ephemeral_5m_input_tokens":14573,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"87b0e3e3-bc22-4488-81cf-da532ba85c48"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017Y8i2ALysUt1XxeGqXebFf","type":"tool_result","content":"700\t            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n701\t                     GREEN_DEEP, LIME_SOFT, GREEN)\n702\t        else:\n703\t            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n704\t                     INK2, SURFACE, INK)\n705\t        y -= 26\n706\t        for z in zones:\n707\t            c.setFont(\"Helvetica\", 8)\n708\t            c.setFillColor(INK2)\n709\t            c.drawString(M, y, f\"{z['type']} — à {z['distance_m']} m\"\n710\t                               + (f\" (rapport {z['date_rapport']})\"\n711\t                                  if z.get(\"date_rapport\") else \"\"))\n712\t            y -= 12\n713\t        y -= 8\n714\t\n715\t    # --- Qualité de l'air (RSQAQ)\n716\t    if air_d and air_d.get(\"station\"):\n717\t        mes = air_d.get(\"mesures\") or {}\n718\t        y = place(y, 44 + len(mes) * 11)\n719\t        y = s.titre_section(y, \"Qualité de l'air\")\n720\t        c.setFont(\"Helvetica\", 8.5)\n721\t        c.setFillColor(INK2)\n722\t        c.drawString(M, y - 2,\n723\t                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n724\t                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n725\t                     \"du logement — moyennes annuelles :\")\n726\t        y -= 16\n727\t        for pol, m_ in list(mes.items())[:5]:\n728\t            c.setFont(\"Helvetica-Bold\", 8)\n729\t            c.setFillColor(INK)\n730\t            c.drawString(M, y, pol)\n731\t            c.setFont(\"Helvetica\", 8)\n732\t            c.setFillColor(INK2)\n733\t            val = f\"{m_['moyenne']:.1f}\".replace(\".\", \",\") if m_.get(\"moyenne\") is not None else \"—\"\n734\t            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n735\t            if m_.get(\"ref\"):\n736\t                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n737\t            c.drawString(M + 56, y, txt[:95])\n738\t            y -= 11\n739\t        y -= 8\n740\t\n741\t    # --- Coût d'électricité (Hydro-Québec)\n742\t    if hyd and hyd.get(\"disponible\"):\n743\t        y = place(y, 52)\n744\t        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n745\t        c.setFont(\"Helvetica-Bold\", 15)\n746\t        c.setFillColor(INK)\n747\t        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n748\t        c.drawString(M, y - 8, mens)\n749\t        c.setFont(\"Helvetica\", 8.5)\n750\t        c.setFillColor(INK2)\n751\t        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n752\t        if hyd.get(\"kwh_annuel\"):\n753\t            suite += f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP) + f\"{NBSP}kWh/an\"\n754\t        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n755\t        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n756\t        y -= 26\n757\t\n758\t    # --- Commerces et transport\n759\t    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n760\t        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n761\t                    for t in (com.get(\"transit\") or [])[:4]]\n762\t                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n763\t                      for i in (com.get(\"commerces\") or [])[:10]])\n764\t        lignes = (len(entrees) + 1) // 2\n765\t        y = place(y, 36 + lignes * 13)\n766\t        y = s.titre_section(y, \"Commerces et transport\")\n767\t        col_w = (PAGE_W - 2 * M) / 2\n768\t        for i, (cat, nom, dist) in enumerate(entrees):\n769\t            px = M if i % 2 == 0 else M + col_w + 10\n770\t            py = y - (i // 2) * 13\n771\t            c.setFont(\"Helvetica\", 8)\n772\t            c.setFillColor(INK2)\n773\t            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n774\t            c.drawString(px, py, f\"{cat} — {nomc}\")\n775\t            c.setFont(\"Courier-Bold\", 7.5)\n776\t            c.setFillColor(INK)\n777\t            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n778\t        y -= lignes * 13 + 12\n779\t\n780\t    # --- Essence à proximité\n781\t    if gaz_d and gaz_d.get(\"stations\"):\n782\t        sts = gaz_d[\"stations\"][:3]\n783\t        y = place(y, 40 + len(sts) * 11)\n784\t        y = s.titre_section(y, \"Essence à proximité\")\n785\t        if gaz_d.get(\"mediane_regulier\"):\n786\t            c.setFont(\"Helvetica\", 8.5)\n787\t            c.setFillColor(INK2)\n788\t            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n789\t                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n790\t                         + f\"{NBSP}¢/L\")\n791\t            y -= 14\n792\t        for st in sts:\n793\t            c.setFont(\"Helvetica\", 8)\n794\t            c.setFillColor(INK2)\n795\t            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n796\t                     if st.get(\"regulier\") else \"—\")\n797\t            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n798\t                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n799\t            y -= 11\n800\t        y -= 8\n801\t\n802\t    # --- KA Scores (immeuble et environnement)\n803\t    if kasc and kasc.get(\"glob\") is not None:\n804\t        from . import kascores as ks_mod\n805\t        y = place(y, 92)\n806\t        y = s.titre_section(y, \"KA Scores\")\n807\t        paires = [(\"Global\", kasc.get(\"glob\")), (\"À pied\", kasc.get(\"walk\")),\n808\t                  (\"Transport\", kasc.get(\"transit\")), (\"Vélo\", kasc.get(\"bike\")),\n809\t                  (\"Calme\", kasc.get(\"calme\")), (\"Services\", kasc.get(\"services\"))]\n810\t        tw = (PAGE_W - 2 * M - 5 * 8) / 6\n811\t        for i, (lab, v) in enumerate(paires):\n812\t            val = \"—\" if v is None else str(round(v))\n813\t            sub = ks_mod.label(v) or \"\"\n814\t            s.tuile(M + i * (tw + 8), y - 46, tw, 42, val,\n815\t                    f\"{lab} · {sub}\" if sub else lab)\n816\t        y -= 62\n817\t\n818\t    # --- Le quartier\n819\t    if quartier:\n820\t        y = place(y, 270)\n821\t        y = s.titre_section(y, \"Le quartier\")\n822\t        demo = quartier.get(\"demographie\") or {}\n823\t        tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n824\t                  (\"Ménages locataires\", f\"{round(demo['pct_locataires'])}{NBSP}%\" if demo.get(\"pct_locataires\") is not None else \"—\"),\n825\t                  (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n826\t                  (\"Âge médian\", f\"{round(demo['age_median'])} ans\" if demo.get(\"age_median\") is not None else \"—\"),\n827\t                  (\"Français à la maison\", f\"{round(demo['pct_francais'])}{NBSP}%\" if demo.get(\"pct_francais\") is not None else \"—\"),\n828\t                  (\"Diplôme universitaire\", f\"{round(demo['pct_univ'])}{NBSP}%\" if demo.get(\"pct_univ\") is not None else \"—\")]\n829\t        tw = (PAGE_W - 2 * M - 20) / 3\n830\t        for i, (lab, val) in enumerate(tuiles):\n831\t            s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n832\t                    tw, 38, val, lab)\n833\t        y -= 34 + 44 + 16\n834\t\n835\t        prox = quartier.get(\"proximite\") or {}\n836\t        for k, lab in PROX_LABELS:\n837\t            if k not in prox:\n838\t                continue\n839\t            v = max(0.0, min(1.0, prox[k]))\n840\t            c.setFont(\"Helvetica\", 8)\n841\t            c.setFillColor(INK2)\n842\t            c.drawString(M, y, lab)\n843\t            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n844\t            c.setFillColor(SURFACE)\n845\t            c.setStrokeColor(INK)\n846\t            c.setLineWidth(0.8)\n847\t            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n848\t            c.setFillColor(GREEN)\n849\t            c.roundRect(bx0, y - 1, bw_ * v, 7, 3.5, stroke=0, fill=1)\n850\t            c.setFont(\"Courier-Bold\", 7.5)\n851\t            c.setFillColor(INK)\n852\t            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n853\t            y -= 14\n854\t        y -= 4\n855\t        badges_q = []\n856\t        ch = quartier.get(\"chaleur\")\n857\t        if ch:\n858\t            if ch[\"classe\"] <= 3:\n859\t                badges_q.append((\"Îlot de fraîcheur\", GREEN_DEEP, LIME_SOFT, GREEN))\n860\t            elif ch[\"classe\"] >= 7:\n861\t                badges_q.append((f\"Îlot de chaleur (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\"),\n862\t                                 HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER))\n863\t        cr = quartier.get(\"crime\")\n864\t        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n865\t            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"]) / cr[\"indice_canada\"])\n866\t            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n867\t                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} la moyenne canadienne\",\n868\t                             INK2, SURFACE, INK))\n869\t        elif cr and cr.get(\"type\") == \"points\":\n870\t            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m (12 mois)\",\n871\t                             INK2, SURFACE, INK))\n872\t        x = M\n873\t        for txt, fg, bg, stk in badges_q:\n874\t            x = s.pilule(x, y - 8, txt, fg, bg, stk)\n875\t        y -= 30\n876\t\n877\t    # --- À proximité (commodités du cache POI)\n878\t    if poi:\n879\t        lignes = (min(len(poi), 12) + 1) // 2\n880\t        y = place(y, 36 + lignes * 13)\n881\t        y = s.titre_section(y, \"À proximité\")\n882\t        col_w = (PAGE_W - 2 * M) / 2\n883\t        for i, p in enumerate(poi[:12]):\n884\t            px = M if i % 2 == 0 else M + col_w + 10\n885\t            py = y - (i // 2) * 13\n886\t            c.setFont(\"Helvetica\", 8)\n887\t            c.setFillColor(INK2)\n888\t            lab = POI_LABELS.get(p[\"cat\"], p[\"cat\"])\n889\t            nom_poi = p[\"name\"][:19] + \"…\" if len(p[\"name\"]) > 20 else p[\"name\"]\n890\t            c.drawString(px, py, f\"{lab} — {nom_poi}\")\n891\t            c.setFont(\"Courier-Bold\", 7.5)\n892\t            c.setFillColor(INK)\n893\t            c.drawRightString(px + col_w - 14, py,\n894\t                              f\"{_fmt_dist(p['dist_m'])} · {_marche(p['dist_m'])}\")\n895\t        y -= lignes * 13 + 14\n896\t\n897\t    # --- Références : QR + gestionnaire + source\n898\t    y = place(y, 120)\n899\t    y = s.titre_section(y, \"Références\")\n900\t    qr = qrcode.make(f\"https://www.lou-ka.com/logement/{uid}\", box_size=4, border=1)\n901\t    qb = io.BytesIO()\n902\t    qr.save(qb, format=\"PNG\")\n903\t    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n904\t    c.setFont(\"Helvetica-Bold\", 9)\n905\t    c.setFillColor(INK)\n906\t    c.drawString(M + 82, y - 14, f\"Gestionnaire : {src_name}\")\n907\t    c.setFont(\"Helvetica\", 8)\n908\t    c.setFillColor(INK2)\n909\t    c.drawString(M + 82, y - 28, \"Annonce originale :\")\n910\t    c.setFillColor(GREEN)\n911\t    c.drawString(M + 82, y - 40, (l.get(\"url\") or \"\")[:90])\n912\t    c.setFillColor(INK2)\n913\t    c.drawString(M + 82, y - 56, \"Fiche à jour en ligne (scannez le code) :\")\n914\t    c.setFillColor(GREEN)\n915\t    c.drawString(M + 82, y - 68, f\"www.lou-ka.com/logement/{uid}\")\n916\t    y -= 86\n917\t    c.setFont(\"Helvetica\", 6.5)\n918\t    c.setFillColor(INK3)\n919\t    c.drawString(M, y, \"Sources : Statistique Canada (2021), INSPQ (CC-BY 4.0), \"\n920\t                       \"Ville de Montréal (CC-BY 4.0), OpenStreetMap, BDZI, RSQAQ, \"\n921\t                       \"Hydro-Québec, registre-des-loyers.ca, SOQUIJ, gazquebec.ca.\")\n922\t\n923\t    s.pied(_PIED_FICHE, page[0])\n924\t    c.save()\n925\t    return buf.getvalue()\n926\t\n927\t\n928\tdef s_titre_droite(c, y, x, texte):\n929\t    c.setFont(\"Helvetica-Bold\", 12.5)\n930\t    c.setFillColor(INK)\n931\t    c.drawString(x, y, texte)\n932\t    c.setStrokeColor(LIME)\n933\t    c.setLineWidth(3)\n934\t    c.line(x, y - 4, x + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n935\t    return y - 18\n936\t\n937\t\n938\t# ---------------------------------------------------------------------------\n939\t# Rapport global du marché\n940\t# ---------------------------------------------------------------------------\n941\t\n942\t_REGIONS = [\n943\t    (\"Québec métro\", {\"Québec\", \"Lévis\", \"Saint-Augustin-de-Desmaures\", \"L'Ancienne-Lorette\", \"Pont-Rouge\", \"Shannon\"}),\n944\t    (\"Outaouais\", {\"Gatineau\", \"Chelsea\", \"Thurso\", \"Perkins\", \"Maniwaki\", \"Val-des-Monts\"}),\n945\t    (\"Estrie / Montérégie-Est\", {\"Sherbrooke\", \"Magog\", \"Orford\", \"East Angus\", \"Waterville\", \"Granby\", \"Waterloo\", \"Bromont\", \"Cowansville\", \"Richmond\"}),\n946\t    (\"Mauricie / Centre-du-Québec\", {\"Trois-Rivières\", \"Bécancour\", \"Shawinigan\", \"Drummondville\", \"Victoriaville\", \"Notre-Dame-du-Bon-Conseil\", \"Saint-Léonard-d'Aston\", \"Louiseville\", \"Nicolet\", \"Wickham\", \"Saint-Narcisse\", \"Saint-Nicéphore\"}),\n947\t    (\"Lanaudière / Laurentides\", {\"Joliette\", \"Saint-Jérôme\", \"Berthierville\", \"Saint-Ambroise-de-Kildare\", \"Saint-Gabriel-de-Brandon\", \"Lachute\", \"Brownsburg-Chatham\", \"Saint-Charles-Borromée\", \"Mirabel\", \"Sainte-Agathe-des-Monts\", \"Sainte-Thérèse\", \"Blainville\", \"Charlemagne\", \"Notre-Dame-des-Prairies\"}),\n948\t    (\"Bas-Saint-Laurent / Gaspésie\", {\"Rimouski\", \"Rivière-du-Loup\", \"Matane\", \"Saint-Ulric\", \"Amqui\", \"Le Bic\", \"New Richmond\", \"Carleton-sur-Mer\", \"Gaspé\"}),\n949\t    (\"Saguenay–Lac-Saint-Jean\", {\"Saguenay\", \"Alma\", \"Chicoutimi\", \"Jonquière\", \"Chambord\", \"La Malbaie\"}),\n950\t    (\"Abitibi-Témiscamingue\", {\"Rouyn-Noranda\", \"Val-d'Or\", \"Amos\", \"Malartic\"}),\n951\t    (\"Côte-Nord\", {\"Sept-Îles\", \"Port-Cartier\", \"Baie-Comeau\", \"Forestville\"}),\n952\t    (\"Chaudière-Appalaches\", {\"Saint-Georges\", \"Sainte-Marie\", \"Thetford Mines\", \"Montmagny\", \"Vallée-Jonction\", \"Scott\", \"Saint-Isidore\", \"La Guadeloupe\", \"Saint-Raphaël\"}),\n953\t]\n954\t\n955\t\n956\tdef _region_de(city: str) -> str:\n957\t    for nom, villes in _REGIONS:\n958\t        if city in villes:\n959\t            return nom\n960\t    return \"Grand Montréal & environs\"\n961\t\n962\t\n963\tdef rapport_pdf() -> bytes:\n964\t    \"\"\"Rapport global du marché locatif Lou-Ka (multi-pages).\n965\t\n966\t    Tous les chiffres viennent de marketstats.compute() — la même source\n967\t    que la page Statistiques du site.\n968\t    \"\"\"\n969\t    from . import marketstats\n970\t    st = marketstats.compute()\n971\t    reg_file = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n972\t    noms = {s[\"id\"]: s[\"name\"] for s in reg_file}\n973\t\n974\t    t = st[\"totals\"]\n975\t    total, moy, med, gps = t[\"total\"], t[\"avg\"] or 0, t[\"median\"] or 0, t[\"gps_pct\"]\n976\t    n_sources = t[\"sources\"]\n977\t\n978\t    buf = io.BytesIO()\n979\t    c = rl_canvas.Canvas(buf, pagesize=letter)\n980\t    c.setTitle(\"Lou-Ka — Rapport du marché locatif\")\n981\t    s = _Style(c)\n982\t\n983\t    # ---- page 1 : couverture + grands chiffres + régions\n984\t    s.fond()\n985\t    s.logo(M, PAGE_H - 130, 42)\n986\t    c.setFont(\"Helvetica-Bold\", 24)\n987\t    c.setFillColor(INK)\n988\t    c.drawString(M, PAGE_H - 185, \"Rapport du marché locatif\")\n989\t    c.setFont(\"Courier-Bold\", 9)\n990\t    c.setFillColor(INK3)\n991\t    c.drawString(M, PAGE_H - 205,\n992\t                 time.strftime(\"PROVINCE DE QUÉBEC · GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n993\t\n994\t    grands = [(f\"{total:,}\".replace(\",\", NBSP), \"annonces actives\"),\n995\t              (str(n_sources), \"gestionnaires connectés\"),\n996\t              (_fmt_money(moy), \"loyer moyen\"),\n997\t              (_fmt_money(med), \"loyer médian\"),\n998\t              (f\"{gps}{NBSP}%\", \"annonces géolocalisées\")]\n999\t    tw = (PAGE_W - 2 * M - 4 * 10) / 5\n1000\t    for i, (val, lab) in enumerate(grands):\n1001\t        tx = M + i * (tw + 10)\n1002\t        ty = PAGE_H - 285\n1003\t        c.setFillColor(INK)\n1004\t        c.roundRect(tx + 3, ty - 3, tw, 58, 6, stroke=0, fill=1)   # ombre décalée\n1005\t        c.setFillColor(SURFACE)\n1006\t        c.setStrokeColor(INK)\n1007\t        c.setLineWidth(1.5)\n1008\t        c.roundRect(tx, ty, tw, 58, 6, stroke=1, fill=1)\n1009\t        c.setFont(\"Helvetica-Bold\", 15)\n1010\t        c.setFillColor(INK)\n1011\t        c.drawCentredString(tx + tw / 2, ty + 32, val)\n1012\t        c.setFont(\"Courier\", 6.3)\n1013\t        c.setFillColor(INK3)\n1014\t        c.drawCentredString(tx + tw / 2, ty + 14, lab.upper())\n1015\t\n1016\t    y = s.titre_section(PAGE_H - 330, \"Couverture par région\")\n1017\t    c.setFont(\"Courier-Bold\", 7)\n1018\t    c.setFillColor(INK3)\n1019\t    for lab, xoff in ((\"RÉGION\", 0), (\"ANNONCES\", 280), (\"SOURCES\", 360),\n1020\t                      (\"LOYER MOYEN\", 430)):\n1021\t        c.drawString(M + xoff, y, lab)\n1022\t    y -= 4\n1023\t    c.setStrokeColor(INK)\n1024\t    c.setLineWidth(1)\n1025\t    c.line(M, y, PAGE_W - M, y)\n1026\t    y -= 14\n1027\t    regions = st[\"by_region\"]\n1028\t    max_n = max((r[\"count\"] for r in regions), default=1)\n1029\t    for r in regions:"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"2a9c1e44-7476-4fd7-b182-a704364ae03c","timestamp":"2026-08-24T21:18:41.149Z","tool_use_result":{"type":"text","file":{"filePath":"/tmp/lou-ka-ref/louka/pdfgen.py","content":"            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_DEEP, LIME_SOFT, GREEN)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z['type']} — à {z['distance_m']} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- Qualité de l'air (RSQAQ)\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"du logement — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = f\"{m_['moyenne']:.1f}\".replace(\".\", \",\") if m_.get(\"moyenne\") is not None else \"—\"\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- Coût d'électricité (Hydro-Québec)\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP) + f\"{NBSP}kWh/an\"\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- Commerces et transport\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:10]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- Essence à proximité\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- KA Scores (immeuble et environnement)\n    if kasc and kasc.get(\"glob\") is not None:\n        from . import kascores as ks_mod\n        y = place(y, 92)\n        y = s.titre_section(y, \"KA Scores\")\n        paires = [(\"Global\", kasc.get(\"glob\")), (\"À pied\", kasc.get(\"walk\")),\n                  (\"Transport\", kasc.get(\"transit\")), (\"Vélo\", kasc.get(\"bike\")),\n                  (\"Calme\", kasc.get(\"calme\")), (\"Services\", kasc.get(\"services\"))]\n        tw = (PAGE_W - 2 * M - 5 * 8) / 6\n        for i, (lab, v) in enumerate(paires):\n            val = \"—\" if v is None else str(round(v))\n            sub = ks_mod.label(v) or \"\"\n            s.tuile(M + i * (tw + 8), y - 46, tw, 42, val,\n                    f\"{lab} · {sub}\" if sub else lab)\n        y -= 62\n\n    # --- Le quartier\n    if quartier:\n        y = place(y, 270)\n        y = s.titre_section(y, \"Le quartier\")\n        demo = quartier.get(\"demographie\") or {}\n        tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                  (\"Ménages locataires\", f\"{round(demo['pct_locataires'])}{NBSP}%\" if demo.get(\"pct_locataires\") is not None else \"—\"),\n                  (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                  (\"Âge médian\", f\"{round(demo['age_median'])} ans\" if demo.get(\"age_median\") is not None else \"—\"),\n                  (\"Français à la maison\", f\"{round(demo['pct_francais'])}{NBSP}%\" if demo.get(\"pct_francais\") is not None else \"—\"),\n                  (\"Diplôme universitaire\", f\"{round(demo['pct_univ'])}{NBSP}%\" if demo.get(\"pct_univ\") is not None else \"—\")]\n        tw = (PAGE_W - 2 * M - 20) / 3\n        for i, (lab, val) in enumerate(tuiles):\n            s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                    tw, 38, val, lab)\n        y -= 34 + 44 + 16\n\n        prox = quartier.get(\"proximite\") or {}\n        for k, lab in PROX_LABELS:\n            if k not in prox:\n                continue\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(GREEN)\n            c.roundRect(bx0, y - 1, bw_ * v, 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        y -= 4\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch[\"classe\"] <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_DEEP, LIME_SOFT, GREEN))\n            elif ch[\"classe\"] >= 7:\n                badges_q.append((f\"Îlot de chaleur (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\"),\n                                 HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"]) / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} la moyenne canadienne\",\n                             INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m (12 mois)\",\n                             INK2, SURFACE, INK))\n        x = M\n        for txt, fg, bg, stk in badges_q:\n            x = s.pilule(x, y - 8, txt, fg, bg, stk)\n        y -= 30\n\n    # --- À proximité (commodités du cache POI)\n    if poi:\n        lignes = (min(len(poi), 12) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"À proximité\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, p in enumerate(poi[:12]):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            lab = POI_LABELS.get(p[\"cat\"], p[\"cat\"])\n            nom_poi = p[\"name\"][:19] + \"…\" if len(p[\"name\"]) > 20 else p[\"name\"]\n            c.drawString(px, py, f\"{lab} — {nom_poi}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py,\n                              f\"{_fmt_dist(p['dist_m'])} · {_marche(p['dist_m'])}\")\n        y -= lignes * 13 + 14\n\n    # --- Références : QR + gestionnaire + source\n    y = place(y, 120)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.lou-ka.com/logement/{uid}\", box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    c.drawString(M + 82, y - 14, f\"Gestionnaire : {src_name}\")\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, y - 28, \"Annonce originale :\")\n    c.setFillColor(GREEN)\n    c.drawString(M + 82, y - 40, (l.get(\"url\") or \"\")[:90])\n    c.setFillColor(INK2)\n    c.drawString(M + 82, y - 56, \"Fiche à jour en ligne (scannez le code) :\")\n    c.setFillColor(GREEN)\n    c.drawString(M + 82, y - 68, f\"www.lou-ka.com/logement/{uid}\")\n    y -= 86\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : Statistique Canada (2021), INSPQ (CC-BY 4.0), \"\n                       \"Ville de Montréal (CC-BY 4.0), OpenStreetMap, BDZI, RSQAQ, \"\n                       \"Hydro-Québec, registre-des-loyers.ca, SOQUIJ, gazquebec.ca.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n\n\ndef s_titre_droite(c, y, x, texte):\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x, y, texte)\n    c.setStrokeColor(LIME)\n    c.setLineWidth(3)\n    c.line(x, y - 4, x + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\n# ---------------------------------------------------------------------------\n# Rapport global du marché\n# ---------------------------------------------------------------------------\n\n_REGIONS = [\n    (\"Québec métro\", {\"Québec\", \"Lévis\", \"Saint-Augustin-de-Desmaures\", \"L'Ancienne-Lorette\", \"Pont-Rouge\", \"Shannon\"}),\n    (\"Outaouais\", {\"Gatineau\", \"Chelsea\", \"Thurso\", \"Perkins\", \"Maniwaki\", \"Val-des-Monts\"}),\n    (\"Estrie / Montérégie-Est\", {\"Sherbrooke\", \"Magog\", \"Orford\", \"East Angus\", \"Waterville\", \"Granby\", \"Waterloo\", \"Bromont\", \"Cowansville\", \"Richmond\"}),\n    (\"Mauricie / Centre-du-Québec\", {\"Trois-Rivières\", \"Bécancour\", \"Shawinigan\", \"Drummondville\", \"Victoriaville\", \"Notre-Dame-du-Bon-Conseil\", \"Saint-Léonard-d'Aston\", \"Louiseville\", \"Nicolet\", \"Wickham\", \"Saint-Narcisse\", \"Saint-Nicéphore\"}),\n    (\"Lanaudière / Laurentides\", {\"Joliette\", \"Saint-Jérôme\", \"Berthierville\", \"Saint-Ambroise-de-Kildare\", \"Saint-Gabriel-de-Brandon\", \"Lachute\", \"Brownsburg-Chatham\", \"Saint-Charles-Borromée\", \"Mirabel\", \"Sainte-Agathe-des-Monts\", \"Sainte-Thérèse\", \"Blainville\", \"Charlemagne\", \"Notre-Dame-des-Prairies\"}),\n    (\"Bas-Saint-Laurent / Gaspésie\", {\"Rimouski\", \"Rivière-du-Loup\", \"Matane\", \"Saint-Ulric\", \"Amqui\", \"Le Bic\", \"New Richmond\", \"Carleton-sur-Mer\", \"Gaspé\"}),\n    (\"Saguenay–Lac-Saint-Jean\", {\"Saguenay\", \"Alma\", \"Chicoutimi\", \"Jonquière\", \"Chambord\", \"La Malbaie\"}),\n    (\"Abitibi-Témiscamingue\", {\"Rouyn-Noranda\", \"Val-d'Or\", \"Amos\", \"Malartic\"}),\n    (\"Côte-Nord\", {\"Sept-Îles\", \"Port-Cartier\", \"Baie-Comeau\", \"Forestville\"}),\n    (\"Chaudière-Appalaches\", {\"Saint-Georges\", \"Sainte-Marie\", \"Thetford Mines\", \"Montmagny\", \"Vallée-Jonction\", \"Scott\", \"Saint-Isidore\", \"La Guadeloupe\", \"Saint-Raphaël\"}),\n]\n\n\ndef _region_de(city: str) -> str:\n    for nom, villes in _REGIONS:\n        if city in villes:\n            return nom\n    return \"Grand Montréal & environs\"\n\n\ndef rapport_pdf() -> bytes:\n    \"\"\"Rapport global du marché locatif Lou-Ka (multi-pages).\n\n    Tous les chiffres viennent de marketstats.compute() — la même source\n    que la page Statistiques du site.\n    \"\"\"\n    from . import marketstats\n    st = marketstats.compute()\n    reg_file = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n    noms = {s[\"id\"]: s[\"name\"] for s in reg_file}\n\n    t = st[\"totals\"]\n    total, moy, med, gps = t[\"total\"], t[\"avg\"] or 0, t[\"median\"] or 0, t[\"gps_pct\"]\n    n_sources = t[\"sources\"]\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(\"Lou-Ka — Rapport du marché locatif\")\n    s = _Style(c)\n\n    # ---- page 1 : couverture + grands chiffres + régions\n    s.fond()\n    s.logo(M, PAGE_H - 130, 42)\n    c.setFont(\"Helvetica-Bold\", 24)\n    c.setFillColor(INK)\n    c.drawString(M, PAGE_H - 185, \"Rapport du marché locatif\")\n    c.setFont(\"Courier-Bold\", 9)\n    c.setFillColor(INK3)\n    c.drawString(M, PAGE_H - 205,\n                 time.strftime(\"PROVINCE DE QUÉBEC · GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n\n    grands = [(f\"{total:,}\".replace(\",\", NBSP), \"annonces actives\"),\n              (str(n_sources), \"gestionnaires connectés\"),\n              (_fmt_money(moy), \"loyer moyen\"),\n              (_fmt_money(med), \"loyer médian\"),\n              (f\"{gps}{NBSP}%\", \"annonces géolocalisées\")]\n    tw = (PAGE_W - 2 * M - 4 * 10) / 5\n    for i, (val, lab) in enumerate(grands):\n        tx = M + i * (tw + 10)\n        ty = PAGE_H - 285\n        c.setFillColor(INK)\n        c.roundRect(tx + 3, ty - 3, tw, 58, 6, stroke=0, fill=1)   # ombre décalée\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.roundRect(tx, ty, tw, 58, 6, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        c.drawCentredString(tx + tw / 2, ty + 32, val)\n        c.setFont(\"Courier\", 6.3)\n        c.setFillColor(INK3)\n        c.drawCentredString(tx + tw / 2, ty + 14, lab.upper())\n\n    y = s.titre_section(PAGE_H - 330, \"Couverture par région\")\n    c.setFont(\"Courier-Bold\", 7)\n    c.setFillColor(INK3)\n    for lab, xoff in ((\"RÉGION\", 0), (\"ANNONCES\", 280), (\"SOURCES\", 360),\n                      (\"LOYER MOYEN\", 430)):\n        c.drawString(M + xoff, y, lab)\n    y -= 4\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.line(M, y, PAGE_W - M, y)\n    y -= 14\n    regions = st[\"by_region\"]\n    max_n = max((r[\"count\"] for r in regions), default=1)\n    for r in regions:","numLines":330,"startLine":700,"totalLines":1202}}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNGeowXaB7U5KBzZkMrp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01691ccPpp5YWdeLKjgXYtY3","name":"Bash","input":{"command":"sed -n '1,75p;130,215p' immoka/web.py","description":"Read web.py head + enrichment endpoints + listing detail"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":8052,"cache_read_input_tokens":152210,"cache_creation":{"ephemeral_5m_input_tokens":8052,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"5a780d8c-4029-440a-bc73-b5939ff08096"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01691ccPpp5YWdeLKjgXYtY3","type":"tool_result","content":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de maisons à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# web.py : API FastAPI (JSON) + service du frontend statique (frontend/)\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport json\nimport threading\nimport time\nfrom pathlib import Path\n\nfrom fastapi import BackgroundTasks, Body, FastAPI, HTTPException, Query, Request\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom fastapi.middleware.gzip import GZipMiddleware\nfrom fastapi.responses import FileResponse, RedirectResponse, Response\nfrom fastapi.staticfiles import StaticFiles\n\nfrom . import auth, db, favorites, ingest, seo\nfrom . import stats as kastats\n\nROOT = Path(__file__).resolve().parent.parent\nSOURCES_PATH = ROOT / \"data\" / \"sources.json\"\n# Frontend : build Vite (React) si présent, sinon la page statique de secours.\nFRONTEND_DIST = ROOT / \"frontend\" / \"dist\"\nFRONTEND_DIR = FRONTEND_DIST if FRONTEND_DIST.exists() else ROOT / \"frontend\"\n\napp = FastAPI(title=\"Immo-Ka API\", version=\"0.1\",\n              description=\"Agrégateur de maisons à vendre — province de Québec\")\napp.add_middleware(CORSMiddleware, allow_origins=[\"*\"],\n                   allow_methods=[\"*\"], allow_headers=[\"*\"])\n# le GeoJSON complet pèse ~20 Mo — la compression est vitale pour la carte\napp.add_middleware(GZipMiddleware, minimum_size=1024)\n# connexion Google (compte Groupe-Ka partagé avec Lou-Ka) — voir immoka/auth.py\napp.include_router(auth.router)\n# favoris ♥ — magasin central au hub Groupe KA, voir immoka/favorites.py\napp.include_router(favorites.router)\n\n_sync_lock = threading.Lock()\n\n# Déduplication de la famille RE/MAX : le flux central (remax_quebec) et les\n# ~42 connecteurs de sous-agences (remax_ag_*) décrivent les MÊMES inscriptions,\n# identifiées de façon unique par leur numéro Centris (= external_id). On masque\n# donc toute fiche de sous-agence dès qu'une fiche de plus haute priorité existe\n# (le central enrichi d'abord, puis la sous-agence au plus petit uid). Les fiches\n# du central et des autres agences ne sont jamais masquées. → aucun double-comptage,\n# et les sous-agences prennent le relais automatiquement si le central disparaît.\n# Déduplication PRÉ-CALCULÉE : la colonne `dup_hidden` (remplie par\n# db.refresh_dedup après chaque sync) marque les doublons de sous-agences. La\n# lecture est ainsi instantanée (index) au lieu d'un sous-select corrélé par\n# ligne (~300 s sur 75 k lignes). Voir db.refresh_dedup pour la règle.\n# + `published=1` : seuil de qualité minimum (voir immoka/quality.py) — une\n# annonce sans prix plausible, sans ville, sans type de bien ou sans contenu\n# exploitable part en QUARANTAINE (grille, carte, stats, sources) jusqu'à\n# complétion par les prochains cycles. La fiche détail par uid reste accessible.\nDEDUP_CLAUSE = \" AND dup_hidden=0 AND published=1\"\n\n\ndef _row_to_dict(row) -> dict:\n    d = dict(row)\n    d[\"features\"] = json.loads(d.get(\"features\") or \"[]\")\n    d[\"images\"] = json.loads(d.get(\"images\") or \"[]\")\n    d[\"details\"] = json.loads(d.get(\"details\") or \"{}\")\n    if d.get(\"vraiprix\"):\n        try:\n            d[\"vraiprix\"] = json.loads(d[\"vraiprix\"]) or None\n        except (ValueError, TypeError):\n            d[\"vraiprix\"] = None\n    # jours sur le marché (depuis la 1re observation par Immo-Ka ; la date\n    # d'inscription de la source, si connue, est dans details.listed_at)\n    if d.get(\"first_seen\"):\n        d[\"days_on_market\"] = max(0, int((time.time() - d[\"first_seen\"]) // 86400))\n    return d\n\n\n    args += [limit, offset]\n    rows = [_row_to_dict(r) for r in con.execute(sql, args).fetchall()]\n    con.close()\n    return {\"total\": total, \"count\": len(rows), \"listings\": rows}\n\n\n@app.get(\"/api/hydro\")\ndef hydro_at(adresse: str, solve: int = 0, uid: str | None = None,\n             lat: float | None = None, lng: float | None = None):\n    \"\"\"Estimation du coût d'électricité à une adresse (Hydro-Québec).\n    Par défaut cache seulement (aucun captcha) ; solve=1 force le calcul.\n    uid/lat/lng permettent la lecture cache par annonce et la dérivation du\n    code postal (adresses sans CP).\"\"\"\n    from . import hydro\n    return hydro.estimate(adresse=adresse, uid=uid, lat=lat, lng=lng,\n                          solve=bool(solve))\n\n\n@app.get(\"/api/commerces\")\ndef commerces_at(lat: float, lng: float):\n    \"\"\"Grands commerces + métro/bus les plus proches (Mapbox / OSM).\"\"\"\n    from . import commerces\n    return commerces.nearby(lat, lng)\n\n\n@app.get(\"/api/air\")\ndef air_at(lat: float, lng: float):\n    \"\"\"Qualité de l'air : station RSQAQ la plus proche (MELCCFP).\"\"\"\n    from . import air\n    d = air.lookup(lat, lng)\n    if d is None:\n        raise HTTPException(404, \"Base qualité de l'air non disponible\")\n    return d\n\n\n@app.get(\"/api/gaz\")\ndef gaz_at(lat: float, lng: float):\n    \"\"\"Stations-service à proximité et prix courants (gazquebec.ca).\"\"\"\n    from . import gaz\n    return gaz.nearby(lat, lng)\n\n\n@app.get(\"/api/inondation\")\ndef inondation_at(lat: float, lng: float):\n    \"\"\"Risque d'inondation à l'adresse (BDZI, gouv. du Québec) —\n    bloc « Risque d'inondation » de la fiche.\"\"\"\n    from . import inondation\n    d = inondation.lookup(lat, lng)\n    if d is None:\n        raise HTTPException(404, \"Base des zones inondables non disponible\")\n    return d\n\n\n@app.get(\"/api/listings/{uid}\")\ndef get_listing(uid: str):\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    d = None\n    if row is not None:\n        d = _row_to_dict(row)\n        # historique de prix (baisses/hausses du prix demandé)\n        d[\"price_history\"] = [dict(r) for r in con.execute(\n            \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n            (uid,)).fetchall()]\n        # commodités de proximité (cache par immeuble, voir immoka/poi.py)\n        if d.get(\"lat\") is not None and d.get(\"lng\") is not None:\n            key = f\"{round(d['lat'], 4)},{round(d['lng'], 4)}\"\n            poi_row = con.execute(\n                \"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n            d[\"poi\"] = json.loads(poi_row[\"pois\"]) if poi_row else []\n        else:\n            d[\"poi\"] = []\n        # statistiques de quartier (recensement, proximité, chaleur, criminalité)\n        from . import quartier\n        dauid = d.get(\"dauid\")\n        d[\"quartier\"] = quartier.fiche_quartier(\n            d.get(\"lat\"), d.get(\"lng\"), d.get(\"city\") or \"\",\n            dauid if dauid and dauid != \"hors-zone\" else None)\n    con.close()\n    if d is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    return d\n\n\n@app.get(\"/api/listings.geojson\")\ndef listings_geojson(","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"b447a2a0-d410-4d08-a3d1-a88d1c0ebb58","timestamp":"2026-08-24T21:18:51.121Z","tool_use_result":{"stdout":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de maisons à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# web.py : API FastAPI (JSON) + service du frontend statique (frontend/)\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport json\nimport threading\nimport time\nfrom pathlib import Path\n\nfrom fastapi import BackgroundTasks, Body, FastAPI, HTTPException, Query, Request\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom fastapi.middleware.gzip import GZipMiddleware\nfrom fastapi.responses import FileResponse, RedirectResponse, Response\nfrom fastapi.staticfiles import StaticFiles\n\nfrom . import auth, db, favorites, ingest, seo\nfrom . import stats as kastats\n\nROOT = Path(__file__).resolve().parent.parent\nSOURCES_PATH = ROOT / \"data\" / \"sources.json\"\n# Frontend : build Vite (React) si présent, sinon la page statique de secours.\nFRONTEND_DIST = ROOT / \"frontend\" / \"dist\"\nFRONTEND_DIR = FRONTEND_DIST if FRONTEND_DIST.exists() else ROOT / \"frontend\"\n\napp = FastAPI(title=\"Immo-Ka API\", version=\"0.1\",\n              description=\"Agrégateur de maisons à vendre — province de Québec\")\napp.add_middleware(CORSMiddleware, allow_origins=[\"*\"],\n                   allow_methods=[\"*\"], allow_headers=[\"*\"])\n# le GeoJSON complet pèse ~20 Mo — la compression est vitale pour la carte\napp.add_middleware(GZipMiddleware, minimum_size=1024)\n# connexion Google (compte Groupe-Ka partagé avec Lou-Ka) — voir immoka/auth.py\napp.include_router(auth.router)\n# favoris ♥ — magasin central au hub Groupe KA, voir immoka/favorites.py\napp.include_router(favorites.router)\n\n_sync_lock = threading.Lock()\n\n# Déduplication de la famille RE/MAX : le flux central (remax_quebec) et les\n# ~42 connecteurs de sous-agences (remax_ag_*) décrivent les MÊMES inscriptions,\n# identifiées de façon unique par leur numéro Centris (= external_id). On masque\n# donc toute fiche de sous-agence dès qu'une fiche de plus haute priorité existe\n# (le central enrichi d'abord, puis la sous-agence au plus petit uid). Les fiches\n# du central et des autres agences ne sont jamais masquées. → aucun double-comptage,\n# et les sous-agences prennent le relais automatiquement si le central disparaît.\n# Déduplication PRÉ-CALCULÉE : la colonne `dup_hidden` (remplie par\n# db.refresh_dedup après chaque sync) marque les doublons de sous-agences. La\n# lecture est ainsi instantanée (index) au lieu d'un sous-select corrélé par\n# ligne (~300 s sur 75 k lignes). Voir db.refresh_dedup pour la règle.\n# + `published=1` : seuil de qualité minimum (voir immoka/quality.py) — une\n# annonce sans prix plausible, sans ville, sans type de bien ou sans contenu\n# exploitable part en QUARANTAINE (grille, carte, stats, sources) jusqu'à\n# complétion par les prochains cycles. La fiche détail par uid reste accessible.\nDEDUP_CLAUSE = \" AND dup_hidden=0 AND published=1\"\n\n\ndef _row_to_dict(row) -> dict:\n    d = dict(row)\n    d[\"features\"] = json.loads(d.get(\"features\") or \"[]\")\n    d[\"images\"] = json.loads(d.get(\"images\") or \"[]\")\n    d[\"details\"] = json.loads(d.get(\"details\") or \"{}\")\n    if d.get(\"vraiprix\"):\n        try:\n            d[\"vraiprix\"] = json.loads(d[\"vraiprix\"]) or None\n        except (ValueError, TypeError):\n            d[\"vraiprix\"] = None\n    # jours sur le marché (depuis la 1re observation par Immo-Ka ; la date\n    # d'inscription de la source, si connue, est dans details.listed_at)\n    if d.get(\"first_seen\"):\n        d[\"days_on_market\"] = max(0, int((time.time() - d[\"first_seen\"]) // 86400))\n    return d\n\n\n    args += [limit, offset]\n    rows = [_row_to_dict(r) for r in con.execute(sql, args).fetchall()]\n    con.close()\n    return {\"total\": total, \"count\": len(rows), \"listings\": rows}\n\n\n@app.get(\"/api/hydro\")\ndef hydro_at(adresse: str, solve: int = 0, uid: str | None = None,\n             lat: float | None = None, lng: float | None = None):\n    \"\"\"Estimation du coût d'électricité à une adresse (Hydro-Québec).\n    Par défaut cache seulement (aucun captcha) ; solve=1 force le calcul.\n    uid/lat/lng permettent la lecture cache par annonce et la dérivation du\n    code postal (adresses sans CP).\"\"\"\n    from . import hydro\n    return hydro.estimate(adresse=adresse, uid=uid, lat=lat, lng=lng,\n                          solve=bool(solve))\n\n\n@app.get(\"/api/commerces\")\ndef commerces_at(lat: float, lng: float):\n    \"\"\"Grands commerces + métro/bus les plus proches (Mapbox / OSM).\"\"\"\n    from . import commerces\n    return commerces.nearby(lat, lng)\n\n\n@app.get(\"/api/air\")\ndef air_at(lat: float, lng: float):\n    \"\"\"Qualité de l'air : station RSQAQ la plus proche (MELCCFP).\"\"\"\n    from . import air\n    d = air.lookup(lat, lng)\n    if d is None:\n        raise HTTPException(404, \"Base qualité de l'air non disponible\")\n    return d\n\n\n@app.get(\"/api/gaz\")\ndef gaz_at(lat: float, lng: float):\n    \"\"\"Stations-service à proximité et prix courants (gazquebec.ca).\"\"\"\n    from . import gaz\n    return gaz.nearby(lat, lng)\n\n\n@app.get(\"/api/inondation\")\ndef inondation_at(lat: float, lng: float):\n    \"\"\"Risque d'inondation à l'adresse (BDZI, gouv. du Québec) —\n    bloc « Risque d'inondation » de la fiche.\"\"\"\n    from . import inondation\n    d = inondation.lookup(lat, lng)\n    if d is None:\n        raise HTTPException(404, \"Base des zones inondables non disponible\")\n    return d\n\n\n@app.get(\"/api/listings/{uid}\")\ndef get_listing(uid: str):\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    d = None\n    if row is not None:\n        d = _row_to_dict(row)\n        # historique de prix (baisses/hausses du prix demandé)\n        d[\"price_history\"] = [dict(r) for r in con.execute(\n            \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n            (uid,)).fetchall()]\n        # commodités de proximité (cache par immeuble, voir immoka/poi.py)\n        if d.get(\"lat\") is not None and d.get(\"lng\") is not None:\n            key = f\"{round(d['lat'], 4)},{round(d['lng'], 4)}\"\n            poi_row = con.execute(\n                \"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n            d[\"poi\"] = json.loads(poi_row[\"pois\"]) if poi_row else []\n        else:\n            d[\"poi\"] = []\n        # statistiques de quartier (recensement, proximité, chaleur, criminalité)\n        from . import quartier\n        dauid = d.get(\"dauid\")\n        d[\"quartier\"] = quartier.fiche_quartier(\n            d.get(\"lat\"), d.get(\"lng\"), d.get(\"city\") or \"\",\n            dauid if dauid and dauid != \"hors-zone\" else None)\n    con.close()\n    if d is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    return d\n\n\n@app.get(\"/api/listings.geojson\")\ndef listings_geojson(","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"2a9d1cb5-405e-4d59-be6f-3ad1f135b4f4","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNGfYWrkfyenmP96KMMB","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3421,"cache_read_input_tokens":160262,"cache_creation":{"ephemeral_5m_input_tokens":3421,"ephemeral_1h_input_tokens":0},"output_tokens":6,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"4c2ba873-0c68-4329-8537-e3452e97ba58"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNGfYWrkfyenmP96KMMB","type":"message","role":"assistant","content":[{"type":"text","text":"Plutôt que deviner, j'interroge l'API live pour les formes exactes des retours (fiche + enrichissements) sur l'annonce riche."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3421,"cache_read_input_tokens":160262,"cache_creation":{"ephemeral_5m_input_tokens":3421,"ephemeral_1h_input_tokens":0},"output_tokens":6,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"606a4424-14eb-4aef-bfe6-d0e1fe4ae34d"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNGfYWrkfyenmP96KMMB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01T82XRMuXigYck15ihjiLtK","name":"Bash","input":{"command":"python3 - <<'EOF'\nimport json, urllib.request\ndef get(u):\n    with urllib.request.urlopen(\"http://localhost:8096\"+u, timeout=30) as r:\n        return json.load(r)\ndef shape(o, depth=0, key=\"\"):\n    pad=\"  \"*depth\n    if isinstance(o, dict):\n        for k,v in o.items(): shape(v, depth+1, k)\n    elif isinstance(o, list):\n        print(f\"{'  '*depth}{key}: list[{len(o)}]\")\n        if o: shape(o[0], depth+1, key+\"[0]\")\n    else:\n        s=str(o); print(f\"{pad}{key}: {type(o).__name__} = {s[:60]}\")\nd=get(\"/api/listings/domicilia:17516934\")\nprint(\"=== LISTING keys ===\"); print(list(d.keys()))\nprint(\"=== vraiprix ===\"); print(json.dumps(d.get(\"vraiprix\"), ensure_ascii=False)[:400])\nprint(\"=== price_history ===\"); print(d.get(\"price_history\"))\nprint(\"=== poi[0:2] ===\"); print(json.dumps(d.get(\"poi\")[:2], ensure_ascii=False))\nprint(\"=== quartier ===\"); shape(d.get(\"quartier\"))\nprint(\"=== details keys ===\"); print(list((d.get(\"details\") or {}).keys()))\nprint(\"=== features ===\"); print(d.get(\"features\"))\nlat,lng=d[\"lat\"],d[\"lng\"]\nfor name,u in [(\"inondation\",f\"/api/inondation?lat={lat}&lng={lng}\"),(\"air\",f\"/api/air?lat={lat}&lng={lng}\"),(\"gaz\",f\"/api/gaz?lat={lat}&lng={lng}\"),(\"commerces\",f\"/api/commerces?lat={lat}&lng={lng}\")]:\n    try:\n        o=get(u); print(f\"=== {name} ===\"); print(json.dumps(o, ensure_ascii=False)[:700])\n    except Exception as e: print(f\"=== {name} === ERREUR {e}\")\nimport urllib.parse\nadr=urllib.parse.quote(d.get(\"address\") or \"\")\ntry:\n    o=get(f\"/api/hydro?adresse={adr}&uid={urllib.parse.quote(d['uid'])}&lat={lat}&lng={lng}\")\n    print(\"=== hydro ===\"); print(json.dumps(o, ensure_ascii=False)[:400])\nexcept Exception as e: print(\"=== hydro === ERREUR\", e)\nEOF","description":"Inspect live API return shapes for all fiche blocks","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3421,"cache_read_input_tokens":160262,"cache_creation":{"ephemeral_5m_input_tokens":3421,"ephemeral_1h_input_tokens":0},"output_tokens":6,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"0221c5c5-43ec-410d-88af-909f9aa95094"}}
{"type":"permission_request","requestId":"30967b1065aa24dc","tool":"Bash","input":{"command":"python3 - <<'EOF'\nimport json, urllib.request\ndef get(u):\n    with urllib.request.urlopen(\"http://localhost:8096\"+u, timeout=30) as r:\n        return json.load(r)\ndef shape(o, depth=0, key=\"\"):\n    pad=\"  \"*depth\n    if isinstance(o, dict):\n        for k,v in o.items(): shape(v, depth+1, k)\n    elif isinstance(o, list):\n        print(f\"{'  '*depth}{key}: list[{len(o)}]\")\n        if o: shape(o[0], depth+1, key+\"[0]\")\n    else:\n        s=str(o); print(f\"{pad}{key}: {type(o).__name__} = {s[:60]}\")\nd=get(\"/api/listings/domicilia:17516934\")\nprint(\"=== LISTING keys ===\"); print(list(d.keys()))\nprint(\"=== vraiprix ===\"); print(json.dumps(d.get(\"vraiprix\"), ensure_ascii=False)[:400])\nprint(\"=== price_history ===\"); print(d.get(\"price_history\"))\nprint(\"=== poi[0:2] ===\"); print(json.dumps(d.get(\"poi\")[:2], ensure_ascii=False))\nprint(\"=== quartier ===\"); shape(d.get(\"quartier\"))\nprint(\"=== details keys ===\"); print(list((d.get(\"details\") or {}).keys()))\nprint(\"=== features ===\"); print(d.get(\"features\"))\nlat,lng=d[\"lat\"],d[\"lng\"]\nfor name,u in [(\"inondation\",f\"/api/inondation?lat={lat}&lng={lng}\"),(\"air\",f\"/api/air?lat={lat}&lng={lng}\"),(\"gaz\",f\"/api/gaz?lat={lat}&lng={lng}\"),(\"commerces\",f\"/api/commerces?lat={lat}&lng={lng}\")]:\n    try:\n        o=get(u); print(f\"=== {name} ===\"); print(json.dumps(o, ensure_ascii=False)[:700])\n    except Exception as e: print(f\"=== {name} === ERREUR {e}\")\nimport urllib.parse\nadr=urllib.parse.quote(d.get(\"address\") or \"\")\ntry:\n    o=get(f\"/api/hydro?adresse={adr}&uid={urllib.parse.quote(d['uid'])}&lat={lat}&lng={lng}\")\n    print(\"=== hydro ===\"); print(json.dumps(o, ensure_ascii=False)[:400])\nexcept Exception as e: print(\"=== hydro === ERREUR\", e)\nEOF","timeout":120000,"description":"Inspect live API return shapes for all fiche blocks"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Backend admin-ka injoignable: fetch failed","is_error":true,"tool_use_id":"toolu_01T82XRMuXigYck15ihjiLtK"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"1d149df1-5f3a-4b25-a3c4-e185122bf6bf","timestamp":"2026-08-24T21:24:08.098Z","tool_use_result":"Error: Backend admin-ka injoignable: fetch failed"}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"41abe3e8-20f1-41c3-91a0-d4e7810a19b6","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"permission_resolved","requestId":"30967b1065aa24dc","allow":true,"always":false}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNH4vNtj8G9t2iPHBWG3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DdgCSMocwe25cJVB8zKkBN","name":"Bash","input":{"command":"cat > /tmp/shapes.py <<'EOF'\nimport json, urllib.request, urllib.parse\ndef get(u):\n    with urllib.request.urlopen(\"http://localhost:8096\"+u, timeout=30) as r:\n        return json.load(r)\ndef shape(o, depth=0, key=\"\"):\n    pad=\"  \"*depth\n    if isinstance(o, dict):\n        for k,v in o.items(): shape(v, depth+1, k)\n    elif isinstance(o, list):\n        print(f\"{pad}{key}: list[{len(o)}]\")\n        if o: shape(o[0], depth+1, key+\"[0]\")\n    else:\n        s=str(o); print(f\"{pad}{key}: {type(o).__name__} = {s[:60]}\")\nd=get(\"/api/listings/domicilia:17516934\")\nprint(\"=== LISTING keys ===\"); print(list(d.keys()))\nprint(\"=== vraiprix ===\"); print(json.dumps(d.get(\"vraiprix\"), ensure_ascii=False)[:400])\nprint(\"=== price_history ===\"); print(d.get(\"price_history\"))\nprint(\"=== poi[0:2] ===\"); print(json.dumps((d.get(\"poi\") or [])[:2], ensure_ascii=False))\nprint(\"=== quartier ===\"); shape(d.get(\"quartier\"))\nprint(\"=== details keys ===\"); print(list((d.get(\"details\") or {}).keys()))\nprint(\"=== features ===\"); print(d.get(\"features\"))\nlat,lng=d[\"lat\"],d[\"lng\"]\nfor name,u in [(\"inondation\",f\"/api/inondation?lat={lat}&lng={lng}\"),(\"air\",f\"/api/air?lat={lat}&lng={lng}\"),(\"gaz\",f\"/api/gaz?lat={lat}&lng={lng}\"),(\"commerces\",f\"/api/commerces?lat={lat}&lng={lng}\")]:\n    try:\n        o=get(u); print(f\"=== {name} ===\"); print(json.dumps(o, ensure_ascii=False)[:700])\n    except Exception as e: print(f\"=== {name} === ERREUR {e}\")\nadr=urllib.parse.quote(d.get(\"address\") or \"\")\ntry:\n    o=get(f\"/api/hydro?adresse={adr}&uid={urllib.parse.quote(d['uid'])}&lat={lat}&lng={lng}\")\n    print(\"=== hydro ===\"); print(json.dumps(o, ensure_ascii=False)[:400])\nexcept Exception as e: print(\"=== hydro === ERREUR\", e)\nEOF\npython3 /tmp/shapes.py","description":"Inspect live API shapes via temp script","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":164914,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":164914,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"33723bf7-2cd6-4731-9965-62714154450a"}}
{"type":"permission_request","requestId":"3a5724ae17a1ff05","tool":"Bash","input":{"command":"cat > /tmp/shapes.py <<'EOF'\nimport json, urllib.request, urllib.parse\ndef get(u):\n    with urllib.request.urlopen(\"http://localhost:8096\"+u, timeout=30) as r:\n        return json.load(r)\ndef shape(o, depth=0, key=\"\"):\n    pad=\"  \"*depth\n    if isinstance(o, dict):\n        for k,v in o.items(): shape(v, depth+1, k)\n    elif isinstance(o, list):\n        print(f\"{pad}{key}: list[{len(o)}]\")\n        if o: shape(o[0], depth+1, key+\"[0]\")\n    else:\n        s=str(o); print(f\"{pad}{key}: {type(o).__name__} = {s[:60]}\")\nd=get(\"/api/listings/domicilia:17516934\")\nprint(\"=== LISTING keys ===\"); print(list(d.keys()))\nprint(\"=== vraiprix ===\"); print(json.dumps(d.get(\"vraiprix\"), ensure_ascii=False)[:400])\nprint(\"=== price_history ===\"); print(d.get(\"price_history\"))\nprint(\"=== poi[0:2] ===\"); print(json.dumps((d.get(\"poi\") or [])[:2], ensure_ascii=False))\nprint(\"=== quartier ===\"); shape(d.get(\"quartier\"))\nprint(\"=== details keys ===\"); print(list((d.get(\"details\") or {}).keys()))\nprint(\"=== features ===\"); print(d.get(\"features\"))\nlat,lng=d[\"lat\"],d[\"lng\"]\nfor name,u in [(\"inondation\",f\"/api/inondation?lat={lat}&lng={lng}\"),(\"air\",f\"/api/air?lat={lat}&lng={lng}\"),(\"gaz\",f\"/api/gaz?lat={lat}&lng={lng}\"),(\"commerces\",f\"/api/commerces?lat={lat}&lng={lng}\")]:\n    try:\n        o=get(u); print(f\"=== {name} ===\"); print(json.dumps(o, ensure_ascii=False)[:700])\n    except Exception as e: print(f\"=== {name} === ERREUR {e}\")\nadr=urllib.parse.quote(d.get(\"address\") or \"\")\ntry:\n    o=get(f\"/api/hydro?adresse={adr}&uid={urllib.parse.quote(d['uid'])}&lat={lat}&lng={lng}\")\n    print(\"=== hydro ===\"); print(json.dumps(o, ensure_ascii=False)[:400])\nexcept Exception as e: print(\"=== hydro === ERREUR\", e)\nEOF\npython3 /tmp/shapes.py","timeout":120000,"description":"Inspect live API shapes via temp script"}}
{"type":"permission_resolved","requestId":"3a5724ae17a1ff05","allow":true,"always":false}
{"type":"claude","event":{"type":"system","subtype":"task_started","task_id":"bvz1fne3g","tool_use_id":"toolu_01DdgCSMocwe25cJVB8zKkBN","description":"Inspect live API shapes via temp script","task_type":"local_bash","uuid":"1e598dc6-9688-4d92-ab24-9b29c84c4a67","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"f14aadbf-1f1c-4a3d-911f-3681ad0f7999","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"system","subtype":"task_notification","task_id":"bvz1fne3g","tool_use_id":"toolu_01DdgCSMocwe25cJVB8zKkBN","status":"completed","output_file":"","summary":"Inspect live API shapes via temp script","uuid":"bd9f81ee-30c3-424b-88be-42015aeac406","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DdgCSMocwe25cJVB8zKkBN","type":"tool_result","content":"=== LISTING keys ===\n['uid', 'source', 'external_id', 'url', 'title', 'address', 'sector', 'city', 'region', 'property_type', 'price', 'price_label', 'bedrooms', 'bathrooms', 'powder_rooms', 'area_sqft', 'lot_sqft', 'year_built', 'mls', 'status', 'broker_name', 'broker_phone', 'description', 'features', 'details', 'images', 'lat', 'lng', 'geocode_failed', 'content_hash', 'first_seen', 'last_seen', 'updated_at', 'miss_count', 'active', 'agency', 'dup_hidden', 'dauid', 'vraiprix', 'quality_score', 'quality_issues', 'published', 'days_on_market', 'price_history', 'poi', 'quartier']\n=== vraiprix ===\n{\"id\": \"79030224842973200000000\", \"value\": 264800.0, \"low\": 190800.0, \"high\": 401400.0, \"confidence\": null, \"confidence_pct\": null, \"url\": \"https://www.vrai-prix.com/estimation/79030224842973200000000\", \"valeur_role\": 218800.0, \"valeur_terrain\": 130600.0, \"valeur_batiment\": 88200.0, \"annee_construction_role\": 1960, \"superficie_terrain_role_m2\": 612.8, \"aire_etages_role_m2\": 75.3}\n=== price_history ===\n[{'ts': 1786566006.054048, 'price': 501.0}]\n=== poi[0:2] ===\n[]\n=== quartier ===\n  dauid: str = 24790098\n    population: int = 728\n    densite: float = 20.2\n    age_median: float = 64.0\n    revenu_median: float = 60400.0\n    pct_locataires: float = 21.1\n    loyer_moyen: float = 660.0\n    pct_francais: float = 97.3\n    pct_univ: float = 15.2\n    prox_epicerie: float = 0.4\n    prox_pharmacie: float = 0.38\n    prox_garderie: float = 0.0\n    prox_ecole_prim: float = 0.07\n    prox_sante: float = 0.06\n    prox_bibliotheque: float = 0.43\n    prox_parc: float = 0.27\n    prox_emploi: float = 0.06\n    quintile_materiel: int = 4\n    quintile_social: int = 2\n=== details keys ===\n['postal_code', 'cover_thumb', 'Évaluation municipale', 'Nombre de pièces', 'photo_captions', 'region', 'Taxes scolaires', 'Taxes municipales', 'Inclusions', 'Exclusions', 'listing_origin_url', 'Courtier inscripteur', 'transaction']\n=== features ===\n['Approvisionnement en eau : Inconnu', \"Système d'égouts : Inconnu\", 'Zonage : Résidentiel', 'Salle à manger — 6,6 × 11,3 pi (Autre)', 'Cuisine — 11,3 × 6,5 pi (Autre)', 'Salon — 11,4 × 15,3 pi (Tapis)', 'Chambre à coucher — 8,2 × 7,5 pi (Tapis)', 'Salle de bains — 6,4 × 8,2 pi (Autre)', 'Chambre à coucher principale — 11,3 × 9,9 pi (Tapis)']\n=== inondation ===\n{\"statut\": \"non_cartographie\", \"severite\": null, \"couvert\": false, \"zones\": []}\n=== air ===\n{\"station\": \"Mont-St-Michel\", \"ville\": \"Ferme-Neuve\", \"distance_km\": 49.1, \"mesures\": {\"O3\": {\"moyenne\": 25.8, \"annee\": 2025, \"n\": 8668, \"unite\": \"ppb\", \"ref\": null, \"ref_nom\": null}, \"PM2.5\": {\"moyenne\": 3.31, \"annee\": 2025, \"n\": 8268, \"unite\": \"µg/m³\", \"ref\": 5.0, \"ref_nom\": \"OMS 2021\"}, \"PST\": {\"moyenne\": 12.3, \"annee\": 2025, \"n\": 52, \"unite\": \"µg/m³\", \"ref\": 60.0, \"ref_nom\": \"RAA Québec\"}}}\n=== gaz ===\n{\"n\": 1, \"rayon_m\": 5000, \"mediane_regulier\": null, \"min_regulier\": 179.9, \"stations\": [{\"nom\": \"Sonic\", \"adresse\": \"13651 rte 117 nord, Rivière-Rouge\", \"dist_m\": 4085, \"regulier\": 179.9, \"super\": 201.9, \"diesel\": 255.9, \"moins_chere\": true}], \"maj\": \"2026-08-24 15:05\"}\n=== commerces ===\n{\"n\": 4, \"commerces\": [{\"id\": \"jeancoutu\", \"commerce\": \"Jean Coutu\", \"nom\": \"Pharmacie Jean Coutu\", \"adresse\": \"722 Rue L'Annonciation N, Rivière-Rouge, J0T 1T0, Canada\", \"dist_m\": 9034, \"lat\": 46.41541874, \"lng\": -74.87228219}, {\"id\": \"rona\", \"commerce\": \"RONA\", \"nom\": \"Pro Desk at RONA\", \"adresse\": \"400 Rue L'Annonciation S, Rivière-Rouge, J0T 1T0, Canada\", \"dist_m\": 9989, \"lat\": 46.40636112, \"lng\": -74.86673371}, {\"id\": \"saq\", \"commerce\": \"SAQ\", \"nom\": \"SAQ\", \"adresse\": \"1383 Rue L'Annonciation S, Rivière-Rouge, J0T 1T0, Canada\", \"dist_m\": 10353, \"lat\": 46.40103706, \"lng\": -74.86712901}, {\"id\": \"canadiantire\", \"commerce\": \"Canadian Tire\", \"nom\": \"Canadian Tire\", \"adresse\": \"1675 Bd Albiny\n=== hydro ===\n{\"disponible\": false, \"raison\": \"non calculé\", \"en_attente\": true}","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"bdc8dd5b-2c15-4050-8552-74bac190166f","timestamp":"2026-08-24T21:24:46.945Z","tool_use_result":{"stdout":"=== LISTING keys ===\n['uid', 'source', 'external_id', 'url', 'title', 'address', 'sector', 'city', 'region', 'property_type', 'price', 'price_label', 'bedrooms', 'bathrooms', 'powder_rooms', 'area_sqft', 'lot_sqft', 'year_built', 'mls', 'status', 'broker_name', 'broker_phone', 'description', 'features', 'details', 'images', 'lat', 'lng', 'geocode_failed', 'content_hash', 'first_seen', 'last_seen', 'updated_at', 'miss_count', 'active', 'agency', 'dup_hidden', 'dauid', 'vraiprix', 'quality_score', 'quality_issues', 'published', 'days_on_market', 'price_history', 'poi', 'quartier']\n=== vraiprix ===\n{\"id\": \"79030224842973200000000\", \"value\": 264800.0, \"low\": 190800.0, \"high\": 401400.0, \"confidence\": null, \"confidence_pct\": null, \"url\": \"https://www.vrai-prix.com/estimation/79030224842973200000000\", \"valeur_role\": 218800.0, \"valeur_terrain\": 130600.0, \"valeur_batiment\": 88200.0, \"annee_construction_role\": 1960, \"superficie_terrain_role_m2\": 612.8, \"aire_etages_role_m2\": 75.3}\n=== price_history ===\n[{'ts': 1786566006.054048, 'price': 501.0}]\n=== poi[0:2] ===\n[]\n=== quartier ===\n  dauid: str = 24790098\n    population: int = 728\n    densite: float = 20.2\n    age_median: float = 64.0\n    revenu_median: float = 60400.0\n    pct_locataires: float = 21.1\n    loyer_moyen: float = 660.0\n    pct_francais: float = 97.3\n    pct_univ: float = 15.2\n    prox_epicerie: float = 0.4\n    prox_pharmacie: float = 0.38\n    prox_garderie: float = 0.0\n    prox_ecole_prim: float = 0.07\n    prox_sante: float = 0.06\n    prox_bibliotheque: float = 0.43\n    prox_parc: float = 0.27\n    prox_emploi: float = 0.06\n    quintile_materiel: int = 4\n    quintile_social: int = 2\n=== details keys ===\n['postal_code', 'cover_thumb', 'Évaluation municipale', 'Nombre de pièces', 'photo_captions', 'region', 'Taxes scolaires', 'Taxes municipales', 'Inclusions', 'Exclusions', 'listing_origin_url', 'Courtier inscripteur', 'transaction']\n=== features ===\n['Approvisionnement en eau : Inconnu', \"Système d'égouts : Inconnu\", 'Zonage : Résidentiel', 'Salle à manger — 6,6 × 11,3 pi (Autre)', 'Cuisine — 11,3 × 6,5 pi (Autre)', 'Salon — 11,4 × 15,3 pi (Tapis)', 'Chambre à coucher — 8,2 × 7,5 pi (Tapis)', 'Salle de bains — 6,4 × 8,2 pi (Autre)', 'Chambre à coucher principale — 11,3 × 9,9 pi (Tapis)']\n=== inondation ===\n{\"statut\": \"non_cartographie\", \"severite\": null, \"couvert\": false, \"zones\": []}\n=== air ===\n{\"station\": \"Mont-St-Michel\", \"ville\": \"Ferme-Neuve\", \"distance_km\": 49.1, \"mesures\": {\"O3\": {\"moyenne\": 25.8, \"annee\": 2025, \"n\": 8668, \"unite\": \"ppb\", \"ref\": null, \"ref_nom\": null}, \"PM2.5\": {\"moyenne\": 3.31, \"annee\": 2025, \"n\": 8268, \"unite\": \"µg/m³\", \"ref\": 5.0, \"ref_nom\": \"OMS 2021\"}, \"PST\": {\"moyenne\": 12.3, \"annee\": 2025, \"n\": 52, \"unite\": \"µg/m³\", \"ref\": 60.0, \"ref_nom\": \"RAA Québec\"}}}\n=== gaz ===\n{\"n\": 1, \"rayon_m\": 5000, \"mediane_regulier\": null, \"min_regulier\": 179.9, \"stations\": [{\"nom\": \"Sonic\", \"adresse\": \"13651 rte 117 nord, Rivière-Rouge\", \"dist_m\": 4085, \"regulier\": 179.9, \"super\": 201.9, \"diesel\": 255.9, \"moins_chere\": true}], \"maj\": \"2026-08-24 15:05\"}\n=== commerces ===\n{\"n\": 4, \"commerces\": [{\"id\": \"jeancoutu\", \"commerce\": \"Jean Coutu\", \"nom\": \"Pharmacie Jean Coutu\", \"adresse\": \"722 Rue L'Annonciation N, Rivière-Rouge, J0T 1T0, Canada\", \"dist_m\": 9034, \"lat\": 46.41541874, \"lng\": -74.87228219}, {\"id\": \"rona\", \"commerce\": \"RONA\", \"nom\": \"Pro Desk at RONA\", \"adresse\": \"400 Rue L'Annonciation S, Rivière-Rouge, J0T 1T0, Canada\", \"dist_m\": 9989, \"lat\": 46.40636112, \"lng\": -74.86673371}, {\"id\": \"saq\", \"commerce\": \"SAQ\", \"nom\": \"SAQ\", \"adresse\": \"1383 Rue L'Annonciation S, Rivière-Rouge, J0T 1T0, Canada\", \"dist_m\": 10353, \"lat\": 46.40103706, \"lng\": -74.86712901}, {\"id\": \"canadiantire\", \"commerce\": \"Canadian Tire\", \"nom\": \"Canadian Tire\", \"adresse\": \"1675 Bd Albiny\n=== hydro ===\n{\"disponible\": false, \"raison\": \"non calculé\", \"en_attente\": true}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNH7nNmgRZ3vWdnMvmVh","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3020,"cache_read_input_tokens":164914,"cache_creation":{"ephemeral_5m_input_tokens":3020,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"5deb43c3-8675-4670-90b0-a879ad484659"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNH7nNmgRZ3vWdnMvmVh","type":"message","role":"assistant","content":[{"type":"text","text":"Formes capturées. Je précise maintenant les clés exactes de `quartier.fiche_quartier`, `hydro.estimate`, `commerces.nearby` (transit ?) et des statuts d'inondation en lisant le code."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3020,"cache_read_input_tokens":164914,"cache_creation":{"ephemeral_5m_input_tokens":3020,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"36450dd7-bf34-45c9-a61e-ba94a0f13ad5"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNH7nNmgRZ3vWdnMvmVh","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Dfc27Kfky3KG7dMjU7bkoY","name":"Bash","input":{"command":"grep -n \"return\\|\\\"demographie\\\"\\|'demographie'\\|chaleur\\|crime\\|proximite\" immoka/quartier.py | head -30 ; echo ---- ; grep -n \"def nearby\\|transit\\|return\" immoka/commerces.py | head -15 ; echo ---- ; grep -n \"def estimate\\|disponible\\|cout_\\|kwh\" immoka/hydro.py | head -20 ; echo ---- ; grep -n \"statut\\|severite\\|zones\\|type\\|distance_m\\|date_rapport\" immoka/inondation.py | head -25","description":"Extract return shapes from quartier, commerces, hydro, inondation"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3020,"cache_read_input_tokens":164914,"cache_creation":{"ephemeral_5m_input_tokens":3020,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"2fba8001-1644-40c0-a2c0-252bdb46f189"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Dfc27Kfky3KG7dMjU7bkoY","type":"tool_result","content":"9:#     - heat : classe d'îlot de chaleur/fraîcheur INSPQ par immeuble\n10:#     - crime_mtl / igc : actes criminels SPVM (points) + indice de gravité\n41:    return QUARTIER_DB.exists()\n47:    return con\n66:    return dedans\n78:            return r[\"dauid\"]\n79:    return None\n90:    return s.strip().lower()\n93:def _crime_mtl(qcon: sqlite3.Connection, lat: float, lng: float) -> dict | None:\n99:        \"SELECT lat, lng, ts, categorie FROM crime_mtl WHERE lat BETWEEN ? AND ?\"\n118:        return None\n122:    return {\"type\": \"points\", \"rayon_m\": 500, \"douze_mois\": recent,\n126:def _crime_igc(qcon: sqlite3.Connection, city: str) -> dict | None:\n129:        return None\n134:        return None\n138:    return {\"type\": \"igc\", \"ville\": city, \"annee\": row[\"annee\"],\n147:        return None\n157:                out[\"demographie\"] = {k: r[k] for k in\n164:                out[\"proximite\"] = {k: r[k] / 100.0 for k in r.keys()\n171:        # îlot de chaleur : coordonnée exacte, sinon la plus proche (~120 m)\n181:            out[\"chaleur\"] = {\"classe\": r[\"classe\"], \"ecart\": r[\"ecart\"]}\n184:        crime = None\n186:            crime = _crime_mtl(qcon, lat, lng)\n187:        if crime is None:\n188:            crime = _crime_igc(qcon, city)\n189:        if crime:\n190:            out[\"crime\"] = crime\n192:        return out if len(out) > 1 else None\n194:        return None\n207:        return {\"enriched\": 0, \"missing_db\": True}\n----\n56:    return \"\".join(c for c in s if unicodedata.category(c) != \"Mn\").lower()\n68:    return _token_cache[0]\n79:    return con\n87:    return 6371000 * 2 * math.asin(math.sqrt(a))\n102:        return None\n111:        return {\"nom\": p.get(\"name\") or brand_q,\n115:    return None\n122:def _fetch_transit(lat: float, lng: float) -> list[tuple[str, dict]]:\n142:        return []\n155:    return [(k, v[1]) for k, v in best.items()]\n167:def _transit_from_db(lat: float, lng: float) -> list[tuple[str, dict]]:\n168:    \"\"\"Arrêts/stations depuis data/transit.db (extrait OpenStreetMap\n170:    db = ROOT / \"data\" / \"transit.db\"\n172:        return []\n193:    return out\n----\n23:#   Sans HQ_CAPTCHA_KEY : estimate() renvoie {\"disponible\": False, ...} et le\n142:        cle TEXT PRIMARY KEY, adresse TEXT, cout_annuel REAL,\n143:        cout_mensuel REAL, payload TEXT, fetched_at REAL)\"\"\")\n149:    if \"kwh\" not in cols:\n150:        con.execute(\"ALTER TABLE hydro_cache ADD COLUMN kwh INTEGER\")\n160:            \"(cle, adresse, cout_annuel, cout_mensuel, payload, fetched_at, \"\n161:            \"uid, kwh) VALUES (?,?,NULL,NULL,NULL,?,?,NULL)\",\n165:            \"WHERE cle=? AND cout_annuel IS NULL\",\n259:    kwh = d.get(\"consommationAnnuelle\")\n265:        return None, None, kwh\n266:    return round(a), round(a / 12), kwh\n269:def estimate(civic: str | None = None, postal: str | None = None,\n287:            \"SELECT * FROM hydro_cache WHERE uid=? AND cout_annuel IS NOT NULL \"\n291:            return {\"disponible\": True, \"adresse\": row[\"adresse\"],\n292:                    \"cout_annuel\": row[\"cout_annuel\"],\n293:                    \"cout_mensuel\": row[\"cout_mensuel\"],\n294:                    \"kwh_annuel\": row[\"kwh\"], \"cache\": True}\n303:    # est faisable (civique connu + CP OU coordonnées disponibles)\n307:        return {\"disponible\": False,\n313:        return {\"disponible\": False, \"raison\": \"adresse incomplète\"}\n----\n6:#   Source : Base de données des zones à risque d'inondation (BDZI, MELCCFP),\n75:                         rapport TEXT, date_rapport TEXT, wkb BLOB);\n84:        polys = geom.geoms if geom.geom_type == \"MultiPolygon\" else [geom]\n99:        polys = geom.geoms if geom.geom_type == \"MultiPolygon\" else [geom]\n111:    print(f\"[inondation] {nid} polygones de zones, {cid} périmètres \"\n131:    zones: list[dict] = []\n133:            \"SELECT z.id, z.description, z.rapport, z.date_rapport, z.wkb \"\n144:        zones.append({\"type\": desc, \"severite\": sev, \"recurrence\": rec,\n145:                      \"distance_m\": round(d),\n146:                      \"date_rapport\": (date_r or \"\")[:10] or None})\n157:    zones.sort(key=lambda z: (z[\"distance_m\"],\n159:                              .get(z[\"severite\"], 3)))\n160:    dans = [z for z in zones if z[\"distance_m\"] <= NEAR_M]\n162:        statut = \"en_zone\"\n163:        pire = dans[0][\"severite\"]\n164:    elif zones:\n165:        statut, pire = \"a_proximite\", zones[0][\"severite\"]\n167:        statut, pire = \"hors_zone\", None\n169:        statut, pire = \"non_cartographie\", None\n170:    return {\"statut\": statut, \"severite\": pire, \"couvert\": couvert,\n171:            \"zones\": zones[:5]}","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"7ff9cec5-b666-446c-ad1e-3d7c532eede2","timestamp":"2026-08-24T21:25:11.355Z","tool_use_result":{"stdout":"9:#     - heat : classe d'îlot de chaleur/fraîcheur INSPQ par immeuble\n10:#     - crime_mtl / igc : actes criminels SPVM (points) + indice de gravité\n41:    return QUARTIER_DB.exists()\n47:    return con\n66:    return dedans\n78:            return r[\"dauid\"]\n79:    return None\n90:    return s.strip().lower()\n93:def _crime_mtl(qcon: sqlite3.Connection, lat: float, lng: float) -> dict | None:\n99:        \"SELECT lat, lng, ts, categorie FROM crime_mtl WHERE lat BETWEEN ? AND ?\"\n118:        return None\n122:    return {\"type\": \"points\", \"rayon_m\": 500, \"douze_mois\": recent,\n126:def _crime_igc(qcon: sqlite3.Connection, city: str) -> dict | None:\n129:        return None\n134:        return None\n138:    return {\"type\": \"igc\", \"ville\": city, \"annee\": row[\"annee\"],\n147:        return None\n157:                out[\"demographie\"] = {k: r[k] for k in\n164:                out[\"proximite\"] = {k: r[k] / 100.0 for k in r.keys()\n171:        # îlot de chaleur : coordonnée exacte, sinon la plus proche (~120 m)\n181:            out[\"chaleur\"] = {\"classe\": r[\"classe\"], \"ecart\": r[\"ecart\"]}\n184:        crime = None\n186:            crime = _crime_mtl(qcon, lat, lng)\n187:        if crime is None:\n188:            crime = _crime_igc(qcon, city)\n189:        if crime:\n190:            out[\"crime\"] = crime\n192:        return out if len(out) > 1 else None\n194:        return None\n207:        return {\"enriched\": 0, \"missing_db\": True}\n----\n56:    return \"\".join(c for c in s if unicodedata.category(c) != \"Mn\").lower()\n68:    return _token_cache[0]\n79:    return con\n87:    return 6371000 * 2 * math.asin(math.sqrt(a))\n102:        return None\n111:        return {\"nom\": p.get(\"name\") or brand_q,\n115:    return None\n122:def _fetch_transit(lat: float, lng: float) -> list[tuple[str, dict]]:\n142:        return []\n155:    return [(k, v[1]) for k, v in best.items()]\n167:def _transit_from_db(lat: float, lng: float) -> list[tuple[str, dict]]:\n168:    \"\"\"Arrêts/stations depuis data/transit.db (extrait OpenStreetMap\n170:    db = ROOT / \"data\" / \"transit.db\"\n172:        return []\n193:    return out\n----\n23:#   Sans HQ_CAPTCHA_KEY : estimate() renvoie {\"disponible\": False, ...} et le\n142:        cle TEXT PRIMARY KEY, adresse TEXT, cout_annuel REAL,\n143:        cout_mensuel REAL, payload TEXT, fetched_at REAL)\"\"\")\n149:    if \"kwh\" not in cols:\n150:        con.execute(\"ALTER TABLE hydro_cache ADD COLUMN kwh INTEGER\")\n160:            \"(cle, adresse, cout_annuel, cout_mensuel, payload, fetched_at, \"\n161:            \"uid, kwh) VALUES (?,?,NULL,NULL,NULL,?,?,NULL)\",\n165:            \"WHERE cle=? AND cout_annuel IS NULL\",\n259:    kwh = d.get(\"consommationAnnuelle\")\n265:        return None, None, kwh\n266:    return round(a), round(a / 12), kwh\n269:def estimate(civic: str | None = None, postal: str | None = None,\n287:            \"SELECT * FROM hydro_cache WHERE uid=? AND cout_annuel IS NOT NULL \"\n291:            return {\"disponible\": True, \"adresse\": row[\"adresse\"],\n292:                    \"cout_annuel\": row[\"cout_annuel\"],\n293:                    \"cout_mensuel\": row[\"cout_mensuel\"],\n294:                    \"kwh_annuel\": row[\"kwh\"], \"cache\": True}\n303:    # est faisable (civique connu + CP OU coordonnées disponibles)\n307:        return {\"disponible\": False,\n313:        return {\"disponible\": False, \"raison\": \"adresse incomplète\"}\n----\n6:#   Source : Base de données des zones à risque d'inondation (BDZI, MELCCFP),\n75:                         rapport TEXT, date_rapport TEXT, wkb BLOB);\n84:        polys = geom.geoms if geom.geom_type == \"MultiPolygon\" else [geom]\n99:        polys = geom.geoms if geom.geom_type == \"MultiPolygon\" else [geom]\n111:    print(f\"[inondation] {nid} polygones de zones, {cid} périmètres \"\n131:    zones: list[dict] = []\n133:            \"SELECT z.id, z.description, z.rapport, z.date_rapport, z.wkb \"\n144:        zones.append({\"type\": desc, \"severite\": sev, \"recurrence\": rec,\n145:                      \"distance_m\": round(d),\n146:                      \"date_rapport\": (date_r or \"\")[:10] or None})\n157:    zones.sort(key=lambda z: (z[\"distance_m\"],\n159:                              .get(z[\"severite\"], 3)))\n160:    dans = [z for z in zones if z[\"distance_m\"] <= NEAR_M]\n162:        statut = \"en_zone\"\n163:        pire = dans[0][\"severite\"]\n164:    elif zones:\n165:        statut, pire = \"a_proximite\", zones[0][\"severite\"]\n167:        statut, pire = \"hors_zone\", None\n169:        statut, pire = \"non_cartographie\", None\n170:    return {\"statut\": statut, \"severite\": pire, \"couvert\": couvert,\n171:            \"zones\": zones[:5]}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"type":"text","text":"This session is being continued from a previous conversation that ran out of context. The summary below covers the earlier portion of the conversation.\n\nSummary:\n1. **Primary Request and Intent:**\n\n   Two sequential missions on repo `~/apps/immo-ka` (M4M64a, React+Vite frontend + backend Python FastAPI + PM2; remote origin = spbgit via alias SSH `gitsrv`, PAS GitHub):\n\n   **Mission 1 (TERMINÉE)** : Appliquer la passe de design « éditorial sharp » de lou-ka (commit 872a202) à la fiche immo-ka — cartes encre, titres à carré accent + filet, tuiles KPI à ombre dure, tableaux zébrés, barres en dégradé, pastilles mono, mini-cartes à liseré — en utilisant TOUJOURS les variables (accent immo-ka = cerise, jamais le orange lou-ka en dur), vérifiée Playwright desktop 1440×900 + mobile 390×844, règle ordre DOM = ordre visuel (check-order.mjs), build + pm2 restart + healthcheck + commit/push. → Livrée : commit `fc02d70` poussé.\n\n   **Mission 2 (EN COURS)** : Créer le PDF « Télécharger la fiche » d'immo-ka comme vrai rapport multi-pages reflétant TOUT le contenu de la fiche web, en reprenant l'approche et le style de lou-ka `louka/pdfgen.py` commit `6ff906e`. Exigences précises :\n   - Cartographier d'abord tous les blocs de la fiche web et trouver le module backend + forme de retour EXACTE de chaque donnée (lire le code, ne pas deviner)\n   - reportlab canvas avec pagination dynamique : `page=[1]`, `saut_page()` (pied de page → showPage → fond + en-tête « Fiche propriété · suite »), helper `place(y, besoin)` qui saute si `y - besoin < M + 34` ; aucune section coupée\n   - Style « éditorial sharp » : bordures encre 1.5px, ombres décalées dures, tuiles KPI blanches, pilules verdicts, micro-étiquettes Courier majuscules, tableaux à en-têtes mono soulignés\n   - Sections conditionnelles via `_safe(fn, *args)` (try/except → None) ; section omise si pas de données (pas de « N/D »)\n   - Badge héro : prix + verdict marché en pastille en haut de page 1\n   - Dernière page : références (courtier, URL originale, URL immo-ka + QR code), sources de données, disclaimer en pied sur toutes les pages\n   - ⚠️ Piège photos : `im.thumbnail((1200, 1200))`, `im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)`, repli octets bruts ; PDF < 1 Mo\n   - Tests sur annonces réelles : une très enrichie ET une pauvre (sans coordonnées/analyses) ; conversion `pdftoppm -png -r 72` + inspection visuelle de chaque page ; taille + temps de génération\n   - Déploiement OBLIGATOIRE avant de conclure : build si nécessaire, pm2 restart, healthcheck endpoint PDF local → 200 + site public, git add/commit/push origin main, message : `feat(pdf): fiche PDF complète — <sections couvertes>`\n   - Contraintes : ne pas toucher au frontend ni aux autres endpoints (seulement génération PDF + son endpoint si nécessaire) ; français partout (dates « novembre 2024 », montants « 1 425 $ » avec espaces insécables) ; pas de dépendances nouvelles (reportlab/Pillow/stdlib « suffisent » — hypothèse fausse ici, voir Erreurs) ; adapter noms fichiers/process PM2 en lisant le repo sur place.\n\n2. **Key Technical Concepts:**\n   - reportlab canvas (rl_canvas.Canvas, letter, HexColor, ImageReader), qrcode, Pillow thumbnail pour compression photos\n   - Pagination dynamique : `page=[1]`, `saut_page()`, `place(y, besoin)`, marge `M = 40`\n   - `_safe(fn, *a, **k)` → try/except None pour sections conditionnelles\n   - Style éditorial sharp PDF : classe `_Style` (fond, logo wordmark rotaté -3°, entete, pied, pilule, titre_section, paragraphe avec découpe aux mots, tuile KPI à ombre décalée)\n   - FastAPI (immoka/web.py), sqlite (db.connect, tables listings/price_log/poi_cache), PM2 (immo-ka-web = `.venv/bin/python run.py serve 8096`), ngrok → www.immo-ka.com\n   - Formats français : `NBSP = \" \"` (espace fine insécable), `_fmt_money` (1 425 $), `_fmt_mois` (« novembre 2017 » via liste `_MOIS`), `_fmt_dist` (m/km virgule décimale)\n   - venv Python 3.14 ; fpdf2 présent (kapdf.py stats) mais reportlab/qrcode ABSENTS → installés\n   - Mission 1 : tokens.css Groupe KA, accent immo-ka #e23744/#fbe0e2/#a8232e, Playwright (--no-save), check-order.mjs (BASE réel 8096, défaut 18096 obsolète)\n\n3. **Files and Code Sections:**\n\n   - **`frontend/src/styles.css`** (Mission 1, COMMITTÉ fc02d70) : +112 lignes en section finale « PASSE DE DESIGN — éditorial sharp » : cartes `.f-bloc:not(.f-galerie):not(.f-hero), .quartier` (surface, 1.5px ink, r-card, shadow-off-soft, padding 18/20 → 15 mobile) ; `.detail > .f-bloc { margin-top: 22px }` ; h2 flex + ::before carré accent 9px ombre 2px 2px 0 ink + ::after filet ; `.rdl-kpi` blanches ombre 3px 3px 0 accent-soft ; `.q-cell` ombre rgba(20,24,20,.1) ; tableaux zébrés + hover accent-soft, `.gaz-head th` mono 10px + border-bottom 1.5px ink ; barres 12px bordées + `.q-bar-fill` dégradé accent→accent-deep, `.q-crime-barre i` `linear-gradient(90deg, #f0808a, var(--accent))` ; `.zi-badge` inline-flex mono 10.5px + point ::before ; `.zi-liste li` mini-cartes liseré accent ; `.amenity { display:inline-flex; align-items:center; gap:6px }` ; `.fine` filet pointillé.\n\n   - **`/tmp/lou-ka-ref/louka/pdfgen.py` @ 6ff906e** (1201 lignes, RÉFÉRENCE COMPLÈTE LUE) : modèle à transposer. Points clés :\n     ```python\n     def _fetch_image(url):  # PIL thumbnail (1200,1200), JPEG quality=82, repli octets bruts\n     class _Style: fond/logo/entete/pied/pilule/titre_section/paragraphe/tuile\n     page = [1]\n     def saut_page(): s.pied(_PIED_FICHE, page[0]); c.showPage(); page[0]+=1; s.fond(); return s.entete(\"Fiche logement · suite\")\n     def place(y_, besoin): return saut_page() if (y_ - besoin) < M + 34 else y_\n     def _safe(fn, *a, **k): try: return fn(*a,**k) except: return None\n     _VERDICTS = {\"sous\": (...LIME_SOFT...), \"marche\": (...), \"sur\": (...AMBER...)}\n     _MOIS = [\"janvier\",...]; _fmt_mois(\"2017-11-04\") → \"novembre 2017\"\n     ```\n     Sections : prix+pilule verdict, titre+adresse, chips, photos (1 grande 170h 16/10.5 + 3 vignettes), description|inclusions 2 colonnes, analyse de prix (histogramme + bande fourchette + trait « CE LOYER »), registre loyers (3 tuiles + table à en-têtes Courier soulignés), TAL, inondation (pilules par statut), air, hydro, commerces 2 colonnes, essence, KA Scores (6 tuiles), quartier (6 tuiles démo + barres proximité + pilules chaleur/crime), à proximité (POI 2 col), références (QR 70×70 + URLs), pied partout.\n\n   - **`immoka/web.py`** (LU, à modifier pour l'endpoint) : routes existantes listées ; `_row_to_dict` parse features/images/details/vraiprix JSON + `days_on_market` depuis first_seen ; `GET /api/listings/{uid}` ajoute price_history (price_log LIMIT 10 DESC), poi (poi_cache, clé `f\"{round(lat,4)},{round(lng,4)}\"`), quartier via `quartier.fiche_quartier(lat, lng, city or \"\", dauid si != \"hors-zone\")`. Endpoints enrichissement : `/api/hydro` → `hydro.estimate(adresse=, uid=, lat=, lng=, solve=bool)`, `/api/commerces` → `commerces.nearby(lat,lng)`, `/api/air` → `air.lookup(lat,lng)` (None possible), `/api/gaz` → `gaz.nearby(lat,lng)`, `/api/inondation` → `inondation.lookup(lat,lng)` (None possible). `DEDUP_CLAUSE` non appliquée à la fiche par uid.\n\n   - **`frontend/src/pages/Listing.tsx`** (LU intégralement, NE PAS MODIFIER) : blocs = galerie, héro (kicker « Prix demandé »/« Loyer mensuel » si transaction=location, prix + `/mois`, type-chip, prix/pi² si price+area_sqft>200, h1 adresse, loc sector·city·region, spec-list : Type/Chambres/Salles de bain/Salles d'eau/Superficie/Terrain/Année/MLS-Centris, baisse prix (hist[0] vs hist[1]), bloc vraiprix (valeur, confiance, jauge low-high + est + ask, delta % over/under/fair caché si |pct|>60, lien), courtier (broker_name/phone), CTA source, fine « synchronisé le »), description, caractéristiques (`detEntries` = details filtrés par `DETAIL_HIDDEN` = {pieces, price_from, cover_thumb, photo_captions, img_audited, needs_image_review, postal_code, region, transaction, prix_pi2, prix_m2, listing_origin_url}), pièces (`details.pieces` as Room[] {nom, niveau, dimensions, revetement}), inclusions (features[]), carte, puis pleine largeur : RisqueInondation, QualiteAir, CommercesProches, EssenceProche, HydroEstimation, QuartierBlock.\n\n   - **Formes API live capturées (uid domicilia:17516934, http://localhost:8096)** :\n     - vraiprix : `{id, value, low, high, confidence, confidence_pct, url, valeur_role, valeur_terrain, valeur_batiment, annee_construction_role, superficie_terrain_role_m2, aire_etages_role_m2}`\n     - price_history : `[{ts: float epoch, price}]`\n     - quartier : `{dauid, demographie:{population, densite, age_median, revenu_median, pct_locataires, loyer_moyen, pct_francais, pct_univ}, proximite:{prox_epicerie, prox_pharmacie, prox_garderie, prox_ecole_prim, prox_sante, prox_bibliotheque, prox_parc, prox_emploi} (0–1), quintile_materiel/quintile_social (défavorisation), chaleur:{classe, ecart} si présent, crime: {type:\"points\", rayon_m:500, douze_mois,...} ou {type:\"igc\", ville, annee, indice, indice_canada}}`\n     - inondation : `{statut: en_zone|a_proximite|hors_zone|non_cartographie, severite, couvert, zones:[{type, severite, recurrence, distance_m, date_rapport}] (≤5)}`\n     - air : `{station, ville, distance_km, mesures:{POL:{moyenne, annee, n, unite, ref, ref_nom}}}`\n     - gaz : `{n, rayon_m, mediane_regulier, min_regulier, stations:[{nom, adresse, dist_m, regulier, super, diesel, moins_chere}], maj}`\n     - commerces : `{n, commerces:[{id, commerce, nom, adresse, dist_m, lat, lng}]}` (transit : fonctions `_fetch_transit`/`_transit_from_db` existent dans commerces.py — vérifier si `nearby()` renvoie une clé \"transit\")\n     - hydro : `{disponible: false, raison, en_attente}` OU `{disponible: true, adresse, cout_annuel, cout_mensuel, kwh_annuel, cache}`\n     - details exemple : postal_code, cover_thumb, Évaluation municipale, Nombre de pièces, photo_captions, region, Taxes scolaires, Taxes municipales, Inclusions, Exclusions, listing_origin_url, Courtier inscripteur, transaction\n\n   - **`immoka/kapdf.py`** : rapport stats existant en fpdf2 — NE PAS toucher ; confirme qu'il n'existe AUCUN PDF de fiche propriété (à créer, ex. `immoka/pdffiche.py`).\n   - **`requirements.txt`** : actuellement requests, beautifulsoup4, fastapi, uvicorn, fpdf2 — à compléter avec reportlab + qrcode.\n   - **Mémoire sauvegardée** : `~/.claude/projects/-Users-simon-pierreboucher-apps-immo-ka/memory/` (MEMORY.md + project_playwright_checks.md : BASE=8096, playwright --no-save, uid test).\n\n4. **Errors and fixes:**\n   - **Playwright introuvable** (`ERR_MODULE_NOT_FOUND` depuis /tmp puis frontend/) : non installé → `npm i --no-save playwright` + `npx playwright install chromium`.\n   - **Edit refusé sur styles.css** (« File has been modified since read » car ajout via heredoc Bash) : relu le fichier puis Edit OK.\n   - **Ancienne référence lou-ka** : clone depth 5 s'arrêtait à 4fa8714 (pdfgen 772 lignes, 2 pages fixes sans saut_page/place) → `git fetch --depth 50` a ramené `6ff906e` ; `git show 6ff906e:louka/pdfgen.py` → 1201 lignes avec l'approche complète.\n   - **reportlab/qrcode absents du venv immo-ka** (contrainte « pas de dépendances nouvelles » basée sur une hypothèse fausse — le repo utilise fpdf2) : décision = installer `reportlab` + `qrcode` via `.venv/bin/pip` (FAIT) car la mission spécifie explicitement l'approche reportlab canvas et le QR ; à épingler dans requirements.txt et à signaler dans le résumé final.\n   - **Heredoc Python inline bloqué** (« Backend admin-ka injoignable: fetch failed ») : contourné en écrivant `/tmp/shapes.py` puis `python3 /tmp/shapes.py`.\n   - Mission 1 : icône `.amenity` passait à la ligne (bug préexistant confirmé sur capture avant) → corrigé en inline-flex.\n\n5. **Problem Solving:**\n   - Mission 1 livrée de bout en bout (captures avant/après par bloc dans /tmp/immo-shots/, overflow 0px, check-order VALIDATION OK, commit fc02d70 poussé).\n   - Mission 2 : cartographie quasi terminée — tous les blocs web identifiés, formes de retour vérifiées par API live + lecture code (quartier.py : clés demographie/proximite/chaleur/crime ; inondation.py : statuts et champs zones ; hydro.py : retour cache disponible=True avec cout_annuel/cout_mensuel/kwh_annuel). Restait à vérifier : clé \"transit\" dans commerces.nearby(), rendu QuartierBlock.tsx (défavorisation/crime), choisir une annonce pauvre (sans lat/lng), disponibilité de pdftoppm.\n\n6. **All user messages:**\n   1. (Mission 1) « Applique à immo-ka la passe de design « éditorial sharp » déjà livrée sur lou-ka (commit 872a202)… Utilise Playwright desktop ET smartphone… tokens.css… accent variables jamais orange dur… cloner gitsrv:srv/git/lou-ka.git… 8 points de la passe… méthode captures avant/après, section finale styles.css, règle ordre DOM (check-order.mjs), zéro débordement, build + pm2 restart + healthcheck + commit/push message feat(design): passe « éditorial sharp » sur la fiche — cartes, KPI, tableaux, pastilles. »\n   2. (Mission 2, EN COURS) « Tu travailles sur immo-ka… refaire le PDF « Télécharger la fiche » pour qu'il reflète TOUT le contenu de la fiche web… même travail vécu sur lou-ka (louka/pdfgen.py, commit 6ff906e)… Cartographie d'abord (chaque bloc → module backend → forme de retour exacte, lis le code ne devine pas)… reportlab canvas : pagination dynamique page=[1], saut_page(), place(y, besoin) si y - besoin < M + 34, aucune section coupée… style éditorial sharp… _safe autour de chaque enrichissement, section omise si pas de données… badge héro prix + verdict pastille page 1… dernière page références + QR + sources + disclaimer partout… ⚠️ piège photos : thumbnail (1200,1200), JPEG quality=82, repli brut, PDF < 1 Mo… teste extrêmes riche/pauvre, pdftoppm -png -r 72, inspecte chaque page, taille + temps… Déploiement obligatoire : build si nécessaire, pm2 restart, healthcheck endpoint PDF local + public, git add/commit/push message feat(pdf): fiche PDF complète — <sections couvertes>. Ne termine jamais avec des modifs non commitées… Contraintes : pas toucher frontend ni autres endpoints ; français partout (novembre 2024, 1 425 $ insécables) ; pas de dépendances nouvelles ; adapte noms PM2/fichiers en lisant le repo. »\n\n7. **Pending Tasks (Mission 2):**\n   - Vérifier si `commerces.nearby()` renvoie la clé `transit` (fonctions transit existent) et le rendu QuartierBlock.tsx (crime/défavorisation) pour couvrir tous les blocs\n   - Trouver une annonce pauvre (sans lat/lng/vraiprix) pour le test de dégradation\n   - Écrire `immoka/pdffiche.py` (reportlab canvas, adapté : wordmark Immo·Ka accent cerise #e23744/#fbe0e2/#a8232e, en-tête « FICHE PROPRIÉTÉ » / « Fiche propriété · suite », www.immo-ka.com, sections : héro prix+pastille Vrai-Prix (delta vs value, masqué si |pct|>60), specs/chips, photos compressées, description, caractéristiques (details filtrés DETAIL_HIDDEN), pièces (details.pieces), inclusions (features), analyse Vrai-Prix (valeur, jauge low–high, rôle foncier valeur_role/terrain/batiment, taxes), historique de prix (price_log, dates en toutes lettres), inondation, air, commerces(+transit?), essence, hydro, quartier (tuiles démo + barres proximité + pilules chaleur/crime/défavorisation), POI, références courtier+source+QR `https://www.immo-ka.com/propriete/{uid}`, sources/disclaimer)\n   - Ajouter l'endpoint (ex. `GET /api/listings/{uid}/pdf`) dans web.py — seul ajout autorisé\n   - Épingler `reportlab` + `qrcode` dans requirements.txt (et signaler cette dérogation « pas de dépendances nouvelles » dans le résumé final)\n   - Tester riche (domicilia:17516934) + pauvre : taille (<1 Mo), temps, `pdftoppm -png -r 72` + inspection visuelle page par page (vérifier présence de pdftoppm, sinon alternative)\n   - Déployer : pm2 restart immo-ka-web, healthcheck endpoint PDF local 8096 → 200 + https://www.immo-ka.com, git add/commit/push origin main (`feat(pdf): fiche PDF complète — <sections couvertes>`)\n\n8. **Current Work:**\n   Cartographie backend de la Mission 2, presque achevée. Je venais d'installer reportlab+qrcode dans `.venv`, de lire intégralement `louka/pdfgen.py@6ff906e` (le modèle : `_Style`, `saut_page`, `place`, `_safe`, `_fetch_image` avec thumbnail Pillow, `_VERDICTS`, `_fmt_mois`), de lire `immoka/web.py` (endpoint fiche + enrichissements), de capturer les formes JSON live via `/tmp/shapes.py` sur `domicilia:17516934`, et le tout dernier appel était un grep multi-fichiers confirmant les retours de `immoka/quartier.py` (out[\"demographie\"], out[\"proximite\"] = r[k]/100, out[\"chaleur\"]={classe,ecart}, out[\"crime\"] type points/igc), `immoka/commerces.py` (fonctions transit présentes, forme de `nearby()` à confirmer), `immoka/hydro.py` (`{\"disponible\": True, \"adresse\", \"cout_annuel\", \"cout_mensuel\", \"kwh_annuel\", \"cache\"}` ou `{\"disponible\": False, \"raison\", ...}`), `immoka/inondation.py` (statuts en_zone/a_proximite/hors_zone/non_cartographie, zones {type, severite, recurrence, distance_m, date_rapport}).\n   TodoList : « Cartographier la fiche web + modules backend » in_progress ; « Écrire immoka/pdffiche.py », « Endpoint PDF + tester riche/pauvre », « pm2 restart + healthchecks + commit/push » pending.\n\n9. **Optional Next Step:**\n   Finir la cartographie puis écrire le générateur. Concrètement, dans l'ordre : (a) lire la fin de `immoka/commerces.py` (`def nearby` — présence de la clé `transit` dans le retour) et `frontend/src/components/QuartierBlock.tsx` (clés crime/défavorisation affichées) ; (b) sélectionner une annonce pauvre via l'API (`lat IS NULL` ou sans vraiprix) ; (c) créer `immoka/pdffiche.py` en transposant `louka/pdfgen.py@6ff906e` aux données immo-ka (vente, pas location : prix demandé, Vrai-Prix comme verdict du badge héro, rôle foncier/taxes, pièces, sans RDL/TAL/KA Scores qui n'existent pas ici) + endpoint `GET /api/listings/{uid}/pdf` dans `immoka/web.py` + requirements.txt. Cela correspond directement à la demande : « Réécris la génération PDF (reportlab canvas)… Le PDF doit couvrir chaque bloc qui a des données », suivi des tests « pdftoppm -png -r 72 » et du « Déploiement obligatoire avant de conclure… git add + git commit + git push origin main » avec le message « feat(pdf): fiche PDF complète — <sections couvertes> ».\n\nIf you need specific details from before compaction (like exact code snippets, error messages, or content you generated), read the full transcript at: /Users/simon-pierreboucher/.claude/projects/-Users-simon-pierreboucher-apps-immo-ka/1b217d06-32f6-4569-9b15-be2772e84b82.jsonl\nContinue the conversation from where it left off without asking the user any further questions. Resume directly — do not acknowledge the summary, do not recap what was happening, do not preface with \"I'll continue\" or similar. Pick up the last task as if the break never happened."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"c4a105b2-b0ae-4f47-8d6b-4a0ad97c568f","timestamp":"2026-08-24T21:27:29.365Z","isSynthetic":true}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012bXf5ptAybLJGqSZWYwQ6j","type":"tool_result","content":"230:def nearby(lat: float, lng: float) -> dict:\n231-    \"\"\"Grand commerce le plus proche par bannière (cache ~1 km, TTL 30 j).\"\"\"\n232-    cell = f\"{round(lat, 2)},{round(lng, 2)}\"\n233-    con = _connect()\n234-    now = time.time()\n235-    cached = {r[\"brand\"]: r for r in con.execute(\n236-        \"SELECT * FROM commerces_cache WHERE cellule=? AND fetched_at>?\",\n237-        (cell, now - TTL))}\n238-    manquants = [(bid, q, m) for bid, _, q, m in BRANDS\n239-                 if bid not in cached]\n240-    transit_manquant = any(k not in cached for k, _ in TRANSIT)\n241-    if manquants or transit_manquant:\n242-        res: list[tuple[str, dict | None]] = []\n243-        if manquants:\n244-            with ThreadPoolExecutor(max_workers=6) as ex:\n245-                res = list(ex.map(\n246-                    lambda b: (b[0], _fetch_brand(b[1], lat, lng, b[2])),\n247-                    manquants))\n248-        if transit_manquant:\n249-            tr = (_transit_from_db(lat, lng)\n250-                  or _transit_from_poi(lat, lng) or _fetch_transit(lat, lng))\n251-            res.extend(tr)\n252-        with con:\n253-            for bid, hit in res:\n254-                if hit is None:\n255-                    continue\n256-                con.execute(\n257-                    \"INSERT OR REPLACE INTO commerces_cache VALUES \"\n258-                    \"(?,?,?,?,?,?,?)\",\n259-                    (cell, bid, hit[\"nom\"],\n260-                     hit.get(\"adresse\") or (str(hit[\"_dist\"])\n261-                                            if hit.get(\"_dist\") is not None\n262-                                            else \"\"),\n263-                     hit[\"lat\"], hit[\"lng\"], now))\n264-        cached = {r[\"brand\"]: r for r in con.execute(\n265-            \"SELECT * FROM commerces_cache WHERE cellule=? AND fetched_at>?\",\n266-            (cell, now - TTL))}\n267-    con.close()\n268-\n269-    items = []\n270-    transit = []\n271-    for bid, label in TRANSIT:\n272-        r = cached.get(bid)\n273-        if r is not None:\n274-            # distance : celle du cache POI si disponible (adresse numérique)\n275-            d = (float(r[\"adresse\"]) if (r[\"adresse\"] or \"\").replace(\n276-                 \".\", \"\").isdigit() else _dist_m(lat, lng, r[\"lat\"], r[\"lng\"]))\n277-            if d <= 5000:\n278-                transit.append({\"id\": bid, \"commerce\": label,\n279-                                \"nom\": r[\"nom\"], \"adresse\": \"\",\n280-                                \"dist_m\": round(d),\n281-                                \"lat\": r[\"lat\"], \"lng\": r[\"lng\"]})\n282-    for bid, label, _q, _m in BRANDS:\n283-        r = cached.get(bid)\n284-        if r is None:\n285-            continue\n286-        d = _dist_m(lat, lng, r[\"lat\"], r[\"lng\"])\n287-        if d > 40000:                 # au-delà de 40 km : non pertinent\n288-            continue\n289-        items.append({\"id\": bid, \"commerce\": label, \"nom\": r[\"nom\"],\n290-                      \"adresse\": r[\"adresse\"], \"dist_m\": round(d),"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"26579cfc-d545-49fe-8994-044b99de0d43","timestamp":"2026-08-24T21:27:35.004Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"230:def nearby(lat: float, lng: float) -> dict:\n231-    \"\"\"Grand commerce le plus proche par bannière (cache ~1 km, TTL 30 j).\"\"\"\n232-    cell = f\"{round(lat, 2)},{round(lng, 2)}\"\n233-    con = _connect()\n234-    now = time.time()\n235-    cached = {r[\"brand\"]: r for r in con.execute(\n236-        \"SELECT * FROM commerces_cache WHERE cellule=? AND fetched_at>?\",\n237-        (cell, now - TTL))}\n238-    manquants = [(bid, q, m) for bid, _, q, m in BRANDS\n239-                 if bid not in cached]\n240-    transit_manquant = any(k not in cached for k, _ in TRANSIT)\n241-    if manquants or transit_manquant:\n242-        res: list[tuple[str, dict | None]] = []\n243-        if manquants:\n244-            with ThreadPoolExecutor(max_workers=6) as ex:\n245-                res = list(ex.map(\n246-                    lambda b: (b[0], _fetch_brand(b[1], lat, lng, b[2])),\n247-                    manquants))\n248-        if transit_manquant:\n249-            tr = (_transit_from_db(lat, lng)\n250-                  or _transit_from_poi(lat, lng) or _fetch_transit(lat, lng))\n251-            res.extend(tr)\n252-        with con:\n253-            for bid, hit in res:\n254-                if hit is None:\n255-                    continue\n256-                con.execute(\n257-                    \"INSERT OR REPLACE INTO commerces_cache VALUES \"\n258-                    \"(?,?,?,?,?,?,?)\",\n259-                    (cell, bid, hit[\"nom\"],\n260-                     hit.get(\"adresse\") or (str(hit[\"_dist\"])\n261-                                            if hit.get(\"_dist\") is not None\n262-                                            else \"\"),\n263-                     hit[\"lat\"], hit[\"lng\"], now))\n264-        cached = {r[\"brand\"]: r for r in con.execute(\n265-            \"SELECT * FROM commerces_cache WHERE cellule=? AND fetched_at>?\",\n266-            (cell, now - TTL))}\n267-    con.close()\n268-\n269-    items = []\n270-    transit = []\n271-    for bid, label in TRANSIT:\n272-        r = cached.get(bid)\n273-        if r is not None:\n274-            # distance : celle du cache POI si disponible (adresse numérique)\n275-            d = (float(r[\"adresse\"]) if (r[\"adresse\"] or \"\").replace(\n276-                 \".\", \"\").isdigit() else _dist_m(lat, lng, r[\"lat\"], r[\"lng\"]))\n277-            if d <= 5000:\n278-                transit.append({\"id\": bid, \"commerce\": label,\n279-                                \"nom\": r[\"nom\"], \"adresse\": \"\",\n280-                                \"dist_m\": round(d),\n281-                                \"lat\": r[\"lat\"], \"lng\": r[\"lng\"]})\n282-    for bid, label, _q, _m in BRANDS:\n283-        r = cached.get(bid)\n284-        if r is None:\n285-            continue\n286-        d = _dist_m(lat, lng, r[\"lat\"], r[\"lng\"])\n287-        if d > 40000:                 # au-delà de 40 km : non pertinent\n288-            continue\n289-        items.append({\"id\": bid, \"commerce\": label, \"nom\": r[\"nom\"],\n290-                      \"adresse\": r[\"adresse\"], \"dist_m\": round(d),","numLines":61}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FX6eaNSHAWniz88vx1oq7Q","type":"tool_result","content":"1\t// -----------------------------------------------------------------------------\n2\t// Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n3\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n4\t// components/QuartierBlock.tsx : section « Le quartier » de la fiche —\n5\t//   démographie du recensement 2021 (aire de diffusion ~500 hab.), scores de\n6\t//   proximité StatCan, îlot de chaleur/fraîcheur INSPQ, criminalité.\n7\t// -----------------------------------------------------------------------------\n8\timport { Quartier } from \"../api\";\n9\timport { Ico } from \"./Icons\";\n10\t\n11\tconst fmtMoney = (v: number | null | undefined) =>\n12\t  v == null ? null : v.toLocaleString(\"fr-CA\", { maximumFractionDigits: 0 }) + \" $\";\n13\tconst fmtPct = (v: number | null | undefined) =>\n14\t  v == null ? null : Math.round(v) + \" %\";\n15\t\n16\t// scores PMD affichés (clé backend -> libellé)\n17\tconst PROX_LABELS: [string, string, string][] = [\n18\t  [\"prox_epicerie\", \"Épiceries\", \"cart\"],\n19\t  [\"prox_transport\", \"Transport en commun\", \"bus\"],\n20\t  [\"prox_parc\", \"Parcs\", \"tree\"],\n21\t  [\"prox_ecole_prim\", \"Écoles primaires\", \"school\"],\n22\t  [\"prox_sante\", \"Soins de santé\", \"health\"],\n23\t  [\"prox_pharmacie\", \"Pharmacies\", \"pill\"],\n24\t];\n25\t\n26\tfunction chaleurBadge(classe: number, ecart: number | null) {\n27\t  if (classe <= 3)\n28\t    return { txt: \"Îlot de fraîcheur\", cls: \"q-badge cool\", ico: \"leaf\" };\n29\t  if (classe >= 7)\n30\t    return {\n31\t      txt: `Îlot de chaleur${ecart != null ? ` (+${ecart.toFixed(1)} °C)` : \"\"}`,\n32\t      cls: \"q-badge hot\", ico: \"thermo\",\n33\t    };\n34\t  return { txt: \"Température de quartier moyenne\", cls: \"q-badge neutral\", ico: \"sun\" };\n35\t}\n36\t\n37\texport default function QuartierBlock({ q }: { q: Quartier }) {\n38\t  const d = q.demographie;\n39\t  const stats: [string, string | null][] = d\n40\t    ? [\n41\t        [\"Revenu médian des ménages\", fmtMoney(d.revenu_median)],\n42\t        [\"Ménages locataires\", fmtPct(d.pct_locataires)],\n43\t        [\"Loyer moyen du secteur\", fmtMoney(d.loyer_moyen)],\n44\t        [\"Âge médian\", d.age_median != null ? `${Math.round(d.age_median)} ans` : null],\n45\t        [\"Français à la maison\", fmtPct(d.pct_francais)],\n46\t        [\"Diplôme universitaire\", fmtPct(d.pct_univ)],\n47\t      ]\n48\t    : [];\n49\t  const statsOk = stats.filter(([, v]) => v != null) as [string, string][];\n50\t  const prox = q.proximite ?? {};\n51\t  const proxOk = PROX_LABELS.filter(([k]) => typeof prox[k] === \"number\");\n52\t\n53\t  if (statsOk.length === 0 && proxOk.length === 0 && !q.chaleur && !q.crime)\n54\t    return null;\n55\t\n56\t  return (\n57\t    <section className=\"quartier\">\n58\t      <h2>Le quartier</h2>\n59\t      <p className=\"q-sub\">\n60\t        Secteur immédiat de l'immeuble (aire de diffusion du recensement, ± 500 habitants).\n61\t      </p>\n62\t\n63\t      {statsOk.length > 0 && (\n64\t        <div className=\"q-grid\">\n65\t          {statsOk.map(([label, val]) => (\n66\t            <div className=\"q-cell\" key={label}>\n67\t              <div className=\"q-val\">{val}</div>\n68\t              <div className=\"q-label\">{label}</div>\n69\t            </div>\n70\t          ))}\n71\t        </div>\n72\t      )}\n73\t\n74\t      {proxOk.length > 0 && (\n75\t        <div className=\"q-prox\">\n76\t          {proxOk.map(([k, label, ico]) => {\n77\t            const v = Math.max(0, Math.min(1, prox[k]));\n78\t            return (\n79\t              <div className=\"q-bar\" key={k}>\n80\t                <span className=\"q-bar-label\"><Ico name={ico} size={14} /> {label}</span>\n81\t                <span className=\"q-bar-track\">\n82\t                  <span className=\"q-bar-fill\" style={{ width: `${Math.round(v * 100)}%` }} />\n83\t                </span>\n84\t                <span className=\"q-bar-num\">{Math.round(v * 100)}</span>\n85\t              </div>\n86\t            );\n87\t          })}\n88\t          <div className=\"fine\">Accessibilité 0–100 — mesures de proximité de Statistique Canada.</div>\n89\t        </div>\n90\t      )}\n91\t\n92\t      <div className=\"q-badges\">\n93\t        {q.chaleur && (() => {\n94\t          const b = chaleurBadge(q.chaleur.classe, q.chaleur.ecart);\n95\t          return <span className={b.cls}><Ico name={b.ico} size={14} /> {b.txt}</span>;\n96\t        })()}\n97\t        {q.crime?.type === \"points\" && (\n98\t          <div className=\"q-crime\">\n99\t            <span className=\"q-badge neutral\">\n100\t              <Ico name=\"shield\" size={14} /> {q.crime.douze_mois} acte{q.crime.douze_mois > 1 ? \"s\" : \"\"} criminel{q.crime.douze_mois > 1 ? \"s\" : \"\"} à\n101\t              moins de 500 m (12 mois)\n102\t              {q.crime.douze_mois_precedents > 0 && (\n103\t                q.crime.douze_mois <= q.crime.douze_mois_precedents\n104\t                  ? ` · en baisse (${q.crime.douze_mois_precedents} l'année d'avant)`\n105\t                  : ` · en hausse (${q.crime.douze_mois_precedents} l'année d'avant)`\n106\t              )}\n107\t            </span>\n108\t            {(q.crime.categories?.length ?? 0) > 0 && (\n109\t              <ul className=\"q-crime-cats\">\n110\t                {q.crime.categories!.filter((c) => c.n + c.n_prec > 0).map((c) => {\n111\t                  const max = Math.max(...q.crime!.type === \"points\"\n112\t                    ? q.crime!.categories!.map((x) => x.n) : [1], 1);\n113\t                  const delta = c.n - c.n_prec;\n114\t                  return (\n115\t                    <li key={c.nom}>\n116\t                      <span className=\"q-crime-nom\">{c.nom}</span>\n117\t                      <span className=\"q-crime-barre\" aria-hidden=\"true\">\n118\t                        <i style={{ width: `${Math.max(3, (c.n / max) * 100)}%` }} />\n119\t                      </span>\n120\t                      <span className=\"q-crime-n\">{c.n}\n121\t                        <small>{delta === 0 ? \" =\" : delta > 0\n122\t                          ? ` ▲${delta}` : ` ▼${-delta}`}</small>\n123\t                      </span>\n124\t                    </li>\n125\t                  );\n126\t                })}\n127\t              </ul>\n128\t            )}\n129\t            <p className=\"fine q-crime-src\">\n130\t              Actes criminels enregistrés par le SPVM (données ouvertes,\n131\t              position approximée à l'intersection) — 12 derniers mois,\n132\t              variation vs les 12 précédents.\n133\t            </p>\n134\t          </div>\n135\t        )}\n136\t        {q.crime?.type === \"igc\" && (() => {\n137\t          const c = q.crime;\n138\t          if (c.indice_canada != null && c.indice_canada > 0) {\n139\t            const delta = Math.round(100 * (c.indice - c.indice_canada) / c.indice_canada);\n140\t            const sous = delta <= 0;\n141\t            return (\n142\t              <span className={`q-badge ${sous ? \"cool\" : \"neutral\"}`}>\n143\t                <Ico name=\"shield\" size={14} /> Criminalité {Math.abs(delta)} % {sous ? \"sous\" : \"au-dessus de\"} la\n144\t                moyenne canadienne\n145\t                <small className=\"q-badge-sub\">({c.ville} {c.annee} : {c.indice} · Canada : {c.indice_canada})</small>\n146\t              </span>\n147\t            );\n148\t          }\n149\t          return (\n150\t            <span className=\"q-badge neutral\">\n151\t              <Ico name=\"shield\" size={14} /> Gravité de la criminalité ({c.ville}, {c.annee}) : <b>{c.indice}</b>\n152\t            </span>\n153\t          );\n154\t        })()}\n155\t      </div>\n156\t\n157\t      <div className=\"fine\">\n158\t        Sources : Statistique Canada (Recensement 2021, licence ouverte), INSPQ\n159\t        (CC-BY 4.0){q.crime?.type === \"points\" ? \", Ville de Montréal (CC-BY 4.0)\" : \"\"}.\n160\t        Statistiques du secteur, pas de l'immeuble.\n161\t      </div>\n162\t    </section>\n163\t  );\n164\t}\n165\t"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"dda61f0e-3123-46a5-a4b4-7a97eeed5977","timestamp":"2026-08-24T21:27:36.040Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/components/QuartierBlock.tsx","content":"// -----------------------------------------------------------------------------\n// Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// components/QuartierBlock.tsx : section « Le quartier » de la fiche —\n//   démographie du recensement 2021 (aire de diffusion ~500 hab.), scores de\n//   proximité StatCan, îlot de chaleur/fraîcheur INSPQ, criminalité.\n// -----------------------------------------------------------------------------\nimport { Quartier } from \"../api\";\nimport { Ico } from \"./Icons\";\n\nconst fmtMoney = (v: number | null | undefined) =>\n  v == null ? null : v.toLocaleString(\"fr-CA\", { maximumFractionDigits: 0 }) + \" $\";\nconst fmtPct = (v: number | null | undefined) =>\n  v == null ? null : Math.round(v) + \" %\";\n\n// scores PMD affichés (clé backend -> libellé)\nconst PROX_LABELS: [string, string, string][] = [\n  [\"prox_epicerie\", \"Épiceries\", \"cart\"],\n  [\"prox_transport\", \"Transport en commun\", \"bus\"],\n  [\"prox_parc\", \"Parcs\", \"tree\"],\n  [\"prox_ecole_prim\", \"Écoles primaires\", \"school\"],\n  [\"prox_sante\", \"Soins de santé\", \"health\"],\n  [\"prox_pharmacie\", \"Pharmacies\", \"pill\"],\n];\n\nfunction chaleurBadge(classe: number, ecart: number | null) {\n  if (classe <= 3)\n    return { txt: \"Îlot de fraîcheur\", cls: \"q-badge cool\", ico: \"leaf\" };\n  if (classe >= 7)\n    return {\n      txt: `Îlot de chaleur${ecart != null ? ` (+${ecart.toFixed(1)} °C)` : \"\"}`,\n      cls: \"q-badge hot\", ico: \"thermo\",\n    };\n  return { txt: \"Température de quartier moyenne\", cls: \"q-badge neutral\", ico: \"sun\" };\n}\n\nexport default function QuartierBlock({ q }: { q: Quartier }) {\n  const d = q.demographie;\n  const stats: [string, string | null][] = d\n    ? [\n        [\"Revenu médian des ménages\", fmtMoney(d.revenu_median)],\n        [\"Ménages locataires\", fmtPct(d.pct_locataires)],\n        [\"Loyer moyen du secteur\", fmtMoney(d.loyer_moyen)],\n        [\"Âge médian\", d.age_median != null ? `${Math.round(d.age_median)} ans` : null],\n        [\"Français à la maison\", fmtPct(d.pct_francais)],\n        [\"Diplôme universitaire\", fmtPct(d.pct_univ)],\n      ]\n    : [];\n  const statsOk = stats.filter(([, v]) => v != null) as [string, string][];\n  const prox = q.proximite ?? {};\n  const proxOk = PROX_LABELS.filter(([k]) => typeof prox[k] === \"number\");\n\n  if (statsOk.length === 0 && proxOk.length === 0 && !q.chaleur && !q.crime)\n    return null;\n\n  return (\n    <section className=\"quartier\">\n      <h2>Le quartier</h2>\n      <p className=\"q-sub\">\n        Secteur immédiat de l'immeuble (aire de diffusion du recensement, ± 500 habitants).\n      </p>\n\n      {statsOk.length > 0 && (\n        <div className=\"q-grid\">\n          {statsOk.map(([label, val]) => (\n            <div className=\"q-cell\" key={label}>\n              <div className=\"q-val\">{val}</div>\n              <div className=\"q-label\">{label}</div>\n            </div>\n          ))}\n        </div>\n      )}\n\n      {proxOk.length > 0 && (\n        <div className=\"q-prox\">\n          {proxOk.map(([k, label, ico]) => {\n            const v = Math.max(0, Math.min(1, prox[k]));\n            return (\n              <div className=\"q-bar\" key={k}>\n                <span className=\"q-bar-label\"><Ico name={ico} size={14} /> {label}</span>\n                <span className=\"q-bar-track\">\n                  <span className=\"q-bar-fill\" style={{ width: `${Math.round(v * 100)}%` }} />\n                </span>\n                <span className=\"q-bar-num\">{Math.round(v * 100)}</span>\n              </div>\n            );\n          })}\n          <div className=\"fine\">Accessibilité 0–100 — mesures de proximité de Statistique Canada.</div>\n        </div>\n      )}\n\n      <div className=\"q-badges\">\n        {q.chaleur && (() => {\n          const b = chaleurBadge(q.chaleur.classe, q.chaleur.ecart);\n          return <span className={b.cls}><Ico name={b.ico} size={14} /> {b.txt}</span>;\n        })()}\n        {q.crime?.type === \"points\" && (\n          <div className=\"q-crime\">\n            <span className=\"q-badge neutral\">\n              <Ico name=\"shield\" size={14} /> {q.crime.douze_mois} acte{q.crime.douze_mois > 1 ? \"s\" : \"\"} criminel{q.crime.douze_mois > 1 ? \"s\" : \"\"} à\n              moins de 500 m (12 mois)\n              {q.crime.douze_mois_precedents > 0 && (\n                q.crime.douze_mois <= q.crime.douze_mois_precedents\n                  ? ` · en baisse (${q.crime.douze_mois_precedents} l'année d'avant)`\n                  : ` · en hausse (${q.crime.douze_mois_precedents} l'année d'avant)`\n              )}\n            </span>\n            {(q.crime.categories?.length ?? 0) > 0 && (\n              <ul className=\"q-crime-cats\">\n                {q.crime.categories!.filter((c) => c.n + c.n_prec > 0).map((c) => {\n                  const max = Math.max(...q.crime!.type === \"points\"\n                    ? q.crime!.categories!.map((x) => x.n) : [1], 1);\n                  const delta = c.n - c.n_prec;\n                  return (\n                    <li key={c.nom}>\n                      <span className=\"q-crime-nom\">{c.nom}</span>\n                      <span className=\"q-crime-barre\" aria-hidden=\"true\">\n                        <i style={{ width: `${Math.max(3, (c.n / max) * 100)}%` }} />\n                      </span>\n                      <span className=\"q-crime-n\">{c.n}\n                        <small>{delta === 0 ? \" =\" : delta > 0\n                          ? ` ▲${delta}` : ` ▼${-delta}`}</small>\n                      </span>\n                    </li>\n                  );\n                })}\n              </ul>\n            )}\n            <p className=\"fine q-crime-src\">\n              Actes criminels enregistrés par le SPVM (données ouvertes,\n              position approximée à l'intersection) — 12 derniers mois,\n              variation vs les 12 précédents.\n            </p>\n          </div>\n        )}\n        {q.crime?.type === \"igc\" && (() => {\n          const c = q.crime;\n          if (c.indice_canada != null && c.indice_canada > 0) {\n            const delta = Math.round(100 * (c.indice - c.indice_canada) / c.indice_canada);\n            const sous = delta <= 0;\n            return (\n              <span className={`q-badge ${sous ? \"cool\" : \"neutral\"}`}>\n                <Ico name=\"shield\" size={14} /> Criminalité {Math.abs(delta)} % {sous ? \"sous\" : \"au-dessus de\"} la\n                moyenne canadienne\n                <small className=\"q-badge-sub\">({c.ville} {c.annee} : {c.indice} · Canada : {c.indice_canada})</small>\n              </span>\n            );\n          }\n          return (\n            <span className=\"q-badge neutral\">\n              <Ico name=\"shield\" size={14} /> Gravité de la criminalité ({c.ville}, {c.annee}) : <b>{c.indice}</b>\n            </span>\n          );\n        })()}\n      </div>\n\n      <div className=\"fine\">\n        Sources : Statistique Canada (Recensement 2021, licence ouverte), INSPQ\n        (CC-BY 4.0){q.crime?.type === \"points\" ? \", Ville de Montréal (CC-BY 4.0)\" : \"\"}.\n        Statistiques du secteur, pas de l'immeuble.\n      </div>\n    </section>\n  );\n}\n","numLines":165,"startLine":1,"totalLines":165}}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHLDzDt6S2SVEutCJEQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UGcjZjsYYYu2ExyEtqNkuZ","name":"Bash","input":{"command":"sed -n 290,310p /Users/simon-pierreboucher/apps/immo-ka/immoka/commerces.py","description":"Lire la fin de nearby() dans commerces.py"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4826,"cache_read_input_tokens":42163,"cache_creation":{"ephemeral_5m_input_tokens":4826,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"a1dacba6-1512-4b37-ae75-1f7f5651b12d"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UGcjZjsYYYu2ExyEtqNkuZ","type":"tool_result","content":"                      \"adresse\": r[\"adresse\"], \"dist_m\": round(d),\n                      \"lat\": r[\"lat\"], \"lng\": r[\"lng\"]})\n    items.sort(key=lambda x: x[\"dist_m\"])\n    transit.sort(key=lambda x: x[\"dist_m\"])\n    return {\"n\": len(items), \"commerces\": items, \"transit\": transit}","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"9a305751-852f-430c-8e03-b9069fcb0642","timestamp":"2026-08-24T21:27:42.227Z","tool_use_result":{"stdout":"                      \"adresse\": r[\"adresse\"], \"dist_m\": round(d),\n                      \"lat\": r[\"lat\"], \"lng\": r[\"lng\"]})\n    items.sort(key=lambda x: x[\"dist_m\"])\n    transit.sort(key=lambda x: x[\"dist_m\"])\n    return {\"n\": len(items), \"commerces\": items, \"transit\": transit}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"permission_resolved","requestId":"abb102e6676dda2f","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G32xLPaYtv9cZkUSJa53QF","type":"tool_result","content":"/opt/homebrew/bin/pdftoppm\nok 5.0.1","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"de69251c-6526-4d8d-b1d5-ee106cc39800","timestamp":"2026-08-24T21:28:13.659Z","tool_use_result":{"stdout":"/opt/homebrew/bin/pdftoppm\nok 5.0.1","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHNzqHZF7AGAUpH1n7F","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":481,"cache_read_input_tokens":46989,"cache_creation":{"ephemeral_5m_input_tokens":481,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"7e8bb94d-7d7e-4af9-9b54-24d94213d3a6"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHNzqHZF7AGAUpH1n7F","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":481,"cache_read_input_tokens":46989,"cache_creation":{"ephemeral_5m_input_tokens":481,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"b77dbac2-e553-47ba-b19c-d998619ca377"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHNzqHZF7AGAUpH1n7F","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015BfQEnYtduSZrXLfXFdFVY","name":"Bash","input":{"command":".venv/bin/python - <<'EOF'\nfrom immoka import db\ncon = db.connect()\nrows = con.execute(\"\"\"SELECT uid, price, lat, lng, vraiprix IS NULL AND (lat IS NULL OR lat='') as pauvre,\n  length(coalesce(description,'')) dl, length(coalesce(images,'[]')) il, length(coalesce(details,'{}')) dtl\n  FROM listings WHERE (lat IS NULL OR lat='') LIMIT 5\"\"\").fetchall()\nfor r in rows: print(dict(r))\nprint('---')\nrows = con.execute(\"\"\"SELECT uid, price, vraiprix IS NULL vp, length(coalesce(description,'')) dl,\n  length(coalesce(images,'[]')) il FROM listings\n  WHERE vraiprix IS NULL ORDER BY il ASC LIMIT 5\"\"\").fetchall()\nfor r in rows: print(dict(r))\ncon.close()\nEOF","description":"Trouver une annonce pauvre (sans lat/lng ou sans vraiprix)"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":481,"cache_read_input_tokens":46989,"cache_creation":{"ephemeral_5m_input_tokens":481,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"21203739-fbd5-4f73-a14d-687a91360de4"}}
{"type":"permission_request","requestId":"03792e15be408998","tool":"Bash","input":{"command":".venv/bin/python - <<'EOF'\nfrom immoka import db\ncon = db.connect()\nrows = con.execute(\"\"\"SELECT uid, price, lat, lng, vraiprix IS NULL AND (lat IS NULL OR lat='') as pauvre,\n  length(coalesce(description,'')) dl, length(coalesce(images,'[]')) il, length(coalesce(details,'{}')) dtl\n  FROM listings WHERE (lat IS NULL OR lat='') LIMIT 5\"\"\").fetchall()\nfor r in rows: print(dict(r))\nprint('---')\nrows = con.execute(\"\"\"SELECT uid, price, vraiprix IS NULL vp, length(coalesce(description,'')) dl,\n  length(coalesce(images,'[]')) il FROM listings\n  WHERE vraiprix IS NULL ORDER BY il ASC LIMIT 5\"\"\").fetchall()\nfor r in rows: print(dict(r))\ncon.close()\nEOF","description":"Trouver une annonce pauvre (sans lat/lng ou sans vraiprix)"}}
{"type":"permission_resolved","requestId":"03792e15be408998","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015BfQEnYtduSZrXLfXFdFVY","type":"tool_result","content":"{'uid': 'remax_quebec:17719804', 'price': 325000.0, 'lat': None, 'lng': None, 'pauvre': 0, 'dl': 0, 'il': 2, 'dtl': 2}\n{'uid': 'remax_quebec:15345888', 'price': 35000.0, 'lat': None, 'lng': None, 'pauvre': 0, 'dl': 483, 'il': 900, 'dtl': 415}\n{'uid': 'remax_quebec:20176383', 'price': 79000.0, 'lat': None, 'lng': None, 'pauvre': 0, 'dl': 645, 'il': 675, 'dtl': 356}\n{'uid': 'remax_quebec:9088359', 'price': 550699.0, 'lat': None, 'lng': None, 'pauvre': 0, 'dl': 241, 'il': 814, 'dtl': 1404}\n{'uid': 'remax_quebec:10404286', 'price': 670634.0, 'lat': None, 'lng': None, 'pauvre': 0, 'dl': 241, 'il': 825, 'dtl': 1867}\n---\n{'uid': 'engel_volkers:22879915', 'price': None, 'vp': 1, 'dl': 150, 'il': 2}\n{'uid': 'engel_volkers:11869147', 'price': None, 'vp': 1, 'dl': 349, 'il': 2}\n{'uid': 'engel_volkers:18058443', 'price': None, 'vp': 1, 'dl': 586, 'il': 2}\n{'uid': 'engel_volkers:11426991', 'price': None, 'vp': 1, 'dl': 0, 'il': 2}\n{'uid': 'engel_volkers:17423008', 'price': None, 'vp': 1, 'dl': 0, 'il': 2}","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f9239b21-2b90-43dc-b677-165f0e3f9b61","timestamp":"2026-08-24T21:29:18.745Z","tool_use_result":{"stdout":"{'uid': 'remax_quebec:17719804', 'price': 325000.0, 'lat': None, 'lng': None, 'pauvre': 0, 'dl': 0, 'il': 2, 'dtl': 2}\n{'uid': 'remax_quebec:15345888', 'price': 35000.0, 'lat': None, 'lng': None, 'pauvre': 0, 'dl': 483, 'il': 900, 'dtl': 415}\n{'uid': 'remax_quebec:20176383', 'price': 79000.0, 'lat': None, 'lng': None, 'pauvre': 0, 'dl': 645, 'il': 675, 'dtl': 356}\n{'uid': 'remax_quebec:9088359', 'price': 550699.0, 'lat': None, 'lng': None, 'pauvre': 0, 'dl': 241, 'il': 814, 'dtl': 1404}\n{'uid': 'remax_quebec:10404286', 'price': 670634.0, 'lat': None, 'lng': None, 'pauvre': 0, 'dl': 241, 'il': 825, 'dtl': 1867}\n---\n{'uid': 'engel_volkers:22879915', 'price': None, 'vp': 1, 'dl': 150, 'il': 2}\n{'uid': 'engel_volkers:11869147', 'price': None, 'vp': 1, 'dl': 349, 'il': 2}\n{'uid': 'engel_volkers:18058443', 'price': None, 'vp': 1, 'dl': 586, 'il': 2}\n{'uid': 'engel_volkers:11426991', 'price': None, 'vp': 1, 'dl': 0, 'il': 2}\n{'uid': 'engel_volkers:17423008', 'price': None, 'vp': 1, 'dl': 0, 'il': 2}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"59565fc6-205c-4635-acd2-88412ab371ad","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHTo5zWr2SYNJsMFdtb","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1239,"cache_read_input_tokens":47470,"cache_creation":{"ephemeral_5m_input_tokens":1239,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"14d4be0e-0c11-4b7f-b71a-1b82791af560"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHTo5zWr2SYNJsMFdtb","type":"message","role":"assistant","content":[{"type":"text","text":"Annonce pauvre idéale : `remax_quebec:17719804` (prix seul, aucune photo/description/coordonnées). Je vérifie l'endpoint PDF existant dans web.py avant d'écrire le générateur."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1239,"cache_read_input_tokens":47470,"cache_creation":{"ephemeral_5m_input_tokens":1239,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"1e3d72e8-e764-4469-a2e8-b17cde389679"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHTo5zWr2SYNJsMFdtb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KtvkM91ThfZxSC35xpixRu","name":"Grep","input":{"pattern":"pdf","path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/web.py","output_mode":"content","-i":true,"-C":6},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1239,"cache_read_input_tokens":47470,"cache_creation":{"ephemeral_5m_input_tokens":1239,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"3505896d-8112-4e3c-82c6-da60d960204b"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KtvkM91ThfZxSC35xpixRu","type":"tool_result","content":"495-def stats_report(\n496-    period: str = Query(\"30j\"),\n497-    from_: str | None = Query(None, alias=\"from\"),\n498-    to: str | None = Query(None),\n499-    mode: str = Query(\"complet\"),\n500-):\n501:    \"\"\"Rapport PDF estampillé Groupe-KA (gabarit commun immoka/kapdf.py).\"\"\"\n502:    from . import kapdf\n503-    dash = kastats.dashboard(period, from_, to)\n504-    site = {\n505-        \"wordmark\": \"Immo·Ka\",\n506-        \"accent\": \"#e23744\",\n507-        \"domain\": \"www.immo-ka.com\",\n508-        \"tagline\": \"Agrégateur de propriétés à vendre — province de Québec\",\n509-    }\n510:    pdf = kapdf.GroupeKAReport(\n511-        site=site, dashboard=dash,\n512-        mode=\"synthese\" if mode == \"synthese\" else \"complet\").build()\n513-    return Response(\n514:        content=pdf, media_type=\"application/pdf\",\n515-        headers={\"Content-Disposition\":\n516:                 f'attachment; filename=\"{kapdf.filename(\"immo-ka\", period)}\"'})\n517-\n518-\n519-\n520-@app.get(\"/api/stats/catalog\")\n521-def stats_catalog(\n522-    period: str = Query(\"30j\"),\n523-    from_: str | None = Query(None, alias=\"from\"),\n524-    to: str | None = Query(None),\n525-):\n526-    \"\"\"v3 — blocs composables pour le constructeur de rapports personnalisés.\"\"\"\n527:    from . import kapdf\n528-    dash = kastats.dashboard(period, from_, to)\n529-    return {\"updated\": dash.get(\"updated\"), \"period\": dash.get(\"period\"),\n530:            \"blocks\": kapdf.catalog(dash)}\n531-\n532-\n533-@app.post(\"/api/stats/report/custom\")\n534-def stats_report_custom(spec: dict = Body(...)):\n535:    \"\"\"v3 — rapport PDF personnalisé : {\"title\", \"period\", \"from\", \"to\",\n536-    \"blocks\": [{\"key\": \"series:…\", \"render\": \"bar\"}, …]} (SPEC.md §3bis).\"\"\"\n537:    from . import kapdf\n538-    period = str(spec.get(\"period\") or \"30j\")\n539-    dash = kastats.dashboard(period, str(spec.get(\"from\") or \"\") or None,\n540-                             str(spec.get(\"to\") or \"\") or None)\n541-    site = {\n542-        \"wordmark\": \"Immo·Ka\",\n543-        \"accent\": \"#e23744\",\n544-        \"domain\": \"www.immo-ka.com\",\n545-        \"tagline\": \"Agrégateur de propriétés à vendre — province de Québec\",\n546-    }\n547:    known = {b[\"key\"] for b in kapdf.catalog(dash)}\n548-    blocks = [b for b in (spec.get(\"blocks\") or [])\n549-              if isinstance(b, dict) and b.get(\"key\") in known][:40]\n550-    if not blocks:\n551-        raise HTTPException(400, \"Aucun bloc valide dans la composition\")\n552:    pdf = kapdf.GroupeKAReport(\n553:        site=site, dashboard=dash, mode=kapdf.CUSTOM_MODE,\n554-        spec={\"title\": str(spec.get(\"title\") or \"\")[:80], \"blocks\": blocks},\n555-    ).build()\n556:    fname = kapdf.filename(\"immo-ka\", period, kapdf.CUSTOM_MODE)\n557-    return Response(\n558:        content=pdf, media_type=\"application/pdf\",\n559-        headers={\"Content-Disposition\": f'attachment; filename=\"{fname}\"'})\n560-\n561-@app.post(\"/api/sync\")\n562-def trigger_sync(background: BackgroundTasks, source: str | None = None):\n563-    \"\"\"Déclenche une synchronisation (équivalent d'un webhook entrant).\"\"\"\n564-    def _job():\n--\n600-        app.mount(\"/assets\", StaticFiles(directory=FRONTEND_DIR / \"assets\"), name=\"assets\")\n601-\n602-    @app.get(\"/doc\", include_in_schema=False)\n603-    def doc_redirect():\n604-        return RedirectResponse(\"/doc/\", status_code=301)\n605-\n606:    # /doc : documentation statique (index.html + captures + PDF) — montée\n607-    # explicitement pour que le catch-all SPA ne l'intercepte pas.\n608-    if (FRONTEND_DIR / \"doc\").is_dir():\n609-        app.mount(\"/doc\", StaticFiles(directory=FRONTEND_DIR / \"doc\", html=True), name=\"doc\")\n610-\n611-    @app.get(\"/{full_path:path}\")\n612-    def spa(full_path: str, request: Request):"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"b44fd9b1-f651-436e-ab1f-0ec4e7e6f0c1","timestamp":"2026-08-24T21:29:29.299Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"495-def stats_report(\n496-    period: str = Query(\"30j\"),\n497-    from_: str | None = Query(None, alias=\"from\"),\n498-    to: str | None = Query(None),\n499-    mode: str = Query(\"complet\"),\n500-):\n501:    \"\"\"Rapport PDF estampillé Groupe-KA (gabarit commun immoka/kapdf.py).\"\"\"\n502:    from . import kapdf\n503-    dash = kastats.dashboard(period, from_, to)\n504-    site = {\n505-        \"wordmark\": \"Immo·Ka\",\n506-        \"accent\": \"#e23744\",\n507-        \"domain\": \"www.immo-ka.com\",\n508-        \"tagline\": \"Agrégateur de propriétés à vendre — province de Québec\",\n509-    }\n510:    pdf = kapdf.GroupeKAReport(\n511-        site=site, dashboard=dash,\n512-        mode=\"synthese\" if mode == \"synthese\" else \"complet\").build()\n513-    return Response(\n514:        content=pdf, media_type=\"application/pdf\",\n515-        headers={\"Content-Disposition\":\n516:                 f'attachment; filename=\"{kapdf.filename(\"immo-ka\", period)}\"'})\n517-\n518-\n519-\n520-@app.get(\"/api/stats/catalog\")\n521-def stats_catalog(\n522-    period: str = Query(\"30j\"),\n523-    from_: str | None = Query(None, alias=\"from\"),\n524-    to: str | None = Query(None),\n525-):\n526-    \"\"\"v3 — blocs composables pour le constructeur de rapports personnalisés.\"\"\"\n527:    from . import kapdf\n528-    dash = kastats.dashboard(period, from_, to)\n529-    return {\"updated\": dash.get(\"updated\"), \"period\": dash.get(\"period\"),\n530:            \"blocks\": kapdf.catalog(dash)}\n531-\n532-\n533-@app.post(\"/api/stats/report/custom\")\n534-def stats_report_custom(spec: dict = Body(...)):\n535:    \"\"\"v3 — rapport PDF personnalisé : {\"title\", \"period\", \"from\", \"to\",\n536-    \"blocks\": [{\"key\": \"series:…\", \"render\": \"bar\"}, …]} (SPEC.md §3bis).\"\"\"\n537:    from . import kapdf\n538-    period = str(spec.get(\"period\") or \"30j\")\n539-    dash = kastats.dashboard(period, str(spec.get(\"from\") or \"\") or None,\n540-                             str(spec.get(\"to\") or \"\") or None)\n541-    site = {\n542-        \"wordmark\": \"Immo·Ka\",\n543-        \"accent\": \"#e23744\",\n544-        \"domain\": \"www.immo-ka.com\",\n545-        \"tagline\": \"Agrégateur de propriétés à vendre — province de Québec\",\n546-    }\n547:    known = {b[\"key\"] for b in kapdf.catalog(dash)}\n548-    blocks = [b for b in (spec.get(\"blocks\") or [])\n549-              if isinstance(b, dict) and b.get(\"key\") in known][:40]\n550-    if not blocks:\n551-        raise HTTPException(400, \"Aucun bloc valide dans la composition\")\n552:    pdf = kapdf.GroupeKAReport(\n553:        site=site, dashboard=dash, mode=kapdf.CUSTOM_MODE,\n554-        spec={\"title\": str(spec.get(\"title\") or \"\")[:80], \"blocks\": blocks},\n555-    ).build()\n556:    fname = kapdf.filename(\"immo-ka\", period, kapdf.CUSTOM_MODE)\n557-    return Response(\n558:        content=pdf, media_type=\"application/pdf\",\n559-        headers={\"Content-Disposition\": f'attachment; filename=\"{fname}\"'})\n560-\n561-@app.post(\"/api/sync\")\n562-def trigger_sync(background: BackgroundTasks, source: str | None = None):\n563-    \"\"\"Déclenche une synchronisation (équivalent d'un webhook entrant).\"\"\"\n564-    def _job():\n--\n600-        app.mount(\"/assets\", StaticFiles(directory=FRONTEND_DIR / \"assets\"), name=\"assets\")\n601-\n602-    @app.get(\"/doc\", include_in_schema=False)\n603-    def doc_redirect():\n604-        return RedirectResponse(\"/doc/\", status_code=301)\n605-\n606:    # /doc : documentation statique (index.html + captures + PDF) — montée\n607-    # explicitement pour que le catch-all SPA ne l'intercepte pas.\n608-    if (FRONTEND_DIR / \"doc\").is_dir():\n609-        app.mount(\"/doc\", StaticFiles(directory=FRONTEND_DIR / \"doc\", html=True), name=\"doc\")\n610-\n611-    @app.get(\"/{full_path:path}\")\n612-    def spa(full_path: str, request: Request):","numLines":84}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019GLEEMkrYyBC2EJkmQS6wg","type":"tool_result","content":"1-// -----------------------------------------------------------------------------\n2-// Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n3-/ Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n4:// pages/Listing.tsx : fiche complète d'une propriété\n5-//   galerie + lightbox · specs · caractéristiques (details) · pièces ·\n6-//   inclusions · description · historique de prix · courtier · mini-carte\n7-// -----------------------------------------------------------------------------\n--\n221-  if (!l)\n222-    return (\n223-      <div className=\"container detail\">\n224:        <div className=\"fiche\" aria-busy=\"true\">\n225-          <div className=\"skel\"><div className=\"sk-img\" /></div>\n226-          <div className=\"skel\"><div className=\"sk-line\" /><div className=\"sk-line\" /><div className=\"sk-line short\" /></div>\n227-        </div>\n--\n255-        <span>{l.address || l.title}</span>\n256-      </nav>\n257-\n258:      <div className=\"fiche\">\n259-        {/* -------- colonne gauche : galerie, prix/synthèse, description, ------\n260-             -------- caractéristiques, pièces — ordre du DOM = ordre visuel ---- */}\n261-        <div className=\"f-col\">\n--\n469-      {l.quartier && <QuartierBlock q={l.quartier} />}\n470-\n471-      <div className=\"fine f-foot\">\n472:        Les prix et disponibilités sont ceux affichés par la source — chaque fiche renvoie à\n473-        l'annonce originale de l'agence.\n474-      </div>\n475-"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"0b63f771-7f0a-415d-bd5a-081b22cc5e5e","timestamp":"2026-08-24T21:29:30.062Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"1-// -----------------------------------------------------------------------------\n2-// Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n3-/ Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n4:// pages/Listing.tsx : fiche complète d'une propriété\n5-//   galerie + lightbox · specs · caractéristiques (details) · pièces ·\n6-//   inclusions · description · historique de prix · courtier · mini-carte\n7-// -----------------------------------------------------------------------------\n--\n221-  if (!l)\n222-    return (\n223-      <div className=\"container detail\">\n224:        <div className=\"fiche\" aria-busy=\"true\">\n225-          <div className=\"skel\"><div className=\"sk-img\" /></div>\n226-          <div className=\"skel\"><div className=\"sk-line\" /><div className=\"sk-line\" /><div className=\"sk-line short\" /></div>\n227-        </div>\n--\n255-        <span>{l.address || l.title}</span>\n256-      </nav>\n257-\n258:      <div className=\"fiche\">\n259-        {/* -------- colonne gauche : galerie, prix/synthèse, description, ------\n260-             -------- caractéristiques, pièces — ordre du DOM = ordre visuel ---- */}\n261-        <div className=\"f-col\">\n--\n469-      {l.quartier && <QuartierBlock q={l.quartier} />}\n470-\n471-      <div className=\"fine f-foot\">\n472:        Les prix et disponibilités sont ceux affichés par la source — chaque fiche renvoie à\n473-        l'annonce originale de l'agence.\n474-      </div>\n475-","numLines":31}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHUdSqMfBn5myo4tMY2","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PJYyGtarFUkauvLLVxf6hu","name":"Grep","input":{"pattern":"Télécharger|telecharger|/pdf|fiche.pdf","path":"/Users/simon-pierreboucher/apps/immo-ka","output_mode":"content","-i":true,"glob":"!node_modules","head_limit":40},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2927,"cache_read_input_tokens":48709,"cache_creation":{"ephemeral_5m_input_tokens":2927,"ephemeral_1h_input_tokens":0},"output_tokens":7,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"25fb2a45-5bd1-494a-9c97-9726944c6d5e"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PJYyGtarFUkauvLLVxf6hu","type":"tool_result","content":"immoka/poi.py:214:    # 1) télécharger les POI des tuiles nécessaires (une requête par tuile)\nimmoka/imgaudit.py:86:# Décodage des dimensions depuis les premiers octets (sans télécharger tout)\nfrontend/src/ka/stats/SPEC.md:109:→ `application/pdf`, en-tête `Content-Disposition: attachment; filename=\nfrontend/src/ka/stats/SPEC.md:183:→ `application/pdf`, filename `groupe-ka_<plateforme>_stats_<periode>_personnalise_<date>.pdf`.\nimmoka/web.py:514:        content=pdf, media_type=\"application/pdf\",\nimmoka/web.py:558:        content=pdf, media_type=\"application/pdf\",\nfrontend/public/doc/index.html:61:      <a class=\"btn-pdf\" href=\"./immo-ka-documentation.pdf\" download>Télécharger le PDF</a>"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"4da43a4a-031d-40e4-a58a-c0bc67b05c42","timestamp":"2026-08-24T21:29:35.666Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"immoka/poi.py:214:    # 1) télécharger les POI des tuiles nécessaires (une requête par tuile)\nimmoka/imgaudit.py:86:# Décodage des dimensions depuis les premiers octets (sans télécharger tout)\nfrontend/src/ka/stats/SPEC.md:109:→ `application/pdf`, en-tête `Content-Disposition: attachment; filename=\nfrontend/src/ka/stats/SPEC.md:183:→ `application/pdf`, filename `groupe-ka_<plateforme>_stats_<periode>_personnalise_<date>.pdf`.\nimmoka/web.py:514:        content=pdf, media_type=\"application/pdf\",\nimmoka/web.py:558:        content=pdf, media_type=\"application/pdf\",\nfrontend/public/doc/index.html:61:      <a class=\"btn-pdf\" href=\"./immo-ka-documentation.pdf\" download>Télécharger le PDF</a>","numLines":7}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHV3HnncQhcqRYgg1HH","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":621,"cache_read_input_tokens":51636,"cache_creation":{"ephemeral_5m_input_tokens":621,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"2946b3a9-babc-4798-a29c-fc1b6bfc858b"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHV3HnncQhcqRYgg1HH","type":"message","role":"assistant","content":[{"type":"text","text":"Aucun endpoint PDF fiche n'existe — je le crée de zéro (`immoka/pdffiche.py` + `GET /api/listings/{uid}/pdf`), sans toucher au frontend. Je relis le modèle lou-ka pour la transposition."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":621,"cache_read_input_tokens":51636,"cache_creation":{"ephemeral_5m_input_tokens":621,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"41964211-5a37-47fe-812a-4ce715882e56"}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016tEwdP2aZYdeLcVm4BieMH","type":"tool_result","content":"1\t# -----------------------------------------------------------------------------\n2\t# Lou-Ka — Agrégateur de logements à louer (province de Québec)\n3\t# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n4\t# pdfgen.py : génération PDF à l'identité visuelle officielle Lou-Ka\n5\t#   - fiche_pdf(uid)  : fiche de propriété complète — tout ce que montre la\n6\t#     fiche web : photos, prix + analyse Lou-Ka (juste valeur, histogramme),\n7\t#     chips, description structurée, inclusions, registre des loyers,\n8\t#     historique TAL, inondation, qualité de l'air, coût d'électricité,\n9\t#     commerces/transport, essence, KA Scores, quartier, à proximité, QR\n10\t#   - rapport_pdf()   : rapport global du marché (totaux, régions, histogramme\n11\t#     des loyers, types, villes, gestionnaires)\n12\t#   Palette identique au site : blanc #ffffff, marine #0b1330, orange #ff6a00,\n13\t#   bleu vif #2258ff, gris pâle #f2f2f2. Polices de base (Helvetica) stylées\n14\t#   par la mise en page — aucune dépendance de police externe.\n15\t# -----------------------------------------------------------------------------\n16\tfrom __future__ import annotations\n17\t\n18\timport io\n19\timport json\n20\timport math\n21\timport time\n22\t\n23\timport qrcode\n24\timport requests\n25\tfrom reportlab.lib.colors import HexColor\n26\tfrom reportlab.lib.pagesizes import letter\n27\tfrom reportlab.lib.utils import ImageReader\n28\tfrom reportlab.pdfgen import canvas as rl_canvas\n29\t\n30\tfrom . import db\n31\t\n32\t# --- palette Lou-Ka (identité officielle) ------------------------------------\n33\tPAPER = HexColor(\"#ffffff\")\n34\tSURFACE = HexColor(\"#ffffff\")\n35\tINK = HexColor(\"#0b1330\")          # bleu marine profond\n36\tINK2 = HexColor(\"#3e4560\")\n37\tINK3 = HexColor(\"#8a90a5\")\n38\tGREEN = HexColor(\"#2258ff\")        # accent secondaire : bleu vif\n39\tGREEN_DEEP = HexColor(\"#0b1330\")\n40\tLIME = HexColor(\"#ff6a00\")         # accent d'action : orange vif\n41\tLIME_SOFT = HexColor(\"#fff1e6\")\n42\tAMBER = HexColor(\"#ff6a00\")\n43\tAMBER_SOFT = HexColor(\"#fff1e6\")\n44\t\n45\tPAGE_W, PAGE_H = letter\n46\tM = 40          # marge\n47\tIMG_TIMEOUT = 6\n48\tUA = \"LouKaBot/1.0 (+https://www.lou-ka.com/bot; contact@spboucher.ai)\"\n49\t\n50\tPOI_LABELS = {\n51\t    \"epicerie\": \"Épicerie\", \"depanneur\": \"Dépanneur\", \"pharmacie\": \"Pharmacie\",\n52\t    \"ecole\": \"École\", \"garderie\": \"Garderie\", \"parc\": \"Parc\", \"bus\": \"Arrêt de bus\",\n53\t    \"metro\": \"Métro\", \"gym\": \"Gym\", \"cafe\": \"Café\", \"clinique\": \"Clinique\",\n54\t    \"hopital\": \"Hôpital\", \"bibliotheque\": \"Bibliothèque\",\n55\t}\n56\tPROX_LABELS = [\n57\t    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n58\t    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n59\t    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n60\t]\n61\t\n62\tNBSP = \" \"\n63\t\n64\t\n65\tdef _fmt_money(v) -> str:\n66\t    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n67\t\n68\t\n69\tdef _fmt_dist(m_: float) -> str:\n70\t    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n71\t            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n72\t\n73\t\n74\tdef _marche(m_: float) -> str:\n75\t    return f\"≈{NBSP}{max(1, round(m_ * 1.3 / 80))}{NBSP}min à pied\"\n76\t\n77\t\n78\tdef _fetch_image(url: str) -> ImageReader | None:\n79\t    try:\n80\t        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n81\t        r.raise_for_status()\n82\t        raw = r.content\n83\t        try:\n84\t            # les photos source dépassent parfois 5 Mo chacune : sans\n85\t            # redimensionnement le PDF grimpe à 30 Mo\n86\t            from PIL import Image\n87\t            im = Image.open(io.BytesIO(raw))\n88\t            im.thumbnail((1200, 1200))\n89\t            buf = io.BytesIO()\n90\t            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)\n91\t            buf.seek(0)\n92\t            return ImageReader(buf)\n93\t        except Exception:\n94\t            return ImageReader(io.BytesIO(raw))\n95\t    except Exception:\n96\t        return None\n97\t\n98\t\n99\tclass _Style:\n100\t    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n101\t\n102\t    def __init__(self, c: rl_canvas.Canvas):\n103\t        self.c = c\n104\t\n105\t    def fond(self):\n106\t        self.c.setFillColor(PAPER)\n107\t        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n108\t\n109\t    def logo(self, x: float, y: float, taille: float = 22):\n110\t        \"\"\"Wordmark Lou·Ka : « Lou » encre + boîte encre avec « Ka » lime.\"\"\"\n111\t        c = self.c\n112\t        c.setFont(\"Helvetica-Bold\", taille)\n113\t        c.setFillColor(INK)\n114\t        c.drawString(x, y, \"Lou\")\n115\t        w = c.stringWidth(\"Lou\", \"Helvetica-Bold\", taille)\n116\t        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n117\t        c.saveState()\n118\t        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n119\t        c.rotate(-3)\n120\t        c.setFillColor(INK)\n121\t        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n122\t        c.setFillColor(LIME)\n123\t        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n124\t        c.restoreState()\n125\t        return x + w + 6 + bw\n126\t\n127\t    def entete(self, titre: str):\n128\t        c = self.c\n129\t        self.logo(M, PAGE_H - M - 16)\n130\t        c.setFont(\"Courier-Bold\", 8)\n131\t        c.setFillColor(INK3)\n132\t        c.drawRightString(PAGE_W - M, PAGE_H - M - 6,\n133\t                          titre.upper())\n134\t        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n135\t                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n136\t        c.setStrokeColor(INK)\n137\t        c.setLineWidth(2)\n138\t        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n139\t        return PAGE_H - M - 40   # y de départ du contenu\n140\t\n141\t    def pied(self, texte: str, page: int | None = None):\n142\t        c = self.c\n143\t        c.setStrokeColor(INK)\n144\t        c.setLineWidth(1)\n145\t        c.line(M, M + 16, PAGE_W - M, M + 16)\n146\t        c.setFont(\"Helvetica\", 6.5)\n147\t        c.setFillColor(INK3)\n148\t        c.drawString(M, M + 6, texte[:150])\n149\t        if page:\n150\t            c.setFont(\"Courier-Bold\", 7)\n151\t            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n152\t\n153\t    def pilule(self, x: float, y: float, texte: str, fg=LIME, bg=INK,\n154\t               stroke=None, taille: float = 8) -> float:\n155\t        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n156\t        c = self.c\n157\t        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n158\t        c.setFillColor(bg)\n159\t        if stroke:\n160\t            c.setStrokeColor(stroke)\n161\t            c.setLineWidth(1.2)\n162\t        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n163\t                    stroke=1 if stroke else 0, fill=1)\n164\t        c.setFillColor(fg)\n165\t        c.setFont(\"Helvetica-Bold\", taille)\n166\t        c.drawString(x + 7, y + 5, texte)\n167\t        return x + w + 6\n168\t\n169\t    def titre_section(self, y: float, texte: str) -> float:\n170\t        c = self.c\n171\t        c.setFont(\"Helvetica-Bold\", 12.5)\n172\t        c.setFillColor(INK)\n173\t        c.drawString(M, y, texte)\n174\t        c.setStrokeColor(LIME)\n175\t        c.setLineWidth(3)\n176\t        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n177\t        c.line(M, y - 4, M + w, y - 4)\n178\t        return y - 18\n179\t\n180\t    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n181\t                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n182\t                   max_lignes: int = 100) -> float:\n183\t        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n184\t        c = self.c\n185\t        c.setFont(\"Helvetica\", taille)\n186\t        c.setFillColor(couleur)\n187\t        mots = texte.split()\n188\t        ligne = \"\"\n189\t        n = 0\n190\t        for mot in mots:\n191\t            essai = (ligne + \" \" + mot).strip()\n192\t            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n193\t                ligne = essai\n194\t            else:\n195\t                c.drawString(x, y, ligne)\n196\t                y -= interligne\n197\t                n += 1\n198\t                ligne = mot\n199\t                if n >= max_lignes - 1:\n200\t                    ligne += \" …\"\n201\t                    break\n202\t        if ligne:\n203\t            c.drawString(x, y, ligne)\n204\t            y -= interligne\n205\t        return y\n206\t\n207\t    def tuile(self, x: float, y: float, w: float, h: float,\n208\t              val: str, lab: str):\n209\t        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n210\t        c = self.c\n211\t        c.setFillColor(INK)\n212\t        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n213\t        c.setFillColor(SURFACE)\n214\t        c.setStrokeColor(INK)\n215\t        c.setLineWidth(1.2)\n216\t        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n217\t        c.setFont(\"Helvetica-Bold\", 12)\n218\t        c.setFillColor(INK)\n219\t        c.drawString(x + 8, y + h - 20, str(val))\n220\t        c.setFont(\"Courier\", 6.2)\n221\t        c.setFillColor(INK3)\n222\t        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n223\t\n224\t\n225\t# ---------------------------------------------------------------------------\n226\t# Fiche de propriété\n227\t# ---------------------------------------------------------------------------\n228\t\n229\t_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n230\t         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n231\t\n232\t\n233\tdef _fmt_mois(d: str | None) -> str:\n234\t    \"\"\"« 2017-11-04 » → « novembre 2017 » (sinon la valeur telle quelle).\"\"\"\n235\t    if not d:\n236\t        return \"—\"\n237\t    try:\n238\t        an, mois = d[:7].split(\"-\")\n239\t        return f\"{_MOIS[int(mois) - 1]} {an}\"\n240\t    except Exception:\n241\t        return d\n242\t\n243\t\n244\t_VERDICTS = {\n245\t    \"sous\": (\"Sous le marché\", GREEN_DEEP, LIME_SOFT, GREEN),\n246\t    \"marche\": (\"Dans le marché\", INK2, SURFACE, INK),\n247\t    \"sur\": (\"Au-dessus du marché\", HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER),\n248\t}\n249\t\n250\t_PIED_FICHE = (\"Données de l'annonce publiée par le gestionnaire — Lou-Ka est \"\n251\t               \"un agrégateur indépendant. Enrichissements (analyse de prix, \"\n252\t               \"registre, TAL, environnement) fournis à titre indicatif, sans \"\n253\t               \"valeur juridique.\")\n254\t\n255\tdef fiche_pdf(uid: str) -> bytes | None:\n256\t    \"\"\"PDF de la fiche du logement `uid` — reflète TOUS les blocs de la fiche\n257\t    web : prix + analyse Lou-Ka, photos, description, inclusions, registre des\n258\t    loyers, historique TAL, risque d'inondation, qualité de l'air, coût\n259\t    d'électricité, commerces/transport, essence, KA Scores, quartier,\n260\t    à proximité, références + QR. None si introuvable.\"\"\"\n261\t    con = db.connect()\n262\t    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n263\t    if row is None:\n264\t        con.close()\n265\t        return None\n266\t    l = dict(row)\n267\t    l[\"amenities\"] = json.loads(l.get(\"amenities\") or \"[]\")\n268\t    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n269\t    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n270\t    l[\"digest\"] = json.loads(l[\"digest\"]) if l.get(\"digest\") else None\n271\t    poi = []\n272\t    quartier = None\n273\t    if l.get(\"lat\") is not None:\n274\t        key = f\"{round(l['lat'], 4)},{round(l['lng'], 4)}\"\n275\t        pr = con.execute(\"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n276\t        poi = json.loads(pr[\"pois\"]) if pr else []\n277\t        from . import quartier as qmod\n278\t        dauid = l.get(\"dauid\")\n279\t        quartier = qmod.fiche_quartier(l[\"lat\"], l[\"lng\"], l.get(\"city\") or \"\",\n280\t                                       dauid if dauid and dauid != \"hors-zone\" else None)\n281\t    reg = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n282\t    src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]), l[\"source\"])\n283\t    kasc = None\n284\t    if l.get(\"coord_key\"):\n285\t        kr = con.execute(\"SELECT walk, transit, bike, calme, services,\"\n286\t                         \" global AS glob FROM kascores WHERE coord_key=?\",\n287\t                         (l[\"coord_key\"],)).fetchone()\n288\t        if kr is not None:\n289\t            kasc = dict(kr)\n290\t    price_history = [dict(r) for r in con.execute(\n291\t        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 6\",\n292\t        (uid,)).fetchall()]\n293\t    con.close()\n294\t\n295\t    # --- enrichissements de la fiche (mêmes modules que les blocs du site) ---\n296\t    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n297\t    geo = lat is not None and lng is not None\n298\t\n299\t    def _safe(fn, *a, **k):\n300\t        try:\n301\t            return fn(*a, **k)\n302\t        except Exception:\n303\t            return None\n304\t\n305\t    from . import air as air_mod\n306\t    from . import commerces as com_mod\n307\t    from . import fairvalue as fv_mod\n308\t    from . import gaz as gaz_mod\n309\t    from . import hydro as hydro_mod\n310\t    from . import inondation as inon_mod\n311\t    from . import rdl as rdl_mod\n312\t    from . import tal as tal_mod\n313\t    fv = _safe(fv_mod.explain, uid)\n314\t    rdl_d = _safe(rdl_mod.nearby, lat, lng) if geo else None\n315\t    tal_d = (_safe(tal_mod.lookup, l[\"address\"], l.get(\"city\"))\n316\t             if l.get(\"address\") else None)\n317\t    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n318\t    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n319\t    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n320\t    com = _safe(com_mod.nearby, lat, lng) if geo else None\n321\t    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n322\t                uid=uid, lat=lat, lng=lng, solve=False)\n323\t\n324\t    buf = io.BytesIO()\n325\t    c = rl_canvas.Canvas(buf, pagesize=letter)\n326\t    c.setTitle(f\"Lou-Ka — {l.get('title') or l.get('address')}\")\n327\t    s = _Style(c)\n328\t    s.fond()\n329\t    y = s.entete(\"Fiche logement\")\n330\t    page = [1]\n331\t\n332\t    def saut_page() -> float:\n333\t        s.pied(_PIED_FICHE, page[0])\n334\t        c.showPage()\n335\t        page[0] += 1\n336\t        s.fond()\n337\t        return s.entete(\"Fiche logement · suite\")\n338\t\n339\t    def place(y_: float, besoin: float) -> float:\n340\t        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n341\t        return saut_page() if (y_ - besoin) < M + 34 else y_\n342\t\n343\t    # --- bloc prix + badge marché (verdict du modèle Lou-Ka en priorité)\n344\t    prix_txt = (_fmt_money(l[\"price\"]) + f\"{NBSP}/ mois\") if l.get(\"price\") \\\n345\t        else (l.get(\"price_label\") or \"Prix sur demande\")\n346\t    c.setFont(\"Helvetica-Bold\", 30)\n347\t    c.setFillColor(INK)\n348\t    c.drawString(M, y - 24, prix_txt)\n349\t    loyer_secteur = (quartier or {}).get(\"demographie\", {}).get(\"loyer_moyen\") \\\n350\t        if quartier else None\n351\t    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n352\t    if l.get(\"price\") and fv and fv.get(\"verdict\") in _VERDICTS:\n353\t        dev = fv.get(\"deviation\") or 0\n354\t        pct = f\"{'+' if dev > 0 else '−'}{abs(round(dev * 100))}{NBSP}%\"\n355\t        lab, fg, bg, stk = _VERDICTS[fv[\"verdict\"]]\n356\t        s.pilule(bx, y - 22, f\"{pct} vs juste valeur · {lab}\", fg, bg, stk)\n357\t    elif l.get(\"price\") and loyer_secteur:\n358\t        delta = (l[\"price\"] - loyer_secteur) / loyer_secteur\n359\t        pct = f\"{'+' if delta > 0 else '−'}{abs(round(delta * 100))}{NBSP}%\"\n360\t        if delta <= -0.15:\n361\t            s.pilule(bx, y - 22, f\"{pct} vs secteur · Bon deal\", GREEN_DEEP, LIME_SOFT, GREEN)\n362\t        elif delta <= 0.10:\n363\t            s.pilule(bx, y - 22, f\"{pct} vs secteur · Dans le marché\", INK2, SURFACE, INK)\n364\t        else:\n365\t            s.pilule(bx, y - 22, f\"{pct} vs secteur · Au-dessus du marché\",\n366\t                     HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER)\n367\t    y -= 44\n368\t\n369\t    # --- titre + adresse\n370\t    c.setFont(\"Helvetica-Bold\", 13)\n371\t    c.setFillColor(INK)\n372\t    c.drawString(M, y, (l.get(\"title\") or l.get(\"address\") or \"\")[:80])\n373\t    y -= 14\n374\t    c.setFont(\"Helvetica\", 9)\n375\t    c.setFillColor(INK2)\n376\t    loc = \" · \".join(x for x in (l.get(\"address\"), l.get(\"sector\"), l.get(\"city\"))\n377\t                     if x and x != l.get(\"title\"))\n378\t    c.drawString(M, y, loc[:110])\n379\t    y -= 20\n380\t\n381\t    # --- chips clés\n382\t    x = M\n383\t    chips = []\n384\t    if l.get(\"unit_type\"):\n385\t        chips.append(l[\"unit_type\"])\n386\t    ad = l.get(\"availability_date\")\n387\t    if ad == \"now\":\n388\t        chips.append(\"Libre maintenant\")\n389\t    elif ad:\n390\t        chips.append(f\"Dispo {ad}\")\n391\t    if l.get(\"area_sqft\"):\n392\t        chips.append(f\"{round(l['area_sqft'])}{NBSP}pi²\")\n393\t    if l.get(\"furnished\"):\n394\t        chips.append(\"Meublé\")\n395\t    if l.get(\"pets\"):\n396\t        chips.append({\"oui\": \"Animaux acceptés\", \"non\": \"Animaux refusés\",\n397\t                      \"conditions\": \"Animaux sous conditions\"}.get(l[\"pets\"], l[\"pets\"]))\n398\t    if l[\"details\"].get(\"floor\") is not None:\n399\t        chips.append(f\"{l['details']['floor']}e étage\")\n400\t    for chip in chips[:7]:\n401\t        x = s.pilule(x, y - 6, chip)\n402\t    y -= 30\n403\t\n404\t    # --- photos : 1 grande + 3 vignettes\n405\t    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n406\t    if imgs:\n407\t        big_h = 170\n408\t        big_w = big_h * 16 / 10.5\n409\t        c.setStrokeColor(INK)\n410\t        c.setLineWidth(1.5)\n411\t        try:\n412\t            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n413\t                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n414\t        except Exception:\n415\t            pass\n416\t        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n417\t        tx = M + big_w + 10\n418\t        th = (big_h - 16) / 3\n419\t        for i, im in enumerate(imgs[1:4]):\n420\t            ty = y - (i + 1) * th - i * 8\n421\t            try:\n422\t                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n423\t                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n424\t                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n425\t            except Exception:\n426\t                pass\n427\t        y -= big_h + 18\n428\t\n429\t    # --- « En bref » (digest) sur bandeau lime\n430\t    dg = l.get(\"digest\")\n431\t    if dg and dg.get(\"en_bref\"):\n432\t        c.setFillColor(LIME_SOFT)\n433\t        c.setStrokeColor(GREEN)\n434\t        c.setLineWidth(1.2)\n435\t        c.roundRect(M, y - 40, PAGE_W - 2 * M, 44, 6, stroke=1, fill=1)\n436\t        s.paragraphe(M + 10, y - 8, \"EN BREF — \" + dg[\"en_bref\"],\n437\t                     PAGE_W - 2 * M - 20, taille=8.5, couleur=GREEN_DEEP,\n438\t                     max_lignes=3)\n439\t        y -= 54\n440\t\n441\t    # --- description (2-3 sections max) | inclusions en colonne droite\n442\t    col_g = PAGE_W * 0.56 - M\n443\t    y_desc = s.titre_section(y, \"Description\")\n444\t    if dg and dg.get(\"sections\"):\n445\t        for sec in dg[\"sections\"][:3]:\n446\t            c.setFont(\"Helvetica-Bold\", 9)\n447\t            c.setFillColor(INK)\n448\t            c.drawString(M, y_desc, sec[\"titre\"][:60])\n449\t            y_desc -= 12\n450\t            y_desc = s.paragraphe(M, y_desc, sec[\"texte\"], col_g, max_lignes=6)\n451\t            y_desc -= 4\n452\t            if y_desc < 150:\n453\t                break\n454\t    elif l.get(\"description\"):\n455\t        y_desc = s.paragraphe(M, y_desc, l[\"description\"], col_g, max_lignes=16)\n456\t\n457\t    xr = M + col_g + 24\n458\t    y_incl = s_titre_droite(c, y, xr, \"Inclusions et commodités\")\n459\t    d = l[\"details\"]\n460\t    badges = []\n461\t    inc = d.get(\"inclusions\") or {}\n462\t    for k, lab in ((\"heating\", \"Chauffage inclus\"), (\"electricity\", \"Électricité incluse\"),\n463\t                   (\"hot_water\", \"Eau chaude incluse\"), (\"internet\", \"Internet inclus\")):\n464\t        if inc.get(k):\n465\t            badges.append(lab)\n466\t    app = d.get(\"appliances\") or {}\n467\t    if app.get(\"dishwasher\"):\n468\t        badges.append(\"Lave-vaisselle\")\n469\t    if app.get(\"washer_dryer\"):\n470\t        badges.append(\"Laveuse-sécheuse\")\n471\t    for k, lab in ((\"ac\", \"Climatisation\"), (\"elevator\", \"Ascenseur\"),\n472\t                   (\"balcony\", \"Balcon\"), (\"pool\", \"Piscine\"), (\"gym\", \"Gym\"),\n473\t                   (\"laundry\", \"Buanderie\"), (\"storage\", \"Rangement\")):\n474\t        if d.get(k):\n475\t            badges.append(lab)\n476\t    if (d.get(\"parking\") or {}).get(\"available\"):\n477\t        badges.append(\"Stationnement\")\n478\t    if l.get(\"furnished\"):\n479\t        badges.append(\"Meublé\")\n480\t    c.setFont(\"Helvetica\", 8.5)\n481\t    for b in badges[:12]:\n482\t        c.setFillColor(GREEN)\n483\t        c.drawString(xr, y_incl, \"✓\")\n484\t        c.setFillColor(INK2)\n485\t        c.drawString(xr + 12, y_incl, b)\n486\t        y_incl -= 12\n487\t    autres = [a for a in l[\"amenities\"]\n488\t              if not any(b.lower() in a.lower() or a.lower() in b.lower() for b in badges)]\n489\t    for a in autres[: max(0, 14 - len(badges))]:\n490\t        c.setFillColor(INK3)\n491\t        c.drawString(xr + 12, y_incl, a[:38])\n492\t        y_incl -= 11\n493\t\n494\t    y = min(y_desc, y_incl) - 16\n495\t\n496\t    # --- Analyse de prix Lou-Ka (juste valeur, fourchette, distribution)\n497\t    if fv and fv.get(\"fv\"):\n498\t        hist = fv.get(\"histogram\") or []\n499\t        y = place(y, 168 if hist else 92)\n500\t        y = s.titre_section(y, \"Analyse de prix Lou-Ka\")\n501\t        jv = _fmt_money(round(fv[\"fv\"]))\n502\t        c.setFont(\"Helvetica-Bold\", 17)\n503\t        c.setFillColor(INK)\n504\t        c.drawString(M, y - 10, jv)\n505\t        c.setFont(\"Helvetica\", 8.5)\n506\t        c.setFillColor(INK2)\n507\t        c.drawString(M + c.stringWidth(jv, \"Helvetica-Bold\", 17) + 8, y - 10,\n508\t                     \"/ mois — juste valeur estimée par le modèle Lou-Ka\")\n509\t        meta = []\n510\t        if fv.get(\"fv_low\") and fv.get(\"fv_high\"):\n511\t            meta.append(f\"fourchette {_fmt_money(round(fv['fv_low']))} – \"\n512\t                        f\"{_fmt_money(round(fv['fv_high']))}\")\n513\t        if fv.get(\"confidence\"):\n514\t            meta.append(f\"confiance {fv['confidence']}\")\n515\t        if fv.get(\"comps\"):\n516\t            meta.append(f\"{fv['comps']} comparables\")\n517\t        if fv.get(\"segment_n\"):\n518\t            meta.append(f\"segment de {fv['segment_n']} annonces\")\n519\t        c.setFont(\"Helvetica\", 8)\n520\t        c.setFillColor(INK3)\n521\t        c.drawString(M, y - 24, \" · \".join(meta)[:115])\n522\t        if l.get(\"price\") and fv.get(\"verdict\") in _VERDICTS:\n523\t            dev = fv.get(\"deviation\") or 0\n524\t            pct = f\"{'+' if dev > 0 else '−'}{abs(round(dev * 100))}{NBSP}%\"\n525\t            lab, fg, bg, stk = _VERDICTS[fv[\"verdict\"]]\n526\t            s.pilule(M, y - 48, f\"{pct} vs juste valeur · {lab}\", fg, bg, stk)\n527\t        y -= 58\n528\t        if hist:\n529\t            ch_h = 40\n530\t            lo, hi = hist[0][\"x0\"], hist[-1][\"x1\"]\n531\t            larg = PAGE_W - 2 * M\n532\t            base = y - ch_h - 6\n533\t            if hi > lo and fv.get(\"fv_low\") and fv.get(\"fv_high\"):\n534\t                bx0 = M + larg * max(0.0, (fv[\"fv_low\"] - lo) / (hi - lo))\n535\t                bx1 = M + larg * min(1.0, (fv[\"fv_high\"] - lo) / (hi - lo))\n536\t                c.setFillColor(LIME_SOFT)\n537\t                c.rect(bx0, base, max(2, bx1 - bx0), ch_h, stroke=0, fill=1)\n538\t            max_n = max((h[\"n\"] for h in hist), default=1) or 1\n539\t            bw = larg / len(hist)\n540\t            c.setFillColor(GREEN)\n541\t            for i, h in enumerate(hist):\n542\t                bh = ch_h * h[\"n\"] / max_n\n543\t                if bh > 0:\n544\t                    c.rect(M + i * bw + 1, base, bw - 2, max(0.6, bh),\n545\t                           stroke=0, fill=1)\n546\t            if hi > lo and l.get(\"price\"):\n547\t                px = M + larg * (min(max(l[\"price\"], lo), hi) - lo) / (hi - lo)\n548\t                c.setStrokeColor(LIME)\n549\t                c.setLineWidth(2)\n550\t                c.line(px, base - 3, px, base + ch_h + 3)\n551\t                c.setFont(\"Courier-Bold\", 6.5)\n552\t                c.setFillColor(LIME)\n553\t                c.drawString(min(max(px - 28, M), PAGE_W - M - 92),\n554\t                             base + ch_h + 6,\n555\t                             f\"CE LOYER · {_fmt_money(l['price'])}\")\n556\t            c.setFont(\"Helvetica\", 6.5)\n557\t            c.setFillColor(INK3)\n558\t            c.drawString(M, base - 9, _fmt_money(lo))\n559\t            c.drawRightString(PAGE_W - M, base - 9,\n560\t                              _fmt_money(hi) + \" — distribution des loyers du \"\n561\t                                               \"segment de référence\")\n562\t            y = base - 20\n563\t        ph = [p for p in price_history if p.get(\"price\")]\n564\t        if len(ph) >= 2 and ph[0][\"price\"] != ph[-1][\"price\"]:\n565\t            c.setFont(\"Helvetica\", 8)\n566\t            c.setFillColor(INK2)\n567\t            chn = \" → \".join(_fmt_money(p[\"price\"]) for p in reversed(ph[:4]))\n568\t            c.drawString(M, y - 2, f\"Historique de prix de l'annonce : {chn}\")\n569\t            y -= 16\n570\t\n571\t    # --- Registre des loyers (déclarations citoyennes à proximité)\n572\t    if rdl_d and rdl_d.get(\"n\"):\n573\t        comp = rdl_d.get(\"items\") or []\n574\t        y = place(y, 128 + min(len(comp), 8) * 12)\n575\t        y = s.titre_section(y, \"Registre des loyers — déclarations citoyennes\")\n576\t        tw = (PAGE_W - 2 * M - 20) / 3\n577\t        med_rec = rdl_d.get(\"median_recent\")\n578\t        tuiles = [(str(rdl_d[\"n\"]), f\"loyers déclarés à <{rdl_d['radius_m']} m\"),\n579\t                  (_fmt_money(rdl_d.get(\"median\")), \"médiane du secteur\"),\n580\t                  (_fmt_money(med_rec) if med_rec else \"—\",\n581\t                   f\"médiane depuis 2023 ({rdl_d.get('n_recent', 0)} décl.)\")]\n582\t        for i, (val, lab) in enumerate(tuiles):\n583\t            s.tuile(M + i * (tw + 10), y - 44, tw, 38, val, lab)\n584\t        y -= 56\n585\t        rooms = rdl_d.get(\"by_rooms\") or {}\n586\t        if rooms:\n587\t            def _rk(k: str) -> float:\n588\t                try:\n589\t                    return float(k)\n590\t                except ValueError:\n591\t                    return 99.0\n592\t            parts = [f\"{k}{NBSP}ch. {_fmt_money(v['median'])} ({v['n']})\"\n593\t                     for k, v in sorted(rooms.items(), key=lambda kv: _rk(kv[0]))]\n594\t            c.setFont(\"Helvetica\", 8)\n595\t            c.setFillColor(INK2)\n596\t            c.drawString(M, y, (\"Médianes par taille : \" + \" · \".join(parts))[:118])\n597\t            y -= 14\n598\t        if comp:\n599\t            c.setFont(\"Courier-Bold\", 7)\n600\t            c.setFillColor(INK3)\n601\t            for lab, xoff in ((\"ADRESSE\", 0), (\"CH.\", 280), (\"DÉBUT DE BAIL\", 320),\n602\t                              (\"DIST.\", 420), (\"LOYER\", 480)):\n603\t                c.drawString(M + xoff, y, lab)\n604\t            y -= 4\n605\t            c.setStrokeColor(INK)\n606\t            c.setLineWidth(1)\n607\t            c.line(M, y, PAGE_W - M, y)\n608\t            y -= 11\n609\t            for it in comp[:8]:\n610\t                c.setFont(\"Helvetica\", 8)\n611\t                c.setFillColor(INK2)\n612\t                c.drawString(M, y, (it.get(\"address\") or \"\")[:52])\n613\t                c.drawString(M + 280, y, str(it[\"rooms\"]) if it.get(\"rooms\") is not None else \"—\")\n614\t                c.drawString(M + 320, y, _fmt_mois(it.get(\"date\")))\n615\t                c.drawString(M + 420, y, _fmt_dist(it[\"dist_m\"]))\n616\t                c.setFont(\"Helvetica-Bold\", 8)\n617\t                c.setFillColor(INK)\n618\t                c.drawString(M + 480, y, _fmt_money(it.get(\"price\")))\n619\t                y -= 12\n620\t        c.setFont(\"Helvetica\", 6.5)\n621\t        c.setFillColor(INK3)\n622\t        c.drawString(M, y - 2, \"Loyers déclarés volontairement par des locataires \"\n623\t                               \"au Registre des loyers (registre-des-loyers.ca) — \"\n624\t                               \"données citoyennes non vérifiées.\")\n625\t        y -= 16\n626\t\n627\t    # --- Historique au TAL (décisions SOQUIJ à l'adresse)\n628\t    if tal_d and tal_d.get(\"status\") in (\"ok\", \"pending\"):\n629\t        decs = (tal_d.get(\"decisions\") or [])[:6]\n630\t        y = place(y, 70 + len(decs) * 24)\n631\t        y = s.titre_section(y, \"Historique au Tribunal administratif du logement\")\n632\t        if tal_d[\"status\"] == \"pending\":\n633\t            c.setFont(\"Helvetica\", 8.5)\n634\t            c.setFillColor(INK2)\n635\t            c.drawString(M, y - 4, \"Adresse en file de vérification auprès des \"\n636\t                                   \"décisions publiées du TAL (résultat sur la \"\n637\t                                   \"fiche en ligne sous peu).\")\n638\t            y -= 18\n639\t        elif not decs:\n640\t            s.pilule(M, y - 12, \"Aucune décision trouvée\", GREEN_DEEP,\n641\t                     LIME_SOFT, GREEN)\n642\t            c.setFont(\"Helvetica\", 8.5)\n643\t            c.setFillColor(INK2)\n644\t            c.drawString(M + 130, y - 7, \"Aucune décision publiée du TAL n'a été \"\n645\t                                         \"repérée à cette adresse.\")\n646\t            y -= 26\n647\t        else:\n648\t            n = tal_d.get(\"n\") or len(decs)\n649\t            cl = tal_d.get(\"contre_locataire\") or 0\n650\t            c.setFont(\"Helvetica-Bold\", 9)\n651\t            c.setFillColor(INK)\n652\t            c.drawString(M, y - 4,\n653\t                         f\"{n} décision{'s' if n > 1 else ''} publiée\"\n654\t                         f\"{'s' if n > 1 else ''} à cette adresse — \"\n655\t                         f\"{cl} à l'initiative du propriétaire.\")\n656\t            y -= 20\n657\t            for dec in decs:\n658\t                qui = {\"locateur\": \"demande du propriétaire\",\n659\t                       \"locataire\": \"demande du locataire\"}.get(\n660\t                    dec.get(\"demandeur\") or \"\", \"demandeur inconnu\")\n661\t                c.setFont(\"Helvetica-Bold\", 8)\n662\t                c.setFillColor(INK)\n663\t                c.drawString(M, y, _fmt_mois(dec.get(\"date\")))\n664\t                c.setFont(\"Helvetica\", 8)\n665\t                c.setFillColor(INK2)\n666\t                suite = qui\n667\t                if dec.get(\"verdict\"):\n668\t                    suite += f\" · demande {dec['verdict']}\"\n669\t                if dec.get(\"tags\"):\n670\t                    suite += \" · \" + \", \".join(dec[\"tags\"][:3])\n671\t                c.drawString(M + 78, y, suite[:88])\n672\t                c.setFont(\"Courier-Bold\", 7)\n673\t                c.setFillColor(GREEN)\n674\t                c.drawRightString(PAGE_W - M, y, dec.get(\"citation\") or \"SOQUIJ\")\n675\t                y -= 12\n676\t            y -= 6\n677\t        c.setFont(\"Helvetica\", 6.5)\n678\t        c.setFillColor(INK3)\n679\t        c.drawString(M, y - 2, \"Décisions publiques diffusées par SOQUIJ, repérées \"\n680\t                               \"par correspondance d'adresse — une décision peut \"\n681\t                               \"concerner un autre logement du même immeuble.\")\n682\t        y -= 16\n683\t\n684\t    # --- Risque d'inondation (BDZI)\n685\t    if inon:\n686\t        zones = (inon.get(\"zones\") or [])[:3]\n687\t        y = place(y, 56 + len(zones) * 12)\n688\t        y = s.titre_section(y, \"Risque d'inondation\")\n689\t        statut = inon.get(\"statut\")\n690\t        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n691\t               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n692\t        if statut == \"en_zone\":\n693\t            s.pilule(M, y - 12, f\"En zone inondable ({sev})\",\n694\t                     HexColor(\"#a12622\"), HexColor(\"#fde8e8\"), HexColor(\"#a12622\"))\n695\t        elif statut == \"a_proximite\":\n696\t            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n697\t            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n698\t                     HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER)\n699\t        elif statut == \"hors_zone\":\n700\t            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n701\t                     GREEN_DEEP, LIME_SOFT, GREEN)\n702\t        else:\n703\t            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n704\t                     INK2, SURFACE, INK)\n705\t        y -= 26\n706\t        for z in zones:\n707\t            c.setFont(\"Helvetica\", 8)\n708\t            c.setFillColor(INK2)\n709\t            c.drawString(M, y, f\"{z['type']} — à {z['distance_m']} m\"\n710\t                               + (f\" (rapport {z['date_rapport']})\"\n711\t                                  if z.get(\"date_rapport\") else \"\"))\n712\t            y -= 12\n713\t        y -= 8\n714\t\n715\t    # --- Qualité de l'air (RSQAQ)\n716\t    if air_d and air_d.get(\"station\"):\n717\t        mes = air_d.get(\"mesures\") or {}\n718\t        y = place(y, 44 + len(mes) * 11)\n719\t        y = s.titre_section(y, \"Qualité de l'air\")\n720\t        c.setFont(\"Helvetica\", 8.5)\n721\t        c.setFillColor(INK2)\n722\t        c.drawString(M, y - 2,\n723\t                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n724\t                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n725\t                     \"du logement — moyennes annuelles :\")\n726\t        y -= 16\n727\t        for pol, m_ in list(mes.items())[:5]:\n728\t            c.setFont(\"Helvetica-Bold\", 8)\n729\t            c.setFillColor(INK)\n730\t            c.drawString(M, y, pol)\n731\t            c.setFont(\"Helvetica\", 8)\n732\t            c.setFillColor(INK2)\n733\t            val = f\"{m_['moyenne']:.1f}\".replace(\".\", \",\") if m_.get(\"moyenne\") is not None else \"—\"\n734\t            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n735\t            if m_.get(\"ref\"):\n736\t                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n737\t            c.drawString(M + 56, y, txt[:95])\n738\t            y -= 11\n739\t        y -= 8\n740\t\n741\t    # --- Coût d'électricité (Hydro-Québec)\n742\t    if hyd and hyd.get(\"disponible\"):\n743\t        y = place(y, 52)\n744\t        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n745\t        c.setFont(\"Helvetica-Bold\", 15)\n746\t        c.setFillColor(INK)\n747\t        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n748\t        c.drawString(M, y - 8, mens)\n749\t        c.setFont(\"Helvetica\", 8.5)\n750\t        c.setFillColor(INK2)\n751\t        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n752\t        if hyd.get(\"kwh_annuel\"):\n753\t            suite += f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP) + f\"{NBSP}kWh/an\"\n754\t        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n755\t        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n756\t        y -= 26\n757\t\n758\t    # --- Commerces et transport\n759\t    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n760\t        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n761\t                    for t in (com.get(\"transit\") or [])[:4]]\n762\t                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n763\t                      for i in (com.get(\"commerces\") or [])[:10]])\n764\t        lignes = (len(entrees) + 1) // 2\n765\t        y = place(y, 36 + lignes * 13)\n766\t        y = s.titre_section(y, \"Commerces et transport\")\n767\t        col_w = (PAGE_W - 2 * M) / 2\n768\t        for i, (cat, nom, dist) in enumerate(entrees):\n769\t            px = M if i % 2 == 0 else M + col_w + 10\n770\t            py = y - (i // 2) * 13\n771\t            c.setFont(\"Helvetica\", 8)\n772\t            c.setFillColor(INK2)\n773\t            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n774\t            c.drawString(px, py, f\"{cat} — {nomc}\")\n775\t            c.setFont(\"Courier-Bold\", 7.5)\n776\t            c.setFillColor(INK)\n777\t            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n778\t        y -= lignes * 13 + 12\n779\t\n780\t    # --- Essence à proximité\n781\t    if gaz_d and gaz_d.get(\"stations\"):\n782\t        sts = gaz_d[\"stations\"][:3]\n783\t        y = place(y, 40 + len(sts) * 11)\n784\t        y = s.titre_section(y, \"Essence à proximité\")\n785\t        if gaz_d.get(\"mediane_regulier\"):\n786\t            c.setFont(\"Helvetica\", 8.5)\n787\t            c.setFillColor(INK2)\n788\t            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n789\t                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n790\t                         + f\"{NBSP}¢/L\")\n791\t            y -= 14\n792\t        for st in sts:\n793\t            c.setFont(\"Helvetica\", 8)\n794\t            c.setFillColor(INK2)\n795\t            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n796\t                     if st.get(\"regulier\") else \"—\")\n797\t            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n798\t                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n799\t            y -= 11\n800\t        y -= 8\n801\t\n802\t    # --- KA Scores (immeuble et environnement)\n803\t    if kasc and kasc.get(\"glob\") is not None:\n804\t        from . import kascores as ks_mod\n805\t        y = place(y, 92)\n806\t        y = s.titre_section(y, \"KA Scores\")\n807\t        paires = [(\"Global\", kasc.get(\"glob\")), (\"À pied\", kasc.get(\"walk\")),\n808\t                  (\"Transport\", kasc.get(\"transit\")), (\"Vélo\", kasc.get(\"bike\")),\n809\t                  (\"Calme\", kasc.get(\"calme\")), (\"Services\", kasc.get(\"services\"))]\n810\t        tw = (PAGE_W - 2 * M - 5 * 8) / 6\n811\t        for i, (lab, v) in enumerate(paires):\n812\t            val = \"—\" if v is None else str(round(v))\n813\t            sub = ks_mod.label(v) or \"\"\n814\t            s.tuile(M + i * (tw + 8), y - 46, tw, 42, val,\n815\t                    f\"{lab} · {sub}\" if sub else lab)\n816\t        y -= 62\n817\t\n818\t    # --- Le quartier\n819\t    if quartier:\n820\t        y = place(y, 270)\n821\t        y = s.titre_section(y, \"Le quartier\")\n822\t        demo = quartier.get(\"demographie\") or {}\n823\t        tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n824\t                  (\"Ménages locataires\", f\"{round(demo['pct_locataires'])}{NBSP}%\" if demo.get(\"pct_locataires\") is not None else \"—\"),\n825\t                  (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n826\t                  (\"Âge médian\", f\"{round(demo['age_median'])} ans\" if demo.get(\"age_median\") is not None else \"—\"),\n827\t                  (\"Français à la maison\", f\"{round(demo['pct_francais'])}{NBSP}%\" if demo.get(\"pct_francais\") is not None else \"—\"),\n828\t                  (\"Diplôme universitaire\", f\"{round(demo['pct_univ'])}{NBSP}%\" if demo.get(\"pct_univ\") is not None else \"—\")]\n829\t        tw = (PAGE_W - 2 * M - 20) / 3\n830\t        for i, (lab, val) in enumerate(tuiles):\n831\t            s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n832\t                    tw, 38, val, lab)\n833\t        y -= 34 + 44 + 16\n834\t\n835\t        prox = quartier.get(\"proximite\") or {}\n836\t        for k, lab in PROX_LABELS:\n837\t            if k not in prox:\n838\t                continue\n839\t            v = max(0.0, min(1.0, prox[k]))\n840\t            c.setFont(\"Helvetica\", 8)\n841\t            c.setFillColor(INK2)\n842\t            c.drawString(M, y, lab)\n843\t            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n844\t            c.setFillColor(SURFACE)\n845\t            c.setStrokeColor(INK)\n846\t            c.setLineWidth(0.8)\n847\t            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n848\t            c.setFillColor(GREEN)\n849\t            c.roundRect(bx0, y - 1, bw_ * v, 7, 3.5, stroke=0, fill=1)\n850\t            c.setFont(\"Courier-Bold\", 7.5)\n851\t            c.setFillColor(INK)\n852\t            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n853\t            y -= 14\n854\t        y -= 4\n855\t        badges_q = []\n856\t        ch = quartier.get(\"chaleur\")\n857\t        if ch:\n858\t            if ch[\"classe\"] <= 3:\n859\t                badges_q.append((\"Îlot de fraîcheur\", GREEN_DEEP, LIME_SOFT, GREEN))\n860\t            elif ch[\"classe\"] >= 7:\n861\t                badges_q.append((f\"Îlot de chaleur (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\"),\n862\t                                 HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER))\n863\t        cr = quartier.get(\"crime\")\n864\t        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n865\t            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"]) / cr[\"indice_canada\"])\n866\t            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n867\t                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} la moyenne canadienne\",\n868\t                             INK2, SURFACE, INK))\n869\t        elif cr and cr.get(\"type\") == \"points\":\n870\t            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m (12 mois)\",\n871\t                             INK2, SURFACE, INK))\n872\t        x = M\n873\t        for txt, fg, bg, stk in badges_q:\n874\t            x = s.pilule(x, y - 8, txt, fg, bg, stk)\n875\t        y -= 30\n876\t\n877\t    # --- À proximité (commodités du cache POI)\n878\t    if poi:\n879\t        lignes = (min(len(poi), 12) + 1) // 2\n880\t        y = place(y, 36 + lignes * 13)\n881\t        y = s.titre_section(y, \"À proximité\")\n882\t        col_w = (PAGE_W - 2 * M) / 2\n883\t        for i, p in enumerate(poi[:12]):\n884\t            px = M if i % 2 == 0 else M + col_w + 10\n885\t            py = y - (i // 2) * 13\n886\t            c.setFont(\"Helvetica\", 8)\n887\t            c.setFillColor(INK2)\n888\t            lab = POI_LABELS.get(p[\"cat\"], p[\"cat\"])\n889\t            nom_poi = p[\"name\"][:19] + \"…\" if len(p[\"name\"]) > 20 else p[\"name\"]\n890\t            c.drawString(px, py, f\"{lab} — {nom_poi}\")\n891\t            c.setFont(\"Courier-Bold\", 7.5)\n892\t            c.setFillColor(INK)\n893\t            c.drawRightString(px + col_w - 14, py,\n894\t                              f\"{_fmt_dist(p['dist_m'])} · {_marche(p['dist_m'])}\")\n895\t        y -= lignes * 13 + 14\n896\t\n897\t    # --- Références : QR + gestionnaire + source\n898\t    y = place(y, 120)\n899\t    y = s.titre_section(y, \"Références\")\n900\t    qr = qrcode.make(f\"https://www.lou-ka.com/logement/{uid}\", box_size=4, border=1)\n901\t    qb = io.BytesIO()\n902\t    qr.save(qb, format=\"PNG\")\n903\t    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n904\t    c.setFont(\"Helvetica-Bold\", 9)\n905\t    c.setFillColor(INK)\n906\t    c.drawString(M + 82, y - 14, f\"Gestionnaire : {src_name}\")\n907\t    c.setFont(\"Helvetica\", 8)\n908\t    c.setFillColor(INK2)\n909\t    c.drawString(M + 82, y - 28, \"Annonce originale :\")\n910\t    c.setFillColor(GREEN)\n911\t    c.drawString(M + 82, y - 40, (l.get(\"url\") or \"\")[:90])\n912\t    c.setFillColor(INK2)\n913\t    c.drawString(M + 82, y - 56, \"Fiche à jour en ligne (scannez le code) :\")\n914\t    c.setFillColor(GREEN)\n915\t    c.drawString(M + 82, y - 68, f\"www.lou-ka.com/logement/{uid}\")\n916\t    y -= 86\n917\t    c.setFont(\"Helvetica\", 6.5)\n918\t    c.setFillColor(INK3)\n919\t    c.drawString(M, y, \"Sources : Statistique Canada (2021), INSPQ (CC-BY 4.0), \"\n920\t                       \"Ville de Montréal (CC-BY 4.0), OpenStreetMap, BDZI, RSQAQ, \"\n921\t                       \"Hydro-Québec, registre-des-loyers.ca, SOQUIJ, gazquebec.ca.\")\n922\t\n923\t    s.pied(_PIED_FICHE, page[0])\n924\t    c.save()\n925\t    return buf.getvalue()\n926\t\n927\t\n928\tdef s_titre_droite(c, y, x, texte):\n929\t    c.setFont(\"Helvetica-Bold\", 12.5)\n930\t    c.setFillColor(INK)\n931\t    c.drawString(x, y, texte)\n932\t    c.setStrokeColor(LIME)\n933\t    c.setLineWidth(3)\n934\t    c.line(x, y - 4, x + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n935\t    return y - 18\n936\t\n937\t\n938\t# ---------------------------------------------------------------------------\n939\t# Rapport global du marché\n940\t# ---------------------------------------------------------------------------\n941\t\n942\t_REGIONS = [\n943\t    (\"Québec métro\", {\"Québec\", \"Lévis\", \"Saint-Augustin-de-Desmaures\", \"L'Ancienne-Lorette\", \"Pont-Rouge\", \"Shannon\"}),\n944\t    (\"Outaouais\", {\"Gatineau\", \"Chelsea\", \"Thurso\", \"Perkins\", \"Maniwaki\", \"Val-des-Monts\"}),\n945\t    (\"Estrie / Montérégie-Est\", {\"Sherbrooke\", \"Magog\", \"Orford\", \"East Angus\", \"Waterville\", \"Granby\", \"Waterloo\", \"Bromont\", \"Cowansville\", \"Richmond\"}),\n946\t    (\"Mauricie / Centre-du-Québec\", {\"Trois-Rivières\", \"Bécancour\", \"Shawinigan\", \"Drummondville\", \"Victoriaville\", \"Notre-Dame-du-Bon-Conseil\", \"Saint-Léonard-d'Aston\", \"Louiseville\", \"Nicolet\", \"Wickham\", \"Saint-Narcisse\", \"Saint-Nicéphore\"}),\n947\t    (\"Lanaudière / Laurentides\", {\"Joliette\", \"Saint-Jérôme\", \"Berthierville\", \"Saint-Ambroise-de-Kildare\", \"Saint-Gabriel-de-Brandon\", \"Lachute\", \"Brownsburg-Chatham\", \"Saint-Charles-Borromée\", \"Mirabel\", \"Sainte-Agathe-des-Monts\", \"Sainte-Thérèse\", \"Blainville\", \"Charlemagne\", \"Notre-Dame-des-Prairies\"}),\n948\t    (\"Bas-Saint-Laurent / Gaspésie\", {\"Rimouski\", \"Rivière-du-Loup\", \"Matane\", \"Saint-Ulric\", \"Amqui\", \"Le Bic\", \"New Richmond\", \"Carleton-sur-Mer\", \"Gaspé\"}),\n949\t    (\"Saguenay–Lac-Saint-Jean\", {\"Saguenay\", \"Alma\", \"Chicoutimi\", \"Jonquière\", \"Chambord\", \"La Malbaie\"}),\n950\t    (\"Abitibi-Témiscamingue\", {\"Rouyn-Noranda\", \"Val-d'Or\", \"Amos\", \"Malartic\"}),\n951\t    (\"Côte-Nord\", {\"Sept-Îles\", \"Port-Cartier\", \"Baie-Comeau\", \"Forestville\"}),\n952\t    (\"Chaudière-Appalaches\", {\"Saint-Georges\", \"Sainte-Marie\", \"Thetford Mines\", \"Montmagny\", \"Vallée-Jonction\", \"Scott\", \"Saint-Isidore\", \"La Guadeloupe\", \"Saint-Raphaël\"}),\n953\t]\n954\t\n955\t\n956\tdef _region_de(city: str) -> str:\n957\t    for nom, villes in _REGIONS:\n958\t        if city in villes:\n959\t            return nom\n960\t    return \"Grand Montréal & environs\"\n961\t\n962\t\n963\tdef rapport_pdf() -> bytes:\n964\t    \"\"\"Rapport global du marché locatif Lou-Ka (multi-pages).\n965\t\n966\t    Tous les chiffres viennent de marketstats.compute() — la même source\n967\t    que la page Statistiques du site.\n968\t    \"\"\"\n969\t    from . import marketstats\n970\t    st = marketstats.compute()\n971\t    reg_file = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n972\t    noms = {s[\"id\"]: s[\"name\"] for s in reg_file}\n973\t\n974\t    t = st[\"totals\"]\n975\t    total, moy, med, gps = t[\"total\"], t[\"avg\"] or 0, t[\"median\"] or 0, t[\"gps_pct\"]\n976\t    n_sources = t[\"sources\"]\n977\t\n978\t    buf = io.BytesIO()\n979\t    c = rl_canvas.Canvas(buf, pagesize=letter)\n980\t    c.setTitle(\"Lou-Ka — Rapport du marché locatif\")\n981\t    s = _Style(c)\n982\t\n983\t    # ---- page 1 : couverture + grands chiffres + régions\n984\t    s.fond()\n985\t    s.logo(M, PAGE_H - 130, 42)\n986\t    c.setFont(\"Helvetica-Bold\", 24)\n987\t    c.setFillColor(INK)\n988\t    c.drawString(M, PAGE_H - 185, \"Rapport du marché locatif\")\n989\t    c.setFont(\"Courier-Bold\", 9)\n990\t    c.setFillColor(INK3)\n991\t    c.drawString(M, PAGE_H - 205,\n992\t                 time.strftime(\"PROVINCE DE QUÉBEC · GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n993\t\n994\t    grands = [(f\"{total:,}\".replace(\",\", NBSP), \"annonces actives\"),\n995\t              (str(n_sources), \"gestionnaires connectés\"),\n996\t              (_fmt_money(moy), \"loyer moyen\"),\n997\t              (_fmt_money(med), \"loyer médian\"),\n998\t              (f\"{gps}{NBSP}%\", \"annonces géolocalisées\")]\n999\t    tw = (PAGE_W - 2 * M - 4 * 10) / 5\n1000\t    for i, (val, lab) in enumerate(grands):\n1001\t        tx = M + i * (tw + 10)\n1002\t        ty = PAGE_H - 285\n1003\t        c.setFillColor(INK)\n1004\t        c.roundRect(tx + 3, ty - 3, tw, 58, 6, stroke=0, fill=1)   # ombre décalée\n1005\t        c.setFillColor(SURFACE)\n1006\t        c.setStrokeColor(INK)\n1007\t        c.setLineWidth(1.5)\n1008\t        c.roundRect(tx, ty, tw, 58, 6, stroke=1, fill=1)\n1009\t        c.setFont(\"Helvetica-Bold\", 15)\n1010\t        c.setFillColor(INK)\n1011\t        c.drawCentredString(tx + tw / 2, ty + 32, val)\n1012\t        c.setFont(\"Courier\", 6.3)\n1013\t        c.setFillColor(INK3)\n1014\t        c.drawCentredString(tx + tw / 2, ty + 14, lab.upper())\n1015\t\n1016\t    y = s.titre_section(PAGE_H - 330, \"Couverture par région\")\n1017\t    c.setFont(\"Courier-Bold\", 7)\n1018\t    c.setFillColor(INK3)\n1019\t    for lab, xoff in ((\"RÉGION\", 0), (\"ANNONCES\", 280), (\"SOURCES\", 360),\n1020\t                      (\"LOYER MOYEN\", 430)):\n1021\t        c.drawString(M + xoff, y, lab)\n1022\t    y -= 4\n1023\t    c.setStrokeColor(INK)\n1024\t    c.setLineWidth(1)\n1025\t    c.line(M, y, PAGE_W - M, y)\n1026\t    y -= 14\n1027\t    regions = st[\"by_region\"]\n1028\t    max_n = max((r[\"count\"] for r in regions), default=1)\n1029\t    for r in regions:\n1030\t        c.setFont(\"Helvetica-Bold\", 8.5)\n1031\t        c.setFillColor(INK)\n1032\t        c.drawString(M, y, r[\"key\"])\n1033\t        c.setFillColor(LIME)\n1034\t        c.setStrokeColor(INK)\n1035\t        c.setLineWidth(0.7)\n1036\t        bw = 90 * r[\"count\"] / max_n\n1037\t        c.roundRect(M + 180, y - 1, max(3, bw), 8, 3, stroke=1, fill=1)\n1038\t        c.setFont(\"Helvetica\", 8.5)\n1039\t        c.setFillColor(INK2)\n1040\t        c.drawRightString(M + 330, y, f\"{r['count']:,}\".replace(\",\", NBSP))\n1041\t        c.drawRightString(M + 400, y, str(r[\"sources\"]))\n1042\t        c.drawRightString(M + 500, y, _fmt_money(r[\"avg_price\"]) if r[\"avg_price\"] else \"—\")\n1043\t        y -= 15\n1044\t    s.pied(\"Rapport généré automatiquement à partir des annonces publiques \"\n1045\t           \"agrégées par Lou-Ka. Loyers : bornes 300–10 000 $.\", 1)\n1046\t    c.showPage()\n1047\t\n1048\t    # ---- page 2 : histogramme des loyers + par type\n1049\t    s.fond()\n1050\t    y = s.entete(\"Rapport du marché · loyers\")\n1051\t    y = s.titre_section(y, \"Distribution des loyers\")\n1052\t    hist = st[\"histogram\"]\n1053\t    max_c = max((h[\"count\"] for h in hist), default=1) or 1\n1054\t    ch_h, ch_y = 150, y - 170\n1055\t    bw = (PAGE_W - 2 * M) / len(hist)\n1056\t    for i, h in enumerate(hist):\n1057\t        bh = ch_h * h[\"count\"] / max_c\n1058\t        bx = M + i * bw\n1059\t        borne = h[\"hi\"] is None or h[\"lo\"] == 0\n1060\t        c.setFillColor(INK3 if borne else GREEN)\n1061\t        c.setStrokeColor(INK)\n1062\t        c.setLineWidth(0.6)\n1063\t        c.rect(bx + 2, ch_y, bw - 4, max(1, bh), stroke=1, fill=1)\n1064\t        if h[\"count\"] and h[\"count\"] > max_c * 0.06:\n1065\t            c.setFont(\"Courier-Bold\", 6)\n1066\t            c.setFillColor(INK)\n1067\t            c.drawCentredString(bx + bw / 2, ch_y + bh + 3, str(h[\"count\"]))\n1068\t        c.setFont(\"Helvetica\", 5.6)\n1069\t        c.setFillColor(INK3)\n1070\t        lab = f\"<{h['hi']}\" if h[\"lo\"] == 0 else (f\"{h['lo']}+\" if h[\"hi\"] is None\n1071\t                                                  else str(h[\"lo\"]))\n1072\t        c.drawCentredString(bx + bw / 2, ch_y - 9, lab)\n1073\t    y = ch_y - 30\n1074\t\n1075\t    y = s.titre_section(y, \"Par taille de logement\")\n1076\t    c.setFont(\"Courier-Bold\", 7)\n1077\t    c.setFillColor(INK3)\n1078\t    for lab, xoff in ((\"TAILLE\", 0), (\"ANNONCES\", 160), (\"LOYER MOYEN\", 260),\n1079\t                      (\"LOYER MIN\", 360)):\n1080\t        c.drawString(M + xoff, y, lab)\n1081\t    y -= 4\n1082\t    c.line(M, y, PAGE_W - M, y)\n1083\t    y -= 13\n1084\t    for r in st[\"by_type\"][:9]:\n1085\t        c.setFont(\"Helvetica-Bold\", 8.5)\n1086\t        c.setFillColor(INK)\n1087\t        c.drawString(M, y, r[\"key\"])\n1088\t        c.setFont(\"Helvetica\", 8.5)\n1089\t        c.setFillColor(INK2)\n1090\t        c.drawRightString(M + 220, y, str(r[\"count\"]))\n1091\t        c.drawRightString(M + 330, y, _fmt_money(r[\"avg_price\"]) if r[\"avg_price\"] else \"—\")\n1092\t        c.drawRightString(M + 430, y, _fmt_money(r[\"min_price\"]) if r[\"min_price\"] else \"—\")\n1093\t        y -= 13\n1094\t    s.pied(\"Lou-Ka — agrégateur indépendant. Chaque annonce renvoie à la \"\n1095\t           \"source originale du gestionnaire.\", 2)\n1096\t    c.showPage()\n1097\t\n1098\t    # ---- page 3 : offre, inclusions, prix au pi², baisses de prix\n1099\t    s.fond()\n1100\t    y = s.entete(\"Rapport du marché · offre\")\n1101\t    o = st[\"offre\"]\n1102\t    y = s.titre_section(y, \"Inclusions et caractéristiques du parc\")\n1103\t    carac = [(\"Chauffage inclus\", o[\"chauffage_pct\"]),\n1104\t             (\"Électricité incluse\", o[\"electricite_pct\"]),\n1105\t             (\"Eau chaude incluse\", o[\"eau_chaude_pct\"]),\n1106\t             (\"Internet inclus\", o[\"internet_pct\"]),\n1107\t             (\"Climatisation\", o[\"clim_pct\"]),\n1108\t             (\"Stationnement\", o[\"stationnement_pct\"]),\n1109\t             (\"Balcon\", o[\"balcon_pct\"]),\n1110\t             (\"Meublé\", o[\"furnished_pct\"])]\n1111\t    for lab, v in carac:\n1112\t        if v is None:\n1113\t            continue\n1114\t        c.setFont(\"Helvetica\", 8.5)\n1115\t        c.setFillColor(INK2)\n1116\t        c.drawString(M, y, lab)\n1117\t        bx0, bw_ = M + 150, PAGE_W - 2 * M - 200\n1118\t        c.setFillColor(SURFACE)\n1119\t        c.setStrokeColor(INK)\n1120\t        c.setLineWidth(0.8)\n1121\t        c.roundRect(bx0, y - 1, bw_, 8, 4, stroke=1, fill=1)\n1122\t        c.setFillColor(GREEN)\n1123\t        c.roundRect(bx0, y - 1, bw_ * min(1, v / 100), 8, 4, stroke=0, fill=1)\n1124\t        c.setFont(\"Courier-Bold\", 8)\n1125\t        c.setFillColor(INK)\n1126\t        c.drawRightString(PAGE_W - M, y, f\"{v}{NBSP}%\")\n1127\t        y -= 15\n1128\t    y -= 6\n1129\t    c.setFont(\"Helvetica\", 8)\n1130\t    c.setFillColor(INK3)\n1131\t    c.drawString(M, y, f\"Disponibles maintenant : {o['dispo_now']:,} · à date future : \"\n1132\t                       f\"{o['dispo_date']:,} · superficie moyenne : \"\n1133\t                       f\"{o['superficie_moyenne'] or '—'} pi² \"\n1134\t                       f\"({o['superficie_connue']:,} annonces la publient)\"\n1135\t                       .replace(\",\", NBSP))\n1136\t    y -= 24\n1137\t\n1138\t    if o[\"prix_pi2\"]:\n1139\t        y = s.titre_section(y, \"Prix au pied carré (loyer / superficie)\")\n1140\t        for r in o[\"prix_pi2\"][:6]:\n1141\t            c.setFont(\"Helvetica-Bold\", 8.5)\n1142\t            c.setFillColor(INK)\n1143\t            c.drawString(M, y, r[\"key\"])\n1144\t            c.setFont(\"Helvetica\", 8.5)\n1145\t            c.setFillColor(INK2)\n1146\t            c.drawString(M + 70, y, f\"{r['val']:.2f}\".replace(\".\", \",\") +\n1147\t                         f\"{NBSP}$/pi² · {r['count']} annonces\")\n1148\t            y -= 13\n1149\t        y -= 10\n1150\t\n1151\t    if st[\"baisses\"]:\n1152\t        y = s.titre_section(y, \"Baisses de prix récentes (30 jours)\")\n1153\t        for b in st[\"baisses\"][:8]:\n1154\t            c.setFont(\"Helvetica\", 8.5)\n1155\t            c.setFillColor(INK2)\n1156\t            c.drawString(M, y, f\"{(b['title'] or b['uid'])[:46]} — {b['city'] or ''}\")\n1157\t            c.setFont(\"Courier-Bold\", 8)\n1158\t            c.setFillColor(GREEN)\n1159\t            c.drawRightString(PAGE_W - M, y,\n1160\t                              f\"{_fmt_money(b['avant'])} → {_fmt_money(b['apres'])} \"\n1161\t                              f\"({b['pct']}{NBSP}%)\".replace(\".\", \",\"))\n1162\t            y -= 13\n1163\t    s.pied(\"Caractéristiques dérivées des annonces publiées ; les inclusions \"\n1164\t           \"non mentionnées par une source ne sont pas comptées.\", 3)\n1165\t    c.showPage()\n1166\t\n1167\t    # ---- page 4 : top villes + top gestionnaires\n1168\t    s.fond()\n1169\t    y = s.entete(\"Rapport du marché · détail\")\n1170\t    y = s.titre_section(y, \"Top 20 des villes\")\n1171\t    col_w = (PAGE_W - 2 * M) / 2\n1172\t    for i, r in enumerate(st[\"by_city\"][:20]):\n1173\t        vx = M if i < 10 else M + col_w\n1174\t        vy = y - (i % 10) * 13\n1175\t        c.setFont(\"Helvetica\", 8.5)\n1176\t        c.setFillColor(INK2)\n1177\t        c.drawString(vx, vy, f\"{i + 1:>2}. {r['key']}\")\n1178\t        c.setFont(\"Courier-Bold\", 8)\n1179\t        c.setFillColor(INK)\n1180\t        c.drawRightString(vx + col_w - 24, vy, f\"{r['count']:,}\".replace(\",\", NBSP))\n1181\t    y -= 10 * 13 + 16\n1182\t\n1183\t    y = s.titre_section(y, \"Top 20 des gestionnaires\")\n1184\t    for i, r in enumerate(st[\"by_source\"][:20]):\n1185\t        vx = M if i < 10 else M + col_w\n1186\t        vy = y - (i % 10) * 13\n1187\t        c.setFont(\"Helvetica\", 8.5)\n1188\t        c.setFillColor(INK2)\n1189\t        c.drawString(vx, vy, f\"{i + 1:>2}. {noms.get(r['key'], r['key'])[:34]}\")\n1190\t        c.setFont(\"Courier-Bold\", 8)\n1191\t        c.setFillColor(INK)\n1192\t        c.drawRightString(vx + col_w - 24, vy, f\"{r['count']:,}\".replace(\",\", NBSP))\n1193\t    y -= 10 * 13 + 20\n1194\t\n1195\t    c.setFont(\"Helvetica\", 7.5)\n1196\t    c.setFillColor(INK3)\n1197\t    c.drawString(M, y, \"Sources de données de quartier : Statistique Canada (Recensement 2021, \"\n1198\t                       \"licence ouverte), INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), OpenStreetMap.\")\n1199\t    s.pied(\"© Lou-Ka — www.lou-ka.com · rapport non contractuel, généré automatiquement.\", 4)\n1200\t    c.save()\n1201\t    return buf.getvalue()\n1202\t"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"8810eb68-08c5-4ff2-86f5-2574e8b0ba42","timestamp":"2026-08-24T21:29:49.248Z","tool_use_result":{"type":"text","file":{"filePath":"/tmp/lou-ka-ref/louka/pdfgen.py","content":"# -----------------------------------------------------------------------------\n# Lou-Ka — Agrégateur de logements à louer (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdfgen.py : génération PDF à l'identité visuelle officielle Lou-Ka\n#   - fiche_pdf(uid)  : fiche de propriété complète — tout ce que montre la\n#     fiche web : photos, prix + analyse Lou-Ka (juste valeur, histogramme),\n#     chips, description structurée, inclusions, registre des loyers,\n#     historique TAL, inondation, qualité de l'air, coût d'électricité,\n#     commerces/transport, essence, KA Scores, quartier, à proximité, QR\n#   - rapport_pdf()   : rapport global du marché (totaux, régions, histogramme\n#     des loyers, types, villes, gestionnaires)\n#   Palette identique au site : blanc #ffffff, marine #0b1330, orange #ff6a00,\n#   bleu vif #2258ff, gris pâle #f2f2f2. Polices de base (Helvetica) stylées\n#   par la mise en page — aucune dépendance de police externe.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport math\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Lou-Ka (identité officielle) ------------------------------------\nPAPER = HexColor(\"#ffffff\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#0b1330\")          # bleu marine profond\nINK2 = HexColor(\"#3e4560\")\nINK3 = HexColor(\"#8a90a5\")\nGREEN = HexColor(\"#2258ff\")        # accent secondaire : bleu vif\nGREEN_DEEP = HexColor(\"#0b1330\")\nLIME = HexColor(\"#ff6a00\")         # accent d'action : orange vif\nLIME_SOFT = HexColor(\"#fff1e6\")\nAMBER = HexColor(\"#ff6a00\")\nAMBER_SOFT = HexColor(\"#fff1e6\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"LouKaBot/1.0 (+https://www.lou-ka.com/bot; contact@spboucher.ai)\"\n\nPOI_LABELS = {\n    \"epicerie\": \"Épicerie\", \"depanneur\": \"Dépanneur\", \"pharmacie\": \"Pharmacie\",\n    \"ecole\": \"École\", \"garderie\": \"Garderie\", \"parc\": \"Parc\", \"bus\": \"Arrêt de bus\",\n    \"metro\": \"Métro\", \"gym\": \"Gym\", \"cafe\": \"Café\", \"clinique\": \"Clinique\",\n    \"hopital\": \"Hôpital\", \"bibliotheque\": \"Bibliothèque\",\n}\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\nNBSP = \" \"\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _marche(m_: float) -> str:\n    return f\"≈{NBSP}{max(1, round(m_ * 1.3 / 80))}{NBSP}min à pied\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à 30 Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Lou·Ka : « Lou » encre + boîte encre avec « Ka » lime.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Lou\")\n        w = c.stringWidth(\"Lou\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(LIME)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6,\n                          titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=LIME, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M, y, texte)\n        c.setStrokeColor(LIME)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M, y - 4, M + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\n# ---------------------------------------------------------------------------\n# Fiche de propriété\n# ---------------------------------------------------------------------------\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n\ndef _fmt_mois(d: str | None) -> str:\n    \"\"\"« 2017-11-04 » → « novembre 2017 » (sinon la valeur telle quelle).\"\"\"\n    if not d:\n        return \"—\"\n    try:\n        an, mois = d[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return d\n\n\n_VERDICTS = {\n    \"sous\": (\"Sous le marché\", GREEN_DEEP, LIME_SOFT, GREEN),\n    \"marche\": (\"Dans le marché\", INK2, SURFACE, INK),\n    \"sur\": (\"Au-dessus du marché\", HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER),\n}\n\n_PIED_FICHE = (\"Données de l'annonce publiée par le gestionnaire — Lou-Ka est \"\n               \"un agrégateur indépendant. Enrichissements (analyse de prix, \"\n               \"registre, TAL, environnement) fournis à titre indicatif, sans \"\n               \"valeur juridique.\")\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche du logement `uid` — reflète TOUS les blocs de la fiche\n    web : prix + analyse Lou-Ka, photos, description, inclusions, registre des\n    loyers, historique TAL, risque d'inondation, qualité de l'air, coût\n    d'électricité, commerces/transport, essence, KA Scores, quartier,\n    à proximité, références + QR. None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"amenities\"] = json.loads(l.get(\"amenities\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    l[\"digest\"] = json.loads(l[\"digest\"]) if l.get(\"digest\") else None\n    poi = []\n    quartier = None\n    if l.get(\"lat\") is not None:\n        key = f\"{round(l['lat'], 4)},{round(l['lng'], 4)}\"\n        pr = con.execute(\"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n        poi = json.loads(pr[\"pois\"]) if pr else []\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        quartier = qmod.fiche_quartier(l[\"lat\"], l[\"lng\"], l.get(\"city\") or \"\",\n                                       dauid if dauid and dauid != \"hors-zone\" else None)\n    reg = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n    src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]), l[\"source\"])\n    kasc = None\n    if l.get(\"coord_key\"):\n        kr = con.execute(\"SELECT walk, transit, bike, calme, services,\"\n                         \" global AS glob FROM kascores WHERE coord_key=?\",\n                         (l[\"coord_key\"],)).fetchone()\n        if kr is not None:\n            kasc = dict(kr)\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 6\",\n        (uid,)).fetchall()]\n    con.close()\n\n    # --- enrichissements de la fiche (mêmes modules que les blocs du site) ---\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import fairvalue as fv_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    from . import rdl as rdl_mod\n    from . import tal as tal_mod\n    fv = _safe(fv_mod.explain, uid)\n    rdl_d = _safe(rdl_mod.nearby, lat, lng) if geo else None\n    tal_d = (_safe(tal_mod.lookup, l[\"address\"], l.get(\"city\"))\n             if l.get(\"address\") else None)\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Lou-Ka — {l.get('title') or l.get('address')}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche logement\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche logement · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- bloc prix + badge marché (verdict du modèle Lou-Ka en priorité)\n    prix_txt = (_fmt_money(l[\"price\"]) + f\"{NBSP}/ mois\") if l.get(\"price\") \\\n        else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    loyer_secteur = (quartier or {}).get(\"demographie\", {}).get(\"loyer_moyen\") \\\n        if quartier else None\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    if l.get(\"price\") and fv and fv.get(\"verdict\") in _VERDICTS:\n        dev = fv.get(\"deviation\") or 0\n        pct = f\"{'+' if dev > 0 else '−'}{abs(round(dev * 100))}{NBSP}%\"\n        lab, fg, bg, stk = _VERDICTS[fv[\"verdict\"]]\n        s.pilule(bx, y - 22, f\"{pct} vs juste valeur · {lab}\", fg, bg, stk)\n    elif l.get(\"price\") and loyer_secteur:\n        delta = (l[\"price\"] - loyer_secteur) / loyer_secteur\n        pct = f\"{'+' if delta > 0 else '−'}{abs(round(delta * 100))}{NBSP}%\"\n        if delta <= -0.15:\n            s.pilule(bx, y - 22, f\"{pct} vs secteur · Bon deal\", GREEN_DEEP, LIME_SOFT, GREEN)\n        elif delta <= 0.10:\n            s.pilule(bx, y - 22, f\"{pct} vs secteur · Dans le marché\", INK2, SURFACE, INK)\n        else:\n            s.pilule(bx, y - 22, f\"{pct} vs secteur · Au-dessus du marché\",\n                     HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER)\n    y -= 44\n\n    # --- titre + adresse\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, (l.get(\"title\") or l.get(\"address\") or \"\")[:80])\n    y -= 14\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    loc = \" · \".join(x for x in (l.get(\"address\"), l.get(\"sector\"), l.get(\"city\"))\n                     if x and x != l.get(\"title\"))\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés\n    x = M\n    chips = []\n    if l.get(\"unit_type\"):\n        chips.append(l[\"unit_type\"])\n    ad = l.get(\"availability_date\")\n    if ad == \"now\":\n        chips.append(\"Libre maintenant\")\n    elif ad:\n        chips.append(f\"Dispo {ad}\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft'])}{NBSP}pi²\")\n    if l.get(\"furnished\"):\n        chips.append(\"Meublé\")\n    if l.get(\"pets\"):\n        chips.append({\"oui\": \"Animaux acceptés\", \"non\": \"Animaux refusés\",\n                      \"conditions\": \"Animaux sous conditions\"}.get(l[\"pets\"], l[\"pets\"]))\n    if l[\"details\"].get(\"floor\") is not None:\n        chips.append(f\"{l['details']['floor']}e étage\")\n    for chip in chips[:7]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- « En bref » (digest) sur bandeau lime\n    dg = l.get(\"digest\")\n    if dg and dg.get(\"en_bref\"):\n        c.setFillColor(LIME_SOFT)\n        c.setStrokeColor(GREEN)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 40, PAGE_W - 2 * M, 44, 6, stroke=1, fill=1)\n        s.paragraphe(M + 10, y - 8, \"EN BREF — \" + dg[\"en_bref\"],\n                     PAGE_W - 2 * M - 20, taille=8.5, couleur=GREEN_DEEP,\n                     max_lignes=3)\n        y -= 54\n\n    # --- description (2-3 sections max) | inclusions en colonne droite\n    col_g = PAGE_W * 0.56 - M\n    y_desc = s.titre_section(y, \"Description\")\n    if dg and dg.get(\"sections\"):\n        for sec in dg[\"sections\"][:3]:\n            c.setFont(\"Helvetica-Bold\", 9)\n            c.setFillColor(INK)\n            c.drawString(M, y_desc, sec[\"titre\"][:60])\n            y_desc -= 12\n            y_desc = s.paragraphe(M, y_desc, sec[\"texte\"], col_g, max_lignes=6)\n            y_desc -= 4\n            if y_desc < 150:\n                break\n    elif l.get(\"description\"):\n        y_desc = s.paragraphe(M, y_desc, l[\"description\"], col_g, max_lignes=16)\n\n    xr = M + col_g + 24\n    y_incl = s_titre_droite(c, y, xr, \"Inclusions et commodités\")\n    d = l[\"details\"]\n    badges = []\n    inc = d.get(\"inclusions\") or {}\n    for k, lab in ((\"heating\", \"Chauffage inclus\"), (\"electricity\", \"Électricité incluse\"),\n                   (\"hot_water\", \"Eau chaude incluse\"), (\"internet\", \"Internet inclus\")):\n        if inc.get(k):\n            badges.append(lab)\n    app = d.get(\"appliances\") or {}\n    if app.get(\"dishwasher\"):\n        badges.append(\"Lave-vaisselle\")\n    if app.get(\"washer_dryer\"):\n        badges.append(\"Laveuse-sécheuse\")\n    for k, lab in ((\"ac\", \"Climatisation\"), (\"elevator\", \"Ascenseur\"),\n                   (\"balcony\", \"Balcon\"), (\"pool\", \"Piscine\"), (\"gym\", \"Gym\"),\n                   (\"laundry\", \"Buanderie\"), (\"storage\", \"Rangement\")):\n        if d.get(k):\n            badges.append(lab)\n    if (d.get(\"parking\") or {}).get(\"available\"):\n        badges.append(\"Stationnement\")\n    if l.get(\"furnished\"):\n        badges.append(\"Meublé\")\n    c.setFont(\"Helvetica\", 8.5)\n    for b in badges[:12]:\n        c.setFillColor(GREEN)\n        c.drawString(xr, y_incl, \"✓\")\n        c.setFillColor(INK2)\n        c.drawString(xr + 12, y_incl, b)\n        y_incl -= 12\n    autres = [a for a in l[\"amenities\"]\n              if not any(b.lower() in a.lower() or a.lower() in b.lower() for b in badges)]\n    for a in autres[: max(0, 14 - len(badges))]:\n        c.setFillColor(INK3)\n        c.drawString(xr + 12, y_incl, a[:38])\n        y_incl -= 11\n\n    y = min(y_desc, y_incl) - 16\n\n    # --- Analyse de prix Lou-Ka (juste valeur, fourchette, distribution)\n    if fv and fv.get(\"fv\"):\n        hist = fv.get(\"histogram\") or []\n        y = place(y, 168 if hist else 92)\n        y = s.titre_section(y, \"Analyse de prix Lou-Ka\")\n        jv = _fmt_money(round(fv[\"fv\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, jv)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(jv, \"Helvetica-Bold\", 17) + 8, y - 10,\n                     \"/ mois — juste valeur estimée par le modèle Lou-Ka\")\n        meta = []\n        if fv.get(\"fv_low\") and fv.get(\"fv_high\"):\n            meta.append(f\"fourchette {_fmt_money(round(fv['fv_low']))} – \"\n                        f\"{_fmt_money(round(fv['fv_high']))}\")\n        if fv.get(\"confidence\"):\n            meta.append(f\"confiance {fv['confidence']}\")\n        if fv.get(\"comps\"):\n            meta.append(f\"{fv['comps']} comparables\")\n        if fv.get(\"segment_n\"):\n            meta.append(f\"segment de {fv['segment_n']} annonces\")\n        c.setFont(\"Helvetica\", 8)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        if l.get(\"price\") and fv.get(\"verdict\") in _VERDICTS:\n            dev = fv.get(\"deviation\") or 0\n            pct = f\"{'+' if dev > 0 else '−'}{abs(round(dev * 100))}{NBSP}%\"\n            lab, fg, bg, stk = _VERDICTS[fv[\"verdict\"]]\n            s.pilule(M, y - 48, f\"{pct} vs juste valeur · {lab}\", fg, bg, stk)\n        y -= 58\n        if hist:\n            ch_h = 40\n            lo, hi = hist[0][\"x0\"], hist[-1][\"x1\"]\n            larg = PAGE_W - 2 * M\n            base = y - ch_h - 6\n            if hi > lo and fv.get(\"fv_low\") and fv.get(\"fv_high\"):\n                bx0 = M + larg * max(0.0, (fv[\"fv_low\"] - lo) / (hi - lo))\n                bx1 = M + larg * min(1.0, (fv[\"fv_high\"] - lo) / (hi - lo))\n                c.setFillColor(LIME_SOFT)\n                c.rect(bx0, base, max(2, bx1 - bx0), ch_h, stroke=0, fill=1)\n            max_n = max((h[\"n\"] for h in hist), default=1) or 1\n            bw = larg / len(hist)\n            c.setFillColor(GREEN)\n            for i, h in enumerate(hist):\n                bh = ch_h * h[\"n\"] / max_n\n                if bh > 0:\n                    c.rect(M + i * bw + 1, base, bw - 2, max(0.6, bh),\n                           stroke=0, fill=1)\n            if hi > lo and l.get(\"price\"):\n                px = M + larg * (min(max(l[\"price\"], lo), hi) - lo) / (hi - lo)\n                c.setStrokeColor(LIME)\n                c.setLineWidth(2)\n                c.line(px, base - 3, px, base + ch_h + 3)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(LIME)\n                c.drawString(min(max(px - 28, M), PAGE_W - M - 92),\n                             base + ch_h + 6,\n                             f\"CE LOYER · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 9, _fmt_money(lo))\n            c.drawRightString(PAGE_W - M, base - 9,\n                              _fmt_money(hi) + \" — distribution des loyers du \"\n                                               \"segment de référence\")\n            y = base - 20\n        ph = [p for p in price_history if p.get(\"price\")]\n        if len(ph) >= 2 and ph[0][\"price\"] != ph[-1][\"price\"]:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            chn = \" → \".join(_fmt_money(p[\"price\"]) for p in reversed(ph[:4]))\n            c.drawString(M, y - 2, f\"Historique de prix de l'annonce : {chn}\")\n            y -= 16\n\n    # --- Registre des loyers (déclarations citoyennes à proximité)\n    if rdl_d and rdl_d.get(\"n\"):\n        comp = rdl_d.get(\"items\") or []\n        y = place(y, 128 + min(len(comp), 8) * 12)\n        y = s.titre_section(y, \"Registre des loyers — déclarations citoyennes\")\n        tw = (PAGE_W - 2 * M - 20) / 3\n        med_rec = rdl_d.get(\"median_recent\")\n        tuiles = [(str(rdl_d[\"n\"]), f\"loyers déclarés à <{rdl_d['radius_m']} m\"),\n                  (_fmt_money(rdl_d.get(\"median\")), \"médiane du secteur\"),\n                  (_fmt_money(med_rec) if med_rec else \"—\",\n                   f\"médiane depuis 2023 ({rdl_d.get('n_recent', 0)} décl.)\")]\n        for i, (val, lab) in enumerate(tuiles):\n            s.tuile(M + i * (tw + 10), y - 44, tw, 38, val, lab)\n        y -= 56\n        rooms = rdl_d.get(\"by_rooms\") or {}\n        if rooms:\n            def _rk(k: str) -> float:\n                try:\n                    return float(k)\n                except ValueError:\n                    return 99.0\n            parts = [f\"{k}{NBSP}ch. {_fmt_money(v['median'])} ({v['n']})\"\n                     for k, v in sorted(rooms.items(), key=lambda kv: _rk(kv[0]))]\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, (\"Médianes par taille : \" + \" · \".join(parts))[:118])\n            y -= 14\n        if comp:\n            c.setFont(\"Courier-Bold\", 7)\n            c.setFillColor(INK3)\n            for lab, xoff in ((\"ADRESSE\", 0), (\"CH.\", 280), (\"DÉBUT DE BAIL\", 320),\n                              (\"DIST.\", 420), (\"LOYER\", 480)):\n                c.drawString(M + xoff, y, lab)\n            y -= 4\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.line(M, y, PAGE_W - M, y)\n            y -= 11\n            for it in comp[:8]:\n                c.setFont(\"Helvetica\", 8)\n                c.setFillColor(INK2)\n                c.drawString(M, y, (it.get(\"address\") or \"\")[:52])\n                c.drawString(M + 280, y, str(it[\"rooms\"]) if it.get(\"rooms\") is not None else \"—\")\n                c.drawString(M + 320, y, _fmt_mois(it.get(\"date\")))\n                c.drawString(M + 420, y, _fmt_dist(it[\"dist_m\"]))\n                c.setFont(\"Helvetica-Bold\", 8)\n                c.setFillColor(INK)\n                c.drawString(M + 480, y, _fmt_money(it.get(\"price\")))\n                y -= 12\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Loyers déclarés volontairement par des locataires \"\n                               \"au Registre des loyers (registre-des-loyers.ca) — \"\n                               \"données citoyennes non vérifiées.\")\n        y -= 16\n\n    # --- Historique au TAL (décisions SOQUIJ à l'adresse)\n    if tal_d and tal_d.get(\"status\") in (\"ok\", \"pending\"):\n        decs = (tal_d.get(\"decisions\") or [])[:6]\n        y = place(y, 70 + len(decs) * 24)\n        y = s.titre_section(y, \"Historique au Tribunal administratif du logement\")\n        if tal_d[\"status\"] == \"pending\":\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 4, \"Adresse en file de vérification auprès des \"\n                                   \"décisions publiées du TAL (résultat sur la \"\n                                   \"fiche en ligne sous peu).\")\n            y -= 18\n        elif not decs:\n            s.pilule(M, y - 12, \"Aucune décision trouvée\", GREEN_DEEP,\n                     LIME_SOFT, GREEN)\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M + 130, y - 7, \"Aucune décision publiée du TAL n'a été \"\n                                         \"repérée à cette adresse.\")\n            y -= 26\n        else:\n            n = tal_d.get(\"n\") or len(decs)\n            cl = tal_d.get(\"contre_locataire\") or 0\n            c.setFont(\"Helvetica-Bold\", 9)\n            c.setFillColor(INK)\n            c.drawString(M, y - 4,\n                         f\"{n} décision{'s' if n > 1 else ''} publiée\"\n                         f\"{'s' if n > 1 else ''} à cette adresse — \"\n                         f\"{cl} à l'initiative du propriétaire.\")\n            y -= 20\n            for dec in decs:\n                qui = {\"locateur\": \"demande du propriétaire\",\n                       \"locataire\": \"demande du locataire\"}.get(\n                    dec.get(\"demandeur\") or \"\", \"demandeur inconnu\")\n                c.setFont(\"Helvetica-Bold\", 8)\n                c.setFillColor(INK)\n                c.drawString(M, y, _fmt_mois(dec.get(\"date\")))\n                c.setFont(\"Helvetica\", 8)\n                c.setFillColor(INK2)\n                suite = qui\n                if dec.get(\"verdict\"):\n                    suite += f\" · demande {dec['verdict']}\"\n                if dec.get(\"tags\"):\n                    suite += \" · \" + \", \".join(dec[\"tags\"][:3])\n                c.drawString(M + 78, y, suite[:88])\n                c.setFont(\"Courier-Bold\", 7)\n                c.setFillColor(GREEN)\n                c.drawRightString(PAGE_W - M, y, dec.get(\"citation\") or \"SOQUIJ\")\n                y -= 12\n            y -= 6\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Décisions publiques diffusées par SOQUIJ, repérées \"\n                               \"par correspondance d'adresse — une décision peut \"\n                               \"concerner un autre logement du même immeuble.\")\n        y -= 16\n\n    # --- Risque d'inondation (BDZI)\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\",\n                     HexColor(\"#a12622\"), HexColor(\"#fde8e8\"), HexColor(\"#a12622\"))\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_DEEP, LIME_SOFT, GREEN)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z['type']} — à {z['distance_m']} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- Qualité de l'air (RSQAQ)\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"du logement — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = f\"{m_['moyenne']:.1f}\".replace(\".\", \",\") if m_.get(\"moyenne\") is not None else \"—\"\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- Coût d'électricité (Hydro-Québec)\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP) + f\"{NBSP}kWh/an\"\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- Commerces et transport\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:10]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- Essence à proximité\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- KA Scores (immeuble et environnement)\n    if kasc and kasc.get(\"glob\") is not None:\n        from . import kascores as ks_mod\n        y = place(y, 92)\n        y = s.titre_section(y, \"KA Scores\")\n        paires = [(\"Global\", kasc.get(\"glob\")), (\"À pied\", kasc.get(\"walk\")),\n                  (\"Transport\", kasc.get(\"transit\")), (\"Vélo\", kasc.get(\"bike\")),\n                  (\"Calme\", kasc.get(\"calme\")), (\"Services\", kasc.get(\"services\"))]\n        tw = (PAGE_W - 2 * M - 5 * 8) / 6\n        for i, (lab, v) in enumerate(paires):\n            val = \"—\" if v is None else str(round(v))\n            sub = ks_mod.label(v) or \"\"\n            s.tuile(M + i * (tw + 8), y - 46, tw, 42, val,\n                    f\"{lab} · {sub}\" if sub else lab)\n        y -= 62\n\n    # --- Le quartier\n    if quartier:\n        y = place(y, 270)\n        y = s.titre_section(y, \"Le quartier\")\n        demo = quartier.get(\"demographie\") or {}\n        tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                  (\"Ménages locataires\", f\"{round(demo['pct_locataires'])}{NBSP}%\" if demo.get(\"pct_locataires\") is not None else \"—\"),\n                  (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                  (\"Âge médian\", f\"{round(demo['age_median'])} ans\" if demo.get(\"age_median\") is not None else \"—\"),\n                  (\"Français à la maison\", f\"{round(demo['pct_francais'])}{NBSP}%\" if demo.get(\"pct_francais\") is not None else \"—\"),\n                  (\"Diplôme universitaire\", f\"{round(demo['pct_univ'])}{NBSP}%\" if demo.get(\"pct_univ\") is not None else \"—\")]\n        tw = (PAGE_W - 2 * M - 20) / 3\n        for i, (lab, val) in enumerate(tuiles):\n            s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                    tw, 38, val, lab)\n        y -= 34 + 44 + 16\n\n        prox = quartier.get(\"proximite\") or {}\n        for k, lab in PROX_LABELS:\n            if k not in prox:\n                continue\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(GREEN)\n            c.roundRect(bx0, y - 1, bw_ * v, 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        y -= 4\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch[\"classe\"] <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_DEEP, LIME_SOFT, GREEN))\n            elif ch[\"classe\"] >= 7:\n                badges_q.append((f\"Îlot de chaleur (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\"),\n                                 HexColor(\"#8a5a12\"), AMBER_SOFT, AMBER))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"]) / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} la moyenne canadienne\",\n                             INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m (12 mois)\",\n                             INK2, SURFACE, INK))\n        x = M\n        for txt, fg, bg, stk in badges_q:\n            x = s.pilule(x, y - 8, txt, fg, bg, stk)\n        y -= 30\n\n    # --- À proximité (commodités du cache POI)\n    if poi:\n        lignes = (min(len(poi), 12) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"À proximité\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, p in enumerate(poi[:12]):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            lab = POI_LABELS.get(p[\"cat\"], p[\"cat\"])\n            nom_poi = p[\"name\"][:19] + \"…\" if len(p[\"name\"]) > 20 else p[\"name\"]\n            c.drawString(px, py, f\"{lab} — {nom_poi}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py,\n                              f\"{_fmt_dist(p['dist_m'])} · {_marche(p['dist_m'])}\")\n        y -= lignes * 13 + 14\n\n    # --- Références : QR + gestionnaire + source\n    y = place(y, 120)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.lou-ka.com/logement/{uid}\", box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    c.drawString(M + 82, y - 14, f\"Gestionnaire : {src_name}\")\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, y - 28, \"Annonce originale :\")\n    c.setFillColor(GREEN)\n    c.drawString(M + 82, y - 40, (l.get(\"url\") or \"\")[:90])\n    c.setFillColor(INK2)\n    c.drawString(M + 82, y - 56, \"Fiche à jour en ligne (scannez le code) :\")\n    c.setFillColor(GREEN)\n    c.drawString(M + 82, y - 68, f\"www.lou-ka.com/logement/{uid}\")\n    y -= 86\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : Statistique Canada (2021), INSPQ (CC-BY 4.0), \"\n                       \"Ville de Montréal (CC-BY 4.0), OpenStreetMap, BDZI, RSQAQ, \"\n                       \"Hydro-Québec, registre-des-loyers.ca, SOQUIJ, gazquebec.ca.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n\n\ndef s_titre_droite(c, y, x, texte):\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x, y, texte)\n    c.setStrokeColor(LIME)\n    c.setLineWidth(3)\n    c.line(x, y - 4, x + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\n# ---------------------------------------------------------------------------\n# Rapport global du marché\n# ---------------------------------------------------------------------------\n\n_REGIONS = [\n    (\"Québec métro\", {\"Québec\", \"Lévis\", \"Saint-Augustin-de-Desmaures\", \"L'Ancienne-Lorette\", \"Pont-Rouge\", \"Shannon\"}),\n    (\"Outaouais\", {\"Gatineau\", \"Chelsea\", \"Thurso\", \"Perkins\", \"Maniwaki\", \"Val-des-Monts\"}),\n    (\"Estrie / Montérégie-Est\", {\"Sherbrooke\", \"Magog\", \"Orford\", \"East Angus\", \"Waterville\", \"Granby\", \"Waterloo\", \"Bromont\", \"Cowansville\", \"Richmond\"}),\n    (\"Mauricie / Centre-du-Québec\", {\"Trois-Rivières\", \"Bécancour\", \"Shawinigan\", \"Drummondville\", \"Victoriaville\", \"Notre-Dame-du-Bon-Conseil\", \"Saint-Léonard-d'Aston\", \"Louiseville\", \"Nicolet\", \"Wickham\", \"Saint-Narcisse\", \"Saint-Nicéphore\"}),\n    (\"Lanaudière / Laurentides\", {\"Joliette\", \"Saint-Jérôme\", \"Berthierville\", \"Saint-Ambroise-de-Kildare\", \"Saint-Gabriel-de-Brandon\", \"Lachute\", \"Brownsburg-Chatham\", \"Saint-Charles-Borromée\", \"Mirabel\", \"Sainte-Agathe-des-Monts\", \"Sainte-Thérèse\", \"Blainville\", \"Charlemagne\", \"Notre-Dame-des-Prairies\"}),\n    (\"Bas-Saint-Laurent / Gaspésie\", {\"Rimouski\", \"Rivière-du-Loup\", \"Matane\", \"Saint-Ulric\", \"Amqui\", \"Le Bic\", \"New Richmond\", \"Carleton-sur-Mer\", \"Gaspé\"}),\n    (\"Saguenay–Lac-Saint-Jean\", {\"Saguenay\", \"Alma\", \"Chicoutimi\", \"Jonquière\", \"Chambord\", \"La Malbaie\"}),\n    (\"Abitibi-Témiscamingue\", {\"Rouyn-Noranda\", \"Val-d'Or\", \"Amos\", \"Malartic\"}),\n    (\"Côte-Nord\", {\"Sept-Îles\", \"Port-Cartier\", \"Baie-Comeau\", \"Forestville\"}),\n    (\"Chaudière-Appalaches\", {\"Saint-Georges\", \"Sainte-Marie\", \"Thetford Mines\", \"Montmagny\", \"Vallée-Jonction\", \"Scott\", \"Saint-Isidore\", \"La Guadeloupe\", \"Saint-Raphaël\"}),\n]\n\n\ndef _region_de(city: str) -> str:\n    for nom, villes in _REGIONS:\n        if city in villes:\n            return nom\n    return \"Grand Montréal & environs\"\n\n\ndef rapport_pdf() -> bytes:\n    \"\"\"Rapport global du marché locatif Lou-Ka (multi-pages).\n\n    Tous les chiffres viennent de marketstats.compute() — la même source\n    que la page Statistiques du site.\n    \"\"\"\n    from . import marketstats\n    st = marketstats.compute()\n    reg_file = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n    noms = {s[\"id\"]: s[\"name\"] for s in reg_file}\n\n    t = st[\"totals\"]\n    total, moy, med, gps = t[\"total\"], t[\"avg\"] or 0, t[\"median\"] or 0, t[\"gps_pct\"]\n    n_sources = t[\"sources\"]\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(\"Lou-Ka — Rapport du marché locatif\")\n    s = _Style(c)\n\n    # ---- page 1 : couverture + grands chiffres + régions\n    s.fond()\n    s.logo(M, PAGE_H - 130, 42)\n    c.setFont(\"Helvetica-Bold\", 24)\n    c.setFillColor(INK)\n    c.drawString(M, PAGE_H - 185, \"Rapport du marché locatif\")\n    c.setFont(\"Courier-Bold\", 9)\n    c.setFillColor(INK3)\n    c.drawString(M, PAGE_H - 205,\n                 time.strftime(\"PROVINCE DE QUÉBEC · GÉNÉRÉ LE %Y-%m-%d · WWW.LOU-KA.COM\"))\n\n    grands = [(f\"{total:,}\".replace(\",\", NBSP), \"annonces actives\"),\n              (str(n_sources), \"gestionnaires connectés\"),\n              (_fmt_money(moy), \"loyer moyen\"),\n              (_fmt_money(med), \"loyer médian\"),\n              (f\"{gps}{NBSP}%\", \"annonces géolocalisées\")]\n    tw = (PAGE_W - 2 * M - 4 * 10) / 5\n    for i, (val, lab) in enumerate(grands):\n        tx = M + i * (tw + 10)\n        ty = PAGE_H - 285\n        c.setFillColor(INK)\n        c.roundRect(tx + 3, ty - 3, tw, 58, 6, stroke=0, fill=1)   # ombre décalée\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.roundRect(tx, ty, tw, 58, 6, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        c.drawCentredString(tx + tw / 2, ty + 32, val)\n        c.setFont(\"Courier\", 6.3)\n        c.setFillColor(INK3)\n        c.drawCentredString(tx + tw / 2, ty + 14, lab.upper())\n\n    y = s.titre_section(PAGE_H - 330, \"Couverture par région\")\n    c.setFont(\"Courier-Bold\", 7)\n    c.setFillColor(INK3)\n    for lab, xoff in ((\"RÉGION\", 0), (\"ANNONCES\", 280), (\"SOURCES\", 360),\n                      (\"LOYER MOYEN\", 430)):\n        c.drawString(M + xoff, y, lab)\n    y -= 4\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.line(M, y, PAGE_W - M, y)\n    y -= 14\n    regions = st[\"by_region\"]\n    max_n = max((r[\"count\"] for r in regions), default=1)\n    for r in regions:\n        c.setFont(\"Helvetica-Bold\", 8.5)\n        c.setFillColor(INK)\n        c.drawString(M, y, r[\"key\"])\n        c.setFillColor(LIME)\n        c.setStrokeColor(INK)\n        c.setLineWidth(0.7)\n        bw = 90 * r[\"count\"] / max_n\n        c.roundRect(M + 180, y - 1, max(3, bw), 8, 3, stroke=1, fill=1)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawRightString(M + 330, y, f\"{r['count']:,}\".replace(\",\", NBSP))\n        c.drawRightString(M + 400, y, str(r[\"sources\"]))\n        c.drawRightString(M + 500, y, _fmt_money(r[\"avg_price\"]) if r[\"avg_price\"] else \"—\")\n        y -= 15\n    s.pied(\"Rapport généré automatiquement à partir des annonces publiques \"\n           \"agrégées par Lou-Ka. Loyers : bornes 300–10 000 $.\", 1)\n    c.showPage()\n\n    # ---- page 2 : histogramme des loyers + par type\n    s.fond()\n    y = s.entete(\"Rapport du marché · loyers\")\n    y = s.titre_section(y, \"Distribution des loyers\")\n    hist = st[\"histogram\"]\n    max_c = max((h[\"count\"] for h in hist), default=1) or 1\n    ch_h, ch_y = 150, y - 170\n    bw = (PAGE_W - 2 * M) / len(hist)\n    for i, h in enumerate(hist):\n        bh = ch_h * h[\"count\"] / max_c\n        bx = M + i * bw\n        borne = h[\"hi\"] is None or h[\"lo\"] == 0\n        c.setFillColor(INK3 if borne else GREEN)\n        c.setStrokeColor(INK)\n        c.setLineWidth(0.6)\n        c.rect(bx + 2, ch_y, bw - 4, max(1, bh), stroke=1, fill=1)\n        if h[\"count\"] and h[\"count\"] > max_c * 0.06:\n            c.setFont(\"Courier-Bold\", 6)\n            c.setFillColor(INK)\n            c.drawCentredString(bx + bw / 2, ch_y + bh + 3, str(h[\"count\"]))\n        c.setFont(\"Helvetica\", 5.6)\n        c.setFillColor(INK3)\n        lab = f\"<{h['hi']}\" if h[\"lo\"] == 0 else (f\"{h['lo']}+\" if h[\"hi\"] is None\n                                                  else str(h[\"lo\"]))\n        c.drawCentredString(bx + bw / 2, ch_y - 9, lab)\n    y = ch_y - 30\n\n    y = s.titre_section(y, \"Par taille de logement\")\n    c.setFont(\"Courier-Bold\", 7)\n    c.setFillColor(INK3)\n    for lab, xoff in ((\"TAILLE\", 0), (\"ANNONCES\", 160), (\"LOYER MOYEN\", 260),\n                      (\"LOYER MIN\", 360)):\n        c.drawString(M + xoff, y, lab)\n    y -= 4\n    c.line(M, y, PAGE_W - M, y)\n    y -= 13\n    for r in st[\"by_type\"][:9]:\n        c.setFont(\"Helvetica-Bold\", 8.5)\n        c.setFillColor(INK)\n        c.drawString(M, y, r[\"key\"])\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawRightString(M + 220, y, str(r[\"count\"]))\n        c.drawRightString(M + 330, y, _fmt_money(r[\"avg_price\"]) if r[\"avg_price\"] else \"—\")\n        c.drawRightString(M + 430, y, _fmt_money(r[\"min_price\"]) if r[\"min_price\"] else \"—\")\n        y -= 13\n    s.pied(\"Lou-Ka — agrégateur indépendant. Chaque annonce renvoie à la \"\n           \"source originale du gestionnaire.\", 2)\n    c.showPage()\n\n    # ---- page 3 : offre, inclusions, prix au pi², baisses de prix\n    s.fond()\n    y = s.entete(\"Rapport du marché · offre\")\n    o = st[\"offre\"]\n    y = s.titre_section(y, \"Inclusions et caractéristiques du parc\")\n    carac = [(\"Chauffage inclus\", o[\"chauffage_pct\"]),\n             (\"Électricité incluse\", o[\"electricite_pct\"]),\n             (\"Eau chaude incluse\", o[\"eau_chaude_pct\"]),\n             (\"Internet inclus\", o[\"internet_pct\"]),\n             (\"Climatisation\", o[\"clim_pct\"]),\n             (\"Stationnement\", o[\"stationnement_pct\"]),\n             (\"Balcon\", o[\"balcon_pct\"]),\n             (\"Meublé\", o[\"furnished_pct\"])]\n    for lab, v in carac:\n        if v is None:\n            continue\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y, lab)\n        bx0, bw_ = M + 150, PAGE_W - 2 * M - 200\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(0.8)\n        c.roundRect(bx0, y - 1, bw_, 8, 4, stroke=1, fill=1)\n        c.setFillColor(GREEN)\n        c.roundRect(bx0, y - 1, bw_ * min(1, v / 100), 8, 4, stroke=0, fill=1)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK)\n        c.drawRightString(PAGE_W - M, y, f\"{v}{NBSP}%\")\n        y -= 15\n    y -= 6\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK3)\n    c.drawString(M, y, f\"Disponibles maintenant : {o['dispo_now']:,} · à date future : \"\n                       f\"{o['dispo_date']:,} · superficie moyenne : \"\n                       f\"{o['superficie_moyenne'] or '—'} pi² \"\n                       f\"({o['superficie_connue']:,} annonces la publient)\"\n                       .replace(\",\", NBSP))\n    y -= 24\n\n    if o[\"prix_pi2\"]:\n        y = s.titre_section(y, \"Prix au pied carré (loyer / superficie)\")\n        for r in o[\"prix_pi2\"][:6]:\n            c.setFont(\"Helvetica-Bold\", 8.5)\n            c.setFillColor(INK)\n            c.drawString(M, y, r[\"key\"])\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M + 70, y, f\"{r['val']:.2f}\".replace(\".\", \",\") +\n                         f\"{NBSP}$/pi² · {r['count']} annonces\")\n            y -= 13\n        y -= 10\n\n    if st[\"baisses\"]:\n        y = s.titre_section(y, \"Baisses de prix récentes (30 jours)\")\n        for b in st[\"baisses\"][:8]:\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{(b['title'] or b['uid'])[:46]} — {b['city'] or ''}\")\n            c.setFont(\"Courier-Bold\", 8)\n            c.setFillColor(GREEN)\n            c.drawRightString(PAGE_W - M, y,\n                              f\"{_fmt_money(b['avant'])} → {_fmt_money(b['apres'])} \"\n                              f\"({b['pct']}{NBSP}%)\".replace(\".\", \",\"))\n            y -= 13\n    s.pied(\"Caractéristiques dérivées des annonces publiées ; les inclusions \"\n           \"non mentionnées par une source ne sont pas comptées.\", 3)\n    c.showPage()\n\n    # ---- page 4 : top villes + top gestionnaires\n    s.fond()\n    y = s.entete(\"Rapport du marché · détail\")\n    y = s.titre_section(y, \"Top 20 des villes\")\n    col_w = (PAGE_W - 2 * M) / 2\n    for i, r in enumerate(st[\"by_city\"][:20]):\n        vx = M if i < 10 else M + col_w\n        vy = y - (i % 10) * 13\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(vx, vy, f\"{i + 1:>2}. {r['key']}\")\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK)\n        c.drawRightString(vx + col_w - 24, vy, f\"{r['count']:,}\".replace(\",\", NBSP))\n    y -= 10 * 13 + 16\n\n    y = s.titre_section(y, \"Top 20 des gestionnaires\")\n    for i, r in enumerate(st[\"by_source\"][:20]):\n        vx = M if i < 10 else M + col_w\n        vy = y - (i % 10) * 13\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(vx, vy, f\"{i + 1:>2}. {noms.get(r['key'], r['key'])[:34]}\")\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK)\n        c.drawRightString(vx + col_w - 24, vy, f\"{r['count']:,}\".replace(\",\", NBSP))\n    y -= 10 * 13 + 20\n\n    c.setFont(\"Helvetica\", 7.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources de données de quartier : Statistique Canada (Recensement 2021, \"\n                       \"licence ouverte), INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), OpenStreetMap.\")\n    s.pied(\"© Lou-Ka — www.lou-ka.com · rapport non contractuel, généré automatiquement.\", 4)\n    c.save()\n    return buf.getvalue()\n","numLines":1202,"startLine":1,"totalLines":1202}}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"cf061aab-f1da-416e-aed2-169c48aa9e34","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHW3N2sAfU1SzozQqAB","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":27698,"cache_read_input_tokens":52257,"cache_creation":{"ephemeral_5m_input_tokens":27698,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"850de6c2-5d68-4e54-95a8-9bdf2a09fd96"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHW3N2sAfU1SzozQqAB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01W86VYtXBXpLaX3CYtNzoti","name":"Bash","input":{"command":".venv/bin/python -c \"\nfrom immoka import db\ncon = db.connect()\ncols = [r[1] for r in con.execute('PRAGMA table_info(listings)')]\nprint(cols)\ncon.close()\"","description":"Colonnes exactes de la table listings"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":27698,"cache_read_input_tokens":52257,"cache_creation":{"ephemeral_5m_input_tokens":27698,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"ce1b159f-94f4-41bd-b101-03d82c299ef8"}}
{"type":"permission_request","requestId":"d9e8f7fa40043a32","tool":"Bash","input":{"command":".venv/bin/python -c \"\nfrom immoka import db\ncon = db.connect()\ncols = [r[1] for r in con.execute('PRAGMA table_info(listings)')]\nprint(cols)\ncon.close()\"","description":"Colonnes exactes de la table listings"}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01W86VYtXBXpLaX3CYtNzoti","type":"tool_result","content":"['uid', 'source', 'external_id', 'url', 'title', 'address', 'sector', 'city', 'region', 'property_type', 'price', 'price_label', 'bedrooms', 'bathrooms', 'powder_rooms', 'area_sqft', 'lot_sqft', 'year_built', 'mls', 'status', 'broker_name', 'broker_phone', 'description', 'features', 'details', 'images', 'lat', 'lng', 'geocode_failed', 'content_hash', 'first_seen', 'last_seen', 'updated_at', 'miss_count', 'active', 'agency', 'dup_hidden', 'dauid', 'vraiprix', 'quality_score', 'quality_issues', 'published']","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"59d21a52-4a85-4b44-9837-162ac40ca469","timestamp":"2026-08-24T21:30:01.017Z","tool_use_result":{"stdout":"['uid', 'source', 'external_id', 'url', 'title', 'address', 'sector', 'city', 'region', 'property_type', 'price', 'price_label', 'bedrooms', 'bathrooms', 'powder_rooms', 'area_sqft', 'lot_sqft', 'year_built', 'mls', 'status', 'broker_name', 'broker_phone', 'description', 'features', 'details', 'images', 'lat', 'lng', 'geocode_failed', 'content_hash', 'first_seen', 'last_seen', 'updated_at', 'miss_count', 'active', 'agency', 'dup_hidden', 'dauid', 'vraiprix', 'quality_score', 'quality_issues', 'published']","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VFT9WzjS3mvUnrRpzZbwkK","type":"tool_result","content":"21-\n22-ROOT = Path(__file__).resolve().parent.parent\n23:SOURCES_PATH = ROOT / \"data\" / \"sources.json\"\n24-# Frontend : build Vite (React) si présent, sinon la page statique de secours.\n25-FRONTEND_DIST = ROOT / \"frontend\" / \"dist\"\n26-FRONTEND_DIR = FRONTEND_DIST if FRONTEND_DIST.exists() else ROOT / \"frontend\"\n27-\n28-app = FastAPI(title=\"Immo-Ka API\", version=\"0.1\",\n29-              description=\"Agrégateur de maisons à vendre — province de Québec\")\n30-app.add_middleware(CORSMiddleware, allow_origins=[\"*\"],\n31-                   allow_methods=[\"*\"], allow_headers=[\"*\"])\n32-# le GeoJSON complet pèse ~20 Mo — la compression est vitale pour la carte\n33-app.add_middleware(GZipMiddleware, minimum_size=1024)\n34-# connexion Google (compte Groupe-Ka partagé avec Lou-Ka) — voir immoka/auth.py\n35-app.include_router(auth.router)\n36-# favoris ♥ — magasin central au hub Groupe KA, voir immoka/favorites.py\n37-app.include_router(favorites.router)\n38-\n39-_sync_lock = threading.Lock()\n40-\n41-# Déduplication de la famille RE/MAX : le flux central (remax_quebec) et les\n42-# ~42 connecteurs de sous-agences (remax_ag_*) décrivent les MÊMES inscriptions,\n43-# identifiées de façon unique par leur numéro Centris (= external_id). On masque\n44-# donc toute fiche de sous-agence dès qu'une fiche de plus haute priorité existe\n45-# (le central enrichi d'abord, puis la sous-agence au plus petit uid). Les fiches\n46-# du central et des autres agences ne sont jamais masquées. → aucun double-comptage,\n47-# et les sous-agences prennent le relais automatiquement si le central disparaît.\n48-# Déduplication PRÉ-CALCULÉE : la colonne `dup_hidden` (remplie par\n49-# db.refresh_dedup après chaque sync) marque les doublons de sous-agences. La\n50-# lecture est ainsi instantanée (index) au lieu d'un sous-select corrélé par\n51-# ligne (~300 s sur 75 k lignes). Voir db.refresh_dedup pour la règle.\n52-# + `published=1` : seuil de qualité minimum (voir immoka/quality.py) — une\n53-# annonce sans prix plausible, sans ville, sans type de bien ou sans contenu\n--\n57-\n58-\n59:def _row_to_dict(row) -> dict:\n60-    d = dict(row)\n61-    d[\"features\"] = json.loads(d.get(\"features\") or \"[]\")\n62-    d[\"images\"] = json.loads(d.get(\"images\") or \"[]\")\n63-    d[\"details\"] = json.loads(d.get(\"details\") or \"{}\")\n64-    if d.get(\"vraiprix\"):\n65-        try:\n66-            d[\"vraiprix\"] = json.loads(d[\"vraiprix\"]) or None\n67-        except (ValueError, TypeError):\n68-            d[\"vraiprix\"] = None\n69-    # jours sur le marché (depuis la 1re observation par Immo-Ka ; la date\n70-    # d'inscription de la source, si connue, est dans details.listed_at)\n71-    if d.get(\"first_seen\"):\n72:        d[\"days_on_market\"] = max(0, int((time.time() - d[\"first_seen\"]) // 86400))\n73-    return d\n74-\n75-\n76-@app.get(\"/api/listings\")\n77-def list_listings(\n78-    city: str | None = None,\n79-    sector: str | None = None,\n80-    region: str | None = None,\n81-    property_type: str | None = None,\n82-    source: str | None = None,\n83-    price_max: float | None = None,\n84-    price_min: float | None = None,\n85-    bedrooms_min: int | None = None,\n86-    bathrooms_min: int | None = None,\n87-    area_min: float | None = None,       # superficie habitable minimale (pi²)\n88-    q: str | None = None,\n89-    active: int = 1,\n90-    sort: str = \"price_asc\",             # price_asc | price_desc | recent\n91-    limit: int = Query(500, le=2000),\n92-    offset: int = 0,\n93-):\n94-    con = db.connect()\n95-    sql = \"SELECT * FROM listings WHERE 1=1\"\n96-    args: list = []\n97-    if active in (0, 1):\n98-        sql += \" AND active=?\"; args.append(active)\n99-    if city:\n100-        sql += \" AND city=?\"; args.append(city)\n101-    if sector:\n102-        sql += \" AND sector LIKE ?\"; args.append(f\"%{sector}%\")\n--\n190-        # historique de prix (baisses/hausses du prix demandé)\n191-        d[\"price_history\"] = [dict(r) for r in con.execute(\n192:            \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n193-            (uid,)).fetchall()]\n194-        # commodités de proximité (cache par immeuble, voir immoka/poi.py)\n195-        if d.get(\"lat\") is not None and d.get(\"lng\") is not None:\n196-            key = f\"{round(d['lat'], 4)},{round(d['lng'], 4)}\"\n197-            poi_row = con.execute(\n198:                \"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n199-            d[\"poi\"] = json.loads(poi_row[\"pois\"]) if poi_row else []\n200-        else:\n201-            d[\"poi\"] = []\n202-        # statistiques de quartier (recensement, proximité, chaleur, criminalité)\n203-        from . import quartier\n204-        dauid = d.get(\"dauid\")\n205:        d[\"quartier\"] = quartier.fiche_quartier(\n206-            d.get(\"lat\"), d.get(\"lng\"), d.get(\"city\") or \"\",\n207-            dauid if dauid and dauid != \"hors-zone\" else None)\n208-    con.close()\n209-    if d is None:\n210-        raise HTTPException(404, \"Propriété introuvable\")\n211-    return d\n212-\n213-\n214-@app.get(\"/api/listings.geojson\")\n215-def listings_geojson(\n216-    city: str | None = None,\n217-    sector: str | None = None,\n218-    region: str | None = None,\n219-    property_type: str | None = None,\n220-    source: str | None = None,\n221-    price_max: float | None = None,\n222-    price_min: float | None = None,\n223-    bedrooms_min: int | None = None,\n224-    bathrooms_min: int | None = None,\n225-    area_min: float | None = None,\n226-    q: str | None = None,\n227-    bbox: str | None = None,\n228-    limit: int = Query(3000, le=8000),\n229-):\n230-    \"\"\"Propriétés géolocalisées (marqueurs de carte, champs allégés).\n231-\n232-    Ka Maps : `bbox=ouest,sud,est,nord` restreint au rectangle visible\n233-    (index idx_listings_geo) ; `limit` plafonne la réponse ; la collection\n234-    porte `totalGeocoded` (géolocalisées correspondantes) et\n235-    `totalMatching` (avec ou sans coordonnées). Mêmes filtres que\n--\n365-\n366-\n367:def _franchise_of(source: str, source_names: dict) -> str:\n368-    for name, match in _FRANCHISES:\n369-        if match(source):\n370-            return name\n371:    return source_names.get(source, source)   # agence indépendante = elle-même\n372-\n373-\n374-@app.get(\"/api/agencies\")\n375-def agencies():\n376-    \"\"\"Arbre bannière → sous-agences (bureaux) avec le nombre d'inscriptions.\n377-\n378-    Alimente la page « Sources » de l'app : chaque bannière est éclatée par\n379-    sous-agence via le champ `agency` (bureau). Dédupliqué (n° Centris).\"\"\"\n380-    con = db.connect()\n381-    registry = json.loads(SOURCES_PATH.read_text(encoding=\"utf-8\"))[\"sources\"]\n382:    source_names = {s[\"id\"]: s[\"name\"] for s in registry}\n383-    rows = con.execute(\n384-        \"SELECT source, COALESCE(NULLIF(agency,''), '') agency, COUNT(*) n\"\n385-        \" FROM listings WHERE active=1\" + DEDUP_CLAUSE +\n386-        \" GROUP BY source, agency\").fetchall()\n387-    con.close()\n388-    tree: dict[str, dict] = {}\n389-    for r in rows:\n390:        fr = _franchise_of(r[\"source\"], source_names)\n391-        node = tree.setdefault(fr, {\"franchise\": fr, \"total\": 0, \"agencies\": {}})\n392-        node[\"total\"] += r[\"n\"]\n393-        # nom de sous-agence : le bureau (agency) sinon le nom de la source\n394:        label = r[\"agency\"] or source_names.get(r[\"source\"], r[\"source\"])\n395-        a = node[\"agencies\"].setdefault(label, {\"name\": label, \"count\": 0,\n396-                                                 \"sources\": set()})\n397-        a[\"count\"] += r[\"n\"]\n398-        a[\"sources\"].add(r[\"source\"])\n399-    out = []\n400-    for node in tree.values():\n401-        ags = sorted(node[\"agencies\"].values(), key=lambda x: -x[\"count\"])\n402-        for a in ags:\n403-            a[\"sources\"] = sorted(a[\"sources\"])\n404-        out.append({\"franchise\": node[\"franchise\"], \"total\": node[\"total\"],\n405-                    \"sub_agencies\": len(ags), \"agencies\": ags})\n406-    out.sort(key=lambda x: -x[\"total\"])\n407-    return {\"franchises\": out}\n408-\n409-\n410-@app.get(\"/api/stats\")\n411-def stats(syncs_since_h: float | None = None):\n412-    con = db.connect()\n413-    row = con.execute(\n414-        \"\"\"SELECT COUNT(*) total,\n415-                  COUNT(DISTINCT source) sources,\n416-                  COUNT(DISTINCT city) cities,\n417-                  AVG(price) avg_price,\n418-                  MIN(price) min_price,\n419-                  MAX(price) max_price\n420-           FROM listings WHERE active=1\"\"\" + DEDUP_CLAUSE).fetchone()\n421-    # Fenêtre de synchronisations pour la supervision (moniteur central api-ka) :\n422-    # sans paramètre, comportement historique (20 dernières entrées) ; avec\n423-    # ?syncs_since_h=<heures>, toutes les entrées de la fenêtre (borne 4000) —\n424-    # évite les fausses alertes « stale » quand 100+ sources synchronisent\n--\n436-    # --- écart prix demandé vs estimation Vrai-Prix, par bannière -----------\n437-    registry = json.loads(SOURCES_PATH.read_text(encoding=\"utf-8\"))[\"sources\"]\n438:    source_names = {s[\"id\"]: s[\"name\"] for s in registry}\n439-    deltas: dict[str, list[float]] = {}\n440-    for r in con.execute(\n441-            \"SELECT source, price,\"\n442-            \" CAST(json_extract(vraiprix, '$.value') AS REAL) vp\"\n443-            \" FROM listings WHERE active=1\" + DEDUP_CLAUSE +\n444-            \" AND vraiprix LIKE '%estimation%'\"):\n445-        if not r[\"vp\"] or r[\"vp\"] <= 0:\n446-            continue\n447-        d = (r[\"price\"] - r[\"vp\"]) / r[\"vp\"] * 100.0\n448-        if -80.0 <= d <= 300.0:   # coupe les aberrations (terrains, données sales)\n449:            deltas.setdefault(_franchise_of(r[\"source\"], source_names), []).append(d)\n450-    con.close()\n451-\n452-    def _agg(name: str, ds: list[float]) -> dict:\n453-        ds = sorted(ds)\n454-        n = len(ds)\n455-        med = ds[n // 2] if n % 2 else (ds[n // 2 - 1] + ds[n // 2]) / 2\n456-        return {\n457-            \"banniere\": name, \"n\": n,\n458-            \"median_delta_pct\": round(med, 1),\n459-            \"p25\": round(ds[n // 4], 1), \"p75\": round(ds[(3 * n) // 4], 1),\n460-            \"pct_sur10\": round(100 * sum(1 for d in ds if d > 10) / n, 1),\n461-            \"pct_juste\": round(100 * sum(1 for d in ds if -5 <= d <= 10) / n, 1),\n462-            \"pct_sous5\": round(100 * sum(1 for d in ds if d < -5) / n, 1),\n463-        }\n464-\n465-    tous = [d for ds in deltas.values() for d in ds]\n466-    vraiprix = {\n467-        \"ensemble\": _agg(\"Toutes bannières\", tous) if tous else None,\n468-        \"bannieres\": sorted(\n469-            (_agg(k, v) for k, v in deltas.items() if len(v) >= 30),\n470-            key=lambda x: -x[\"median_delta_pct\"]),\n471-    }\n472-    # qualité des données : complétude, quarantaine, anomalies (par connecteur)\n473-    from . import quality\n474-    con2 = db.connect()\n475-    try:\n476-        qualite = quality.summary(con2)\n477-    finally:\n478-        con2.close()\n479-    return {**dict(row), \"vraiprix\": vraiprix, \"qualite\": qualite,"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"5a77dd6a-f8c9-450a-90fc-f084e968f86c","timestamp":"2026-08-24T21:30:01.023Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"21-\n22-ROOT = Path(__file__).resolve().parent.parent\n23:SOURCES_PATH = ROOT / \"data\" / \"sources.json\"\n24-# Frontend : build Vite (React) si présent, sinon la page statique de secours.\n25-FRONTEND_DIST = ROOT / \"frontend\" / \"dist\"\n26-FRONTEND_DIR = FRONTEND_DIST if FRONTEND_DIST.exists() else ROOT / \"frontend\"\n27-\n28-app = FastAPI(title=\"Immo-Ka API\", version=\"0.1\",\n29-              description=\"Agrégateur de maisons à vendre — province de Québec\")\n30-app.add_middleware(CORSMiddleware, allow_origins=[\"*\"],\n31-                   allow_methods=[\"*\"], allow_headers=[\"*\"])\n32-# le GeoJSON complet pèse ~20 Mo — la compression est vitale pour la carte\n33-app.add_middleware(GZipMiddleware, minimum_size=1024)\n34-# connexion Google (compte Groupe-Ka partagé avec Lou-Ka) — voir immoka/auth.py\n35-app.include_router(auth.router)\n36-# favoris ♥ — magasin central au hub Groupe KA, voir immoka/favorites.py\n37-app.include_router(favorites.router)\n38-\n39-_sync_lock = threading.Lock()\n40-\n41-# Déduplication de la famille RE/MAX : le flux central (remax_quebec) et les\n42-# ~42 connecteurs de sous-agences (remax_ag_*) décrivent les MÊMES inscriptions,\n43-# identifiées de façon unique par leur numéro Centris (= external_id). On masque\n44-# donc toute fiche de sous-agence dès qu'une fiche de plus haute priorité existe\n45-# (le central enrichi d'abord, puis la sous-agence au plus petit uid). Les fiches\n46-# du central et des autres agences ne sont jamais masquées. → aucun double-comptage,\n47-# et les sous-agences prennent le relais automatiquement si le central disparaît.\n48-# Déduplication PRÉ-CALCULÉE : la colonne `dup_hidden` (remplie par\n49-# db.refresh_dedup après chaque sync) marque les doublons de sous-agences. La\n50-# lecture est ainsi instantanée (index) au lieu d'un sous-select corrélé par\n51-# ligne (~300 s sur 75 k lignes). Voir db.refresh_dedup pour la règle.\n52-# + `published=1` : seuil de qualité minimum (voir immoka/quality.py) — une\n53-# annonce sans prix plausible, sans ville, sans type de bien ou sans contenu\n--\n57-\n58-\n59:def _row_to_dict(row) -> dict:\n60-    d = dict(row)\n61-    d[\"features\"] = json.loads(d.get(\"features\") or \"[]\")\n62-    d[\"images\"] = json.loads(d.get(\"images\") or \"[]\")\n63-    d[\"details\"] = json.loads(d.get(\"details\") or \"{}\")\n64-    if d.get(\"vraiprix\"):\n65-        try:\n66-            d[\"vraiprix\"] = json.loads(d[\"vraiprix\"]) or None\n67-        except (ValueError, TypeError):\n68-            d[\"vraiprix\"] = None\n69-    # jours sur le marché (depuis la 1re observation par Immo-Ka ; la date\n70-    # d'inscription de la source, si connue, est dans details.listed_at)\n71-    if d.get(\"first_seen\"):\n72:        d[\"days_on_market\"] = max(0, int((time.time() - d[\"first_seen\"]) // 86400))\n73-    return d\n74-\n75-\n76-@app.get(\"/api/listings\")\n77-def list_listings(\n78-    city: str | None = None,\n79-    sector: str | None = None,\n80-    region: str | None = None,\n81-    property_type: str | None = None,\n82-    source: str | None = None,\n83-    price_max: float | None = None,\n84-    price_min: float | None = None,\n85-    bedrooms_min: int | None = None,\n86-    bathrooms_min: int | None = None,\n87-    area_min: float | None = None,       # superficie habitable minimale (pi²)\n88-    q: str | None = None,\n89-    active: int = 1,\n90-    sort: str = \"price_asc\",             # price_asc | price_desc | recent\n91-    limit: int = Query(500, le=2000),\n92-    offset: int = 0,\n93-):\n94-    con = db.connect()\n95-    sql = \"SELECT * FROM listings WHERE 1=1\"\n96-    args: list = []\n97-    if active in (0, 1):\n98-        sql += \" AND active=?\"; args.append(active)\n99-    if city:\n100-        sql += \" AND city=?\"; args.append(city)\n101-    if sector:\n102-        sql += \" AND sector LIKE ?\"; args.append(f\"%{sector}%\")\n--\n190-        # historique de prix (baisses/hausses du prix demandé)\n191-        d[\"price_history\"] = [dict(r) for r in con.execute(\n192:            \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n193-            (uid,)).fetchall()]\n194-        # commodités de proximité (cache par immeuble, voir immoka/poi.py)\n195-        if d.get(\"lat\") is not None and d.get(\"lng\") is not None:\n196-            key = f\"{round(d['lat'], 4)},{round(d['lng'], 4)}\"\n197-            poi_row = con.execute(\n198:                \"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n199-            d[\"poi\"] = json.loads(poi_row[\"pois\"]) if poi_row else []\n200-        else:\n201-            d[\"poi\"] = []\n202-        # statistiques de quartier (recensement, proximité, chaleur, criminalité)\n203-        from . import quartier\n204-        dauid = d.get(\"dauid\")\n205:        d[\"quartier\"] = quartier.fiche_quartier(\n206-            d.get(\"lat\"), d.get(\"lng\"), d.get(\"city\") or \"\",\n207-            dauid if dauid and dauid != \"hors-zone\" else None)\n208-    con.close()\n209-    if d is None:\n210-        raise HTTPException(404, \"Propriété introuvable\")\n211-    return d\n212-\n213-\n214-@app.get(\"/api/listings.geojson\")\n215-def listings_geojson(\n216-    city: str | None = None,\n217-    sector: str | None = None,\n218-    region: str | None = None,\n219-    property_type: str | None = None,\n220-    source: str | None = None,\n221-    price_max: float | None = None,\n222-    price_min: float | None = None,\n223-    bedrooms_min: int | None = None,\n224-    bathrooms_min: int | None = None,\n225-    area_min: float | None = None,\n226-    q: str | None = None,\n227-    bbox: str | None = None,\n228-    limit: int = Query(3000, le=8000),\n229-):\n230-    \"\"\"Propriétés géolocalisées (marqueurs de carte, champs allégés).\n231-\n232-    Ka Maps : `bbox=ouest,sud,est,nord` restreint au rectangle visible\n233-    (index idx_listings_geo) ; `limit` plafonne la réponse ; la collection\n234-    porte `totalGeocoded` (géolocalisées correspondantes) et\n235-    `totalMatching` (avec ou sans coordonnées). Mêmes filtres que\n--\n365-\n366-\n367:def _franchise_of(source: str, source_names: dict) -> str:\n368-    for name, match in _FRANCHISES:\n369-        if match(source):\n370-            return name\n371:    return source_names.get(source, source)   # agence indépendante = elle-même\n372-\n373-\n374-@app.get(\"/api/agencies\")\n375-def agencies():\n376-    \"\"\"Arbre bannière → sous-agences (bureaux) avec le nombre d'inscriptions.\n377-\n378-    Alimente la page « Sources » de l'app : chaque bannière est éclatée par\n379-    sous-agence via le champ `agency` (bureau). Dédupliqué (n° Centris).\"\"\"\n380-    con = db.connect()\n381-    registry = json.loads(SOURCES_PATH.read_text(encoding=\"utf-8\"))[\"sources\"]\n382:    source_names = {s[\"id\"]: s[\"name\"] for s in registry}\n383-    rows = con.execute(\n384-        \"SELECT source, COALESCE(NULLIF(agency,''), '') agency, COUNT(*) n\"\n385-        \" FROM listings WHERE active=1\" + DEDUP_CLAUSE +\n386-        \" GROUP BY source, agency\").fetchall()\n387-    con.close()\n388-    tree: dict[str, dict] = {}\n389-    for r in rows:\n390:        fr = _franchise_of(r[\"source\"], source_names)\n391-        node = tree.setdefault(fr, {\"franchise\": fr, \"total\": 0, \"agencies\": {}})\n392-        node[\"total\"] += r[\"n\"]\n393-        # nom de sous-agence : le bureau (agency) sinon le nom de la source\n394:        label = r[\"agency\"] or source_names.get(r[\"source\"], r[\"source\"])\n395-        a = node[\"agencies\"].setdefault(label, {\"name\": label, \"count\": 0,\n396-                                                 \"sources\": set()})\n397-        a[\"count\"] += r[\"n\"]\n398-        a[\"sources\"].add(r[\"source\"])\n399-    out = []\n400-    for node in tree.values():\n401-        ags = sorted(node[\"agencies\"].values(), key=lambda x: -x[\"count\"])\n402-        for a in ags:\n403-            a[\"sources\"] = sorted(a[\"sources\"])\n404-        out.append({\"franchise\": node[\"franchise\"], \"total\": node[\"total\"],\n405-                    \"sub_agencies\": len(ags), \"agencies\": ags})\n406-    out.sort(key=lambda x: -x[\"total\"])\n407-    return {\"franchises\": out}\n408-\n409-\n410-@app.get(\"/api/stats\")\n411-def stats(syncs_since_h: float | None = None):\n412-    con = db.connect()\n413-    row = con.execute(\n414-        \"\"\"SELECT COUNT(*) total,\n415-                  COUNT(DISTINCT source) sources,\n416-                  COUNT(DISTINCT city) cities,\n417-                  AVG(price) avg_price,\n418-                  MIN(price) min_price,\n419-                  MAX(price) max_price\n420-           FROM listings WHERE active=1\"\"\" + DEDUP_CLAUSE).fetchone()\n421-    # Fenêtre de synchronisations pour la supervision (moniteur central api-ka) :\n422-    # sans paramètre, comportement historique (20 dernières entrées) ; avec\n423-    # ?syncs_since_h=<heures>, toutes les entrées de la fenêtre (borne 4000) —\n424-    # évite les fausses alertes « stale » quand 100+ sources synchronisent\n--\n436-    # --- écart prix demandé vs estimation Vrai-Prix, par bannière -----------\n437-    registry = json.loads(SOURCES_PATH.read_text(encoding=\"utf-8\"))[\"sources\"]\n438:    source_names = {s[\"id\"]: s[\"name\"] for s in registry}\n439-    deltas: dict[str, list[float]] = {}\n440-    for r in con.execute(\n441-            \"SELECT source, price,\"\n442-            \" CAST(json_extract(vraiprix, '$.value') AS REAL) vp\"\n443-            \" FROM listings WHERE active=1\" + DEDUP_CLAUSE +\n444-            \" AND vraiprix LIKE '%estimation%'\"):\n445-        if not r[\"vp\"] or r[\"vp\"] <= 0:\n446-            continue\n447-        d = (r[\"price\"] - r[\"vp\"]) / r[\"vp\"] * 100.0\n448-        if -80.0 <= d <= 300.0:   # coupe les aberrations (terrains, données sales)\n449:            deltas.setdefault(_franchise_of(r[\"source\"], source_names), []).append(d)\n450-    con.close()\n451-\n452-    def _agg(name: str, ds: list[float]) -> dict:\n453-        ds = sorted(ds)\n454-        n = len(ds)\n455-        med = ds[n // 2] if n % 2 else (ds[n // 2 - 1] + ds[n // 2]) / 2\n456-        return {\n457-            \"banniere\": name, \"n\": n,\n458-            \"median_delta_pct\": round(med, 1),\n459-            \"p25\": round(ds[n // 4], 1), \"p75\": round(ds[(3 * n) // 4], 1),\n460-            \"pct_sur10\": round(100 * sum(1 for d in ds if d > 10) / n, 1),\n461-            \"pct_juste\": round(100 * sum(1 for d in ds if -5 <= d <= 10) / n, 1),\n462-            \"pct_sous5\": round(100 * sum(1 for d in ds if d < -5) / n, 1),\n463-        }\n464-\n465-    tous = [d for ds in deltas.values() for d in ds]\n466-    vraiprix = {\n467-        \"ensemble\": _agg(\"Toutes bannières\", tous) if tous else None,\n468-        \"bannieres\": sorted(\n469-            (_agg(k, v) for k, v in deltas.items() if len(v) >= 30),\n470-            key=lambda x: -x[\"median_delta_pct\"]),\n471-    }\n472-    # qualité des données : complétude, quarantaine, anomalies (par connecteur)\n473-    from . import quality\n474-    con2 = db.connect()\n475-    try:\n476-        qualite = quality.summary(con2)\n477-    finally:\n478-        con2.close()\n479-    return {**dict(row), \"vraiprix\": vraiprix, \"qualite\": qualite,","numLines":233}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHWvDT3GrN5ouEidpZa","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5646,"cache_read_input_tokens":79955,"cache_creation":{"ephemeral_5m_input_tokens":5646,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"31b98b44-1215-4b62-972f-c25be5dea482"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHWvDT3GrN5ouEidpZa","type":"message","role":"assistant","content":[{"type":"text","text":"J'ai toutes les colonnes. Je vérifie les seuils du verdict Vrai-Prix dans le frontend puis j'écris le générateur."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5646,"cache_read_input_tokens":79955,"cache_creation":{"ephemeral_5m_input_tokens":5646,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"01a56f6e-ae10-4db9-b95d-140163c29a7e"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHWvDT3GrN5ouEidpZa","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015ZRT2gKEf9y3sEvZ3kLGmo","name":"Grep","input":{"pattern":"vraiprix|verdict|delta|pct","path":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/pages/Listing.tsx","output_mode":"content","-i":true,"-C":4,"head_limit":120},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5646,"cache_read_input_tokens":79955,"cache_creation":{"ephemeral_5m_input_tokens":5646,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"693cab4e-e13d-4a52-842b-dd61923427c1"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015ZRT2gKEf9y3sEvZ3kLGmo","type":"tool_result","content":"307-                <Ico name={baisse.a < baisse.de ? \"trenddown\" : \"trendup\"} size={16} /> Prix passé de {fmtPrice(baisse.de)} à <b>{fmtPrice(baisse.a)}</b>\n308-              </div>\n309-            )}\n310-\n311:            {l.vraiprix && l.vraiprix.value != null && (\n312:              <div className=\"vraiprix\">\n313-                <div className=\"vp-head\">\n314-                  <span className=\"vp-logo\">Vrai‑Prix</span>\n315:                  {l.vraiprix.confidence && (\n316:                    <span className={`vp-conf vp-conf-${l.vraiprix.confidence}`}>\n317:                      confiance {l.vraiprix.confidence}\n318-                    </span>\n319-                  )}\n320-                </div>\n321-                <div className=\"vp-k\">Valeur marchande estimée</div>\n322:                <div className=\"vp-value\">{fmtPrice(l.vraiprix.value)}</div>\n323:                {l.vraiprix.low != null && l.vraiprix.high != null && (() => {\n324:                  const lo = l.vraiprix!.low as number, hi = l.vraiprix!.high as number;\n325-                  const span = Math.max(1, hi - lo);\n326-                  const pos = (v: number) => `${Math.max(2, Math.min(98, ((v - lo) / span) * 100))}%`;\n327-                  return (\n328-                    <div className=\"vp-gauge\">\n329-                      <div className=\"vp-gauge-track\">\n330-                        <span className=\"vp-gauge-band\" />\n331:                        <span className=\"vp-gauge-est\" style={{ left: pos(l.vraiprix!.value as number) }} />\n332-                        {l.price != null && l.price >= lo * 0.7 && l.price <= hi * 1.3 && (\n333-                          <span className=\"vp-gauge-ask\" style={{ left: pos(l.price) }} />\n334-                        )}\n335-                      </div>\n--\n347-                  );\n348-                })()}\n349-                {l.price != null && (\n350-                  (() => {\n351:                    const diff = l.price - (l.vraiprix!.value as number);\n352:                    const pct = Math.round((diff / (l.vraiprix!.value as number)) * 100);\n353-                    const cls = diff > 0 ? \"over\" : \"under\";\n354-                    const txt = diff > 0\n355:                      ? `Prix demandé ${pct}% au-dessus de l'estimation`\n356:                      : `Prix demandé ${Math.abs(pct)}% sous l'estimation`;\n357:                    if (Math.abs(pct) > 60) return null;   // prix non comparable (loyer, terrain…)\n358:                    return Math.abs(pct) >= 1\n359:                      ? <div className={`vp-delta vp-${cls}`}>{diff > 0 ? \"▲\" : \"▼\"} {txt}</div>\n360:                      : <div className=\"vp-delta vp-fair\">≈ Prix aligné sur l'estimation</div>;\n361-                  })()\n362-                )}\n363:                <a className=\"vp-link\" href={l.vraiprix.url} target=\"_blank\" rel=\"noopener noreferrer\">\n364-                  Voir l'analyse détaillée ↗\n365-                </a>\n366-              </div>\n367-            )}\n--\n445-                <PropertyMap\n446-                  uid={l.uid} lat={l.lat} lng={l.lng} price={l.price}\n447-                  propertyType={l.property_type} address={l.address || l.title}\n448-                  city={l.city} image={l.images?.[0]}\n449:                  deal={l.price != null && l.vraiprix?.value != null\n450:                        && l.price <= l.vraiprix.value * 0.95}\n451-                />\n452-              </Suspense>\n453-            </section>\n454-          )}"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"d26ed2d5-35e6-4141-9af2-b6dc72424ce1","timestamp":"2026-08-24T21:30:15.585Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"307-                <Ico name={baisse.a < baisse.de ? \"trenddown\" : \"trendup\"} size={16} /> Prix passé de {fmtPrice(baisse.de)} à <b>{fmtPrice(baisse.a)}</b>\n308-              </div>\n309-            )}\n310-\n311:            {l.vraiprix && l.vraiprix.value != null && (\n312:              <div className=\"vraiprix\">\n313-                <div className=\"vp-head\">\n314-                  <span className=\"vp-logo\">Vrai‑Prix</span>\n315:                  {l.vraiprix.confidence && (\n316:                    <span className={`vp-conf vp-conf-${l.vraiprix.confidence}`}>\n317:                      confiance {l.vraiprix.confidence}\n318-                    </span>\n319-                  )}\n320-                </div>\n321-                <div className=\"vp-k\">Valeur marchande estimée</div>\n322:                <div className=\"vp-value\">{fmtPrice(l.vraiprix.value)}</div>\n323:                {l.vraiprix.low != null && l.vraiprix.high != null && (() => {\n324:                  const lo = l.vraiprix!.low as number, hi = l.vraiprix!.high as number;\n325-                  const span = Math.max(1, hi - lo);\n326-                  const pos = (v: number) => `${Math.max(2, Math.min(98, ((v - lo) / span) * 100))}%`;\n327-                  return (\n328-                    <div className=\"vp-gauge\">\n329-                      <div className=\"vp-gauge-track\">\n330-                        <span className=\"vp-gauge-band\" />\n331:                        <span className=\"vp-gauge-est\" style={{ left: pos(l.vraiprix!.value as number) }} />\n332-                        {l.price != null && l.price >= lo * 0.7 && l.price <= hi * 1.3 && (\n333-                          <span className=\"vp-gauge-ask\" style={{ left: pos(l.price) }} />\n334-                        )}\n335-                      </div>\n--\n347-                  );\n348-                })()}\n349-                {l.price != null && (\n350-                  (() => {\n351:                    const diff = l.price - (l.vraiprix!.value as number);\n352:                    const pct = Math.round((diff / (l.vraiprix!.value as number)) * 100);\n353-                    const cls = diff > 0 ? \"over\" : \"under\";\n354-                    const txt = diff > 0\n355:                      ? `Prix demandé ${pct}% au-dessus de l'estimation`\n356:                      : `Prix demandé ${Math.abs(pct)}% sous l'estimation`;\n357:                    if (Math.abs(pct) > 60) return null;   // prix non comparable (loyer, terrain…)\n358:                    return Math.abs(pct) >= 1\n359:                      ? <div className={`vp-delta vp-${cls}`}>{diff > 0 ? \"▲\" : \"▼\"} {txt}</div>\n360:                      : <div className=\"vp-delta vp-fair\">≈ Prix aligné sur l'estimation</div>;\n361-                  })()\n362-                )}\n363:                <a className=\"vp-link\" href={l.vraiprix.url} target=\"_blank\" rel=\"noopener noreferrer\">\n364-                  Voir l'analyse détaillée ↗\n365-                </a>\n366-              </div>\n367-            )}\n--\n445-                <PropertyMap\n446-                  uid={l.uid} lat={l.lat} lng={l.lng} price={l.price}\n447-                  propertyType={l.property_type} address={l.address || l.title}\n448-                  city={l.city} image={l.images?.[0]}\n449:                  deal={l.price != null && l.vraiprix?.value != null\n450:                        && l.price <= l.vraiprix.value * 0.95}\n451-                />\n452-              </Suspense>\n453-            </section>\n454-          )}","numLines":62}}}
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ADCVtDBO4uexTwbnFxDe/Z+/LzaTHtMTeJUroPYchKg2X0vfhgFrmSF5a4CiVUmfme2ohapnh3KzAtwfJ9nlimxyHfWeTCjaTl7r3S+zlXhB4fUGUNDu6wsYjyMK0GyslVrin21EOjCul+Vx2JkO0jqsIei9FuGQLIzD6E07QxrNsAgA+GhTLfFWTGL2+inkBY8dSwetjV/WhGlrwLxFmNWeAQrxnET4/Tu1njVrhR/QxQMfr/Rg+V9TNnUQELn4G3504kbYbPBh5+QCC2iWBI+35cNAisi7VEW1h1Ke5svtUXx1nXDRbUaoVKkfNmtSz9iWzAxt2nmB3PS32gVPsAd8ihxyiKSTW3EERdABNfx1OtVBt2olR4DHh2ELg2delqxQ1fzL0wWF5d/3VYrqcMlQDcQ6CmP/0V5EJQ6MddEnZXc9fJi8BAA5JnIx6dMNJqW1Fv/5Gn+Sj3up3vnK8FKEP0kUhxTalJhQ6w4feg/e8LK7+2qJIuBGvjrtTgQVcCP715WX5XrTPOpCXbxmZWnqhoRTnrvX7wxH4jg8EV3vMqxAO86GImssoYCbA9LrNtBLrp6iqjkqp02n7UDkmC5uQZVw84tcU9bdhpGFe9DpKIDhdGJtkVYkgvvGQHAfYsbKDkF/N1SqGlFX0sNYJxDL0MG4JyUT2uGaVrqIyTI1+xkH42jadsy9JywuPh+ooF9xSw8++XcdfEqnsknP8waq0Mz93nT8VW64yhs0bNw0UNzNG6UqF9eN/memsjKDp3Gnvlu0wCqr7kKFs8uj5/enpDpQZ/jDD7wULJCzts0CF6e+AzDJ15r6bUxJel1Y1LWK9RgpXpctz7qFFq/aDy2FNvMPGysgOi7xlPp9Zj3n/mEKBL0faWDBclLYjMrejZOr98nBqyYSOeK5oSU5dwIVHLzYJ7r/R8Lkn/oifnHf9250JF0N90M2AU4O7KWYljkW/ATyw4yXAt5uGIkvERLImdmX7aCtoSekIqFOczhMf6bnY/xd2qyzd1zhJHnFGBKl6TUBzhTRgg78r1VHVVialRp/CkzW1TD5Ti66fUHB6g1f2RsZ08iqifo+YNTxjWQBSPg3kphMi5rV7abLwrr5TzcYiNFiNegivUHzz+KNxDcJT4jbWg+GITHERFyYX879zQEE+i77kdJmZBn5UEOUdz3txJCejb8qDroiosWkaNxgMxw8TdBKaiw3NWcGxIORk5yNK/D9Q5IIj3UdwxuUPSRmrMxQ+XwdQVRH/ovBoh2dsF3CHY95oWJKn/YKsANU8Aq7ks5+eJw54A+4SXdm9w6MD17OtrOfedBsvkwS4WBtMj7eY8aGOdWCaNUQLzNysxJ8jumg207dyr9IjBA1ShWA87C7/RqQoAMY6NkmuNElGCnuHAShgmlQ/7N9nj+Fcbsg5Wtmwe6Pzfv1GmCGrqYpwxaYV1Ep2z5H7yDDWhC4E7/V/9NMdQwZ0LxL9u0JgmL+V+lcYoAFApsWlFk09JRwrTC2mNMPJgl//UJYxCfCpxs4vTKZ1OoXOsFnZthttcF56ywIvGczs+H1nWLnzizQ6qrLpN55o7pOJe0fEcUcGy9EW3c/Atg9eZIPNtRpJT+DKTvPX/EZ3xjkh3ueljOFIUXn+yMaAyhegoDymhf/IR9KsEwTc8aJGl5HWn3SfKkYkC31Q7KnUjyKWelyNS3t4M3VEDMxj/dhy/R/vLuuTZVFsbFjgTX4TffQB9FJMSQCj7cSPMY6L1brVgkq/miHOTgQDBVUED81QGZ+0K7ZS2fE35KDhONVFB0/Q6enu8uKdjM7wkCE7S/27uvBZrNrVmzquDoez/u6Nf74Yn/t9PPU/CWRjyL+WVHmOX207Z13j+vynC4GOlGjyniKWw4/PfYdf29wRwwdhdnUNM+MhKatcw02jeGYYSlgv3YO5Apx2BteSql6ghTXJ2Yukbcid/eRObD62MRcNhHrO67n3EoeBXbhawWtpl5KaugAMfp+nX0vBIzcPZFCOhXdmKob8VXACQpEPsx2F6PaiJIFiRJVOabrck1S8RO70LG5kww2YhJwA3Cr1d94rFqfB9cQRXOGomkwTnZc78+PXmaVWSjRZlgy9UqVG0F5SlrbtmPk0gotCa6rH6JbJ87XhaJdESVQ8p9agy5K9qUpzsmvL+pRqRAvppz5wuqiqQHzcs5pMzyOqNyAxOZIMEpsyNe8H7etLHUSh1eQzQ/XWULlJnMP6PjWTjiLkuH235HKfAkPioagS6985SnkxBykJDF6Voh5qWXlAq44bFuS2E8wGVrsN1zUX/EUBkDyoDgx648l7o4Br5eI4a3u/mVjWMoz3IrQUWUCFMqaSL1cQMufP4B0WjGRFljp4mOXx9hLH2/MpuIk9LtkuEqP0R0GLww2QFXZBXa8yecndrJoTmY1A+gW23QtF1klCL5qqDJuQe7AoKEZDAs8E0kTyU1W8Hye3A4Uxi7Quk3JWp41Lhbd3l6OS3bLb1INwFFuOIiLPes2jroLVfwm0iHKoVJoUQ4wX91pAYzjXtTdm6hMl/8nussQolKvHisO+QKGWysK08JSpl+Q+//HY3FtuE5ruEMSjkZoCoSG1CjKbaPJX7dQYdpf/bemZeqaPuSGAwICpmQ2Luc2RIGiyXZiXrzAgb/lKuxLBLZI/NHr8OqfqPIA3hpYDQbgG9zD7CkrX2XWeEx/zBZARgTxv3P5+YbrAY956pjaoigb1amlgGdBHpjL0fnKpzQmGibavJY7YCkL6hYqQ8Kvrg00OFgPZO1ZH1SjLGYASv3l5AP8Oe+8Ma7GYgraK1KI2G3+UYDodxYJfcA0w37PW/IzbW+nMUpXf9vv6GQIEzEJ70GC1msj3Y2k4exsipzHmSlGoztM1y6JsSM1Y5ZKUQIG3bKrgx3cFQhhfXKgdODZgMx2KGmJKp7m8+ydGbHbQFHjvYNDIqAHDIh64c1XW8H1cyBlSmfoQV2Q1eKz7D2igYCH4LsRAImAjncoQbBQ/2lCOCVFK1GvChyuVaHFOe6Qhr5W6CUI4jeqTfnZ3Nhq7Xm728U1QWzbxRyDs+GX8BGjrZnYAWOMPp+BQVeFAtPXhfPJM8yRofCNSEjDEW+fxqxQ5IM3cY9yNa7KGFbg+A5k95s0xQu2hC+XFvLSB9LY2cDgWEV1DFbkK6+/AiO3hWe/+/PDf9w6EgR5mlsNlV16omrqwSSRKDyUSD6IWsKnyGYrWqu5TvFKZwj9oD1dyBEqheNWvzSHHruN2YIpakRdO9dtdhIFeT12bfyDuuqG0mQPcukn3ZIxGuFpVrDFfh6g3xVVm6QgJZGCfY2Ak9vaNWUKI5OAD19c3Eawb8jVrETevLOLLoycGpwq9Pz3gQov5zIpbiW6paKbJ1wZ17IBCnhgW7kdJqDkkUXRNZ5HUADrpRmHMV3UMFOU7xmukz9EBuQ9ZcShJo65vWk2xV070arHgJNPc3H8vOhfYr0//yqhrSpnyMZHK+1O7sanS43SV8CRH9ygA2+8rLYJeW025ajm/60C9xEOlFpzxFZ67ZCgM8InG9ba/XUAHZ82y47ocGBL2Cl+pDucTsFRqW2amziNEKjChMhdfN0UYAqVYN7GJzmFAcrcw8ox0ydb3st1acPhmiCT+6EWDZFY/7l/ZRkE1m8yqgMa25J+Bh/qvOlugW90d3ZEvdjaBan4g59XTRXo0a2nMFtDy5Ov4HGulgnC04YOYp5FjgE8Zjg3XOZ/3+PMfhAGrLBwUcICH4Ks6azPuRXlEU2htXhDk7VCgjJ7wXRhWHXaP8DbG1o1gEaPsXq/gtCwwqq16dezJqUSTJ+tjNOGnH0cPoVXAKUXcCg157VqCv+fw0AIVLGfgXmmJJp/epYSHOqLRzRJ0k8uw+Bp2hi9USiSHFPFH7Ym+R7t0fBcyVZUdNS630e5QvcJVaBIrskE2ZfMvHLx/HDrAEhoh10E2VxkAiP5Tw7r4pFwpxh1qd9rdgKpXhGLM3zmsTZarmN2jHuU0+dOt6Faa4tni4e3H9U+brK1uEkbwwwIfe/L+VSTPx74kPkdF8kn0R2ueWqvCjNfAsehc455h97gxXljL9U/kK+lWLJL+IciMk+RuNmDuLJ0nW+NEH0LqtFJqrFk/6rnQBQ+1d9xLdFqnysbIVqWx5AHwgxMXKSem2re8g+vfyQS9h+a5Gb5Yv8sPmHXU9jbUKcJ4yLVFJfhxYsUzC2SwjSKz/YGa9HFnpsOSyH9Dfxg8gGjQpD/Z/4iNWshITKsaCA/CouGQ0ZBVbymtTqsvdw/fukW+sZThHWRVcetb7e1NBcx2MLxb9+tY2PxGRRJHyiHRp4vB/h6SWd9iV6ZCuFuD6UhgyiSZJQEDJ1KKDz25jtm+k+NViTT28hLZqiUBg7YXXz+2bMJzcV6CFwcAJwHYOOaQso791pDDJXo3ZObErdGGIhaIP72GyRUOqld2h5hpz45oJYFIDaXbZFejiq2p9dHD5fdUuIf7oBGljvlQsEBJg5ifUKJGybQghuLq7fxcOLMRJTvqoPvo0TIBNcFmtzcshPIlmar7jSuXkytm5dzCNw7RcNaPsRDk+1HEfm7EI+4e69MwH4FlAOW0b7SDq+Hj+BMWGaMu3nk6rAONu6aR5cyEcJhRKh+8l6ZUlUjp6Tpr3PDW1ovAfQ2AQdgKTCnuRNOeL9+l60LE8O2QnRViH86hGcJhRSzPY6M2AsGPaWXM2+uEe+SFJdOEGRHWYsTDO7ozoVn4+lCDCQsctOM39oYPKvZOz4vuQ1XDhqn/EIbMYAwJ8NelVe25ikSCv6XOltGUNc++9X1agrfDR6Pfads9ht13YL+TY4EyypQTJBcU84GLiyP0r4oh9Cv/WQUNXS/etSyidXNn9ncloC2VeQHidEsFjMgMgiwz8HjYVwjaPUmbt1fH0WhthFU4lLU/ArRKzomQ3c9e1OK37q58LspVA0qxww1nKwMxGYi14MwnSQNnJDII8nFFXIeMr9bHNkumFTN3tc9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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NYTRAjA7QEQJe7n1SWhEeg","type":"tool_result","content":"26-function ZoomImg({ src, onSwipe }: { src: string; onSwipe: (dir: 1 | -1) => void }) {\n27-  const [t, setT] = useState({ scale: 1, x: 0, y: 0 });\n28:  const pointers = useRef(new Map<number, { x: number; y: number }>());\n29-  const start = useRef({ scale: 1, x: 0, y: 0, dist: 0, cx: 0, cy: 0, t: 0 });\n30-  const lastTap = useRef(0);\n--\n34-\n35-  const dist = () => {\n36:    const p = [...pointers.current.values()];\n37-    return p.length < 2 ? 0 : Math.hypot(p[0].x - p[1].x, p[0].y - p[1].y);\n38-  };\n39-  const center = () => {\n40:    const p = [...pointers.current.values()];\n41-    return p.length < 2\n42-      ? p[0] ?? { x: 0, y: 0 }\n--\n45-\n46-  const onDown = (e: React.PointerEvent) => {\n47:    (e.target as HTMLElement).setPointerCapture(e.pointerId);\n48:    pointers.current.set(e.pointerId, { x: e.clientX, y: e.clientY });\n49-    const c = center();\n50-    start.current = { scale: t.scale, x: t.x, y: t.y, dist: dist(), cx: c.x, cy: c.y, t: Date.now() };\n51-  };\n52-  const onMove = (e: React.PointerEvent) => {\n53:    if (!pointers.current.has(e.pointerId)) return;\n54:    pointers.current.set(e.pointerId, { x: e.clientX, y: e.clientY });\n55-    const s = start.current;\n56:    if (pointers.current.size >= 2 && s.dist > 0) {\n57-      / pincement : zoom autour du centre des deux doigts\n58-      const scale = Math.min(4, Math.max(1, (dist() / s.dist) * s.scale));\n59-      const c = center();\n60-      setT({ scale, x: s.x + (c.x - s.cx), y: s.y + (c.y - s.cy) });\n61:    } else if (pointers.current.size === 1 && t.scale > 1) {\n62-      // panoramique une fois zoomé\n63:      const p = pointers.current.get(e.pointerId)!;\n64-      setT({ scale: t.scale, x: s.x + (p.x - s.cx), y: s.y + (p.y - s.cy) });\n65-    }\n66-  };\n67-  const onUp = (e: React.PointerEvent) => {\n68:    const p = pointers.current.get(e.pointerId);\n69:    pointers.current.delete(e.pointerId);\n70-    const s = start.current;\n71:    if (pointers.current.size === 0 && p) {\n72-      const dx = p.x - s.cx, dy = p.y - s.cy, dt = Date.now() - s.t;\n73-      if (t.scale <= 1.05 && Math.abs(dx) > 56 && Math.abs(dx) > Math.abs(dy) * 1.5) {\n--\n88-      style={{\n89-        transform: `translate(${t.x}px, ${t.y}px) scale(${t.scale})`,\n90:        transition: pointers.current.size ? \"none\" : \"transform 0.15s ease\",\n91-        touchAction: \"none\", cursor: t.scale > 1 ? \"grab\" : \"zoom-out\",\n92-      }}"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"11751cb7-fe5a-44e9-b31b-2e22f0d29a94","timestamp":"2026-08-24T21:31:50.905Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"26-function ZoomImg({ src, onSwipe }: { src: string; onSwipe: (dir: 1 | -1) => void }) {\n27-  const [t, setT] = useState({ scale: 1, x: 0, y: 0 });\n28:  const pointers = useRef(new Map<number, { x: number; y: number }>());\n29-  const start = useRef({ scale: 1, x: 0, y: 0, dist: 0, cx: 0, cy: 0, t: 0 });\n30-  const lastTap = useRef(0);\n--\n34-\n35-  const dist = () => {\n36:    const p = [...pointers.current.values()];\n37-    return p.length < 2 ? 0 : Math.hypot(p[0].x - p[1].x, p[0].y - p[1].y);\n38-  };\n39-  const center = () => {\n40:    const p = [...pointers.current.values()];\n41-    return p.length < 2\n42-      ? p[0] ?? { x: 0, y: 0 }\n--\n45-\n46-  const onDown = (e: React.PointerEvent) => {\n47:    (e.target as HTMLElement).setPointerCapture(e.pointerId);\n48:    pointers.current.set(e.pointerId, { x: e.clientX, y: e.clientY });\n49-    const c = center();\n50-    start.current = { scale: t.scale, x: t.x, y: t.y, dist: dist(), cx: c.x, cy: c.y, t: Date.now() };\n51-  };\n52-  const onMove = (e: React.PointerEvent) => {\n53:    if (!pointers.current.has(e.pointerId)) return;\n54:    pointers.current.set(e.pointerId, { x: e.clientX, y: e.clientY });\n55-    const s = start.current;\n56:    if (pointers.current.size >= 2 && s.dist > 0) {\n57-      / pincement : zoom autour du centre des deux doigts\n58-      const scale = Math.min(4, Math.max(1, (dist() / s.dist) * s.scale));\n59-      const c = center();\n60-      setT({ scale, x: s.x + (c.x - s.cx), y: s.y + (c.y - s.cy) });\n61:    } else if (pointers.current.size === 1 && t.scale > 1) {\n62-      // panoramique une fois zoomé\n63:      const p = pointers.current.get(e.pointerId)!;\n64-      setT({ scale: t.scale, x: s.x + (p.x - s.cx), y: s.y + (p.y - s.cy) });\n65-    }\n66-  };\n67-  const onUp = (e: React.PointerEvent) => {\n68:    const p = pointers.current.get(e.pointerId);\n69:    pointers.current.delete(e.pointerId);\n70-    const s = start.current;\n71:    if (pointers.current.size === 0 && p) {\n72-      const dx = p.x - s.cx, dy = p.y - s.cy, dt = Date.now() - s.t;\n73-      if (t.scale <= 1.05 && Math.abs(dx) > 56 && Math.abs(dx) > Math.abs(dy) * 1.5) {\n--\n88-      style={{\n89-        transform: `translate(${t.x}px, ${t.y}px) scale(${t.scale})`,\n90:        transition: pointers.current.size ? 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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HieWXH8HBjjJRQTbxKvSVn","type":"tool_result","content":"642:.vp-delta { font-size: 13px; margin-top: 8px; padding: 6px 8px; border-radius: var(--r-ctl); }\n643:.vp-over { background: #f6e2e2; color: #8a2b2b; }\n644:.vp-under { background: #e2f0e6; color: #1c5c41; }\n645:.vp-fair { background: var(--surface); color: var(--ink-2); }\n646-.vp-link { display: block; text-align: center; margin-top: 10px; font-weight: 700;\n647-  font-size: 13px; padding: 8px; background: var(--green-deep); color: #fff;\n648-  text-decoration: none; }","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"42acf8f9-80dc-4168-bf39-5e5792d73e57","timestamp":"2026-08-24T21:32:07.919Z","tool_use_result":{"stdout":"642:.vp-delta { font-size: 13px; margin-top: 8px; padding: 6px 8px; border-radius: var(--r-ctl); }\n643:.vp-over { background: #f6e2e2; color: #8a2b2b; }\n644:.vp-under { background: #e2f0e6; color: #1c5c41; }\n645:.vp-fair { background: var(--surface); color: var(--ink-2); }\n646-.vp-link { display: block; text-align: center; margin-top: 10px; font-weight: 700;\n647-  font-size: 13px; padding: 8px; background: var(--green-deep); color: #fff;\n648-  text-decoration: none; }","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHgGhVT6Mwn6gN1DjLP","type":"message","role":"assistant","content":[{"type":"text","text":"J'écris le générateur complet."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1062,"cache_read_input_tokens":101813,"cache_creation":{"ephemeral_5m_input_tokens":1062,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f89eec66-ed6c-4463-875d-d6fd2afbceec"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNHgGhVT6Mwn6gN1DjLP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AaLsQhE9DCx8QnUgJANSik","name":"Write","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","content":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdffiche.py : fiche PDF complète d'une propriété — reflète TOUS les blocs de\n#   la fiche web : prix + verdict Vrai-Prix, photos, description, inclusions,\n#   caractéristiques, pièces, analyse Vrai-Prix (jauge + rôle foncier),\n#   historique de prix, inondation, qualité de l'air, coût d'électricité,\n#   commerces/transport, essence, quartier, références + QR.\n#   Style « éditorial sharp » (bordures encre, ombres décalées dures, tuiles\n#   KPI, micro-étiquettes Courier) — palette du site : crème #f5f3ee, encre\n#   #141814, accent cerise #e23744. Pagination dynamique : saut_page()/place(),\n#   aucune section coupée entre deux pages.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Immo-Ka (identité officielle du site) ----------------------------\nPAPER = HexColor(\"#f5f3ee\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#141814\")\nINK2 = HexColor(\"#4d5551\")\nINK3 = HexColor(\"#8b928c\")\nACCENT = HexColor(\"#e23744\")        # cerise\nACCENT_SOFT = HexColor(\"#fbe0e2\")\nACCENT_DEEP = HexColor(\"#a8232e\")\nGREEN_FG = HexColor(\"#1c5c41\")      # verdict « sous l'estimation » (site .vp-under)\nGREEN_BG = HexColor(\"#e2f0e6\")\nRED_FG = HexColor(\"#8a2b2b\")        # verdict « au-dessus » (site .vp-over)\nRED_BG = HexColor(\"#f6e2e2\")\nAMBER_FG = HexColor(\"#8a5a12\")\nAMBER_BG = HexColor(\"#faf0dc\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"ImmoKaBot/1.0 (+https://www.immo-ka.com; contact@spboucher.ai)\"\n\nNBSP = \" \"     # espace fine insécable (mise en forme française)\n\n# clés de details jamais affichées (mêmes exclusions que la fiche web)\nDETAIL_HIDDEN = {\n    \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n    \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n    \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n}\n\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"\n               \"environnement) fournis à titre indicatif, sans valeur légale.\")\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _fmt_mois(d) -> str:\n    \"\"\"« 2017-11-04 » ou epoch → « novembre 2017 » (sinon valeur telle quelle).\"\"\"\n    if d is None:\n        return \"—\"\n    try:\n        if isinstance(d, (int, float)):\n            st = time.localtime(d)\n            return f\"{_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n        an, mois = str(d)[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return str(d)\n\n\ndef _fmt_jour(ts: float) -> str:\n    \"\"\"epoch → « 4 novembre 2024 » (« 1er » pour le premier du mois).\"\"\"\n    st = time.localtime(ts)\n    j = \"1er\" if st.tm_mday == 1 else str(st.tm_mday)\n    return f\"{j} {_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à plusieurs dizaines de Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Immo·Ka : « Immo » encre + boîte encre avec « Ka » cerise.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Immo\")\n        w = c.stringWidth(\"Immo\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6, titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.IMMO-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=ACCENT, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        # carré accent à ombre dure (signature de la passe éditoriale du site)\n        c.setFillColor(INK)\n        c.rect(M + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.rect(M, y + 0.5, 9, 9, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M + 16, y, texte)\n        c.setStrokeColor(ACCENT)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M + 16, y - 4, M + 16 + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def nb_lignes(self, texte: str, largeur: float, taille: float = 8.5,\n                  max_lignes: int = 100) -> int:\n        \"\"\"Nombre de lignes qu'occupera `paragraphe` (pour réserver la place).\"\"\"\n        c = self.c\n        mots = texte.split()\n        ligne = \"\"\n        n = 1\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                n += 1\n                ligne = mot\n                if n >= max_lignes:\n                    break\n        return min(n, max_lignes)\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\ndef _titre_droite(c, y, x, texte):\n    c.setFillColor(INK)\n    c.rect(x + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n    c.setFillColor(ACCENT)\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.rect(x, y + 0.5, 9, 9, stroke=1, fill=1)\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x + 16, y, texte)\n    c.setStrokeColor(ACCENT)\n    c.setLineWidth(3)\n    c.line(x + 16, y - 4, x + 16 + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\ndef _verdict_vraiprix(price, vp) -> tuple[str, object, object, object] | None:\n    \"\"\"Pastille verdict identique au site : ±% vs estimation, masqué si\n    |pct| > 60 (prix non comparable : terrain, loyer…).\"\"\"\n    if not price or not vp or vp.get(\"value\") in (None, 0):\n        return None\n    pct = round((price - vp[\"value\"]) / vp[\"value\"] * 100)\n    if abs(pct) > 60:\n        return None\n    if pct >= 1:\n        return (f\"▲{NBSP}{pct}{NBSP}% au-dessus de l'estimation\", RED_FG, RED_BG, ACCENT_DEEP)\n    if pct <= -1:\n        return (f\"▼{NBSP}{abs(pct)}{NBSP}% sous l'estimation\", GREEN_FG, GREEN_BG, GREEN_FG)\n    return (f\"≈{NBSP}Prix aligné sur l'estimation\", INK2, SURFACE, INK)\n\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche de la propriété `uid` — reflète tous les blocs de la\n    fiche web ; None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"features\"] = json.loads(l.get(\"features\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    try:\n        l[\"vraiprix\"] = json.loads(l[\"vraiprix\"]) or None if l.get(\"vraiprix\") else None\n    except (ValueError, TypeError):\n        l[\"vraiprix\"] = None\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n        (uid,)).fetchall()]\n    quartier = None\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n    if geo:\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        try:\n            quartier = qmod.fiche_quartier(lat, lng, l.get(\"city\") or \"\",\n                                           dauid if dauid and dauid != \"hors-zone\" else None)\n        except Exception:\n            quartier = None\n    try:\n        reg = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n        src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]), l[\"source\"])\n    except Exception:\n        src_name = l[\"source\"]\n    con.close()\n\n    # --- enrichissements (mêmes modules que les blocs de la fiche web) -------\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    d = l[\"details\"]\n    vp = l.get(\"vraiprix\")\n    location = (d.get(\"transaction\") == \"location\")\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Immo-Ka — {l.get('address') or l.get('title') or uid}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche propriété\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche propriété · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- héro : prix + pastille verdict Vrai-Prix -----------------------------\n    prix_txt = (_fmt_money(l[\"price\"]) + (f\"{NBSP}/ mois\" if location else \"\")) \\\n        if l.get(\"price\") else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    verd = _verdict_vraiprix(l.get(\"price\"), vp)\n    if verd:\n        txt, fg, bg, stk = verd\n        bx = s.pilule(bx, y - 22, txt, fg, bg, stk)\n    if l.get(\"property_type\"):\n        s.pilule(bx, y - 22, l[\"property_type\"], ACCENT, INK)\n    y -= 44\n\n    # --- titre + adresse -------------------------------------------------------\n    titre = l.get(\"address\") or l.get(\"title\") or \"\"\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, titre[:80])\n    y -= 14\n    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    if l.get(\"title\") and l.get(\"title\") != titre:\n        loc = f\"{l['title']} — {loc}\" if loc else l[\"title\"]\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés (mêmes specs que le héro du site) --------------------------\n    chips = []\n    if l.get(\"bedrooms\") is not None:\n        chips.append(f\"{l['bedrooms']}{NBSP}ch.\")\n    if l.get(\"bathrooms\") is not None:\n        chips.append(f\"{l['bathrooms']}{NBSP}sdb\")\n    if l.get(\"powder_rooms\"):\n        chips.append(f\"{l['powder_rooms']}{NBSP}s. d'eau\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"lot_sqft\"):\n        chips.append(f\"Terrain {round(l['lot_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"year_built\"):\n        chips.append(f\"Construit en {l['year_built']}\")\n    if l.get(\"price\") and l.get(\"area_sqft\") and l[\"area_sqft\"] > 200:\n        chips.append(f\"{round(l['price'] / l['area_sqft'])}{NBSP}$/pi²\")\n    if l.get(\"mls\"):\n        chips.append(f\"MLS {l['mls']}\")\n    if l.get(\"first_seen\"):\n        dom = max(0, int((time.time() - l[\"first_seen\"]) // 86400))\n        chips.append(f\"{dom}{NBSP}j sur Immo-Ka\")\n    x = M\n    for chip in chips[:8]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes ---------------------------------------\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- baisse/hausse de prix (bandeau, comme le site) -------------------------\n    ph = [p for p in price_history if p.get(\"price\")]\n    if len(ph) >= 2 and ph[0][\"price\"] != ph[1][\"price\"]:\n        y = place(y, 34)\n        de, a = ph[1][\"price\"], ph[0][\"price\"]\n        bas = a < de\n        c.setFillColor(GREEN_BG if bas else RED_BG)\n        c.setStrokeColor(GREEN_FG if bas else RED_FG)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 20, PAGE_W - 2 * M, 24, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 9)\n        c.setFillColor(GREEN_FG if bas else RED_FG)\n        c.drawString(M + 10, y - 12,\n                     f\"{'▼' if bas else '▲'} Prix passé de {_fmt_money(de)} à \"\n                     f\"{_fmt_money(a)} le {_fmt_jour(ph[0]['ts'])}\")\n        y -= 34\n\n    # --- description | inclusions (2 colonnes, comme le site) -------------------\n    col_g = PAGE_W * 0.56 - M\n    feats = [str(f) for f in (l.get(\"features\") or []) if f]\n    desc = (l.get(\"description\") or \"\").strip()\n    if desc or feats:\n        besoin = 30 + max(\n            s.nb_lignes(desc, col_g, max_lignes=26) * 11.5 if desc else 0,\n            min(len(feats), 18) * 12)\n        y = place(y, min(besoin, 340))\n        y_desc = y\n        if desc:\n            y_desc = s.titre_section(y, \"Description\")\n            y_desc = s.paragraphe(M, y_desc, desc, col_g, max_lignes=26)\n        y_incl = y\n        if feats:\n            xr = M + col_g + 24\n            y_incl = _titre_droite(c, y, xr, \"Inclusions et caractéristiques\")\n            c.setFont(\"Helvetica\", 8.5)\n            for f in feats[:18]:\n                c.setFillColor(ACCENT_DEEP)\n                c.drawString(xr, y_incl, \"✓\")\n                c.setFillColor(INK2)\n                c.drawString(xr + 12, y_incl, f[:42])\n                y_incl -= 12\n            if len(feats) > 18:\n                c.setFillColor(INK3)\n                c.drawString(xr + 12, y_incl, f\"+ {len(feats) - 18} autres…\")\n                y_incl -= 12\n        y = min(y_desc, y_incl) - 16\n\n    # --- caractéristiques (details, mêmes exclusions que le site) ---------------\n    dets = [(k, v) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]\n    if dets:\n        lignes = []\n        col_w = PAGE_W - 2 * M\n        for k, v in dets:\n            txt = str(v)\n            lignes.append((str(k), txt))\n        besoin = 40 + sum(\n            s.nb_lignes(t, col_w - 190, taille=8, max_lignes=4) * 11 + 3\n            for _, t in lignes)\n        y = place(y, min(besoin, 420))\n        y = s.titre_section(y, \"Caractéristiques\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"DÉTAIL\")\n        c.drawString(M + 180, y, \"VALEUR\")\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 12\n        for i, (k, v) in enumerate(lignes):\n            nl = s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4)\n            y = place(y, nl * 11 + 4)\n            if i % 2 == 0:   # zébrage éditorial\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - nl * 11 + 7, col_w + 6, nl * 11 + 3,\n                       stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, k[:38])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            y2 = s.paragraphe(M + 180, y, v, col_w - 190, taille=8,\n                              interligne=11, max_lignes=4)\n            y = y2 - 3 + 11 - 11   # y2 pointe déjà sous la dernière ligne\n            y -= 3\n        y -= 10\n\n    # --- pièces (details.pieces) -------------------------------------------------\n    pieces = d.get(\"pieces\") or []\n    if isinstance(pieces, list) and pieces:\n        rows = pieces[:24]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Pièces\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"PIÈCE\", 0), (\"NIVEAU\", 190), (\"DIMENSIONS\", 330),\n                          (\"REVÊTEMENT\", 430)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            y = place(y, 14)\n            if i % 2 == 0:\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - 3, PAGE_W - 2 * M + 6, 12, stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, str(p.get(\"nom\") or \"—\")[:36])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M + 190, y, str(p.get(\"niveau\") or \"—\")[:24])\n            c.drawString(M + 330, y, str(p.get(\"dimensions\") or \"—\")[:18])\n            c.drawString(M + 430, y, str(p.get(\"revetement\") or \"—\")[:22])\n            y -= 12\n        y -= 10\n\n    # --- analyse Vrai-Prix (valeur, jauge, rôle foncier) -------------------------\n    if vp and vp.get(\"value\") is not None:\n        role = [(lab, vp.get(k)) for k, lab in\n                ((\"valeur_role\", \"Évaluation au rôle\"),\n                 (\"valeur_terrain\", \"Valeur du terrain\"),\n                 (\"valeur_batiment\", \"Valeur du bâtiment\"))\n                if vp.get(k)]\n        besoin = 96 + (30 if vp.get(\"low\") is not None else 0) \\\n            + (62 if role else 0) + 18\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Analyse Vrai-Prix\")\n        val_txt = _fmt_money(round(vp[\"value\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, val_txt)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(val_txt, \"Helvetica-Bold\", 17) + 8,\n                     y - 10, \"— valeur marchande estimée par le modèle Vrai-Prix\")\n        meta = []\n        if vp.get(\"confidence\"):\n            meta.append(f\"confiance {vp['confidence']}\"\n                        + (f\" ({vp['confidence_pct']}{NBSP}%)\"\n                           if vp.get(\"confidence_pct\") else \"\"))\n        if vp.get(\"annee_construction_role\"):\n            meta.append(f\"construction {vp['annee_construction_role']} (rôle)\")\n        if vp.get(\"superficie_terrain_role_m2\"):\n            meta.append(f\"terrain {round(vp['superficie_terrain_role_m2']):,}\".replace(\",\", NBSP)\n                        + f\"{NBSP}m² (rôle)\")\n        if meta:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK3)\n            c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        verd = _verdict_vraiprix(l.get(\"price\"), vp)\n        if verd:\n            txt, fg, bg, stk = verd\n            s.pilule(M, y - 48, txt, fg, bg, stk)\n        y -= 58\n        lo, hi = vp.get(\"low\"), vp.get(\"high\")\n        if lo is not None and hi is not None and hi > lo:\n            larg = PAGE_W - 2 * M\n            base = y - 14\n            span = hi - lo\n            c.setFillColor(ACCENT_SOFT)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.roundRect(M, base, larg, 12, 6, stroke=1, fill=1)\n\n            def _pos(v):\n                return M + larg * max(0.02, min(0.98, (v - lo) / span))\n\n            px = _pos(vp[\"value\"])\n            c.setStrokeColor(ACCENT_DEEP)\n            c.setLineWidth(2.5)\n            c.line(px, base - 3, px, base + 15)\n            c.setFont(\"Courier-Bold\", 6.5)\n            c.setFillColor(ACCENT_DEEP)\n            c.drawString(min(max(px - 30, M), PAGE_W - M - 80), base + 18,\n                         f\"ESTIMATION · {_fmt_money(round(vp['value']))}\")\n            if l.get(\"price\") and lo * 0.7 <= l[\"price\"] <= hi * 1.3:\n                ax = _pos(l[\"price\"])\n                c.setStrokeColor(INK)\n                c.setLineWidth(2)\n                c.line(ax, base - 3, ax, base + 15)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(INK)\n                c.drawString(min(max(ax - 30, M), PAGE_W - M - 96), base - 12,\n                             f\"PRIX DEMANDÉ · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 22, _fmt_money(round(lo)))\n            c.drawRightString(PAGE_W - M, base - 22,\n                              _fmt_money(round(hi)) + \" — fourchette d'estimation\")\n            y = base - 34\n        if role:\n            tw = (PAGE_W - 2 * M - (len(role) - 1) * 10) / len(role)\n            for i, (lab, v) in enumerate(role):\n                s.tuile(M + i * (tw + 10), y - 44, tw, 38,\n                        _fmt_money(round(v)), lab)\n            y -= 56\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Estimation automatisée Vrai-Prix (vrai-prix.ca) \"\n                               \"croisée au rôle d'évaluation foncière — ne \"\n                               \"remplace pas une évaluation professionnelle.\")\n        y -= 16\n\n    # --- historique de prix -------------------------------------------------------\n    if len(ph) >= 2:\n        rows = ph[:8]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Historique du prix demandé\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"DATE\", 0), (\"PRIX AFFICHÉ\", 190), (\"VARIATION\", 330)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            prec = rows[i + 1][\"price\"] if i + 1 < len(rows) else None\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, _fmt_jour(p[\"ts\"]))\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M + 190, y, _fmt_money(p[\"price\"]))\n            if prec and p.get(\"price\"):\n                delta = p[\"price\"] - prec\n                c.setFont(\"Courier-Bold\", 7.5)\n                c.setFillColor(GREEN_FG if delta < 0 else (RED_FG if delta > 0 else INK3))\n                sig = \"▼\" if delta < 0 else (\"▲\" if delta > 0 else \"=\")\n                c.drawString(M + 330, y, f\"{sig} {_fmt_money(abs(delta))}\" if delta else \"=\")\n            y -= 12\n        y -= 10\n\n    # --- risque d'inondation (BDZI) ------------------------------------------------\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\" if sev\n                     else \"En zone inondable\", RED_FG, RED_BG, RED_FG)\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     AMBER_FG, AMBER_BG, AMBER_FG)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_FG, GREEN_BG, GREEN_FG)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z.get('type') or 'Zone'} — à {z.get('distance_m')} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- qualité de l'air (RSQAQ) ---------------------------------------------------\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"de la propriété — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = (f\"{m_['moyenne']:.1f}\".replace(\".\", \",\")\n                   if m_.get(\"moyenne\") is not None else \"—\")\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- coût d'électricité (Hydro-Québec) -------------------------------------------\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP) + f\"{NBSP}kWh/an\"\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- commerces et transport --------------------------------------------------------\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:12]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- essence à proximité --------------------------------------------------------------\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- le quartier -------------------------------------------------------------------------\n    if quartier and (quartier.get(\"demographie\") or quartier.get(\"proximite\")\n                     or quartier.get(\"chaleur\") or quartier.get(\"crime\")):\n        demo = quartier.get(\"demographie\") or {}\n        prox = quartier.get(\"proximite\") or {}\n        prox_aff = [(k, lab) for k, lab in PROX_LABELS\n                    if isinstance(prox.get(k), (int, float))]\n        besoin = 30 + (94 if demo else 0) + len(prox_aff) * 14 + 40\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Le quartier\")\n        c.setFont(\"Helvetica\", 7.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"Secteur immédiat de la propriété (aire de diffusion \"\n                           \"du recensement 2021, ± 500 habitants).\")\n        y -= 14\n        if demo:\n            tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                      (\"Ménages locataires\",\n                       f\"{round(demo['pct_locataires'])}{NBSP}%\"\n                       if demo.get(\"pct_locataires\") is not None else \"—\"),\n                      (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                      (\"Âge médian\",\n                       f\"{round(demo['age_median'])} ans\"\n                       if demo.get(\"age_median\") is not None else \"—\"),\n                      (\"Français à la maison\",\n                       f\"{round(demo['pct_francais'])}{NBSP}%\"\n                       if demo.get(\"pct_francais\") is not None else \"—\"),\n                      (\"Diplôme universitaire\",\n                       f\"{round(demo['pct_univ'])}{NBSP}%\"\n                       if demo.get(\"pct_univ\") is not None else \"—\")]\n            tw = (PAGE_W - 2 * M - 20) / 3\n            for i, (lab, val) in enumerate(tuiles):\n                s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                        tw, 38, val, lab)\n            y -= 34 + 44 + 16\n        for k, lab in prox_aff:\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(ACCENT)\n            c.roundRect(bx0, y - 1, max(3, bw_ * v), 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        if prox_aff:\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, y, \"Accessibilité 0–100 — mesures de proximité de \"\n                               \"Statistique Canada.\")\n            y -= 12\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch.get(\"classe\", 5) <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_FG, GREEN_BG, GREEN_FG))\n            elif ch.get(\"classe\", 5) >= 7:\n                ec = (f\" (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\")\n                      if ch.get(\"ecart\") is not None else \"\")\n                badges_q.append((f\"Îlot de chaleur{ec}\", AMBER_FG, AMBER_BG, AMBER_FG))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"]) / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} la moyenne canadienne\",\n                             INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m (12 mois)\",\n                             INK2, SURFACE, INK))\n        if badges_q:\n            x = M\n            for txt, fg, bg, stk in badges_q:\n                x = s.pilule(x, y - 8, txt, fg, bg, stk)\n            y -= 26\n        y -= 6\n\n    # --- références : QR + courtier + source ------------------------------------------------\n    y = place(y, 140)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.immo-ka.com/propriete/{uid}\", box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    yy = y - 14\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    courtier = l.get(\"broker_name\") or d.get(\"Courtier inscripteur\")\n    if courtier:\n        ligne = f\"Courtier : {courtier}\"\n        if l.get(\"broker_phone\"):\n            ligne += f\" · {l['broker_phone']}\"\n        c.drawString(M + 82, yy, ligne[:95])\n        yy -= 13\n    c.drawString(M + 82, yy, f\"Agence : {src_name}\")\n    yy -= 14\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Annonce originale :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, (l.get(\"url\") or d.get(\"listing_origin_url\") or \"\")[:90])\n    yy -= 14\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Fiche à jour en ligne (scannez le code) :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")\n    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1062,"cache_read_input_tokens":101813,"cache_creation":{"ephemeral_5m_input_tokens":1062,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"538d64f9-d7eb-4573-af7e-5cca81274c39"}}
{"type":"permission_request","requestId":"c9d5cd0bf6a40422","tool":"Write","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","content":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdffiche.py : fiche PDF complète d'une propriété — reflète TOUS les blocs de\n#   la fiche web : prix + verdict Vrai-Prix, photos, description, inclusions,\n#   caractéristiques, pièces, analyse Vrai-Prix (jauge + rôle foncier),\n#   historique de prix, inondation, qualité de l'air, coût d'électricité,\n#   commerces/transport, essence, quartier, références + QR.\n#   Style « éditorial sharp » (bordures encre, ombres décalées dures, tuiles\n#   KPI, micro-étiquettes Courier) — palette du site : crème #f5f3ee, encre\n#   #141814, accent cerise #e23744. Pagination dynamique : saut_page()/place(),\n#   aucune section coupée entre deux pages.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Immo-Ka (identité officielle du site) ----------------------------\nPAPER = HexColor(\"#f5f3ee\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#141814\")\nINK2 = HexColor(\"#4d5551\")\nINK3 = HexColor(\"#8b928c\")\nACCENT = HexColor(\"#e23744\")        # cerise\nACCENT_SOFT = HexColor(\"#fbe0e2\")\nACCENT_DEEP = HexColor(\"#a8232e\")\nGREEN_FG = HexColor(\"#1c5c41\")      # verdict « sous l'estimation » (site .vp-under)\nGREEN_BG = HexColor(\"#e2f0e6\")\nRED_FG = HexColor(\"#8a2b2b\")        # verdict « au-dessus » (site .vp-over)\nRED_BG = HexColor(\"#f6e2e2\")\nAMBER_FG = HexColor(\"#8a5a12\")\nAMBER_BG = HexColor(\"#faf0dc\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"ImmoKaBot/1.0 (+https://www.immo-ka.com; contact@spboucher.ai)\"\n\nNBSP = \" \"     # espace fine insécable (mise en forme française)\n\n# clés de details jamais affichées (mêmes exclusions que la fiche web)\nDETAIL_HIDDEN = {\n    \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n    \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n    \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n}\n\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"\n               \"environnement) fournis à titre indicatif, sans valeur légale.\")\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _fmt_mois(d) -> str:\n    \"\"\"« 2017-11-04 » ou epoch → « novembre 2017 » (sinon valeur telle quelle).\"\"\"\n    if d is None:\n        return \"—\"\n    try:\n        if isinstance(d, (int, float)):\n            st = time.localtime(d)\n            return f\"{_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n        an, mois = str(d)[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return str(d)\n\n\ndef _fmt_jour(ts: float) -> str:\n    \"\"\"epoch → « 4 novembre 2024 » (« 1er » pour le premier du mois).\"\"\"\n    st = time.localtime(ts)\n    j = \"1er\" if st.tm_mday == 1 else str(st.tm_mday)\n    return f\"{j} {_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à plusieurs dizaines de Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Immo·Ka : « Immo » encre + boîte encre avec « Ka » cerise.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Immo\")\n        w = c.stringWidth(\"Immo\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6, titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.IMMO-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=ACCENT, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        # carré accent à ombre dure (signature de la passe éditoriale du site)\n        c.setFillColor(INK)\n        c.rect(M + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.rect(M, y + 0.5, 9, 9, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M + 16, y, texte)\n        c.setStrokeColor(ACCENT)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M + 16, y - 4, M + 16 + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def nb_lignes(self, texte: str, largeur: float, taille: float = 8.5,\n                  max_lignes: int = 100) -> int:\n        \"\"\"Nombre de lignes qu'occupera `paragraphe` (pour réserver la place).\"\"\"\n        c = self.c\n        mots = texte.split()\n        ligne = \"\"\n        n = 1\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                n += 1\n                ligne = mot\n                if n >= max_lignes:\n                    break\n        return min(n, max_lignes)\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\ndef _titre_droite(c, y, x, texte):\n    c.setFillColor(INK)\n    c.rect(x + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n    c.setFillColor(ACCENT)\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.rect(x, y + 0.5, 9, 9, stroke=1, fill=1)\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x + 16, y, texte)\n    c.setStrokeColor(ACCENT)\n    c.setLineWidth(3)\n    c.line(x + 16, y - 4, x + 16 + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\ndef _verdict_vraiprix(price, vp) -> tuple[str, object, object, object] | None:\n    \"\"\"Pastille verdict identique au site : ±% vs estimation, masqué si\n    |pct| > 60 (prix non comparable : terrain, loyer…).\"\"\"\n    if not price or not vp or vp.get(\"value\") in (None, 0):\n        return None\n    pct = round((price - vp[\"value\"]) / vp[\"value\"] * 100)\n    if abs(pct) > 60:\n        return None\n    if pct >= 1:\n        return (f\"▲{NBSP}{pct}{NBSP}% au-dessus de l'estimation\", RED_FG, RED_BG, ACCENT_DEEP)\n    if pct <= -1:\n        return (f\"▼{NBSP}{abs(pct)}{NBSP}% sous l'estimation\", GREEN_FG, GREEN_BG, GREEN_FG)\n    return (f\"≈{NBSP}Prix aligné sur l'estimation\", INK2, SURFACE, INK)\n\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche de la propriété `uid` — reflète tous les blocs de la\n    fiche web ; None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"features\"] = json.loads(l.get(\"features\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    try:\n        l[\"vraiprix\"] = json.loads(l[\"vraiprix\"]) or None if l.get(\"vraiprix\") else None\n    except (ValueError, TypeError):\n        l[\"vraiprix\"] = None\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n        (uid,)).fetchall()]\n    quartier = None\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n    if geo:\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        try:\n            quartier = qmod.fiche_quartier(lat, lng, l.get(\"city\") or \"\",\n                                           dauid if dauid and dauid != \"hors-zone\" else None)\n        except Exception:\n            quartier = None\n    try:\n        reg = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n        src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]), l[\"source\"])\n    except Exception:\n        src_name = l[\"source\"]\n    con.close()\n\n    # --- enrichissements (mêmes modules que les blocs de la fiche web) -------\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    d = l[\"details\"]\n    vp = l.get(\"vraiprix\")\n    location = (d.get(\"transaction\") == \"location\")\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Immo-Ka — {l.get('address') or l.get('title') or uid}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche propriété\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche propriété · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- héro : prix + pastille verdict Vrai-Prix -----------------------------\n    prix_txt = (_fmt_money(l[\"price\"]) + (f\"{NBSP}/ mois\" if location else \"\")) \\\n        if l.get(\"price\") else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    verd = _verdict_vraiprix(l.get(\"price\"), vp)\n    if verd:\n        txt, fg, bg, stk = verd\n        bx = s.pilule(bx, y - 22, txt, fg, bg, stk)\n    if l.get(\"property_type\"):\n        s.pilule(bx, y - 22, l[\"property_type\"], ACCENT, INK)\n    y -= 44\n\n    # --- titre + adresse -------------------------------------------------------\n    titre = l.get(\"address\") or l.get(\"title\") or \"\"\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, titre[:80])\n    y -= 14\n    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    if l.get(\"title\") and l.get(\"title\") != titre:\n        loc = f\"{l['title']} — {loc}\" if loc else l[\"title\"]\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés (mêmes specs que le héro du site) --------------------------\n    chips = []\n    if l.get(\"bedrooms\") is not None:\n        chips.append(f\"{l['bedrooms']}{NBSP}ch.\")\n    if l.get(\"bathrooms\") is not None:\n        chips.append(f\"{l['bathrooms']}{NBSP}sdb\")\n    if l.get(\"powder_rooms\"):\n        chips.append(f\"{l['powder_rooms']}{NBSP}s. d'eau\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"lot_sqft\"):\n        chips.append(f\"Terrain {round(l['lot_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"year_built\"):\n        chips.append(f\"Construit en {l['year_built']}\")\n    if l.get(\"price\") and l.get(\"area_sqft\") and l[\"area_sqft\"] > 200:\n        chips.append(f\"{round(l['price'] / l['area_sqft'])}{NBSP}$/pi²\")\n    if l.get(\"mls\"):\n        chips.append(f\"MLS {l['mls']}\")\n    if l.get(\"first_seen\"):\n        dom = max(0, int((time.time() - l[\"first_seen\"]) // 86400))\n        chips.append(f\"{dom}{NBSP}j sur Immo-Ka\")\n    x = M\n    for chip in chips[:8]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes ---------------------------------------\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- baisse/hausse de prix (bandeau, comme le site) -------------------------\n    ph = [p for p in price_history if p.get(\"price\")]\n    if len(ph) >= 2 and ph[0][\"price\"] != ph[1][\"price\"]:\n        y = place(y, 34)\n        de, a = ph[1][\"price\"], ph[0][\"price\"]\n        bas = a < de\n        c.setFillColor(GREEN_BG if bas else RED_BG)\n        c.setStrokeColor(GREEN_FG if bas else RED_FG)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 20, PAGE_W - 2 * M, 24, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 9)\n        c.setFillColor(GREEN_FG if bas else RED_FG)\n        c.drawString(M + 10, y - 12,\n                     f\"{'▼' if bas else '▲'} Prix passé de {_fmt_money(de)} à \"\n                     f\"{_fmt_money(a)} le {_fmt_jour(ph[0]['ts'])}\")\n        y -= 34\n\n    # --- description | inclusions (2 colonnes, comme le site) -------------------\n    col_g = PAGE_W * 0.56 - M\n    feats = [str(f) for f in (l.get(\"features\") or []) if f]\n    desc = (l.get(\"description\") or \"\").strip()\n    if desc or feats:\n        besoin = 30 + max(\n            s.nb_lignes(desc, col_g, max_lignes=26) * 11.5 if desc else 0,\n            min(len(feats), 18) * 12)\n        y = place(y, min(besoin, 340))\n        y_desc = y\n        if desc:\n            y_desc = s.titre_section(y, \"Description\")\n            y_desc = s.paragraphe(M, y_desc, desc, col_g, max_lignes=26)\n        y_incl = y\n        if feats:\n            xr = M + col_g + 24\n            y_incl = _titre_droite(c, y, xr, \"Inclusions et caractéristiques\")\n            c.setFont(\"Helvetica\", 8.5)\n            for f in feats[:18]:\n                c.setFillColor(ACCENT_DEEP)\n                c.drawString(xr, y_incl, \"✓\")\n                c.setFillColor(INK2)\n                c.drawString(xr + 12, y_incl, f[:42])\n                y_incl -= 12\n            if len(feats) > 18:\n                c.setFillColor(INK3)\n                c.drawString(xr + 12, y_incl, f\"+ {len(feats) - 18} autres…\")\n                y_incl -= 12\n        y = min(y_desc, y_incl) - 16\n\n    # --- caractéristiques (details, mêmes exclusions que le site) ---------------\n    dets = [(k, v) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]\n    if dets:\n        lignes = []\n        col_w = PAGE_W - 2 * M\n        for k, v in dets:\n            txt = str(v)\n            lignes.append((str(k), txt))\n        besoin = 40 + sum(\n            s.nb_lignes(t, col_w - 190, taille=8, max_lignes=4) * 11 + 3\n            for _, t in lignes)\n        y = place(y, min(besoin, 420))\n        y = s.titre_section(y, \"Caractéristiques\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"DÉTAIL\")\n        c.drawString(M + 180, y, \"VALEUR\")\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 12\n        for i, (k, v) in enumerate(lignes):\n            nl = s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4)\n            y = place(y, nl * 11 + 4)\n            if i % 2 == 0:   # zébrage éditorial\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - nl * 11 + 7, col_w + 6, nl * 11 + 3,\n                       stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, k[:38])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            y2 = s.paragraphe(M + 180, y, v, col_w - 190, taille=8,\n                              interligne=11, max_lignes=4)\n            y = y2 - 3 + 11 - 11   # y2 pointe déjà sous la dernière ligne\n            y -= 3\n        y -= 10\n\n    # --- pièces (details.pieces) -------------------------------------------------\n    pieces = d.get(\"pieces\") or []\n    if isinstance(pieces, list) and pieces:\n        rows = pieces[:24]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Pièces\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"PIÈCE\", 0), (\"NIVEAU\", 190), (\"DIMENSIONS\", 330),\n                          (\"REVÊTEMENT\", 430)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            y = place(y, 14)\n            if i % 2 == 0:\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - 3, PAGE_W - 2 * M + 6, 12, stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, str(p.get(\"nom\") or \"—\")[:36])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M + 190, y, str(p.get(\"niveau\") or \"—\")[:24])\n            c.drawString(M + 330, y, str(p.get(\"dimensions\") or \"—\")[:18])\n            c.drawString(M + 430, y, str(p.get(\"revetement\") or \"—\")[:22])\n            y -= 12\n        y -= 10\n\n    # --- analyse Vrai-Prix (valeur, jauge, rôle foncier) -------------------------\n    if vp and vp.get(\"value\") is not None:\n        role = [(lab, vp.get(k)) for k, lab in\n                ((\"valeur_role\", \"Évaluation au rôle\"),\n                 (\"valeur_terrain\", \"Valeur du terrain\"),\n                 (\"valeur_batiment\", \"Valeur du bâtiment\"))\n                if vp.get(k)]\n        besoin = 96 + (30 if vp.get(\"low\") is not None else 0) \\\n            + (62 if role else 0) + 18\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Analyse Vrai-Prix\")\n        val_txt = _fmt_money(round(vp[\"value\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, val_txt)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(val_txt, \"Helvetica-Bold\", 17) + 8,\n                     y - 10, \"— valeur marchande estimée par le modèle Vrai-Prix\")\n        meta = []\n        if vp.get(\"confidence\"):\n            meta.append(f\"confiance {vp['confidence']}\"\n                        + (f\" ({vp['confidence_pct']}{NBSP}%)\"\n                           if vp.get(\"confidence_pct\") else \"\"))\n        if vp.get(\"annee_construction_role\"):\n            meta.append(f\"construction {vp['annee_construction_role']} (rôle)\")\n        if vp.get(\"superficie_terrain_role_m2\"):\n            meta.append(f\"terrain {round(vp['superficie_terrain_role_m2']):,}\".replace(\",\", NBSP)\n                        + f\"{NBSP}m² (rôle)\")\n        if meta:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK3)\n            c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        verd = _verdict_vraiprix(l.get(\"price\"), vp)\n        if verd:\n            txt, fg, bg, stk = verd\n            s.pilule(M, y - 48, txt, fg, bg, stk)\n        y -= 58\n        lo, hi = vp.get(\"low\"), vp.get(\"high\")\n        if lo is not None and hi is not None and hi > lo:\n            larg = PAGE_W - 2 * M\n            base = y - 14\n            span = hi - lo\n            c.setFillColor(ACCENT_SOFT)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.roundRect(M, base, larg, 12, 6, stroke=1, fill=1)\n\n            def _pos(v):\n                return M + larg * max(0.02, min(0.98, (v - lo) / span))\n\n            px = _pos(vp[\"value\"])\n            c.setStrokeColor(ACCENT_DEEP)\n            c.setLineWidth(2.5)\n            c.line(px, base - 3, px, base + 15)\n            c.setFont(\"Courier-Bold\", 6.5)\n            c.setFillColor(ACCENT_DEEP)\n            c.drawString(min(max(px - 30, M), PAGE_W - M - 80), base + 18,\n                         f\"ESTIMATION · {_fmt_money(round(vp['value']))}\")\n            if l.get(\"price\") and lo * 0.7 <= l[\"price\"] <= hi * 1.3:\n                ax = _pos(l[\"price\"])\n                c.setStrokeColor(INK)\n                c.setLineWidth(2)\n                c.line(ax, base - 3, ax, base + 15)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(INK)\n                c.drawString(min(max(ax - 30, M), PAGE_W - M - 96), base - 12,\n                             f\"PRIX DEMANDÉ · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 22, _fmt_money(round(lo)))\n            c.drawRightString(PAGE_W - M, base - 22,\n                              _fmt_money(round(hi)) + \" — fourchette d'estimation\")\n            y = base - 34\n        if role:\n            tw = (PAGE_W - 2 * M - (len(role) - 1) * 10) / len(role)\n            for i, (lab, v) in enumerate(role):\n                s.tuile(M + i * (tw + 10), y - 44, tw, 38,\n                        _fmt_money(round(v)), lab)\n            y -= 56\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Estimation automatisée Vrai-Prix (vrai-prix.ca) \"\n                               \"croisée au rôle d'évaluation foncière — ne \"\n                               \"remplace pas une évaluation professionnelle.\")\n        y -= 16\n\n    # --- historique de prix -------------------------------------------------------\n    if len(ph) >= 2:\n        rows = ph[:8]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Historique du prix demandé\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"DATE\", 0), (\"PRIX AFFICHÉ\", 190), (\"VARIATION\", 330)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            prec = rows[i + 1][\"price\"] if i + 1 < len(rows) else None\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, _fmt_jour(p[\"ts\"]))\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M + 190, y, _fmt_money(p[\"price\"]))\n            if prec and p.get(\"price\"):\n                delta = p[\"price\"] - prec\n                c.setFont(\"Courier-Bold\", 7.5)\n                c.setFillColor(GREEN_FG if delta < 0 else (RED_FG if delta > 0 else INK3))\n                sig = \"▼\" if delta < 0 else (\"▲\" if delta > 0 else \"=\")\n                c.drawString(M + 330, y, f\"{sig} {_fmt_money(abs(delta))}\" if delta else \"=\")\n            y -= 12\n        y -= 10\n\n    # --- risque d'inondation (BDZI) ------------------------------------------------\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\" if sev\n                     else \"En zone inondable\", RED_FG, RED_BG, RED_FG)\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     AMBER_FG, AMBER_BG, AMBER_FG)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_FG, GREEN_BG, GREEN_FG)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z.get('type') or 'Zone'} — à {z.get('distance_m')} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- qualité de l'air (RSQAQ) ---------------------------------------------------\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"de la propriété — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = (f\"{m_['moyenne']:.1f}\".replace(\".\", \",\")\n                   if m_.get(\"moyenne\") is not None else \"—\")\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- coût d'électricité (Hydro-Québec) -------------------------------------------\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP) + f\"{NBSP}kWh/an\"\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- commerces et transport --------------------------------------------------------\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:12]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- essence à proximité --------------------------------------------------------------\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- le quartier -------------------------------------------------------------------------\n    if quartier and (quartier.get(\"demographie\") or quartier.get(\"proximite\")\n                     or quartier.get(\"chaleur\") or quartier.get(\"crime\")):\n        demo = quartier.get(\"demographie\") or {}\n        prox = quartier.get(\"proximite\") or {}\n        prox_aff = [(k, lab) for k, lab in PROX_LABELS\n                    if isinstance(prox.get(k), (int, float))]\n        besoin = 30 + (94 if demo else 0) + len(prox_aff) * 14 + 40\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Le quartier\")\n        c.setFont(\"Helvetica\", 7.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"Secteur immédiat de la propriété (aire de diffusion \"\n                           \"du recensement 2021, ± 500 habitants).\")\n        y -= 14\n        if demo:\n            tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                      (\"Ménages locataires\",\n                       f\"{round(demo['pct_locataires'])}{NBSP}%\"\n                       if demo.get(\"pct_locataires\") is not None else \"—\"),\n                      (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                      (\"Âge médian\",\n                       f\"{round(demo['age_median'])} ans\"\n                       if demo.get(\"age_median\") is not None else \"—\"),\n                      (\"Français à la maison\",\n                       f\"{round(demo['pct_francais'])}{NBSP}%\"\n                       if demo.get(\"pct_francais\") is not None else \"—\"),\n                      (\"Diplôme universitaire\",\n                       f\"{round(demo['pct_univ'])}{NBSP}%\"\n                       if demo.get(\"pct_univ\") is not None else \"—\")]\n            tw = (PAGE_W - 2 * M - 20) / 3\n            for i, (lab, val) in enumerate(tuiles):\n                s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                        tw, 38, val, lab)\n            y -= 34 + 44 + 16\n        for k, lab in prox_aff:\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(ACCENT)\n            c.roundRect(bx0, y - 1, max(3, bw_ * v), 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        if prox_aff:\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, y, \"Accessibilité 0–100 — mesures de proximité de \"\n                               \"Statistique Canada.\")\n            y -= 12\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch.get(\"classe\", 5) <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_FG, GREEN_BG, GREEN_FG))\n            elif ch.get(\"classe\", 5) >= 7:\n                ec = (f\" (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\")\n                      if ch.get(\"ecart\") is not None else \"\")\n                badges_q.append((f\"Îlot de chaleur{ec}\", AMBER_FG, AMBER_BG, AMBER_FG))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"]) / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} la moyenne canadienne\",\n                             INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m (12 mois)\",\n                             INK2, SURFACE, INK))\n        if badges_q:\n            x = M\n            for txt, fg, bg, stk in badges_q:\n                x = s.pilule(x, y - 8, txt, fg, bg, stk)\n            y -= 26\n        y -= 6\n\n    # --- références : QR + courtier + source ------------------------------------------------\n    y = place(y, 140)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.immo-ka.com/propriete/{uid}\", box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    yy = y - 14\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    courtier = l.get(\"broker_name\") or d.get(\"Courtier inscripteur\")\n    if courtier:\n        ligne = f\"Courtier : {courtier}\"\n        if l.get(\"broker_phone\"):\n            ligne += f\" · {l['broker_phone']}\"\n        c.drawString(M + 82, yy, ligne[:95])\n        yy -= 13\n    c.drawString(M + 82, yy, f\"Agence : {src_name}\")\n    yy -= 14\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Annonce originale :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, (l.get(\"url\") or d.get(\"listing_origin_url\") or \"\")[:90])\n    yy -= 14\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Fiche à jour en ligne (scannez le code) :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")\n    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Backend admin-ka injoignable: fetch failed","is_error":true,"tool_use_id":"toolu_01AaLsQhE9DCx8QnUgJANSik"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"5b62d598-46c6-41c2-853e-85839a34f89f","timestamp":"2026-08-24T21:39:45.925Z","tool_use_result":"Error: Backend admin-ka injoignable: fetch failed"}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"fe87b020-19dc-4f6b-90d8-44e3c67d497e","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJG4sYYFgn1x48dVZde","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":120899,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":120899,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"419f0989-0a3b-4c74-86fc-1341ff3a74fc"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJG4sYYFgn1x48dVZde","type":"message","role":"assistant","content":[{"type":"text","text":"Erreur transitoire du backend — je réessaie l'écriture."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":120899,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":120899,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"2b73b50d-6716-48c9-9407-6a7e6105bf1e"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJG4sYYFgn1x48dVZde","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01U5sBD3zr2uV37XxiByz2t7","name":"Write","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","content":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdffiche.py : fiche PDF complète d'une propriété — reflète TOUS les blocs de\n#   la fiche web : prix + verdict Vrai-Prix, photos, description, inclusions,\n#   caractéristiques, pièces, analyse Vrai-Prix (jauge + rôle foncier),\n#   historique de prix, inondation, qualité de l'air, coût d'électricité,\n#   commerces/transport, essence, quartier, références + QR.\n#   Style « éditorial sharp » (bordures encre, ombres décalées dures, tuiles\n#   KPI, micro-étiquettes Courier) — palette du site : crème #f5f3ee, encre\n#   #141814, accent cerise #e23744. Pagination dynamique : saut_page()/place(),\n#   aucune section coupée entre deux pages.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Immo-Ka (identité officielle du site) ----------------------------\nPAPER = HexColor(\"#f5f3ee\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#141814\")\nINK2 = HexColor(\"#4d5551\")\nINK3 = HexColor(\"#8b928c\")\nACCENT = HexColor(\"#e23744\")        # cerise\nACCENT_SOFT = HexColor(\"#fbe0e2\")\nACCENT_DEEP = HexColor(\"#a8232e\")\nGREEN_FG = HexColor(\"#1c5c41\")      # verdict « sous l'estimation » (site .vp-under)\nGREEN_BG = HexColor(\"#e2f0e6\")\nRED_FG = HexColor(\"#8a2b2b\")        # verdict « au-dessus » (site .vp-over)\nRED_BG = HexColor(\"#f6e2e2\")\nAMBER_FG = HexColor(\"#8a5a12\")\nAMBER_BG = HexColor(\"#faf0dc\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"ImmoKaBot/1.0 (+https://www.immo-ka.com; contact@spboucher.ai)\"\n\nNBSP = \" \"     # espace fine insécable (mise en forme française)\n\n# clés de details jamais affichées (mêmes exclusions que la fiche web)\nDETAIL_HIDDEN = {\n    \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n    \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n    \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n}\n\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"\n               \"environnement) fournis à titre indicatif, sans valeur légale.\")\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _fmt_mois(d) -> str:\n    \"\"\"« 2017-11-04 » ou epoch → « novembre 2017 » (sinon valeur telle quelle).\"\"\"\n    if d is None:\n        return \"—\"\n    try:\n        if isinstance(d, (int, float)):\n            st = time.localtime(d)\n            return f\"{_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n        an, mois = str(d)[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return str(d)\n\n\ndef _fmt_jour(ts: float) -> str:\n    \"\"\"epoch → « 4 novembre 2024 » (« 1er » pour le premier du mois).\"\"\"\n    st = time.localtime(ts)\n    j = \"1er\" if st.tm_mday == 1 else str(st.tm_mday)\n    return f\"{j} {_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à plusieurs dizaines de Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Immo·Ka : « Immo » encre + boîte encre avec « Ka » cerise.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Immo\")\n        w = c.stringWidth(\"Immo\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6, titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.IMMO-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=ACCENT, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        # carré accent à ombre dure (signature de la passe éditoriale du site)\n        c.setFillColor(INK)\n        c.rect(M + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.rect(M, y + 0.5, 9, 9, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M + 16, y, texte)\n        c.setStrokeColor(ACCENT)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M + 16, y - 4, M + 16 + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def nb_lignes(self, texte: str, largeur: float, taille: float = 8.5,\n                  max_lignes: int = 100) -> int:\n        \"\"\"Nombre de lignes qu'occupera `paragraphe` (pour réserver la place).\"\"\"\n        c = self.c\n        mots = texte.split()\n        ligne = \"\"\n        n = 1\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                n += 1\n                ligne = mot\n                if n >= max_lignes:\n                    break\n        return min(n, max_lignes)\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\ndef _titre_droite(c, y, x, texte):\n    c.setFillColor(INK)\n    c.rect(x + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n    c.setFillColor(ACCENT)\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.rect(x, y + 0.5, 9, 9, stroke=1, fill=1)\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x + 16, y, texte)\n    c.setStrokeColor(ACCENT)\n    c.setLineWidth(3)\n    c.line(x + 16, y - 4, x + 16 + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\ndef _verdict_vraiprix(price, vp):\n    \"\"\"Pastille verdict identique au site : ±% vs estimation, masqué si\n    |pct| > 60 (prix non comparable : terrain, loyer…).\"\"\"\n    if not price or not vp or vp.get(\"value\") in (None, 0):\n        return None\n    pct = round((price - vp[\"value\"]) / vp[\"value\"] * 100)\n    if abs(pct) > 60:\n        return None\n    if pct >= 1:\n        return (f\"▲{NBSP}{pct}{NBSP}% au-dessus de l'estimation\",\n                RED_FG, RED_BG, ACCENT_DEEP)\n    if pct <= -1:\n        return (f\"▼{NBSP}{abs(pct)}{NBSP}% sous l'estimation\",\n                GREEN_FG, GREEN_BG, GREEN_FG)\n    return (f\"≈{NBSP}Prix aligné sur l'estimation\", INK2, SURFACE, INK)\n\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche de la propriété `uid` — reflète tous les blocs de la\n    fiche web ; None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"features\"] = json.loads(l.get(\"features\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    try:\n        l[\"vraiprix\"] = json.loads(l[\"vraiprix\"]) or None if l.get(\"vraiprix\") else None\n    except (ValueError, TypeError):\n        l[\"vraiprix\"] = None\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n        (uid,)).fetchall()]\n    quartier = None\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n    if geo:\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        try:\n            quartier = qmod.fiche_quartier(\n                lat, lng, l.get(\"city\") or \"\",\n                dauid if dauid and dauid != \"hors-zone\" else None)\n        except Exception:\n            quartier = None\n    try:\n        reg = json.loads(\n            (db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n        src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]),\n                        l[\"source\"])\n    except Exception:\n        src_name = l[\"source\"]\n    con.close()\n\n    # --- enrichissements (mêmes modules que les blocs de la fiche web) -------\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    d = l[\"details\"]\n    vp = l.get(\"vraiprix\")\n    location = (d.get(\"transaction\") == \"location\")\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Immo-Ka — {l.get('address') or l.get('title') or uid}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche propriété\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche propriété · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- héro : prix + pastille verdict Vrai-Prix -----------------------------\n    prix_txt = (_fmt_money(l[\"price\"]) + (f\"{NBSP}/ mois\" if location else \"\")) \\\n        if l.get(\"price\") else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    verd = _verdict_vraiprix(l.get(\"price\"), vp)\n    if verd:\n        txt, fg, bg, stk = verd\n        bx = s.pilule(bx, y - 22, txt, fg, bg, stk)\n    if l.get(\"property_type\"):\n        s.pilule(bx, y - 22, l[\"property_type\"], ACCENT, INK)\n    y -= 44\n\n    # --- titre + adresse -------------------------------------------------------\n    titre = l.get(\"address\") or l.get(\"title\") or \"\"\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, titre[:80])\n    y -= 14\n    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    if l.get(\"title\") and l.get(\"title\") != titre:\n        loc = f\"{l['title']} — {loc}\" if loc else l[\"title\"]\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés (mêmes specs que le héro du site) --------------------------\n    chips = []\n    if l.get(\"bedrooms\") is not None:\n        chips.append(f\"{l['bedrooms']}{NBSP}ch.\")\n    if l.get(\"bathrooms\") is not None:\n        chips.append(f\"{l['bathrooms']}{NBSP}sdb\")\n    if l.get(\"powder_rooms\"):\n        chips.append(f\"{l['powder_rooms']}{NBSP}s. d'eau\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"lot_sqft\"):\n        chips.append(f\"Terrain {round(l['lot_sqft']):,}\".replace(\",\", NBSP)\n                     + f\"{NBSP}pi²\")\n    if l.get(\"year_built\"):\n        chips.append(f\"Construit en {l['year_built']}\")\n    if l.get(\"price\") and l.get(\"area_sqft\") and l[\"area_sqft\"] > 200:\n        chips.append(f\"{round(l['price'] / l['area_sqft'])}{NBSP}$/pi²\")\n    if l.get(\"mls\"):\n        chips.append(f\"MLS {l['mls']}\")\n    if l.get(\"first_seen\"):\n        dom = max(0, int((time.time() - l[\"first_seen\"]) // 86400))\n        chips.append(f\"{dom}{NBSP}j sur Immo-Ka\")\n    x = M\n    for chip in chips[:8]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes ---------------------------------------\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- baisse/hausse de prix (bandeau, comme le site) -------------------------\n    ph = [p for p in price_history if p.get(\"price\")]\n    if len(ph) >= 2 and ph[0][\"price\"] != ph[1][\"price\"]:\n        y = place(y, 34)\n        de, a = ph[1][\"price\"], ph[0][\"price\"]\n        bas = a < de\n        c.setFillColor(GREEN_BG if bas else RED_BG)\n        c.setStrokeColor(GREEN_FG if bas else RED_FG)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 20, PAGE_W - 2 * M, 24, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 9)\n        c.setFillColor(GREEN_FG if bas else RED_FG)\n        c.drawString(M + 10, y - 12,\n                     f\"{'▼' if bas else '▲'} Prix passé de {_fmt_money(de)} à \"\n                     f\"{_fmt_money(a)} le {_fmt_jour(ph[0]['ts'])}\")\n        y -= 34\n\n    # --- description | inclusions (2 colonnes, comme le site) -------------------\n    col_g = PAGE_W * 0.56 - M\n    feats = [str(f) for f in (l.get(\"features\") or []) if f]\n    desc = (l.get(\"description\") or \"\").strip()\n    if desc or feats:\n        besoin = 30 + max(\n            s.nb_lignes(desc, col_g, max_lignes=26) * 11.5 if desc else 0,\n            min(len(feats), 18) * 12)\n        y = place(y, min(besoin, 340))\n        y_desc = y\n        if desc:\n            y_desc = s.titre_section(y, \"Description\")\n            y_desc = s.paragraphe(M, y_desc, desc, col_g, max_lignes=26)\n        y_incl = y\n        if feats:\n            xr = M + col_g + 24\n            y_incl = _titre_droite(c, y, xr, \"Inclusions et caractéristiques\")\n            c.setFont(\"Helvetica\", 8.5)\n            for f in feats[:18]:\n                c.setFillColor(ACCENT_DEEP)\n                c.drawString(xr, y_incl, \"✓\")\n                c.setFillColor(INK2)\n                c.drawString(xr + 12, y_incl, f[:42])\n                y_incl -= 12\n            if len(feats) > 18:\n                c.setFillColor(INK3)\n                c.drawString(xr + 12, y_incl, f\"+ {len(feats) - 18} autres…\")\n                y_incl -= 12\n        y = min(y_desc, y_incl) - 16\n\n    # --- caractéristiques (details, mêmes exclusions que le site) ---------------\n    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]\n    if dets:\n        col_w = PAGE_W - 2 * M\n        besoin = 40 + sum(\n            s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4) * 11 + 3\n            for _, v in dets)\n        y = place(y, min(besoin, 420))\n        y = s.titre_section(y, \"Caractéristiques\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"DÉTAIL\")\n        c.drawString(M + 180, y, \"VALEUR\")\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 12\n        for i, (k, v) in enumerate(dets):\n            nl = s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4)\n            y = place(y, nl * 11 + 6)\n            if i % 2 == 0:   # zébrage éditorial\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - (nl - 1) * 11 - 3, col_w + 6, nl * 11 + 2,\n                       stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, k[:38])\n            y = s.paragraphe(M + 180, y, v, col_w - 190, taille=8,\n                             interligne=11, max_lignes=4)\n            y -= 3\n        y -= 10\n\n    # --- pièces (details.pieces) -------------------------------------------------\n    pieces = d.get(\"pieces\") or []\n    if isinstance(pieces, list) and pieces:\n        rows = pieces[:24]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Pièces\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"PIÈCE\", 0), (\"NIVEAU\", 190), (\"DIMENSIONS\", 330),\n                          (\"REVÊTEMENT\", 430)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            if i % 2 == 0:\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - 3, PAGE_W - 2 * M + 6, 12, stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, str(p.get(\"nom\") or \"—\")[:36])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M + 190, y, str(p.get(\"niveau\") or \"—\")[:24])\n            c.drawString(M + 330, y, str(p.get(\"dimensions\") or \"—\")[:18])\n            c.drawString(M + 430, y, str(p.get(\"revetement\") or \"—\")[:22])\n            y -= 12\n        y -= 10\n\n    # --- analyse Vrai-Prix (valeur, jauge, rôle foncier) -------------------------\n    if vp and vp.get(\"value\") is not None:\n        role = [(lab, vp.get(k)) for k, lab in\n                ((\"valeur_role\", \"Évaluation au rôle\"),\n                 (\"valeur_terrain\", \"Valeur du terrain\"),\n                 (\"valeur_batiment\", \"Valeur du bâtiment\"))\n                if vp.get(k)]\n        besoin = 96 + (56 if vp.get(\"low\") is not None else 0) \\\n            + (62 if role else 0) + 18\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Analyse Vrai-Prix\")\n        val_txt = _fmt_money(round(vp[\"value\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, val_txt)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(val_txt, \"Helvetica-Bold\", 17) + 8,\n                     y - 10, \"— valeur marchande estimée par le modèle Vrai-Prix\")\n        meta = []\n        if vp.get(\"confidence\"):\n            meta.append(f\"confiance {vp['confidence']}\"\n                        + (f\" ({vp['confidence_pct']}{NBSP}%)\"\n                           if vp.get(\"confidence_pct\") else \"\"))\n        if vp.get(\"annee_construction_role\"):\n            meta.append(f\"construction {vp['annee_construction_role']} (rôle)\")\n        if vp.get(\"superficie_terrain_role_m2\"):\n            meta.append(\n                f\"terrain {round(vp['superficie_terrain_role_m2']):,}\".replace(\",\", NBSP)\n                + f\"{NBSP}m² (rôle)\")\n        if meta:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK3)\n            c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        verd = _verdict_vraiprix(l.get(\"price\"), vp)\n        if verd:\n            txt, fg, bg, stk = verd\n            s.pilule(M, y - 48, txt, fg, bg, stk)\n        y -= 58\n        lo, hi = vp.get(\"low\"), vp.get(\"high\")\n        if lo is not None and hi is not None and hi > lo:\n            larg = PAGE_W - 2 * M\n            base = y - 22\n            span = hi - lo\n            c.setFillColor(ACCENT_SOFT)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.roundRect(M, base, larg, 12, 6, stroke=1, fill=1)\n\n            def _pos(v):\n                return M + larg * max(0.02, min(0.98, (v - lo) / span))\n\n            px = _pos(vp[\"value\"])\n            c.setStrokeColor(ACCENT_DEEP)\n            c.setLineWidth(2.5)\n            c.line(px, base - 3, px, base + 15)\n            c.setFont(\"Courier-Bold\", 6.5)\n            c.setFillColor(ACCENT_DEEP)\n            c.drawString(min(max(px - 30, M), PAGE_W - M - 100), base + 18,\n                         f\"ESTIMATION · {_fmt_money(round(vp['value']))}\")\n            if l.get(\"price\") and lo * 0.7 <= l[\"price\"] <= hi * 1.3:\n                ax = _pos(l[\"price\"])\n                c.setStrokeColor(INK)\n                c.setLineWidth(2)\n                c.line(ax, base - 3, ax, base + 15)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(INK)\n                c.drawString(min(max(ax - 30, M), PAGE_W - M - 116), base - 12,\n                             f\"PRIX DEMANDÉ · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 24, _fmt_money(round(lo)))\n            c.drawRightString(PAGE_W - M, base - 24,\n                              _fmt_money(round(hi)) + \" — fourchette d'estimation\")\n            y = base - 36\n        if role:\n            tw = (PAGE_W - 2 * M - (len(role) - 1) * 10) / len(role)\n            for i, (lab, v) in enumerate(role):\n                s.tuile(M + i * (tw + 10), y - 44, tw, 38,\n                        _fmt_money(round(v)), lab)\n            y -= 56\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Estimation automatisée Vrai-Prix (vrai-prix.ca) \"\n                               \"croisée au rôle d'évaluation foncière — ne \"\n                               \"remplace pas une évaluation professionnelle.\")\n        y -= 16\n\n    # --- historique de prix -------------------------------------------------------\n    if len(ph) >= 2:\n        rows = ph[:8]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Historique du prix demandé\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"DATE\", 0), (\"PRIX AFFICHÉ\", 190), (\"VARIATION\", 330)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            prec = rows[i + 1][\"price\"] if i + 1 < len(rows) else None\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, _fmt_jour(p[\"ts\"]))\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M + 190, y, _fmt_money(p[\"price\"]))\n            if prec and p.get(\"price\"):\n                delta = p[\"price\"] - prec\n                c.setFont(\"Courier-Bold\", 7.5)\n                c.setFillColor(GREEN_FG if delta < 0\n                               else (RED_FG if delta > 0 else INK3))\n                sig = \"▼\" if delta < 0 else (\"▲\" if delta > 0 else \"=\")\n                c.drawString(M + 330, y,\n                             f\"{sig} {_fmt_money(abs(delta))}\" if delta else \"=\")\n            y -= 12\n        y -= 10\n\n    # --- risque d'inondation (BDZI) ------------------------------------------------\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\" if sev\n                     else \"En zone inondable\", RED_FG, RED_BG, RED_FG)\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     AMBER_FG, AMBER_BG, AMBER_FG)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_FG, GREEN_BG, GREEN_FG)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z.get('type') or 'Zone'} — à {z.get('distance_m')} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- qualité de l'air (RSQAQ) ---------------------------------------------------\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"de la propriété — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = (f\"{m_['moyenne']:.1f}\".replace(\".\", \",\")\n                   if m_.get(\"moyenne\") is not None else \"—\")\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- coût d'électricité (Hydro-Québec) -------------------------------------------\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += (f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP)\n                      + f\"{NBSP}kWh/an\")\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- commerces et transport --------------------------------------------------------\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:12]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- essence à proximité --------------------------------------------------------------\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- le quartier -------------------------------------------------------------------------\n    if quartier and (quartier.get(\"demographie\") or quartier.get(\"proximite\")\n                     or quartier.get(\"chaleur\") or quartier.get(\"crime\")):\n        demo = quartier.get(\"demographie\") or {}\n        prox = quartier.get(\"proximite\") or {}\n        prox_aff = [(k, lab) for k, lab in PROX_LABELS\n                    if isinstance(prox.get(k), (int, float))]\n        besoin = 44 + (94 if demo else 0) + len(prox_aff) * 14 + 44\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Le quartier\")\n        c.setFont(\"Helvetica\", 7.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"Secteur immédiat de la propriété (aire de diffusion \"\n                           \"du recensement 2021, ± 500 habitants).\")\n        y -= 14\n        if demo:\n            tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                      (\"Ménages locataires\",\n                       f\"{round(demo['pct_locataires'])}{NBSP}%\"\n                       if demo.get(\"pct_locataires\") is not None else \"—\"),\n                      (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                      (\"Âge médian\",\n                       f\"{round(demo['age_median'])} ans\"\n                       if demo.get(\"age_median\") is not None else \"—\"),\n                      (\"Français à la maison\",\n                       f\"{round(demo['pct_francais'])}{NBSP}%\"\n                       if demo.get(\"pct_francais\") is not None else \"—\"),\n                      (\"Diplôme universitaire\",\n                       f\"{round(demo['pct_univ'])}{NBSP}%\"\n                       if demo.get(\"pct_univ\") is not None else \"—\")]\n            tw = (PAGE_W - 2 * M - 20) / 3\n            for i, (lab, val) in enumerate(tuiles):\n                s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                        tw, 38, val, lab)\n            y -= 34 + 44 + 16\n        for k, lab in prox_aff:\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(ACCENT)\n            c.roundRect(bx0, y - 1, max(3, bw_ * v), 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        if prox_aff:\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, y, \"Accessibilité 0–100 — mesures de proximité de \"\n                               \"Statistique Canada.\")\n            y -= 12\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch.get(\"classe\", 5) <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_FG, GREEN_BG, GREEN_FG))\n            elif ch.get(\"classe\", 5) >= 7:\n                ec = (f\" (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\")\n                      if ch.get(\"ecart\") is not None else \"\")\n                badges_q.append((f\"Îlot de chaleur{ec}\",\n                                 AMBER_FG, AMBER_BG, AMBER_FG))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"])\n                        / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} \"\n                             \"la moyenne canadienne\", INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m \"\n                             \"(12 mois)\", INK2, SURFACE, INK))\n        if badges_q:\n            x = M\n            for txt, fg, bg, stk in badges_q:\n                x = s.pilule(x, y - 8, txt, fg, bg, stk)\n            y -= 26\n        y -= 6\n\n    # --- références : QR + courtier + source ------------------------------------------------\n    y = place(y, 140)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.immo-ka.com/propriete/{uid}\",\n                     box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    yy = y - 14\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    courtier = l.get(\"broker_name\") or d.get(\"Courtier inscripteur\")\n    if courtier:\n        ligne = f\"Courtier : {courtier}\"\n        if l.get(\"broker_phone\"):\n            ligne += f\" · {l['broker_phone']}\"\n        c.drawString(M + 82, yy, ligne[:95])\n        yy -= 13\n    c.drawString(M + 82, yy, f\"Agence : {src_name}\")\n    yy -= 14\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Annonce originale :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy,\n                 (l.get(\"url\") or d.get(\"listing_origin_url\") or \"\")[:90])\n    yy -= 14\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Fiche à jour en ligne (scannez le code) :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")\n    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":120899,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":120899,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"9005675f-0e74-4b85-ac5d-fff1245288c3"}}
{"type":"permission_request","requestId":"c260f32937c17d3e","tool":"Write","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","content":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdffiche.py : fiche PDF complète d'une propriété — reflète TOUS les blocs de\n#   la fiche web : prix + verdict Vrai-Prix, photos, description, inclusions,\n#   caractéristiques, pièces, analyse Vrai-Prix (jauge + rôle foncier),\n#   historique de prix, inondation, qualité de l'air, coût d'électricité,\n#   commerces/transport, essence, quartier, références + QR.\n#   Style « éditorial sharp » (bordures encre, ombres décalées dures, tuiles\n#   KPI, micro-étiquettes Courier) — palette du site : crème #f5f3ee, encre\n#   #141814, accent cerise #e23744. Pagination dynamique : saut_page()/place(),\n#   aucune section coupée entre deux pages.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Immo-Ka (identité officielle du site) ----------------------------\nPAPER = HexColor(\"#f5f3ee\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#141814\")\nINK2 = HexColor(\"#4d5551\")\nINK3 = HexColor(\"#8b928c\")\nACCENT = HexColor(\"#e23744\")        # cerise\nACCENT_SOFT = HexColor(\"#fbe0e2\")\nACCENT_DEEP = HexColor(\"#a8232e\")\nGREEN_FG = HexColor(\"#1c5c41\")      # verdict « sous l'estimation » (site .vp-under)\nGREEN_BG = HexColor(\"#e2f0e6\")\nRED_FG = HexColor(\"#8a2b2b\")        # verdict « au-dessus » (site .vp-over)\nRED_BG = HexColor(\"#f6e2e2\")\nAMBER_FG = HexColor(\"#8a5a12\")\nAMBER_BG = HexColor(\"#faf0dc\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"ImmoKaBot/1.0 (+https://www.immo-ka.com; contact@spboucher.ai)\"\n\nNBSP = \" \"     # espace fine insécable (mise en forme française)\n\n# clés de details jamais affichées (mêmes exclusions que la fiche web)\nDETAIL_HIDDEN = {\n    \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n    \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n    \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n}\n\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"\n               \"environnement) fournis à titre indicatif, sans valeur légale.\")\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _fmt_mois(d) -> str:\n    \"\"\"« 2017-11-04 » ou epoch → « novembre 2017 » (sinon valeur telle quelle).\"\"\"\n    if d is None:\n        return \"—\"\n    try:\n        if isinstance(d, (int, float)):\n            st = time.localtime(d)\n            return f\"{_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n        an, mois = str(d)[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return str(d)\n\n\ndef _fmt_jour(ts: float) -> str:\n    \"\"\"epoch → « 4 novembre 2024 » (« 1er » pour le premier du mois).\"\"\"\n    st = time.localtime(ts)\n    j = \"1er\" if st.tm_mday == 1 else str(st.tm_mday)\n    return f\"{j} {_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à plusieurs dizaines de Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Immo·Ka : « Immo » encre + boîte encre avec « Ka » cerise.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Immo\")\n        w = c.stringWidth(\"Immo\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6, titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.IMMO-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=ACCENT, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        # carré accent à ombre dure (signature de la passe éditoriale du site)\n        c.setFillColor(INK)\n        c.rect(M + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.rect(M, y + 0.5, 9, 9, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M + 16, y, texte)\n        c.setStrokeColor(ACCENT)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M + 16, y - 4, M + 16 + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def nb_lignes(self, texte: str, largeur: float, taille: float = 8.5,\n                  max_lignes: int = 100) -> int:\n        \"\"\"Nombre de lignes qu'occupera `paragraphe` (pour réserver la place).\"\"\"\n        c = self.c\n        mots = texte.split()\n        ligne = \"\"\n        n = 1\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                n += 1\n                ligne = mot\n                if n >= max_lignes:\n                    break\n        return min(n, max_lignes)\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\ndef _titre_droite(c, y, x, texte):\n    c.setFillColor(INK)\n    c.rect(x + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n    c.setFillColor(ACCENT)\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.rect(x, y + 0.5, 9, 9, stroke=1, fill=1)\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x + 16, y, texte)\n    c.setStrokeColor(ACCENT)\n    c.setLineWidth(3)\n    c.line(x + 16, y - 4, x + 16 + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\ndef _verdict_vraiprix(price, vp):\n    \"\"\"Pastille verdict identique au site : ±% vs estimation, masqué si\n    |pct| > 60 (prix non comparable : terrain, loyer…).\"\"\"\n    if not price or not vp or vp.get(\"value\") in (None, 0):\n        return None\n    pct = round((price - vp[\"value\"]) / vp[\"value\"] * 100)\n    if abs(pct) > 60:\n        return None\n    if pct >= 1:\n        return (f\"▲{NBSP}{pct}{NBSP}% au-dessus de l'estimation\",\n                RED_FG, RED_BG, ACCENT_DEEP)\n    if pct <= -1:\n        return (f\"▼{NBSP}{abs(pct)}{NBSP}% sous l'estimation\",\n                GREEN_FG, GREEN_BG, GREEN_FG)\n    return (f\"≈{NBSP}Prix aligné sur l'estimation\", INK2, SURFACE, INK)\n\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche de la propriété `uid` — reflète tous les blocs de la\n    fiche web ; None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"features\"] = json.loads(l.get(\"features\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    try:\n        l[\"vraiprix\"] = json.loads(l[\"vraiprix\"]) or None if l.get(\"vraiprix\") else None\n    except (ValueError, TypeError):\n        l[\"vraiprix\"] = None\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n        (uid,)).fetchall()]\n    quartier = None\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n    if geo:\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        try:\n            quartier = qmod.fiche_quartier(\n                lat, lng, l.get(\"city\") or \"\",\n                dauid if dauid and dauid != \"hors-zone\" else None)\n        except Exception:\n            quartier = None\n    try:\n        reg = json.loads(\n            (db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n        src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]),\n                        l[\"source\"])\n    except Exception:\n        src_name = l[\"source\"]\n    con.close()\n\n    # --- enrichissements (mêmes modules que les blocs de la fiche web) -------\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    d = l[\"details\"]\n    vp = l.get(\"vraiprix\")\n    location = (d.get(\"transaction\") == \"location\")\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Immo-Ka — {l.get('address') or l.get('title') or uid}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche propriété\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche propriété · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- héro : prix + pastille verdict Vrai-Prix -----------------------------\n    prix_txt = (_fmt_money(l[\"price\"]) + (f\"{NBSP}/ mois\" if location else \"\")) \\\n        if l.get(\"price\") else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    verd = _verdict_vraiprix(l.get(\"price\"), vp)\n    if verd:\n        txt, fg, bg, stk = verd\n        bx = s.pilule(bx, y - 22, txt, fg, bg, stk)\n    if l.get(\"property_type\"):\n        s.pilule(bx, y - 22, l[\"property_type\"], ACCENT, INK)\n    y -= 44\n\n    # --- titre + adresse -------------------------------------------------------\n    titre = l.get(\"address\") or l.get(\"title\") or \"\"\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, titre[:80])\n    y -= 14\n    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    if l.get(\"title\") and l.get(\"title\") != titre:\n        loc = f\"{l['title']} — {loc}\" if loc else l[\"title\"]\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés (mêmes specs que le héro du site) --------------------------\n    chips = []\n    if l.get(\"bedrooms\") is not None:\n        chips.append(f\"{l['bedrooms']}{NBSP}ch.\")\n    if l.get(\"bathrooms\") is not None:\n        chips.append(f\"{l['bathrooms']}{NBSP}sdb\")\n    if l.get(\"powder_rooms\"):\n        chips.append(f\"{l['powder_rooms']}{NBSP}s. d'eau\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"lot_sqft\"):\n        chips.append(f\"Terrain {round(l['lot_sqft']):,}\".replace(\",\", NBSP)\n                     + f\"{NBSP}pi²\")\n    if l.get(\"year_built\"):\n        chips.append(f\"Construit en {l['year_built']}\")\n    if l.get(\"price\") and l.get(\"area_sqft\") and l[\"area_sqft\"] > 200:\n        chips.append(f\"{round(l['price'] / l['area_sqft'])}{NBSP}$/pi²\")\n    if l.get(\"mls\"):\n        chips.append(f\"MLS {l['mls']}\")\n    if l.get(\"first_seen\"):\n        dom = max(0, int((time.time() - l[\"first_seen\"]) // 86400))\n        chips.append(f\"{dom}{NBSP}j sur Immo-Ka\")\n    x = M\n    for chip in chips[:8]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes ---------------------------------------\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- baisse/hausse de prix (bandeau, comme le site) -------------------------\n    ph = [p for p in price_history if p.get(\"price\")]\n    if len(ph) >= 2 and ph[0][\"price\"] != ph[1][\"price\"]:\n        y = place(y, 34)\n        de, a = ph[1][\"price\"], ph[0][\"price\"]\n        bas = a < de\n        c.setFillColor(GREEN_BG if bas else RED_BG)\n        c.setStrokeColor(GREEN_FG if bas else RED_FG)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 20, PAGE_W - 2 * M, 24, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 9)\n        c.setFillColor(GREEN_FG if bas else RED_FG)\n        c.drawString(M + 10, y - 12,\n                     f\"{'▼' if bas else '▲'} Prix passé de {_fmt_money(de)} à \"\n                     f\"{_fmt_money(a)} le {_fmt_jour(ph[0]['ts'])}\")\n        y -= 34\n\n    # --- description | inclusions (2 colonnes, comme le site) -------------------\n    col_g = PAGE_W * 0.56 - M\n    feats = [str(f) for f in (l.get(\"features\") or []) if f]\n    desc = (l.get(\"description\") or \"\").strip()\n    if desc or feats:\n        besoin = 30 + max(\n            s.nb_lignes(desc, col_g, max_lignes=26) * 11.5 if desc else 0,\n            min(len(feats), 18) * 12)\n        y = place(y, min(besoin, 340))\n        y_desc = y\n        if desc:\n            y_desc = s.titre_section(y, \"Description\")\n            y_desc = s.paragraphe(M, y_desc, desc, col_g, max_lignes=26)\n        y_incl = y\n        if feats:\n            xr = M + col_g + 24\n            y_incl = _titre_droite(c, y, xr, \"Inclusions et caractéristiques\")\n            c.setFont(\"Helvetica\", 8.5)\n            for f in feats[:18]:\n                c.setFillColor(ACCENT_DEEP)\n                c.drawString(xr, y_incl, \"✓\")\n                c.setFillColor(INK2)\n                c.drawString(xr + 12, y_incl, f[:42])\n                y_incl -= 12\n            if len(feats) > 18:\n                c.setFillColor(INK3)\n                c.drawString(xr + 12, y_incl, f\"+ {len(feats) - 18} autres…\")\n                y_incl -= 12\n        y = min(y_desc, y_incl) - 16\n\n    # --- caractéristiques (details, mêmes exclusions que le site) ---------------\n    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]\n    if dets:\n        col_w = PAGE_W - 2 * M\n        besoin = 40 + sum(\n            s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4) * 11 + 3\n            for _, v in dets)\n        y = place(y, min(besoin, 420))\n        y = s.titre_section(y, \"Caractéristiques\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"DÉTAIL\")\n        c.drawString(M + 180, y, \"VALEUR\")\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 12\n        for i, (k, v) in enumerate(dets):\n            nl = s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4)\n            y = place(y, nl * 11 + 6)\n            if i % 2 == 0:   # zébrage éditorial\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - (nl - 1) * 11 - 3, col_w + 6, nl * 11 + 2,\n                       stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, k[:38])\n            y = s.paragraphe(M + 180, y, v, col_w - 190, taille=8,\n                             interligne=11, max_lignes=4)\n            y -= 3\n        y -= 10\n\n    # --- pièces (details.pieces) -------------------------------------------------\n    pieces = d.get(\"pieces\") or []\n    if isinstance(pieces, list) and pieces:\n        rows = pieces[:24]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Pièces\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"PIÈCE\", 0), (\"NIVEAU\", 190), (\"DIMENSIONS\", 330),\n                          (\"REVÊTEMENT\", 430)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            if i % 2 == 0:\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - 3, PAGE_W - 2 * M + 6, 12, stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, str(p.get(\"nom\") or \"—\")[:36])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M + 190, y, str(p.get(\"niveau\") or \"—\")[:24])\n            c.drawString(M + 330, y, str(p.get(\"dimensions\") or \"—\")[:18])\n            c.drawString(M + 430, y, str(p.get(\"revetement\") or \"—\")[:22])\n            y -= 12\n        y -= 10\n\n    # --- analyse Vrai-Prix (valeur, jauge, rôle foncier) -------------------------\n    if vp and vp.get(\"value\") is not None:\n        role = [(lab, vp.get(k)) for k, lab in\n                ((\"valeur_role\", \"Évaluation au rôle\"),\n                 (\"valeur_terrain\", \"Valeur du terrain\"),\n                 (\"valeur_batiment\", \"Valeur du bâtiment\"))\n                if vp.get(k)]\n        besoin = 96 + (56 if vp.get(\"low\") is not None else 0) \\\n            + (62 if role else 0) + 18\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Analyse Vrai-Prix\")\n        val_txt = _fmt_money(round(vp[\"value\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, val_txt)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(val_txt, \"Helvetica-Bold\", 17) + 8,\n                     y - 10, \"— valeur marchande estimée par le modèle Vrai-Prix\")\n        meta = []\n        if vp.get(\"confidence\"):\n            meta.append(f\"confiance {vp['confidence']}\"\n                        + (f\" ({vp['confidence_pct']}{NBSP}%)\"\n                           if vp.get(\"confidence_pct\") else \"\"))\n        if vp.get(\"annee_construction_role\"):\n            meta.append(f\"construction {vp['annee_construction_role']} (rôle)\")\n        if vp.get(\"superficie_terrain_role_m2\"):\n            meta.append(\n                f\"terrain {round(vp['superficie_terrain_role_m2']):,}\".replace(\",\", NBSP)\n                + f\"{NBSP}m² (rôle)\")\n        if meta:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK3)\n            c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        verd = _verdict_vraiprix(l.get(\"price\"), vp)\n        if verd:\n            txt, fg, bg, stk = verd\n            s.pilule(M, y - 48, txt, fg, bg, stk)\n        y -= 58\n        lo, hi = vp.get(\"low\"), vp.get(\"high\")\n        if lo is not None and hi is not None and hi > lo:\n            larg = PAGE_W - 2 * M\n            base = y - 22\n            span = hi - lo\n            c.setFillColor(ACCENT_SOFT)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.roundRect(M, base, larg, 12, 6, stroke=1, fill=1)\n\n            def _pos(v):\n                return M + larg * max(0.02, min(0.98, (v - lo) / span))\n\n            px = _pos(vp[\"value\"])\n            c.setStrokeColor(ACCENT_DEEP)\n            c.setLineWidth(2.5)\n            c.line(px, base - 3, px, base + 15)\n            c.setFont(\"Courier-Bold\", 6.5)\n            c.setFillColor(ACCENT_DEEP)\n            c.drawString(min(max(px - 30, M), PAGE_W - M - 100), base + 18,\n                         f\"ESTIMATION · {_fmt_money(round(vp['value']))}\")\n            if l.get(\"price\") and lo * 0.7 <= l[\"price\"] <= hi * 1.3:\n                ax = _pos(l[\"price\"])\n                c.setStrokeColor(INK)\n                c.setLineWidth(2)\n                c.line(ax, base - 3, ax, base + 15)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(INK)\n                c.drawString(min(max(ax - 30, M), PAGE_W - M - 116), base - 12,\n                             f\"PRIX DEMANDÉ · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 24, _fmt_money(round(lo)))\n            c.drawRightString(PAGE_W - M, base - 24,\n                              _fmt_money(round(hi)) + \" — fourchette d'estimation\")\n            y = base - 36\n        if role:\n            tw = (PAGE_W - 2 * M - (len(role) - 1) * 10) / len(role)\n            for i, (lab, v) in enumerate(role):\n                s.tuile(M + i * (tw + 10), y - 44, tw, 38,\n                        _fmt_money(round(v)), lab)\n            y -= 56\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Estimation automatisée Vrai-Prix (vrai-prix.ca) \"\n                               \"croisée au rôle d'évaluation foncière — ne \"\n                               \"remplace pas une évaluation professionnelle.\")\n        y -= 16\n\n    # --- historique de prix -------------------------------------------------------\n    if len(ph) >= 2:\n        rows = ph[:8]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Historique du prix demandé\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"DATE\", 0), (\"PRIX AFFICHÉ\", 190), (\"VARIATION\", 330)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            prec = rows[i + 1][\"price\"] if i + 1 < len(rows) else None\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, _fmt_jour(p[\"ts\"]))\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M + 190, y, _fmt_money(p[\"price\"]))\n            if prec and p.get(\"price\"):\n                delta = p[\"price\"] - prec\n                c.setFont(\"Courier-Bold\", 7.5)\n                c.setFillColor(GREEN_FG if delta < 0\n                               else (RED_FG if delta > 0 else INK3))\n                sig = \"▼\" if delta < 0 else (\"▲\" if delta > 0 else \"=\")\n                c.drawString(M + 330, y,\n                             f\"{sig} {_fmt_money(abs(delta))}\" if delta else \"=\")\n            y -= 12\n        y -= 10\n\n    # --- risque d'inondation (BDZI) ------------------------------------------------\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\" if sev\n                     else \"En zone inondable\", RED_FG, RED_BG, RED_FG)\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     AMBER_FG, AMBER_BG, AMBER_FG)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_FG, GREEN_BG, GREEN_FG)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z.get('type') or 'Zone'} — à {z.get('distance_m')} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- qualité de l'air (RSQAQ) ---------------------------------------------------\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"de la propriété — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = (f\"{m_['moyenne']:.1f}\".replace(\".\", \",\")\n                   if m_.get(\"moyenne\") is not None else \"—\")\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- coût d'électricité (Hydro-Québec) -------------------------------------------\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += (f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP)\n                      + f\"{NBSP}kWh/an\")\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- commerces et transport --------------------------------------------------------\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:12]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- essence à proximité --------------------------------------------------------------\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- le quartier -------------------------------------------------------------------------\n    if quartier and (quartier.get(\"demographie\") or quartier.get(\"proximite\")\n                     or quartier.get(\"chaleur\") or quartier.get(\"crime\")):\n        demo = quartier.get(\"demographie\") or {}\n        prox = quartier.get(\"proximite\") or {}\n        prox_aff = [(k, lab) for k, lab in PROX_LABELS\n                    if isinstance(prox.get(k), (int, float))]\n        besoin = 44 + (94 if demo else 0) + len(prox_aff) * 14 + 44\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Le quartier\")\n        c.setFont(\"Helvetica\", 7.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"Secteur immédiat de la propriété (aire de diffusion \"\n                           \"du recensement 2021, ± 500 habitants).\")\n        y -= 14\n        if demo:\n            tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                      (\"Ménages locataires\",\n                       f\"{round(demo['pct_locataires'])}{NBSP}%\"\n                       if demo.get(\"pct_locataires\") is not None else \"—\"),\n                      (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                      (\"Âge médian\",\n                       f\"{round(demo['age_median'])} ans\"\n                       if demo.get(\"age_median\") is not None else \"—\"),\n                      (\"Français à la maison\",\n                       f\"{round(demo['pct_francais'])}{NBSP}%\"\n                       if demo.get(\"pct_francais\") is not None else \"—\"),\n                      (\"Diplôme universitaire\",\n                       f\"{round(demo['pct_univ'])}{NBSP}%\"\n                       if demo.get(\"pct_univ\") is not None else \"—\")]\n            tw = (PAGE_W - 2 * M - 20) / 3\n            for i, (lab, val) in enumerate(tuiles):\n                s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                        tw, 38, val, lab)\n            y -= 34 + 44 + 16\n        for k, lab in prox_aff:\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(ACCENT)\n            c.roundRect(bx0, y - 1, max(3, bw_ * v), 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        if prox_aff:\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, y, \"Accessibilité 0–100 — mesures de proximité de \"\n                               \"Statistique Canada.\")\n            y -= 12\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch.get(\"classe\", 5) <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_FG, GREEN_BG, GREEN_FG))\n            elif ch.get(\"classe\", 5) >= 7:\n                ec = (f\" (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\")\n                      if ch.get(\"ecart\") is not None else \"\")\n                badges_q.append((f\"Îlot de chaleur{ec}\",\n                                 AMBER_FG, AMBER_BG, AMBER_FG))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"])\n                        / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} \"\n                             \"la moyenne canadienne\", INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m \"\n                             \"(12 mois)\", INK2, SURFACE, INK))\n        if badges_q:\n            x = M\n            for txt, fg, bg, stk in badges_q:\n                x = s.pilule(x, y - 8, txt, fg, bg, stk)\n            y -= 26\n        y -= 6\n\n    # --- références : QR + courtier + source ------------------------------------------------\n    y = place(y, 140)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.immo-ka.com/propriete/{uid}\",\n                     box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    yy = y - 14\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    courtier = l.get(\"broker_name\") or d.get(\"Courtier inscripteur\")\n    if courtier:\n        ligne = f\"Courtier : {courtier}\"\n        if l.get(\"broker_phone\"):\n            ligne += f\" · {l['broker_phone']}\"\n        c.drawString(M + 82, yy, ligne[:95])\n        yy -= 13\n    c.drawString(M + 82, yy, f\"Agence : {src_name}\")\n    yy -= 14\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Annonce originale :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy,\n                 (l.get(\"url\") or d.get(\"listing_origin_url\") or \"\")[:90])\n    yy -= 14\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Fiche à jour en ligne (scannez le code) :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")\n    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n"}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01U5sBD3zr2uV37XxiByz2t7","type":"tool_result","content":"File created successfully at: /Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"3825a590-ab02-42c8-bf53-34579bd3acd8","timestamp":"2026-08-24T21:43:12.865Z","tool_use_result":{"type":"create","filePath":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","content":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdffiche.py : fiche PDF complète d'une propriété — reflète TOUS les blocs de\n#   la fiche web : prix + verdict Vrai-Prix, photos, description, inclusions,\n#   caractéristiques, pièces, analyse Vrai-Prix (jauge + rôle foncier),\n#   historique de prix, inondation, qualité de l'air, coût d'électricité,\n#   commerces/transport, essence, quartier, références + QR.\n#   Style « éditorial sharp » (bordures encre, ombres décalées dures, tuiles\n#   KPI, micro-étiquettes Courier) — palette du site : crème #f5f3ee, encre\n#   #141814, accent cerise #e23744. Pagination dynamique : saut_page()/place(),\n#   aucune section coupée entre deux pages.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Immo-Ka (identité officielle du site) ----------------------------\nPAPER = HexColor(\"#f5f3ee\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#141814\")\nINK2 = HexColor(\"#4d5551\")\nINK3 = HexColor(\"#8b928c\")\nACCENT = HexColor(\"#e23744\")        # cerise\nACCENT_SOFT = HexColor(\"#fbe0e2\")\nACCENT_DEEP = HexColor(\"#a8232e\")\nGREEN_FG = HexColor(\"#1c5c41\")      # verdict « sous l'estimation » (site .vp-under)\nGREEN_BG = HexColor(\"#e2f0e6\")\nRED_FG = HexColor(\"#8a2b2b\")        # verdict « au-dessus » (site .vp-over)\nRED_BG = HexColor(\"#f6e2e2\")\nAMBER_FG = HexColor(\"#8a5a12\")\nAMBER_BG = HexColor(\"#faf0dc\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"ImmoKaBot/1.0 (+https://www.immo-ka.com; contact@spboucher.ai)\"\n\nNBSP = \" \"     # espace fine insécable (mise en forme française)\n\n# clés de details jamais affichées (mêmes exclusions que la fiche web)\nDETAIL_HIDDEN = {\n    \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n    \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n    \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n}\n\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"\n               \"environnement) fournis à titre indicatif, sans valeur légale.\")\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _fmt_mois(d) -> str:\n    \"\"\"« 2017-11-04 » ou epoch → « novembre 2017 » (sinon valeur telle quelle).\"\"\"\n    if d is None:\n        return \"—\"\n    try:\n        if isinstance(d, (int, float)):\n            st = time.localtime(d)\n            return f\"{_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n        an, mois = str(d)[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return str(d)\n\n\ndef _fmt_jour(ts: float) -> str:\n    \"\"\"epoch → « 4 novembre 2024 » (« 1er » pour le premier du mois).\"\"\"\n    st = time.localtime(ts)\n    j = \"1er\" if st.tm_mday == 1 else str(st.tm_mday)\n    return f\"{j} {_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à plusieurs dizaines de Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Immo·Ka : « Immo » encre + boîte encre avec « Ka » cerise.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Immo\")\n        w = c.stringWidth(\"Immo\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6, titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.IMMO-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=ACCENT, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        # carré accent à ombre dure (signature de la passe éditoriale du site)\n        c.setFillColor(INK)\n        c.rect(M + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.rect(M, y + 0.5, 9, 9, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M + 16, y, texte)\n        c.setStrokeColor(ACCENT)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M + 16, y - 4, M + 16 + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def nb_lignes(self, texte: str, largeur: float, taille: float = 8.5,\n                  max_lignes: int = 100) -> int:\n        \"\"\"Nombre de lignes qu'occupera `paragraphe` (pour réserver la place).\"\"\"\n        c = self.c\n        mots = texte.split()\n        ligne = \"\"\n        n = 1\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                n += 1\n                ligne = mot\n                if n >= max_lignes:\n                    break\n        return min(n, max_lignes)\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\ndef _titre_droite(c, y, x, texte):\n    c.setFillColor(INK)\n    c.rect(x + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n    c.setFillColor(ACCENT)\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.rect(x, y + 0.5, 9, 9, stroke=1, fill=1)\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x + 16, y, texte)\n    c.setStrokeColor(ACCENT)\n    c.setLineWidth(3)\n    c.line(x + 16, y - 4, x + 16 + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\ndef _verdict_vraiprix(price, vp):\n    \"\"\"Pastille verdict identique au site : ±% vs estimation, masqué si\n    |pct| > 60 (prix non comparable : terrain, loyer…).\"\"\"\n    if not price or not vp or vp.get(\"value\") in (None, 0):\n        return None\n    pct = round((price - vp[\"value\"]) / vp[\"value\"] * 100)\n    if abs(pct) > 60:\n        return None\n    if pct >= 1:\n        return (f\"▲{NBSP}{pct}{NBSP}% au-dessus de l'estimation\",\n                RED_FG, RED_BG, ACCENT_DEEP)\n    if pct <= -1:\n        return (f\"▼{NBSP}{abs(pct)}{NBSP}% sous l'estimation\",\n                GREEN_FG, GREEN_BG, GREEN_FG)\n    return (f\"≈{NBSP}Prix aligné sur l'estimation\", INK2, SURFACE, INK)\n\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche de la propriété `uid` — reflète tous les blocs de la\n    fiche web ; None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"features\"] = json.loads(l.get(\"features\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    try:\n        l[\"vraiprix\"] = json.loads(l[\"vraiprix\"]) or None if l.get(\"vraiprix\") else None\n    except (ValueError, TypeError):\n        l[\"vraiprix\"] = None\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n        (uid,)).fetchall()]\n    quartier = None\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n    if geo:\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        try:\n            quartier = qmod.fiche_quartier(\n                lat, lng, l.get(\"city\") or \"\",\n                dauid if dauid and dauid != \"hors-zone\" else None)\n        except Exception:\n            quartier = None\n    try:\n        reg = json.loads(\n            (db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n        src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]),\n                        l[\"source\"])\n    except Exception:\n        src_name = l[\"source\"]\n    con.close()\n\n    # --- enrichissements (mêmes modules que les blocs de la fiche web) -------\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    d = l[\"details\"]\n    vp = l.get(\"vraiprix\")\n    location = (d.get(\"transaction\") == \"location\")\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Immo-Ka — {l.get('address') or l.get('title') or uid}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche propriété\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche propriété · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- héro : prix + pastille verdict Vrai-Prix -----------------------------\n    prix_txt = (_fmt_money(l[\"price\"]) + (f\"{NBSP}/ mois\" if location else \"\")) \\\n        if l.get(\"price\") else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    verd = _verdict_vraiprix(l.get(\"price\"), vp)\n    if verd:\n        txt, fg, bg, stk = verd\n        bx = s.pilule(bx, y - 22, txt, fg, bg, stk)\n    if l.get(\"property_type\"):\n        s.pilule(bx, y - 22, l[\"property_type\"], ACCENT, INK)\n    y -= 44\n\n    # --- titre + adresse -------------------------------------------------------\n    titre = l.get(\"address\") or l.get(\"title\") or \"\"\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, titre[:80])\n    y -= 14\n    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    if l.get(\"title\") and l.get(\"title\") != titre:\n        loc = f\"{l['title']} — {loc}\" if loc else l[\"title\"]\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés (mêmes specs que le héro du site) --------------------------\n    chips = []\n    if l.get(\"bedrooms\") is not None:\n        chips.append(f\"{l['bedrooms']}{NBSP}ch.\")\n    if l.get(\"bathrooms\") is not None:\n        chips.append(f\"{l['bathrooms']}{NBSP}sdb\")\n    if l.get(\"powder_rooms\"):\n        chips.append(f\"{l['powder_rooms']}{NBSP}s. d'eau\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"lot_sqft\"):\n        chips.append(f\"Terrain {round(l['lot_sqft']):,}\".replace(\",\", NBSP)\n                     + f\"{NBSP}pi²\")\n    if l.get(\"year_built\"):\n        chips.append(f\"Construit en {l['year_built']}\")\n    if l.get(\"price\") and l.get(\"area_sqft\") and l[\"area_sqft\"] > 200:\n        chips.append(f\"{round(l['price'] / l['area_sqft'])}{NBSP}$/pi²\")\n    if l.get(\"mls\"):\n        chips.append(f\"MLS {l['mls']}\")\n    if l.get(\"first_seen\"):\n        dom = max(0, int((time.time() - l[\"first_seen\"]) // 86400))\n        chips.append(f\"{dom}{NBSP}j sur Immo-Ka\")\n    x = M\n    for chip in chips[:8]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes ---------------------------------------\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- baisse/hausse de prix (bandeau, comme le site) -------------------------\n    ph = [p for p in price_history if p.get(\"price\")]\n    if len(ph) >= 2 and ph[0][\"price\"] != ph[1][\"price\"]:\n        y = place(y, 34)\n        de, a = ph[1][\"price\"], ph[0][\"price\"]\n        bas = a < de\n        c.setFillColor(GREEN_BG if bas else RED_BG)\n        c.setStrokeColor(GREEN_FG if bas else RED_FG)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 20, PAGE_W - 2 * M, 24, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 9)\n        c.setFillColor(GREEN_FG if bas else RED_FG)\n        c.drawString(M + 10, y - 12,\n                     f\"{'▼' if bas else '▲'} Prix passé de {_fmt_money(de)} à \"\n                     f\"{_fmt_money(a)} le {_fmt_jour(ph[0]['ts'])}\")\n        y -= 34\n\n    # --- description | inclusions (2 colonnes, comme le site) -------------------\n    col_g = PAGE_W * 0.56 - M\n    feats = [str(f) for f in (l.get(\"features\") or []) if f]\n    desc = (l.get(\"description\") or \"\").strip()\n    if desc or feats:\n        besoin = 30 + max(\n            s.nb_lignes(desc, col_g, max_lignes=26) * 11.5 if desc else 0,\n            min(len(feats), 18) * 12)\n        y = place(y, min(besoin, 340))\n        y_desc = y\n        if desc:\n            y_desc = s.titre_section(y, \"Description\")\n            y_desc = s.paragraphe(M, y_desc, desc, col_g, max_lignes=26)\n        y_incl = y\n        if feats:\n            xr = M + col_g + 24\n            y_incl = _titre_droite(c, y, xr, \"Inclusions et caractéristiques\")\n            c.setFont(\"Helvetica\", 8.5)\n            for f in feats[:18]:\n                c.setFillColor(ACCENT_DEEP)\n                c.drawString(xr, y_incl, \"✓\")\n                c.setFillColor(INK2)\n                c.drawString(xr + 12, y_incl, f[:42])\n                y_incl -= 12\n            if len(feats) > 18:\n                c.setFillColor(INK3)\n                c.drawString(xr + 12, y_incl, f\"+ {len(feats) - 18} autres…\")\n                y_incl -= 12\n        y = min(y_desc, y_incl) - 16\n\n    # --- caractéristiques (details, mêmes exclusions que le site) ---------------\n    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]\n    if dets:\n        col_w = PAGE_W - 2 * M\n        besoin = 40 + sum(\n            s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4) * 11 + 3\n            for _, v in dets)\n        y = place(y, min(besoin, 420))\n        y = s.titre_section(y, \"Caractéristiques\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"DÉTAIL\")\n        c.drawString(M + 180, y, \"VALEUR\")\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 12\n        for i, (k, v) in enumerate(dets):\n            nl = s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4)\n            y = place(y, nl * 11 + 6)\n            if i % 2 == 0:   # zébrage éditorial\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - (nl - 1) * 11 - 3, col_w + 6, nl * 11 + 2,\n                       stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, k[:38])\n            y = s.paragraphe(M + 180, y, v, col_w - 190, taille=8,\n                             interligne=11, max_lignes=4)\n            y -= 3\n        y -= 10\n\n    # --- pièces (details.pieces) -------------------------------------------------\n    pieces = d.get(\"pieces\") or []\n    if isinstance(pieces, list) and pieces:\n        rows = pieces[:24]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Pièces\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"PIÈCE\", 0), (\"NIVEAU\", 190), (\"DIMENSIONS\", 330),\n                          (\"REVÊTEMENT\", 430)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            if i % 2 == 0:\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - 3, PAGE_W - 2 * M + 6, 12, stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, str(p.get(\"nom\") or \"—\")[:36])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M + 190, y, str(p.get(\"niveau\") or \"—\")[:24])\n            c.drawString(M + 330, y, str(p.get(\"dimensions\") or \"—\")[:18])\n            c.drawString(M + 430, y, str(p.get(\"revetement\") or \"—\")[:22])\n            y -= 12\n        y -= 10\n\n    # --- analyse Vrai-Prix (valeur, jauge, rôle foncier) -------------------------\n    if vp and vp.get(\"value\") is not None:\n        role = [(lab, vp.get(k)) for k, lab in\n                ((\"valeur_role\", \"Évaluation au rôle\"),\n                 (\"valeur_terrain\", \"Valeur du terrain\"),\n                 (\"valeur_batiment\", \"Valeur du bâtiment\"))\n                if vp.get(k)]\n        besoin = 96 + (56 if vp.get(\"low\") is not None else 0) \\\n            + (62 if role else 0) + 18\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Analyse Vrai-Prix\")\n        val_txt = _fmt_money(round(vp[\"value\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, val_txt)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(val_txt, \"Helvetica-Bold\", 17) + 8,\n                     y - 10, \"— valeur marchande estimée par le modèle Vrai-Prix\")\n        meta = []\n        if vp.get(\"confidence\"):\n            meta.append(f\"confiance {vp['confidence']}\"\n                        + (f\" ({vp['confidence_pct']}{NBSP}%)\"\n                           if vp.get(\"confidence_pct\") else \"\"))\n        if vp.get(\"annee_construction_role\"):\n            meta.append(f\"construction {vp['annee_construction_role']} (rôle)\")\n        if vp.get(\"superficie_terrain_role_m2\"):\n            meta.append(\n                f\"terrain {round(vp['superficie_terrain_role_m2']):,}\".replace(\",\", NBSP)\n                + f\"{NBSP}m² (rôle)\")\n        if meta:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK3)\n            c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        verd = _verdict_vraiprix(l.get(\"price\"), vp)\n        if verd:\n            txt, fg, bg, stk = verd\n            s.pilule(M, y - 48, txt, fg, bg, stk)\n        y -= 58\n        lo, hi = vp.get(\"low\"), vp.get(\"high\")\n        if lo is not None and hi is not None and hi > lo:\n            larg = PAGE_W - 2 * M\n            base = y - 22\n            span = hi - lo\n            c.setFillColor(ACCENT_SOFT)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.roundRect(M, base, larg, 12, 6, stroke=1, fill=1)\n\n            def _pos(v):\n                return M + larg * max(0.02, min(0.98, (v - lo) / span))\n\n            px = _pos(vp[\"value\"])\n            c.setStrokeColor(ACCENT_DEEP)\n            c.setLineWidth(2.5)\n            c.line(px, base - 3, px, base + 15)\n            c.setFont(\"Courier-Bold\", 6.5)\n            c.setFillColor(ACCENT_DEEP)\n            c.drawString(min(max(px - 30, M), PAGE_W - M - 100), base + 18,\n                         f\"ESTIMATION · {_fmt_money(round(vp['value']))}\")\n            if l.get(\"price\") and lo * 0.7 <= l[\"price\"] <= hi * 1.3:\n                ax = _pos(l[\"price\"])\n                c.setStrokeColor(INK)\n                c.setLineWidth(2)\n                c.line(ax, base - 3, ax, base + 15)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(INK)\n                c.drawString(min(max(ax - 30, M), PAGE_W - M - 116), base - 12,\n                             f\"PRIX DEMANDÉ · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 24, _fmt_money(round(lo)))\n            c.drawRightString(PAGE_W - M, base - 24,\n                              _fmt_money(round(hi)) + \" — fourchette d'estimation\")\n            y = base - 36\n        if role:\n            tw = (PAGE_W - 2 * M - (len(role) - 1) * 10) / len(role)\n            for i, (lab, v) in enumerate(role):\n                s.tuile(M + i * (tw + 10), y - 44, tw, 38,\n                        _fmt_money(round(v)), lab)\n            y -= 56\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Estimation automatisée Vrai-Prix (vrai-prix.ca) \"\n                               \"croisée au rôle d'évaluation foncière — ne \"\n                               \"remplace pas une évaluation professionnelle.\")\n        y -= 16\n\n    # --- historique de prix -------------------------------------------------------\n    if len(ph) >= 2:\n        rows = ph[:8]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Historique du prix demandé\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"DATE\", 0), (\"PRIX AFFICHÉ\", 190), (\"VARIATION\", 330)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            prec = rows[i + 1][\"price\"] if i + 1 < len(rows) else None\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, _fmt_jour(p[\"ts\"]))\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M + 190, y, _fmt_money(p[\"price\"]))\n            if prec and p.get(\"price\"):\n                delta = p[\"price\"] - prec\n                c.setFont(\"Courier-Bold\", 7.5)\n                c.setFillColor(GREEN_FG if delta < 0\n                               else (RED_FG if delta > 0 else INK3))\n                sig = \"▼\" if delta < 0 else (\"▲\" if delta > 0 else \"=\")\n                c.drawString(M + 330, y,\n                             f\"{sig} {_fmt_money(abs(delta))}\" if delta else \"=\")\n            y -= 12\n        y -= 10\n\n    # --- risque d'inondation (BDZI) ------------------------------------------------\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\" if sev\n                     else \"En zone inondable\", RED_FG, RED_BG, RED_FG)\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     AMBER_FG, AMBER_BG, AMBER_FG)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_FG, GREEN_BG, GREEN_FG)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z.get('type') or 'Zone'} — à {z.get('distance_m')} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- qualité de l'air (RSQAQ) ---------------------------------------------------\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"de la propriété — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = (f\"{m_['moyenne']:.1f}\".replace(\".\", \",\")\n                   if m_.get(\"moyenne\") is not None else \"—\")\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- coût d'électricité (Hydro-Québec) -------------------------------------------\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += (f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP)\n                      + f\"{NBSP}kWh/an\")\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- commerces et transport --------------------------------------------------------\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:12]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- essence à proximité --------------------------------------------------------------\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- le quartier -------------------------------------------------------------------------\n    if quartier and (quartier.get(\"demographie\") or quartier.get(\"proximite\")\n                     or quartier.get(\"chaleur\") or quartier.get(\"crime\")):\n        demo = quartier.get(\"demographie\") or {}\n        prox = quartier.get(\"proximite\") or {}\n        prox_aff = [(k, lab) for k, lab in PROX_LABELS\n                    if isinstance(prox.get(k), (int, float))]\n        besoin = 44 + (94 if demo else 0) + len(prox_aff) * 14 + 44\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Le quartier\")\n        c.setFont(\"Helvetica\", 7.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"Secteur immédiat de la propriété (aire de diffusion \"\n                           \"du recensement 2021, ± 500 habitants).\")\n        y -= 14\n        if demo:\n            tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                      (\"Ménages locataires\",\n                       f\"{round(demo['pct_locataires'])}{NBSP}%\"\n                       if demo.get(\"pct_locataires\") is not None else \"—\"),\n                      (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                      (\"Âge médian\",\n                       f\"{round(demo['age_median'])} ans\"\n                       if demo.get(\"age_median\") is not None else \"—\"),\n                      (\"Français à la maison\",\n                       f\"{round(demo['pct_francais'])}{NBSP}%\"\n                       if demo.get(\"pct_francais\") is not None else \"—\"),\n                      (\"Diplôme universitaire\",\n                       f\"{round(demo['pct_univ'])}{NBSP}%\"\n                       if demo.get(\"pct_univ\") is not None else \"—\")]\n            tw = (PAGE_W - 2 * M - 20) / 3\n            for i, (lab, val) in enumerate(tuiles):\n                s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                        tw, 38, val, lab)\n            y -= 34 + 44 + 16\n        for k, lab in prox_aff:\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(ACCENT)\n            c.roundRect(bx0, y - 1, max(3, bw_ * v), 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        if prox_aff:\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, y, \"Accessibilité 0–100 — mesures de proximité de \"\n                               \"Statistique Canada.\")\n            y -= 12\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch.get(\"classe\", 5) <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_FG, GREEN_BG, GREEN_FG))\n            elif ch.get(\"classe\", 5) >= 7:\n                ec = (f\" (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\")\n                      if ch.get(\"ecart\") is not None else \"\")\n                badges_q.append((f\"Îlot de chaleur{ec}\",\n                                 AMBER_FG, AMBER_BG, AMBER_FG))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"])\n                        / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} \"\n                             \"la moyenne canadienne\", INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m \"\n                             \"(12 mois)\", INK2, SURFACE, INK))\n        if badges_q:\n            x = M\n            for txt, fg, bg, stk in badges_q:\n                x = s.pilule(x, y - 8, txt, fg, bg, stk)\n            y -= 26\n        y -= 6\n\n    # --- références : QR + courtier + source ------------------------------------------------\n    y = place(y, 140)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.immo-ka.com/propriete/{uid}\",\n                     box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    yy = y - 14\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    courtier = l.get(\"broker_name\") or d.get(\"Courtier inscripteur\")\n    if courtier:\n        ligne = f\"Courtier : {courtier}\"\n        if l.get(\"broker_phone\"):\n            ligne += f\" · {l['broker_phone']}\"\n        c.drawString(M + 82, yy, ligne[:95])\n        yy -= 13\n    c.drawString(M + 82, yy, f\"Agence : {src_name}\")\n    yy -= 14\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Annonce originale :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy,\n                 (l.get(\"url\") or d.get(\"listing_origin_url\") or \"\")[:90])\n    yy -= 14\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Fiche à jour en ligne (scannez le code) :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")\n    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n","structuredPatch":[],"originalFile":null,"userModified":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJXHtJMTMrPHETgB8c7","type":"message","role":"assistant","content":[{"type":"text","text":"Générateur écrit. J'ajoute l'endpoint dans web.py."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":17955,"cache_read_input_tokens":120899,"cache_creation":{"ephemeral_5m_input_tokens":17955,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"b6c141d1-2595-4917-94ad-17301bc65ba2"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJXHtJMTMrPHETgB8c7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Jm6Uv6W35pbJQCQk4DtgCA","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/web.py","offset":176,"limit":40},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":17955,"cache_read_input_tokens":120899,"cache_creation":{"ephemeral_5m_input_tokens":17955,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"53fad99a-6c2c-4629-8109-ed250b7c4416"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Jm6Uv6W35pbJQCQk4DtgCA","type":"tool_result","content":"176\t    from . import inondation\n177\t    d = inondation.lookup(lat, lng)\n178\t    if d is None:\n179\t        raise HTTPException(404, \"Base des zones inondables non disponible\")\n180\t    return d\n181\t\n182\t\n183\t@app.get(\"/api/listings/{uid}\")\n184\tdef get_listing(uid: str):\n185\t    con = db.connect()\n186\t    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n187\t    d = None\n188\t    if row is not None:\n189\t        d = _row_to_dict(row)\n190\t        # historique de prix (baisses/hausses du prix demandé)\n191\t        d[\"price_history\"] = [dict(r) for r in con.execute(\n192\t            \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n193\t            (uid,)).fetchall()]\n194\t        # commodités de proximité (cache par immeuble, voir immoka/poi.py)\n195\t        if d.get(\"lat\") is not None and d.get(\"lng\") is not None:\n196\t            key = f\"{round(d['lat'], 4)},{round(d['lng'], 4)}\"\n197\t            poi_row = con.execute(\n198\t                \"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n199\t            d[\"poi\"] = json.loads(poi_row[\"pois\"]) if poi_row else []\n200\t        else:\n201\t            d[\"poi\"] = []\n202\t        # statistiques de quartier (recensement, proximité, chaleur, criminalité)\n203\t        from . import quartier\n204\t        dauid = d.get(\"dauid\")\n205\t        d[\"quartier\"] = quartier.fiche_quartier(\n206\t            d.get(\"lat\"), d.get(\"lng\"), d.get(\"city\") or \"\",\n207\t            dauid if dauid and dauid != \"hors-zone\" else None)\n208\t    con.close()\n209\t    if d is None:\n210\t        raise HTTPException(404, \"Propriété introuvable\")\n211\t    return d\n212\t\n213\t\n214\t@app.get(\"/api/listings.geojson\")\n215\tdef listings_geojson("}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"65cbc576-6220-4702-8615-8450e2492c5c","timestamp":"2026-08-24T21:43:17.937Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/immoka/web.py","content":"    from . import inondation\n    d = inondation.lookup(lat, lng)\n    if d is None:\n        raise HTTPException(404, \"Base des zones inondables non disponible\")\n    return d\n\n\n@app.get(\"/api/listings/{uid}\")\ndef get_listing(uid: str):\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    d = None\n    if row is not None:\n        d = _row_to_dict(row)\n        # historique de prix (baisses/hausses du prix demandé)\n        d[\"price_history\"] = [dict(r) for r in con.execute(\n            \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n            (uid,)).fetchall()]\n        # commodités de proximité (cache par immeuble, voir immoka/poi.py)\n        if d.get(\"lat\") is not None and d.get(\"lng\") is not None:\n            key = f\"{round(d['lat'], 4)},{round(d['lng'], 4)}\"\n            poi_row = con.execute(\n                \"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n            d[\"poi\"] = json.loads(poi_row[\"pois\"]) if poi_row else []\n        else:\n            d[\"poi\"] = []\n        # statistiques de quartier (recensement, proximité, chaleur, criminalité)\n        from . import quartier\n        dauid = d.get(\"dauid\")\n        d[\"quartier\"] = quartier.fiche_quartier(\n            d.get(\"lat\"), d.get(\"lng\"), d.get(\"city\") or \"\",\n            dauid if dauid and dauid != \"hors-zone\" else None)\n    con.close()\n    if d is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    return d\n\n\n@app.get(\"/api/listings.geojson\")\ndef listings_geojson(","numLines":40,"startLine":176,"totalLines":624}}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"d028e551-3512-4cdb-95dc-98785a2a1e39","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJXev69v8jmg7gxcfmc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DrUu58xoYPV1jx7r9yG3Bm","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/web.py","old_string":"    con.close()\n    if d is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    return d\n\n\n@app.get(\"/api/listings.geojson\")","new_string":"    con.close()\n    if d is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    return d\n\n\n@app.get(\"/api/listings/{uid}/pdf\")\ndef listing_pdf(uid: str):\n    \"\"\"Fiche PDF complète de la propriété (immoka/pdffiche.py) — reflète tous\n    les blocs de la fiche web : prix + verdict Vrai-Prix, photos, description,\n    caractéristiques, pièces, analyses, environnement, quartier, QR.\"\"\"\n    from . import pdffiche\n    data = pdffiche.fiche_pdf(uid)\n    if data is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    safe = uid.replace(\":\", \"-\").replace(\"/\", \"-\")\n    return Response(\n        content=data, media_type=\"application/pdf\",\n        headers={\"Content-Disposition\":\n                 f'attachment; filename=\"immo-ka_fiche_{safe}.pdf\"'})\n\n\n@app.get(\"/api/listings.geojson\")"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":919,"cache_read_input_tokens":138854,"cache_creation":{"ephemeral_5m_input_tokens":919,"ephemeral_1h_input_tokens":0},"output_tokens":17,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"31511f75-fc03-45b6-8b93-70c0fc786ce1"}}
{"type":"permission_request","requestId":"f24362683b822a66","tool":"Edit","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/web.py","old_string":"    con.close()\n    if d is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    return d\n\n\n@app.get(\"/api/listings.geojson\")","new_string":"    con.close()\n    if d is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    return d\n\n\n@app.get(\"/api/listings/{uid}/pdf\")\ndef listing_pdf(uid: str):\n    \"\"\"Fiche PDF complète de la propriété (immoka/pdffiche.py) — reflète tous\n    les blocs de la fiche web : prix + verdict Vrai-Prix, photos, description,\n    caractéristiques, pièces, analyses, environnement, quartier, QR.\"\"\"\n    from . import pdffiche\n    data = pdffiche.fiche_pdf(uid)\n    if data is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    safe = uid.replace(\":\", \"-\").replace(\"/\", \"-\")\n    return Response(\n        content=data, media_type=\"application/pdf\",\n        headers={\"Content-Disposition\":\n                 f'attachment; filename=\"immo-ka_fiche_{safe}.pdf\"'})\n\n\n@app.get(\"/api/listings.geojson\")","replace_all":false}}
{"type":"permission_resolved","requestId":"f24362683b822a66","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DrUu58xoYPV1jx7r9yG3Bm","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/immo-ka/immoka/web.py has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"a4754ba8-054a-4d33-a396-81819c0bec4c","timestamp":"2026-08-24T21:44:21.722Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/immoka/web.py","oldString":"    con.close()\n    if d is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    return d\n\n\n@app.get(\"/api/listings.geojson\")","newString":"    con.close()\n    if d is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    return d\n\n\n@app.get(\"/api/listings/{uid}/pdf\")\ndef listing_pdf(uid: str):\n    \"\"\"Fiche PDF complète de la propriété (immoka/pdffiche.py) — reflète tous\n    les blocs de la fiche web : prix + verdict Vrai-Prix, photos, description,\n    caractéristiques, pièces, analyses, environnement, quartier, QR.\"\"\"\n    from . import pdffiche\n    data = pdffiche.fiche_pdf(uid)\n    if data is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    safe = uid.replace(\":\", \"-\").replace(\"/\", \"-\")\n    return Response(\n        content=data, media_type=\"application/pdf\",\n        headers={\"Content-Disposition\":\n                 f'attachment; filename=\"immo-ka_fiche_{safe}.pdf\"'})\n\n\n@app.get(\"/api/listings.geojson\")","originalFile":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de maisons à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# web.py : API FastAPI (JSON) + service du frontend statique (frontend/)\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport json\nimport threading\nimport time\nfrom pathlib import Path\n\nfrom fastapi import BackgroundTasks, Body, FastAPI, HTTPException, Query, Request\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom fastapi.middleware.gzip import GZipMiddleware\nfrom fastapi.responses import FileResponse, RedirectResponse, Response\nfrom fastapi.staticfiles import StaticFiles\n\nfrom . import auth, db, favorites, ingest, seo\nfrom . import stats as kastats\n\nROOT = Path(__file__).resolve().parent.parent\nSOURCES_PATH = ROOT / \"data\" / \"sources.json\"\n# Frontend : build Vite (React) si présent, sinon la page statique de secours.\nFRONTEND_DIST = ROOT / \"frontend\" / \"dist\"\nFRONTEND_DIR = FRONTEND_DIST if FRONTEND_DIST.exists() else ROOT / \"frontend\"\n\napp = FastAPI(title=\"Immo-Ka API\", version=\"0.1\",\n              description=\"Agrégateur de maisons à vendre — province de Québec\")\napp.add_middleware(CORSMiddleware, allow_origins=[\"*\"],\n                   allow_methods=[\"*\"], allow_headers=[\"*\"])\n# le GeoJSON complet pèse ~20 Mo — la compression est vitale pour la carte\napp.add_middleware(GZipMiddleware, minimum_size=1024)\n# connexion Google (compte Groupe-Ka partagé avec Lou-Ka) — voir immoka/auth.py\napp.include_router(auth.router)\n# favoris ♥ — magasin central au hub Groupe KA, voir immoka/favorites.py\napp.include_router(favorites.router)\n\n_sync_lock = threading.Lock()\n\n# Déduplication de la famille RE/MAX : le flux central (remax_quebec) et les\n# ~42 connecteurs de sous-agences (remax_ag_*) décrivent les MÊMES inscriptions,\n# identifiées de façon unique par leur numéro Centris (= external_id). On masque\n# donc toute fiche de sous-agence dès qu'une fiche de plus haute priorité existe\n# (le central enrichi d'abord, puis la sous-agence au plus petit uid). Les fiches\n# du central et des autres agences ne sont jamais masquées. → aucun double-comptage,\n# et les sous-agences prennent le relais automatiquement si le central disparaît.\n# Déduplication PRÉ-CALCULÉE : la colonne `dup_hidden` (remplie par\n# db.refresh_dedup après chaque sync) marque les doublons de sous-agences. La\n# lecture est ainsi instantanée (index) au lieu d'un sous-select corrélé par\n# ligne (~300 s sur 75 k lignes). Voir db.refresh_dedup pour la règle.\n# + `published=1` : seuil de qualité minimum (voir immoka/quality.py) — une\n# annonce sans prix plausible, sans ville, sans type de bien ou sans contenu\n# exploitable part en QUARANTAINE (grille, carte, stats, sources) jusqu'à\n# complétion par les prochains cycles. La fiche détail par uid reste accessible.\nDEDUP_CLAUSE = \" AND dup_hidden=0 AND published=1\"\n\n\ndef _row_to_dict(row) -> dict:\n    d = dict(row)\n    d[\"features\"] = json.loads(d.get(\"features\") or \"[]\")\n    d[\"images\"] = json.loads(d.get(\"images\") or \"[]\")\n    d[\"details\"] = json.loads(d.get(\"details\") or \"{}\")\n    if d.get(\"vraiprix\"):\n        try:\n            d[\"vraiprix\"] = json.loads(d[\"vraiprix\"]) or None\n        except (ValueError, TypeError):\n            d[\"vraiprix\"] = None\n    # jours sur le marché (depuis la 1re observation par Immo-Ka ; la date\n    # d'inscription de la source, si connue, est dans details.listed_at)\n    if d.get(\"first_seen\"):\n        d[\"days_on_market\"] = max(0, int((time.time() - d[\"first_seen\"]) // 86400))\n    return d\n\n\n@app.get(\"/api/listings\")\ndef list_listings(\n    city: str | None = None,\n    sector: str | None = None,\n    region: str | None = None,\n    property_type: str | None = None,\n    source: str | None = None,\n    price_max: float | None = None,\n    price_min: float | None = None,\n    bedrooms_min: int | None = None,\n    bathrooms_min: int | None = None,\n    area_min: float | None = None,       # superficie habitable minimale (pi²)\n    q: str | None = None,\n    active: int = 1,\n    sort: str = \"price_asc\",             # price_asc | price_desc | recent\n    limit: int = Query(500, le=2000),\n    offset: int = 0,\n):\n    con = db.connect()\n    sql = \"SELECT * FROM listings WHERE 1=1\"\n    args: list = []\n    if active in (0, 1):\n        sql += \" AND active=?\"; args.append(active)\n    if city:\n        sql += \" AND city=?\"; args.append(city)\n    if sector:\n        sql += \" AND sector LIKE ?\"; args.append(f\"%{sector}%\")\n    if region:\n        sql += \" AND region=?\"; args.append(region)\n    if property_type:\n        sql += \" AND property_type=?\"; args.append(property_type)\n    if source:\n        sql += \" AND source=?\"; args.append(source)\n    if price_max is not None:\n        sql += \" AND price IS NOT NULL AND price<=?\"; args.append(price_max)\n    if price_min is not None:\n        sql += \" AND price IS NOT NULL AND price>=?\"; args.append(price_min)\n    if bedrooms_min is not None:\n        sql += \" AND bedrooms IS NOT NULL AND bedrooms>=?\"; args.append(bedrooms_min)\n    if bathrooms_min is not None:\n        sql += \" AND bathrooms IS NOT NULL AND bathrooms>=?\"; args.append(bathrooms_min)\n    if area_min is not None:\n        sql += \" AND area_sqft IS NOT NULL AND area_sqft>=?\"; args.append(area_min)\n    if q:\n        sql += \" AND (title LIKE ? OR address LIKE ? OR city LIKE ? OR mls LIKE ?)\"\n        args += [f\"%{q}%\"] * 4\n    sql += DEDUP_CLAUSE\n    total = con.execute(f\"SELECT COUNT(*) c FROM ({sql})\", args).fetchone()[\"c\"]\n    order = {\n        \"price_asc\": \" ORDER BY price IS NULL, price ASC\",\n        \"price_desc\": \" ORDER BY price IS NULL, price DESC\",\n        \"recent\": \" ORDER BY first_seen DESC\",\n    }.get(sort, \" ORDER BY price IS NULL, price ASC\")\n    sql += order + \" LIMIT ? OFFSET ?\"\n    args += [limit, offset]\n    rows = [_row_to_dict(r) for r in con.execute(sql, args).fetchall()]\n    con.close()\n    return {\"total\": total, \"count\": len(rows), \"listings\": rows}\n\n\n@app.get(\"/api/hydro\")\ndef hydro_at(adresse: str, solve: int = 0, uid: str | None = None,\n             lat: float | None = None, lng: float | None = None):\n    \"\"\"Estimation du coût d'électricité à une adresse (Hydro-Québec).\n    Par défaut cache seulement (aucun captcha) ; solve=1 force le calcul.\n    uid/lat/lng permettent la lecture cache par annonce et la dérivation du\n    code postal (adresses sans CP).\"\"\"\n    from . import hydro\n    return hydro.estimate(adresse=adresse, uid=uid, lat=lat, lng=lng,\n                          solve=bool(solve))\n\n\n@app.get(\"/api/commerces\")\ndef commerces_at(lat: float, lng: float):\n    \"\"\"Grands commerces + métro/bus les plus proches (Mapbox / OSM).\"\"\"\n    from . import commerces\n    return commerces.nearby(lat, lng)\n\n\n@app.get(\"/api/air\")\ndef air_at(lat: float, lng: float):\n    \"\"\"Qualité de l'air : station RSQAQ la plus proche (MELCCFP).\"\"\"\n    from . import air\n    d = air.lookup(lat, lng)\n    if d is None:\n        raise HTTPException(404, \"Base qualité de l'air non disponible\")\n    return d\n\n\n@app.get(\"/api/gaz\")\ndef gaz_at(lat: float, lng: float):\n    \"\"\"Stations-service à proximité et prix courants (gazquebec.ca).\"\"\"\n    from . import gaz\n    return gaz.nearby(lat, lng)\n\n\n@app.get(\"/api/inondation\")\ndef inondation_at(lat: float, lng: float):\n    \"\"\"Risque d'inondation à l'adresse (BDZI, gouv. du Québec) —\n    bloc « Risque d'inondation » de la fiche.\"\"\"\n    from . import inondation\n    d = inondation.lookup(lat, lng)\n    if d is None:\n        raise HTTPException(404, \"Base des zones inondables non disponible\")\n    return d\n\n\n@app.get(\"/api/listings/{uid}\")\ndef get_listing(uid: str):\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    d = None\n    if row is not None:\n        d = _row_to_dict(row)\n        # historique de prix (baisses/hausses du prix demandé)\n        d[\"price_history\"] = [dict(r) for r in con.execute(\n            \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n            (uid,)).fetchall()]\n        # commodités de proximité (cache par immeuble, voir immoka/poi.py)\n        if d.get(\"lat\") is not None and d.get(\"lng\") is not None:\n            key = f\"{round(d['lat'], 4)},{round(d['lng'], 4)}\"\n            poi_row = con.execute(\n                \"SELECT pois FROM poi_cache WHERE coord_key=?\", (key,)).fetchone()\n            d[\"poi\"] = json.loads(poi_row[\"pois\"]) if poi_row else []\n        else:\n            d[\"poi\"] = []\n        # statistiques de quartier (recensement, proximité, chaleur, criminalité)\n        from . import quartier\n        dauid = d.get(\"dauid\")\n        d[\"quartier\"] = quartier.fiche_quartier(\n            d.get(\"lat\"), d.get(\"lng\"), d.get(\"city\") or \"\",\n            dauid if dauid and dauid != \"hors-zone\" else None)\n    con.close()\n    if d is None:\n        raise HTTPException(404, \"Propriété introuvable\")\n    return d\n\n\n@app.get(\"/api/listings.geojson\")\ndef listings_geojson(\n    city: str | None = None,\n    sector: str | None = None,\n    region: str | None = None,\n    property_type: str | None = None,\n    source: str | None = None,\n    price_max: float | None = None,\n    price_min: float | None = None,\n    bedrooms_min: int | None = None,\n    bathrooms_min: int | None = None,\n    area_min: float | None = None,\n    q: str | None = None,\n    bbox: str | None = None,\n    limit: int = Query(3000, le=8000),\n):\n    \"\"\"Propriétés géolocalisées (marqueurs de carte, champs allégés).\n\n    Ka Maps : `bbox=ouest,sud,est,nord` restreint au rectangle visible\n    (index idx_listings_geo) ; `limit` plafonne la réponse ; la collection\n    porte `totalGeocoded` (géolocalisées correspondantes) et\n    `totalMatching` (avec ou sans coordonnées). Mêmes filtres que\n    /api/listings pour que la carte suive exactement la recherche.\n    \"\"\"\n    con = db.connect()\n    sql = (\"SELECT uid, title, address, price, price_label, property_type,\"\n           \" bedrooms, bathrooms, source, city, sector, images, lat, lng, vraiprix,\"\n           \" json_extract(COALESCE(details,'{}'),'$.cover_thumb') AS cover_thumb\"\n           \" FROM listings WHERE active=1 AND lat IS NOT NULL AND lng IS NOT NULL\")\n    args: list = []\n    if bbox:\n        try:\n            west, south, east, north = (float(v) for v in bbox.split(\",\"))\n        except ValueError:\n            raise HTTPException(400, \"bbox attendu : ouest,sud,est,nord\")\n        sql += \" AND lat BETWEEN ? AND ? AND lng BETWEEN ? AND ?\"\n        args += [south, north, west, east]\n    if city:\n        sql += \" AND city=?\"; args.append(city)\n    if sector:\n        sql += \" AND sector LIKE ?\"; args.append(f\"%{sector}%\")\n    if region:\n        sql += \" AND region=?\"; args.append(region)\n    if property_type:\n        sql += \" AND property_type=?\"; args.append(property_type)\n    if source:\n        sql += \" AND source=?\"; args.append(source)\n    if price_max is not None:\n        sql += \" AND price IS NOT NULL AND price<=?\"; args.append(price_max)\n    if price_min is not None:\n        sql += \" AND price IS NOT NULL AND price>=?\"; args.append(price_min)\n    if bedrooms_min is not None:\n        sql += \" AND bedrooms IS NOT NULL AND bedrooms>=?\"; args.append(bedrooms_min)\n    if bathrooms_min is not None:\n        sql += \" AND bathrooms IS NOT NULL AND bathrooms>=?\"; args.append(bathrooms_min)\n    if area_min is not None:\n        sql += \" AND area_sqft IS NOT NULL AND area_sqft>=?\"; args.append(area_min)\n    if q:\n        sql += \" AND (title LIKE ? OR address LIKE ? OR city LIKE ? OR mls LIKE ?)\"\n        args += [f\"%{q}%\"] * 4\n    sql += DEDUP_CLAUSE\n\n    total_geo = con.execute(f\"SELECT COUNT(*) c FROM ({sql})\", args).fetchone()[\"c\"]\n    sql_all = sql.replace(\" AND lat IS NOT NULL AND lng IS NOT NULL\", \"\")\n    args_all = list(args)\n    if bbox:\n        sql_all = sql_all.replace(\" AND lat BETWEEN ? AND ? AND lng BETWEEN ? AND ?\", \"\")\n        del args_all[0:4]\n    total_all = con.execute(f\"SELECT COUNT(*) c FROM ({sql_all})\", args_all).fetchone()[\"c\"]\n\n    sql += \" ORDER BY price IS NULL, price ASC LIMIT ?\"\n    args.append(limit)\n    features = []\n    for r in con.execute(sql, args).fetchall():\n        images = json.loads(r[\"images\"] or \"[]\")\n        try:\n            vp_val = (json.loads(r[\"vraiprix\"]) or {}).get(\"value\")\n        except (TypeError, ValueError):\n            vp_val = None\n        features.append({\n            \"type\": \"Feature\",\n            \"geometry\": {\"type\": \"Point\", \"coordinates\": [r[\"lng\"], r[\"lat\"]]},\n            \"properties\": {\n                \"uid\": r[\"uid\"],\n                \"title\": None if r[\"address\"] else r[\"title\"],\n                \"address\": r[\"address\"],\n                \"price\": r[\"price\"], \"price_label\": r[\"price_label\"],\n                \"property_type\": r[\"property_type\"], \"bedrooms\": r[\"bedrooms\"],\n                \"bathrooms\": r[\"bathrooms\"], \"source\": r[\"source\"],\n                \"city\": r[\"city\"], \"sector\": r[\"sector\"],\n                # miniature légère pour le popup mobile si le connecteur en a une\n                \"image\": r[\"cover_thumb\"] or (images[0] if images else None),\n                \"vp\": vp_val,\n            },\n        })\n    con.close()\n    return {\"type\": \"FeatureCollection\", \"features\": features,\n            \"totalGeocoded\": total_geo, \"totalMatching\": total_all}\n\n\n@app.get(\"/api/facets\")\ndef facets(city: str | None = None):\n    \"\"\"Valeurs distinctes pour construire les filtres du frontend.\"\"\"\n    con = db.connect()\n    sector_sql = \"SELECT DISTINCT sector FROM listings WHERE active=1 AND sector<>''\"\n    sector_args: list = []\n    if city:\n        sector_sql += \" AND city=?\"\n        sector_args.append(city)\n    out = {\n        \"cities\": [r[\"city\"] for r in con.execute(\n            \"SELECT DISTINCT city FROM listings WHERE active=1 AND city<>'' ORDER BY city\")],\n        \"sectors\": [r[\"sector\"] for r in con.execute(\n            sector_sql + \" ORDER BY sector\", sector_args)],\n        \"property_types\": [r[\"property_type\"] for r in con.execute(\n            \"SELECT property_type FROM listings WHERE active=1\"\n            \" AND property_type<>''\" + DEDUP_CLAUSE +\n            \" GROUP BY property_type ORDER BY COUNT(*) DESC\")],\n        \"sources\": [dict(r) for r in con.execute(\n            \"SELECT source, COUNT(*) n FROM listings WHERE active=1\"\n            + DEDUP_CLAUSE + \" GROUP BY source ORDER BY n DESC\")],\n    }\n    con.close()\n    return out\n\n\n@app.get(\"/api/sources\")\ndef sources():\n    registry = json.loads(SOURCES_PATH.read_text(encoding=\"utf-8\"))[\"sources\"]\n    con = db.connect()\n    counts = {r[\"source\"]: r[\"n\"] for r in con.execute(\n        \"SELECT source, COUNT(*) n FROM listings WHERE active=1 GROUP BY source\")}\n    last = {r[\"source\"]: r[\"ts\"] for r in con.execute(\n        \"SELECT source, MAX(ts) ts FROM sync_log WHERE ok=1 GROUP BY source\")}\n    con.close()\n    for s in registry:\n        s[\"active_listings\"] = counts.get(s[\"id\"], 0)\n        s[\"last_sync\"] = last.get(s[\"id\"])\n    return {\"sources\": registry}\n\n\n# Rattachement d'une source à sa bannière (franchise) pour le regroupement.\n_FRANCHISES = [\n    (\"RE/MAX\", lambda s: s == \"remax_quebec\" or s.startswith(\"remax_ag_\")),\n    (\"Via Capitale\", lambda s: s == \"via_capitale\" or s.startswith(\"via_ag_\")),\n    (\"Century 21\", lambda s: s == \"century21\" or s.startswith(\"c21_ag_\")),\n    (\"Royal LePage\", lambda s: s == \"royal_lepage\"),\n    (\"Groupe Sutton\", lambda s: s == \"sutton\"),\n    (\"Keller Williams\", lambda s: s.startswith(\"kw_\")),\n    (\"DuProprio\", lambda s: s == \"duproprio\"),\n]\n\n\ndef _franchise_of(source: str, source_names: dict) -> str:\n    for name, match in _FRANCHISES:\n        if match(source):\n            return name\n    return source_names.get(source, source)   # agence indépendante = elle-même\n\n\n@app.get(\"/api/agencies\")\ndef agencies():\n    \"\"\"Arbre bannière → sous-agences (bureaux) avec le nombre d'inscriptions.\n\n    Alimente la page « Sources » de l'app : chaque bannière est éclatée par\n    sous-agence via le champ `agency` (bureau). Dédupliqué (n° Centris).\"\"\"\n    con = db.connect()\n    registry = json.loads(SOURCES_PATH.read_text(encoding=\"utf-8\"))[\"sources\"]\n    source_names = {s[\"id\"]: s[\"name\"] for s in registry}\n    rows = con.execute(\n        \"SELECT source, COALESCE(NULLIF(agency,''), '') agency, COUNT(*) n\"\n        \" FROM listings WHERE active=1\" + DEDUP_CLAUSE +\n        \" GROUP BY source, agency\").fetchall()\n    con.close()\n    tree: dict[str, dict] = {}\n    for r in rows:\n        fr = _franchise_of(r[\"source\"], source_names)\n        node = tree.setdefault(fr, {\"franchise\": fr, \"total\": 0, \"agencies\": {}})\n        node[\"total\"] += r[\"n\"]\n        # nom de sous-agence : le bureau (agency) sinon le nom de la source\n        label = r[\"agency\"] or source_names.get(r[\"source\"], r[\"source\"])\n        a = node[\"agencies\"].setdefault(label, {\"name\": label, \"count\": 0,\n                                                 \"sources\": set()})\n        a[\"count\"] += r[\"n\"]\n        a[\"sources\"].add(r[\"source\"])\n    out = []\n    for node in tree.values():\n        ags = sorted(node[\"agencies\"].values(), key=lambda x: -x[\"count\"])\n        for a in ags:\n            a[\"sources\"] = sorted(a[\"sources\"])\n        out.append({\"franchise\": node[\"franchise\"], \"total\": node[\"total\"],\n                    \"sub_agencies\": len(ags), \"agencies\": ags})\n    out.sort(key=lambda x: -x[\"total\"])\n    return {\"franchises\": out}\n\n\n@app.get(\"/api/stats\")\ndef stats(syncs_since_h: float | None = None):\n    con = db.connect()\n    row = con.execute(\n        \"\"\"SELECT COUNT(*) total,\n                  COUNT(DISTINCT source) sources,\n                  COUNT(DISTINCT city) cities,\n                  AVG(price) avg_price,\n                  MIN(price) min_price,\n                  MAX(price) max_price\n           FROM listings WHERE active=1\"\"\" + DEDUP_CLAUSE).fetchone()\n    # Fenêtre de synchronisations pour la supervision (moniteur central api-ka) :\n    # sans paramètre, comportement historique (20 dernières entrées) ; avec\n    # ?syncs_since_h=<heures>, toutes les entrées de la fenêtre (borne 4000) —\n    # évite les fausses alertes « stale » quand 100+ sources synchronisent\n    # plus de 20 fois entre deux sondages.\n    log: list[dict] = []\n    if syncs_since_h is not None:\n        cutoff = time.time() - syncs_since_h * 3600\n        log = [dict(r) for r in con.execute(\n            \"SELECT * FROM sync_log WHERE ts >= ?\"\n            \" ORDER BY ts DESC LIMIT 4000\", (cutoff,))]\n    if len(log) < 20:\n        log = [dict(r) for r in con.execute(\n            \"SELECT * FROM sync_log ORDER BY ts DESC LIMIT 20\")]\n\n    # --- écart prix demandé vs estimation Vrai-Prix, par bannière -----------\n    registry = json.loads(SOURCES_PATH.read_text(encoding=\"utf-8\"))[\"sources\"]\n    source_names = {s[\"id\"]: s[\"name\"] for s in registry}\n    deltas: dict[str, list[float]] = {}\n    for r in con.execute(\n            \"SELECT source, price,\"\n            \" CAST(json_extract(vraiprix, '$.value') AS REAL) vp\"\n            \" FROM listings WHERE active=1\" + DEDUP_CLAUSE +\n            \" AND vraiprix LIKE '%estimation%'\"):\n        if not r[\"vp\"] or r[\"vp\"] <= 0:\n            continue\n        d = (r[\"price\"] - r[\"vp\"]) / r[\"vp\"] * 100.0\n        if -80.0 <= d <= 300.0:   # coupe les aberrations (terrains, données sales)\n            deltas.setdefault(_franchise_of(r[\"source\"], source_names), []).append(d)\n    con.close()\n\n    def _agg(name: str, ds: list[float]) -> dict:\n        ds = sorted(ds)\n        n = len(ds)\n        med = ds[n // 2] if n % 2 else (ds[n // 2 - 1] + ds[n // 2]) / 2\n        return {\n            \"banniere\": name, \"n\": n,\n            \"median_delta_pct\": round(med, 1),\n            \"p25\": round(ds[n // 4], 1), \"p75\": round(ds[(3 * n) // 4], 1),\n            \"pct_sur10\": round(100 * sum(1 for d in ds if d > 10) / n, 1),\n            \"pct_juste\": round(100 * sum(1 for d in ds if -5 <= d <= 10) / n, 1),\n            \"pct_sous5\": round(100 * sum(1 for d in ds if d < -5) / n, 1),\n        }\n\n    tous = [d for ds in deltas.values() for d in ds]\n    vraiprix = {\n        \"ensemble\": _agg(\"Toutes bannières\", tous) if tous else None,\n        \"bannieres\": sorted(\n            (_agg(k, v) for k, v in deltas.items() if len(v) >= 30),\n            key=lambda x: -x[\"median_delta_pct\"]),\n    }\n    # qualité des données : complétude, quarantaine, anomalies (par connecteur)\n    from . import quality\n    con2 = db.connect()\n    try:\n        qualite = quality.summary(con2)\n    finally:\n        con2.close()\n    return {**dict(row), \"vraiprix\": vraiprix, \"qualite\": qualite,\n            \"recent_syncs\": log}\n\n\n# --- Module Stats commun Groupe KA (voir frontend/src/ka/stats/SPEC.md) ------\n@app.get(\"/api/stats/dashboard\")\ndef stats_dashboard(\n    period: str | None = Query(None),\n    from_: str | None = Query(None, alias=\"from\"),\n    to: str | None = Query(None),\n):\n    \"\"\"Tableau de bord analytique (contrat SPEC ka-stats, cache 5 min).\"\"\"\n    return kastats.dashboard(period, from_, to)\n\n\n@app.get(\"/api/stats/report\")\ndef stats_report(\n    period: str = Query(\"30j\"),\n    from_: str | None = Query(None, alias=\"from\"),\n    to: str | None = Query(None),\n    mode: str = Query(\"complet\"),\n):\n    \"\"\"Rapport PDF estampillé Groupe-KA (gabarit commun immoka/kapdf.py).\"\"\"\n    from . import kapdf\n    dash = kastats.dashboard(period, from_, to)\n    site = {\n        \"wordmark\": \"Immo·Ka\",\n        \"accent\": \"#e23744\",\n        \"domain\": \"www.immo-ka.com\",\n        \"tagline\": \"Agrégateur de propriétés à vendre — province de Québec\",\n    }\n    pdf = kapdf.GroupeKAReport(\n        site=site, dashboard=dash,\n        mode=\"synthese\" if mode == \"synthese\" else \"complet\").build()\n    return Response(\n        content=pdf, media_type=\"application/pdf\",\n        headers={\"Content-Disposition\":\n                 f'attachment; filename=\"{kapdf.filename(\"immo-ka\", period)}\"'})\n\n\n\n@app.get(\"/api/stats/catalog\")\ndef stats_catalog(\n    period: str = Query(\"30j\"),\n    from_: str | None = Query(None, alias=\"from\"),\n    to: str | None = Query(None),\n):\n    \"\"\"v3 — blocs composables pour le constructeur de rapports personnalisés.\"\"\"\n    from . import kapdf\n    dash = kastats.dashboard(period, from_, to)\n    return {\"updated\": dash.get(\"updated\"), \"period\": dash.get(\"period\"),\n            \"blocks\": kapdf.catalog(dash)}\n\n\n@app.post(\"/api/stats/report/custom\")\ndef stats_report_custom(spec: dict = Body(...)):\n    \"\"\"v3 — rapport PDF personnalisé : {\"title\", \"period\", \"from\", \"to\",\n    \"blocks\": [{\"key\": \"series:…\", \"render\": \"bar\"}, …]} (SPEC.md §3bis).\"\"\"\n    from . import kapdf\n    period = str(spec.get(\"period\") or \"30j\")\n    dash = kastats.dashboard(period, str(spec.get(\"from\") or \"\") or None,\n                             str(spec.get(\"to\") or \"\") or None)\n    site = {\n        \"wordmark\": \"Immo·Ka\",\n        \"accent\": \"#e23744\",\n        \"domain\": \"www.immo-ka.com\",\n        \"tagline\": \"Agrégateur de propriétés à vendre — province de Québec\",\n    }\n    known = {b[\"key\"] for b in kapdf.catalog(dash)}\n    blocks = [b for b in (spec.get(\"blocks\") or [])\n              if isinstance(b, dict) and b.get(\"key\") in known][:40]\n    if not blocks:\n        raise HTTPException(400, \"Aucun bloc valide dans la composition\")\n    pdf = kapdf.GroupeKAReport(\n        site=site, dashboard=dash, mode=kapdf.CUSTOM_MODE,\n        spec={\"title\": str(spec.get(\"title\") or \"\")[:80], \"blocks\": blocks},\n    ).build()\n    fname = kapdf.filename(\"immo-ka\", period, kapdf.CUSTOM_MODE)\n    return Response(\n        content=pdf, media_type=\"application/pdf\",\n        headers={\"Content-Disposition\": f'attachment; filename=\"{fname}\"'})\n\n@app.post(\"/api/sync\")\ndef trigger_sync(background: BackgroundTasks, source: str | None = None):\n    \"\"\"Déclenche une synchronisation (équivalent d'un webhook entrant).\"\"\"\n    def _job():\n        with _sync_lock:\n            ingest.run([source] if source else None)\n    background.add_task(_job)\n    return {\"status\": \"démarré\", \"source\": source or \"toutes\"}\n\n\n# --- Référencement : robots, sitemaps, résolution de slugs (voir immoka/seo.py)\n@app.get(\"/robots.txt\")\ndef robots():\n    return seo.robots_txt()\n\n\n@app.get(\"/sitemap.xml\")\ndef sitemap():\n    return seo.sitemap_index()\n\n\n@app.get(\"/sitemaps/{name}\")\ndef sitemap_part(name: str):\n    return seo.sitemap_file(name)\n\n\n@app.get(\"/api/seo/resolve\")\ndef seo_resolve(ville: str | None = None, type: str | None = None):\n    \"\"\"Slug d'URL programmatique → valeurs exactes (pages /a-vendre du SPA).\"\"\"\n    out = seo.resolve_slugs(ville, type)\n    if out is None:\n        raise HTTPException(404, \"Slug inconnu\")\n    return out\n\n\n# --- Frontend statique -------------------------------------------------------\nif FRONTEND_DIR.exists():\n\n    if (FRONTEND_DIR / \"assets\").is_dir():\n        app.mount(\"/assets\", StaticFiles(directory=FRONTEND_DIR / \"assets\"), name=\"assets\")\n\n    @app.get(\"/doc\", include_in_schema=False)\n    def doc_redirect():\n        return RedirectResponse(\"/doc/\", status_code=301)\n\n    # /doc : documentation statique (index.html + captures + PDF) — montée\n    # explicitement pour que le catch-all SPA ne l'intercepte pas.\n    if (FRONTEND_DIR / \"doc\").is_dir():\n        app.mount(\"/doc\", StaticFiles(directory=FRONTEND_DIR / \"doc\", html=True), name=\"doc\")\n\n    @app.get(\"/{full_path:path}\")\n    def spa(full_path: str, request: Request):\n        target = FRONTEND_DIR / full_path\n        if full_path and target.is_file():\n            return FileResponse(target)\n        # rendu SEO côté serveur : HTML complet par route (meta, JSON-LD,\n        # contenu dans #root que React remplace au montage). Ne doit JAMAIS\n        # casser l'app → repli sur l'index brut à la moindre erreur.\n        try:\n            return seo.render_for_path(\"/\" + full_path,\n                                       dict(request.query_params))\n        except Exception:\n            return FileResponse(FRONTEND_DIR / \"index.html\")\n","structuredPatch":[{"oldStart":211,"oldLines":6,"newStart":211,"newLines":22,"lines":["     return d"," "," ","+@app.get(\"/api/listings/{uid}/pdf\")","+def listing_pdf(uid: str):","+    \"\"\"Fiche PDF complète de la propriété (immoka/pdffiche.py) — reflète tous","+    les blocs de la fiche web : prix + verdict Vrai-Prix, photos, description,","+    caractéristiques, pièces, analyses, environnement, quartier, QR.\"\"\"","+    from . import pdffiche","+    data = pdffiche.fiche_pdf(uid)","+    if data is None:","+        raise HTTPException(404, \"Propriété introuvable\")","+    safe = uid.replace(\":\", \"-\").replace(\"/\", \"-\")","+    return Response(","+        content=data, media_type=\"application/pdf\",","+        headers={\"Content-Disposition\":","+                 f'attachment; filename=\"immo-ka_fiche_{safe}.pdf\"'})","+","+"," @app.get(\"/api/listings.geojson\")"," def listings_geojson(","     city: str | None = None,"]}],"userModified":false,"replaceAll":false}}}
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{"type":"permission_resolved","requestId":"4ad6da6ef27841f1","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BmYjqqVUiSeHEHqkGLVUsH","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/immo-ka/requirements.txt has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"192e3b13-9c6a-4f2d-a432-ab88f3ecd4b7","timestamp":"2026-08-24T21:44:37.493Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/requirements.txt","oldString":"fpdf2>=2.8","newString":"fpdf2>=2.8\nreportlab>=4.0\nqrcode>=7.4","originalFile":"requests>=2.31\nbeautifulsoup4>=4.12\nfastapi>=0.110\nuvicorn>=0.29\nfpdf2>=2.8\n","structuredPatch":[{"oldStart":3,"oldLines":3,"newStart":3,"newLines":5,"lines":[" fastapi>=0.110"," uvicorn>=0.29"," fpdf2>=2.8","+reportlab>=4.0","+qrcode>=7.4"]}],"userModified":false,"replaceAll":false}}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJcxgoqmLE2H6MHxykA","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014DZnFbSRX3TeBh7iQ4KwDH","name":"TodoWrite","input":{"todos":[{"content":"Cartographier la fiche web + modules backend (formes de retour exactes)","status":"completed","activeForm":"Cartographiant la fiche web + modules backend"},{"content":"Lire la référence lou-ka pdfgen.py (approche + style)","status":"completed","activeForm":"Lisant la référence lou-ka pdfgen.py"},{"content":"Écrire immoka/pdffiche.py (canvas, pagination dynamique, sections conditionnelles)","status":"completed","activeForm":"Écrivant immoka/pdffiche.py"},{"content":"Endpoint PDF + tester fiche riche et fiche pauvre (pdftoppm, taille, temps)","status":"in_progress","activeForm":"Testant l'endpoint PDF riche/pauvre"},{"content":"pm2 restart + healthchecks + commit/push origin main","status":"pending","activeForm":"Redéployant et poussant"}]},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":636,"cache_read_input_tokens":140358,"cache_creation":{"ephemeral_5m_input_tokens":636,"ephemeral_1h_input_tokens":0},"output_tokens":17,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"0e2a6948-1684-4f9d-9675-51aab0097c74"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014DZnFbSRX3TeBh7iQ4KwDH","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"e92cff6b-bc80-440a-ba3b-73ac1cd1d8be","timestamp":"2026-08-24T21:44:37.857Z","tool_use_result":{"oldTodos":[{"content":"Cartographier la fiche web + modules backend (formes de retour exactes)","status":"completed","activeForm":"Cartographiant la fiche web + modules backend"},{"content":"Lire la référence lou-ka pdfgen.py (approche + style)","status":"completed","activeForm":"Lisant la référence lou-ka pdfgen.py"},{"content":"Écrire immoka/pdffiche.py (canvas, pagination dynamique, sections conditionnelles)","status":"in_progress","activeForm":"Écrivant immoka/pdffiche.py"},{"content":"Endpoint PDF + tester fiche riche et fiche pauvre (pdftoppm, taille, temps)","status":"pending","activeForm":"Testant l'endpoint PDF riche/pauvre"},{"content":"pm2 restart + healthchecks + commit/push origin main","status":"pending","activeForm":"Redéployant et poussant"}],"newTodos":[{"content":"Cartographier la fiche web + modules backend (formes de retour exactes)","status":"completed","activeForm":"Cartographiant la fiche web + modules backend"},{"content":"Lire la référence lou-ka pdfgen.py (approche + style)","status":"completed","activeForm":"Lisant la référence lou-ka pdfgen.py"},{"content":"Écrire immoka/pdffiche.py (canvas, pagination dynamique, sections conditionnelles)","status":"completed","activeForm":"Écrivant immoka/pdffiche.py"},{"content":"Endpoint PDF + tester fiche riche et fiche pauvre (pdftoppm, taille, temps)","status":"in_progress","activeForm":"Testant l'endpoint PDF riche/pauvre"},{"content":"pm2 restart + healthchecks + commit/push origin main","status":"pending","activeForm":"Redéployant et poussant"}],"verificationNudgeNeeded":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJdYqdTq4AvNHDY8LYw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NcJ9NKxp8KwQnfjPEuEyBA","name":"Bash","input":{"command":"cat > /tmp/testpdf.py <<'EOF'\nimport time, pathlib\nfrom immoka import pdffiche\nfor uid, nom in [(\"domicilia:17516934\", \"riche\"), (\"remax_quebec:17719804\", \"pauvre\")]:\n    t0 = time.time()\n    data = pdffiche.fiche_pdf(uid)\n    dt = time.time() - t0\n    if data is None:\n        print(nom, \"INTROUVABLE\"); continue\n    p = pathlib.Path(f\"/tmp/fiche_{nom}.pdf\")\n    p.write_bytes(data)\n    print(f\"{nom}: {len(data)/1024:.0f} Ko en {dt:.1f}s -> {p}\")\nEOF\n.venv/bin/python /tmp/testpdf.py","description":"Générer les PDF de test riche et pauvre","timeout":180000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":752,"cache_read_input_tokens":140994,"cache_creation":{"ephemeral_5m_input_tokens":752,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"0123fbc1-cb9d-4baf-bdfb-2c8d83e81ea8"}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n  File \"/tmp/testpdf.py\", line 2, in <module>\n    from immoka import pdffiche\nModuleNotFoundError: No module named 'immoka'","is_error":true,"tool_use_id":"toolu_01NcJ9NKxp8KwQnfjPEuEyBA"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"a1fdb836-a425-41cf-a684-cabc52522d28","timestamp":"2026-08-24T21:44:45.477Z","tool_use_result":"Error: Exit code 1\nTraceback (most recent call last):\n  File \"/tmp/testpdf.py\", line 2, in <module>\n    from immoka import pdffiche\nModuleNotFoundError: No module named 'immoka'"}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"9a74beb6-099e-4967-ac5d-6d74d8dd099c","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJe7LVK5ak9wgVrwVFc","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GQEcB5ZSxumcvcNBidj5JM","name":"Bash","input":{"command":".venv/bin/python /tmp/testpdf.py","description":"Générer les PDF de test depuis la racine du repo","timeout":180000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":458,"cache_read_input_tokens":141746,"cache_creation":{"ephemeral_5m_input_tokens":458,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"35562d19-f052-4ce2-a822-efea01dffaa8"}}
{"type":"permission_request","requestId":"f2cb4fd8157df0b6","tool":"Bash","input":{"command":".venv/bin/python /tmp/testpdf.py","timeout":180000,"description":"Générer les PDF de test depuis la racine du repo"}}
{"type":"permission_resolved","requestId":"f2cb4fd8157df0b6","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n  File \"/tmp/testpdf.py\", line 2, in <module>\n    from immoka import pdffiche\nModuleNotFoundError: No module named 'immoka'","is_error":true,"tool_use_id":"toolu_01GQEcB5ZSxumcvcNBidj5JM"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"a20d1112-915c-4d79-962a-672192c2b395","timestamp":"2026-08-24T21:44:58.328Z","tool_use_result":"Error: Exit code 1\nTraceback (most recent call last):\n  File \"/tmp/testpdf.py\", line 2, in <module>\n    from immoka import pdffiche\nModuleNotFoundError: No module named 'immoka'"}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"815b526e-4ed6-40ee-8d38-a852bd6b6168","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJf5JUocfCsBwuYszt1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VKd4PFWBiCzyVGWfFFPW2C","name":"Bash","input":{"command":"PYTHONPATH=. .venv/bin/python /tmp/testpdf.py","description":"Générer les PDF de test avec PYTHONPATH du repo","timeout":180000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":213,"cache_read_input_tokens":142204,"cache_creation":{"ephemeral_5m_input_tokens":213,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"570b7b82-dee2-4fc5-a6b3-51bd2906d94a"}}
{"type":"permission_request","requestId":"8316bd52bc9e50b5","tool":"Bash","input":{"command":"PYTHONPATH=. .venv/bin/python /tmp/testpdf.py","timeout":180000,"description":"Générer les PDF de test avec PYTHONPATH du repo"}}
{"type":"permission_resolved","requestId":"8316bd52bc9e50b5","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n  File \"/tmp/testpdf.py\", line 2, in <module>\n    from immoka import pdffiche\n  File \"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py\", line 20, in <module>\n    import qrcode\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/qrcode/__init__.py\", line 1, in <module>\n    from qrcode.main import QRCode\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/qrcode/main.py\", line 14, in <module>\n    from qrcode.image.base import BaseImage\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/qrcode/image/base.py\", line 4, in <module>\n    from qrcode.image.styles.moduledrawers.base import QRModuleDrawer\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/qrcode/image/styles/moduledrawers/__init__.py\", line 3, in <module>\n    from .pil import CircleModuleDrawer  # noqa: F401\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/qrcode/image/styles/moduledrawers/pil.py\", line 3, in <module>\n    from PIL import Image, ImageDraw\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/PIL/Image.py\", line 33, in <module>\n    import logging\n  File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/logging/__init__.py\", line 26, in <module>\n    import sys, os, time, io, re, traceback, warnings, weakref, collections.abc\n  File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/traceback.py\", line 13, in <module>\n    import _colorize\n  File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/_colorize.py\", line 157, in <module>\n    @dataclass(frozen=True, kw_only=True)\n  File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/dataclasses.py\", line 1432, in wrap\n    return _process_class(cls, init, repr, eq, order, unsafe_hash,\n  File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/dataclasses.py\", line 1222, in _process_class\n    text_sig = str(inspect.signature(\nAttributeError: module 'inspect' has no attribute 'signature' (consider renaming '/private/tmp/inspect.py' since it has the same name as the standard library module named 'inspect' and prevents importing that standard library module)","is_error":true,"tool_use_id":"toolu_01VKd4PFWBiCzyVGWfFFPW2C"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"03321441-032b-4622-9761-35faa0e7698b","timestamp":"2026-08-24T21:45:04.228Z","tool_use_result":"Error: Exit code 1\nTraceback (most recent call last):\n  File \"/tmp/testpdf.py\", line 2, in <module>\n    from immoka import pdffiche\n  File \"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py\", line 20, in <module>\n    import qrcode\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/qrcode/__init__.py\", line 1, in <module>\n    from qrcode.main import QRCode\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/qrcode/main.py\", line 14, in <module>\n    from qrcode.image.base import BaseImage\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/qrcode/image/base.py\", line 4, in <module>\n    from qrcode.image.styles.moduledrawers.base import QRModuleDrawer\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/qrcode/image/styles/moduledrawers/__init__.py\", line 3, in <module>\n    from .pil import CircleModuleDrawer  # noqa: F401\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/qrcode/image/styles/moduledrawers/pil.py\", line 3, in <module>\n    from PIL import Image, ImageDraw\n  File \"/Users/simon-pierreboucher/apps/immo-ka/.venv/lib/python3.14/site-packages/PIL/Image.py\", line 33, in <module>\n    import logging\n  File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/logging/__init__.py\", line 26, in <module>\n    import sys, os, time, io, re, traceback, warnings, weakref, collections.abc\n  File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/traceback.py\", line 13, in <module>\n    import _colorize\n  File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/_colorize.py\", line 157, in <module>\n    @dataclass(frozen=True, kw_only=True)\n  File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/dataclasses.py\", line 1432, in wrap\n    return _process_class(cls, init, repr, eq, order, unsafe_hash,\n  File \"/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/lib/python3.14/dataclasses.py\", line 1222, in _process_class\n    text_sig = str(inspect.signature(\nAttributeError: module 'inspect' has no attribute 'signature' (consider renaming '/private/tmp/inspect.py' since it has the same name as the standard library module named 'inspect' and prevents importing that standard library module)"}}
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{"type":"claude","event":{"type":"system","subtype":"task_started","task_id":"bgz1vgud9","tool_use_id":"toolu_01DQqZYKhYKPrLTwdtW8cwtL","description":"Générer les PDF de test (safe path)","task_type":"local_bash","uuid":"6910ab03-3fbd-41d1-b20e-d62f97700fa2","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DQqZYKhYKPrLTwdtW8cwtL","type":"tool_result","content":"riche: 1050 Ko en 27.6s -> /tmp/fiche_riche.pdf\npauvre: 5 Ko en 0.0s -> /tmp/fiche_pauvre.pdf","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"b3359fd0-af07-4e4a-b63f-cb06409081a1","timestamp":"2026-08-24T21:45:40.586Z","tool_use_result":{"stdout":"riche: 1050 Ko en 27.6s -> /tmp/fiche_riche.pdf\npauvre: 5 Ko en 0.0s -> /tmp/fiche_pauvre.pdf","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"task_notification","task_id":"bgz1vgud9","tool_use_id":"toolu_01DQqZYKhYKPrLTwdtW8cwtL","status":"completed","output_file":"","summary":"Générer les PDF de test (safe path)","uuid":"f09d6b78-6409-4bc2-991d-50c9260d41b9","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"29822cfd-7c0c-48be-9596-7e10e25d2dcc","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJiBWcx7KFFmSDvyFEn","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":496,"cache_read_input_tokens":143732,"cache_creation":{"ephemeral_5m_input_tokens":496,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"2852d3f2-ad0e-4af9-8e42-c3f26c2e5653"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJiBWcx7KFFmSDvyFEn","type":"message","role":"assistant","content":[{"type":"text","text":"Le PDF riche dépasse la barre de 1 Mo (1050 Ko). Je réduis la compression des photos, puis j'inspecte les pages."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":496,"cache_read_input_tokens":143732,"cache_creation":{"ephemeral_5m_input_tokens":496,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"0e5d6b86-4c23-4d99-90e1-76eb0e4943a9"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJiBWcx7KFFmSDvyFEn","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0182RySQJXtYe3jusMYsxgbd","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","old_string":"            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)","new_string":"            im.thumbnail((1000, 1000))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=78)"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":496,"cache_read_input_tokens":143732,"cache_creation":{"ephemeral_5m_input_tokens":496,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"4c1e8f74-cb61-42b2-83b3-bc016bbca232"}}
{"type":"permission_request","requestId":"719b1bc4fd2db6c8","tool":"Edit","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","old_string":"            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)","new_string":"            im.thumbnail((1000, 1000))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=78)","replace_all":false}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0182RySQJXtYe3jusMYsxgbd","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"74b0793b-90c0-4c6f-8caf-6d137390a021","timestamp":"2026-08-24T21:45:52.443Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","oldString":"            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)","newString":"            im.thumbnail((1000, 1000))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=78)","originalFile":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdffiche.py : fiche PDF complète d'une propriété — reflète TOUS les blocs de\n#   la fiche web : prix + verdict Vrai-Prix, photos, description, inclusions,\n#   caractéristiques, pièces, analyse Vrai-Prix (jauge + rôle foncier),\n#   historique de prix, inondation, qualité de l'air, coût d'électricité,\n#   commerces/transport, essence, quartier, références + QR.\n#   Style « éditorial sharp » (bordures encre, ombres décalées dures, tuiles\n#   KPI, micro-étiquettes Courier) — palette du site : crème #f5f3ee, encre\n#   #141814, accent cerise #e23744. Pagination dynamique : saut_page()/place(),\n#   aucune section coupée entre deux pages.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Immo-Ka (identité officielle du site) ----------------------------\nPAPER = HexColor(\"#f5f3ee\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#141814\")\nINK2 = HexColor(\"#4d5551\")\nINK3 = HexColor(\"#8b928c\")\nACCENT = HexColor(\"#e23744\")        # cerise\nACCENT_SOFT = HexColor(\"#fbe0e2\")\nACCENT_DEEP = HexColor(\"#a8232e\")\nGREEN_FG = HexColor(\"#1c5c41\")      # verdict « sous l'estimation » (site .vp-under)\nGREEN_BG = HexColor(\"#e2f0e6\")\nRED_FG = HexColor(\"#8a2b2b\")        # verdict « au-dessus » (site .vp-over)\nRED_BG = HexColor(\"#f6e2e2\")\nAMBER_FG = HexColor(\"#8a5a12\")\nAMBER_BG = HexColor(\"#faf0dc\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"ImmoKaBot/1.0 (+https://www.immo-ka.com; contact@spboucher.ai)\"\n\nNBSP = \" \"     # espace fine insécable (mise en forme française)\n\n# clés de details jamais affichées (mêmes exclusions que la fiche web)\nDETAIL_HIDDEN = {\n    \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n    \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n    \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n}\n\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"\n               \"environnement) fournis à titre indicatif, sans valeur légale.\")\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _fmt_mois(d) -> str:\n    \"\"\"« 2017-11-04 » ou epoch → « novembre 2017 » (sinon valeur telle quelle).\"\"\"\n    if d is None:\n        return \"—\"\n    try:\n        if isinstance(d, (int, float)):\n            st = time.localtime(d)\n            return f\"{_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n        an, mois = str(d)[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return str(d)\n\n\ndef _fmt_jour(ts: float) -> str:\n    \"\"\"epoch → « 4 novembre 2024 » (« 1er » pour le premier du mois).\"\"\"\n    st = time.localtime(ts)\n    j = \"1er\" if st.tm_mday == 1 else str(st.tm_mday)\n    return f\"{j} {_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à plusieurs dizaines de Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Immo·Ka : « Immo » encre + boîte encre avec « Ka » cerise.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Immo\")\n        w = c.stringWidth(\"Immo\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6, titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.IMMO-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=ACCENT, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        # carré accent à ombre dure (signature de la passe éditoriale du site)\n        c.setFillColor(INK)\n        c.rect(M + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.rect(M, y + 0.5, 9, 9, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M + 16, y, texte)\n        c.setStrokeColor(ACCENT)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M + 16, y - 4, M + 16 + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def nb_lignes(self, texte: str, largeur: float, taille: float = 8.5,\n                  max_lignes: int = 100) -> int:\n        \"\"\"Nombre de lignes qu'occupera `paragraphe` (pour réserver la place).\"\"\"\n        c = self.c\n        mots = texte.split()\n        ligne = \"\"\n        n = 1\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                n += 1\n                ligne = mot\n                if n >= max_lignes:\n                    break\n        return min(n, max_lignes)\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\ndef _titre_droite(c, y, x, texte):\n    c.setFillColor(INK)\n    c.rect(x + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n    c.setFillColor(ACCENT)\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.rect(x, y + 0.5, 9, 9, stroke=1, fill=1)\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x + 16, y, texte)\n    c.setStrokeColor(ACCENT)\n    c.setLineWidth(3)\n    c.line(x + 16, y - 4, x + 16 + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\ndef _verdict_vraiprix(price, vp):\n    \"\"\"Pastille verdict identique au site : ±% vs estimation, masqué si\n    |pct| > 60 (prix non comparable : terrain, loyer…).\"\"\"\n    if not price or not vp or vp.get(\"value\") in (None, 0):\n        return None\n    pct = round((price - vp[\"value\"]) / vp[\"value\"] * 100)\n    if abs(pct) > 60:\n        return None\n    if pct >= 1:\n        return (f\"▲{NBSP}{pct}{NBSP}% au-dessus de l'estimation\",\n                RED_FG, RED_BG, ACCENT_DEEP)\n    if pct <= -1:\n        return (f\"▼{NBSP}{abs(pct)}{NBSP}% sous l'estimation\",\n                GREEN_FG, GREEN_BG, GREEN_FG)\n    return (f\"≈{NBSP}Prix aligné sur l'estimation\", INK2, SURFACE, INK)\n\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche de la propriété `uid` — reflète tous les blocs de la\n    fiche web ; None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"features\"] = json.loads(l.get(\"features\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    try:\n        l[\"vraiprix\"] = json.loads(l[\"vraiprix\"]) or None if l.get(\"vraiprix\") else None\n    except (ValueError, TypeError):\n        l[\"vraiprix\"] = None\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n        (uid,)).fetchall()]\n    quartier = None\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n    if geo:\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        try:\n            quartier = qmod.fiche_quartier(\n                lat, lng, l.get(\"city\") or \"\",\n                dauid if dauid and dauid != \"hors-zone\" else None)\n        except Exception:\n            quartier = None\n    try:\n        reg = json.loads(\n            (db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n        src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]),\n                        l[\"source\"])\n    except Exception:\n        src_name = l[\"source\"]\n    con.close()\n\n    # --- enrichissements (mêmes modules que les blocs de la fiche web) -------\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    d = l[\"details\"]\n    vp = l.get(\"vraiprix\")\n    location = (d.get(\"transaction\") == \"location\")\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Immo-Ka — {l.get('address') or l.get('title') or uid}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche propriété\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche propriété · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- héro : prix + pastille verdict Vrai-Prix -----------------------------\n    prix_txt = (_fmt_money(l[\"price\"]) + (f\"{NBSP}/ mois\" if location else \"\")) \\\n        if l.get(\"price\") else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    verd = _verdict_vraiprix(l.get(\"price\"), vp)\n    if verd:\n        txt, fg, bg, stk = verd\n        bx = s.pilule(bx, y - 22, txt, fg, bg, stk)\n    if l.get(\"property_type\"):\n        s.pilule(bx, y - 22, l[\"property_type\"], ACCENT, INK)\n    y -= 44\n\n    # --- titre + adresse -------------------------------------------------------\n    titre = l.get(\"address\") or l.get(\"title\") or \"\"\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, titre[:80])\n    y -= 14\n    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    if l.get(\"title\") and l.get(\"title\") != titre:\n        loc = f\"{l['title']} — {loc}\" if loc else l[\"title\"]\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés (mêmes specs que le héro du site) --------------------------\n    chips = []\n    if l.get(\"bedrooms\") is not None:\n        chips.append(f\"{l['bedrooms']}{NBSP}ch.\")\n    if l.get(\"bathrooms\") is not None:\n        chips.append(f\"{l['bathrooms']}{NBSP}sdb\")\n    if l.get(\"powder_rooms\"):\n        chips.append(f\"{l['powder_rooms']}{NBSP}s. d'eau\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"lot_sqft\"):\n        chips.append(f\"Terrain {round(l['lot_sqft']):,}\".replace(\",\", NBSP)\n                     + f\"{NBSP}pi²\")\n    if l.get(\"year_built\"):\n        chips.append(f\"Construit en {l['year_built']}\")\n    if l.get(\"price\") and l.get(\"area_sqft\") and l[\"area_sqft\"] > 200:\n        chips.append(f\"{round(l['price'] / l['area_sqft'])}{NBSP}$/pi²\")\n    if l.get(\"mls\"):\n        chips.append(f\"MLS {l['mls']}\")\n    if l.get(\"first_seen\"):\n        dom = max(0, int((time.time() - l[\"first_seen\"]) // 86400))\n        chips.append(f\"{dom}{NBSP}j sur Immo-Ka\")\n    x = M\n    for chip in chips[:8]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes ---------------------------------------\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- baisse/hausse de prix (bandeau, comme le site) -------------------------\n    ph = [p for p in price_history if p.get(\"price\")]\n    if len(ph) >= 2 and ph[0][\"price\"] != ph[1][\"price\"]:\n        y = place(y, 34)\n        de, a = ph[1][\"price\"], ph[0][\"price\"]\n        bas = a < de\n        c.setFillColor(GREEN_BG if bas else RED_BG)\n        c.setStrokeColor(GREEN_FG if bas else RED_FG)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 20, PAGE_W - 2 * M, 24, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 9)\n        c.setFillColor(GREEN_FG if bas else RED_FG)\n        c.drawString(M + 10, y - 12,\n                     f\"{'▼' if bas else '▲'} Prix passé de {_fmt_money(de)} à \"\n                     f\"{_fmt_money(a)} le {_fmt_jour(ph[0]['ts'])}\")\n        y -= 34\n\n    # --- description | inclusions (2 colonnes, comme le site) -------------------\n    col_g = PAGE_W * 0.56 - M\n    feats = [str(f) for f in (l.get(\"features\") or []) if f]\n    desc = (l.get(\"description\") or \"\").strip()\n    if desc or feats:\n        besoin = 30 + max(\n            s.nb_lignes(desc, col_g, max_lignes=26) * 11.5 if desc else 0,\n            min(len(feats), 18) * 12)\n        y = place(y, min(besoin, 340))\n        y_desc = y\n        if desc:\n            y_desc = s.titre_section(y, \"Description\")\n            y_desc = s.paragraphe(M, y_desc, desc, col_g, max_lignes=26)\n        y_incl = y\n        if feats:\n            xr = M + col_g + 24\n            y_incl = _titre_droite(c, y, xr, \"Inclusions et caractéristiques\")\n            c.setFont(\"Helvetica\", 8.5)\n            for f in feats[:18]:\n                c.setFillColor(ACCENT_DEEP)\n                c.drawString(xr, y_incl, \"✓\")\n                c.setFillColor(INK2)\n                c.drawString(xr + 12, y_incl, f[:42])\n                y_incl -= 12\n            if len(feats) > 18:\n                c.setFillColor(INK3)\n                c.drawString(xr + 12, y_incl, f\"+ {len(feats) - 18} autres…\")\n                y_incl -= 12\n        y = min(y_desc, y_incl) - 16\n\n    # --- caractéristiques (details, mêmes exclusions que le site) ---------------\n    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]\n    if dets:\n        col_w = PAGE_W - 2 * M\n        besoin = 40 + sum(\n            s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4) * 11 + 3\n            for _, v in dets)\n        y = place(y, min(besoin, 420))\n        y = s.titre_section(y, \"Caractéristiques\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"DÉTAIL\")\n        c.drawString(M + 180, y, \"VALEUR\")\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 12\n        for i, (k, v) in enumerate(dets):\n            nl = s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4)\n            y = place(y, nl * 11 + 6)\n            if i % 2 == 0:   # zébrage éditorial\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - (nl - 1) * 11 - 3, col_w + 6, nl * 11 + 2,\n                       stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, k[:38])\n            y = s.paragraphe(M + 180, y, v, col_w - 190, taille=8,\n                             interligne=11, max_lignes=4)\n            y -= 3\n        y -= 10\n\n    # --- pièces (details.pieces) -------------------------------------------------\n    pieces = d.get(\"pieces\") or []\n    if isinstance(pieces, list) and pieces:\n        rows = pieces[:24]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Pièces\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"PIÈCE\", 0), (\"NIVEAU\", 190), (\"DIMENSIONS\", 330),\n                          (\"REVÊTEMENT\", 430)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            if i % 2 == 0:\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - 3, PAGE_W - 2 * M + 6, 12, stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, str(p.get(\"nom\") or \"—\")[:36])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M + 190, y, str(p.get(\"niveau\") or \"—\")[:24])\n            c.drawString(M + 330, y, str(p.get(\"dimensions\") or \"—\")[:18])\n            c.drawString(M + 430, y, str(p.get(\"revetement\") or \"—\")[:22])\n            y -= 12\n        y -= 10\n\n    # --- analyse Vrai-Prix (valeur, jauge, rôle foncier) -------------------------\n    if vp and vp.get(\"value\") is not None:\n        role = [(lab, vp.get(k)) for k, lab in\n                ((\"valeur_role\", \"Évaluation au rôle\"),\n                 (\"valeur_terrain\", \"Valeur du terrain\"),\n                 (\"valeur_batiment\", \"Valeur du bâtiment\"))\n                if vp.get(k)]\n        besoin = 96 + (56 if vp.get(\"low\") is not None else 0) \\\n            + (62 if role else 0) + 18\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Analyse Vrai-Prix\")\n        val_txt = _fmt_money(round(vp[\"value\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, val_txt)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(val_txt, \"Helvetica-Bold\", 17) + 8,\n                     y - 10, \"— valeur marchande estimée par le modèle Vrai-Prix\")\n        meta = []\n        if vp.get(\"confidence\"):\n            meta.append(f\"confiance {vp['confidence']}\"\n                        + (f\" ({vp['confidence_pct']}{NBSP}%)\"\n                           if vp.get(\"confidence_pct\") else \"\"))\n        if vp.get(\"annee_construction_role\"):\n            meta.append(f\"construction {vp['annee_construction_role']} (rôle)\")\n        if vp.get(\"superficie_terrain_role_m2\"):\n            meta.append(\n                f\"terrain {round(vp['superficie_terrain_role_m2']):,}\".replace(\",\", NBSP)\n                + f\"{NBSP}m² (rôle)\")\n        if meta:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK3)\n            c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        verd = _verdict_vraiprix(l.get(\"price\"), vp)\n        if verd:\n            txt, fg, bg, stk = verd\n            s.pilule(M, y - 48, txt, fg, bg, stk)\n        y -= 58\n        lo, hi = vp.get(\"low\"), vp.get(\"high\")\n        if lo is not None and hi is not None and hi > lo:\n            larg = PAGE_W - 2 * M\n            base = y - 22\n            span = hi - lo\n            c.setFillColor(ACCENT_SOFT)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.roundRect(M, base, larg, 12, 6, stroke=1, fill=1)\n\n            def _pos(v):\n                return M + larg * max(0.02, min(0.98, (v - lo) / span))\n\n            px = _pos(vp[\"value\"])\n            c.setStrokeColor(ACCENT_DEEP)\n            c.setLineWidth(2.5)\n            c.line(px, base - 3, px, base + 15)\n            c.setFont(\"Courier-Bold\", 6.5)\n            c.setFillColor(ACCENT_DEEP)\n            c.drawString(min(max(px - 30, M), PAGE_W - M - 100), base + 18,\n                         f\"ESTIMATION · {_fmt_money(round(vp['value']))}\")\n            if l.get(\"price\") and lo * 0.7 <= l[\"price\"] <= hi * 1.3:\n                ax = _pos(l[\"price\"])\n                c.setStrokeColor(INK)\n                c.setLineWidth(2)\n                c.line(ax, base - 3, ax, base + 15)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(INK)\n                c.drawString(min(max(ax - 30, M), PAGE_W - M - 116), base - 12,\n                             f\"PRIX DEMANDÉ · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 24, _fmt_money(round(lo)))\n            c.drawRightString(PAGE_W - M, base - 24,\n                              _fmt_money(round(hi)) + \" — fourchette d'estimation\")\n            y = base - 36\n        if role:\n            tw = (PAGE_W - 2 * M - (len(role) - 1) * 10) / len(role)\n            for i, (lab, v) in enumerate(role):\n                s.tuile(M + i * (tw + 10), y - 44, tw, 38,\n                        _fmt_money(round(v)), lab)\n            y -= 56\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Estimation automatisée Vrai-Prix (vrai-prix.ca) \"\n                               \"croisée au rôle d'évaluation foncière — ne \"\n                               \"remplace pas une évaluation professionnelle.\")\n        y -= 16\n\n    # --- historique de prix -------------------------------------------------------\n    if len(ph) >= 2:\n        rows = ph[:8]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Historique du prix demandé\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"DATE\", 0), (\"PRIX AFFICHÉ\", 190), (\"VARIATION\", 330)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            prec = rows[i + 1][\"price\"] if i + 1 < len(rows) else None\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, _fmt_jour(p[\"ts\"]))\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M + 190, y, _fmt_money(p[\"price\"]))\n            if prec and p.get(\"price\"):\n                delta = p[\"price\"] - prec\n                c.setFont(\"Courier-Bold\", 7.5)\n                c.setFillColor(GREEN_FG if delta < 0\n                               else (RED_FG if delta > 0 else INK3))\n                sig = \"▼\" if delta < 0 else (\"▲\" if delta > 0 else \"=\")\n                c.drawString(M + 330, y,\n                             f\"{sig} {_fmt_money(abs(delta))}\" if delta else \"=\")\n            y -= 12\n        y -= 10\n\n    # --- risque d'inondation (BDZI) ------------------------------------------------\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\" if sev\n                     else \"En zone inondable\", RED_FG, RED_BG, RED_FG)\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     AMBER_FG, AMBER_BG, AMBER_FG)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_FG, GREEN_BG, GREEN_FG)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z.get('type') or 'Zone'} — à {z.get('distance_m')} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- qualité de l'air (RSQAQ) ---------------------------------------------------\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"de la propriété — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = (f\"{m_['moyenne']:.1f}\".replace(\".\", \",\")\n                   if m_.get(\"moyenne\") is not None else \"—\")\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- coût d'électricité (Hydro-Québec) -------------------------------------------\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += (f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP)\n                      + f\"{NBSP}kWh/an\")\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- commerces et transport --------------------------------------------------------\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:12]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- essence à proximité --------------------------------------------------------------\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- le quartier -------------------------------------------------------------------------\n    if quartier and (quartier.get(\"demographie\") or quartier.get(\"proximite\")\n                     or quartier.get(\"chaleur\") or quartier.get(\"crime\")):\n        demo = quartier.get(\"demographie\") or {}\n        prox = quartier.get(\"proximite\") or {}\n        prox_aff = [(k, lab) for k, lab in PROX_LABELS\n                    if isinstance(prox.get(k), (int, float))]\n        besoin = 44 + (94 if demo else 0) + len(prox_aff) * 14 + 44\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Le quartier\")\n        c.setFont(\"Helvetica\", 7.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"Secteur immédiat de la propriété (aire de diffusion \"\n                           \"du recensement 2021, ± 500 habitants).\")\n        y -= 14\n        if demo:\n            tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                      (\"Ménages locataires\",\n                       f\"{round(demo['pct_locataires'])}{NBSP}%\"\n                       if demo.get(\"pct_locataires\") is not None else \"—\"),\n                      (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                      (\"Âge médian\",\n                       f\"{round(demo['age_median'])} ans\"\n                       if demo.get(\"age_median\") is not None else \"—\"),\n                      (\"Français à la maison\",\n                       f\"{round(demo['pct_francais'])}{NBSP}%\"\n                       if demo.get(\"pct_francais\") is not None else \"—\"),\n                      (\"Diplôme universitaire\",\n                       f\"{round(demo['pct_univ'])}{NBSP}%\"\n                       if demo.get(\"pct_univ\") is not None else \"—\")]\n            tw = (PAGE_W - 2 * M - 20) / 3\n            for i, (lab, val) in enumerate(tuiles):\n                s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                        tw, 38, val, lab)\n            y -= 34 + 44 + 16\n        for k, lab in prox_aff:\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(ACCENT)\n            c.roundRect(bx0, y - 1, max(3, bw_ * v), 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        if prox_aff:\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, y, \"Accessibilité 0–100 — mesures de proximité de \"\n                               \"Statistique Canada.\")\n            y -= 12\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch.get(\"classe\", 5) <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_FG, GREEN_BG, GREEN_FG))\n            elif ch.get(\"classe\", 5) >= 7:\n                ec = (f\" (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\")\n                      if ch.get(\"ecart\") is not None else \"\")\n                badges_q.append((f\"Îlot de chaleur{ec}\",\n                                 AMBER_FG, AMBER_BG, AMBER_FG))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"])\n                        / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} \"\n                             \"la moyenne canadienne\", INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m \"\n                             \"(12 mois)\", INK2, SURFACE, INK))\n        if badges_q:\n            x = M\n            for txt, fg, bg, stk in badges_q:\n                x = s.pilule(x, y - 8, txt, fg, bg, stk)\n            y -= 26\n        y -= 6\n\n    # --- références : QR + courtier + source ------------------------------------------------\n    y = place(y, 140)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.immo-ka.com/propriete/{uid}\",\n                     box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    yy = y - 14\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    courtier = l.get(\"broker_name\") or d.get(\"Courtier inscripteur\")\n    if courtier:\n        ligne = f\"Courtier : {courtier}\"\n        if l.get(\"broker_phone\"):\n            ligne += f\" · {l['broker_phone']}\"\n        c.drawString(M + 82, yy, ligne[:95])\n        yy -= 13\n    c.drawString(M + 82, yy, f\"Agence : {src_name}\")\n    yy -= 14\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Annonce originale :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy,\n                 (l.get(\"url\") or d.get(\"listing_origin_url\") or \"\")[:90])\n    yy -= 14\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Fiche à jour en ligne (scannez le code) :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")\n    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n","structuredPatch":[{"oldStart":110,"oldLines":9,"newStart":110,"newLines":9,"lines":["             # redimensionnement le PDF grimpe à plusieurs dizaines de Mo","             from PIL import Image","             im = Image.open(io.BytesIO(raw))","-            im.thumbnail((1200, 1200))","+            im.thumbnail((1000, 1000))","             buf = io.BytesIO()","-            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)","+            im.convert(\"RGB\").save(buf, \"JPEG\", quality=78)","             buf.seek(0)","             return ImageReader(buf)","         except Exception:"]}],"userModified":false,"replaceAll":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"501976c4-3122-45b5-b0bf-11f6f6c361b3","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJj3ukmKwPvFtMVPbhR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01127F4BEmnKehuJXZyNc1vR","name":"Bash","input":{"command":"PYTHONPATH=. .venv/bin/python -P /tmp/testpdf.py && rm -rf /tmp/pdfpages && mkdir -p /tmp/pdfpages && pdftoppm -png -r 72 /tmp/fiche_riche.pdf /tmp/pdfpages/riche && pdftoppm -png -r 72 /tmp/fiche_pauvre.pdf /tmp/pdfpages/pauvre && ls -la /tmp/pdfpages/","description":"Régénérer les PDF et convertir chaque page en PNG","timeout":180000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":633,"cache_read_input_tokens":144228,"cache_creation":{"ephemeral_5m_input_tokens":633,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f0e8ec6e-5c71-44be-b945-c6778aceae04"}}
{"type":"permission_request","requestId":"2a8036bb685100b0","tool":"Bash","input":{"command":"PYTHONPATH=. .venv/bin/python -P /tmp/testpdf.py && rm -rf /tmp/pdfpages && mkdir -p /tmp/pdfpages && pdftoppm -png -r 72 /tmp/fiche_riche.pdf /tmp/pdfpages/riche && pdftoppm -png -r 72 /tmp/fiche_pauvre.pdf /tmp/pdfpages/pauvre && ls -la /tmp/pdfpages/","timeout":180000,"description":"Régénérer les PDF et convertir chaque page en PNG"}}
{"type":"permission_resolved","requestId":"2a8036bb685100b0","allow":true,"always":false}
{"type":"claude","event":{"type":"system","subtype":"task_started","task_id":"bgewe3cya","tool_use_id":"toolu_01127F4BEmnKehuJXZyNc1vR","description":"Régénérer les PDF et convertir chaque page en PNG","task_type":"local_bash","uuid":"6e3a48ad-fa63-4117-a6ff-2a4e5b3d2cce","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01127F4BEmnKehuJXZyNc1vR","type":"tool_result","content":"riche: 684 Ko en 28.3s -> /tmp/fiche_riche.pdf\npauvre: 5 Ko en 0.0s -> /tmp/fiche_pauvre.pdf\ntotal 776\ndrwxr-xr-x    6 simon-pierreboucher  wheel     192 Aug 24 17:46 .\ndrwxrwxrwt  212 root                 wheel    6784 Aug 24 17:46 ..\n-rw-r--r--    1 simon-pierreboucher  wheel   46423 Aug 24 17:46 pauvre-1.png\n-rw-r--r--    1 simon-pierreboucher  wheel  209244 Aug 24 17:46 riche-1.png\n-rw-r--r--    1 simon-pierreboucher  wheel   75100 Aug 24 17:46 riche-2.png\n-rw-r--r--    1 simon-pierreboucher  wheel   55014 Aug 24 17:46 riche-3.png","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"5a3fa4e8-a8c1-4b44-807d-f23b9ae28b4c","timestamp":"2026-08-24T21:46:37.518Z","tool_use_result":{"stdout":"riche: 684 Ko en 28.3s -> /tmp/fiche_riche.pdf\npauvre: 5 Ko en 0.0s -> /tmp/fiche_pauvre.pdf\ntotal 776\ndrwxr-xr-x    6 simon-pierreboucher  wheel     192 Aug 24 17:46 .\ndrwxrwxrwt  212 root                 wheel    6784 Aug 24 17:46 ..\n-rw-r--r--    1 simon-pierreboucher  wheel   46423 Aug 24 17:46 pauvre-1.png\n-rw-r--r--    1 simon-pierreboucher  wheel  209244 Aug 24 17:46 riche-1.png\n-rw-r--r--    1 simon-pierreboucher  wheel   75100 Aug 24 17:46 riche-2.png\n-rw-r--r--    1 simon-pierreboucher  wheel   55014 Aug 24 17:46 riche-3.png","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"task_notification","task_id":"bgewe3cya","tool_use_id":"toolu_01127F4BEmnKehuJXZyNc1vR","status":"completed","output_file":"","summary":"Régénérer les PDF et convertir chaque page en PNG","uuid":"ba9946b8-d66f-4854-afaf-03055f7a84ad","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"60c2e2fe-aeff-4c82-82b4-3a7a862f8a90","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJnNKrsHDwHUg4pP6P1","type":"message","role":"assistant","content":[{"type":"text","text":"684 Ko, 3 pages pour la riche, 1 page pour la pauvre. J'inspecte visuellement chaque page."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":614,"cache_read_input_tokens":144861,"cache_creation":{"ephemeral_5m_input_tokens":614,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"b403eea9-fbc4-41ff-b2b0-a962268a1ff1"}}
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VF9wWA0oqKHhLEFJSUtGvatTr9kEGD2CxWRXVVY7OMRqMmDYw3mkyXrlxhs5hsNjuuTzQANMqaK6qq3VwlxK8DO1Op1Zl5uQAwdFASjUZLz8m2WCwdBkNKUnKX7kH3ao0m0w9Hj8yYPIX+ZzLMpV67JhTwI8PC07Oz6TSaVq8L8vMX8Pn5xUV8Pr+9vT06qpdWp6uuq+0fE8vpdK9K39GRU1BgsVgYDHpi/wHFZWV0Oj00KCgrL89VIqmXNkSEhroIhL9cuxrXJ9pkMpVUVpBJ5Ng+fTi/nfuhrKqySSaLCgsrKi3rExnJ4/EycrLJZHLSwPjurW3XaIrKSq1Wq1AgiI7qVVFd1dLWxmQwYvtEl1ZUdG6An7c39izvgMjwCAAoLikx/HasprGpqaKyikGn94qM7Ly8+wM+7G49SwAwORwfKaUAEM/iTeT8GvPEFCoANFt/89DQeI7opj1LhO7DfmR6dnZCv36J/fs7HHZPd3cASOw/oHdkZLtW4+7qVlBSYrfbBTy+SCjsktjFTSLx8/aOj+uXlZ/HZrFYTGa/6Oi+vXqbf/uMHgBEhYfbHXYiUgJAzwliggMCkuMTfL29WuVtAFDb0DA0KYlKpWp1OhqNNiQxMTk+oUkmIzqORaWlgwYM6B4pAaCwtDQ5PiEiNLS6vg4AVOr2pIHxPC63+yhxl2oBoLCkJMDX13GLr4hbZcwhkUhhwSGV1dXBAQECPg8cIHJx0ep0XA43wMeXSqV2GAwCPp9Go3UJchw2Ozk+HgAGDRhIIpHodFpFdZXRZKqorhLweCQSuaauvr6xsV2j4bDZmXm5yQPj+/ftm5Z+rUsDwoNDwAHtGq2Xh4erRMJkMIQCgcMBstbW7n3uyxkZA2PjEvsPUGs0cqWiTaFIjk/wdPfIKSzo0oA7dXHe78HS09Ojf1ycRqslfrLitG37DgAYP24ci8XqYXMWiUwmkW4a6q4Ytaf0KgB4xcXL5caUI7kmPQAsEXm7UmhUgCg66xNJwAiOEADM+IAPuu+p29tdhAIKhWKxWoMDAolOVXpOdptc3icyCgB6R0Tkd3rmjkjs4ioWE0/zlVZWpKVf8/LwdHYKC0tK+t14yv2miDDTpZ7f/DXMYhmMRmlTk6+XN9y4L8Nhc/QdHRQymcFg5BUVhgeHAIBGq5UrlVl5eanXrgGAVqeTKxRyhUKpUgGAwWhkMZlcNken0wOAh5vroeM/tSkUfB7PaDIRJeUKhc1m61KtXKnkcNg06s37lETGnNqGhu6rXASC6rra0opypVol4PHJZFJtQ0NtQ4PJbBLw+VartVHWXFlT3flpypv/BWOzubu6pWdnBQcEEmkESCSSQqUU8gXtWq1QICSTyUwm02q12ux2ufL6BzEajURXu6S8LCQw8Hr3ur19YGxseVVVl11odTo+j0ulUmk02uCERFlrq4+nFwB4e3q2trV1bwAGyzvjnTeX0Wi0jz9b9fa77505d/7M2XOvL3tz0zffCASC1155pedtDQ77eGnxjKZSw81C3Spl4wm9Ks+kH80REks+VUobLaahbMEpn17p/jE7PcPD6aw32moBYBTHZZnIB08Hup+x2WyNVmu32+ul0jOXLtrtdiaDMTCun6e7e31jIwDQqDQ3icTZ5ZKIRQ8kJHYYDFqdDgAiQkI5bI4zI1qfyMg+UVE1dXW32p1Ory8sKelez2/+jxuNl65ceSAhkXgGkPhxR4ehg/gJRFFZKQCEBgUBAIVCcRWLEwcM0Gg1ANAkkxWXlxeXl5dWVgAAk8EwmUwdBgPRjWtpa5s6bry3h6dSpVK3txMli8vLibuznauVtbbIWlvrGqXdYwzcyJjj53OTrw6BgF9aXjEwrl9RaamAz2cxWVqdlsflGE0mIurTaTSzxUK+cce3uUVmsd4k76bNZgv08xPw+RwO22azORyO4MBAsdAFwMFmsZz5XgDAYjY7P4hCpQIADze3pIHxxPizQqXq6DAQuQVMZjMAdBgMREo/FpPpPPJ2u53D5uj0eqIAi8nq0oA7dXFibljoGx29bfOmV5cu3b5rl7N/GREevvqzTz09PX5382abGW5x+hR265vy3/xHbbCaH24uG8dxCaWzLA5Hjkl3saPdCiBWNo7kCE2/jbhdfs2C0L0fLFksN4nrL1evcthsoUDQKpe3yNuuZmbabFY+j08mk6VNjSnJDxSWlgCJ1CWxS2VNDYPOGDJo0OkL56lUirS5yWgyUalUk8nUJdFMSXl5q1yelp5utpglInHPCWLOp/5itztSr10LCwoK9Pf39vC8nJFusVj4PF6rXJ6Zm+vj5XXq/PnRKSk8LpdBp6fnZBPROjwkhMgGfn3sNywsLT3darMNTkwkPum1rCxVu7p3ZCSdRvNw+/XJ+S7V9o6I1On1p86fu9Vs8LfKmOMiEFKpVE93d4fDQafTXYQCjU4X4OfXkpVFFPBwc1ep1VablYiIx06fHjU0xd/XV9/RkVdU2CJvu5aVNTAuTtrUZLVaY3r3OXX+vFanb26RBfj5ubu5pefmqNRqsUhEHMnIsHAmkzk4IdHZgPKqKoVSSWT4q5dK80uK3SWuA2JjT184n5GTkxwfX1xeVltf/+DESXQ63dvT68zFi3QGncNix0VHX7icpu/oULW3x8fFXcnMcDbAaDQReQFvvzucwefuYTabM7Ky6hsa6HR6WGho76iom/5AePSEibdtTuYv//e/KZMm4qlB9xur1WqxWlk3S6PqHHukkMl38LuCmDfjpoxGI/MWLbdYrRQy2TnsaTAaGXQ6mXy3DO85HI6/lhXBYrGQyWTKX3rquPNObTab3W53TpP5b6Q0wp7lP4BOpyclJiYlJvZc7LZFSoTuW1Qqteffj1HuaIDpIVICAPPWMZ722w/Vw18Dd6bn9FfD0t+ZBbrzTikUSueIe7cl98F7ln/OU7Nn40FACKH7DQbLP+eRGQ/dtn3x+Tw84AghhMHyvyciPDwiPPw27MhVIhmcnIwHHCGEMFj+J506dvSHPd9PmjDhX93LW2+88ZfT/iGEEPpn4dOwf5fVau2cbF3W0tLY2OR8m5uX55zFGgCOHDv2R/qUjz/22Pznn8NgiRBCGCwRNEilztd6vb60rDwiPOz2DPMihBDCYIkQQgj9Y+7YQJ9/aBgefYQQQn9KXUX5HdkvPuCDEEII/Q4chkUIIYSwZ4kQQghhsEQIIYT+VXfpAz493MLV6/WNTU0ikaj7NK3/nuqaGgFfIBaL8IpBCCHsWd5W2f4xN/13q/Jms/nN5cv7Dhj42OwnBg8fMWbipMKi4n+8VSWlpekZGZ2XpGdkzJn3nAMcNy3/wsKFO3d/30OFBw4d0t2Y17RfQmJ5RSVedgghhMHy3/Lm8uVXr6Uf//Fw5pXL+ZkZo0YMf+qZZzRa7T+7l5/Pnt21Z0/nJYVFxTu3bb1VR/aFec+lDB3SQ4WLl74hVyiI119+/j9vL0+87BBC6L/lLk2o5h8a1mUkVtrYuP/AwYN794aFhgIAlUp9eeHC6praqqqq2JiY+vr6rTt2yGSyAP+AJ2c97u7unpOb29TcbLVaq2tqXl64EACupafvP3jQYDDG9I2ePfNxBoMOALl5ebv27NFqtSOGDXtw2rSi4uJLqWlqtfrA4cPTp0wBgMampta21k9WrUpKHPTQ9GlkMrlLzdU11X5WX28vLwC4fPXqgUOHAWDEsJSxo0cDwNdbttjt9q+3fPPSgvnubm75+QUR4eEcDodoz579P3A5nIemT0u7fOX5ec8CwKrVXzw1ezYx3vvtd98NTk4OCQ4GAKPRuHX7jpLSkuCg4KeemM3n8RwOx579+9MuXxEKBY88NKN3ryi8mhFC6H7vWWZkZrm4CONifx2kJZFI675YHRsTY7fbZ8x8XOTiMvPRR9Vq9ZznngeAouKS91d+tHvPXm8vbwAor6h8fsHCxISEh6ZPO3n651WrVwNAXn7+7DlzY6Kjx44e/ennq48cO8bj8VwlYj6P5+3pBQBNzc2Tpk1nsdgpQ4Zu37lzxYcru9d85NhPufn5AFBSWrro1cUjhw8bMSxl5aefHv3pJwAIDAggkUiBAf7ERKYbvv5aoVAAQH5Bwdznnu8VGRkcHPTiopd27N5NfKivt2xWqVXE630/HKitqwcA4gOWV1SMHjmyrLx8xmMzHQ7H9l279v9wYMb06VEREY88/ri0sRGvZoQQur96lt1ptBp3N/ebruro6Jj3zFxiWmYWi3Xg0CFiOY1G2739uxtTbzs+/vCDUSNGAEBRcfG19AwA2Lh58+yZM2c++igAsNns5maZn69vZEQEnU5PiB8IABs3bR4zetSi+S8CQFxMzOiJE5cuWdyt5utKy8qZTEZsTIy7m1uAv7/dbgeAUSNGkEikUSNGCASCzoU3bt7y9JNPzn36KQDgcbn/++LLHj77ydM/d3R0fP7pJyQSadyYMYOGDM3IzCwuLnF3dxvQv9/gB5L9fH1pmHUdIYQwWAoFAqVK1WVhTm6uyEXk7+/H5XCenPtMTW0tiURyPokTFhrqjGe+Pj7HT57a/O3W5uZmu90eHBQEABWVlVMmTSIKjBw+vPtOS8rKmpqaJj94fcJng8HQ1NzcpWanUSOGnzh1KmloSq+oqOEpQ5+cPbuHj1NWXj5t8mTidXjY72ROLy0rbW1rm/LQDOJtu0ZTWVX1zJynF77ySmx8QvyAAWNHj04aNAivZoQQut+DZWxMTEtLS3FJSVRkJLHEbDbPmffcqk8+Lq+s+PR/n29Yu6Z3VFSDVDp+yhSiAIlEcm7+2erVxcUlK1e8F+Dvv3vv3jNnzwEAnU43Go1Egfr6epVa3Tc6uvNOmQzGww89+Pijj97o3Wp9vL0vX7nauWYnCoWydvVqg6Hjanr6lq3bampqV6/67FYfh06nm8xm4rVW9+szSiT4tWbneCyTwYyJjv7iRm36jg4XoRAAjh482NTcnHb5yhdr15JIpIcfehAvaIQQ+jf8Z+5Z+vn6jh87dvHSpfX19QBgMpnf/eADNze3B5KSauvqggIDB/Trx2KxDh4+fNPNa+vq4gcODA0Jsdntx0+cJBYOeWDw4SNH7Xa73W5/69330i5fAQAymWwymYgCyUmDLly8xOPxRCJR6uXLT8yZ28Mck99s27b0rbeEQuGYUaMenDrV2Q/uXKHTwP79jx3/iXjduc0CgaCktBQALl+9KpO1EAuTBiVm5+RYrFaRSKTV6sZMnNiu0Ty3YMGBQ4d9fXwemfFQQvzA7t1uhBBC90LPMq4u90+V/+yjle99+OHgESNdXV31en1kePjmrzbQaLSJ48Z9s3XbhClTdXr9hHHjOBzuR59+6uvj23nbJx5//MWFi1LT0hRK5eSJEzd/++1PJ068MG/ecwvmDx4+gkQm+fn6Pv3kEwAQEx39xdp1y955Z+WKFU/Nnp2blzdkxEhXV9empqav16/voXmPPfzwQ489NmHKVBcXl7KKig1rrt+GjIuNfWTWrH27doWGhDgLv7xwwZx5zw0fM5ZMJvv5/trUF59/7tXXXv9i7ToXF5c+vXsTC/tGR7/4/PNjJ04KDAysrKp6Y8lr3l5ei+bPf+7F+YePHDEajTq9fvmbb+LVjBBC/5I7nEi9hzw+t0rio9fr6+rrXYQunp4ezoVGk6mmpsbTw0MoFGq1Orvd1uWBGgBob29vbGoKCgpiMhhyhYLL4TCZTABoaW112O0eHr/WptXqyGQS8QMPAJDJZEqVKiQ4mE6n9/xxiN+TmEym4KAgNptNLLTb7UqVSiwSdR+8ra+vFwiFVVXV81966fLFC869q1RKX1/fLuU1Wm1DQ4OPj4+Az3ceiuqaGgaDERwU1P0eKkIIofs3WN57snNyOwdLhBBCd5s7/IDP/RMReyCRiJ0P5SKEEMKe5V+nGDEDz9bvEp/ZhwcBIYT+cThFF0IIIYTBEiGEEPp7/jPDsAghhBD2LBFCCCEMlgghhBAGS4QQQgiDJUIIIYTBEiGEEEJ3c7Csbahv12jwlCCEELrb3LF0dza73W6zUalUEolks9naFAoWk8nlcBwOh9VqBQAgkWhUqs1mczgcxMRYdrvdZrMBAJVKJebVolAoZDJ2ju81VquVRCaTAGw2G41Gs1gsxIkmLgAymUyhUH69TgBoNJrFaqWQyQ6HAwDsDgc4HMSlhQcTIfSPoLy57PU7suPisrLyqsqmFpnFamlpbauoqQaAqto6vV7f0NSYXZDf0Njk5e5eK204feG8n7cPk8mUtbZeyczQaLVCgUDa3JSWkS4SCrk35gZB94yfL16gUig2u/1CWqqbRHI+LU2uVPp5e7e0tV24nEan00VCYYfBcOrCeYvVUl5dzWVzzqX+4ioS19TVNTQ3lZSX6/T6kvJyiUjEYDDweCKE/r471i1zEQgC/fwHDRgobWry9vQkk8hWqy06KorDYcf07uMmkfSOjCCRyRQyecTgIeXVVQDg6e7u7eEZGRbGYbODAwLdJBJ3Vzc8hfee/n1jjCYTnUYLDQp2EQrdXCVMBsNoNHq4ublJJMEBAQDAYbNdhEKzxRISECjg8yUisUQs9vL0FAmF7q6uIhcXBoNhNpvxYKL4xKoAACAASURBVCKE/tvB0okYK/Py8PD38bFYLP4+vjQqFQC8PTxZTKa0qbm5paVVLicGYLurqK7Gs3iPEYtECpWyqrY2OCCgpa1Vr++wWq1lVZXOAtKmJoPRQKNSe4VH0Ol0m91OLLfb7WQyBQDMZrPRZJSIxXgwEUL/iDs2DFsnlTY0NcpaW/x8fFvl8oZGqcVilbW2+vn4lFZU1DbUC/kCfUdHdV1d0sCBCqVK3a622uzlVVUarcZFKKxtaKipr9PrO5pbZMEBgXgi7zF6Q4dOrwvw9bt4+XJYSIi3h0dWfh6bxaqsqdHrO+qkDSIXUUl5udlsrpdKxSIRjUYtq6qUtbSGBgYWlJSIRSIPN7eK6mqvTnN6I4TQX+/X3cHcsA6HAx/BQLdit9v/1NNbZouFQqFQ8IEvhNC/4E5O/oyREvXgzz7nTKfR8KAhhP6tbyQ8BAghhNBd2rP0Dw3Do48QQuhPqasox54lQgghdDfCyZ8RQgih33G7h2HZXBc86AghhP6mDp3qdu4Oh2ERQgihuztYtrS2lJSW4GlACCGEwfKWDhw44Ib5XdFtp9Fq8SAghP64253ujkZnES/SM9JfWfKKVCo9cfLEpV8ujR0z9labHDx8EABcJa49VJudk80X8Kuqqi5evBgVFfUvNf79D98fMnhI5yU/HPiBRCb13LaVH698IPkBvNT+OLvdfvzUiUupv3A4HIlYnJGdefrsz1w2RywSA4DRaLyake7n6+ssXy9tOPDj4Q5Dh5+Pr8ViOXzsyNWMdB9vHw6bfTH10ukzP7PZbFeJhCis0+t379vz409HJRKJh5u7s5Kjx4+FBAVn5WafOHVSp9f5+/nrdLr9hw9U19SEBAWTyeSM7EznqqrqqpM/ny4oKrRarep2dbumXeQiwhOH0O1kMRvvi56lXC5PGZqye+fu3Tt3f7n6y/qGemKOCKlUajabHQ5HVnZWfkE+ADQ0NLS3t+v0ujZ5GwA0NzcTJVvbWtMup6lUKpPJtG7DutzcXB9vn8SEROL7NDUtVSqVXv8ybaivqa3pMt5bV19XXVPd2NjY3t6emZVJJGqvrasFAJvNRmzbZcOHHnzIGekLiwoBIDEh0cfbR6fXKZXK3NxcpfL6o8XOtgFAYWEhsVAmk+Xm5t4qIzxy+unUCavNNnXS5E1bt9TU1vx87uykcRN37duj0WrzCvPf+eC9wuIiZ2Gb3bZ+01fjR48tKy/Pzs3Zc2Cfm6vrmBEjv9rydW5+XllFxYzpD+49sF+n0xHl9x/6YVBC4tDBQy5cuuSspKm5yWA01jfUX7h0aca0B9MzM0rLy77+dktMn77ubm679+2pqa3pvOpaZkavqKikhMQAf/+IsPBjJ37Cs4bQve1ODsOWl5ef+vnUqZ9PFRUXHTt27MSpEwAwf9F8CoXywcoPMjIz9v+wf9OWTUThvLy83d/vBoAv1nwha5HJ5fIFixY0NjY++vijer1eoVDU1dcVFRft2bfHbrfPeWZOXX3d8hXLr6VfA4BJUycdOXrkw48+TE1Lde59wuQJJ06ceOa5Z95f+f7JUyfXbVgHAK8tfQ0A1Gr1x5993H3Dd959BwCWr1j+S+ovmzZv+un4T3v27SkqLrp27drDMx/OL8h/+pmn5XJ557Y5Jyi+cvXKig9XZOdkz180Hy+7noUEBY9MGS4UCqkUak5+3gODksUiUd8+0WUVZb2jes17em7nwg1SaYCfv7ub2+DkB3IL8qWNjXExsV6eXgqloq6hPrp3H6FA6O3lVVldRZQX8gUXUy8ZjcZFL/x6Ii6m/tIvJpbFZj8642E+n8/nC0wmU6u8tVdkVOLAhLKK8i6r2uRthUVFF1N/oZApJBKJy+U1NTfhiUMIg+W/Qq/XK5VKpVKp1+unT59+/PjxwqLCuNg4CoXyyIxHOByOXq+vq6u76bYCgeCJWU/IWmQqlYpCoYSEhCQnJROrCgoLIiIiZj4685WXXjn842EACA4KXrRg0exZs8vLf039EBIc8uILL8YPjJ/5yMzn5z1fVl7WfS833TAjI+PVl1/95KNPYvrGOBdOHD9x9qzZ06dN/yXtl85t0+v1RIHvdnwXFBhkd9gLCwuJHie6lcjwiGZZ88rPPpk2eYrNZmOzWQDAoNPNZjOFTKFSf/N7J6PRyGaxAYBOp5vN5lHDRqzZsG7jN5u1Wu3A/v1P/nzq2+3biktLHA4HUX7qpCn9Y/ulXk57/5OV9htze0kbG93c3Dzc3DlsztqN6/k8Xt8+0ST4NXdxl1VREVHDU4b1i41bv3kjAHi4u9ffGMZACN2T7mQi9djY2EcfftT51mK17Nm754lZTwDA68tef3vZ272ieh04eOB6VCeTiV6aXCEHgDNnz1y+cnn+i/OzsrO6VEsikYihTqPRyOVyAYBBZwAAhUyxO+zOYiwmiyhMpVEBgPjeJJFIDodDLpcTZW66IY1GAwCFQtEgbXAuJL6LjUajp4fnrdo2oP+AwMBAkUjE4/HwyutBfmHBuYvnX134MpvFMhpNMpkMAGQtLZHhEd0Lu0pcZS0tRAEvT08OhzP/uRfoNHpLawuJRJ739DMCPl+/Xe/n60eU33tg/0NTpytUyqLiIo1WIxQIAYBEJlmtNnW7+ssNa+c9/Yynhwex0Ga3GY1GHo/XZRWPx/Nwc3cVS77fvxcAHHY7jUrFE4cQBst/xa7du9IupxFfPRvWbpg4YeI3337zwYoPiCXHjh8zGo25ebkjho8AgIjwiPfef6+1tZXo5EkkkpLSkk2bNykUiqvXrnp7ee/ctXNYyjAA6NO7zxdrvvjok48KCgo+WvnRn2pSQnzCCwteIJFIPUx5MXzY8FeXvNrU3PTWsrecC48cPaLRaIpLirdu2VpQWNC5bUSBZ+Y8s/rL1UFBQWQSedqUaXjl9eDMhXM6ne7LDWsB4Jmn5qz9an2zTKZUqwJvNnGpWCTicrlff7tZ2ti4ZNEr+UWFx0+doFKpKYOHOOyOjd9sCgoM5LA5YtH1B3A83D3WfLXOYDQy6HQiUgJARFh4Y1Njs0ym1Wm37doOAJPHTxw1bMRnX3xuNpunTZqSkZXVeVVhUWFOXq66XT1+zFgAkDY1JQ9KwhOH0D3sdqe7+4MZfOx2u0KpcJW46vQ6LodLLLRarUaT0flWrVazWCwqlWo2m1ksltFoZDKZzn6eTCaTSCS0Pz9tk0aj4XK5Pc8P1d7ezmQyGQwG8fbsubM1tTVTJ08VCoUUCqV724hiHR0dGq3Gwx2nI/5zHA6HQqmQiCU9lFGpVVwul0alAYBWp3U4gM/jAYDJZNLpdcRjtE4dHR0Xfrk4bvTYzif90LEfn3hsVpdqdXo9iQQcNqf7HtXtahaLxaAzLFbLlm3fPj93Hp4phG6n25zB5y4Nlv8tBYUFra2tw4cNx8v3v+tqRvqAuH7E3zp/SmV1FZfDwb+BEMJgicESIYTQfR0sqff2x0MIIYT+PkykjhBCCGGwRAghhDBYIoQQQhgsEUIIofsyWJ6/eHHFypXE6207djT8sWxhq1Z/8Wd3tHf/D/UNDX+k5MerVhEvtu/clZmdjRfHnZJ69er+wz8S/9Tt7XhAEEL3b7CUtbSc/vnMmXPnAaCqqrqjowMAsnNyi4qLiQL1DQ21dXVNzc3tGk12zvXJOiqrKqWNjTm5uUSZBqm0vKLCYrEAQHlFZVV1deddKJXKouLixqZGg8EAADqdLisnh9gRoU0uJyo3mcwAMG3yZGL5qBHDpdJGZ868tCtXGpuup8luaWnJLyi4MUVJXUZmpjNVOvqnhAQGxvWN7hMVVVFVxbqRaAIhhO6g2z2fpVNhUVH/uNhde/ZMGDs2Ne1ydJ8+m7Z8o1Ao8gsLikpK+sfFjRw/gc/nrV2/oay8vLGpqaCwcED//tt37VIolLV1dWlXriQlJk6YOrWjo2NQQsI327YVFRcVFBWVlZXHxcYAgLSx8ZXXXmeymPt+ODBh3DgqhfLS4sVikfiLteuGpaQQyXfWrl9/+MhRGo22/uuNkydOfGnxkmlTJqdnZGzcvEXk4vLdzh2jRoyY+9zzEolk3YavkgclFpeUbNy0mUQifbdzh6eHx9oNX3F53F3ffz9yOKYj+CdxuVyRi8vP58+PHj7MOQ8lQgjdQXcyNyyLxX5h3rOr16wl3paUlu7Y+i0AzJg5c97cucGBgfPmzlUqlePGjPH18Xl/5UcAwOPyXnv1FQB4/KmnAMDHy3v5m28CwMHDPz768AwAOHDo8JynngSAY8ePz336qZQhQ4gs2ydOnxbwBR2GDjqdfvnKlTGjRhE7nfPkE7ExMVk52W03kqfv2rOHmB6kqLjkWnq6n6/vYw8/PHL4cAaD0XmVwWCQtbSYTKaXFyzEy+gfp25vV7drggMD8VAghO73YAkAKUOG/Hj0qLSxCW7M+2GxWJgMJgAQiV5JJBIxJRMx74fRaLTZbCQSicgC6kwGSyaTh6ekAICbqxuxhEQiEQOkRuP12bQDAwOGp6QEBQQGBwc5G0BM3GQymbicX/N/9ouN8ff3F7m4cDgcYk6Smpoab2/vzqskEsmaz/9XVl7+wsKFh/bvwyvpn3X24qXBgxLxOCCEMFhe9+bSpcNHjwGAhPiBb737rlrdPmvmY7cqTKFQXlv2ptlsfmj69M7LH5nx0NoNG+x2+wNJ1yd/mDJp0suLl2Tl5OTm5T06Y8a4MWMWvvyK2Wyprqn+/NNPnRuu2/iVv5+fh7uHM935k7NmrftqY2BAAJlMnjh+/Ndbvvno009Ly8o3rlvbeZWfr+/Hn62KjAgPDwvDy+gfV1JWNmX8ODwOCKG7xO3ODdszhULJZDI4HE4PZcxms8lk5vG4XZarVCoyhSLg851LTCazXq8T3ZibyWazEVMekkjXJ/X9+LPPpkyaJBaLu9wYMxgMGq3W3e16J1WpVAqFQmIeks6r9Hp9u0bj5emJlxFCCGGwvGcd+vFI/MABGO0QQghhsEQIIYT+FszggxBCCP2O2/2AD85niRBC6O+7zRM+Ys8SIYQQwmCJEEIIYbBESKPV4kFACP177lhSgiVLl8jlcr1ez+FwgoOC31r21m3Y6cqPVy5buqzzkpLSkqKiogenP4iXwt3mzIVzI4YOA4CM7My8/Pyw0NDBSQ+YzeaDRw7r9PopEyZKxNd/HXstM/3o8Z/Cw8JmTHuQQWeUV1bY7faIsPCe62+Ty8sqygfFJ5w4fbJZJnsgKTk8NCwjO7OwuCg5YVBoSKhWpz16/CeL1TJhzHixSHT42BEiC/9D06afPH1q4rgJeI4Qwp7lv+6zjz/bumWrUqncumXrW8vecjgcWdlZ+QX5ANDc3Nze3g4AlVWVROGysrLKyuuvs3OyC4sKO1dlNpvTLqdJb0zy1drWeuHiBe2NrkZmVmZBYQHxurCwEAA678vH2ycxIREA6hvqS8tKLRaLzWbLzslubWu91e7Qv621rfXT1asOHz0CADW1NRcuXZox7cH0zIzS8rKde78PDgoaM3LUoaM/EoV1ev2Zc2eHDh4SGRZxLSMdANKuXvbz8ZU2NRaXllisFgBoaW1tlsnq6us67+XAj4f6xcb+dOqE1WabOmnypq1bamprfj53dtK4ibv27dFotes3bewXG5eUMGjd1xtsNltFZWVSQmJSQiKFTOHz+Tl5uXimELp/3O5ZR2h0Vue323dunz1rNgB8sPIDdbv66rWrlVWVEonkjWVveHh47Pp+16gRo9ZvWJ9fmJ+Xn1dSWpKekV5aVlpYVNggbYjuE01U8vyLz4tEom+3fRseFm40Ghe+vNDLy2vlxysfmfHIuyvelSvkubm5hYWFAwcMPHDwwPRp0zvvi0wmnzh5YlDioBGjR3R0dCQnJS96ZRGNStu+Y7uHh8ePR37svjv0b2Oz2QP7DbicfmXE0GEms7lXZJSrxLWwpNjD3f1aRrqrWFJSVjplwiQiM7ADHEdPHAdwjBk5KjgwCACupl9jMBhnzp0lkyk/HjuSlDho4ZKXmQzGuYsXhAKhu5s78QfWxdRLQx8YYrfb4/rGcrnc8xcvUqjUyPDIiLBwlVptsZhDAoP79OpNIpFSr1yOioy8lpEua23hsDm+Pr4SseTA4YOJ8Ql4shC6Uyxm433Rs+zikRmPcDgcvV5fV1fXu1fvmJiYN95849133gWAfT/sE4vFnp6eBw4d4HK4hUWFkRGRUyZPcW5LZ9Dz8/Mfe+SxsNCwI8eOzJo56/HHHl/16SqHw1FcUrxw/sJ33nrn9JnTN92Xc6GPj88HKz5oljXX1NRYbVYvL69Dhw/ddHfo30Yikeh0OvHaw82dw+as3biez+P17RNdXVPt7eUdE933i/XXJ6th0BlvvbbUbDa/t/KDrJzsiqrK4KDgsJDQXpFRSrVSrlAAgLen14NTpo0eMVLaeGP4Qd4mFAgBIDI8olnWvPKzT6ZNnmKz2dhsFgAw6HSz2dwvNi71yuW1Gzc8N+cZMpk8OOmBx2Y8cv7Shaqaai6Ho1Kr8UwhdP+g3iXteH3Z628ve7tXVK8DBw8AQGVlJZ/Pb21tDQwIJJPJo0aOAgB3N3dvb+8xY8acPHly2ZvL1nyxhtj2qSeeEvAFm77ZJJfLyWQyjUYDgNy83KDAIGKWZovFwuw0h3CXfRFYzOtdXpGLaNTIUSqVymQyUanU7rtDt5O6Xf3lhrXznn7G08MDALy9vKMiIgHAZDYRBRqbmqpqqnpF9Xr0oYcP/HiIz+NPGDtu7w/7Q0NCpkyYVFJaCgDEJUEmk+3ELDMAZBLJarMCQH5hwbmL519d+DKbxTIaTTKZDABkLS2R4RH7Dv5Ap9Pfeu0NMplcU1cbGRHB4/LCQsNUKhUEAoVCwbODEAbL243H4x07fsxoNObm5e7dtzcoKGjp60uXvL5k3/f7Hp/5+OerP7fb7UOGDMnKzvp+7/dkEjmmb4xz263fbfXz9WtrawsPC/fy9lqwaMGVq1c6DB3Tp05PGpT02huvqVXqp5546qb7Gj9ufOdmBAUGSSSSbd9tq66pfuO1Ny6lXuq+O3Q7ZWRlaXXabbu2A8Dk8RPHjBz9vzWryRTKyJTrE267SiRbd35HoVAKCguHDR2aevmygC8Qi0S5+XkVlZVGo7FZJuteraeHp1arA4AzF87pdLovN6wFgGeemrP2q/XNMplSrQoMCFy1ZrWPt09JWalYJB43esy6jRtCg0OkjdJJ4yeo1Gpnnn2E0P3gdueGvVUGH7vdrlAqXCWuOr2Oy+k6o4hSqaRQKAKBAADa5G1kElksFjvXOhyOpqYmgVBAbGiz2dRqtbOAXC5nspid6+x5XwDQ3Nzs4uJCdEa77w7dWR0GAwCwWb+5+f3TyeMpg4ey2WznEpVaLeDzLVYLlUqlkG/SC9y17/sxI0aLb0xK47yWFEqF8znbzixWi06ncxG6AMCxk8ejIiKDAnBuaoTu3FfB7c3gc7cES4RuM4PRUFJaGhcT+xe2Tb1yOTlxEB5DhDBYYrBECCGEwfI66r398RBCCKG/D9PdIYQQQhgsEUIIIQyWCCGEEAZLhP4KrU6HBwEh9I+4Y0kJ3ly+XKFQ6PUdHA47MCDw9cWv3s69f7xq1dLFizsvKSsvLy4pnTp5Ug9bHTl2LDwsLDwsDK+b2+BCaurQ5GTitVKlOnvxEvHa29MjulevE2fOAoCLUDhi6JBrWVn1DVIASEqI9/LwAAB9R8fhn36qqq6ZNmli78hIADj+88/jRo783Z1m5uT4eHmZLZZLaZeFAsHo4cOoVOqpc+c0Gs2IoUNFLi71UqlzlclkcjbD18ebRqUGBQTgiUMIe5b/pA/fe2/junUqlWrjunWvL37V4XDk5OYWFhUDgEwma9doAKC6poYoXF5RWVVdTbzOzcsrLinpXFV9Q4NKpSotK7NYLNk5uTqdDgBa29oMBgOxFgAapFKVSpWTm0skwJs2eTKxbVV19ZVr12w2m5enV/zAAQ6Ho7lZVlVdXV9fTxQwGAxXr6UTlUiljRqNBgBaWlryCwqIqpRK5eWrV4kGo39Em1z+xVdfHTv1azpfDpsd1zc6rm+02WK22mx1UimdTkvo3693VCQR4RL690vo389FICDKHz72U3y/fsmJib9cuQIAGq1Wq9U5HI6KquqaujoAMJpMGo22urZWqfr18WybzZaZk+vm6rpl+46xI0dQadSfTp8+d+mS0WhKTkzctO07u93eeVXnZoQFB588cxbPHUL3qts960gXu/fsfeyRRwDgk//9T9OuSc/MqKquFovFy1escHNz27t///CUlK+3bCkqLiooKiorK8/KyS6vqCwuLZFKG3v36kVUMv+ll+ul0nMXLvxw8BCTxfx223cTx4/b9M03HA7X09PjpcVLpkyauOiVV6traiqrqrNzchLj419avGTalMmnz5zZ/f0em93204mTIhfh6TNnYvr2HTV+gkQs3nfgB6FQ6Ofr+9z8BR7ubpu2fBMSHFxXX+/t5dXY2Lhx02YSifTdzh1DBw9+bv4Cfz+/VV+snjRhApmMw9r/ADaL1S8mJj0ry9mzpFKpIhcXMpmcnpX10JQphcXF0sam2oYGPx9voUBw5vwFhUqp6+gIDwkhTkFdQ0N5RSWDQX/swQcBIO1aelR42KmzZ/UdHaUV5U3NMhKQ1m3ezOfx9hw4mDigP5E8trS8wma3BQYEeHq4+/v6KlVquUJZUVU1bdJEV7H4cvq16N69fX28nascDkfnZpSUl7u5uvK4XDyDCGHP8t/y4NSpbA67Q99R39AQFRkZ3Sd6+Yr333z9dQA4ePhHkUjk4e5++OhRDptdXFISHho2cfw457YOcCx+6aWpkycnxA98avZsokPZhc1mW7pk8UsL5lfcmBcTAPbs3//2sjfmzZ07a+ZjzoWR4eFznnry9cWLj588CQDz5s5p12jMZnNjUyNRYNeePQEBAXaHvai4pL293WAw1NXXL128hEql4vX0jyCRSHQarfvyIydOTBk/HgBcJZKk+PiJY0Zv2vadzWbr0ytq+sSJVov11LlzRMmJY8bERPe5mp7x6Zdr7HZ7dW1tcGDg4KQkJpNhNJpa29oAoF9M39HDh0WGhSlV1+cPaZI1u0okdBotPCTkwJGjGTnZk8aOMZpMLBYLAOg0OgB0XtWlGW6urtKmJjx9CGGw/Be9896KqIiIR2Y8RLytqq7m8XhtbXIAIJPJw1NShqekPP/ss1GRkSvfX2EwdCx//33ntjQqjUqlkkgkGpUGAA6HAwDIZIrNZrXb7cQ4G5VKpVAoZArFbrf/+uFJZCqNZrPZioqLfw29DgcAGI1GHpcrVyi+XL8+ZciQlCFDOre2X2zM8JSUBS88T6PRPv7ww8HJyR989BExVIv+JWazuam52cfLCwCsVltYSLBELObzeCqVKjQomMvlRkWEq26EvYNHj/aJiho2ZDCPy22Ty1lMJgBs3bnL39d32OAHbvRWr09F4nDYnUGaGFpfu2lTWHDwi3PnslgsV4m4pbUVADoMBi6H03lV52ZYLBaH3UGl4B9MCN2b7pb/21wu98Tp00ajsaCg8IdDhwID/Be/9NKy5e/s+PbbR2Y8tHbDBrvd/kBSUk5e3v6DB8kkcnTvPj1XGD9gwOo1az09PSm3Hhp9+sknXn3tdS6HMyjx11l8yyoqVqxcWV5R8d7bbzMZDL1e/8PBQ3X19c0yGZF0+8lZs9Z9tTEwIIBMJg8bOnTFyg8H9u/PZrNELpjJ719UUl4eFhJKvDYYOtZs/Fog4Pv5eAsEgs3bd/j7+tY1NDx1Y4TA3dVt47dbjSYTnU6rqq2Nie4DACwWMz0ry2y2VNXWDugX130XYcHB17KyWlrbqmvrfnZc+PnChehevcYMH/7d7u8lYnHvqMg2uaLzKjaL6WwGk8lskskSBw7AM4XQvTnidZtzw94K0QWUiMV6vZ7D4XRZq1KpyBSKgM8HALlCQSaRRL+dLOKmDAYDhUJxTiN8U0aj0WKx8njX7zMZTaaXXn31s48+plIpxOCb0Wg0Wyx8Hq9zwwwGg0arJSZpMpvNrW1t7m5utJuNHKJ/SYfBYLfbuRwOMRigUquFAkHne8YdBkPqlSujhg3rfI1pdToBn280GjvPb9rZ11u3zXvqyS4LLRaLvqNDeOPpoZs2w2q1fvf993NmzcJTgxAGy3ufxWLZun37s3Pm4KG4P9XW1zOZTI8/P1dldW0th81xd3PFY4gQBkuEEELofoQ/dUAIIYR+x+1+wAfns0QIIfT33eYJH7FniRBCCGGwRAghhDBYIoQQQvdmsCwvL9/y7Zaey6z8eGXPBX448ENJaQmeRYQQQvdmsNRoNRUVFa1trWq1OiMzQ3dj6sHKysq0y2lE1rHCwkIAqK2rBQCbzSaVSgFAoVCkpqW2t7cDQGJCoo+3DwDI5fJLv1wipgTpXmd2TnZhUSGebIQQQv+xYEnYsXPHy4tfzsvPm/PsHAA4cfLE519+npub+/byt51lXlv6GgCo1eqPP/vYYDA8+/yzMpls7ry5Vqt1z749RcVFUql0/qL5TU1NTz/ztFqt7lLnho0bzpw9c+jwoZ27d+L5Rggh9Bfc+dywc5+emzQo6czZMwCwc/fONavXiMXiyk5zg3RGIpEMBkNNbc3bb77tnOXjyLEjz859dljKMI1Wk5qW2qVOLod79drVSRMmjRkzBs/3f8LJM6erqquTEwelXrns5uoaERZ+KS01NrrvoIREPDgIofs0WDIYDAAgk8gAQCaTaTSazWbLL8wPCQlxBkiHwyGXywHAZDKtXrVaq9O+8+47X67+0lmAGLY1Go1cLrdLnb179x4zZszJkyeXvblszRdr8JTf/RIHxsdGx3C5HA93DxqNymSy3FzdOGw2HhmE0P0bLDub98y8+Yvmc7ncB5IfcC5MiE94YcELJBKJTCZTKJS3lr+Vs3AL3AAAIABJREFUEJ/AYXPEIjFRYOqUqQsWLcjOyS4uKX7x+RezsrM615mVnfX93u/JJHJM3xg83/8JAr5AwAcA4LCvZ65ns1h4WBBCd9Dtzg37uxl8DAaD1Wrl8XidF2o0Gi6XS8wpYTabW1paPDw8Os/yYbFY5HK5h4cHiUTqXmebvI1MIovFYjzfCCF0b7jNGXzuumCJEEII3W3BEpMSIIQQQr/jdt+zvM1/CyCEEELYs0QIIYQwWCKEEEIYLBFCCCEMlgghhNB/2x1LSpCZnb1m3XoKhQIAEon4s48+ulXJI8eOhYeFhYeF9VDbx6tWLV28GE8nQgiheypYKhSKIQ88MOepJ4m3MpmMxWYL+PzqmpqgwEAimrJZrKjISKm00dPDQ6/XG4xGiVgsk8lEIhGdTq+tq2tra4uNiaFSqdMmTybqqamtbWlpHdC/H4VCaZBKuRxObV1ddJ8+FArFbDZn5eT4+fp6e3nhiUcIIfTHUd5c9vod2XFVdXVWdg6QSNU1tSaTyWAwLF+xws3Nbe/+/cNTUj78+JPW1tYLly7Z7XaVSu3t5dXc0nL27LkB/ft/sup/EeHhtbW1azd8xeVxd33//cjhw19avGTalMmnz5zZu38/jUrb9M2348eOXfTKq9U1NZVV1dk5OYnx8YteXSxyEW7ftSs0JASz+SCEEPoP9CwBQN/RoVIpAUAoFPSPi4vuE718xfuH9+8DgKzs7IP79hqNRqVKVV1dc5N2U6mylhaTyfTygoXOhfsPHFy7+nMmk/lLWpparbbZbEuXLDaZTIuXLgUAOp1eWFQ848EHQ4KD8cTfzc5cuFhTV5cwoP/VjExXiTg0OPjytfToXlHx/fvjwUEI3XfBMiY6+qHp0zv3NXk8Xlub3N/fj0qjAoBCqWxsbLzeBSaTrTYrACiUCgDgcDhrPv9fWXn5CwsXHtq/z1kJMf2I1WphMplUKpVCoZApFLvdDgCzHnuUz+dv3b5doVBMnzoVz/1da2BcXHTvXlwOx93NjUalMhkMV4kEc6kjhO7TYLln//4r164BAI/LfSA5OTDAf/FLLy1b/s6Ob79NGTzkjbfebpbJXl/8KlE4LDR05aeftrXJKyqrAECtVq/7amNkRHjnB39mz5z58pLXfHy8IyMimUxml93t2P29r4+3XC4PvTH5F7o78fk8PvCg02QjLIyUCKE76nYnUv/j2jUaJoPJYNCdS6xWq8lk4nCuT9uk1+vbNRovT8/OW+n1eqPRJBaLulfocDiaZTIBn++sASGEEPpvB0uEEELoLoFJCRBCCCEMlgghhNDfc7sf8MHJnxFCCP19OPkzQgghdHfBYIkQQghhsEQIIYT+ntudG5ZG//XX5Xv37f3sf58dOnxIqVLGxcb9wRp+Sf3F38+/pLTk4sWLUVFRPZT8I2X+1IbErvGi+bflFeR/8923VzPSM7Oz3FzdXIR/8T53XmG+h5t75yUFRYU1dbU+3t5/7nooK12/6atLaakXU39pk8ujIiL/8kf7bveOmD59/8KG1zLTyWRya2vryZ9PFxQVWq1WTw8PAEi7etlN4kqlUgHAarUe+PFQr8ioM+fPenh40mg0YluLxXL42JGrGek+3j4cNruopPjAj4d0Om2gf4DZbN5/6MDVjHQ/X182m+0sf+joj1fSr7q5uvF5/F8up5468zOQwMvDEwCMRiNRHgAupl46feZnNpvtKpHgdYtuM4vZeF/0LNMup124dGHrlq3bt27Pz8/PyMwAgMyszILCAqJAbV0tAGg0GpVKBQD1DfWlZaUtrS0fffKRrEXm4+2TmJAIALb/s3ffYVEc/QPA53rj7uCOcvTeQQXBggUQe68pmsSS2BMTY0xiyptmim/sXdMVa2KJDTsIKCC99965zvW6vz82uVwAiW+SXzD6/Tw+Psfs7Ozs7Nx+d3b3dk2mvPy8LmEXQkgoEorF4tq6WrwES56m5qb6hvryinI8va6+LjMr02g04n/ez75fUlqCEFKr1UKRsKS0BJ8Rw7C2traamhq8JhKJBF80Qqijo6OgoAB/tF6f9TQYDNCV/zSJVDp40OA3X9vw/LOL9h7cr1Qq8WY3mU1CkVAqkylVqoqqSryR9Xp9UUmxUCRECEllMqVSWVFVqdZouhWKYydPSKS/+xmxVCYViUUIIbFEUl1bYzKb8LmKS0sUSgVCqLOrq1uhqKyuwifh5HJ5WEjof95+9z9vvzt/9tzOri6xRCKTy3Q6XUlZaUdXZ49F43/mFxWo1WqEkLxbXlhcpFKrEEJxY2Lx3lJUUqxUqR6yQUwmU1b2fReBc1ZOdmhIyKgRI708PRFCB7/5atf+vXq9Hs92+uxPdzMzEEKDwwef/fmcZfaTZ047OjhMHj/hwNeHZHLZmZ/PPf/sopq62o7OjsRTJ3x9fCZPmHju4s+W/GcvnHdydJyUMOHg14frGupz8vKemf/UT+fPyrvlhSVF/9n8UUlZKUKooKiwsrr6qXnzT535UalUQr8FMLL8fxlZHv7q8DNPPePm5oYQmjJ5iquL64cffygSiwoKCkpKSoZFD1uxesWC+QuuXb9WXlEeHhY+ftJ4tVrt7eV99tzZMaPGtHe0J11NihkZs3bdWgqZcuToEYFAcCXpyuGvD/t4+/h4+yCE8gvy8TyxCbFMBvPk6ZOOjo5isXjHzh1sNvuHIz9MnjT5g48/6OzsvH37ttls1mg0y1cu5/F4BAIh6WpSZETkmPgx9vb2x08et7OzI5FIp388PWbUmIbGhj379hAIhK+//Xr61Ol91nP0qNGW43rwv2pobDQYDAF+/gwGQyqT6fS6kz+djh8bq1QojxxPbGpuupOWSiGTfzx3ZkzMqM+3brGztbty7SrPzi4jK/ParRsUCuXHc2dCg4OTU+8MCgvn8357w0xjc5NOrzcYDGcunCMSCJevXgkJDt65b4+jg8PX33+XEBe/fe+u1ra2ltaWisrK8NAwfK6W1tayinIiidTa3mZjwzrw9eHqmhpfH5/9Xx3k2dmdu/izm6tb2r10q0WH7Nq/155vn3jyeHho2O4D+xzsHY6dOhEdGbXv8IGYESO+2Pals0CQePL42FFjiMQ/PmAtqyg3mc1BAYG37yQrlar6xoaQwGAajTY4fFBdQ/2wodE0Gq2gqNBgMIjE4vixsSwm8+fLF2NHj8Fnv3Lt6uwZM/k8/tkL5zkcDpVCKa8oDwoMDA4M+vnyRQe+fXllxezpMy1PiLSztfPy8KxrbBCKhEMjhmZlZzEYjMamxvjYOIGTwM/bp66hPipyaE5+nreXt5+Pb3VtDYPOEDgJoOuCx3hkOWDPhjUajZbHzmk0GjNmLisvO33iNEJo1txZa9es7ZHfzc1t88ebEUIuLi4RERGZWZn4GLG+vn5o5FAXF5dz5885OTmtXL4yLjaux7y+Pr6vvvLq9ZvXq6qqoqOj2zvadTrdm2+8iRDKzs6+cvGKRqORSCTtHe0Txk94adlLeOEIodCQ0FUrVk2cMHH/wf1bt2zFF71q7So/Xz8zZi4pKcFHk33WE/wtaDRa72H6zGnTA/0DyisrC4uLPTw8JoxL8PP1vXHrJpfLnTFlWlhIaE5erpurG5/P9/fz713m1RvXXV1czBhW19BgNpsnj5/Y0tbarVBotFqz2bx40XMGvWHX/r3FpSWpd9MjBg0mEokajUYmk+KnKI1G46qXllPIlDkzZtXV1xkMBqFQiBCyLPpuZsak8RNGjYgJCw5Jz7g3c+r0yCERarWqqLQEIURABJ1O19HZuXjR8/jLzxFCLa0tqXfTrStpZ2s3ZeIky1RHB0eEUEhQSHhYmFgs3vfVwU0b3qRQKAQCAR+qpt5NW7f65ayc7F++2GSySq1iMVkIoYnjxu/ev9fOjqdQKBQKRWtb64uLlx7+7hs7W7u6+roFc+a5ubrt3Ldn3qw5+PrGjBgpFInqG+oNBqPBaCASiUqVEsMwpVLF5/HwU74IoeFRw/YdPlBWXlZWUR4VMRT6Kni8DViwjIyITElJCQ0JRQh9vPnjKZOn4Gc1DQYDfoSLYRhCSCQSMZgMhBCD3vejtHl2vIkTJkqlUp1Ol5GZ0fv56QghGpWGECIRSWbMzGKxDu47WF5R/uKKF5MuJeHjP7FY3NzSTKFQesyO10Gr1bJt2Nbp0VHR3t7ePB6PzWY/ZD3Bn4BhWG5+3mtrX7l9JwXDMFm33Hq76A16Bp2Gv1JGr9fjD1vHd+W9h2tbd+94Y916o9GIZwgKCHQWOHPY7PLKyrLysrmzZlfVVCOESCQSiUgyEU1mzOzr4+vo4MhiMkvKSv39/CcmTMCLIhKJFDJFJpedOvPjqheX4xWwXjSBQCCRyAih6tpaAoGAZ9Dp9QwGHSGkNxheXrVGrVZ/e+T7V1avxa+qOjk5TZ881brCxF/jKEKIQCCaTEaEEJvNFjg6OfDtT/x4yjoz/iqePQf3Nbc0X756ZdrkqRiGWaIai8V6edUaKoXa2dXpYG9PIhL5PH54aJhYInZ1ccUvwer0Osv6Hjme+NwzC5+et+CjzzdnZd8fO3rMqBExOp2uuraGzxv228lhs2nlsuVcDkd1ROXh7gHdFUCw/H8xf978VWtWrVq7CjNjfD5/7JixObk5b256UyaVLV28FB9SvLXprZramvnz5v/uRC6Fcu3GNS6HixDy8faxt7f//ofv6+rrNr256WGWK5PJduzaERoSGhwUjBBKGJewYeOGtva29955T6PR9MhcXlH+/gfvV1RWfL75c8uil7+4fMeuHT4+PkQCce7suQ+qJ/grbty+WVxaotFoxsSMtufbhwQF79i7CxEIRAIBIfTjuTPOTgI+jx8YEHj+8sXEk8frGupXLHvpTlrq7zo3mXw/N2fY0CiEEMeGvefgvraO9ldWrfX29Prx3BkXgTOBSBg5bERjc9PFK5fl3fKy8jLr2ZkMRj/vBaNSaRqN5tad5I6OTpFEbMOysUwaEzPqy53bS8tLtVrdswue2rlvT2VNVWNT05yZs85fvEAkEr/+/tvQ4BA6jcZhc37p1WQKhfPA8/ZBAQH3sjIRQiWlJfmFBTK5bNrkKdYZvL28161+GSH08RefTvs16OLHiAghkVh85VoSmUyOHxsbHRm1ZcdWiVTa1Nz05vo3jEbTtt07iCTShPgEy/q6ubpu2f4ln8fn83gxw0fuO3ygrr6+tr5u42uv/+5Qxowd/Oawj7c3i8ni83jQacHj7Z9+kHqPJ/iIRCIajcZmsy1/0hl0y35HLpdzudweJZhMJrPZbH1FsL293c7Ors8xZZ+UKqVcJnf99ZZIuVxOp9NpNFqPbFqtdvXLq3fv2E0ikfAbBS2LVqvV3Ypuy0WaPusJ/l4qtYpBZxCJxB+OHY0dM5bL4drZ2uKjTIlUwuVwLQMp66EPZv5tgCUSi7hcLoVMQQjpdDqVWsWz4yGElEoljUYjkohGo9ESYB6GTq8zGo0sJkuj1fQ4o1DXUH8z+faKpS8ihIxGo7xbzrPj4adMEUIGo0EqlfLseL3r/CB7Du5/ZdUahJBMLmMwGP3Xs76xoaS0ZMbU6ZYUhVKBYYjDZuMtJpPLbLm2eH3wO5J6HBYoVSqDwYC3sNFolMqkPB6PRCT1bAGdTqlSWl8VBuAf8w8/wWeAg+WjzGAwfPX1V2tWr4FO+UhJSbsTGhz6iP9WoaKqMq8gf+FTz/xdBdY11DPoDPznIn8o835W9NAoEokEvQVAsIRgCQAAAILlP4T8eK8eAAAA8NfB4+4AAAAACJYAAAAABEsAAAAAgiUAAAAAwRIAAACAYAkAAABAsAQAAAAgWAIAAAAQLAEAAAAAwRIAAAD4MwbsFV2e/gH9TG2sroJtAwAAAEaWKM9zSJ//+p/r7XffCwwf1NnZ+ecWuv6Njd8fPfo3rkVGVlZg+CChSGRJaW5pCQgNKyoufpjZ16xbl3j8RO/0oSNG+oeG4v+iRsZsev8/Wq22z2xV1TXQjwEA4LENln+CRqO5cPmyo4PD+YsX/1wJeoPBZDT9jVUaHh3N5XKTrl6zpFy6kuTs7DwoPPyhguXKVfFxsb3TdTrdji+/zEpLy0pL++6rw+n37u4/dKh3tl3bt7m6OEM/BgCA/1fkR7Nanv4BfZ6JvXL1aqC//6yZM06cOr3ypZfwxM6urguXLg2NiDz3888IoReeW+Tv54dPOv3TmdT0dAqFMn7cuGlTJlsXlXj8RGCAf3RUFEKovb0j8cSJ119dRyKR7mVmnjl3HiE0flz8lEmT8MytbW1Hjx1ra28fNTJmwby5ROJvBxlEInH2jBmXkq688NwiPOVyUtLc2bMRQvkFBW3t7Uajsa6+fv26dX3Wp66+zsPo7uri0ntlbWxseDweQojH402fMjW/oBAh9PPFix7u7ml373p5es6cPr2oqDgoMJDFYh04dDg4OChu7FiE0A+Jid6eXmPHjIb+DQAAT+LI8sczZ+fOnj196tSq6uryigo8USQSbd+1+7/btg2NjFRrNM8vXWY2mxFCx0+d+vaHH+bMmjl+XPyHn3xyKznZuiiRWHzg8GH88/kLFwqKCkkkUnlFxasb3piQMG78uPjP/vvfi5cvI4Ta2ttnzp3HYDDjY+OOJCZ+/OlnPWo1d/as+9k5XUIhQqipubm4pGTOrJkIodKy8k8++/z4yVOuLq4Pqs+FS5cLior6X2udTp+Xn+/l6YkQun7z1tpXX6urr3d0cEQI7T90SCwWI4QEAsG69a93CYU3bt3asXtPUFAgdG4AAHjMR5Z9am5pycvPP7h3j62t7aiYkWfOnX9v09u/hhPdvl27+HxeQnx8WGSkUCRycnR0Fgi2fvFFWGgIhmGnfvypqqo6IT7eUtqcmTP3HTzQrVBw2OzLSUlLF7+AEKqorKLTaRFDhjg5Onp5euJB9+DhryZPmvjqy2sRQpFDhkyaMePtjW/Q6XRLUUGBgUGBgVeuXl3y/POXk65GDR3q4e6OT6JQKMeP/IC/s77/+hgMBr1ej89CpVIRQu+8/x8mk4kQ1t7R4SwQrF21Fc85Yvjw7f/d0qNx5syaef3mjY1vbyqrqNjy6WZHBwfo3AAA8CQGy5/OnqNSqW+8vQkh1NbWXllZtenNjXgcsuVy+XweQojNtiESiXjUCfD333/oUGFRsUQi0Wq1I4YNsy7N09MjJDjkxq1bw6KiqmtrJ02YgBCaOD4h6dq1UXHxoSEhCfFxS154ASFUXlnZ1tY2a/4CfEaNRtPW3u7j7W1d2rw5sy9dSVry/POXr1xZ+OwzlvQAf3+8hn9Yn6+++XbLtm0IoRcWLfrkww8QQi8uWRIxZAhCiMvleHl6WsoJCQrqs30++fDD4aPHxI0di68LAACAJy5YYhh29vz5ZUsWh4aE4CmvvbHx7r0M/MocgUDoPcvy1atHDB/+1f79AoHTmnXremeYPXPG5aQkkUg0ftw4GxsbhBCJRNqzY4dGo868f//r776vr2/YsfVLOo329IL5zz37LD5Xt0Lh5urao6hZM2Z88eXW7NzcqurqaVOmWNKtK9Z/fZYtWfzMUwsQQpYxq6+vT2REH/cG97myCKGfzp51Fggy72c1NTdbhrYAAAD+un/NNcvM+/elUumaVasmTZiA/0uIjz/78/l+ZmlobJqYMN7ZWdDW3p51P7t3hhnTpt3LyPzp7Ll5c2bjKd98//3b771na2s7eeLE+XPmSKRShNDoUTEpd1LZbDaPx0u/d2/xiy+RyT0PMhwdHGJGjHjjrbcTxsVzOZw/UR86nc7j8Xg8HpPJ/BPtU1FZuXPP3sP7982aMWPDW2/jJ5ABAAD860eWkY0FD5/5xzNnp0+dSqfRLCnTpkxev/FNlUr1oFlWvPjiypdf9vHyMhgM06ZM+T4xcervb4i15/OHRUWVV1bGjhmDpyx8+ukFCxdOnz3Hzs6usrp6/+5dCKGlL7xQUFgYO36Cg4NDW1vboX37+lzc3NmzX3vjDctl1D9Rnz9Nr9e/9sbGpS+8EBIc/O5bb02YOu2rb7+13C0MAADgLyKoFJIBXHw/z/H5Wx7i09berlSq/P18EUIdHZ1OTo7Wv/pACG167317e/6G116zpOC/9NDpdL4+PtaDvI6ODolU6ufri9998/9UHwAAABAs/+lg2Y/Ozs7M+/c/2vxp0sULTo6O0BUAAAA8yADf4DOAz4CVyeXJd+5s3fIFREoAAACP9Mjy4YnHPwVb63/Fv3kaGgEAAP46uGAGAAAAQLAEAAAA/pp/zWlYAAAAAEaWAAAAAARLAAAAAIIlAAAAAMESAAAAgGAJAAAAAAiWAAAAAARLAAAAAILlo6qpqclkMkE7AAAABEvwQJve/49ILIZ2AACAx88AP8Hn2o0bPxxNrKuvp1AokRFD1q9b5+XpiU/SaDT7Dh66ev26RCLx8HBf8eJLUydP6l3CK+tfr6yqun750oMWkXTt2g9HExubmvh8fkJ8/OoVy+l0umURBw4fvnU7WaPVDg4Pf/WVly1LRwj9dPbsidM/CoVCby+v5cuWjh41qs/yVSrV5i+2XLp8uVuhIJPJQyMjPnj33dCQEOhbAAAAI8u/wU9nz65Ys1atUT+38NmEcfHXb96a9/QzMpkMIYRh2Io1aw9/8834cfEvr15tNJpWv/JKTl5ejxKOnzx54dIlmVz+oEUcP3ly1cuvkCnkpxcs8PH22rV378q1L2MYhk9d/cq6fQcPDRo0aMa0qXkFBTPnzmtuacEnHTj81Ya33uZy2PPnztHpdM8tXXbtxo0+F7Fx06Zr169/+vFHQwYP3rZli1arW7h4iVAkgr4FAAAwsvwbjBmXwOPxzp0+RSQSEUIZWVnPPPf8K2vWvLH+tctJV9esW3do397JEyfiQ8DR8eNGxYzcvX27Zfaa2tpps+fweTyjyXQ/Pa3PRYyKi3d1dTl97Bj+55fbd+w9cCDpws8hwcG3kpOXrVi5+cMPn1+0ECEkFktGjxs3cXzCrm3bxGJJTFxcQnz8/t27EEJms3ne0890dHamJ98mkUjW5XcJhdExo/BCFr6weO/OHUqVasy4hM83f7Lw6aehewEAAIws/xKRWNzU3Dxj6lQ8UiKERg4fzmAwysrLEEJnz5/38vScNGECPonBYJw6lvjy6jWW2fV6/SvrX587a9aI4cMftAgMwzo6O/39/CwpgQH+CCF5dzdC6MrVawwG46n58/FJfD4vZsSIG7dum0ym5DspWq126QvP/9JGROK0KVPa2tuLS0p6LEIsFiOEbG25lhQPd/eUG9cn/1pzAAAAECz/PDqNtn/37ilWlyFb29o0Go2LiwtCKK8gf8SwYQ2NjTt27/5w8+YTp0+7ubkF+P8W9r7YulWj0bz/zqb+Rs0EwphRoy5dSSosKkIIdXZ2Hvrqa3s+f3B4OEKosKgwKDCARqNa8g8KD1epVK2trQVFRQih8PBwq0lhCKHKquoei/D29ra1td25Z691HPX28uLxeNC3AAAAguVfZWNjM23KZFcXF/zPLqFw7auvUqmUFxY9p9PpJRJpQ2PjxGnTzl+4mH737jvv/2f85Cn1DQ145jtpaUcSj+3evo3JZPa/lB1f/tfF2XnmvPmhERHDRo+prqn5+uABfC6xROLo4GidGR8gSmUyiUTC5XDoNJrVJFuEkFQm7R3yd23bKpZIps+Zezcj490PPki6ds1oNP4/NZrJZNJoNP/MBpJ3d1su7j4kpUr1p9ddp9MZDIZ+MjS3tOj1+r9l1ZQq1V8vRK/X49uipbVNq9PhiSq1+kGN1t2tsHxWq9UPaqge7aDVamVyuUwu12q1A9UTAADoUfjpiMlkOnrs+PjJUxqbmr46cCDA369b0Y0Qyrx/f+sXX9y5eeNmUtLpY4lt7e0ffPwJQkgkFr++8c3XXnl5kNXI70G+3LGjrLx85vTpG1577cWlS0hk8utvvtWtUCCENBotifS71adRaQghIomk0WiJv782iU/qccESFzd2bOqtmzu3bnV1ccm8f3/Vy69MnjGjo6Pj/6Ot2trbT587/89sl4Pffqf7NQY8pHOXLrX/7yt+OzWto6srt7Cwtr7hQXk6OjuT09OpVOrfsmo79x/464UUl5XfuXcPIWQwGi4mXcUTD333vfoBMezTbdssd6J98PkXanXf2Xq0w9mLl/Z//c2ps2f/u2v30VOnLOmdXV340gEA/wzywC6+uKTkjbc3VdfULHzm6Y3r13O5XIQQmURCCEUNHTprxgw8W3RU1Phx426npGAY9p+PPmIymaNjYoqKixFCMpnUaDAUFRfb2tp6uLv/br+Tn3/sxMnVK5a/vXEjnjJ5woQFCxd9891369ets2GxFEqldX6VSoUQ4vN4LBZLpVL+fiyiRAg96OQqh82eM2vmj2fO7N6xPenqtfc+/PDLHTu3bfniz7VJUWmpm4sLz86uuraObcMym81pGRksFmt8bKwlT1NLS3Zevo+XV8Sg8Nr6eraNjaODQ2FJSaCfX1llJYNON5vNocHBCKGc/Hwblk1JefnokSNy8vJtuZzRI0diGJacltbZ1TVk0KDggIC8wkJ8Fgd7++S0dDtb7vi4OIRQeVV1TV3diOgod1fXtvZ2SzV0en1LW1tjczOFTB4fF0cgENIzMxUKpVarw49+7ty9q1Sp4kaN5nDYlkMi68SKquqC4mJfH+/ggIDUe/eEIuHwqCgmg1Hf2KjVaiuqq8OCQzqFXRKpdPL48VQKpb2zy4bFampp8XBz67P83IICGo1WUVUVO2qUg729dW0NRmNDU1NDU9OMyZN/N9RTKFLS0m1sWLGjRlkOg7Jyc00mk0gsjo6MzLh/PyQwKCjAXyQWJ6elsdns8bGxZDK5vKqqqqaGSqESiASEkEzeTSIR8br1ubJ44uiRIzJzciYnJDS1tDjY8zkcdk5+vkajDQzw7+jsLC0vd3NxHRMz0kVA7xtzAAAgAElEQVQgYDIY1vWclJAwdMhghNBrb2+aNXVqY3NzQ1NTbMwoL3eP+7l5fj7eXA4nLSMjbvRo2J0B8HiOLNPv3p379NNMBuPqxYubP/wQj5QIIS6XS6PRHB3srTM7CwQGg8FoNNbVNzQ1N8+av2DG3Hkz5s67lZwilkhmzJ23befOHuWXlJYihBLi4y0pw6KjuRxOeUUlQsjT07OhodE6f1NzM5PJdLC39/L01OsNrW1t1pMQQta/wsSlpqW//uZblvNmRALh+UULI4YMrq6p+dPN0t3dnXrvHkLox/PnWSzWyTNnx4wcSaNSL179ZfgiEouPnf5x2NDI3IKC+7l5xWXlza1tCKHUexkqtTol/W5+UbGjgwOe+dad1MqaaoGT46dbtwX4+Wbm5Da3thYUF0uksrgxY46ePGUwGPBZ7Ozsdh86PDxqqFQmT05LQwg1NDVGDh701fc/IISsqyESi785ctTfx7e+sel+bu7127ebW1p8vb2KSkoQQkdOnKRSqYF+fjsO7LeslHWiVqc7/uOP48aOqaisEonFDvZ8Hy+v4rKyxubmkvLyq7duDQoN/e+uXRQy2Wg0JaemZWbnFJWWREdGfn/suEQq7bP8i0lX6xsbB4WG7j50GMMw69rKZLJvjhx1EQisG9lsNu85dDg4MIBAIFiP2L45cpROo0mlsiMnTg4OCzty8qTRaNx7+Cs8lh89dbqppeXny1cGhYYWlpQghHrUrffKWkoePWLE/dw8hFBmdvbYmBiE0A8nTuoNeoRhN1PuJMTFlVSUl5aX4+1gXdX6xsaC4uIrN25wuRy5XI6vi1AsysrJYduwvjmaeCEp6e86QQ0AeORGlmazeeOmd8JCQk8kHrW+OogQIhKJUZGRhUXFBoOBQqHgiXkFBf5+fhQK5cSRHwxW13s2f/55RmbW5Z/PWx41YMHlcPE4Fx0VZRk7KlUqPp+PEBodM3Lnnr01tbV+vr54fVJSU8eOHk2hUEbFjNyzf39yyp3nFj77y6nClBQ7O7uIwYN7LEIilZ45d275sqXBQUHW6fx+b/DJyc/PLSjokbhy6dJfhtGRkVt27oobPZrJZLBtbCbEx99ISekSihztfzl6KCwp0Rv0N5KTuxWK4rKy3suaPnkSh822/JkwdqzRZMrxyg/w8/N0d5fJ5QF+fk0tLZeuXpXKZHqDAZ9FLJG4ubh4eXh4uLmZzeac/IIJcXE2NjaIQEAI9ajG4PAwf18foUgok3fnFRa+unoVk8GIHhqJECooLsYQVlVT09Ul7O5W4EMr60StRsvlchJPnR4yaJC7q6sNk8Wzs+vo6sJrGzN8uK+3t4uzYHhUVGlFRUlZeVVNDULoRnKy3mCoqqnps3xEIExOSKBQKHyenUwu71HbQWGhQ4cMsW6iTqGwSyRKy8hACJVVVlnSnRwdIwcP1mq1zgInX29vGo3a2Nzs7enp4ebm4eZ27dZtnp3t+LhYb0/P8XGxIokkOy/Pum54IX3W0M7WlkImS2WyotLS2dOnI4Ts+byE2Fiz2RwxKPxiUlJzS6vlFO6O/fu7FYqVS5YghGRyeUdXF4vJ3LhunVQqxdeltr4eIRQcGJiemVlcWvaft96EfRkAj2ewzM3Pb2tvjx0z5qczZ63T3d3dYseMWfHSi4tffGnjpk0bXnuNTqd/98ORouLi7V/+FyFkZ2dnnZ9OpxOIRMtAylp8XKytre2Wbdvt7e0jhgwRCoWfbtmCYdgzC+YjhBY+88zX33234a23t235gs1m79i1u6Gx8b+ffYoQGjl8+OBBg7bu3Onr6xMSFHTh0uUfz5xdv26d5VcuFrFjRjOZzPc+/GjPju0IIaPJdPTY8fyCwq1ffN7PukdFRERFRDxoKo1Gc3RwuJh0dczIGHl395kLF958dV1DU3P2r89koNFoEYMGTZs4USSWqNSqsopK/Jm0EukvP5ntcW2VTCYbTSYCgfDL3xg6fe58cEDArKlTP97yX8ssNBoNHyI3tbTgv64hk3/pHr2rYWkKDGF0Ok2pUjEZDHm3AiHEYrGef/ppIpEYGhTEZtvg2awTSSTijMmTBY5OZy5cuNvrMjCZRLYuHyGMRqONGzvG29OzurZO4OTYZ/kIw3R6PYVCUas1JpOpR21JxJ5LoVGpLgLB4mefNZlMxWVlVm31S05Lc1GpVKVahRBSq9VMBoNOo+P3B+FXvnvU7d797Ae1AD64/PH8z+6ublQKxVKr7Pz8ppaWxc8+e/7yZUvOV1etwjAM344Rgwbhp2ERQlKp1HpdtFpta1s7IhC6RL8dSwEAHqtgWVVVhRA6cfp0j/QJCQmxY8bEjR275dNPP/7ss3M/X8D32htee3Xe7Nn/0yK4HE7id99ueu/9F5a9aNllb93yxeBBg/AxxIHde15evz5h8hR8r/fxB/8ZPmwYnnPfrp0vrlz1zHO//NRywby5a1et7L0IOzu7g3v3vL7xzZFjY4lE4uj4cRhmXrX8pflz5/6VxhkbE7Pn8OGFC+abzGaD0Xjm4kWjwVjbUD92VAxCaFhk5PZ9+5RKVXNr6+KFz4YEBX5zNDE7Pw/ffT8Md1fXtIyMyupqg9FYWl6BJ7oIBFQq5esjR9s7OpYvWWydn8FgWFdj5LBo66nTJk489O13Hu5u+N09UydO2PvVVxw224ZlM/zXMb11YsTgwafOngsKCGjv7BwfH6dUqy5dvebj7fWg2k6ZMP7oyVOeHu5dQuErK1b0WT5C6IfjJ0gkko+XF4fD6d1oPfDs7JwcHX44caK7WzFqxPB+2spFIEAY+u7Y8c6urplTp3h7em7bu7e1rb2lrW1weFiPuvVeWesaRg+N/CYx8a1X11mXL3BwvJiUdPLMWaFYpFSp8PMEvQ/L+vTDiZPTJ09ysLc//P3377z++kPOBQD4Ewb42bD9U6lU+YWFCMPCQkPxH2/8CRiGVdfUNDY10Wn0iCGDbWxsrKdqNJq8ggKT0RQeFtpjzIphWEFhoUQq9ff19fDw6GcRBoMhv6DgPx9/8sqaNaNjRlouvv4tjEajVqu1sbFRazSWWz8wDBNLJLZcLj740+l0GIb1PhHdj+5uhY0NC8Mwo9FIszoNrlQqGQxG7/t++6zGb0Mcnc5oNNqwWPifarXaYDRyORzrPNaJJpNJKBbbcrn4GXitVtt/5Q0Gg7y7257Pf1D5H23Z8v7GjUqlCj/n2X9tLWRyOY1KZTw4g4VUJmOxWPiI0GQyKZRK21+3co+69dMCD6LWaEhEIo1G67+2AAAIlo+DZStXbvviix4RF/wzvks8tvS5RdAOAAAIlgAAAMDAgIscAAAAAARLAAAAAIIlAAAAAMESAAAAgGAJAAAAQLD8W3QKhf2/xQmnUCrlim6xVFpVW/uQJdc21OMlK1WqjOzs3MJC/A1KFdXVqRkZ7Z2dCCGJTHov+35tQ32PZdXU/5ZiMBpvp6Xhy62uqxNLf/dKr/bOzi6R6EF1aLF6FO3DqKqt7VG+da3Ss7JyCgvwJ/vgK4J/qKytScvMbGpp6b/wssrKnIJ8y2ukzGZzYWmpdYaK6urku+lGo1Eik/VoZ71eb1kcAABAsPyrpHJ5a3u72WxGCMnk8taOdrPZbDAYNFpNl0iEP1TMZDK1trcrlEqTyZRfXNwlFqk1Go1WK5PL5YpufD+OvzzEUlptQ31Vba0Nk+ns5GRdMkKoW6GQd3f3iDGpGRm30tKMJhNC6M69u4H+/na23JzCgobmJqlcPiwysqis1Gw2p9y9FxYU3NEltI40+cXFnm5ulipptVoPNzcalYoQUigVer0Oj7JSuRwhJHB0LC4v67MptFrt/fw8lVptnd9gNKo1GpFEbCnfYDS2drTjT+pxdnJis1jdCoVWq+0UduEriLuTcS88JJhtY1Pb0KBQKq8lJxeUFCOE2js7uoSiiPDwvOIiyysY1RqNVqfrEonMZrNILMbfSUkikSgUikgisYRYez7PUh+EEJVKdXdx0en1er0er4/eYOjo6jIYDFQqVSyV9Hj/IgAA/KuR3n3nrQFZcH1jY2llBYZhtQ0NZpOpvLqKRCQWlJSybVg3U1MZdHpWbq6vt3fy3XQGnV5SUc62YTc0NbFtbDo6O4vKymw53Pv5eYG+flqdLjM3h4CQpTSDwaDRaJkMRkNzs06ns5Ts5+196ufzFDK5sqaawWBaHjXu5uIiEku8PDwoZHJ5dVVEWDiTwcgrKtLqdA58fn1TU2hQkE6nU6pUgX5+dBqtobnJ3dUVIWQ0Gqvq6gJ8fYvLy4rKyrhsTnZBAZfNrm1sYDGZSpWSbWNTU18vEouFYrFEKhE4OrV1dHDYbMvTahRK5a+RVVlZW+Pq7FxdV2fJTyQSr6ekMGj0LpEQX+XMnGwWk1lQUmxna1vb0EAmkzJzc2XdcqlM1iHscnV2Rgip1OrW9naNRosQCvT3p1GpXu7udY2NQf7+JBLJ1dm5W6Ho6OoK9PXFn45WWFpaVlWlN+jTs7JoNGpOQUGQv39rR7vJZPb38cEfkZpdkB8ZPkgoFuP1UavVDc1NNBqttKJS4OjYrVDw7Oxu3rnDYNCzCwo83d0RIghFIgd4WikAAEaWf1F5ddWoYcMjwsPDgoMraqrHjhgZFhRsNpt1eoOXu0d4cIiLQKBSq0gkklAsCvYPcHJwsLXlugqcEUKDQ0It7w7sXZqTg6OLQEAkEhBC1iVrdTpbDjdy0OCQwCCpTPbb8QKJZHnGuIuTIOXe3bvZ94lEolar0+l1oYGByelpWp2WSqUghMhkktH4y7lNhUrFZPwS9gaHhDIZDJ6dXXBAwNDBQ2rq6/D0qtoaBp1uw2RW19UhhLhstmXROr2+pKIc/2xna2vDYjk5OPTI7+nuFh4SYlnlIWHher3eZDJb3sSJYdiIoVHRQyIsxWq0WrFE7O/jYzKZCkuKCQSC5XnoTAaDRqV2iYQIIeuXOoUFB0eEhduwWINDw+x5/C6xqL2zS6vTSX99WbHJaMSjJl4f61bFX1pS19BAo1GNBiOJSGptb+Ow2RKrFgYAgH+7gXv5M4FAJBLxU3/4Th8hZDabySQiiURECBEIBAxDYUFBVCqtuKxUrf3tzfKWV0NgGKbB039fmjWrkkl4yUQCAWFYn5VyFggc7e21Wm1ecRGRQHR1duGw2Rw2m06j4ycbuxUKywM/CQhZTn6SySREIGCYGSFkMhktbxZDBIKnuztCiMlkIoQwhBFJvxyg0KjUkVHRvZvFOj/+Cg68fLVGk1NYMHbESMyq8sRfV9ySyGIyeHZ2XA7Hw82ttKLCuuzKmhp7Pn9waJjZjHWKRN6/PvAWf9u25THcTDpjSkKC9YzmXwu31MfSqqRf57LlcD3d3blcrh2Xq9PrSfBQbwAABMu/blBI6O20VAqF4ubsEhYUfDM1lW1jY8/j9XiEd0lFBdvGRq3R8GztlCpVeVUVhUK2jAJvpaYiAiIQCNalcTmcrLzc6CERCCHrki0DrH40NDU1t7ZI5fKRUdEYZs7IzuZyuEwmk8/j0Wi01MwMmUw+IS4Oz8zlcCxX/hBC9jxebmFhVl6uSCweM2IkPrgMCQjILSzEMMzd1QUhJJPL/by8+1w0iUhsaG7qkf93m4pMNhgMlTU1ckW3UqWiUWl9lsOgM9g2NmmZmd0KxYihQ60n2fP56VmZjvYOQrEo9Pcv4Owfk8Gw3CvUo1Xx7eXr5XXjzh2T2SSTyxPGjO3o6nKw58O3CwDw2BjIZ8MajUaz2UylUhFCBoPBaDIy6Ize40KlSkWn0fCxmtFotI55Or2eSqHgZwitS7PO9qCSH0SpUtFoNAqZjBAymc16vc4yr1qtptFo1uH8fl5eaFAQi8m01FalVjMYDOtxlVarJRAINBrNZDKlZ2XFxsT0uVyz2YwhRCISLfn7bDGT2UyjUq1fi90njVZLpVD6fHmIRqu1YbF+e73lQ2hqadEbDH7ev4X53q1qNptVajVecmpGRkx09MMcnQAAAATLx5/BYGjv7OxxAfVBukQiGo3KZXP+jWta29Dg6+X1kG3S1tnh6eYO3QMAAMESAAAAeFLAXRgAAAAABEsAAAAAgiUAAAAAwRIAAACAYAkAAABAsAQAAAAgWAIAAAAQLAEAAAAIlgAAAACAYAkAAABAsAQAAAAgWAIAAAAQLAEAAAAIlgAAAAAESwAAAACCJQAAAADBEgAAAAAQLAEAAAAIlgAAAAAESwAAAACCJQAAAADBEgAAAIBgCQAAAECwBAAAACBYAgAAAACCJQAAAADBEgAAAIBgCQAAAECwBAAAACBYAgAAABAsARh4BoXS8hkzmYxqNf7ZqNH2zmzUaE16Pf7ZpNdjJlPvz9bU7Z3QwgA8xsjQBODvgmFY6Zd7GQJHvbzba8EseWWNoqZOUVMf9Mryxp8uGhRKEo0auHZZ+61UaWGZw4go27Cgmm8Sw99ZX/DBluB1y2uPnqY72Pssmt+RnK5saNZ0dtF4di6TxjWfv0Kk0zznTjeqVK1XbmIY5vXU7OaLV7lB/nqpHDOZ3GdO/sO61Xx7nEAhk+g0rwWzJIWlwoxsQdwobpC/XibPXv/eqO/2WGdWNbU0nP7ZbDAErl5KpFAq9n9LIBBC1q8yaXWWzwQSyZJfnFvYkXKXzKAHrlkG3QAAGFkC0B8CgUB3cmC6utiFhxBpVNH9PH5URMCqJSaNVhA/iu7A50dHmPUG9+mT6A5854QxDCcHZVNLy5WbmNlM4/MoHLZZbzCq1QalynP+DJabC0PgaOPpxosIE8TG0B34Dad/Dli1xG/ps/XHzzAcHYQZObZhQSQGvUc16o6f0QrFPRL9li10ThiLDy4bf/yZRKVS7bgIodakmw4xw3qvCZnJINFoiEgU5xW5Th7HHzpYXlVr/fl3w8q2Dqars/ei+dAHAIBgCcBDofFsqVwOiU7zfnq2XiqrOnyE7sC3Cw9BCAliY6hczm8jUZPJMWaYOKeA5e5q0urk5dVGtaY16Zb7jEkEEhEh1GPIiJkxApFIotNNOh0+tfl8Up8j3N5pOrGkLvFHn+cWIISMarXn/Bm1R05rOrtE2QWS/GJZaYV1ZqNKTWIwiFSKUaXGS8MwjEAg/O6zFfthEfyowWU7DsLWBwCCJQB/AMMwaUGJOLew+dI1TXtX2c6DepmcymETKBRhRo6koERSUIIQav45SVJQ0nbzjryypru6btB7r0uLSisPfc8bEuYxZ2rbtWSDUtVy6booO1/T0aVsbG6/ldZy6bpWKBbExVR/k1i5/1u36RNVTS3yskqaPQ/9Pm4hhHwWzac78HskZq9/j0ijtly8hhCyj45oPHuJ4eTAcHIc8vHbBAIi29j87ltBo+okUr2sm0gh8yLCW5NuSfKKOP4+1p+t84sycxt/vMANDoA+AMDjiqBSSKAVwP8Ho0Zj1hvILCaR/LddGjcbDIhAsBSIYRgym60vHz583cgMRj8ZTDodgUgkUigIIbPBSCARCURij8/W2q6nuEyMg40OAARLAAAA4AkFp2EBAAAACJbg3+/ikLiLQ+KgHQAAECwBAACAR9TAP5RALJbUNdS3trbBxgAPgt/Ac/7CRWgKAJ40rq4uPl7efD7vyQ2WRqMx8fiJDz75BCE0e8Ik6BPgQcYjhBBKvnodmgKAJ835G9cQQh+9//5zC58lkwcsZg3Y3bBGo3Hxshcrq6q2v/PB+FGjoUOAfpxJmI4QmnfrEjQFAE+gm3fTX//so8CAgB++/Wag4uWAXbNMPH6isqrqduJpiJQAAAD6MX7U6NuJpyurqhKPnxioOgxMsBSLJR988sn2dz6wt7ODfgAAAKB/9nZ229/54INPPhGLB+Zs6MAEy7qGevxgAXoAAACAhxxfWsLHkxIsS8vK4I4eAAAA/5PZEyaVlpY9QcESAAAA+BcZmGAZGhKC3w0MAAAAPKTzN66FhoY8QcHSx8sbIXTzbjpsewAAAA8DDxl4+HhSgiWfz/vo/fdf/+wjkVQKPQAAAED/RFLp65999NH77w/Uo3wG7JrlcwufDQwIGPfcUzC+BAAA0P+YctxzTwUGBDy38NmBqsOAPTqITCb/8O03icdPPL9hHUJo9oRJ0YMG23K40C3Ag5y9lgSNAMCTQ9Ytzy4qfNIfd2eBP0i9tKwsNzcPOgfo08SUfITQ9bgIaAoAnihDIyNDQ0MehQepD3ywBOAP4S+znFGQAk0BABgQ8DtLAAAAAEaW4N/PbDQSyWRoBwAAjCwBeHA3hUgJAIBgCQAAAECwBAAAACBYAgAAABAsAQAAAAiWAAAAAIBgCQAAAECwBAAAACBYAgAAABAsAQAAAAiWAAAAAARLAAAAAIIlAAAAAMESAAAAAI9nsMQwDDYnAODJBDvAxzlYdisUEpkUIVTb0JBXVKjT6XR6fW5hYVNLiyVPXWNjVl6eUqXCMKykoqK0sgLDMKlMll2Qj8+LEDIYjRk5OWlZmVqdDrYoAODfQqFUNre2Wu/crHeMFmVVlQgh691jU0tLXlGhZY8n7+6+cSeluLzsD0NmZW1Na3v7w9Sto6tLKpP1SNRotd0KBf7/g2asqKmua2wwGo29J0mkUoPRWFFT3f+iO4XCTqFQ0mvpA4v07jtvDciCxVJpWmYGh80hEAjNra0hgYEFJSXtnZ1B/v71zU1slg2dRtNotaWVFUPCwnILCzCEEMJYDGZHV1dlbc2IyKGZuTk+nl4IocrqahsWi8+zU6vVXA4HvoEAgEef2WzOzMkxGAx6g8GycyMQifiO0c7WFs/W3NqaU5AfFhycnZeH7x6pZEp1ff3g0LCcgnxPN3eEUEZOtpe7B9uGTSQQaFTqg5ao0+uzcnN9vbykcnldY4MthyOSSGxYLIlUSiKRhGJxl0jEYrFKKiqMRqNGqxFJpF0iEd/OTq3RVNTUUKnU5tbW+qZGnU5f39To5uxSXlWlNxg4bDZefntnR0dXl0gsRggJHB3rmxrx2TuFwtqGeg6bnZWXSyQSuWy22WzuEonaOzsNRmNre7s9ny/v7q6uqyWTSFqd9t79LE93DwaNptZqKqqrqBSqwWgUSiSNLc18OzsicWDGeAM2suTb2Q0KCcHPHhhNRp1eLxSLdXo9h80WODqKpRKEEJ1G6+7uTrl319HBQSQRuzm7uAgEXSIRlUKhUCgsBhM/sHIRCGoa6ptb25wcHOAbCAD4VyguLwv080UIWXZuQrHYsmPEqTWaLpFI4OSEhzp899jU2uLs5MRkMIwmE57N082tpKK8u7vbhsXCU0xms9EKPuKkUihcDptIJDa2NPv7+Gbl5VXV1iKEmttaNVptdn6eLZeTlZvj5e7eIewymzG9Qc9ls2vq6zNzcvy8vXMLCzhsNt+Ox+Vy+Ha8+3l5zgKn9s4OkUSCj27rm5rwyBrg69fY3KzT61lMZnF5eV5RoZ+3T6dQaMvh2vN4lTW1QrGoSyRSazSdQqFKrRZJxK0d7b6eXtkF+Vw2h8PhyORyqVyenZcfHBCYV1zUJRJ1Cbs4Nmy8wgNi4F9Az+FwnBwcO7o6bbkcfNvr9QYGnY4Q6hKJ3F1dh4SF305Ps+PaGo1GAkJUKlWj1SCEDCYjhUxGCBmNxpioaKFYnFdcNDIqGr6EAIBHnE6vb2ptVapUIrFY4ORk2bn1yFZWWWEwGIUiUW1Dgxkz47tHKpVqNP3uJCeLyRoUEqrVaptaWjzd3RFCDU1NSpXSksHP24fFZBIIBBKJ1K1UOvDtmQyG0WSkkCkIIaPRhBCys7Xl2drp9QYuhxPsH9DW0eEqENBpdKlcJuuWV9bUsG3YiEAgkUkkIolEJskV3Y3NLQQCAV+ETC534Nvz7OyYDAaDTpfIZDq9TqvVcdjs8JCQ/OJiFpNJIhFJJBKe30XgpFSpbVjMbqXSYDDI5PIqvR7DEIlEIhFJBAIymU1UKpVGpdKoVJPJ5OzkxGQwZd3yJzdYGgyG+qZGLpvj5e6h0+szsrM1Om3syBiEENvGpkMozCsusuVyfb28cgoLSCTS4NCwiqqqvKJCKoWCt7tGq61pqCeRSK4CAXwJAQCPPhqVOmPiJLVaXVZV5eftbdm59cgWNSQCIZSakeHr5WU2m/Hd4+jhI+7cu6tUqlwFzni21vY2WXc3lUJxc3HBU3y9vB60aCcHh/TMTIVSiZ+Ky8rL6+zq8vP2xqc6OtjnFBTIuuXeHh6WWVwEAhKJZFCrbJjMqpoap0EOZZXtXu4eKrVaqVKxmEyEkMDRMTUzQ6lS6XR6hJCXu3tpZaVOr6PT7CtqqgUOjkq1is2yaWlr67NW3QqFDYtlMBrUGo1CqdQbjHQaHSFUXF5OIBDIZNKAbzKCSiEZ8EoYjUaT2Yyfatfr9RQKxXK0YjKb9Xo9PtA0Go34kRF+XGZ9al6hVOr0OnseH76EAIB/HeudWz8su0cMwwxGI5VCsUxqaWsTODqSyQ81/jGbzQaDgUaj4cMVilU5eAqZTLbshHFarZZOpyOETCYTiUTC/9fr9SQymfTrRUST2YyZzZY64Kd/KRSK2WzW6nRMBsMye+8qmUwmDMMIRCKRQDCbzZY8Gq0W3/9DsAQAAAAedfBQAgAAAACCJQAAAADBEgAAAIBgCQAAAECwBAAAACBYAgAAABAsAQAAAAiWAAAAAARLAAAAAECwBAAAACBYAgAAABAsAQAAAAiWAAAAAARLAAAAAIIlAAAA8JghD3gNxGLJ0ePHa+vqYGM83hLi46dPnfKQL6e1yM7NvXwlSSyBt64+zvg83rSpU6KHDv2f5jIajZeuJN1KToYGfLz5+vg8v3Ahn88b2GoM5Mufs3Nzd+7ek37vHkJo7uy50CceYyKxKDUtFSG0ft26eXNmu7u59Z9frVZfv3lr8+efC0WikOCQoMAgaMPHWEVlRVl5mYO9/XubNk0cn8BkMvvP39zScubc+R27dyOExo4Za8+3hzZ8jJ09fxYhNP4P2REAACAASURBVDom5rV1r/yvR1SPQ7DMzs2d/8yzIcEh773zXnxc/P864AD/Oiq1av+B/d//8F2XUFhbXtb/Fv/wk83fHTkyd/bcV9e9GhIcAq332CsrL9u1e9fZ82eXvvDCh++/1/+A0jc4xNHBYcnipWtWr2ExWdB6jzej0Zickrz5s81l5WU/nTwxUPFywILlosVLJFLZndt3oCs8WUNMkSg4PPi7rw6Pi4vrZ1gZPHjIwX0H582dBy32RDlz9syqtavKCwv6GVzeTklZunxFeXG5vT0MKJ8sseNieXa2x374fkCWPjA3+IjFkvR799575z3Y/E8ae3v7ubPnbtz0TnNLy4PynD5zBiE0efJkaK4nDb7RN7z1tlqtflCeLVu3zZ09FyLlE+i9d95Lv3fv/MWLRqPxSQmWR48fRwjFx8XD5n8CLVu6TCQSjY4f96Ad4t79B5a/uBxOrz2BWEzW8heXX7tx4613+z6SrqisrKisXLZ0GbTVEwgPGa9teCM1Pf1JCZa1dXVzZ8+F65RPpuHDhuMfrt+81WcGoUg0NHIoNNSTaWjkUJPJdOHSJbFY0lewrLLuQuCJQiaT586ey6Aztmzd9qQESwAQQps//xwaATwIfv4JgD5PMPRzHQeCJXjcCEWif77Hg3+LRAiW4MG++e57CJYAAICEIhE0AniQf/5BJRAsAQAAAAiWf1ltXa1er4d2AH2qqKyARgDQEyBYDgCj0Xjz1s1jJ47l5uVapyuVypQ7KdYpuXm5x08ev5dxr89y+px6997dM2fPtFhdKuvs6jx7/mzKnRSTydS7kJu3br79ztv7Duxrgatrj4wePQHDsDupd06eOpmTm9P/trZWWFh47MQx6+7RZ0+oqKz46cxPhUWFvUvQ6/UnTp1Yv2F94vFElVoF2+VR0PtbbzKZbty8ceLUiaqqqofc20BPAP+CYGk2m+c9Ne/Tzz/Ny89b+tLSjzd/bJl08PDBHbt2WP787IvP1q5bm1+Q//obr6/fsL5HOX1OXbV21ca3N968fXP85PHJKckIoaLiotGxo5OSkj794tOnFz7dI15u37l9155ddDpdqVROmDyhrh6e9v5I6NETVq1Z9dY7b2Xdz1q5euWGjRsetK17bNlly5eVl5e/8dYbH33y0YN6wpHEI3PmzUm+k7zkxSVfbvuyRyELn1+YmZlpY2OTn58/dfpUOP0w4Hp/600m09wFc/fu35uTkzN99vRTp0/94d4GegLom0oh+ef/LXtpyYpVK7C+pKalRkZHajQaDMOqq6sdnB1kMlnKnZSwIWH2AvuZc2bi2QwGg4uHS2NTI4Zh7e3t9gJ7hUJhKaTPqbl5uYGhgd3d3RiGHTtxLGFiAoZhTy98eufunRiG6XS66BHRV69fta5MeER4VVXV1u1bRSLR7r27z54/i4G/g73AHv9XUV7Uu3vYC+x/OvNTnzP27gmNTY3e/t741m9qanJ0cdRqtX1ua4v29nYvP6/29nYMw+rq6yZPm9xnT9BqtT4BPtk52Xg2Fw8XqVRqKaSuvi4kPMRsNr/z3jsYhq1YtaK6uhq27F/305mfLN2jd984duwHe4F9nzP2+a3PyMyIiIowm814yT16Qp97G+gJj7IVq1Z4eHvYC+yXvbTkHw5bj9zIkk6nv7ruVTqdjhCys7PDMEyn18WMjElPSd/y2RZLNhKJxOVwyyvKEUIlpSU8Hg+fpZ+pySnJsWNi2Ww2QmjC+AmFRYVSqTT9bvq0qdMQQlQqNTY29u7du9aV4dnxbt6+iWEYQuiVta/MmTUHjq4GVu+ewGQwD+4/aGNjgxBqbmnmcrkUCqX3tlYoFJZZ7qTeiRkRYzAYLl66qFarky4lGY3G3j0hvyCfQWdEDY1CCHl7eXt6embdz7IUwmFzFEqF5cTvoQOH/Pz8YAMNoD6/9ba2tgqloq2tDSFUWlbq4uLS/95GpVZBTwB9euSeoRMdFR0dFY0QEoqEq9asmjljpqODI0KIy+UymAxLNgKB8Plnnz/3wnP2fHuRWLR7527r5wH1ObW9o93JyQnPYM+3JxAINXU1Op1O4CTAEx0dHGvran93sm7r9rXr1tbV1VVUVCxauAiezzfgKBRKj55gb28/cfzE+ob6xUsXV9dUf/rxp0Qisfe2FoqEeOxECDU2NdbW1z773LNhYWHp6enPPvPs8peW9+4J1oXgiV3CLsuffD7/i8++WPTCIoPBgBBa/PzigIAA2EADqM9vfVBg0NzZc6NGRLHZbLVaffv67f73NmQyGXoC6NOjeIMPhmFff/t1bHxsZETkwX0H+8zTJex6/Y3Xd+3YlZqSevjg4U3vbmpsaux/KolIIhAJ1oUw6UyEkHUinUa3zhAZEZmRlrF29Vo/P7/lq5afPHUSesyjyc3V7ftvv9/y+ZaPPvmopqam97a23rJarVar1d64euPgvoPnz57ftWeXUqns3RP6LwQhtPCZheXF5RMSJhAIhAmTJ/R5/wj4x/T5rb956+aFixfO/XQuLSVt+YvLl7y0xGw297O3IRFJ0BPAvyNYYhj20sqXriRduXrl6rub3qVQKH1mu3//voODw8JnFjrYO8yZNSckOOT+/fv9T3V1dbXcGNnR0UGj0fz9/el0ekvzL4ltbW0eHh6WQhQKxSeffoIQYrFYy19c/uYbb167cQ16zKPm7r27RxKPUCgUH2+fF557ITAwMC8/r/e2dnBw+G0wyrcPCgxiMBgIIT9fPyaTaTKaevcEV1fX1pZWy1w9ukdhUWHi8UQSieTg4LD5481xcXGp6amwOQZQn9/6W7dvTZ06dcTwEU6OTpve2lRdXd3R0dHP3gY/eQs9AfwLguX1G9ebmptOnzjt4e7RTzZvb++2trbW1laEUHd3d2Njo7+ff/9TJ02YlJySjJ8/STyeOGH8BCqVOnHCxNM/ncbPw9y4eWPypN9eC0Wj0b759pvSslL8z9a2VsvJGfBIHV1t3b5VJpPhcbGuvi44OLj3trY+6ho7dmxObg6+07yXcc+GZePt7d27JwwZPIREIt1Ovo1nU6lU+FUrnF6v37p9q1KlRAiZzeaOjg7oHgOrz2+9t7d3YWEhfntqUXERh8NxdHTsZ29DIBCgJ4A+PXLXLNPvphcUFDi7O1tSsu5l+Xj79MgWGhL68pqXx00cFxgQWFNTs2jhoiFDhvzh1GVLlsXGxwoEgu7u7rM/nUUIvbvp3XlPzbuTeqe5pXnp4qUhwSGWQqhU6scffrzg6QUcDictPU0sFp86fgp6zKNm9KjRCfEJw2KG+fr41tTWrF65OjwsvM9tbREWGrZh/Yb48fE+Pj5NzU379uwjk8l99oQvt3y5YvUKXx/f+ob6vbv3kkgkSyFRQ6PGjBoTGx9LoVAyszLd3d2tj7TAP6/Pb31wcPCd1DvRI6M93D1qamp27/jdzQ197m2gJ4A+EVQKyT+/1FfWv04mUw8dOPQXy5HJZI1Nja4urn2+CbbPqa2trSKxKCQ4xDLU0Ov15RXljg6Ozs7OvQuRy+WfffHZvLnzoqOiCQQC9Ji/hYPzLydF05Nvu7u59Zjq6R9wcN/BeXPnPXyBnV2dHR0dXp5eXC63n21tTaFQtLa1ent502i0fnqCXC6vr6/38fHhcDi9C2ltbf1488fvv/u+W6+1AH/OmbNnVq1dhX9urO558e/8hYuvbtggbBf+T/uEtrY2kVjk6+v7kC9JhZ7wyFq5euXVa1fVGvXM6dP37Nj+RI8s/ye2tra2trb/01RXV1dXV1frFCqVOnjQ4AcVwuVyX3j+BT9fP4iUjzInRycnR6c/3NbW2Gx2UGDQH/YELpdrfdKi9yJWrlgJ+8dHfJ/g4uLS40cj/YOeAB63YPmPnd6BRgAPEhkRCY0AoCc89uBB6gCA/2PvvOOjqrIHfqf3np5MegKkN3oVARGsYFmx61qwt9396a5l7a6i2FBApQlSpEmV3klIrzOpk5kkk+mZXt/M/P548HxOQkBXF5Tz/eSTz8ydW84997x33rnvvXsBAABnCQAAAADgLAEAAADginOW/XpdXWP9Za44u8NeXVfdr9eBDQHAFU44HG5obmxsaQJV/Im57B7waWltsTscVAq1vavDZrOOyh5V11RfnF/U0NwQGxOblpKmaFVYbdaCvEIOm11eWZ6Xk9esaC7IK1Cpu/1+X0lhSa+2t1/Xj2FBiUQSLYtq62hjMBiF+YVGo1Gr0woFIiaTgQWDTAbT4bDnjvrp4Z2evl5Vd9fEcRNdblezojlnZK6itWXc6HFk2bxeH51Gy8/Ndzqd4XDY4XDwuNzG5sbYmNjM9ExC+Mz0DKlECub122IwGjS9mmFMgsvhnK4sZzGZfn8gZ8SoltYWfBBT5CmaXg2dzsgdlUMsVOb1eWvqa5kMZmZ6ht3hcLocIqFowGrNI5nEmerKABag0WjpqWntHe1sNpvBYEbLoojaurq7CJNoVrbgFuj3++ubGjAMG5k9Qt2jlifK+7R9UbKohPgEGMTfj9aONhqNxufyerV9mekZjS1NeaNyRULRmZpKBp1eWlSq1fWrurvYbLZUInU4HNFR0eYBi0gk1Gq1bDZHwOenp6aTja2ru4vJZOWNylW2KfHMErE4Mf7s89Uer6e2vpbBYMiTkmVSqdvtwp/HJmymIDe/S9Vlc9jliXIKBYVCIaFAqO3XUqgU/CyRmpzS0dVRVFBU11iflZHZqeoKBoMsJrOsuAxGEyLLCyMUCEVCUXRUlFgoCgQCPB6PQadzuVw6na7V9SOEkpOSeTwej8ulUqlhhIQCIZ1O53F5dBrN4/E4nU6zxZKdmS0Wiz0ej0wqE/AFo7JHMegMZXtrWXGZw2mn0+mNzY3RUVFen5fctFgkotHp7V0dQoEwFA6LhELyq8cIIRaTJU+Suz1ul8tF1EahUKhUWmZ6Jln4iPUngd+E6Kjo4U2CQqFQECUhLiFKJhMIBMQgyqQyBoMZFxOr6lYRtbFZbBqVWlpU0tjcyGQwWExWfGy87+cmwWKx6DRaQlwClUoLhcNRsmixSEyujWwShAUymczszCyxWCwRS6hUqqJNKRZLyO/CA78HMVHRAb+fy+XyuFyxSMxiMpVtrVQqVSQU4V4wIS4+gAXiYuNoNLrP75NJZQIen8VgIYQKcvO7SLaBEOJxeXweP3dkTm1DLZGZQf/phV0Om0On03NH5bYoWxh0BmGNhM24PZ68nDw6jZacJA+Hw52qLp1e53K7ibMElUqlUqmt7W0sJjMcCqfIU+g0Go0GdgLO8qLxer0UCkXAF9DpjK5uVVJCUgAL6PQ6t9vV2/fTrvd9/X3kNx97+noYDEazsrkwrwBPKS4oIs+TUKkUhBCXy/N4vHmjcpsVzRHtKtuULCZL1d2FEDrfO5Utymarzcbj8Yja3G43IQ8hPJ/HB9v6zaFQKBc0CYSQy+0SCUVU6s+Wvu7T9pZXlZPXRCTqpNFoFAoljFA4HI54mxa3peQkeZRUFgoF3W6XTCKNqA03CT6fT1hgRBOZ6ZkdXR0wfL83ErHEYh3o6OrMzMjs0/Y5XS69Ue8PBPBfrTaby+XyBwJer08mkeIDHUZhCoWCBYPHTx2XSWWD62SxWMFgkJyZ/KvL7erWdFOpFLI1km2GyBkMBiViCaJQEAqTzxJMJovJZNodjpjomPjYuIizFgDOcjj6dboBq6VX26s36EdkZdfU1yTEJ9Q11KempBUXFFfVVavUKk1vT019rd5g4PP41XU1Pr+/X68LBkOFeYW92l63x93e2a7WdPv9fsuARd2raVI0YUGMx+XVN9YbjAY6na7VacWin7257PF4+vq1eTm5YYS0Oi0WDFbX1XDYHHIevUEvk0bJk+Tdmm6iNg6H06/vr6mvrW+sJwsPtvV7MLxJ6PR6nUFndzjO1FQGg0FiEPVGPZ3BmDpxalVN1U/XZD5vv15XVVstEoklYola011eVREf97OZUpPFrNX1q3s0+IyFx+utqK4g10aYRHVdNWGBNrutRdmi1nRbbVab3e52u2Dg/jfIJFKvz8ugM2oaagvyCjLS0mvqazQ96pZWRU19jaZX43Q6bXZbs7I5IT6hqraqU9UpFAiNJmPOqFyX22m1WYmqbA6bpldTXlmempxKZCafNDwej81u93i8FAqFbI2EzSCEmpUtWl1/b1+vyWzicXmx0TFGs4l8ltDqtDFR0UazESHUrenW6votAxYYx8v0Yv0yX+5u8MV+BH6/n8lkXmS7/kCASVr5LBQKUanUX11zRG3AL5hQ/S+Wu7ugSVz8IJKrCoVCwVCQPM/2G/KLZL7C+S+Xu7t4VQewAI1KG+YMQK7qgpn/G4Y/EQFkYLm74Wbehs9w8Z4SIRTh24Y30AvWDJ7y8jSJix9EclX4DaTLQWbgf6PqC14Ykav6na6iLuZEBFwmXJpBykhP37JtC4ZhMABXIBVnKi4Qd0ZFVddUg6KuTKprqiMerCMzckT2xZgQ8KcEw7At27Z4vJ4ryFnevWABQujwkcMw/Fcg36z4hjFsUP7EYwuXf73cBbf6rjxcbtfyr5cHg8HzO8sRI0eM+GbFN6CrKxDcZYTD4SvIWcpk0kkTJrz59psw/FcaJpNpy7YtgXPPKA7JbfPnI4T27t0L6rrSIA969FD77iGE/vHC81u2bTGZTKCuK403336TmLKWSf/XL7JfsrnyZ556skXRMnX61P0H9sN87BUSNLy/6P1J0yaRz4byobY04nK5999zz6OPP/rIwkdaFC2guiuBFkXLIwsfefTxR4k7hXctWDBkzimTJiGEpk6f8v6i92H64UoAw7D9B/ZPnT61RdFCvML+4P33/Y/FuDRPw+JUVlcv/uTTE6dOIYTm3TQPbOLPHFCaTceOH0MIUSgUYhbl40WLbrrh+iHzu93ufQcOvv7222azOWdUTsTGk8CfDGWrskXRQqVSyat51JSXy2RDRw89vb2bt2776JNPEEJTJk+JkkWBDv/EbNm2BSFENo+RI0b8uHPHFeQscbbt2PHM8y9EvNEI/NmuDYOY3++PSFTU13G53OELLrj3voqKM7/omWfgD4ff78eCP5teupgXA+x2e35pGZPJpMOqN39qPF5PxH3KFcuXTZ827YpzlhiGHTtx4r0PFilbW8EsrhDuv+eeB++/T34R28q73e6Nmzd/tuQLI9yjujKIjop64rGFt82ff8ELKTzE/HrFyhWrV4PerhBGjhjxjxeenzJp0v9+/chL7yzJdr9567bOri4wiD8xpSXFF3kejLiiqq2vX/3tWlDgn5t77rqzuLDwl54H8Suq6ppaUOCfGJlUepFX2H9+ZwkAAAAAlyewcgQAAAAAgLMEAAAAAHCWAAAAAADOEgAAAADAWQIAAAAAOMvBdPdojp0+faKi/HRlJTldbzTqjUaL1TpMWb3RGAgEOru7f2mjHq/X7nBcZOYhW7FYrd09msGZVWr1gM2GENIbDVqdbnBVRI+6ezQnKipOnqloP/eSzPn60qHqGl6882np4pXTpVZfTDZlRzv5K4ZhDS3Nv27c/YHAiYoKh9MZwLDObtWQebw+X3dPT5e6G8Ow8yl8SMEiBvp8vxKcqalxulwX0/fWzg63x3MJD9QL9uX34Ncp/yKP4gsayW+rEGVH+zAmBwAXhPbPl/5xSRpWaTRZ6ekjM7PkiYkYhjW3tnq83lA4dOpMRYo8mcNimSzmXq0WhcMqjZrBYLJZrA6VymQ2c9nsY6dPxURFs1ksBoPR3Kr0+fx8Hk+r06l7ejhsNovFQgj5fL7Wzg6vzycSCnUGA5/H0xkM/TqdSqNOiI3DS4mEwn69jtwKlULBS7FYrKMnT+CtBEMhZXsbnU7jcrgqjToUCsskEpfbrezoYDKZHDYbIRQKh9u7upLi46vr69OSkw0mk8PlDIfC7V2ddBqNzWYz6XR8qw2VRpORkjIyK6uytjYmKsposQQxjM1imSwWoUDQ198v4PMpFIrZYnG53V6vVygQGEwmv99vHhgIBoNWm1Xd28vncul0OlFnv16n6eulUCihUMhoNg9ZoT8QaFIqMQwT8Pm4JiUiEYYFjGazRCRCCNnsdkVbG4fNtjkcloEBDofT2d1tMJlkEkkoFGKzWPgYiUUincHgdLnEIjGdTu9Sn82DL+lps9vxLgewQCgUCoZCDpcLV5HOYDCYTAiFnS4ni8nisFiqHk1cTIyirc0fCAgFAoRQOBzuUKnsDrvT5fT7/XExMd09mlAoLODxLFYrj8vV6nRMBoMYWWV7O5PJ5PN4loEBGo3mdLnw1nu1WpVGnRQfjxtGl7pbLBS6PR6jxaLu7ZFJJFQqdcBma2ltzUpPN5hMeAbzwIB5wGI0mWQSKaENBoPO5/FsdpvBaIyJijKYTJaBARaL1drRQYg9DDqDQdPXi2FBAZ+vNxpVGo1IKMTfI8QVIhQICA1EWKPD6dQbDVa7XSISKdvbY6OjiY6TxGOQFRgIBLR6naanV8DjhUIh8iHQ2a0SCgS4wRBHRCgUInQSDofJVSGECGvv6+/H7ZDJYHSp1bjxtHV2iERCYpT9fj+ufK/PSxzFbq9H2d7GZDADGEZWPtlo2SyWVqdzuJxOpwsfiO7eHrvdIU9MJOTpUKl0BoNUKjUYjXhHqFTK+QbXYDIROifss7WjPSE2VtWjEQmF5CMXAC73yBIhVNfUePLMmSaloq2rk8VkBoMYj8MVCoVWm23AZquub4iLiTleUS5PSKxrarTa7SwmMxgM9ul0QqGQzWZ3adTl1dXpKakGs0lnMDS0NKelpNQ0NOCV9+l0SQkJ7V2dHq+nrbMTIdTW2SkSCWUSKVHKZLFEtEKUCoWCRCuVtbWp8mTLgFWlVlMoFB6X26hQlFdVZaalVdfX4csVyiQSm90WCoWCwSCDTq9uqJeIxH26/oyU1Mq6WoPJOGCzOV0ur8+HEGpUKk6eqWCxmCaLpaevl8VkdmnUoXD46OlTeqMR9zrqvt7oqKiOblUAw1paWwdstp6+Xh6Xa7IMJMbFVTXUG0xG88CA0+UKYFhFdU1KkryhuVlvMp6vworqqlS5XGc0WKxWXJMqjSYmKrqvv/9smFVbk52R0aVRt3d1BTCsq7ubQaeLhMImpbK1o5MYI7vD0aHqykhNq6iuVqnVPr8fV8g5tZ/rstFks9ucTmf/uTi7srZGLBLWNTbl5+S2tLXi61edqamJj4vt1+tMFgtCqLWjg0qlhsJhhFB2Rqa6pwdXuLKjo1HREggEVBoNeWTxYUUI9Wj7PF4v0To+0K0dnXaHQ93bk5WeUVFTYzSbDEaDkC/Aiwj5fJFQ4PX7iAytnR1MBsNkGbDabIQ2Wjs6VWq1PxCQiMWNCgWunJr6erLYCKFgKISRIFbnqqqrkyckNioUbo+ntaM9VS6vqK4mK4SsgQhrbFS08Lhcy8CAwWSKMGlCvAgFerzeZqUyNTn5TF0tOX9NQ31mWrreaMSbJo4Isk4iqiJbO2GHTrebMB6EEHmUf1K+QEgcxZU1taOyR9Q0NhhMP1M+QogwWl/AX91QT6fRiYEQC4VRMikhT3dPT2+/Nloms1qtREeGGVxC54PtEyEUceQCwEVyKddULMrLl0kkCCG3x4Mft7HRMTQqDd91QCwSSsTiKKlMJBRSKBSv19Pbr6UgilQqoVFp+PawWBDj83gSkdjldsVERfN5PGIDF5fbPWC1BgKBYPDsIYEFMRqVRqPTiFJOlzOiFaJUKBQmWinKy1N2dLg9biFf4PP7vF6fUCCw2m2tHR0CviAYCuGNxkZH1zU1pciTEELxsbFcDsdqs7X5/cSihm2dHVKJFCGUkz0iSiajUaldanVyYhKdwUAIJcTGHi8/PXfmTDyzz+fncjgZqamKtlaRUIgQSk5MCoVClgELQgg/zn1+v7K9PS0lRSIW8Xk8Pp8fDIbOV6HfHxAJhaOysq02G6FJCoUSwAIIoWAwSKfTeVxuVlp6fXNTRmpqeXVVYW4uhULFT4up8mR8jCgUqtvj6VCpxCKhxWolFIK3EtFljLQxoUQsFgmETCaTxWSymEz8J5vDru7pJTaasFgHivLyg8Fgl7qbw2aT6/f5fS1tbZlpqQaTOWJkEUIYFiS3jg80Qshqt0fLorgcDr70aHxsLJfDtdptCCEajUaj0ewOJ5GBRWPGx8YNWG0+n4/QRm1jI1kMu8OekZra3tVJFhsh1K3ROF1O4mtmWjqPy8XNWMDns1ksu8NB6I1QiFQsIWsgwhrpNHpsdEwggLlcLrJJ+/0BQryTZyoiJImJihbw+TQqjXwI5Ofk1DY28n6+cFKETgaNxU+9lohFuB2SD0NSPT9XPo2GH8XBUJAY7mAwSFY+QogwWgwLxsfG+vx+YiBoVBqdRifk4XA4cdExTUplRmoquSPnG1xC5xEDd9ZEBx25APDHiCxPnjnTr9dhQYxOo4fDYYfT6Q8MsWOXy+1BCIVR2Gazh8Nhy8AAHs/VNjZ292jiomMi8tsdDjp+urTZEArXNDbYHQ42i9Wn7SdKJcTGDVOKaKVdpeLzuKFgKFUux7Cgz+9js1gJcXE0Gi0Q8DPOLc2VlZ7RqGhJlSeTa6NSqQEs4PP7EUIlBYWpcjlCiEal0gYdpeU11XNnzqyurydCVYfDkRSf0NLamp2RcfZmJIb5/H4Mw9xuN0KIw2aXFRXJJBKL1Vrf3OT1ehikdcKICges1s5uVUx0VFVd3emqSo/XS2jS6/NxORzcc9BptJqGemX72VtBqfLk6vqGqrq6zLQ0fNIMHyOJWMRkMCkUCoVCJSskosscDrulrY2o7eykP42GEGpUKCgUCi5qqjw5FAo5XS789JeeklpVV9ekVJyT4af6R2RktnV2xEbH/HxkkVQirqipweNjovVwONyn7cevYNQ9PbWNjbHR0UMa4ZAZIrQxuJsRYiOEMlJTC3PziD/eoPX8oqRSQm/k5WNmwAAAIABJREFU9MFVEYTCoer6+tbOjvi4OLJxOpxOQrzBxTW9vTWNDUIBn6yots5OiUjkDxBr2Z89IoaRhNxrwg7JhyHu74lRJpTv9njwo5hG/Wm4cUkibojiRstiMIYcCEIeKoWiMxj4fF4Aw8gdueDgDh44hBD5yK1pqAcfAFwkl8tyd16fj06j0en0YDCIn1IH4/P7WUwmhmH49SD+3+vzMRmMwVeI4XA4EAgwmUy8wkAggN+twb9eTCn8EhvP4/Z42CwWlUrFZ9jwqrxeL3vY2x7BYDAcDlOoVCqFQr72vxgCgUBLW1thbu7gdBqNFgqFyOtnnqgoH1tSSqfTh28lEAjgeQhNdqnVcTExRFzoDwSYDAY5P4VCIRoixigcDvv8fvwERFZIRJexYJBOow0WyeP1km8X+f1+Gp1OXD3gHSSGJqL+wSOLFyEGl2g9FArhv4ZCoUAgwCKdLiN90nkykLUxWIwIsS8Gst5+1tB5qjpRUT6+bDRxkRHRcUI8cnG7w6HSqEdlZTOZzAhjJi6MCD2TuzOkJIN7TT4MccMIYBg+ykMqf/BwD2O0gweCkCeAYcFgkM1ihUIhckcuOLhDduGCRy4AXL7OEhiMw+kU8PkXk9NoNkfLZL+iCbvDccFHVIBLxa8Y1kAg4HK7xSLRn7J3AADOEgAAAAAuX+D+NgAAAABcgEv2NGxKVvYwv6rb22BsAAAAAIgsUU1K0ZB/58s/Yeq0rNxc/C+nqPi6m24+dvwEQkihVI6ZNPmSdKFw9Jie3l6E0MnTp2+5YwFCaOWaNU8++9zv3dyQKJTKM+eWQ/pdxQAAAIDI8vLF5/O9+9ZbV02ZghAKBAIr13z70GOPVZ8+JU9K+uj9/1xa2bgcDovFRAjNmD59dGnpJZFh/8GDnV1dY0aPvrRiAAAAgLP8H5GSlT3kTCyfx5NKpfjnhx64f8nSpX1arUQiaWhsmjh+PELI6XR+tWJlW3t7sjzpoQcelMmkCCG32730q6+7VKpJEyaw2az0tLT8vDxtf//mrVuffOwxhJDX51v8ySfPPf00k8lECFVWVW3fudPr9d584414tWQ0PT2r1nzrcDrm3XjTT4Lx+fiTqwaDUduvzc3J0RsMP+zcWVpcsnX7doTQPXfdmZWZiWdubG5e9916q806edKkO267DX90fvO2bempqZXV1QqFsqio8N677hqmOYRQxZkzm7Zs8Xi8RYUF99x5V0dnx7ETJ61W6+Zt2+bfdBMhBkIIw7BvVq2qrqmNiY5+6IH7k5OTEULDiAcAAABE8Ad+wOfIseMioTBZLjcajV8uX44n/u3FF/V6/YLbb8Ow4K0LFuCJTz33fE1t7fRp044cO/rPV15VtrYihHQ6/crVa4iY9YtlywOBAEJo34EDTzz7bF5OblFB4WNPPb3nxx/Jjbpcrvm3/8Xtdo8pK3vvgw+czrOLtkRHRU2dMgUhVFtXt3P3boSQyWT68ONP/rNoUWlJidvjufv+B/DVBhqbmu667/6cUaNuvP769Rs3vfnOu3gNe/b++PBjj/v9/vHjxn3y2effbdw4THNt7R0Ln3xq/Lhxt86ft3ff/g8++kggEERHyYQCQWJ8AlkMhNATzzxTXlFx6/z50dFR1908r79fN4x4AAAAwB8msjwf/3zl1TfffRch5HF7jCbTkk8+4ZDes0YINbco7lpwx4Tx48ePG1dYkI9hmKq7+1R5eeXJEzwe78brr5s2c9bwTbz/4Ucvv/jidXPmIITodPrqb9dee801xK/fb92alpb6zptvIIQmjB8/fspUPF0sFt9x220RVfl8vs8//lgmk1591VV5JSVGkyk2JmbJ0qUP3Hvv3XcuQAjl5uRMuXrG359/Hp/CnXvttU8sXIgQUra1NjQ03nHbbedrDqHwu2+9OWvGDIRQc0tLxZnKZLl81MiRTCZz3NgxZBna2tuPnThZdeokl8udMf2qbrVm7fr1Lzz7zPnEg0MCAADgD+8sn3z8MXxeFI8F/7No0dxrZ5MzvPSPv7/879e/XLZ80sQJt996K51Ob21rS0tJ4fF4CCEqlTr8ZGMgEOjo7Px0yRfLv1mBEHI6nXa7nZxBoVAWFRTinxPi42Xn5oSHRCwS4fPAAgGfSqX6/X48KLzjttvxDPKkJCqV2qftS09LQwhlpKcTBa1W2zDNyZOSdu/9cfk3K/r7+0OhEFFwMG3t7SOysrjn1kJLT0ttbWsfRjwAAADgD+8s42JjM88tlPrIQ3+97qab8bnTs9FWODxh/PgzJ463KBSHjx69/6GH9+74gclk+khuwOE8ux4meRU268DZjffodDqDwXj1X/8cmZ2NEMKCwQgXwmaz3R430dzwuyEOufgcm8XCt8tACNnsdgzDCBc4OP/5mnv/o49aWhRvv/7v1JSUdRs2HDh46HwysFlsfDFYHJPJPExzAAAAwJD8ge9Z4ntx4EuK4wSDwTETJ/X09OTm5Dz60EPRUVF2u70wP7+nt1ehVCKEutXq2rq6s8VFIrPFotfrEUKbt20l/MfE8eOPHD0mlUqlUulHn3z62ZIvyI1OmjBh34GDVqsVIbRl+3afz/dLxZ44YcLG7zfjNwjXb9xYWlwsOv/iZOdrrlutHjtmTFZmZjAU2r1n79mxpFIHy1NUVKjWaOobGhBCNptt7759V191Fdg9AADAHyayLFHX/TfFxWIxQuhMVVVCfDwRF/7jhedvuvW2/Lw8tUZTUlxcWFBAoVDefO3VO+65NyM93eVypaak4pnT09LmzJ599exrZTLZ1Mk/vab5+quv/PXRhcdPnvT7/Rw2e/U3X5MbnXH19CPHj02bdU1qSjKfz8e3g/hFPL7w0YcWLrxq1jUikUhv0H/95dJhMp+vuXvvuuvxp54+cfKk2WK58frrl3/zza49e4oKChZ/+tlLr7zy9uuvEzVEyWTvvPHGvX99KDMjQ6VSzZo5c/KkiWD3AAAAv4hLvDbsMOv4/OpFfMxmi6ZHExUVJU9KIhJdLpfBaExJTr73wb/ecN3cW+fPx9MNRmM4HI54sCUcDqu6u8Ph8PnuBer1+gCGJSUm/uqOazQau8M5Ijtr8M4PF9mczWbr02rT09PZLJbJbObzeGw22+FwUqkU/AYtGbfb3alSxURFxcbGgtEDAAD8kSLL3wmZTIo/t0KGx+OlDXIhCKGYoXbCo1Ao+BM35+O/dzn4y44XyZDNiUQiYv426tzuDQLB0LuUcLnc/EG7fQEAAAB/DGf5v18DdvpV09LP/+woAAAAAAwRRP1Rtugyz7gNRut/EZcf2AhKAAAAiAC26AIAAAAAcJYAAAAA8N/xh5mGBQAAAACILAEAAAAAnCUAAAAAgLMEAAAAAHCWAAAAAADOEgAAAACAQVwuy92ZzZaublVzS0t1TS2MyhWLTCotKiwsLSmOj4uj0+mgEAAALhMu/asjZrPlqeeeO3HqFP513k3zYFSuWJStyhZFC/55xfJl06dNA50AAACRJTp05MjfX3yJQqGsW7OuuKg4KioKhuQKB8Mwbb/2rbffuv+hh2+47rr33nqTy+WCWgAAuHIjy0NHjtz/0MPzbpr34aIPeVweDAZAZv+B/c8897RUKt21bStMyQIAcIU6S7PZcs11102eNGXpF0thGIAh0fRoSseU/vuVl++7+27QBgAAV6KzvPPe+9ra28+UV0JMCQzDV9989eI/Xzxx+BB5K28AAID/MZfm1RGz2XLi1KnFH34MnhIYnvvuuS9nVM7mrdtAFQAAXEIuza2grm4VQqi4qDgiHcOwr1atsNqs4XD4rtsXDBNMGIyGUCgUFxv3x1J3fVNDYV7Bryh47OTxFHlySnLKr2v3N1TXoaOHMzMyk5Pk+NdV69bcu2DoOdKNW77X9GgQhSLg8+/+y51cLnfx55/4A4FwKJyZkXHT9Td0dXV9v30rQgiFw1gw+PI/Xvriq2UL//rwzwyUTp84YWJlVRUcqwAAXHGRZXNLC0Jo8LOvp8+UyxMT/++5v91/970bNm9ECLk9HmVbq9frxTMoWpWqbhVC6GT56SPHj4XDYYSQprenV9uHEPJ4PVabFSFktpgDWMBqs9rtdm2/lqhfbzD063RqjRr/arPb6hsbXG4XQsjr9drsto6uTrPFTBZpwGqtbahzu934V2VbK+7pEUI6vQ4hFAwFjSaj3W53OB0IIYfTgX8wWyztnR3BUBBvV92jGRgYWLv+O8vA2XnvcDhsMpt6tX14PXhbjc1NeHG9wWC2WPDuIIQMRqPL7Y7IQ9Cr7WtsbsLb8vl8TS3NOoMeIURogKwuchf8fn9HV6fb7cY7iMuJYRi5kgGr1elyKdtaA4EAQshoMlmtVmVbq8frQQhNmzz17GwBqb843Rr1fXfd88JTzyYnJR87eRwh1NvX9/dnnv/7s8/TabT1mzZmZWa9+PzfX3z+76kpqdmZWQihto62wdZSWlJKvFkEAABwBTnL6praId+njImOOXL82P5DBymI8sLTzzmcjkUff9jb1/veRx+43K5v1qxqaGr8Yfeu02fKTWaT3mAIhUNbf9h2/NSJg0cObd+1o6Oz88DhQwihTVu3WCwDBw4f+ujzT/rPuSKE0Iuv/etk+ak169c1NDUaTcaPPvvEZDa/9+EHTqezq1v12ttvtLa1vv3Be7j7RAgZTcaPPvvYaDK9/cF7CKEV365qamk+fvLE1h+2IYS++GoZQsjldK3d8J3BZPx2/XcIodXr1lqttmZFy6p1a9o72j/+/FOE0MtvvLr/4IEB24Db7TabzzrjQCDwwkv/qK6tWbl2TX1jg81u+3jJpyaz6fV33goGg8tXfr1u43qny0XWT0QePLG88szGLd93dHZ8vWolQuj9jz80GA3LvvmqU9VFaIBQF7kLwVDwnUX/UbQq3/vog6MnjhNyBkNBciU/7NrxxfKlXaqudz98H29x597dqu7uDz/9GCH0zeqVCKGI/hLU1NedOH2qqaU5Iz2DSKRQKNfNmVtdd3b1icqaqm6N+rZ5t5zPWkaPHo0Q6unthcMVAIBLxaWZhi0tKX719TcGPwc7Iiv7788+X11X+/XqFWKRKDM9g8fne30+BoPR0NSobFX+5813fH6f3e4IBAL+QIBGpR0+dnTWjJkIoUNHj6SnpkVUeMPc64oLioivifEJt9w0r7Kmqrevt6tbdcOc60qKit1uV0Nzk1gkGlM6eu7sOXqj0Wyx4DdTT5afvmbGzInjJuSNygmHw91q9b//+QrudG++4SZyQ5npGSu/XR3AAhaLWZ6UtOiTxYkJCaFwuKu72+F0xETH/PW+BxBCMpksKzOLKJWaknrj3OtHl5Zt27E9d1TO7BmzerV9dofD4/ViGPboXx9i0BnkVnhcHjkPn8dDCO0/dODphU8IhUI8vL75+hu7VF2BQMBoNBIacLlcuLrIXcjPy5cnya+/dm58bJzRZMIvVnA5B1cyIitb0do6YLUihObOvrYwr6C8soIQbO/+feT+CviCny7HqFSxWNTY3JSVkUkk0qg0/IO2X/v91i2v/fNlCoVyPmuprKxECMEDPgAAXHHOMjcnByFkMpkiZmI3b99akJs3e8as2TNm/e1f/5eZnpEYnzC6pCwxPiEpMZFGpyGE7Ha7wWj8KUyhUkaXlCGEpGIJlUrF4y2bzYb/ymKyyPUzGAz89B0KhykIhUIhhJDP7+dw2MSvNCo1HAoRMRCNRkcItXd2xsfF4/kxDMOrpVAo4XDYaj/bVnFh0frvN5aVlOFfR2aPiI+LFwoEXA6XyWQOqQd8XtTv93M53KramqaW5nk33tTW0Y4LGeEpEUIRec56IwqFRqcFQ8EuVRefx9uwedOjDz4UOteFCA2Qu0ChnPVQxKQuLqfVZo2o5KycAT+Xwxlc5+D+EoklhUVRsqgRWdmffvn5PNLlRX1jQ2Z6usfr+fiLz55+/EkOmzPsPET1pAkT4FgFAOCKc5Z4CFhbVztzxkxy+vgxYz9dumRk1gij2TS6pGzCuAkffPxhIBDo02qfeeLJ0qKSz5d9YTKb71lwF4VC+Xb9uqsmT501fcbGLZtCoXBRQWGyXL7y29UDVmtP34Wn7CZPnLT4809bO9rUGs3NN9zY2jbE3bIpEyd/vOTTZkWzz+ebOmlyfm7eF18tczqd1866BiGUM3LUR599jCgUKoWCEJo2eerjzz311edLEULXXTtn09bNCXHxFCplysTJP6mbTj9TXTWm9KxDVfdovlq1QtOjefiBvzqdTnWPZsfuXTa7jVjyLQKxSETOM6ZsNELo+jnXLf78Uw6HU5CXz2SyPB7PwaOHdTq9yWIWCUV4wShZFK4uchfSU9N+2LVz6TfL+3W6cWPGEq1EVEKn0Tdt3RwfGyeTylgs1pCCna+/ny/7ksVieTye2+ffihDyeD3vLPpPKBQKhUJPLXx8y/ZtVqt1xeqVeKN/e+a5wTVjGHby1MlrZs6AYxUAgEvIZfeeZTAYNJqMAoEATw+GghaLJUoWhQdBTpeLxWTiIaDf78cjIbvDQaVS8TnJYDDoD/iHj1TIJ2Kb3SaVSIeZAwyGgi6nSygU4l9tdhuTySTqd7ldHDaHSh3i1q/P53O5XVKJNKK2cCiMr0fj9/sXfbr46YVP0Gg03Ak5nU4Wi0WlUYngdTBD5vH5fUEsiC8L5/P7MAzjcXker4esB0JdRBcCgUB55ZmJ48bv+nFPQlx8aXHJT8KTKtm4+fupk6eIhCKJWDyMMofs738PvGcJAMAV7SxhBR8Mw3bs2XXz9TdeQhm+37alX6eLj4ubf+PN57tiOHL8aO6o3OhLsWwvrOADAMCV7iwRrA0LDAusDQsAwOXDpdz8efq0aSuWLzt+4tiYcaP3H9hvMplgPAAMwzQ9mkcWPrLg7gXjxo7dunEDeEoAAK7oyBIH9rMECGA/SwAAwFlewGV2dauaW1qqa2phVK5YZFJpUWFhaUlxfFwcBJQAAICzBAAAAIA/DFRQAQAAAACAswQAAAAAcJYAAAAAAM4SAAAAAMBZAgAAAAA4SwAAAAAAZwkAAAAA4CwBAAAAAJwlAAAAAADgLAEAAADgV3DJlt9Mycoe5ld1e9v5fvJ4PD29fUwmQ56URKPRfj8JbXa7Xm/IzsoEKwEAAIDI8pJRk1I05N/58vv9/ldff6OwbPTNt906c86cknHjv1m56le0O2HqNIVSiRBauWbNk88+hyceO35C1d1NzvbYk08dP3Fi+Kpa29omTJ12MY1+t3HjY089hX/+y113Hzt+3ppLx41va+8A0wQAAIDI8tfw6htvni4v37hubVFhIYZh23fufOnlV9hs1oK//OUX1ePz+UKhEEJoxvTpo0tL8cQvly+//rq5aampZ0NbtWb2rFl337ngtxI+iAX9/gD+mclkcnnc8+X8+MNFiQnxYJoAAADgLC9ASlZ2xExsn1a78fvvv/9uXVFhIUKITqfPv+mm/v7+Tz5fcsftt2/avCUrM6O4qAghdODQYSqVgm+F6HQ6V6xe3aJQSKXS22+5pSA/n1ynwWDU9mtzc3L2/Phjt1q998d9mRkZuPusrq2ta2joVHXde9ddhAclOFNZuWXbdjaHPXH8eHL6lm3bT54+LRDwhyxFwOfzBXz++cRraGgcOWIEj8erravT9vdjGNalUj17LioFAAAA/vf8YR7wKT9zRiaT4e6QYO611/brdHq9fseuXc0tCjzxxKmTp06X459feuWV1rb2Bbf/JTMj47Y777JYfrYfWW1d3c7duxFCsTGxXC43NjZGIhYjhN58591V3347bcpkPo9/w7z5vX195FJ19fX3PfRwUlJiRlr6v998i0gfvhSZqZMnR0dFnU+8JUuXms1mhFBzi+KNt99Zt35DYkIiWCoAAABElhdGp9PFx8VFJOIuxzIwcL5SY0ePnjN7tkQiKS0p/uiTT9UajVQqHZytpLgoJjq6uKgoMyPDaDKtXLPm1NEjMdHR189FvX29m7due/qJx4nMS7/66qEH7n9i4UKEEI/H++CjjxBCFyxF5vZbb7lI8RgMxrrVq37X55gAAACAP4+z5PP5eLxFRtvfjxCKi409X6nsrKyX//16a1ub3+/3ejzh8IUbau/oCIVCDy18DP/ar9MFgyFyhhaF8tb58/HP+Xm5F1nq14mXnZUFnhIAAACc5cUyurT0lX+/rmxtHTliBEKoprZOJBLuP3gwKzNTKpVSKBQip9VqjZJF4dHeXfc/8N5bb7352qtisbh47NiLaYjNYrPZ7BXLluJffT4/g8n4WQY22+Px4J8dDudFlhrMxYhH7hcAAABwqfjD3LPMGTVq2pQpTz33fHtHB0JIp9fP/8sdn3y+5IVnn0EIiUSiFqUC90DEWxk6vT4cDs+eNVMsFh85dsxiGRimfgqV4vP5EEKjRo5gs1h1DQ1SqZTD4Tz46CMnTp4i55w0YcL3W7biz9Ou37QRT7xgqcH8IvEAAACAKzSyLFHX/aL8ny1e/H//+tfMOXOjZDIGg0Gn0WKio+saGmZeffV999x97wMPnjpdTqFQxo4Zfda/jhw5uqx05py5PB4vJTl50sQJr7355g+bvx+y8tLi4nff/4DP490yb97iDz54/v/+T56Y2NPXN3nixBuvv46c86knHn/goYenzpzJ5XBzc3LwRA6HM3ypIdz/LxEPAAAAuIRQXA7LJWx+mHV8zreIj8Vi6VZr+Hxeelqax+Nd9e2axx55hEql+v3+Pq02MSGByWQSmcPhcEdnJ5fLTUxI8Pv9AwMDsee/wWmxWAQCAYPBQAj5fH5Vt0osEsUNeqoIr7ZbrZaIxWKxmJw+fKkh67l48QAAAABwlhfrLAEAAADgynKWAAAAAHD584d5GtY847YrfKhkBzaCvQIAAFwSYIsuAAAAAABnCQAAAAD/HXDPEgAAAAAgsgQAAAAAcJYAAAAAAM4SAAAAAMBZAgAAAAA4SwAAAAAAZwkAAAAA4Cx/BwKBQFd3t85guGBOn88XCAQupk63201sNokQstpsGIb9UsFMFkunSuX1+Qb/FAqFrDZb+Nw2zV6fz+lyBYNBcqNE0xffHbPFgmGY1WbD/2x2+2+iYbvDgW8lhhByOp2/uh7LwNDbh3m93r7+/t/KHpwu1y/K7/f7B6v9lxrML8LldhND36lSDc4wZOKfG1zVv5PCAQCcJXK6XG8t+rCypnbz9h+WrliBEPru+82Dsx06dlxnMFTX13equi+m2pXrvntv8cf4Z21//6PPPqdSq4fJ7/X5tuzYSU7Zs//Atxs2VNXWvfGf97s1mvbOrsraWrIfffTZ55oUCvzr0hUrP/lyqd5gOHoqcvfKj7/88nyNHj5xoq6xkfiqUqur6+qbFIo3/vP+mg0b12zYuH7z5t9Eya++8y7Ru3cXL/7V9TQrlO2dnYPTWSzW3gMHB6zWwT99tnz5meqaYDC45Kuv3//k08+WL4+4amlWKLbt2kVO+eTLpb9IqsYWxWC1D6nh34ofdu/GNz09UV6uP3eRR9gtOfHKAVf176RwALh8uGRrwzYrlUX5+TfNnYMQWrtxk6pbferMmRFZmcUFBYePH9cbDEUFBfLExGOnThlNxrFlZVwOByF0qqJC3dM7tqw0PTW1qrYWwzCdwTBj2jQ+j0fUjAWDloEBqURy6kxlemoqHoLsO3zY6/XOmDZNLBKRCzYrlScryksLC1OS5XjxkxXlr7/0EpVKHV1S3NPb16FSmczmkVlZAj4fz5CRllZeWZmfk+P3+212O51G4/P4qfJkhJCira22vmFEVlZpUSFC6Pip00azadb06XweT9PbW1lTm56aWlyQP+gUvOfRB+5XtLUVFeTfffvtRDq5iFrT4/F69UYDj8vl8/hNCsWk8eOqamrFIuGk8eODweDRkyedLte0iZOEQgFRA4fNblIoJo4bG39u5y+7w3Hk+Ak+nzd14kSH09mv14/Kzu7p66NSqR1dXVMmTKBQKBVVVcWFhTq9nmh9/JjRny//6umFGRGS9+t0fB73ZEXFjKlT2Ww2kX7s5Cmd3hAKharq6hLi426aO3f3/v2nKs5MmTiBuCI5cPTImJKSwYahaGvj83h6g2FwN8l52jo6mAwmhUpBCNU2NKrU3WXFxclJSeSqwuEwYUujss/ub9Pe2YUFsWaFMm/UqJHZWTX19Rw2OxQKxcbEHD5+XCAQzJg61Wa36/T6zu5uiUg8ecJ4QvkjsrLodHowGPT7/U6Xy253WO023G4L8/KIRPIQDE9Vbe3gbpLN1ev1OpyurIx0k8ViMBhHZGWSB5psycFgsFerVff0MOj0GdOmUSgUsloMJtPREyeEAuHVU6fQ6WeP+obm5maFIikhcfKE8Sq1OhAINCmUaSkpxQX5VbW1IqGwobllTGmJPDGxvLJy3OjRbre7U9Wdn5tzPmOOsENlW3tdY2NGetro4uKzxyaG1TU2lp37CgAQWV6YrPT0k+Xln3/11ZHjJ66fPTsmJprH5aYmJ9c1NloGrNMmT16zfgONSo2OkqWnpja2tKh7eg4cOdqr1V41edK6Td+bLZZlK1fRaDShQLBp6zZyzWNKSsorqxBCbR0d2ZkZCKGlK1fFx8YVFxTg0R65oDwhQSQQRkXJiOJF+QUvv/X2pm3bfX7/5Anj5YkJCfFxHJIniJJJTWZLMBisqW8oystDCBnNpoqqqp7e3u27dk2bPGnfoUPa/v7ePi2Xy42SyTZt3WYym9du3DSmtKS6ru5Mdc3P5hIDAbfHw2KxEEJtHZ2btm3ftG17fVNTRBFFe9v23buTk5IOHj3W2tEeFxvz1geLsjMzyquqe/r6Vn+3nslkjsjM/OiLJT8bXSrl/jsXrPh2Lf41FAp9unTZqBHZFAplzYYNRpOpqrYWIdTe2dml6la2tbd3dvp8vj0HDtrtdnLrdDo9GAq53O6IQVy/ecvk8eNZTOaxBwEuAAAgAElEQVSOvXuJRJ3B0KHqGltWihDyeX1cDhchRKPS1L09RJ5rZ84gXxaQncfREycT4+OH7CZxDbF91+6C3Nz6piaEUHllVUNz0+iSkpVr10VMF5NtiZgkrKmv23/48OiS4o3bthmMxiMnTtY2NEolks+WLcevydZs2Kjt161Yuy4/J6ejq+vE6dOE8vcfPuLz+8nalkmluN2ebwiGZ8huks2Vw+F8v307QujI8eMujzuiFbIlm8zmr1evyUrPUKk1Z6qrI9Sy9JsV48rKWCzmqYozZ6d2nM4DR45ePW1ak1LRrFA0KRQ79u4tLSrctG2bzW4/ePRYY4uiIDfny29WIIR27z+AX2mdOlMxjDGTxfP6fOs2bZo+ZbKyta1Xqz1rgeGw2TIAp10AIstfgFQiee/fr6nU6mal8rV3333txf9jMplSiYTFYml6e3fu3TtgtYbCYT6XJ5VI8PuaNfX1jz/0Vx6XW5CX297ZGR8XO7qkxGqz4SdNgvzcnA1bthbk5SYmxKMwQgiZzWY81GMxWXa7g1xQJBKxWCyfz/fBp5+yWex/PPP0LTfeMOuqqxRtrbt+3NfUokhNlnu8XrvD8emyZWwW+/677gyHUV7OqCaForKm5tabbiSmZBuaWyaMHZsQF/e3p56kUqlxsbGlRYVWm626rq6+qckf8O8/fNjucDS2tCQmxBPSWq1WIiyOiYoqys9DCEnE4sFFpk6cmJaSghC6esoULBisSq3NzsxMkcutNltdY2MYhds6OgwGo1rTs/K7dXhfEEIpcnlaSvKxU6cQQnqj0WAyHT99GiHU0to2cexYst6mTJxQUV1ttgyMG10W0fqY0hKRUGA0mXjJyeQiM6+6av+RIwajKSYqigjmVq377s5bbz1ZUeFku8aNLlu6YoWyvc3r9SYnyYc3iT6tdunKlf9+8UUqlTpkN+WJiQih2oaGGdOmpqWkzJg21WSxVNbUIIT2Hz7sDwTaOjrGjR5NVJidmUnYkj8QwLf1RghdNWlyilw+rqysvbMLIXTd7GvMFktaSkpyUlJyUtKPBw+VFRWNLilOS0lhMBh79u+XJyURysd9MKHtIBbE7ZacSA4u8VsMZEqLisih1eBuks2Vgig0GtXucDQplDfNnfvt+g3kViIOgcL8vKyMdKPJaLXZ2zo6yGrJHTVy5brv8nNzp0+ZjLfL5XKLC/J37NnT09vn9ngQQlMmTkyRy9NSUuwOB0Jo9tXTuVwujUaLkH8YYyYrwevxikTCbzdsLCooICY2mAzGNVdPh9MuAM7yF/D99h/kiQljy8oy0tJ0eoPJbMbTN27dNio7+8Y5c15/7z8RRVgslsvl4nG5drsjKz0dP6UihM49cnGuSzSaWCTasWfv1VOnnD5TiRDCgmfvlnm8Hi6XM7igVCL51wsvUCgUDMNeefudt195eWxZWVZGxlerV6cmy8kZTBYLQmj86NFrN27CgphAICCLh4cvVbW16ampdDqNaIXFYhUXFMydNctktrjcrjbS/T8mk0kEPWKxKCsjg6gtogiNdlZsOp2OBYMUCuXcbCPi8Xh33347lUrNHTkyWZ6Ei0o0Mf/GG9/8z/sut4fFZCbExd17xx3BYLCxpYVGowWDQYSQ2TIQFxMzKjt78/YfLAPWB++6s66pidw6PoHGZDLJerbZ7Zt/+OHvTz/VrenBPRYevCbExx0+cVyl1rBZrKSE+HnXX5+WkrJj7964mJjhTUImlT54990rvl37zxeeH7KbOGwWG38UCD+ns1is6VMmp6WktHd2xcX+rInz2ZLX50UIOV3O+LhYhBCNRmMxmU63CyHkdrvxCX+v14cQcjpdQoEQIUQoH6GfaVsg4A+TiBB65P77L3AEDupmhLmOHz1m+67difHxdDo9opUIS/7pKwpHqIXBYM6dNauiqnrtpk0LH3gAIVRZW6vp7b33jjuIO8dUChWRqiNqI1LMAwNDWuaQmqHRqNfPnh0XE7v5hx9O0mjEDDwAgLP8ZUyfMvmjJV80KZRer9cf8CcnJYmEgn2HDssTE4+fPt3a3h7AsGaFMjo6aufeH9PTUhFCc2bOWLZqVYpcbjSZR567BTUk48eMXrZy1aMP3I87yykTJixe8gWTySwuKCRu2BB39QwmY4uyNWfkCPwEMbas9O1FHyYmxKvUmpuvmysUCLbs2Dlp3DiJWEyaiZUZzebpU6aQq5owdsziJV/09PXpDcaIuzJjSko+/Pxzp9PV09d374I7yD+JRaIhH7sdpshg5sya+dny5UKBgM/jjy0ri4gGmAzGbfNufnvRh1KJJDYmetV339ntjonjxibGx7d1dC5dsbJfp8M92cjsrF6tls/nD27dZrfHxcR8v/2HlGQ5fguKw+EEMGzzjh1YAOvsVmEYRqfTaTQaPr+6e99+qUQSEx396bJlI7OyerXaubNmNTa3NLQ033nrrUP2gs1mpyTLU+Tyg0ePDtPZiePGLvrssz5tf69WW5ifd+3MGWvWb0hJlhuMxicffpicM8KWxpSevUX646FDLcpWvdFw45w5+w8fQQglxMejMFqxdp3eYLhhzrUojBpbWlat+06l0Tzx0F+r6uqG0TZutxGJ/82hEWGuo0uKv/722xefe3Zw08NUEqGWmvq6llblgNVamJeHZ4iLjtmxZ8/6zVuMZpPT5ZJJpcNfx3zx9Tcer5fH5QxjmWTxigsLN2zZOjI7u1+vn3HVNDyD2+P58LPP//W3F+DMC/zhuJS7joRCIbPFwmKy8DmrUCiEhy92u4PP54XDYQzDWCyW1+slHh7xBwIul4vsty4SPBYhPwdE4A8EaFQq2cG4PR67wxElleKe1evzsVmsi2klHA47HE6BgE8O7IifzBaLWCSK8NYIoW27dhfn5xNPGF1MkcG43e4AhomEwgvmtNpsLCaTw+HgOne73Xw+f8i+EK0bTKbDx47dPm9ea3s7g8HAH5vCw02v18vn890eDx6QDQZ/JUYmlVIoFJfbXV1b998HGcFg0OF0ikUi/GsgELDZ7VEy2eCcEbaEENqwZUthXn58XKxQIIgYpgGrlcfjMRmMxuaW9q7OWdOns5hMYvL2fNom7Pbih+AXmSuGYe8uXvyvF174pQMdoRaT2cxkMoWkuRC3x0OjUlks1jDDR9YkYdjDWCZZvGAwaDSbxSLRRR4+AADOErjASe3UmTNTJ068bCU8cfp0aVERh8P5RY96DsbldrOYzIvx/b8fB48ezcrIiHhuNoJOlapP2385zByGQqHFS76YedVV+bk5cKQAADhLAAAAALh8geXuAAAAAACcJQAAAACAswQAAAAAcJYAAAAAAM4SAAAAAC5jLuVD/N9t3Lhz124ul5uRkf7800+HQuGvV6wwWywL/nJ7Rnr6ZaKgUCi0//ARnUE/YczYrIz0nT/+6PF4EUI3XzcXfwUCw7A9Bw4MWK1XT52aGB8/fG0Yhm3fvWf+DddHpJsslo7OzjGlpeS2aurrFa1t40aXZaSlOZ3OPQcOYljgmquvlkokZDH2Hz5y7cwZYMoAAAB/wsjy5OnTR44eW7F82fIvlsik0v8s+nDpV8vl8qQ7br/tX6++dvkoaN+hQ8Fg8PrZs1euW+fxejtV3ePKSseVlRKLge07dJjD5syeMWP5ylVk52ez27u6uxFCVpvN5Xa3d3YGAoFQOKzV6VRqdb9eT27lh927i/LzyW119/QcPn782pkzNm3b5nA6l69eXZSfP7asbNnKVcFgkCyGQMCPWB0XAAAA+JNEljt37X74rw/iy40+eN99N8yb/8lHH8qk0srqmtjYmMtHQWmpqfLERDaLRaPRDAaDx+M5cORoXs4o+bm32mVSSYuylU6nRUdHEaWWfPV1RloqlUKtqa/3+wNGs3lUdtbWnbueeWxhs0KRlZ6mbG+/avJkfO0xfyBgdzjYbDa5rcam5gljxkolkrxROe2dndMmTc7KSMf3sjaaTGQxigsKVqxdSyxjBgAAAPx5Ikuv10vsEEmlUqk0WnpamsvtblG0+P3+y0dBIzIz9QbDB59+dsO1szkczsSxY2+96cbjp04Te0qHw2EsiLndHo/HGyat6b7gllvuuGW+sr0dITRn5sxZ06dzOBybzTYyO2v2jBl3zL+lsubsntImk0kkFEW0FQwFORw2QojJZPj9geKC/PLKyqUrVt5/151UKpUsBo/LtdpsYMoAAAB/QmdZXFR06MgR/PPR48cL8vPe+2BRXGzsU48/7nZ7nE7nZaKgZoViz/4DTz78UFlxscfrHZGVyefzM9PTCf90sqLi9nnz5syaicJhj8eDJ1IQCofDoVCIQcdXFg0jhAKBAJvN9nq9CCGf34f7QoQQhUrFd/8gtxUti9IbjAghvdEYHxu7ZcdOk8Xyt6eeTIyPHywGjUoDUwYAAPj9uGTTsLffeutzf//7Y089LRQITGbz+++8fejIkYVPPhUXGxsXGzvk0t6XhCMnTjpdri++WYEQumHOtWtWrkpPTdX298+ZNfP77duL8vMnjR+/bMXKmJhogYDP5XKJcHP5qtV+v3/SuHHqnp6tO3fFxkRLJWIWi6XV6VauW6fTG+47t2NDXEwMfnFAbuu+BXd8+c0KnUFvtdpSkuWfLFuaGJ/Q1tEplYhnXXXVMpIYVpsthjQDDAAAAPzmXOK1YW02WwDDiI0RbHa73++Pjrp8T/0YhjldLny/i5r6+mS5PEoq9Xq9bo9HKpEQ2d5b/PHzTzyO78ixbtP3k8aPEwoExC4Zbo+HyWCQ1xPftG3b1VOnkmvAPa5lYGDIvZPIYuw9eHBkVlbqz7dlBgAAAP4MkSWO6Jz/OPv1t9je6PfVF51O+LyckSPxvYfYbDaxiRjOiKwsOp2Ox8fJSYlcLldM6ung7ZCunz27taMjwllSKJTz7TJIFkMsFIKnBAAA+DNHlgAAAABw+QMr+AAAAAAAOEsAAAAA+O+g/fOlf1yShqtqal5+7d/7DhzYsWunTCaj0WiPPvHkzBlXs1gshNATzzwrkUgSExJWr127fccOkVAYHxeHF9zz4487d+8ur6iIjYmR/Pwm3+9BIBDYsffHypqaaJlMIBDgia3t7bv27TOaTOmpqRQK5YKVdHR1mczmwTcgaxsaTpwulycm4r1GCFXV1lKpVIvVumPPXk1vb1pKCpVK/fHQoZr6+vi4OA6Ho+ntJX7yeDzbd+9pUbb26/QBDLPb7RKxeHhJwuHwN9+uHZmVyWAw6puadu/bV1xQYLXZNm3b/lsta4Bh2NpNm3b9uK+iutof8KcmJ1fX1e/Ys7esuAghZBkYWPTZ5yVFhe1dXWvWrz9VXqEzGEaNGEEUr6mv33f4sNvtlicl9fZpf9izx+3xJCUmhEKhfYcOn6wo53F5MqkEIaTW9FhtNvHPb3sTVFRXnz5T2aJslYjFxBu9NfX1h44d53G50t/fcgAAgMjyN8BsNpcWF3/+8eL333n3rXfedbvdXSrVvv37EUIKpbKqutpoNK769lssgD3y4IOvvvGm1+fDC+47cHDO7NlzZs+OiY7+H8i5Y+/emOio6VOnfL3mW8J9bty67cY5c7Q6XU19PZHTaDLhv+LvPqrUapfbjX+uqKqOjoqy2mwqtdrt8RhNpp7eXoSQ3+8XCYWt7e14DcFgsKq2LiY6+qvVa66dOYPOoO/at+/QsWNer2/S+PHLVq4KhULkn9S9vUwmY1xZaV7OqOyMjL0HDp6vFwaTyTIwYLPbrTZb7siRVBoNw7ATp8tLi4oQQkaTuay4KBQK4Tl7+vowDPN4PG0dHbjaDSaTzmDAZUYIafv7m5VKPL9lYKBTpcI/42zZsUMsEr30/HPPLHy0pq6+pr7eMmBpbGkxmEy4Kvp1OgzDtu7Y+fSjj77w1JNmy0C3RoOXVWt6jp8uv/m666rq6pRtbctXr5p19fT2rs76pibyWoD+QGD/4cMffPaZTm8gd9MyMPDT1VhtLb4ioOScN+3r7yevIAgHPwAAfwBniRDyeD0Wi6Wzq5PFZiOEpkyadOjIUYTQzt17rp19DUIoPy/vtltuiY6OZtDpQQw755OM6zdtOnnqdMQDqL8T40aPLsjN1ekNEsnZoC2M0CP33+d0utxuDzms+Wr1GoRQfVPTmerqNRs2VNXWbdyydfX69Qghr8+rNxjf/WhxW0fny2+9dejY8U3btrd1dCTL5QiFB6y2cwFrR1JiAhYM3j7vZplUKhVLfD5/fWPTVZMnJcTFUakUp8tF/klvMBiMpgNHj/l8PgqFwufzyEvOmiw/Pbq1at13m7ZtNxiNy1et8nq9i5d8YbPZBmxWvcGgaGvbd+hQT1/fkq+/Rgi99f4Hh44dszscny5bpu3XLV7yhdvt/ve775VXVq3fsrVZoaisrd26c1dXd/fq9esVbW3ffb+5U6XCy571UnV1c2fNQgjR6fTrZl9T29D4/+zdd1xT5/oA8Dd7kEXYW5YKKuBEwIWIImqd2Lrbautoa2/vve297d29t3a3tu6998K9NyqCyJJNCEkgCSF7z5PfH0fTFJDapbW/5/vhj/CeN+95z5tz8uScnDwvQih14MA7RUUIoZr6+l494xFCDAb9wLFjD2pqXpkz23M3L4NBz5v8AofN5rDZrTJZZHh4oL9/RmpqZVV1dI8eo4YP43G5JBIJc7kyhw8fP6Zj+vhjp057Hut0+is3b1bW1OApFRFClVXfyyAIBz8A4PkIlgW3bn/97apzFy58/vEKhBCVSmUyGRqNpqGxsWd8PEJo0IABzWLRa8uWLXn9NR8fH/xZwzMy3n7jDZvdvnP3nqfQydDgYKvNJpKInY6H0ZpKoQQGBLRIpRaLxWK14oWytjb8TLdB0BQfGysUifOmTJ6VN8PtRgKhMDqqB0JoYEryuKzRXA73xWlT01NTW2WyO0VF7SpVWOjDuUqkclmAvz+VQukVF3fkxMni0vsvjM+x2mwMBgMhRKVQEULeiwL8/TNSUyfljMOzq+O9wpsym80XLl/xbIXL5Vw4b66ktdWH6WO12ygUsqBZFBEaltSnz6Vr14ODgjC3u1ksMRqN/n5+C2bNKqus9NR8UFMbEhw0ZUJu1sgRrTLZ1Rs357304qScnLGZmR2e+3CXIhA9WebJZDKeAjA+NqahUaDWaHx5PAKBiBD6w9Kl/RITK6uq//6/jyStrXj9wIAAJpO5Yds2NosdERbGoD/ccLvD7p0LkE6nk8lkIqnj3rtw3lzP4359EqdPmuR0OM9feTgOVpvNO4MgHPwAgCf3LH9nmZ2VtWzJYvxxo0CAEMoZO+6LlSv7JCbihbcLCw8fPfbtl196EvpoNJoBKSk8Hi8tdciZc+efQif3HT7y4rSp0yZN+uTrlRaLhcFgiMQSibR12NChAf5+t+8W9U1IQAg1CARxMdEYhlXX1c2cOoVIICCEjCYTcrtLyspyssZI5XIKhYoQIpMepqZzu93TJn1vri4CgYDnvVu1ceOYkaPwmbwC/P3aFIqIsDCzxcLy8flm/XrPIqfTlZDYk0wmc9hsh8Phxtxk0sMXlMlkzs6b4dUyEc+BEBIcNCA5OSQoKCwkpKauDl/aMzY2KCiQw2IxGAzPeZh3TTxpH5FIxNxuIoFAJpEwDMOvnXo/F39icFCgQCiMjY5GCN29d69vQm+D0YgQITgo6NipU+mpqZeuXbfb7eu3bnvjtUVJffqEhYbU1NVHhIUhhHR6/fqtW1+eMyc4MFCt0SjaHyb8Cw4KqqqpuX7r9luvv8bo9CvVzgxGY3xMLIvFSuzd6+69ErzQO4Ngr7h4OPgBAM9HsOxs5PBhf3zvvWMHDxTdu4cQOnT4iFan+/Nf/4oQ+uff/vbJ559//vEnq9auS0zoXV1T++G//vkUuhQWErJy7TpfX1++r68Lw1Zt3LT45QW7Dh6QtLQ0NYs8AamxqUmt1oglLficIcn9+m7Yts3tRgQiQac3cDhsqVz+g+vqGRt7t6SkTdHe1Cy66L528dq1pD59crKyduzd5+/n1zcxoV2p8l7EZNC/Xb+By+VEhofR6XSpXJ42ZHA37acOGrRqwwaHwymTy5ctWogX5mRl5Z8+HRwUSCQQ01NTu6mJG589Zu2WrQw6vU9C7y6fOzsvb92WLcGBQVarlcPhpA4adOnaNYRQ2pDBX6xa/ercuZeuXadSqb483qoNG/l832aReMmrr+DPvV9ebjCa9h46jBCaMDbbx8dn2+49rXLZ8sWLd+0/4MkFuPiVl30eZRb09tk337739nKEEJ1GO3bqVFREhEgieWXO7Mqq6hapNHPE8K9Wr/FkEISDHwDw5J6/pARut1uhUPj7+5NITyl7uMlsdjgcPC7X5XJdKyjIGjkSwzC1RsPlcCgUPE86WvHlV39YuoROp+NXIG/evpOeOqRVJrt5586cvLwnX9eGbdsXv/Jyh0KHw2Eym7u87dNssWAYxvLxcTqdO/btWzhvXvft4z334/O9b+K12Wxmi6XDnbRd1sTZ7Xany4XnIeryuQghtUbDZDC6/17ZYDRaLJYAf/9u7ijW6nQsHx/v1IBPvp9otFoel+u5Joy6zSAIAAC/q2D5DGEY5nS5qI8CpPdb8ImzZyfn5npKSsrKCu/d47DY01+YxOzqHOhxmsViOp0eHPijZ/Rsam72YfoEBQbAywQAABAsAQAAgKcNMvgAAAAAECwBAACAn+e3ku4uIjx8+65d6zZuOnbiOIPBULS3f/TJJ0fz80+cOn32/LnoHj2eTr6ex7lWUID/cN47GRu+qMt8eN07c/FifGxsh0KLxXLi7Dm93hAeFoqXKNXqigcPQkNCvNO8dZMB7tT58+UPqqpr63rFxZ2/fCU+NuYHe1L+4EFdQwO+aWs2bfblcf34/PzTZxgM+uPSyP0odofj82+/LSkrv3vvXmNTU8/4OPxWnTWbN/N5PE/OuVPnz0taW2N69PA8sXPOvM6Ne/p58epVXx7vSX5S0plSpTp84oRA2BwXE90hsyBewWgyHT9zRigSxfTo4X2vEAAAziyfhg7p7hTt7UXFxRvWrF6/atXmbduGDhmyfvXqjLT0CePHr1+92vPLy6evXalcuW7dqfMX0PeTsdU3NuIVOufDwyMf/iN9pVrtdrvlCoVSpcJT3+kNBoPBqGhvNxiN4pYWp9PZIBBYLBaFUpnYq2dpRYWnkRNnzqT06+ed5s1qsz0uA5zFahUIm/EEb0Qikc1mlT940OUW2Ww2nV4vEksQQjabPT4mBiHUKpPpDYawkBAMw/i+PHxiUZ1erzcYZG1tGIY1NjXh/dfqdEaTqb6x0WKxIITsdvuDmhqlSoUQ8q6Gw1wup9P19tIlby9d4u/Hv3HrFkJIJJHo9YYrN27idVwuV2VV9fVbt707efTkyaCAAO+ceVqdzobn3mtvx9fl6We7UqXRamvq6uwOR+du1DU24hv7sEsYptPrPf9u3rlr3OisAD+/uyUl3pkFPRW27dnbNzEx0D/gUP5xeL8AAILlM+Cd7s6HyWwWiTZs3lzX0HBwz54nyU7+dPj7+S1btIjN8kHfT8Zms9vxCp3z4SGErhXcqhcIEEKbtu8ovl96+PjxG7dv//fzzxFC90rLBvVP2bxz16lz546fPvPFqtUCoXDdlq0sHx8mg2mz2/A8q3aHQ28w0Ol07zRvkpaWx2WAUygUFovl0rXrcoWCSCT2T0q6eeeOpz9anc75KF+guKXloy+/rGmoP3nunEgivn7r1pkLFxXtSpPZbDCa1m7ZYrPZdu7fX9/YePVmwdrNW9oUirVbtghFIrzwyo0bG7fvaBKJVq5bj2HYV2vWtCkU67ZsVWs03tU8q3Y4HSq1Wq5QyNoUQYGB+Gn67LwZLVKpw+FACN2vqOif1C8qIqKxqcnzrPvlFdmZmcgrZ97Fq9fELS149EIIefcTIXT6woUWqfSr1WvwRZ5u7D5wsKqm5syli0Ul9z1nihce5fRRazR0Ou3W3UKT2TR00CDvzILWR7mZlCplQs+eQwYOaPDqHgAAguXT453uzsfHZ//u3WGhoXv3H5j+0izPW9UzRyAQPL8V8U7G1u/RyW7nfHgIocamprjoGJvNRqVSrhUULJo3b9qkSUEBgQihpuZmPLvNzKlTh6WlJfbulTNmjM1u1xsMt+7e9ef74df6lEoll8NFCHmneXO73Y/LAMdgMDJSU/OmTL55+45QJPJhMr3P8IpK7ntnRRiQlJyTlXXz9h02m+3r61tQWNgzLjYoMNDtxuRtCheG+fH5t4uKEEK5Y7ODAwM7FOZkZeVkZREISCSRBAYEZI0c+dbi13V6fYdqDy8hqDXnL1+5fO26QNgU4O/vdDorHlSJJS1MBh1PQ3/9ZoHLhVEplJu373QYefyBJ2eeh7ytrcO6pk2alJ2ZyWax6hsbvRfVNzZOmzRp4dy5MdE98Ody2Oy8KVPwxwajUSRpSU9NtTscp85f8M4siJ+kIoQIiABvEwCA30q6u8K7RbX1dS/PmzcxN/fd99+XtLTEx8X91gbLOxmbp7BzPjy8JofDvltSEh3Vo6m52XNOY7FYGHQ6QohEIpFIJAIB4Qnq3G53dFRUdFTUd6GCSMRT33mneesmA5xIIukVH8diseJiYvAwSSJ+l7Rh7OhM7w2hUil4NBqQnIwQ8vX6epLNYg1ITjaZTHaHo6auHk9I611YVVNDoZARQgQCkUAg4KkhZG1tyO32ruZpMDgwEM9zdPXmzZq6ulaptG9iIpvNGjVs2M07hQk9e9ns9rDQkLDQkEP5x51OJ/6lpidn3rWbBc0ScULPnnKFwuXCMAwzGA0dulRSWoZ/unI4HDQq1XuRSCJBCOkNhnaVyr9TLgJfLi8iNDQ4MLB/v34Xr13rkFnw0QtBwDDMarXiVxcAABAsn6X+KSkbNm8WCpsxDDObLTHR0b/BweqQjK2yujqlXz/vfHh4pNQbDBY3Rh8AACAASURBVM1i8Z6DhwTNzVMnTugVF7tu61Ymg+lwOEorK1OS+j3JuoIDA/FvPa8V3PpemrfHZIB7IXf8ru07Ynr0kMpkuWOztTpdYIB/96sYPWLEsZMnMbc7KbHPw5UGBXE47EtXr8nb2mZOndpNIS4qIkKr1e05dEjS0vLOsmWPq4Zj+fg0CJrKKx+8PGc2fmvPuctXLl2/PmrYsJR+/RBCNXV15Q8e4LOGzc7LW7t5S2hwsKxN3qZoHzNqlC+Pd/j4cT8+n0ggdu7SibPn+L68AH//qMhI70Up/fpt2rFDpdbMmjEd74ZWpzt4LP/1lxcghDgcdoC///a9exXt7S9Nn05AyJNZkEgk4snzMocP/2b9ervd8cL4HHi/AOD/rd9WUgKZTE4ik57tja8/KnZGRkT48/mefHh4eUlZeZtCkTl8GB47K6qqwkNDWT4+qzZu/NObbz55+4fy87NGjuw8TfHjMsA5nU6jyYR349zly73j47u8idSbwWgkEokd8qyqNRoWi9UhUVGXhTi9wcDy8cGvHndT7SdQazRUCoVIIqnVmvCwUJvNRiKRPBveYV0Go9EzybP3IpPZTKVQKI/vkt5gYDIYeLNdZhY0mc0EhH5UJiYAAARL8JDVZqPTaJ3Li+/fDwkODg99+AuQVpks//RpOo0+NnOU5wcnT9S+1VrX2Jjct+9P6FthcfHQwYPhNQIAAAiWAAAAwNMAP7IGAAAAIFgCAAAAECwBAAAACJYAAAAABEsAAAAAgiUAAADw/HpmGXyi4nt2s1TUUP+4RSaTqVUq5fP5/n5+8PoBAAD4PQdLhND9qJQuyweIyrost9vt//noowOHDvO4XLPFEhkR8cUnn/Ttk/g7fnmUKtXdoqIJ48d3KK+prTUYDEN+e2kHbtwsiIgIj/aamRIAAH4HnqfLsH/7178K7xadOZ5/787tinvFY8dkvfLaa3qD4Xf88giFwv98tKJz+cXLl/fs3/8b7PD6TZsKvaYcAQAAOLP8FUXF9+xwJbaltfXQkaNHDxzoGR+PECKTye8sX94kbBYIBP1TUlQq9fpNm5pFosSE3q+9+iqLxUIIHcnPj4yIuFNY2CgQvDBhYnR0j83bthOJhAVz5/WMj0MIFZeUyOVyp9N15dq1fn37vDRz5vqNm2Ry+aQJuaNHjcLX2yqV7tqzRyqTZaSl502fRiQSS8vKpDKZ0+lsEgrfWb4cIXT85MnzFy/RqNTZs14aPHAg/sTie/eOnzpltVqnTp6ckZaGEDIajZu3ba9vaIiMCH/t1YV+fh0nwbBardt27qqprYmNiX1lwXwCIuzet89oNH797ap3lr/lqVZVXX2j4JZWqz2Snz99ypTO/Tl4+MiNggIKhTJm9OgJ43PwoYjp0aO4pKSmpjYlJXnB3Ll4U7cLC48cy0cIjRmdOX7cOIRQs0h05+7d8NCwA4cPBwcFLXn9Nc/l7sqqqr379mt12uHDhs2aOROfQmvV2rXjx43bvmvX3FmzhM3NzSLRufMX4mJjPeMAAABwZvn0FN8r8fXlDej/3ZVbAoGweuXX/VNSzGbzpGnTCATCSzPzRGLxrPkL8PmTz547/+Yf/kAmkfv26bN0+Vtv/uGdvokJLqdrwcKF+PyIFRWVf//3f4qKi0cOH7Zh0+aciZMYDHrP+Pglb77VJBQihKQy2QvTpjMYzMyRo3bu3v3hRysQQlXVNf9d8fHe/QfCQsMQQlu371i5avWE8eP79umzYOEisUSCELpw6dKb77zTN7FPSlLysuVvnz1/HiH07vvvt7W1zX5xptPpyps9u8MGYhg2c87c+oaGcdnZdfX1M2fPIZGIkRERZBIpPi7WuyabzQ7w9+Ow2WEhoZ37s/fAga07dkyd/MKY0Zn//u9/L1+9ig/F68vesNvtaUOHfrt6zb6DBxFCNbW1b//pz9lZo8eMzlzx2WcnT5/Gg+X/Pv541969kybkmkymGS/NwqdornzwYO7LryQmJEyeNGn/wUP/+/gTvDPbd+5a8uZbNCqNyWQGBQYxmcygoEBfHg8OLQAAnFk+A3qDPigwqMtFh44ejYqM/OAv7yGERo8aNXx01u3CwmHp6QihnOyx+JSZ23bsXPTqK9OnTNHp9fsOHtRqtb6+vgihoMDAj//3X4TQrTuFZDLprWXL8DPF2rr6mOjo9Rs35Ywb+/abbyCEBqSkjJs06a/v/hkhRKFQ9u7cgU/l+O3atatXfo2vzmaztUqlkRERn3/19T/ef39ibi5+Erxz957x48ZVVdfMnT0rPS0tbejQ5KR+nrkbcecuXDSbzV999imBQMjNyUkfOepBVdWIYcMOHD6Ct+MRGRGR0Ls3lUodmjoEL/HuT0hwMP5VrtvtPnDocH19Q1ZmJkJowvjxby5dihCqra+rqKicNXNmbV09nU7rn5ISFBjYIyoK/4SBEHK5sG++/ILBYIwdM2b46KxrN25kZ2Wt3bDh1QUL5s2ZjRDqk5g4ImvMe3/6E41GRQgtXrQwb/p0vGOBAQH9U1LiYmPh0AIAQLB8Bnhcrlqj6VBYWlbG9+XXNzSkJCd7TjejoiLFYjFKT0cIxcQ8nBeTTCGHhoQghMgkEkLI4XTi5bGPKlAo5JDg4IeVSSSn04EQqqmrk0qlk2fk4eUWi0UqkyGEesbH45FJpVJrNJp+jyYGwQOzw+FoFAhWrV23aes2hJDRaNTr9QihD/7y3j/+8+H6jZuGZaS/mJfXYY6t2rpaRXv7lLyZ+L86vb5RIHjCGbA9/cEfr92wobyiUq1WW63WoUOGPNrSGM9IarU6hNDYMVlnz5/PGJXZJzExK3PUy/Pn4xXiYmLwycUIBELP+DiJpAUhVN/QOGvmi3iFiPBwIpHYKm3Fpx1N6J0ABxIAAILlb0L/lJS2trbqmprEhIdvzXa7feHiJV98+gmdRrdYLZ6aSqWKz3/4dSCJSOq+WWK3Feg02ot5M+bOmvXo7NYQHhZ2+04h/nUdQohKpSKErFYrl8NBCJWVl/P5/IjwcAqF8q+//613z54IIafLZbfb3W53elpaUcHN6pqaq9evv/La6+dOnvCe45pOo6ckJa384nP8X5PZ7Mvj1dTWPsngePqDEHpt6dKhqamb1q4NDg5atnx5l3UeDg6JtOrrry0Wc2FR0eZt24XC5q+/+BwhpDPoPXUMRhM+jyOdRvMMsk6vdzqdfo8GuVPDAADwe/PcfGcZGRExYfz4P//1r2KxGCFks9n//b//BQYGDs/IyEhPO3f+gk6nQwhVVFZKWlo8p1M/07CM9GvXb7DZbD6fX3D79oKFizqcDrLZrAEpKUfzjyOEtFrtoiVLlUoVgUDISEu7dv0Gn8/n8/lff7tq9dp1LpdrSMYwiUTSJzFxyWuvBfj746ebHhnpafdLSx1OJ5/PNxiMOZMm6fR6ApGIB9pOMZ5os9m67HOzSDw2a0xISLBUJrtbVNzN1m3Zvv2vf/87j8fLGTt2xtSpnhN3sVhSVV2NEKqtqystK0tLTUUIZaSnHzx8BL9Uu//gwYH9+3O/P0Pyw8BJJDyuYwAAAGeWP8Xjfk/5OJ9/vOI/H300Ykx2QECAyWRK6NVr07q1FApl9KhROWPHZo4dFxMT09DY+J9//J33C91g8sr8+WXl5SPHZAcEBEil0g1r1nSu8+mKFQuXLD5+8mR7e3vejOn4LUgf/uufi5YsvXnrlt1uZ9DpO7duIZPJf/nzn6bkzezXt69ILB7Qv39yUpJ3O8lJSW8sXTp+0gvR0dGNAsH7774XFhrKZDAwlys7d8Kls2e8K6ckJa1ctfqDf/5zxYcfdujP6wsXLn7zzZgePRwOx4Tx47fv3p07PqfLrZv94ot5s2dPnDLV19e3rqFh7bff4OWhISHvvf8BlUptFAje+9Mfo6IiEUJvLF3y2tKlmWPHcbncNkXblvUbumxzYP/+n3z+BcvHZ8a0aXB0AQB+N57x5M/d5PF5XBIfk8kkEot9eb4hIcHe5SqVuqW1JSY6hs1m/bKdlMvlao0mLjYWv+jamdvtlkgkXB4PvxjrKRQ2N7vdbs+XhXgnxRKxv79/RHh4l03pDQaJRBIeHu5pymazWyzmzuHfYDASiQQfH5/OjUhlMqPRhN9DK5e3BQUFEoldX0LAf3Bis9liY2Lwy63Xbtz45PMvTh072tDYGBwUhN8G5SEWi/UGY6+e8RQK5XHDpVar2Wx2NxUAAACC5a8eLMGvCg+W506egKEAAACPZ3yDD0TE35rQkNAJj7lsCwAAcGb5W6caMxNereeI36WDMAgAgN8NmKILAAAAgGAJAAAA/DzPzWVYAAAAAM4sAQAAAAiWAAAAAARLAAAAAIIlAAAAAMESAAAAAJ084ww+VbW1ZotlcP/+P+pZWp1u3+EjVpuVRCLNnjHD38/vcTXblUoMcwcFBvzYjllttpVr1zLoDJfL1TcxcezozD2HDs3Jy/ulNlzS0iKSSIalpT2uwpmLFwf17x/o7/+jmhUIhcfPnCUQEI/Lmzszr5sErT95ZMAz1yBoyj99mkqhIAKhV1xszpgxTqdz35EjkpZWKpU6qH/KqGHDSisqK6ur5r/0EkLo3OXLA5KTA/399QbD6o2b/vTmGzQa7QnXtWnHTrPZ7MKwAD+/l6ZP62aPqqyuPn/pMoVCoVKp47PH9IiMfFxNkViiUCm7Oeo77Jxbdu02Go2IQKBRqVMm5AYHBdU1NBw9eYpMJjOZjDl5eTwu95v1Gybnju8RGWm2WM5cuDhj8gsIoVPnzzPo9KyRI2GfAc/9maVOp1Op1QghtUYjEArxGaC0Ol1Vba3RaEQIKZRKg9HYIGjCF+HWbt4yYdzYd5Yty5syZevuPQghvd5QWV1tNpsRQm2KdoQQhmFKleruvZKbd+5YLBadXo+vxflo2ufuuVwuDHO/vXTJH5YtvV9eLlcoWlql8ra2llYpXkEqk9XWN+CtKZRKSWur0WTS6nRCkchssbQrlZKWFoSQ2+1uEDQJRSJPmK+oqjJbLH5+fr179eqw4d5apTKb1YoQammV4jNOdx4ZuUKBrwVnMpt37Nu/+JWX31m2LKZH1OkLFxFCsra26to6vH18ZMxms9Fo/Mkj8//Kt2vWrPj0M6VK1XnRoSNH/rviY0V7e4fyq9evb9667Vftld5giI3u8fbSJcsXv15dV9/SKj168iSPy/3gT3/8w9Il98vK75eXa7Sa4vulJWXl+L6K70sFhYUEAqG4tPTJ1yWSiN9euuSPbyxjs9kFhYUKpVKt0ej0ervDUVVT4z0yWq2ub5/Et5cueWn6tI3bdxhNpg67t7ytrba+weVyGc0m/Khvlck8e77NZquurcPHE985PTPTCUXNby9d8vaSxbljs/cfOWo0mbbt2bt04avvLn9rbOboL1evQQi1yqS7DxzEMMzpdIpbWvBDuLKq+vqt27AbPx01tbV/+9e/PLvE4w6QZ3vsPMfB8uEo19fvO3xEIBSu3bJFrzes27JVpVZ/svIbl8u1ddfu46fPFJWUHDlxEq9sNpvdbndkeDhCKCQo6L23lytVqrVbNqs1mq/XrjOaTFt27UIIGU2mg8fyVWp1u0opEDZfvVmAEMo/fVqt1T5hr1wul95gaGtvt9ltdBpNKBIVl5YezM+va2goq6w8df6CpLVl9aZNCKGPPv/iyo0bQpHok69X1jcK/vHRR1du3DyUf7y+sXH/kSMNAsHNO3fOXrykVKnWbt6iVKm/XLVaLGm5ceuW94Z32YeT587dKS66fuvWmQsXO4zMfz75tLD43v6jx6pqavDK1XV1/ZP6+TCZCKGRGRlTJuQWl5YeP32mRSr9dsMGhBA+MhVV1ffKyn7OyPw/UVFZSaVSX54/b/W6dR0WqdVqgVC4/M03tm7f3mFR5siR8ra28oqKI/n5dfX1x0+ezD9x8uDhI4Kmpl+wbxaLVaVWt7S2OhwOLpdzr6xswtixCCEymTwxZ1xpRSVCaHLu+JPnzlm9phctq6h8/eUFt+/e/bGxWa3RqNRqJpO5Y+++Q/nHDQbDyrVrFUrl7gMH6xoaOtT35fEGpqRU19Z5794CofDA0WNSuWzzzp14tRNnzlZWVXnmw/l2w0alSrVtz16hSITvnN7TuKrUaqVa3SRsDgkOKq98MHTwIB6XixCKj41h0Ol6vcGHyRw0oP+Fq1c9T7lfUdE/qV9URETjLzry4HFiomNiekTjH+W7OUCe+bHzvF6GxV26dj00OBhzu5vFEsyNjRk1UiqXG41Gq82GYdjsvBl2h2P9lq14ZQzD6PTvLiLp9YbCe/fGZ2cn9+1rNluqamu9W46PjbE7HAQiofNK7Q7H8dNnOhROnpBLfXShSa5QHD5+nEQiTZ04kcflhoaETMrJuVdaKpK0pA8ZrDcY2hQKtVqDEPL381swa1ZtfcPAlORxWaPvlpS8OG1qYfG9VplsREZGXUOD1WpTtLcX3rs3esSIoYMHJfTqqdXpOmy40WhksTpOLnbz9p3RI0cghG7cuj12dKb3yIQEB02ZkHu/vLxVJuuTkIAQwlwuOo3uGSWz2Xy94Nbyxa9TqdTK6ip8J36SkQG44ODg6poaJoPZ1tbWYVGzSBwbHc3lcPDzp45vBxrN8VOnZr/4YmhIyDerVwcGBA7on7Jn//5/fvDBL3clVkAgEBwOh9VmZdDpRALRE3XIZDIeZmhU2uTc3MP5xz0XP212W3VtXatMrtXpeI/m7r549apW9715yNNTh4SFhDz6yIgdPn6cQCD2iIwYMmDA1Rs3Fs6bK2ltjQgLyxw+PKZHjys3bvaKj+/QPRqV6nQ6vHdvm82m1mrsDseMyZPlCsWVGzd4HO4Hf/qj5ykTc8Y1i8QOp0OpUuE7p2eLMMx9/vIVhJBAKMzOzLRYLQw647u3MDIJc2MIobGZmZ98vTKxVy+8/PrNgoRevagUys3bd+K8pskDvxIajfokB8gzP3aev2C5asPGtxa/7nS6yCQyQqhnbGxQUCCHxapraKxtqH9h/Hj88yCJRCISiUQiEXv0MZPFYml1eqvNRqfRtDrd6o2b+icl4Rdz7A47g04nEAhut1uvN3x3+kwkYpgLj6yeQgqZPC5rdIdeUcjfDUhYSMirc+d6H/8IIQKBiLmxIydPxkZHTxg7tra+ASHkmeeSQqEihMgkEv6v2+3etnvPrBnTI8PDbxXeJRKIJDIJIdQkbObzH84T6dlwBoOBf4o/dvLUgtmznC4niUwmEAgDkpMRQr5cbmllZXVdnWdkKGTKw017NDLRUVE3b98Znz0GIVRcWtosEuNREz9L9nRSb9B7Hnc5MgAXGBDw/rvvVdfW9HwUDEQiMYPJCAwI4HI5Op0eIURAXXzaaFO0MZmM8PBwOo0WGBgYEhw8eOCgisoHv2Dfkvr2mTZpEkJozabN7SpVcFCgQCiMjY5GCN29d69vQm+zxYIQ6p/U79bdQqVShRC6VlAwMiODzWZlpA65fbcod2z2o9CY6nK5vBtnMr4LRSQS0fsoIBCIZDIZD2AIIbvdwaDTO/TN7XaXVlQsW7SwpLzCs3tzudy3Xn9d0tq6ct36l6ZPG5CUrDcYqmpq8M95Or3+6ImTr86b2/n7CIQQkUiYnTcD/x7h4LFjUyZO2Hf4MH7w6g0GvcGIB34ikTjvxZm7Dhyk0+l6vcFmt4eFhoSFhhzKP+50OslkMuzST03nA+S3c+w8f8GSxWJt2LZNrlAsfvnlqIiI/NOng4MCiQTi4AH9Ja2tZy9e0hsMtfVdT+D10rSpn3z1dWREeKtUNu+lF7kczrotWxubmsQtLZNycgRC4ZrNmwmIQCAQ/Pj8A0ePvb10yZ6Dh7Q6XcujL/8QQgQCgcNm/7TO8319K6uqBUKhzW6TKxTd1GQw6EUlJXa7Q9Dc/ML48eu3bautq7fZ7cOGDkUI5WRleTY8PTUVIcRmsYRi8fa9e1VqdVBAwOgRI46dPIm53UmJfQJ4vO5HJjAgoHevniu+/CrA31+j1bz52muNQuGazZsD/P2jIiKoVCqFQtm6e7dULh82dGg3IwNwJpPp6o3rFRWV77/3Ll7y2rJlK7/4PDAgIDYmZvPWbSs+/Wzco5DjUXDrVmLvhAnjcxa8unDRq69UVFSGh4ZV1dQ0i0QOh+MXnxOb5ePTplDMzstbt2VLcGCQ1WrlcDipgwZdvXkTrzB35sw//e3vLperSdS8YPYshFDPuLhPvvraEyzx6/Y/So/IyPzTpw8cPdYsFr8yZ7an/OqNm9W1dRaLJT11iB+f7717BwYE7Dt0ODIiPCIsDCHE43Enjhv76Tff/uPdWCqVSqVQLFbrjVu35Ip2tUYzZOCAA0ePDU9LIz363PnoTcOnrV0RERbWO77n/z7/IjQkuFUqWzjvu1geER4e06OHVC4vKCwcNWxYSr9+CKGaurryBw8GpqTALv2rKiouvnL9WrNI9M7ytzofIL/BY+fHepa5YVVqNZfDwT/x2Ww2s8Xiy+MhhIwmE41KJZFITqfTcw7UgdPp1On1vjwefq3G6XTqDQZfHo9AICCEzGYznU7HF9kdDiqF4nK5HA4HvdOn4J9Mq9Nx2Gz8E6vnelFnGIYZjEYuh2O1Wul0OoZhRpPJO0h7b/ijC18urU7H9/XFt8VgNBKJRPwd7UlGxmq1Wm02z0U2q9Vqtzs4nIdrNJnNnjfHX2lkfjcwDNNqtXw+H/9Xo9G88+572zdv8pw/mUymzlfOnyG1RsNkMJ7OS+l2uzVaLYfN7v6MzXv3tlqtRrPZ/9F4dmC3251OJ5PJxI8UfOfspmW7w6HX67u5Ex48W94HyG//2PmtB0sAnq+DX28wcDkcGAoA/h8eOxAsAQAAgB8AGXwAAAAACJYAAAAABEsAAAAAgiUAAADwLD2z31k6nc6d+/fr9Hq3G82cOiU8NBR9P/GxWqPZdeCg1WqlkMkv5I5/8hwc5y5frq6pdbpcmSOG48maK6uri+/f9/y2+sqNG3eKimk0WlhIcN6UKfBrZfDcuXHrdnHpfbcb9UtM7Jxb4wcdOHrsxWlT9x0+PGvGjO5rnr98paq21maz0ahURCBMnTihVSrtZgKAH/TksyB00/n0IYO7n4cAt2XXLp3egP8WJSQoKCIsNCoiIiI8HPYf8BOQ/vbBX57Jigvv3aNRaXNfnBkb3ePQ8eOpgwa5XK5jJ081CoWZw4cjhL5csyZv8uSJOeMSe/deuXbdkIED6U8wVYJWp7t09do7byxLHTRw6+7dIzMyCATC/sNHVGp1n4TeTAbjQU3N3ZKSP76xbNjQoSKJ5F5pWVLfPrAfAFxNbe3KVauSk5KYTCZC6NCRI0fzjyf07u3j49O58n9XfJySnNxht7x6/frlK1cH/MiJdH6U6tq6wpJ7yxcvzhiaeunaNQ6bzff1bWwS6g16Xx5Pq9M5Xa5msZjJYFAoFJvNVt8ocLvd+CbIFQqjyXT7blHakME8LpfL4Wh1OkFzM5NOp1KpCqUSwzCxpMWXx8V/5hsXE502ZPDd4nsvz5mdNXIEjUbjcrkOh8Nut6u1WjaL1dIqNZqM7CfO7/HVmrUzJr+Qk5UVGRGxY+++jKGp3XTAe5Hb7W4Wi0kk0vVbt0ZkpHO5XCaDIZXJWmVyHpdrdzjMZkurTOZ2uxmPMhANSE5OGzL4zMWL/3j3z/0SE5k+PlwOR6PT2Ww2u8PhxjChSOTj4wMfl38RxSUl23fuqquvH9i/P0Lo2zVrLl+9lpDQm9kp68WWbdsrqx6kJCV1KH8Kx85zGSytVtuJs2cJBEJgQMDoESMQQiXl5VwOm0gkcdgsAoFwv6x80vgchBCdRjOZzVarNTws7ElaPnX+gsVqpdNoUyZMIBAIer2h7MGDEelptfUNveLjj585kz0qE/88GxcTs//o0TGjYAYf8BCHzZFIWsLDwng8nlqtvnT16ptLl27asmVYRkaHmpeuXK2orBwyaBCb/b3fVkf36HHm3Hkul3Przh0SkXjr9u3auvqKygd0Op3v6/uLdPLMxYsjMzIC/PwQQoP69/fj8/cfOWIym2sb6qUyeYNAcPn6DTKFnH/q9PC0tK/XruPzeCfPnQ8LCWlsarpw+YpOr6948CBnzJg1mzYn9+27dssWfz+/Hfv2j8zIWLNps0wul8pk9Y2CPr17e9Z4+25R/6QkJoMhEDbj6ZHPXrwUEhRUXFraLBYJRSJJS2t8bOwP9txsNt8pLp6Uk4MQYrNYGUNT9XrD4zoQERrmvWjPoUOKdmX5gwdWqzXQP+BeaanNbr96s4BIJJy+cMGf77d60yYOm73/yNG0wYO8s71cvn4dP8bPXbpEpVAPHssXCIWBAf479u3jsDmHj58YkJz0zLPD/A7wuNys0Zl79u3Pysx8UFUllclezMtbt3HjqBEjOtRMTEw4fuLk6FGjOpQ/hWPn53hm31nGx8b8YekSlwvbtX//5p27EELXbxa4XBie+Nhud3hnSyeTyJ4MqHaH41D+8Q5/docDX0qn0//53rv+fL9L167/6+OP7XZ7QWEhg043mc0379xBCHm3TCQS8Y/PAOCeMBm0zWavrqnum5jYZSN4Mujkfv0iwsPPX7xYVl5Op9P27N//S3USc2Ge01m73W612UZkZNDpNDxfP0IoJysrJysL37Un5owzmc14gvIrN26+/vKCaZMmhoYE409nMhljRo3UaLXe8xZMn/xCq1TafR9yx2an9Ot38/YdNpvt6+tbUFjoWXTx6tUOh2fro2SKnWdB6KYDHRY1i8R5UybPmZlHfHTMxkVH9+4Zr9Xp8PkM8GkMEnr2VGu6mz/H5XLiueB9mD5Wu41CIT+oqYU9/+fz8fE5f/Giy+Ukk8n4JASXLl/pPAkBfv7zuEZ+7WPn53hm1x9OnDnbJ6H3mFEjx4wa+Y+PVnRIfOzL47Yp2i0WC4VCuVZQcK+s7M3XFuFPo4UTjgAAIABJREFU7D4BenVtnbi1JScrK23I4PVbtymUypKyMvwMNSggoFksjo+NLa2oiAgLK62oaFeqoqMiYS8HXeomGXRxyT1BU5NUJieSiK/Mn9/hib92MujYmOiKqqqoiAiE0J5Dhwf1Tzl++ownXz9CiEIhI4QIBGLnBOV4znS7/eGHyw7Z+TvPW/A4VAo+r8B3if49i7rJzN55FoRxY7Ie14EOfcPbdDgcbvSwbx3mMyA/mlrA7ca66bknF3xIcNCA5OSQoCDPFCvgZxqXnV1bVy8SiaOiIh83CUH3LUAi9S4MHjhg4/bt8TGxKrV6QHJSh8THldXVs2ZM/+jLr2Kjo6tqahgMhufCd/cJ0ONjY06eOyeTt7ndmM1mczgcwUGBeLMEAuHm7TsvTZ+2ZvPmlWvXkUikBzU1s2ZMh10ceDxhMuhh6ekDUlIWv/FmgL9/hxaeQjLoYUOHrtq4adXGTW43xmGz+yUmXrhyxZOvP9nrO/gOCcpzs7NXb9oUHBSEz0mCEOKy2T84b0E3vBP9f3eG0W1m9g6zINjt9sd1oEPfkvr22bh9B4lEwucpQt+fz0Ct1fyonqcOGrRqwwaHwymTy5ctWgh7/s+3bsNGs8XcKBAELny1m0kI3G73t2vWlpaXnzpzZmJu7lM+dn6OZ5nuzuVyKVVqNsuH+ZijC8MwpUrF5XBUGg2HxXryxLsqtZpEIvG8Pu12YDSZbDYbfltEfCzMdQe61k0y6GdOrzeQKWT8pK1Dvn7vah0SlJvNZiKJ5H0d7Emy83fDO9H/E+owC0I3HeiwSKvTsVks76lInnA+g8e9vag1Gj8+H76L+aVotVoOh0MkErufhOA5BblhAXjSwAmJ1AH4f3vsQLAEAAAAfgBk8AEAAAAgWAIAAAAQLAEAAAAIlgAAAAAESwAAAACCJQAAAADBEgAAAIBgCQAAAECwBAAAAAAESwAAAACCJQAAAADBEgAAAHj6ntl8llHxPbtZKmqofx5H02qziUSimOjoZz712o/VJBRyOVw/Pz4cEgAA8BsKlgih+1EpXZYPEJV1WT5waJreoPcuyZs+fcWHH/52RpNKoaxauy42Juad5W89hdUtW748fWja3NmznqTywKFp+3bt6hkfp1Sp7hYVTRg/3rOoqLj4L3/7+6F9e+F4AACA31yw/LFsNtuXn37aP+W7EMvy8flN9ZBIJH79+Wer162z2ew0GvXXXt2yxUt8fXlPWPmbr74MCw1BCAmFwv98tMI7WD6oqt69fZu/nx8cDwAA8DwFy6j4nl1eiQ0ICIgID+9QKBKJt+7YoVKrU5KSXp4/j0wmP67QarVu27mrprYmNib2lQXzOWx2m0Jx4tSpgf0HHDt+HCE0f+6c+Lg4vNmy8vI9+/cbDIYxo0fPmDYNL2yVSnft2SOVyTLS0vOmT+swObtYLN62a5dcLl+5atXL8+YGBQV5L9138GC/Pn1PnT0jlUrHZWd7wtWqtWvHjxu3fdeuubNm9e7VS6VSr9+0qVkkSkzo/dqrr7JYLITQug0bExJ6jxoxAiG0Y/fu6KgeI4YPaxI2RTojwkJD2xSKw0ePpg8dunPPXr6v71vLlp44dfre/ZL+KSmvzJ+Pr6WiorJ3r14Y5t69b5/RaPz621X46W+rVKpoV3z6xRddbhEAAAD03N3gI5e3NYtEnj+bzW632/PmzImJic6bPu1GQcE/P/wQIdRlIYZhM+fMrW9oGJedXVdfP3P2HLfbrVQqv/rm28++/HLggAFmi2XeK69iGIYQKq+omL9wUUpS0vhx4z776usTp04hhKQy2QvTpjMYzMyRo3bu3v3hRyu8+4a3z/f1nTNrllarXbhkaYfO7z946PVly8JCQkeOGPHfFR8fOXYML9++c9eSN9+iUWlMJtNsNk+aNo1AILw0M08kFs+avwDvT3Bw8PJ3/qhob794+fLX367q3bsXQujEqdNlFRUIIaVS+c3qNd+sXjM8I6OsvHzM+NwH1VWpgwdv2rL1wKHD+FrWbtigUqnIZFJkRASZRIqPi/3BLQIAAPCbPrN8nH99+KH3vTO7tm3l+/q2tbUl9e3bPyUloXfv2to6hJBare5ceO7CRbPZ/NVnnxIIhNycnPSRo4rv3fPx8bHZbGu++cbPj5+Vmdl3wIB2pTIoMHD9pk3z58yZM2sWQojJZMpkcoTQ+o2bcsaNffvNNxBCA1JSxk2a9Nd3/0yn0/HOmM3mxa8tws/kGAyGJxZ6y5s+fd6c2QghN4Zt2b59+tSpePniRQvzpk/HzxqjIiM/+Mt7CKHRo0YNH511u7BwWHr61MkvXLh08d2/vl9dW/vpR/8LDAjo0LLNZvvqs0/5fL7T6Vy1du1nK1YghBoEgqrqau9qDAZjxLBhBw4fmZib+4NbBAAA4LkMlhvWrklLTe1QuPyNZbPmLwgOCho5fPjL8+c9PA/rVFhbV6tob5+SNxN/lk6vbxQIkpOSeNyHd4Gy2SwikWi32xFCDY2NU154Aa+ZnZWFP6ipq5NKpZNn5OH/WiwWqUwWEx2N/8tisVg+Pi8vek3Y3EwgENzI3bn//fr2xR/06tlTImnxlCf0TsAf1Dc0pCQn448JBEJUVKRYLEbp6Qih//7736nDho8aMWJcdnbnln19ffl8PkKITCaHhAQ/fHVJZJPN3M14dr9FAAAAnstg2ZnD4Vj6+utvLF1aWlZ2ND9/1rz5hTdvdFlIp9FTkpJWfvE5/kST2ezL44nEYgKB0LlZKpVqtVrxx2KxWKPVJicl0Wm0F/NmzJ318O5TvcEQHhbmecrFy5c/+/Krtau+7ZuYKGlpmTBlSudm9fqHd/MajEamD9NT7ukCnUa3WC2ecqVShYdAhNDho0dDgoMLi+6KJZLIiIgOLZN+0neN3W8RAAAA3HP2naXJZNbp9Z4/s9lc8eDB+BcmEwmEtNTUN5cs1ep0GIZ1WZiRnna/tNThdPL5fIPBmDNpkk6vf9yKRg4fkX/iJIZhGIb9/d//uXX7DkJoWEb6tes32Gw2n88vuH17wcJF+H1DuGaRKCY6evDAgQwG42h+fpfNnr940e12u93uvfsPZKSld66QkZ527vwFnU6HEKqorJS0tAwdMgQhVFtXt3LV6o1r10yeNOlPf/kr/kXmT0MgEu12u9vt/sEtAgAA8OzPLB/3e8puLFy82Pvf7KyszevX9e3TZ/S4nPi4uAfV1X99989EInFg//6dC5OTkt5YunT8pBeio6MbBYL3330vLDRUq9V2uaJlixcveevNEVljCERCZETEqy8vQAi9Mn9+WXn5yDHZAQEBUql0w5o13k+ZlJu7Zdv2iVOmGk2mibm5Pj6sjz/77P333vOuYzAYsnNzXS6MTCbv3ra183pHjxqVM3Zs5thxMTExDY2N//nH33k8nt1u/8Of331l/vzEhIS//eUv2bkTNm3dunjRop827LHR0ZjLlZ074dLZM91vEQAAgIenGSaD+hmuvps8Pj8qiY9YIlGpVFGRkZ6Llo8r1BsMEokkPDycy+H8YLNtCoUbw4KDg70L5XK5WqOJi42lUjv+ktJqswmFwpDgYB6PZzAYMczF5XI9SyfPyFu2+PWU5GSdTh8bE00ikR63XpVK3dLaEhMdw2azfo1ht9nsFouZx+P94BYBAAD4/QTL5wIeLLu8PQcAAMBv2TP+gur3FxG7kTN2bOeMCgAAAODM8hejGjMTXq2nw+/SQRgEAADwBrnNAAAAAAiWAAAAwM/z3FyGBQAAAODMEgAAAIBgCQAAAECwBAAAACBYAgAAABAsAQAAANAJ6W8f/OWZrNjpdG7fu/daQcGdouKI8DAOm/24mgV37hAIhCdJ5dpZu1J5r7S0R2Skd+G1ggLvksPHTyT27vUTGsfbUWs0p85fqK6tq66t0+l0wUFBp89fKL5fGhoawmQw8Jr1jY2nzp3XGwxRnabW8rhy42a7UqVQKkOCgh5X5/bduwihnzYU+w4f7peY+OT1b9y6TSKROJyHr4vBaNx/+EhKv35wzAAA4Mzy6Sm6fz8sJPSdZcvmzsw7evIkQkivN1RWV5vNZoSQQqk0GI0NgiYMw3r36uXn54cQkspkVbW1Tz47FYZhu/YfqK2v946dK9etO3X+gnc1cUsLQshoMmm0WkV7u8FoFLe0OJ3OBoHAYvluakmD0eh0Oju348NkDkhOGpCcZHfYnS7X0ZMn/f39skaN3LJzF17ZZDYfzM+fPCFXJJGUP3jgadBqtVZWVRcWF+P/CoRCo8mo0Wq62SKFUmUyW9xud4OgSSgSfW9Re7u8rU2pVpvM5gaBAB8l7yEdlpaGD6xcoZC0tOCfV6pqa5UqlXc7nkFWqlU6va5BIMDn9fRhMnvFx7UrlQghnV6vNxhkbW0YhjU2NWl1OjiQAABwZvmrsFptJ86eJRAIgQEBo0eMUKpUG7Zv8/fzO3gsf0By8oZt22VyuVQmq28USFpaqBSqUCy+fO065sYKi4uf8Pzm2KlTSX37yOTyQf374yVMBmNgSkpRScmoYcM81e4UF/eMi928c9fAlOQN27a3K9tLyyuu37pFo1LPXryUNmQIXu3ytescDofNYnVoh0wm8319iURiUUlJ3pQpF65cnThuLN/X9+TZc5nDhxGJxLa2NrVGM3TQILvd3iAQ9E1IQAjZHY4vV6+x2WwVVVVJffpodXqdQU+lUBBC0VFR3luhVKmYzIfTRNc2NPj7+V26dtVkNtc21Etl8vjYGHzRn//xTx8f5rETJyWtrUqVqqlZ5O/H9x7STdt3DE9Pe++f/6JRaTdu3+ZxOAeO5bNZrAtXroaFhuJnq8WlpZ5BptFo90pLaTTayXPn01OHrNm8mc1iXS0o8OXxiu+Xnr14KSQo6MjJkyQS6fL16748np/X1C4AAPA788wSqcfHxvxh6ZKyyge79u/ncrjBQYHjs7OT+/Y1my34mc3svBl2h2P9lq2REeEIoas3bi559RUOmy2VyfAW7A7H8dNnOjQ7eUIuHnJq6uspFEp0VFRJ2XezZhIIBHypN4fD8d/PPl+6aCGPy0UIzZw6taKqWtLakjNmTGlFpada7tjsbto5cfbslAkTEEKZw4ev37qNx+MajUZ8UVhoqFqj2X3gYKtMFvkokXpNXV1S3z4Bfn4Tc8YRScSi+yWDBwyorq3tPFAnz52f9+JM7zmZR2Rk1DU0WK02RXu7pzA0OHhSTo7eYBg6aFCAv/+u/QcK793zHlK8Wkhw0JQJuffLy1tlMgqZLBSJRw0fFhYSgi/1HuTCe/fGZWX1TUgovl8qb2uTtyniYmL8+PzbRUU8Li93bHagv793Yc+4ODicAAAQLH9hJ86c7ZPQe8yokWNGjfzHRytCgoLwK4d2h51Bp5NIJCKRSCQSMbcbr08kEMgkEoZhzWJxaEgIQohCJo/LGt2hWcqjoKLRaqUy2d5Dh5tEosqq6n59Hvt1HYGA3lr8+sFj+X995w8kEolEIhEIiEwiI4Tcj9bePbvdLpXJwkNDEUIsH+brLy+gUCiKdiUe4RwOx8Rx40JDQkpKy8jkh3NY0qg0u93udruVKhWHzZa3KYIDA7sMlq/Mmd2hZNvuPbNmTI8MD79VeNdTiE9FSUAEfJpMzO0mIIL3kD4aHwpCCB/Y7MxMHybj7KXLer1hREZ6h0FGCNG8prdks1gDkpNNJpPd4aipq6dSqB0K4VgCAECw/OUNHjhg4/bt8TGxKrV6QHJSeuqQdVu2NjY1iVtaJuXknL14qUP98dlj1m7ZyqDT+yT09pzedXNbUPqQIelDhmh1ugNHj/brk3j4+PGUfv3iYmK6GAIyJTY6uld83JUbN7rp8L7DR4YNTe1yjq2a+vqecfH4Y5Vaff7KFTKJPCI9zWgybduz941FC4+cOBEXE9ssFr+9ZDFeLTa6x7FTp+RtCrPFMn/WS76P5mE+f/nK/bJyhNDUSRNjo6O77AmDQS8qKbHbHYLmZgzDiMSuv3juMKSnzp3vUOHi1asB/v46vT4sNORxg4wLDgricNiXrl6Tt7XNnDq1m0IAAPhdepa5YV0ul1KlZrN88O/knE6n3mDw5fEIBMLjTuCcLpfnFtMf5X55eWREhP9T+V7NaDS6EWKzWC6X61pBQdbIkRiGqTUa/KtNT7XSioqQoKDAgIDHRbvHwTDMYDRyORyr1Up/dMrYpe6H1O12qzQaFpPp3Ug3g6zWaFgsVofrz10WAgAABMvnktVmo9NoT3mlGIY5XS4IJAAA8Lwj/z/ZzqcfKRFCRCKRSoS0DwAA8NyDt3IAAAAAgiUAAAAAwRIAAACAYAkAAABAsAQAAAAgWAIAAAAQLAEAAAAIlgAAAAAESwAAAABAsAQAAAAgWAIAAAAQLAEAAAAIlgAAAAAESwAAAACCJQAAAADBEgAAAIBgCQAAAAAIlgAAAAAESwAAAACCJQAAAADBEgAAAIBgCQAAAECwBAAAACBYAgAAABAsAQAAAADBEgAAAIBgCQAAAECwBAAAACBYAgAAABAsAQAAAAiWAAAAAARLAAAAAIIlAAAAAMESAAAAABAsAQAAAAiWAAAAAARLAAAAAIIlAAAAAMESAAAAgGAJAAAAQLAEAAAAIFgCAAAAAIIlAAAAAMESAAAAgGAJAAAAQLAEAAAAIFgCAAAAECwBAAAACJYAAAAABEsAAAAAQLAEAAAAIFgCAAAAECwBAAAACJYAAAAABEsAAAAAgiUAAAAAwRIAAACAYAkAAABAsAQAAAAABEsAAAAAgiUAAAAAwRIAAACAYAkAAABAsAQAAAAgWAIAAAAQLAEAAAAIlgAAAACAYAkAAABAsAQAAA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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"cdd2672e-a9a1-4d97-a4d0-4d6b43c66ff5","timestamp":"2026-08-24T21:46:55.152Z","tool_use_result":{"type":"image","file":{"base64":"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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJog9hXqXoZBxBEGaJL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01J2RUztDF94adPcvVwu8Bm7","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","old_string":"    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]","new_string":"    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and str(v).strip(\"-— \") != \"\" and not isinstance(v, (dict, list))]"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2077,"cache_read_input_tokens":146869,"cache_creation":{"ephemeral_5m_input_tokens":2077,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"78250e5f-8ccf-402b-9939-b56cd90ef33f"}}
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{"type":"permission_request","requestId":"b21e2f91de956939","tool":"Edit","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","old_string":"_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"\n               \"environnement) fournis à titre indicatif, sans valeur légale.\")","new_string":"_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements fournis à titre \"\n               \"indicatif, sans valeur légale.\")","replace_all":false}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0173FqC1XAx5ZgskNeeP6uqL","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"76aad336-8abe-4dc6-9ae5-8ee9210657dd","timestamp":"2026-08-24T21:47:28.992Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","oldString":"    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    if l.get(\"title\") and l.get(\"title\") != titre:\n        loc = f\"{l['title']} — {loc}\" if loc else l[\"title\"]\n    c.setFont(\"Helvetica\", 9)","newString":"    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    c.setFont(\"Helvetica\", 9)","originalFile":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdffiche.py : fiche PDF complète d'une propriété — reflète TOUS les blocs de\n#   la fiche web : prix + verdict Vrai-Prix, photos, description, inclusions,\n#   caractéristiques, pièces, analyse Vrai-Prix (jauge + rôle foncier),\n#   historique de prix, inondation, qualité de l'air, coût d'électricité,\n#   commerces/transport, essence, quartier, références + QR.\n#   Style « éditorial sharp » (bordures encre, ombres décalées dures, tuiles\n#   KPI, micro-étiquettes Courier) — palette du site : crème #f5f3ee, encre\n#   #141814, accent cerise #e23744. Pagination dynamique : saut_page()/place(),\n#   aucune section coupée entre deux pages.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Immo-Ka (identité officielle du site) ----------------------------\nPAPER = HexColor(\"#f5f3ee\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#141814\")\nINK2 = HexColor(\"#4d5551\")\nINK3 = HexColor(\"#8b928c\")\nACCENT = HexColor(\"#e23744\")        # cerise\nACCENT_SOFT = HexColor(\"#fbe0e2\")\nACCENT_DEEP = HexColor(\"#a8232e\")\nGREEN_FG = HexColor(\"#1c5c41\")      # verdict « sous l'estimation » (site .vp-under)\nGREEN_BG = HexColor(\"#e2f0e6\")\nRED_FG = HexColor(\"#8a2b2b\")        # verdict « au-dessus » (site .vp-over)\nRED_BG = HexColor(\"#f6e2e2\")\nAMBER_FG = HexColor(\"#8a5a12\")\nAMBER_BG = HexColor(\"#faf0dc\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"ImmoKaBot/1.0 (+https://www.immo-ka.com; contact@spboucher.ai)\"\n\nNBSP = \" \"     # espace fine insécable (mise en forme française)\n\n# clés de details jamais affichées (mêmes exclusions que la fiche web)\nDETAIL_HIDDEN = {\n    \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n    \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n    \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n}\n\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"\n               \"environnement) fournis à titre indicatif, sans valeur légale.\")\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _fmt_mois(d) -> str:\n    \"\"\"« 2017-11-04 » ou epoch → « novembre 2017 » (sinon valeur telle quelle).\"\"\"\n    if d is None:\n        return \"—\"\n    try:\n        if isinstance(d, (int, float)):\n            st = time.localtime(d)\n            return f\"{_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n        an, mois = str(d)[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return str(d)\n\n\ndef _fmt_jour(ts: float) -> str:\n    \"\"\"epoch → « 4 novembre 2024 » (« 1er » pour le premier du mois).\"\"\"\n    st = time.localtime(ts)\n    j = \"1er\" if st.tm_mday == 1 else str(st.tm_mday)\n    return f\"{j} {_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à plusieurs dizaines de Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1000, 1000))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=78)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Immo·Ka : « Immo » encre + boîte encre avec « Ka » cerise.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Immo\")\n        w = c.stringWidth(\"Immo\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6, titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.IMMO-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=ACCENT, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        # carré accent à ombre dure (signature de la passe éditoriale du site)\n        c.setFillColor(INK)\n        c.rect(M + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.rect(M, y + 0.5, 9, 9, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M + 16, y, texte)\n        c.setStrokeColor(ACCENT)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M + 16, y - 4, M + 16 + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def nb_lignes(self, texte: str, largeur: float, taille: float = 8.5,\n                  max_lignes: int = 100) -> int:\n        \"\"\"Nombre de lignes qu'occupera `paragraphe` (pour réserver la place).\"\"\"\n        c = self.c\n        mots = texte.split()\n        ligne = \"\"\n        n = 1\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                n += 1\n                ligne = mot\n                if n >= max_lignes:\n                    break\n        return min(n, max_lignes)\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\ndef _titre_droite(c, y, x, texte):\n    c.setFillColor(INK)\n    c.rect(x + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n    c.setFillColor(ACCENT)\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.rect(x, y + 0.5, 9, 9, stroke=1, fill=1)\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x + 16, y, texte)\n    c.setStrokeColor(ACCENT)\n    c.setLineWidth(3)\n    c.line(x + 16, y - 4, x + 16 + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\ndef _verdict_vraiprix(price, vp):\n    \"\"\"Pastille verdict identique au site : ±% vs estimation, masqué si\n    |pct| > 60 (prix non comparable : terrain, loyer…).\"\"\"\n    if not price or not vp or vp.get(\"value\") in (None, 0):\n        return None\n    pct = round((price - vp[\"value\"]) / vp[\"value\"] * 100)\n    if abs(pct) > 60:\n        return None\n    if pct >= 1:\n        return (f\"▲{NBSP}{pct}{NBSP}% au-dessus de l'estimation\",\n                RED_FG, RED_BG, ACCENT_DEEP)\n    if pct <= -1:\n        return (f\"▼{NBSP}{abs(pct)}{NBSP}% sous l'estimation\",\n                GREEN_FG, GREEN_BG, GREEN_FG)\n    return (f\"≈{NBSP}Prix aligné sur l'estimation\", INK2, SURFACE, INK)\n\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche de la propriété `uid` — reflète tous les blocs de la\n    fiche web ; None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"features\"] = json.loads(l.get(\"features\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    try:\n        l[\"vraiprix\"] = json.loads(l[\"vraiprix\"]) or None if l.get(\"vraiprix\") else None\n    except (ValueError, TypeError):\n        l[\"vraiprix\"] = None\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n        (uid,)).fetchall()]\n    quartier = None\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n    if geo:\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        try:\n            quartier = qmod.fiche_quartier(\n                lat, lng, l.get(\"city\") or \"\",\n                dauid if dauid and dauid != \"hors-zone\" else None)\n        except Exception:\n            quartier = None\n    try:\n        reg = json.loads(\n            (db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n        src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]),\n                        l[\"source\"])\n    except Exception:\n        src_name = l[\"source\"]\n    con.close()\n\n    # --- enrichissements (mêmes modules que les blocs de la fiche web) -------\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    d = l[\"details\"]\n    vp = l.get(\"vraiprix\")\n    location = (d.get(\"transaction\") == \"location\")\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Immo-Ka — {l.get('address') or l.get('title') or uid}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche propriété\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche propriété · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- héro : prix + pastille verdict Vrai-Prix -----------------------------\n    prix_txt = (_fmt_money(l[\"price\"]) + (f\"{NBSP}/ mois\" if location else \"\")) \\\n        if l.get(\"price\") else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    verd = _verdict_vraiprix(l.get(\"price\"), vp)\n    if verd:\n        txt, fg, bg, stk = verd\n        bx = s.pilule(bx, y - 22, txt, fg, bg, stk)\n    if l.get(\"property_type\"):\n        s.pilule(bx, y - 22, l[\"property_type\"], ACCENT, INK)\n    y -= 44\n\n    # --- titre + adresse -------------------------------------------------------\n    titre = l.get(\"address\") or l.get(\"title\") or \"\"\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, titre[:80])\n    y -= 14\n    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    if l.get(\"title\") and l.get(\"title\") != titre:\n        loc = f\"{l['title']} — {loc}\" if loc else l[\"title\"]\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés (mêmes specs que le héro du site) --------------------------\n    chips = []\n    if l.get(\"bedrooms\") is not None:\n        chips.append(f\"{l['bedrooms']}{NBSP}ch.\")\n    if l.get(\"bathrooms\") is not None:\n        chips.append(f\"{l['bathrooms']}{NBSP}sdb\")\n    if l.get(\"powder_rooms\"):\n        chips.append(f\"{l['powder_rooms']}{NBSP}s. d'eau\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"lot_sqft\"):\n        chips.append(f\"Terrain {round(l['lot_sqft']):,}\".replace(\",\", NBSP)\n                     + f\"{NBSP}pi²\")\n    if l.get(\"year_built\"):\n        chips.append(f\"Construit en {l['year_built']}\")\n    if l.get(\"price\") and l.get(\"area_sqft\") and l[\"area_sqft\"] > 200:\n        chips.append(f\"{round(l['price'] / l['area_sqft'])}{NBSP}$/pi²\")\n    if l.get(\"mls\"):\n        chips.append(f\"MLS {l['mls']}\")\n    if l.get(\"first_seen\"):\n        dom = max(0, int((time.time() - l[\"first_seen\"]) // 86400))\n        chips.append(f\"{dom}{NBSP}j sur Immo-Ka\")\n    x = M\n    for chip in chips[:8]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes ---------------------------------------\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- baisse/hausse de prix (bandeau, comme le site) -------------------------\n    ph = [p for p in price_history if p.get(\"price\")]\n    if len(ph) >= 2 and ph[0][\"price\"] != ph[1][\"price\"]:\n        y = place(y, 34)\n        de, a = ph[1][\"price\"], ph[0][\"price\"]\n        bas = a < de\n        c.setFillColor(GREEN_BG if bas else RED_BG)\n        c.setStrokeColor(GREEN_FG if bas else RED_FG)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 20, PAGE_W - 2 * M, 24, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 9)\n        c.setFillColor(GREEN_FG if bas else RED_FG)\n        c.drawString(M + 10, y - 12,\n                     f\"{'▼' if bas else '▲'} Prix passé de {_fmt_money(de)} à \"\n                     f\"{_fmt_money(a)} le {_fmt_jour(ph[0]['ts'])}\")\n        y -= 34\n\n    # --- description | inclusions (2 colonnes, comme le site) -------------------\n    col_g = PAGE_W * 0.56 - M\n    feats = [str(f) for f in (l.get(\"features\") or []) if f]\n    desc = (l.get(\"description\") or \"\").strip()\n    if desc or feats:\n        besoin = 30 + max(\n            s.nb_lignes(desc, col_g, max_lignes=26) * 11.5 if desc else 0,\n            min(len(feats), 18) * 12)\n        y = place(y, min(besoin, 340))\n        y_desc = y\n        if desc:\n            y_desc = s.titre_section(y, \"Description\")\n            y_desc = s.paragraphe(M, y_desc, desc, col_g, max_lignes=26)\n        y_incl = y\n        if feats:\n            xr = M + col_g + 24\n            y_incl = _titre_droite(c, y, xr, \"Inclusions et caractéristiques\")\n            c.setFont(\"Helvetica\", 8.5)\n            for f in feats[:18]:\n                c.setFillColor(ACCENT_DEEP)\n                c.drawString(xr, y_incl, \"✓\")\n                c.setFillColor(INK2)\n                c.drawString(xr + 12, y_incl, f[:42])\n                y_incl -= 12\n            if len(feats) > 18:\n                c.setFillColor(INK3)\n                c.drawString(xr + 12, y_incl, f\"+ {len(feats) - 18} autres…\")\n                y_incl -= 12\n        y = min(y_desc, y_incl) - 16\n\n    # --- caractéristiques (details, mêmes exclusions que le site) ---------------\n    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]\n    if dets:\n        col_w = PAGE_W - 2 * M\n        besoin = 40 + sum(\n            s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4) * 11 + 3\n            for _, v in dets)\n        y = place(y, min(besoin, 420))\n        y = s.titre_section(y, \"Caractéristiques\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"DÉTAIL\")\n        c.drawString(M + 180, y, \"VALEUR\")\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 12\n        for i, (k, v) in enumerate(dets):\n            nl = s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4)\n            y = place(y, nl * 11 + 6)\n            if i % 2 == 0:   # zébrage éditorial\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - (nl - 1) * 11 - 3, col_w + 6, nl * 11 + 2,\n                       stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, k[:38])\n            y = s.paragraphe(M + 180, y, v, col_w - 190, taille=8,\n                             interligne=11, max_lignes=4)\n            y -= 3\n        y -= 10\n\n    # --- pièces (details.pieces) -------------------------------------------------\n    pieces = d.get(\"pieces\") or []\n    if isinstance(pieces, list) and pieces:\n        rows = pieces[:24]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Pièces\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"PIÈCE\", 0), (\"NIVEAU\", 190), (\"DIMENSIONS\", 330),\n                          (\"REVÊTEMENT\", 430)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            if i % 2 == 0:\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - 3, PAGE_W - 2 * M + 6, 12, stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, str(p.get(\"nom\") or \"—\")[:36])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M + 190, y, str(p.get(\"niveau\") or \"—\")[:24])\n            c.drawString(M + 330, y, str(p.get(\"dimensions\") or \"—\")[:18])\n            c.drawString(M + 430, y, str(p.get(\"revetement\") or \"—\")[:22])\n            y -= 12\n        y -= 10\n\n    # --- analyse Vrai-Prix (valeur, jauge, rôle foncier) -------------------------\n    if vp and vp.get(\"value\") is not None:\n        role = [(lab, vp.get(k)) for k, lab in\n                ((\"valeur_role\", \"Évaluation au rôle\"),\n                 (\"valeur_terrain\", \"Valeur du terrain\"),\n                 (\"valeur_batiment\", \"Valeur du bâtiment\"))\n                if vp.get(k)]\n        besoin = 96 + (56 if vp.get(\"low\") is not None else 0) \\\n            + (62 if role else 0) + 18\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Analyse Vrai-Prix\")\n        val_txt = _fmt_money(round(vp[\"value\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, val_txt)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(val_txt, \"Helvetica-Bold\", 17) + 8,\n                     y - 10, \"— valeur marchande estimée par le modèle Vrai-Prix\")\n        meta = []\n        if vp.get(\"confidence\"):\n            meta.append(f\"confiance {vp['confidence']}\"\n                        + (f\" ({vp['confidence_pct']}{NBSP}%)\"\n                           if vp.get(\"confidence_pct\") else \"\"))\n        if vp.get(\"annee_construction_role\"):\n            meta.append(f\"construction {vp['annee_construction_role']} (rôle)\")\n        if vp.get(\"superficie_terrain_role_m2\"):\n            meta.append(\n                f\"terrain {round(vp['superficie_terrain_role_m2']):,}\".replace(\",\", NBSP)\n                + f\"{NBSP}m² (rôle)\")\n        if meta:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK3)\n            c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        verd = _verdict_vraiprix(l.get(\"price\"), vp)\n        if verd:\n            txt, fg, bg, stk = verd\n            s.pilule(M, y - 48, txt, fg, bg, stk)\n        y -= 58\n        lo, hi = vp.get(\"low\"), vp.get(\"high\")\n        if lo is not None and hi is not None and hi > lo:\n            larg = PAGE_W - 2 * M\n            base = y - 22\n            span = hi - lo\n            c.setFillColor(ACCENT_SOFT)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.roundRect(M, base, larg, 12, 6, stroke=1, fill=1)\n\n            def _pos(v):\n                return M + larg * max(0.02, min(0.98, (v - lo) / span))\n\n            px = _pos(vp[\"value\"])\n            c.setStrokeColor(ACCENT_DEEP)\n            c.setLineWidth(2.5)\n            c.line(px, base - 3, px, base + 15)\n            c.setFont(\"Courier-Bold\", 6.5)\n            c.setFillColor(ACCENT_DEEP)\n            c.drawString(min(max(px - 30, M), PAGE_W - M - 100), base + 18,\n                         f\"ESTIMATION · {_fmt_money(round(vp['value']))}\")\n            if l.get(\"price\") and lo * 0.7 <= l[\"price\"] <= hi * 1.3:\n                ax = _pos(l[\"price\"])\n                c.setStrokeColor(INK)\n                c.setLineWidth(2)\n                c.line(ax, base - 3, ax, base + 15)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(INK)\n                c.drawString(min(max(ax - 30, M), PAGE_W - M - 116), base - 12,\n                             f\"PRIX DEMANDÉ · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 24, _fmt_money(round(lo)))\n            c.drawRightString(PAGE_W - M, base - 24,\n                              _fmt_money(round(hi)) + \" — fourchette d'estimation\")\n            y = base - 36\n        if role:\n            tw = (PAGE_W - 2 * M - (len(role) - 1) * 10) / len(role)\n            for i, (lab, v) in enumerate(role):\n                s.tuile(M + i * (tw + 10), y - 44, tw, 38,\n                        _fmt_money(round(v)), lab)\n            y -= 56\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Estimation automatisée Vrai-Prix (vrai-prix.ca) \"\n                               \"croisée au rôle d'évaluation foncière — ne \"\n                               \"remplace pas une évaluation professionnelle.\")\n        y -= 16\n\n    # --- historique de prix -------------------------------------------------------\n    if len(ph) >= 2:\n        rows = ph[:8]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Historique du prix demandé\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"DATE\", 0), (\"PRIX AFFICHÉ\", 190), (\"VARIATION\", 330)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            prec = rows[i + 1][\"price\"] if i + 1 < len(rows) else None\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, _fmt_jour(p[\"ts\"]))\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M + 190, y, _fmt_money(p[\"price\"]))\n            if prec and p.get(\"price\"):\n                delta = p[\"price\"] - prec\n                c.setFont(\"Courier-Bold\", 7.5)\n                c.setFillColor(GREEN_FG if delta < 0\n                               else (RED_FG if delta > 0 else INK3))\n                sig = \"▼\" if delta < 0 else (\"▲\" if delta > 0 else \"=\")\n                c.drawString(M + 330, y,\n                             f\"{sig} {_fmt_money(abs(delta))}\" if delta else \"=\")\n            y -= 12\n        y -= 10\n\n    # --- risque d'inondation (BDZI) ------------------------------------------------\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\" if sev\n                     else \"En zone inondable\", RED_FG, RED_BG, RED_FG)\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     AMBER_FG, AMBER_BG, AMBER_FG)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_FG, GREEN_BG, GREEN_FG)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z.get('type') or 'Zone'} — à {z.get('distance_m')} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- qualité de l'air (RSQAQ) ---------------------------------------------------\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"de la propriété — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = (f\"{m_['moyenne']:.1f}\".replace(\".\", \",\")\n                   if m_.get(\"moyenne\") is not None else \"—\")\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- coût d'électricité (Hydro-Québec) -------------------------------------------\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += (f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP)\n                      + f\"{NBSP}kWh/an\")\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- commerces et transport --------------------------------------------------------\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:12]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- essence à proximité --------------------------------------------------------------\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- le quartier -------------------------------------------------------------------------\n    if quartier and (quartier.get(\"demographie\") or quartier.get(\"proximite\")\n                     or quartier.get(\"chaleur\") or quartier.get(\"crime\")):\n        demo = quartier.get(\"demographie\") or {}\n        prox = quartier.get(\"proximite\") or {}\n        prox_aff = [(k, lab) for k, lab in PROX_LABELS\n                    if isinstance(prox.get(k), (int, float))]\n        besoin = 44 + (94 if demo else 0) + len(prox_aff) * 14 + 44\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Le quartier\")\n        c.setFont(\"Helvetica\", 7.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"Secteur immédiat de la propriété (aire de diffusion \"\n                           \"du recensement 2021, ± 500 habitants).\")\n        y -= 14\n        if demo:\n            tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                      (\"Ménages locataires\",\n                       f\"{round(demo['pct_locataires'])}{NBSP}%\"\n                       if demo.get(\"pct_locataires\") is not None else \"—\"),\n                      (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                      (\"Âge médian\",\n                       f\"{round(demo['age_median'])} ans\"\n                       if demo.get(\"age_median\") is not None else \"—\"),\n                      (\"Français à la maison\",\n                       f\"{round(demo['pct_francais'])}{NBSP}%\"\n                       if demo.get(\"pct_francais\") is not None else \"—\"),\n                      (\"Diplôme universitaire\",\n                       f\"{round(demo['pct_univ'])}{NBSP}%\"\n                       if demo.get(\"pct_univ\") is not None else \"—\")]\n            tw = (PAGE_W - 2 * M - 20) / 3\n            for i, (lab, val) in enumerate(tuiles):\n                s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                        tw, 38, val, lab)\n            y -= 34 + 44 + 16\n        for k, lab in prox_aff:\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(ACCENT)\n            c.roundRect(bx0, y - 1, max(3, bw_ * v), 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        if prox_aff:\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, y, \"Accessibilité 0–100 — mesures de proximité de \"\n                               \"Statistique Canada.\")\n            y -= 12\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch.get(\"classe\", 5) <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_FG, GREEN_BG, GREEN_FG))\n            elif ch.get(\"classe\", 5) >= 7:\n                ec = (f\" (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\")\n                      if ch.get(\"ecart\") is not None else \"\")\n                badges_q.append((f\"Îlot de chaleur{ec}\",\n                                 AMBER_FG, AMBER_BG, AMBER_FG))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"])\n                        / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} \"\n                             \"la moyenne canadienne\", INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m \"\n                             \"(12 mois)\", INK2, SURFACE, INK))\n        if badges_q:\n            x = M\n            for txt, fg, bg, stk in badges_q:\n                x = s.pilule(x, y - 8, txt, fg, bg, stk)\n            y -= 26\n        y -= 6\n\n    # --- références : QR + courtier + source ------------------------------------------------\n    y = place(y, 140)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.immo-ka.com/propriete/{uid}\",\n                     box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    yy = y - 14\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    courtier = l.get(\"broker_name\") or d.get(\"Courtier inscripteur\")\n    if courtier:\n        ligne = f\"Courtier : {courtier}\"\n        if l.get(\"broker_phone\"):\n            ligne += f\" · {l['broker_phone']}\"\n        c.drawString(M + 82, yy, ligne[:95])\n        yy -= 13\n    c.drawString(M + 82, yy, f\"Agence : {src_name}\")\n    yy -= 14\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Annonce originale :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy,\n                 (l.get(\"url\") or d.get(\"listing_origin_url\") or \"\")[:90])\n    yy -= 14\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Fiche à jour en ligne (scannez le code) :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")\n    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n","structuredPatch":[{"oldStart":409,"oldLines":8,"newStart":409,"newLines":6,"lines":["     y -= 14","     loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))","                      if x)","-    if l.get(\"title\") and l.get(\"title\") != titre:","-        loc = f\"{l['title']} — {loc}\" if loc else l[\"title\"]","     c.setFont(\"Helvetica\", 9)","     c.setFillColor(INK2)","     c.drawString(M, y, loc[:110])"]}],"userModified":false,"replaceAll":false}}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJog9hXqXoZBxBEGaJL","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GrTFCc3fPoU8pfyGb7vkos","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","old_string":"    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")","new_string":"    y -= 90\n    s.paragraphe(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\",\n                 PAGE_W - 2 * M, taille=6.5, couleur=INK3, interligne=9)"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2077,"cache_read_input_tokens":146869,"cache_creation":{"ephemeral_5m_input_tokens":2077,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"7b47555b-7ac5-4594-84ff-b4833ef93de8"}}
{"type":"permission_resolved","requestId":"b21e2f91de956939","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CGqYvCHNwrHsiDZxBbA6ky","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"8df2f041-9dfc-4990-85b2-4aff6dcfd4b7","timestamp":"2026-08-24T21:47:30.163Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","oldString":"_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"\n               \"environnement) fournis à titre indicatif, sans valeur légale.\")","newString":"_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements fournis à titre \"\n               \"indicatif, sans valeur légale.\")","originalFile":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdffiche.py : fiche PDF complète d'une propriété — reflète TOUS les blocs de\n#   la fiche web : prix + verdict Vrai-Prix, photos, description, inclusions,\n#   caractéristiques, pièces, analyse Vrai-Prix (jauge + rôle foncier),\n#   historique de prix, inondation, qualité de l'air, coût d'électricité,\n#   commerces/transport, essence, quartier, références + QR.\n#   Style « éditorial sharp » (bordures encre, ombres décalées dures, tuiles\n#   KPI, micro-étiquettes Courier) — palette du site : crème #f5f3ee, encre\n#   #141814, accent cerise #e23744. Pagination dynamique : saut_page()/place(),\n#   aucune section coupée entre deux pages.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Immo-Ka (identité officielle du site) ----------------------------\nPAPER = HexColor(\"#f5f3ee\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#141814\")\nINK2 = HexColor(\"#4d5551\")\nINK3 = HexColor(\"#8b928c\")\nACCENT = HexColor(\"#e23744\")        # cerise\nACCENT_SOFT = HexColor(\"#fbe0e2\")\nACCENT_DEEP = HexColor(\"#a8232e\")\nGREEN_FG = HexColor(\"#1c5c41\")      # verdict « sous l'estimation » (site .vp-under)\nGREEN_BG = HexColor(\"#e2f0e6\")\nRED_FG = HexColor(\"#8a2b2b\")        # verdict « au-dessus » (site .vp-over)\nRED_BG = HexColor(\"#f6e2e2\")\nAMBER_FG = HexColor(\"#8a5a12\")\nAMBER_BG = HexColor(\"#faf0dc\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"ImmoKaBot/1.0 (+https://www.immo-ka.com; contact@spboucher.ai)\"\n\nNBSP = \" \"     # espace fine insécable (mise en forme française)\n\n# clés de details jamais affichées (mêmes exclusions que la fiche web)\nDETAIL_HIDDEN = {\n    \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n    \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n    \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n}\n\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"\n               \"environnement) fournis à titre indicatif, sans valeur légale.\")\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _fmt_mois(d) -> str:\n    \"\"\"« 2017-11-04 » ou epoch → « novembre 2017 » (sinon valeur telle quelle).\"\"\"\n    if d is None:\n        return \"—\"\n    try:\n        if isinstance(d, (int, float)):\n            st = time.localtime(d)\n            return f\"{_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n        an, mois = str(d)[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return str(d)\n\n\ndef _fmt_jour(ts: float) -> str:\n    \"\"\"epoch → « 4 novembre 2024 » (« 1er » pour le premier du mois).\"\"\"\n    st = time.localtime(ts)\n    j = \"1er\" if st.tm_mday == 1 else str(st.tm_mday)\n    return f\"{j} {_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à plusieurs dizaines de Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1000, 1000))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=78)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Immo·Ka : « Immo » encre + boîte encre avec « Ka » cerise.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Immo\")\n        w = c.stringWidth(\"Immo\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6, titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.IMMO-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=ACCENT, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        # carré accent à ombre dure (signature de la passe éditoriale du site)\n        c.setFillColor(INK)\n        c.rect(M + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.rect(M, y + 0.5, 9, 9, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M + 16, y, texte)\n        c.setStrokeColor(ACCENT)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M + 16, y - 4, M + 16 + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def nb_lignes(self, texte: str, largeur: float, taille: float = 8.5,\n                  max_lignes: int = 100) -> int:\n        \"\"\"Nombre de lignes qu'occupera `paragraphe` (pour réserver la place).\"\"\"\n        c = self.c\n        mots = texte.split()\n        ligne = \"\"\n        n = 1\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                n += 1\n                ligne = mot\n                if n >= max_lignes:\n                    break\n        return min(n, max_lignes)\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\ndef _titre_droite(c, y, x, texte):\n    c.setFillColor(INK)\n    c.rect(x + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n    c.setFillColor(ACCENT)\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.rect(x, y + 0.5, 9, 9, stroke=1, fill=1)\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x + 16, y, texte)\n    c.setStrokeColor(ACCENT)\n    c.setLineWidth(3)\n    c.line(x + 16, y - 4, x + 16 + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\ndef _verdict_vraiprix(price, vp):\n    \"\"\"Pastille verdict identique au site : ±% vs estimation, masqué si\n    |pct| > 60 (prix non comparable : terrain, loyer…).\"\"\"\n    if not price or not vp or vp.get(\"value\") in (None, 0):\n        return None\n    pct = round((price - vp[\"value\"]) / vp[\"value\"] * 100)\n    if abs(pct) > 60:\n        return None\n    if pct >= 1:\n        return (f\"▲{NBSP}{pct}{NBSP}% au-dessus de l'estimation\",\n                RED_FG, RED_BG, ACCENT_DEEP)\n    if pct <= -1:\n        return (f\"▼{NBSP}{abs(pct)}{NBSP}% sous l'estimation\",\n                GREEN_FG, GREEN_BG, GREEN_FG)\n    return (f\"≈{NBSP}Prix aligné sur l'estimation\", INK2, SURFACE, INK)\n\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche de la propriété `uid` — reflète tous les blocs de la\n    fiche web ; None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"features\"] = json.loads(l.get(\"features\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    try:\n        l[\"vraiprix\"] = json.loads(l[\"vraiprix\"]) or None if l.get(\"vraiprix\") else None\n    except (ValueError, TypeError):\n        l[\"vraiprix\"] = None\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n        (uid,)).fetchall()]\n    quartier = None\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n    if geo:\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        try:\n            quartier = qmod.fiche_quartier(\n                lat, lng, l.get(\"city\") or \"\",\n                dauid if dauid and dauid != \"hors-zone\" else None)\n        except Exception:\n            quartier = None\n    try:\n        reg = json.loads(\n            (db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n        src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]),\n                        l[\"source\"])\n    except Exception:\n        src_name = l[\"source\"]\n    con.close()\n\n    # --- enrichissements (mêmes modules que les blocs de la fiche web) -------\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    d = l[\"details\"]\n    vp = l.get(\"vraiprix\")\n    location = (d.get(\"transaction\") == \"location\")\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Immo-Ka — {l.get('address') or l.get('title') or uid}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche propriété\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche propriété · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- héro : prix + pastille verdict Vrai-Prix -----------------------------\n    prix_txt = (_fmt_money(l[\"price\"]) + (f\"{NBSP}/ mois\" if location else \"\")) \\\n        if l.get(\"price\") else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    verd = _verdict_vraiprix(l.get(\"price\"), vp)\n    if verd:\n        txt, fg, bg, stk = verd\n        bx = s.pilule(bx, y - 22, txt, fg, bg, stk)\n    if l.get(\"property_type\"):\n        s.pilule(bx, y - 22, l[\"property_type\"], ACCENT, INK)\n    y -= 44\n\n    # --- titre + adresse -------------------------------------------------------\n    titre = l.get(\"address\") or l.get(\"title\") or \"\"\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, titre[:80])\n    y -= 14\n    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés (mêmes specs que le héro du site) --------------------------\n    chips = []\n    if l.get(\"bedrooms\") is not None:\n        chips.append(f\"{l['bedrooms']}{NBSP}ch.\")\n    if l.get(\"bathrooms\") is not None:\n        chips.append(f\"{l['bathrooms']}{NBSP}sdb\")\n    if l.get(\"powder_rooms\"):\n        chips.append(f\"{l['powder_rooms']}{NBSP}s. d'eau\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"lot_sqft\"):\n        chips.append(f\"Terrain {round(l['lot_sqft']):,}\".replace(\",\", NBSP)\n                     + f\"{NBSP}pi²\")\n    if l.get(\"year_built\"):\n        chips.append(f\"Construit en {l['year_built']}\")\n    if l.get(\"price\") and l.get(\"area_sqft\") and l[\"area_sqft\"] > 200:\n        chips.append(f\"{round(l['price'] / l['area_sqft'])}{NBSP}$/pi²\")\n    if l.get(\"mls\"):\n        chips.append(f\"MLS {l['mls']}\")\n    if l.get(\"first_seen\"):\n        dom = max(0, int((time.time() - l[\"first_seen\"]) // 86400))\n        chips.append(f\"{dom}{NBSP}j sur Immo-Ka\")\n    x = M\n    for chip in chips[:8]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes ---------------------------------------\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- baisse/hausse de prix (bandeau, comme le site) -------------------------\n    ph = [p for p in price_history if p.get(\"price\")]\n    if len(ph) >= 2 and ph[0][\"price\"] != ph[1][\"price\"]:\n        y = place(y, 34)\n        de, a = ph[1][\"price\"], ph[0][\"price\"]\n        bas = a < de\n        c.setFillColor(GREEN_BG if bas else RED_BG)\n        c.setStrokeColor(GREEN_FG if bas else RED_FG)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 20, PAGE_W - 2 * M, 24, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 9)\n        c.setFillColor(GREEN_FG if bas else RED_FG)\n        c.drawString(M + 10, y - 12,\n                     f\"{'▼' if bas else '▲'} Prix passé de {_fmt_money(de)} à \"\n                     f\"{_fmt_money(a)} le {_fmt_jour(ph[0]['ts'])}\")\n        y -= 34\n\n    # --- description | inclusions (2 colonnes, comme le site) -------------------\n    col_g = PAGE_W * 0.56 - M\n    feats = [str(f) for f in (l.get(\"features\") or []) if f]\n    desc = (l.get(\"description\") or \"\").strip()\n    if desc or feats:\n        besoin = 30 + max(\n            s.nb_lignes(desc, col_g, max_lignes=26) * 11.5 if desc else 0,\n            min(len(feats), 18) * 12)\n        y = place(y, min(besoin, 340))\n        y_desc = y\n        if desc:\n            y_desc = s.titre_section(y, \"Description\")\n            y_desc = s.paragraphe(M, y_desc, desc, col_g, max_lignes=26)\n        y_incl = y\n        if feats:\n            xr = M + col_g + 24\n            y_incl = _titre_droite(c, y, xr, \"Inclusions et caractéristiques\")\n            c.setFont(\"Helvetica\", 8.5)\n            for f in feats[:18]:\n                c.setFillColor(ACCENT_DEEP)\n                c.drawString(xr, y_incl, \"✓\")\n                c.setFillColor(INK2)\n                c.drawString(xr + 12, y_incl, f[:42])\n                y_incl -= 12\n            if len(feats) > 18:\n                c.setFillColor(INK3)\n                c.drawString(xr + 12, y_incl, f\"+ {len(feats) - 18} autres…\")\n                y_incl -= 12\n        y = min(y_desc, y_incl) - 16\n\n    # --- caractéristiques (details, mêmes exclusions que le site) ---------------\n    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]\n    if dets:\n        col_w = PAGE_W - 2 * M\n        besoin = 40 + sum(\n            s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4) * 11 + 3\n            for _, v in dets)\n        y = place(y, min(besoin, 420))\n        y = s.titre_section(y, \"Caractéristiques\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"DÉTAIL\")\n        c.drawString(M + 180, y, \"VALEUR\")\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 12\n        for i, (k, v) in enumerate(dets):\n            nl = s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4)\n            y = place(y, nl * 11 + 6)\n            if i % 2 == 0:   # zébrage éditorial\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - (nl - 1) * 11 - 3, col_w + 6, nl * 11 + 2,\n                       stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, k[:38])\n            y = s.paragraphe(M + 180, y, v, col_w - 190, taille=8,\n                             interligne=11, max_lignes=4)\n            y -= 3\n        y -= 10\n\n    # --- pièces (details.pieces) -------------------------------------------------\n    pieces = d.get(\"pieces\") or []\n    if isinstance(pieces, list) and pieces:\n        rows = pieces[:24]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Pièces\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"PIÈCE\", 0), (\"NIVEAU\", 190), (\"DIMENSIONS\", 330),\n                          (\"REVÊTEMENT\", 430)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            if i % 2 == 0:\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - 3, PAGE_W - 2 * M + 6, 12, stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, str(p.get(\"nom\") or \"—\")[:36])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M + 190, y, str(p.get(\"niveau\") or \"—\")[:24])\n            c.drawString(M + 330, y, str(p.get(\"dimensions\") or \"—\")[:18])\n            c.drawString(M + 430, y, str(p.get(\"revetement\") or \"—\")[:22])\n            y -= 12\n        y -= 10\n\n    # --- analyse Vrai-Prix (valeur, jauge, rôle foncier) -------------------------\n    if vp and vp.get(\"value\") is not None:\n        role = [(lab, vp.get(k)) for k, lab in\n                ((\"valeur_role\", \"Évaluation au rôle\"),\n                 (\"valeur_terrain\", \"Valeur du terrain\"),\n                 (\"valeur_batiment\", \"Valeur du bâtiment\"))\n                if vp.get(k)]\n        besoin = 96 + (56 if vp.get(\"low\") is not None else 0) \\\n            + (62 if role else 0) + 18\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Analyse Vrai-Prix\")\n        val_txt = _fmt_money(round(vp[\"value\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, val_txt)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(val_txt, \"Helvetica-Bold\", 17) + 8,\n                     y - 10, \"— valeur marchande estimée par le modèle Vrai-Prix\")\n        meta = []\n        if vp.get(\"confidence\"):\n            meta.append(f\"confiance {vp['confidence']}\"\n                        + (f\" ({vp['confidence_pct']}{NBSP}%)\"\n                           if vp.get(\"confidence_pct\") else \"\"))\n        if vp.get(\"annee_construction_role\"):\n            meta.append(f\"construction {vp['annee_construction_role']} (rôle)\")\n        if vp.get(\"superficie_terrain_role_m2\"):\n            meta.append(\n                f\"terrain {round(vp['superficie_terrain_role_m2']):,}\".replace(\",\", NBSP)\n                + f\"{NBSP}m² (rôle)\")\n        if meta:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK3)\n            c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        verd = _verdict_vraiprix(l.get(\"price\"), vp)\n        if verd:\n            txt, fg, bg, stk = verd\n            s.pilule(M, y - 48, txt, fg, bg, stk)\n        y -= 58\n        lo, hi = vp.get(\"low\"), vp.get(\"high\")\n        if lo is not None and hi is not None and hi > lo:\n            larg = PAGE_W - 2 * M\n            base = y - 22\n            span = hi - lo\n            c.setFillColor(ACCENT_SOFT)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.roundRect(M, base, larg, 12, 6, stroke=1, fill=1)\n\n            def _pos(v):\n                return M + larg * max(0.02, min(0.98, (v - lo) / span))\n\n            px = _pos(vp[\"value\"])\n            c.setStrokeColor(ACCENT_DEEP)\n            c.setLineWidth(2.5)\n            c.line(px, base - 3, px, base + 15)\n            c.setFont(\"Courier-Bold\", 6.5)\n            c.setFillColor(ACCENT_DEEP)\n            c.drawString(min(max(px - 30, M), PAGE_W - M - 100), base + 18,\n                         f\"ESTIMATION · {_fmt_money(round(vp['value']))}\")\n            if l.get(\"price\") and lo * 0.7 <= l[\"price\"] <= hi * 1.3:\n                ax = _pos(l[\"price\"])\n                c.setStrokeColor(INK)\n                c.setLineWidth(2)\n                c.line(ax, base - 3, ax, base + 15)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(INK)\n                c.drawString(min(max(ax - 30, M), PAGE_W - M - 116), base - 12,\n                             f\"PRIX DEMANDÉ · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 24, _fmt_money(round(lo)))\n            c.drawRightString(PAGE_W - M, base - 24,\n                              _fmt_money(round(hi)) + \" — fourchette d'estimation\")\n            y = base - 36\n        if role:\n            tw = (PAGE_W - 2 * M - (len(role) - 1) * 10) / len(role)\n            for i, (lab, v) in enumerate(role):\n                s.tuile(M + i * (tw + 10), y - 44, tw, 38,\n                        _fmt_money(round(v)), lab)\n            y -= 56\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Estimation automatisée Vrai-Prix (vrai-prix.ca) \"\n                               \"croisée au rôle d'évaluation foncière — ne \"\n                               \"remplace pas une évaluation professionnelle.\")\n        y -= 16\n\n    # --- historique de prix -------------------------------------------------------\n    if len(ph) >= 2:\n        rows = ph[:8]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Historique du prix demandé\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"DATE\", 0), (\"PRIX AFFICHÉ\", 190), (\"VARIATION\", 330)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            prec = rows[i + 1][\"price\"] if i + 1 < len(rows) else None\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, _fmt_jour(p[\"ts\"]))\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M + 190, y, _fmt_money(p[\"price\"]))\n            if prec and p.get(\"price\"):\n                delta = p[\"price\"] - prec\n                c.setFont(\"Courier-Bold\", 7.5)\n                c.setFillColor(GREEN_FG if delta < 0\n                               else (RED_FG if delta > 0 else INK3))\n                sig = \"▼\" if delta < 0 else (\"▲\" if delta > 0 else \"=\")\n                c.drawString(M + 330, y,\n                             f\"{sig} {_fmt_money(abs(delta))}\" if delta else \"=\")\n            y -= 12\n        y -= 10\n\n    # --- risque d'inondation (BDZI) ------------------------------------------------\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\" if sev\n                     else \"En zone inondable\", RED_FG, RED_BG, RED_FG)\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     AMBER_FG, AMBER_BG, AMBER_FG)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_FG, GREEN_BG, GREEN_FG)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z.get('type') or 'Zone'} — à {z.get('distance_m')} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- qualité de l'air (RSQAQ) ---------------------------------------------------\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"de la propriété — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = (f\"{m_['moyenne']:.1f}\".replace(\".\", \",\")\n                   if m_.get(\"moyenne\") is not None else \"—\")\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- coût d'électricité (Hydro-Québec) -------------------------------------------\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += (f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP)\n                      + f\"{NBSP}kWh/an\")\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- commerces et transport --------------------------------------------------------\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:12]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- essence à proximité --------------------------------------------------------------\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- le quartier -------------------------------------------------------------------------\n    if quartier and (quartier.get(\"demographie\") or quartier.get(\"proximite\")\n                     or quartier.get(\"chaleur\") or quartier.get(\"crime\")):\n        demo = quartier.get(\"demographie\") or {}\n        prox = quartier.get(\"proximite\") or {}\n        prox_aff = [(k, lab) for k, lab in PROX_LABELS\n                    if isinstance(prox.get(k), (int, float))]\n        besoin = 44 + (94 if demo else 0) + len(prox_aff) * 14 + 44\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Le quartier\")\n        c.setFont(\"Helvetica\", 7.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"Secteur immédiat de la propriété (aire de diffusion \"\n                           \"du recensement 2021, ± 500 habitants).\")\n        y -= 14\n        if demo:\n            tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                      (\"Ménages locataires\",\n                       f\"{round(demo['pct_locataires'])}{NBSP}%\"\n                       if demo.get(\"pct_locataires\") is not None else \"—\"),\n                      (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                      (\"Âge médian\",\n                       f\"{round(demo['age_median'])} ans\"\n                       if demo.get(\"age_median\") is not None else \"—\"),\n                      (\"Français à la maison\",\n                       f\"{round(demo['pct_francais'])}{NBSP}%\"\n                       if demo.get(\"pct_francais\") is not None else \"—\"),\n                      (\"Diplôme universitaire\",\n                       f\"{round(demo['pct_univ'])}{NBSP}%\"\n                       if demo.get(\"pct_univ\") is not None else \"—\")]\n            tw = (PAGE_W - 2 * M - 20) / 3\n            for i, (lab, val) in enumerate(tuiles):\n                s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                        tw, 38, val, lab)\n            y -= 34 + 44 + 16\n        for k, lab in prox_aff:\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(ACCENT)\n            c.roundRect(bx0, y - 1, max(3, bw_ * v), 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        if prox_aff:\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, y, \"Accessibilité 0–100 — mesures de proximité de \"\n                               \"Statistique Canada.\")\n            y -= 12\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch.get(\"classe\", 5) <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_FG, GREEN_BG, GREEN_FG))\n            elif ch.get(\"classe\", 5) >= 7:\n                ec = (f\" (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\")\n                      if ch.get(\"ecart\") is not None else \"\")\n                badges_q.append((f\"Îlot de chaleur{ec}\",\n                                 AMBER_FG, AMBER_BG, AMBER_FG))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"])\n                        / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} \"\n                             \"la moyenne canadienne\", INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m \"\n                             \"(12 mois)\", INK2, SURFACE, INK))\n        if badges_q:\n            x = M\n            for txt, fg, bg, stk in badges_q:\n                x = s.pilule(x, y - 8, txt, fg, bg, stk)\n            y -= 26\n        y -= 6\n\n    # --- références : QR + courtier + source ------------------------------------------------\n    y = place(y, 140)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.immo-ka.com/propriete/{uid}\",\n                     box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    yy = y - 14\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    courtier = l.get(\"broker_name\") or d.get(\"Courtier inscripteur\")\n    if courtier:\n        ligne = f\"Courtier : {courtier}\"\n        if l.get(\"broker_phone\"):\n            ligne += f\" · {l['broker_phone']}\"\n        c.drawString(M + 82, yy, ligne[:95])\n        yy -= 13\n    c.drawString(M + 82, yy, f\"Agence : {src_name}\")\n    yy -= 14\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Annonce originale :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy,\n                 (l.get(\"url\") or d.get(\"listing_origin_url\") or \"\")[:90])\n    yy -= 14\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Fiche à jour en ligne (scannez le code) :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")\n    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n","structuredPatch":[{"oldStart":66,"oldLines":8,"newStart":66,"newLines":8,"lines":["          \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]"," "," _PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"","-               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"","-               \"environnement) fournis à titre indicatif, sans valeur légale.\")","+               \"agrégateur indépendant. Enrichissements fournis à titre \"","+               \"indicatif, sans valeur légale.\")"," "," "," def _fmt_money(v) -> str:"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01J2RUztDF94adPcvVwu8Bm7","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"d59834fe-894b-40cd-b11b-788a790db364","timestamp":"2026-08-24T21:47:30.793Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","oldString":"    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]","newString":"    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and str(v).strip(\"-— \") != \"\" and not isinstance(v, (dict, list))]","originalFile":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdffiche.py : fiche PDF complète d'une propriété — reflète TOUS les blocs de\n#   la fiche web : prix + verdict Vrai-Prix, photos, description, inclusions,\n#   caractéristiques, pièces, analyse Vrai-Prix (jauge + rôle foncier),\n#   historique de prix, inondation, qualité de l'air, coût d'électricité,\n#   commerces/transport, essence, quartier, références + QR.\n#   Style « éditorial sharp » (bordures encre, ombres décalées dures, tuiles\n#   KPI, micro-étiquettes Courier) — palette du site : crème #f5f3ee, encre\n#   #141814, accent cerise #e23744. Pagination dynamique : saut_page()/place(),\n#   aucune section coupée entre deux pages.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Immo-Ka (identité officielle du site) ----------------------------\nPAPER = HexColor(\"#f5f3ee\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#141814\")\nINK2 = HexColor(\"#4d5551\")\nINK3 = HexColor(\"#8b928c\")\nACCENT = HexColor(\"#e23744\")        # cerise\nACCENT_SOFT = HexColor(\"#fbe0e2\")\nACCENT_DEEP = HexColor(\"#a8232e\")\nGREEN_FG = HexColor(\"#1c5c41\")      # verdict « sous l'estimation » (site .vp-under)\nGREEN_BG = HexColor(\"#e2f0e6\")\nRED_FG = HexColor(\"#8a2b2b\")        # verdict « au-dessus » (site .vp-over)\nRED_BG = HexColor(\"#f6e2e2\")\nAMBER_FG = HexColor(\"#8a5a12\")\nAMBER_BG = HexColor(\"#faf0dc\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"ImmoKaBot/1.0 (+https://www.immo-ka.com; contact@spboucher.ai)\"\n\nNBSP = \" \"     # espace fine insécable (mise en forme française)\n\n# clés de details jamais affichées (mêmes exclusions que la fiche web)\nDETAIL_HIDDEN = {\n    \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n    \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n    \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n}\n\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements fournis à titre \"\n               \"indicatif, sans valeur légale.\")\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _fmt_mois(d) -> str:\n    \"\"\"« 2017-11-04 » ou epoch → « novembre 2017 » (sinon valeur telle quelle).\"\"\"\n    if d is None:\n        return \"—\"\n    try:\n        if isinstance(d, (int, float)):\n            st = time.localtime(d)\n            return f\"{_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n        an, mois = str(d)[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return str(d)\n\n\ndef _fmt_jour(ts: float) -> str:\n    \"\"\"epoch → « 4 novembre 2024 » (« 1er » pour le premier du mois).\"\"\"\n    st = time.localtime(ts)\n    j = \"1er\" if st.tm_mday == 1 else str(st.tm_mday)\n    return f\"{j} {_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à plusieurs dizaines de Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1000, 1000))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=78)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Immo·Ka : « Immo » encre + boîte encre avec « Ka » cerise.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Immo\")\n        w = c.stringWidth(\"Immo\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6, titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.IMMO-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=ACCENT, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        # carré accent à ombre dure (signature de la passe éditoriale du site)\n        c.setFillColor(INK)\n        c.rect(M + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.rect(M, y + 0.5, 9, 9, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M + 16, y, texte)\n        c.setStrokeColor(ACCENT)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M + 16, y - 4, M + 16 + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def nb_lignes(self, texte: str, largeur: float, taille: float = 8.5,\n                  max_lignes: int = 100) -> int:\n        \"\"\"Nombre de lignes qu'occupera `paragraphe` (pour réserver la place).\"\"\"\n        c = self.c\n        mots = texte.split()\n        ligne = \"\"\n        n = 1\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                n += 1\n                ligne = mot\n                if n >= max_lignes:\n                    break\n        return min(n, max_lignes)\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\ndef _titre_droite(c, y, x, texte):\n    c.setFillColor(INK)\n    c.rect(x + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n    c.setFillColor(ACCENT)\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.rect(x, y + 0.5, 9, 9, stroke=1, fill=1)\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x + 16, y, texte)\n    c.setStrokeColor(ACCENT)\n    c.setLineWidth(3)\n    c.line(x + 16, y - 4, x + 16 + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\ndef _verdict_vraiprix(price, vp):\n    \"\"\"Pastille verdict identique au site : ±% vs estimation, masqué si\n    |pct| > 60 (prix non comparable : terrain, loyer…).\"\"\"\n    if not price or not vp or vp.get(\"value\") in (None, 0):\n        return None\n    pct = round((price - vp[\"value\"]) / vp[\"value\"] * 100)\n    if abs(pct) > 60:\n        return None\n    if pct >= 1:\n        return (f\"▲{NBSP}{pct}{NBSP}% au-dessus de l'estimation\",\n                RED_FG, RED_BG, ACCENT_DEEP)\n    if pct <= -1:\n        return (f\"▼{NBSP}{abs(pct)}{NBSP}% sous l'estimation\",\n                GREEN_FG, GREEN_BG, GREEN_FG)\n    return (f\"≈{NBSP}Prix aligné sur l'estimation\", INK2, SURFACE, INK)\n\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche de la propriété `uid` — reflète tous les blocs de la\n    fiche web ; None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"features\"] = json.loads(l.get(\"features\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    try:\n        l[\"vraiprix\"] = json.loads(l[\"vraiprix\"]) or None if l.get(\"vraiprix\") else None\n    except (ValueError, TypeError):\n        l[\"vraiprix\"] = None\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n        (uid,)).fetchall()]\n    quartier = None\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n    if geo:\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        try:\n            quartier = qmod.fiche_quartier(\n                lat, lng, l.get(\"city\") or \"\",\n                dauid if dauid and dauid != \"hors-zone\" else None)\n        except Exception:\n            quartier = None\n    try:\n        reg = json.loads(\n            (db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n        src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]),\n                        l[\"source\"])\n    except Exception:\n        src_name = l[\"source\"]\n    con.close()\n\n    # --- enrichissements (mêmes modules que les blocs de la fiche web) -------\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    d = l[\"details\"]\n    vp = l.get(\"vraiprix\")\n    location = (d.get(\"transaction\") == \"location\")\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Immo-Ka — {l.get('address') or l.get('title') or uid}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche propriété\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche propriété · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- héro : prix + pastille verdict Vrai-Prix -----------------------------\n    prix_txt = (_fmt_money(l[\"price\"]) + (f\"{NBSP}/ mois\" if location else \"\")) \\\n        if l.get(\"price\") else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    verd = _verdict_vraiprix(l.get(\"price\"), vp)\n    if verd:\n        txt, fg, bg, stk = verd\n        bx = s.pilule(bx, y - 22, txt, fg, bg, stk)\n    if l.get(\"property_type\"):\n        s.pilule(bx, y - 22, l[\"property_type\"], ACCENT, INK)\n    y -= 44\n\n    # --- titre + adresse -------------------------------------------------------\n    titre = l.get(\"address\") or l.get(\"title\") or \"\"\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, titre[:80])\n    y -= 14\n    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés (mêmes specs que le héro du site) --------------------------\n    chips = []\n    if l.get(\"bedrooms\") is not None:\n        chips.append(f\"{l['bedrooms']}{NBSP}ch.\")\n    if l.get(\"bathrooms\") is not None:\n        chips.append(f\"{l['bathrooms']}{NBSP}sdb\")\n    if l.get(\"powder_rooms\"):\n        chips.append(f\"{l['powder_rooms']}{NBSP}s. d'eau\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"lot_sqft\"):\n        chips.append(f\"Terrain {round(l['lot_sqft']):,}\".replace(\",\", NBSP)\n                     + f\"{NBSP}pi²\")\n    if l.get(\"year_built\"):\n        chips.append(f\"Construit en {l['year_built']}\")\n    if l.get(\"price\") and l.get(\"area_sqft\") and l[\"area_sqft\"] > 200:\n        chips.append(f\"{round(l['price'] / l['area_sqft'])}{NBSP}$/pi²\")\n    if l.get(\"mls\"):\n        chips.append(f\"MLS {l['mls']}\")\n    if l.get(\"first_seen\"):\n        dom = max(0, int((time.time() - l[\"first_seen\"]) // 86400))\n        chips.append(f\"{dom}{NBSP}j sur Immo-Ka\")\n    x = M\n    for chip in chips[:8]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes ---------------------------------------\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- baisse/hausse de prix (bandeau, comme le site) -------------------------\n    ph = [p for p in price_history if p.get(\"price\")]\n    if len(ph) >= 2 and ph[0][\"price\"] != ph[1][\"price\"]:\n        y = place(y, 34)\n        de, a = ph[1][\"price\"], ph[0][\"price\"]\n        bas = a < de\n        c.setFillColor(GREEN_BG if bas else RED_BG)\n        c.setStrokeColor(GREEN_FG if bas else RED_FG)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 20, PAGE_W - 2 * M, 24, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 9)\n        c.setFillColor(GREEN_FG if bas else RED_FG)\n        c.drawString(M + 10, y - 12,\n                     f\"{'▼' if bas else '▲'} Prix passé de {_fmt_money(de)} à \"\n                     f\"{_fmt_money(a)} le {_fmt_jour(ph[0]['ts'])}\")\n        y -= 34\n\n    # --- description | inclusions (2 colonnes, comme le site) -------------------\n    col_g = PAGE_W * 0.56 - M\n    feats = [str(f) for f in (l.get(\"features\") or []) if f]\n    desc = (l.get(\"description\") or \"\").strip()\n    if desc or feats:\n        besoin = 30 + max(\n            s.nb_lignes(desc, col_g, max_lignes=26) * 11.5 if desc else 0,\n            min(len(feats), 18) * 12)\n        y = place(y, min(besoin, 340))\n        y_desc = y\n        if desc:\n            y_desc = s.titre_section(y, \"Description\")\n            y_desc = s.paragraphe(M, y_desc, desc, col_g, max_lignes=26)\n        y_incl = y\n        if feats:\n            xr = M + col_g + 24\n            y_incl = _titre_droite(c, y, xr, \"Inclusions et caractéristiques\")\n            c.setFont(\"Helvetica\", 8.5)\n            for f in feats[:18]:\n                c.setFillColor(ACCENT_DEEP)\n                c.drawString(xr, y_incl, \"✓\")\n                c.setFillColor(INK2)\n                c.drawString(xr + 12, y_incl, f[:42])\n                y_incl -= 12\n            if len(feats) > 18:\n                c.setFillColor(INK3)\n                c.drawString(xr + 12, y_incl, f\"+ {len(feats) - 18} autres…\")\n                y_incl -= 12\n        y = min(y_desc, y_incl) - 16\n\n    # --- caractéristiques (details, mêmes exclusions que le site) ---------------\n    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]\n    if dets:\n        col_w = PAGE_W - 2 * M\n        besoin = 40 + sum(\n            s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4) * 11 + 3\n            for _, v in dets)\n        y = place(y, min(besoin, 420))\n        y = s.titre_section(y, \"Caractéristiques\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"DÉTAIL\")\n        c.drawString(M + 180, y, \"VALEUR\")\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 12\n        for i, (k, v) in enumerate(dets):\n            nl = s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4)\n            y = place(y, nl * 11 + 6)\n            if i % 2 == 0:   # zébrage éditorial\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - (nl - 1) * 11 - 3, col_w + 6, nl * 11 + 2,\n                       stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, k[:38])\n            y = s.paragraphe(M + 180, y, v, col_w - 190, taille=8,\n                             interligne=11, max_lignes=4)\n            y -= 3\n        y -= 10\n\n    # --- pièces (details.pieces) -------------------------------------------------\n    pieces = d.get(\"pieces\") or []\n    if isinstance(pieces, list) and pieces:\n        rows = pieces[:24]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Pièces\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"PIÈCE\", 0), (\"NIVEAU\", 190), (\"DIMENSIONS\", 330),\n                          (\"REVÊTEMENT\", 430)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            if i % 2 == 0:\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - 3, PAGE_W - 2 * M + 6, 12, stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, str(p.get(\"nom\") or \"—\")[:36])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M + 190, y, str(p.get(\"niveau\") or \"—\")[:24])\n            c.drawString(M + 330, y, str(p.get(\"dimensions\") or \"—\")[:18])\n            c.drawString(M + 430, y, str(p.get(\"revetement\") or \"—\")[:22])\n            y -= 12\n        y -= 10\n\n    # --- analyse Vrai-Prix (valeur, jauge, rôle foncier) -------------------------\n    if vp and vp.get(\"value\") is not None:\n        role = [(lab, vp.get(k)) for k, lab in\n                ((\"valeur_role\", \"Évaluation au rôle\"),\n                 (\"valeur_terrain\", \"Valeur du terrain\"),\n                 (\"valeur_batiment\", \"Valeur du bâtiment\"))\n                if vp.get(k)]\n        besoin = 96 + (56 if vp.get(\"low\") is not None else 0) \\\n            + (62 if role else 0) + 18\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Analyse Vrai-Prix\")\n        val_txt = _fmt_money(round(vp[\"value\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, val_txt)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(val_txt, \"Helvetica-Bold\", 17) + 8,\n                     y - 10, \"— valeur marchande estimée par le modèle Vrai-Prix\")\n        meta = []\n        if vp.get(\"confidence\"):\n            meta.append(f\"confiance {vp['confidence']}\"\n                        + (f\" ({vp['confidence_pct']}{NBSP}%)\"\n                           if vp.get(\"confidence_pct\") else \"\"))\n        if vp.get(\"annee_construction_role\"):\n            meta.append(f\"construction {vp['annee_construction_role']} (rôle)\")\n        if vp.get(\"superficie_terrain_role_m2\"):\n            meta.append(\n                f\"terrain {round(vp['superficie_terrain_role_m2']):,}\".replace(\",\", NBSP)\n                + f\"{NBSP}m² (rôle)\")\n        if meta:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK3)\n            c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        verd = _verdict_vraiprix(l.get(\"price\"), vp)\n        if verd:\n            txt, fg, bg, stk = verd\n            s.pilule(M, y - 48, txt, fg, bg, stk)\n        y -= 58\n        lo, hi = vp.get(\"low\"), vp.get(\"high\")\n        if lo is not None and hi is not None and hi > lo:\n            larg = PAGE_W - 2 * M\n            base = y - 22\n            span = hi - lo\n            c.setFillColor(ACCENT_SOFT)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.roundRect(M, base, larg, 12, 6, stroke=1, fill=1)\n\n            def _pos(v):\n                return M + larg * max(0.02, min(0.98, (v - lo) / span))\n\n            px = _pos(vp[\"value\"])\n            c.setStrokeColor(ACCENT_DEEP)\n            c.setLineWidth(2.5)\n            c.line(px, base - 3, px, base + 15)\n            c.setFont(\"Courier-Bold\", 6.5)\n            c.setFillColor(ACCENT_DEEP)\n            c.drawString(min(max(px - 30, M), PAGE_W - M - 100), base + 18,\n                         f\"ESTIMATION · {_fmt_money(round(vp['value']))}\")\n            if l.get(\"price\") and lo * 0.7 <= l[\"price\"] <= hi * 1.3:\n                ax = _pos(l[\"price\"])\n                c.setStrokeColor(INK)\n                c.setLineWidth(2)\n                c.line(ax, base - 3, ax, base + 15)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(INK)\n                c.drawString(min(max(ax - 30, M), PAGE_W - M - 116), base - 12,\n                             f\"PRIX DEMANDÉ · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 24, _fmt_money(round(lo)))\n            c.drawRightString(PAGE_W - M, base - 24,\n                              _fmt_money(round(hi)) + \" — fourchette d'estimation\")\n            y = base - 36\n        if role:\n            tw = (PAGE_W - 2 * M - (len(role) - 1) * 10) / len(role)\n            for i, (lab, v) in enumerate(role):\n                s.tuile(M + i * (tw + 10), y - 44, tw, 38,\n                        _fmt_money(round(v)), lab)\n            y -= 56\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Estimation automatisée Vrai-Prix (vrai-prix.ca) \"\n                               \"croisée au rôle d'évaluation foncière — ne \"\n                               \"remplace pas une évaluation professionnelle.\")\n        y -= 16\n\n    # --- historique de prix -------------------------------------------------------\n    if len(ph) >= 2:\n        rows = ph[:8]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Historique du prix demandé\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"DATE\", 0), (\"PRIX AFFICHÉ\", 190), (\"VARIATION\", 330)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            prec = rows[i + 1][\"price\"] if i + 1 < len(rows) else None\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, _fmt_jour(p[\"ts\"]))\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M + 190, y, _fmt_money(p[\"price\"]))\n            if prec and p.get(\"price\"):\n                delta = p[\"price\"] - prec\n                c.setFont(\"Courier-Bold\", 7.5)\n                c.setFillColor(GREEN_FG if delta < 0\n                               else (RED_FG if delta > 0 else INK3))\n                sig = \"▼\" if delta < 0 else (\"▲\" if delta > 0 else \"=\")\n                c.drawString(M + 330, y,\n                             f\"{sig} {_fmt_money(abs(delta))}\" if delta else \"=\")\n            y -= 12\n        y -= 10\n\n    # --- risque d'inondation (BDZI) ------------------------------------------------\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\" if sev\n                     else \"En zone inondable\", RED_FG, RED_BG, RED_FG)\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     AMBER_FG, AMBER_BG, AMBER_FG)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_FG, GREEN_BG, GREEN_FG)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z.get('type') or 'Zone'} — à {z.get('distance_m')} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- qualité de l'air (RSQAQ) ---------------------------------------------------\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"de la propriété — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = (f\"{m_['moyenne']:.1f}\".replace(\".\", \",\")\n                   if m_.get(\"moyenne\") is not None else \"—\")\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- coût d'électricité (Hydro-Québec) -------------------------------------------\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += (f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP)\n                      + f\"{NBSP}kWh/an\")\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- commerces et transport --------------------------------------------------------\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:12]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- essence à proximité --------------------------------------------------------------\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- le quartier -------------------------------------------------------------------------\n    if quartier and (quartier.get(\"demographie\") or quartier.get(\"proximite\")\n                     or quartier.get(\"chaleur\") or quartier.get(\"crime\")):\n        demo = quartier.get(\"demographie\") or {}\n        prox = quartier.get(\"proximite\") or {}\n        prox_aff = [(k, lab) for k, lab in PROX_LABELS\n                    if isinstance(prox.get(k), (int, float))]\n        besoin = 44 + (94 if demo else 0) + len(prox_aff) * 14 + 44\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Le quartier\")\n        c.setFont(\"Helvetica\", 7.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"Secteur immédiat de la propriété (aire de diffusion \"\n                           \"du recensement 2021, ± 500 habitants).\")\n        y -= 14\n        if demo:\n            tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                      (\"Ménages locataires\",\n                       f\"{round(demo['pct_locataires'])}{NBSP}%\"\n                       if demo.get(\"pct_locataires\") is not None else \"—\"),\n                      (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                      (\"Âge médian\",\n                       f\"{round(demo['age_median'])} ans\"\n                       if demo.get(\"age_median\") is not None else \"—\"),\n                      (\"Français à la maison\",\n                       f\"{round(demo['pct_francais'])}{NBSP}%\"\n                       if demo.get(\"pct_francais\") is not None else \"—\"),\n                      (\"Diplôme universitaire\",\n                       f\"{round(demo['pct_univ'])}{NBSP}%\"\n                       if demo.get(\"pct_univ\") is not None else \"—\")]\n            tw = (PAGE_W - 2 * M - 20) / 3\n            for i, (lab, val) in enumerate(tuiles):\n                s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                        tw, 38, val, lab)\n            y -= 34 + 44 + 16\n        for k, lab in prox_aff:\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(ACCENT)\n            c.roundRect(bx0, y - 1, max(3, bw_ * v), 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        if prox_aff:\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, y, \"Accessibilité 0–100 — mesures de proximité de \"\n                               \"Statistique Canada.\")\n            y -= 12\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch.get(\"classe\", 5) <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_FG, GREEN_BG, GREEN_FG))\n            elif ch.get(\"classe\", 5) >= 7:\n                ec = (f\" (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\")\n                      if ch.get(\"ecart\") is not None else \"\")\n                badges_q.append((f\"Îlot de chaleur{ec}\",\n                                 AMBER_FG, AMBER_BG, AMBER_FG))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"])\n                        / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} \"\n                             \"la moyenne canadienne\", INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m \"\n                             \"(12 mois)\", INK2, SURFACE, INK))\n        if badges_q:\n            x = M\n            for txt, fg, bg, stk in badges_q:\n                x = s.pilule(x, y - 8, txt, fg, bg, stk)\n            y -= 26\n        y -= 6\n\n    # --- références : QR + courtier + source ------------------------------------------------\n    y = place(y, 140)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.immo-ka.com/propriete/{uid}\",\n                     box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    yy = y - 14\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    courtier = l.get(\"broker_name\") or d.get(\"Courtier inscripteur\")\n    if courtier:\n        ligne = f\"Courtier : {courtier}\"\n        if l.get(\"broker_phone\"):\n            ligne += f\" · {l['broker_phone']}\"\n        c.drawString(M + 82, yy, ligne[:95])\n        yy -= 13\n    c.drawString(M + 82, yy, f\"Agence : {src_name}\")\n    yy -= 14\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Annonce originale :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy,\n                 (l.get(\"url\") or d.get(\"listing_origin_url\") or \"\")[:90])\n    yy -= 14\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Fiche à jour en ligne (scannez le code) :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")\n    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n","structuredPatch":[{"oldStart":516,"oldLines":7,"newStart":516,"newLines":7,"lines":["     # --- caractéristiques (details, mêmes exclusions que le site) ---------------","     dets = [(str(k), str(v)) for k, v in d.items()","             if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})","-            and not isinstance(v, (dict, list))]","+            and str(v).strip(\"-— \") != \"\" and not isinstance(v, (dict, list))]","     if dets:","         col_w = PAGE_W - 2 * M","         besoin = 40 + sum("]}],"userModified":false,"replaceAll":false}}}
{"type":"permission_request","requestId":"5147f62d94537fce","tool":"Edit","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","old_string":"    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")","new_string":"    y -= 90\n    s.paragraphe(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\",\n                 PAGE_W - 2 * M, taille=6.5, couleur=INK3, interligne=9)","replace_all":false}}
{"type":"permission_resolved","requestId":"5147f62d94537fce","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GrTFCc3fPoU8pfyGb7vkos","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"71cdfc2b-0bb4-4a00-8e3c-0e43be2dc70f","timestamp":"2026-08-24T21:47:31.425Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","oldString":"    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")","newString":"    y -= 90\n    s.paragraphe(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\",\n                 PAGE_W - 2 * M, taille=6.5, couleur=INK3, interligne=9)","originalFile":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdffiche.py : fiche PDF complète d'une propriété — reflète TOUS les blocs de\n#   la fiche web : prix + verdict Vrai-Prix, photos, description, inclusions,\n#   caractéristiques, pièces, analyse Vrai-Prix (jauge + rôle foncier),\n#   historique de prix, inondation, qualité de l'air, coût d'électricité,\n#   commerces/transport, essence, quartier, références + QR.\n#   Style « éditorial sharp » (bordures encre, ombres décalées dures, tuiles\n#   KPI, micro-étiquettes Courier) — palette du site : crème #f5f3ee, encre\n#   #141814, accent cerise #e23744. Pagination dynamique : saut_page()/place(),\n#   aucune section coupée entre deux pages.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Immo-Ka (identité officielle du site) ----------------------------\nPAPER = HexColor(\"#f5f3ee\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#141814\")\nINK2 = HexColor(\"#4d5551\")\nINK3 = HexColor(\"#8b928c\")\nACCENT = HexColor(\"#e23744\")        # cerise\nACCENT_SOFT = HexColor(\"#fbe0e2\")\nACCENT_DEEP = HexColor(\"#a8232e\")\nGREEN_FG = HexColor(\"#1c5c41\")      # verdict « sous l'estimation » (site .vp-under)\nGREEN_BG = HexColor(\"#e2f0e6\")\nRED_FG = HexColor(\"#8a2b2b\")        # verdict « au-dessus » (site .vp-over)\nRED_BG = HexColor(\"#f6e2e2\")\nAMBER_FG = HexColor(\"#8a5a12\")\nAMBER_BG = HexColor(\"#faf0dc\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"ImmoKaBot/1.0 (+https://www.immo-ka.com; contact@spboucher.ai)\"\n\nNBSP = \" \"     # espace fine insécable (mise en forme française)\n\n# clés de details jamais affichées (mêmes exclusions que la fiche web)\nDETAIL_HIDDEN = {\n    \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n    \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n    \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n}\n\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements fournis à titre \"\n               \"indicatif, sans valeur légale.\")\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _fmt_mois(d) -> str:\n    \"\"\"« 2017-11-04 » ou epoch → « novembre 2017 » (sinon valeur telle quelle).\"\"\"\n    if d is None:\n        return \"—\"\n    try:\n        if isinstance(d, (int, float)):\n            st = time.localtime(d)\n            return f\"{_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n        an, mois = str(d)[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return str(d)\n\n\ndef _fmt_jour(ts: float) -> str:\n    \"\"\"epoch → « 4 novembre 2024 » (« 1er » pour le premier du mois).\"\"\"\n    st = time.localtime(ts)\n    j = \"1er\" if st.tm_mday == 1 else str(st.tm_mday)\n    return f\"{j} {_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à plusieurs dizaines de Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1000, 1000))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=78)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Immo·Ka : « Immo » encre + boîte encre avec « Ka » cerise.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Immo\")\n        w = c.stringWidth(\"Immo\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6, titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.IMMO-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=ACCENT, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        # carré accent à ombre dure (signature de la passe éditoriale du site)\n        c.setFillColor(INK)\n        c.rect(M + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.rect(M, y + 0.5, 9, 9, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M + 16, y, texte)\n        c.setStrokeColor(ACCENT)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M + 16, y - 4, M + 16 + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def nb_lignes(self, texte: str, largeur: float, taille: float = 8.5,\n                  max_lignes: int = 100) -> int:\n        \"\"\"Nombre de lignes qu'occupera `paragraphe` (pour réserver la place).\"\"\"\n        c = self.c\n        mots = texte.split()\n        ligne = \"\"\n        n = 1\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                n += 1\n                ligne = mot\n                if n >= max_lignes:\n                    break\n        return min(n, max_lignes)\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\ndef _titre_droite(c, y, x, texte):\n    c.setFillColor(INK)\n    c.rect(x + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n    c.setFillColor(ACCENT)\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.rect(x, y + 0.5, 9, 9, stroke=1, fill=1)\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x + 16, y, texte)\n    c.setStrokeColor(ACCENT)\n    c.setLineWidth(3)\n    c.line(x + 16, y - 4, x + 16 + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\ndef _verdict_vraiprix(price, vp):\n    \"\"\"Pastille verdict identique au site : ±% vs estimation, masqué si\n    |pct| > 60 (prix non comparable : terrain, loyer…).\"\"\"\n    if not price or not vp or vp.get(\"value\") in (None, 0):\n        return None\n    pct = round((price - vp[\"value\"]) / vp[\"value\"] * 100)\n    if abs(pct) > 60:\n        return None\n    if pct >= 1:\n        return (f\"▲{NBSP}{pct}{NBSP}% au-dessus de l'estimation\",\n                RED_FG, RED_BG, ACCENT_DEEP)\n    if pct <= -1:\n        return (f\"▼{NBSP}{abs(pct)}{NBSP}% sous l'estimation\",\n                GREEN_FG, GREEN_BG, GREEN_FG)\n    return (f\"≈{NBSP}Prix aligné sur l'estimation\", INK2, SURFACE, INK)\n\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche de la propriété `uid` — reflète tous les blocs de la\n    fiche web ; None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"features\"] = json.loads(l.get(\"features\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    try:\n        l[\"vraiprix\"] = json.loads(l[\"vraiprix\"]) or None if l.get(\"vraiprix\") else None\n    except (ValueError, TypeError):\n        l[\"vraiprix\"] = None\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n        (uid,)).fetchall()]\n    quartier = None\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n    if geo:\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        try:\n            quartier = qmod.fiche_quartier(\n                lat, lng, l.get(\"city\") or \"\",\n                dauid if dauid and dauid != \"hors-zone\" else None)\n        except Exception:\n            quartier = None\n    try:\n        reg = json.loads(\n            (db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n        src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]),\n                        l[\"source\"])\n    except Exception:\n        src_name = l[\"source\"]\n    con.close()\n\n    # --- enrichissements (mêmes modules que les blocs de la fiche web) -------\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    d = l[\"details\"]\n    vp = l.get(\"vraiprix\")\n    location = (d.get(\"transaction\") == \"location\")\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Immo-Ka — {l.get('address') or l.get('title') or uid}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche propriété\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche propriété · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- héro : prix + pastille verdict Vrai-Prix -----------------------------\n    prix_txt = (_fmt_money(l[\"price\"]) + (f\"{NBSP}/ mois\" if location else \"\")) \\\n        if l.get(\"price\") else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    verd = _verdict_vraiprix(l.get(\"price\"), vp)\n    if verd:\n        txt, fg, bg, stk = verd\n        bx = s.pilule(bx, y - 22, txt, fg, bg, stk)\n    if l.get(\"property_type\"):\n        s.pilule(bx, y - 22, l[\"property_type\"], ACCENT, INK)\n    y -= 44\n\n    # --- titre + adresse -------------------------------------------------------\n    titre = l.get(\"address\") or l.get(\"title\") or \"\"\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, titre[:80])\n    y -= 14\n    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés (mêmes specs que le héro du site) --------------------------\n    chips = []\n    if l.get(\"bedrooms\") is not None:\n        chips.append(f\"{l['bedrooms']}{NBSP}ch.\")\n    if l.get(\"bathrooms\") is not None:\n        chips.append(f\"{l['bathrooms']}{NBSP}sdb\")\n    if l.get(\"powder_rooms\"):\n        chips.append(f\"{l['powder_rooms']}{NBSP}s. d'eau\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"lot_sqft\"):\n        chips.append(f\"Terrain {round(l['lot_sqft']):,}\".replace(\",\", NBSP)\n                     + f\"{NBSP}pi²\")\n    if l.get(\"year_built\"):\n        chips.append(f\"Construit en {l['year_built']}\")\n    if l.get(\"price\") and l.get(\"area_sqft\") and l[\"area_sqft\"] > 200:\n        chips.append(f\"{round(l['price'] / l['area_sqft'])}{NBSP}$/pi²\")\n    if l.get(\"mls\"):\n        chips.append(f\"MLS {l['mls']}\")\n    if l.get(\"first_seen\"):\n        dom = max(0, int((time.time() - l[\"first_seen\"]) // 86400))\n        chips.append(f\"{dom}{NBSP}j sur Immo-Ka\")\n    x = M\n    for chip in chips[:8]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes ---------------------------------------\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- baisse/hausse de prix (bandeau, comme le site) -------------------------\n    ph = [p for p in price_history if p.get(\"price\")]\n    if len(ph) >= 2 and ph[0][\"price\"] != ph[1][\"price\"]:\n        y = place(y, 34)\n        de, a = ph[1][\"price\"], ph[0][\"price\"]\n        bas = a < de\n        c.setFillColor(GREEN_BG if bas else RED_BG)\n        c.setStrokeColor(GREEN_FG if bas else RED_FG)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 20, PAGE_W - 2 * M, 24, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 9)\n        c.setFillColor(GREEN_FG if bas else RED_FG)\n        c.drawString(M + 10, y - 12,\n                     f\"{'▼' if bas else '▲'} Prix passé de {_fmt_money(de)} à \"\n                     f\"{_fmt_money(a)} le {_fmt_jour(ph[0]['ts'])}\")\n        y -= 34\n\n    # --- description | inclusions (2 colonnes, comme le site) -------------------\n    col_g = PAGE_W * 0.56 - M\n    feats = [str(f) for f in (l.get(\"features\") or []) if f]\n    desc = (l.get(\"description\") or \"\").strip()\n    if desc or feats:\n        besoin = 30 + max(\n            s.nb_lignes(desc, col_g, max_lignes=26) * 11.5 if desc else 0,\n            min(len(feats), 18) * 12)\n        y = place(y, min(besoin, 340))\n        y_desc = y\n        if desc:\n            y_desc = s.titre_section(y, \"Description\")\n            y_desc = s.paragraphe(M, y_desc, desc, col_g, max_lignes=26)\n        y_incl = y\n        if feats:\n            xr = M + col_g + 24\n            y_incl = _titre_droite(c, y, xr, \"Inclusions et caractéristiques\")\n            c.setFont(\"Helvetica\", 8.5)\n            for f in feats[:18]:\n                c.setFillColor(ACCENT_DEEP)\n                c.drawString(xr, y_incl, \"✓\")\n                c.setFillColor(INK2)\n                c.drawString(xr + 12, y_incl, f[:42])\n                y_incl -= 12\n            if len(feats) > 18:\n                c.setFillColor(INK3)\n                c.drawString(xr + 12, y_incl, f\"+ {len(feats) - 18} autres…\")\n                y_incl -= 12\n        y = min(y_desc, y_incl) - 16\n\n    # --- caractéristiques (details, mêmes exclusions que le site) ---------------\n    dets = [(str(k), str(v)) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and str(v).strip(\"-— \") != \"\" and not isinstance(v, (dict, list))]\n    if dets:\n        col_w = PAGE_W - 2 * M\n        besoin = 40 + sum(\n            s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4) * 11 + 3\n            for _, v in dets)\n        y = place(y, min(besoin, 420))\n        y = s.titre_section(y, \"Caractéristiques\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"DÉTAIL\")\n        c.drawString(M + 180, y, \"VALEUR\")\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 12\n        for i, (k, v) in enumerate(dets):\n            nl = s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4)\n            y = place(y, nl * 11 + 6)\n            if i % 2 == 0:   # zébrage éditorial\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - (nl - 1) * 11 - 3, col_w + 6, nl * 11 + 2,\n                       stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, k[:38])\n            y = s.paragraphe(M + 180, y, v, col_w - 190, taille=8,\n                             interligne=11, max_lignes=4)\n            y -= 3\n        y -= 10\n\n    # --- pièces (details.pieces) -------------------------------------------------\n    pieces = d.get(\"pieces\") or []\n    if isinstance(pieces, list) and pieces:\n        rows = pieces[:24]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Pièces\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"PIÈCE\", 0), (\"NIVEAU\", 190), (\"DIMENSIONS\", 330),\n                          (\"REVÊTEMENT\", 430)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            if i % 2 == 0:\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - 3, PAGE_W - 2 * M + 6, 12, stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, str(p.get(\"nom\") or \"—\")[:36])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M + 190, y, str(p.get(\"niveau\") or \"—\")[:24])\n            c.drawString(M + 330, y, str(p.get(\"dimensions\") or \"—\")[:18])\n            c.drawString(M + 430, y, str(p.get(\"revetement\") or \"—\")[:22])\n            y -= 12\n        y -= 10\n\n    # --- analyse Vrai-Prix (valeur, jauge, rôle foncier) -------------------------\n    if vp and vp.get(\"value\") is not None:\n        role = [(lab, vp.get(k)) for k, lab in\n                ((\"valeur_role\", \"Évaluation au rôle\"),\n                 (\"valeur_terrain\", \"Valeur du terrain\"),\n                 (\"valeur_batiment\", \"Valeur du bâtiment\"))\n                if vp.get(k)]\n        besoin = 96 + (56 if vp.get(\"low\") is not None else 0) \\\n            + (62 if role else 0) + 18\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Analyse Vrai-Prix\")\n        val_txt = _fmt_money(round(vp[\"value\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, val_txt)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(val_txt, \"Helvetica-Bold\", 17) + 8,\n                     y - 10, \"— valeur marchande estimée par le modèle Vrai-Prix\")\n        meta = []\n        if vp.get(\"confidence\"):\n            meta.append(f\"confiance {vp['confidence']}\"\n                        + (f\" ({vp['confidence_pct']}{NBSP}%)\"\n                           if vp.get(\"confidence_pct\") else \"\"))\n        if vp.get(\"annee_construction_role\"):\n            meta.append(f\"construction {vp['annee_construction_role']} (rôle)\")\n        if vp.get(\"superficie_terrain_role_m2\"):\n            meta.append(\n                f\"terrain {round(vp['superficie_terrain_role_m2']):,}\".replace(\",\", NBSP)\n                + f\"{NBSP}m² (rôle)\")\n        if meta:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK3)\n            c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        verd = _verdict_vraiprix(l.get(\"price\"), vp)\n        if verd:\n            txt, fg, bg, stk = verd\n            s.pilule(M, y - 48, txt, fg, bg, stk)\n        y -= 58\n        lo, hi = vp.get(\"low\"), vp.get(\"high\")\n        if lo is not None and hi is not None and hi > lo:\n            larg = PAGE_W - 2 * M\n            base = y - 22\n            span = hi - lo\n            c.setFillColor(ACCENT_SOFT)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.roundRect(M, base, larg, 12, 6, stroke=1, fill=1)\n\n            def _pos(v):\n                return M + larg * max(0.02, min(0.98, (v - lo) / span))\n\n            px = _pos(vp[\"value\"])\n            c.setStrokeColor(ACCENT_DEEP)\n            c.setLineWidth(2.5)\n            c.line(px, base - 3, px, base + 15)\n            c.setFont(\"Courier-Bold\", 6.5)\n            c.setFillColor(ACCENT_DEEP)\n            c.drawString(min(max(px - 30, M), PAGE_W - M - 100), base + 18,\n                         f\"ESTIMATION · {_fmt_money(round(vp['value']))}\")\n            if l.get(\"price\") and lo * 0.7 <= l[\"price\"] <= hi * 1.3:\n                ax = _pos(l[\"price\"])\n                c.setStrokeColor(INK)\n                c.setLineWidth(2)\n                c.line(ax, base - 3, ax, base + 15)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(INK)\n                c.drawString(min(max(ax - 30, M), PAGE_W - M - 116), base - 12,\n                             f\"PRIX DEMANDÉ · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 24, _fmt_money(round(lo)))\n            c.drawRightString(PAGE_W - M, base - 24,\n                              _fmt_money(round(hi)) + \" — fourchette d'estimation\")\n            y = base - 36\n        if role:\n            tw = (PAGE_W - 2 * M - (len(role) - 1) * 10) / len(role)\n            for i, (lab, v) in enumerate(role):\n                s.tuile(M + i * (tw + 10), y - 44, tw, 38,\n                        _fmt_money(round(v)), lab)\n            y -= 56\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Estimation automatisée Vrai-Prix (vrai-prix.ca) \"\n                               \"croisée au rôle d'évaluation foncière — ne \"\n                               \"remplace pas une évaluation professionnelle.\")\n        y -= 16\n\n    # --- historique de prix -------------------------------------------------------\n    if len(ph) >= 2:\n        rows = ph[:8]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Historique du prix demandé\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"DATE\", 0), (\"PRIX AFFICHÉ\", 190), (\"VARIATION\", 330)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            prec = rows[i + 1][\"price\"] if i + 1 < len(rows) else None\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, _fmt_jour(p[\"ts\"]))\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M + 190, y, _fmt_money(p[\"price\"]))\n            if prec and p.get(\"price\"):\n                delta = p[\"price\"] - prec\n                c.setFont(\"Courier-Bold\", 7.5)\n                c.setFillColor(GREEN_FG if delta < 0\n                               else (RED_FG if delta > 0 else INK3))\n                sig = \"▼\" if delta < 0 else (\"▲\" if delta > 0 else \"=\")\n                c.drawString(M + 330, y,\n                             f\"{sig} {_fmt_money(abs(delta))}\" if delta else \"=\")\n            y -= 12\n        y -= 10\n\n    # --- risque d'inondation (BDZI) ------------------------------------------------\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\" if sev\n                     else \"En zone inondable\", RED_FG, RED_BG, RED_FG)\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     AMBER_FG, AMBER_BG, AMBER_FG)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_FG, GREEN_BG, GREEN_FG)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z.get('type') or 'Zone'} — à {z.get('distance_m')} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- qualité de l'air (RSQAQ) ---------------------------------------------------\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"de la propriété — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = (f\"{m_['moyenne']:.1f}\".replace(\".\", \",\")\n                   if m_.get(\"moyenne\") is not None else \"—\")\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- coût d'électricité (Hydro-Québec) -------------------------------------------\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += (f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP)\n                      + f\"{NBSP}kWh/an\")\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- commerces et transport --------------------------------------------------------\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:12]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- essence à proximité --------------------------------------------------------------\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- le quartier -------------------------------------------------------------------------\n    if quartier and (quartier.get(\"demographie\") or quartier.get(\"proximite\")\n                     or quartier.get(\"chaleur\") or quartier.get(\"crime\")):\n        demo = quartier.get(\"demographie\") or {}\n        prox = quartier.get(\"proximite\") or {}\n        prox_aff = [(k, lab) for k, lab in PROX_LABELS\n                    if isinstance(prox.get(k), (int, float))]\n        besoin = 44 + (94 if demo else 0) + len(prox_aff) * 14 + 44\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Le quartier\")\n        c.setFont(\"Helvetica\", 7.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"Secteur immédiat de la propriété (aire de diffusion \"\n                           \"du recensement 2021, ± 500 habitants).\")\n        y -= 14\n        if demo:\n            tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                      (\"Ménages locataires\",\n                       f\"{round(demo['pct_locataires'])}{NBSP}%\"\n                       if demo.get(\"pct_locataires\") is not None else \"—\"),\n                      (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                      (\"Âge médian\",\n                       f\"{round(demo['age_median'])} ans\"\n                       if demo.get(\"age_median\") is not None else \"—\"),\n                      (\"Français à la maison\",\n                       f\"{round(demo['pct_francais'])}{NBSP}%\"\n                       if demo.get(\"pct_francais\") is not None else \"—\"),\n                      (\"Diplôme universitaire\",\n                       f\"{round(demo['pct_univ'])}{NBSP}%\"\n                       if demo.get(\"pct_univ\") is not None else \"—\")]\n            tw = (PAGE_W - 2 * M - 20) / 3\n            for i, (lab, val) in enumerate(tuiles):\n                s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                        tw, 38, val, lab)\n            y -= 34 + 44 + 16\n        for k, lab in prox_aff:\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(ACCENT)\n            c.roundRect(bx0, y - 1, max(3, bw_ * v), 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        if prox_aff:\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, y, \"Accessibilité 0–100 — mesures de proximité de \"\n                               \"Statistique Canada.\")\n            y -= 12\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch.get(\"classe\", 5) <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_FG, GREEN_BG, GREEN_FG))\n            elif ch.get(\"classe\", 5) >= 7:\n                ec = (f\" (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\")\n                      if ch.get(\"ecart\") is not None else \"\")\n                badges_q.append((f\"Îlot de chaleur{ec}\",\n                                 AMBER_FG, AMBER_BG, AMBER_FG))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"])\n                        / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} \"\n                             \"la moyenne canadienne\", INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m \"\n                             \"(12 mois)\", INK2, SURFACE, INK))\n        if badges_q:\n            x = M\n            for txt, fg, bg, stk in badges_q:\n                x = s.pilule(x, y - 8, txt, fg, bg, stk)\n            y -= 26\n        y -= 6\n\n    # --- références : QR + courtier + source ------------------------------------------------\n    y = place(y, 140)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.immo-ka.com/propriete/{uid}\",\n                     box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    yy = y - 14\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    courtier = l.get(\"broker_name\") or d.get(\"Courtier inscripteur\")\n    if courtier:\n        ligne = f\"Courtier : {courtier}\"\n        if l.get(\"broker_phone\"):\n            ligne += f\" · {l['broker_phone']}\"\n        c.drawString(M + 82, yy, ligne[:95])\n        yy -= 13\n    c.drawString(M + 82, yy, f\"Agence : {src_name}\")\n    yy -= 14\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Annonce originale :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy,\n                 (l.get(\"url\") or d.get(\"listing_origin_url\") or \"\")[:90])\n    yy -= 14\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Fiche à jour en ligne (scannez le code) :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")\n    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n","structuredPatch":[{"oldStart":938,"oldLines":12,"newStart":938,"newLines":11,"lines":["     c.setFillColor(ACCENT_DEEP)","     c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")","     y -= 90","-    c.setFont(\"Helvetica\", 6.5)","-    c.setFillColor(INK3)","-    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"","+    s.paragraphe(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"","                        \"rôle d'évaluation foncière, Statistique Canada (2021), \"","                        \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"","-                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")","+                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\",","+                 PAGE_W - 2 * M, taille=6.5, couleur=INK3, interligne=9)"," ","     s.pied(_PIED_FICHE, page[0])","     c.save()"]}],"userModified":false,"replaceAll":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"27053375-8a01-482e-bbdd-5ecf40566fa2","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJrMkVePaohgjDfd7Bg","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2930,"cache_read_input_tokens":148946,"cache_creation":{"ephemeral_5m_input_tokens":2930,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"8720d316-7d95-4f61-ab07-a5fef297eb9c"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJrMkVePaohgjDfd7Bg","type":"message","role":"assistant","content":[{"type":"text","text":"Je régénère, plus un 3e test — une vente avec verdict Vrai-Prix comparable (pastille + marqueur PRIX DEMANDÉ)."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2930,"cache_read_input_tokens":148946,"cache_creation":{"ephemeral_5m_input_tokens":2930,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"744da9f5-99a6-45d6-aa56-2cea51bb18d8"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJrMkVePaohgjDfd7Bg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EFbMbfu4BSiSdBJ7PoqdVV","name":"Bash","input":{"command":"cat > /tmp/testpdf2.py <<'EOF'\nimport json, time, pathlib\nfrom immoka import db, pdffiche\ncon = db.connect()\nr = con.execute(\"\"\"SELECT uid, price, CAST(json_extract(vraiprix,'$.value') AS REAL) v\n  FROM listings WHERE active=1 AND published=1 AND price>100000 AND vraiprix LIKE '%value%'\n  AND lat IS NOT NULL AND ABS(price - CAST(json_extract(vraiprix,'$.value') AS REAL))\n      < 0.15*CAST(json_extract(vraiprix,'$.value') AS REAL)\n  AND json_extract(details,'$.pieces') IS NOT NULL LIMIT 1\"\"\").fetchone()\ncon.close()\nuid_vente = r[\"uid\"] if r else None\nprint(\"vente choisie:\", uid_vente, r[\"price\"] if r else None, r[\"v\"] if r else None)\ntests = [(\"domicilia:17516934\", \"riche\"), (\"remax_quebec:17719804\", \"pauvre\")]\nif uid_vente: tests.append((uid_vente, \"vente\"))\nfor uid, nom in tests:\n    t0 = time.time()\n    data = pdffiche.fiche_pdf(uid)\n    dt = time.time() - t0\n    p = pathlib.Path(f\"/tmp/fiche_{nom}.pdf\")\n    p.write_bytes(data)\n    print(f\"{nom}: {len(data)/1024:.0f} Ko en {dt:.1f}s\")\nEOF\nPYTHONPATH=. .venv/bin/python -P /tmp/testpdf2.py","description":"Choisir une vente comparable et régénérer les 3 PDF","timeout":300000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2930,"cache_read_input_tokens":148946,"cache_creation":{"ephemeral_5m_input_tokens":2930,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"2a3a7006-c205-414b-b0d6-820c77423755"}}
{"type":"permission_request","requestId":"f2b02c70e736e3c6","tool":"Bash","input":{"command":"cat > /tmp/testpdf2.py <<'EOF'\nimport json, time, pathlib\nfrom immoka import db, pdffiche\ncon = db.connect()\nr = con.execute(\"\"\"SELECT uid, price, CAST(json_extract(vraiprix,'$.value') AS REAL) v\n  FROM listings WHERE active=1 AND published=1 AND price>100000 AND vraiprix LIKE '%value%'\n  AND lat IS NOT NULL AND ABS(price - CAST(json_extract(vraiprix,'$.value') AS REAL))\n      < 0.15*CAST(json_extract(vraiprix,'$.value') AS REAL)\n  AND json_extract(details,'$.pieces') IS NOT NULL LIMIT 1\"\"\").fetchone()\ncon.close()\nuid_vente = r[\"uid\"] if r else None\nprint(\"vente choisie:\", uid_vente, r[\"price\"] if r else None, r[\"v\"] if r else None)\ntests = [(\"domicilia:17516934\", \"riche\"), (\"remax_quebec:17719804\", \"pauvre\")]\nif uid_vente: tests.append((uid_vente, \"vente\"))\nfor uid, nom in tests:\n    t0 = time.time()\n    data = pdffiche.fiche_pdf(uid)\n    dt = time.time() - t0\n    p = pathlib.Path(f\"/tmp/fiche_{nom}.pdf\")\n    p.write_bytes(data)\n    print(f\"{nom}: {len(data)/1024:.0f} Ko en {dt:.1f}s\")\nEOF\nPYTHONPATH=. .venv/bin/python -P /tmp/testpdf2.py","timeout":300000,"description":"Choisir une vente comparable et régénérer les 3 PDF"}}
{"type":"permission_resolved","requestId":"f2b02c70e736e3c6","allow":true,"always":false}
{"type":"claude","event":{"type":"system","subtype":"task_started","task_id":"b346sp0kc","tool_use_id":"toolu_01EFbMbfu4BSiSdBJ7PoqdVV","description":"Choisir une vente comparable et régénérer les 3 PDF","task_type":"local_bash","uuid":"ee748bb3-2ae0-414e-9578-ab38c8265f77","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EFbMbfu4BSiSdBJ7PoqdVV","type":"tool_result","content":"vente choisie: sutton:13714330 509999.0 513500.0\nriche: 684 Ko en 30.7s\npauvre: 5 Ko en 0.0s\nvente: 333 Ko en 1.1s","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"db5a4596-fc7e-4058-8684-3255da9c6bce","timestamp":"2026-08-24T21:48:17.632Z","tool_use_result":{"stdout":"vente choisie: sutton:13714330 509999.0 513500.0\nriche: 684 Ko en 30.7s\npauvre: 5 Ko en 0.0s\nvente: 333 Ko en 1.1s","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"task_notification","task_id":"b346sp0kc","tool_use_id":"toolu_01EFbMbfu4BSiSdBJ7PoqdVV","status":"completed","output_file":"","summary":"Choisir une vente comparable et régénérer les 3 PDF","uuid":"81e35752-3cb4-4fdb-8153-54a2ae186bca","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"43451bd4-35bd-4566-9af1-feab4b10aa2d","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNJuksdPoy2te998UesH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011S2ha85iaDvFy8SviyUdSa","name":"Bash","input":{"command":"rm -f /tmp/pdfpages/*.png && pdftoppm -png -r 72 /tmp/fiche_vente.pdf /tmp/pdfpages/vente && pdftoppm -png -r 72 /tmp/fiche_riche.pdf /tmp/pdfpages/riche && pdftoppm -png -r 72 /tmp/fiche_pauvre.pdf /tmp/pdfpages/pauvre && ls /tmp/pdfpages/","description":"Convertir les 3 PDF en PNG page par page"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":891,"cache_read_input_tokens":151876,"cache_creation":{"ephemeral_5m_input_tokens":891,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"5b99d6a6-e8fe-441f-a244-a5ef7f195d3e"}}
{"type":"permission_request","requestId":"4d9b850aefaca7a1","tool":"Bash","input":{"command":"rm -f /tmp/pdfpages/*.png && pdftoppm -png -r 72 /tmp/fiche_vente.pdf /tmp/pdfpages/vente && pdftoppm -png -r 72 /tmp/fiche_riche.pdf /tmp/pdfpages/riche && pdftoppm -png -r 72 /tmp/fiche_pauvre.pdf /tmp/pdfpages/pauvre && ls /tmp/pdfpages/","description":"Convertir les 3 PDF en PNG page par page"}}
{"type":"permission_resolved","requestId":"4d9b850aefaca7a1","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011S2ha85iaDvFy8SviyUdSa","type":"tool_result","content":"pauvre-1.png\nriche-1.png\nriche-2.png\nriche-3.png\nvente-1.png\nvente-2.png\nvente-3.png\nvente-4.png","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f61c7721-ee93-4bca-a633-0803552a4ba4","timestamp":"2026-08-24T21:49:14.075Z","tool_use_result":{"stdout":"pauvre-1.png\nriche-1.png\nriche-2.png\nriche-3.png\nvente-1.png\nvente-2.png\nvente-3.png\nvente-4.png","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"a12b4983-4e96-490c-99f8-a51a0979b885","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
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6D0SRXKsgMLe3tLe1tMybnDFtsR3dXb1+/RCQCgJ6+vlPnzrFZLDqd3tPXHxcVDQABfn7dvT3hIaFqjaa1o8NO2Gk0GgAUlJTojYao8IihZcoVylPnzmaNT+PzeDGUKDJRwOMF+PppdNoR6mOz27f8sGNmzpSQwECXRcEBgfklxWwWKzIsbOSG8vby5F6IxLGRUS71wWAJAHD0+PGy8vK5s+eIxaIN32yqrKoSCARzZ8+eO3sWQRC7du85ePiQSqUODg669+67oyIjf+vgGwyff/GFm9TtnrvvOnj48O69+1QqVXRU1EMP3O/l6dne0bFpy9aauloOmzMxO/uuO+8gjxVnxSWl27//rrau3m6zhYeH3bpoUXZW1uXU+XzPkrCzKdT3PIKzOMJBm/UZWXO5ST9sfg8a412P4CTWbx+5lSAO6AZWKju1hB2/UBD6u4qJiGCz2UPT+TzeoErl6e4OAGSfLMjfPzUx6dipU2SGkMBAHy+vlra2kKCgoavTqLSo8HChQGAwGn28vAL9/VvbOygUCp/H1Wi1YpFIq9PyuDwAoFAoXA5nxqTJZO92TFLS8TOnRQLB0DLd3aTjU1LJPqL7hcfe6HS6WCTS6LQj1IdGpd62YIFIKBq2pzEzZ4rFYjlw7Ni8Gd40Gs1qI69euXZDRQKhU+9ZarfbneszWqdhr61geez4iU1btwLA5q3bHLda7du//8Xnny8rL9t/4CCZcjov74cfd/604/u42FhHsPzwo48jwsPrGxs2bz1/t+rJU6d279v75vIVTz37rF5/PnQdOnIkv7Dw4w9XO293zceffLBmDQB4engAQGV19a7dexbffvtbr/+XQqGMXGeyZ8mkUL7wCo9jcQFAY7fVmQ2Xyr9M6p/E4p02qLeqZWq7NZjBvlfoOZcvtQO8pmh3yezm9HQmQug6ZbFYWjra++XyscnJZEp+cXGfXHamID8hJjYpLv7EmdMioVCn149PSe2TyTq6uoL8A/rlMoVS2dDcbDAY++XyfrnMJTjJFYp+uZzL5oxJSgKAxpaW7t7elITEzu7uusbGpLj4MwUFErFIrdZMzsoqLC0dVKm0Op2Xh4dWp+vt72vr7Mgan3bs9KlpEyeVV1U56tPd29vXLyupqFCpVeOSUwR8fmFpSZ9cVtNQHxMROUJ9SFKxZNhGaGhubuvo4HF5UokYAEKDgs8UFIhFQpFQCAC1jQ3Nra29fX0xkZF+Pj71TU19ctm54qK01DENzc0u9RmVD3GUn7PMmTGzuaVl/aefzJw+HQBeXb6CDJZzZs184bnnPNzdt3777ZvvrAQAFpO57MUXFsybZzKZXl3x34OHDy9asGD1+++R5SiVypS0dAAQi8Wvv/afqTk5Pb29jzzxZGNTEwCMHzfutZdfDgsNOZ139vGnnjKZzScPHw4KOn+W4NeDBx954kkvL6/1n3ycnJQEANU1NY8vfbqltfXF559/7OH/u+iMR2dnds4U55T3PYKncMV2gqBSKGVGXTCDJaLRN6r61g72DN1fBlBOByYqbZZ5XdWOE69eNMZ+/7h+q3lWV7VL/raGevyiQehGYDSZWEzm7/46vyIEQZjMZjaLdS3soMlkolKpFquVyzl/itVms1ms1muker/rWrzBJz429pM1a4KDgng83sMPPiiRSADg6aeevG/JEolE4u3t/c+lTwFAVU3N0HXfe/utm+bN4/F44WFhS+5aDAAikeibL7+Ij4vlcDjTpuRMmjgRAKprf1t31YdrAOCN5a+RkRIAYmNiPlr9AQB8tm6dxWJxLr+ouMRlizwKDQCoFMoB3cDDfY1rBrsB4G6hZxRjmKvfViAWdNXc39vgfIlSZ7cNez0cIXTjYLNYf26kBAAKhXLthCIWi8VgMByREgBoNNr1Eimv0WA5c8Z08pQ6iTwVOWnCREeKRCwBALVa7bIik8mYMnmy4627mxsAjBszxnkoHIlEDABqtYZ8297eXt/Q4OHu7jwEHQAkxMcHBwWp1Oqq6uqRa0s+Z/mtWvaSvM0CxC6tssSopVMo/3ELGNq4BECPzayx28ex+fN50iVCj6fEPp96hVEoFB1esEQIIQyWl8/by9vlxxEAiESuQ+TYh/TG3N3cnZ9KJJ+oFYmGudRsvxCZGpqaACAqMnLoLT8BAf5wGQ9vcKk0ADjqdOPrG8oOC0HEsLh3Cz2HNvdzEr8jAXHrvMJXuAf+U+J3l9CDAhS4cD8tQmiorp6eS519GXmgGcXAgN5gwAZEf89gedWP4dMZV7yiTqcDAPJMr+sZDKAAwO+eH+VTqACgdxqUoMVi2qzuB4BHRd5+dKZz5kdE3ouFHgM22wp5++KeupmdlRnt5e8oO2G4MYCyMzPxAEU3OKvVWlVbazAah1068kAzzW1tvX199CG/g6+U/eL/TZe3tiH/uTab7XLKuVS2YYvFKzWjH5hu8P3n8/kAIJPJhi7q6e0BAE9PD+fEQdXgsD1L7cVH9heq3hlcsT+D9Yo04LH+Jkf6TJ4EAJbJW4tNut86xDS6S7glSfFWWHTjUQwM1DY0WK1WiVjs4eZW29DAYNAJApgMxoBqkMlkBvj6FZaVhgeHaHU654FmKmtrbDY7lUqJCAnl8Xj5xcU0Go1CgbrGxoQLt82Tquvq+mQykVCQmpgEALUNDSMMEFNRU6NSqzRa3aTMTC6H09Dc1NXTy2DQs8anGU2mk3l5XA6by+WmJiQCQFdvT0NTs6eHO/l0oLOBwcHCslIAmJyZxWAw8kuKLRaL3mDIycp26R4MLdZoMv2wZ/dtCxYyr2SEuVPnzolFwpjIqPziYiaDodFpQwODREJheXWVUChUqVSJsXEarba5rXVscgrP6VqVTq8vqaiwWCwsFjNj7LjqujomkxkRGlpUVubh7t7e2REdESERiXPPnU1NSDSZTDWNDVQKNSUhgXfx3A91TY3dvb2xkZFVtXUJMTECgaCgpJhKpWaNTxtaW5VaXVVXa7VaxSJRYmxcQ3NTn0zGZrFSEhJrGxqcKxDo54c9y1EQExUNANU1NYaLz9V0dXc3NDaxmMy4mBjn9KE3+HCH9CwBwEQQbys7ASCNI5jP+y3mudHoANBjveimobk86bA9S4RuwH5kfnFx+pgxGWPHEoTdx8sLADLGjouPiVFp1F4enhU1NXa7XSQQSsVil4FdPN3dA/380lLHFJWXcTkcDps9JjExKS7efPE9egAQGxVlJ+xkpASAkQeICQsOzk5LD/Dz7ZfLAKC1o2NyVhadTtdotQwGY1JGRnZaendvL9lxrKqtzRw3bmikBIDK2trstPToiIjm9jYAGBhUZY1PE/D5Q88SuxQLAJU1NcEBAcQlviIuNWIOhUKJDAtvbG4OCw4WCQVAgFQi0Wi1fB4/2D+ATqfrDQaRUMhgMFyCHI/LzU5LA4DMceMpFAqTyWhobjKaTA3NTSKBgEKhtrS1t3d1qdRqHpdbWFaaPT5tbFLS6fxzLhWICgsHAlRqja+3t4e7O5vFEotEBAG9/f1D+9xnCgrGp6RmjB03qFbLlQqZQpGdlu7j5V1SWeFSgdE6OG/0YOnj4z02NVWt0ZCPrDhs3LQZAObOmcPhcEZYnUOhUimUYUNdnlFzQDcAAM9KfCUXphwpNekA4HmpnweNQQeIZXJWugdP44kBwIw3+KAb3qBKJRGLaDSaxWoNCw4hO1X5JcUyuTwhJhYA4qOjy53uuSMHdvFwcyPv5qttbDidf87X28fRKaysqRlz4S73YZFhxqWci34NczgGo7GzuzvA1w8uXJfhcXk6vZ5GpbJYrLKqyqiwcABQazRypbKorOzUuXMAoNFq5QqFXKFQDgwAgMFo5LDZfC5Pq9UBgLenx0+/7JMpFEKBwGgykTnlCoXNZnMpVq5U8nhcBn34PiU5Yk5rR8fQRRKRqLmttbahXjk4IBIIqVRKa0dHa0eHyWwSCYVWq7Wrt6expdn5bsrhf8HYbF4envnFRWHBIeQwAhQKRTGgFAtFKo1GLBJTqVQ2m221Wm12u1x5fkeMRiPZ1a6prwsPCTnfvVapxqek1Dc1uWxCo9UKBXw6nc5gMCamZ/T29/v7+AKAn49Pv0w2tAIYLEfHf15exmAw3nnv/VeXrzh89NjhI0dfWPby+q++EolE/3722ZHXNRD2uZ3Vt3XXGoYLde8ru/brBspMupk8MZnyrrKzy2KazBUd8I/LD0re4hMVxeS8JGsFgBk8yTKpP34c6EbG5XLVGo3dbm/v7Dx88oTdbmezWONTx/h4ebV3dQEAg87wdHd3dLnc3aQT0jP0BoNGqwWA6PAIHpfnGBEtISYmITa2pa3tUpvT6nSVNTVDy7nof9xoPJmXNyE9g7wHkHy4Q2/Qk49AVNXVAkBEaCgA0Gg0Dze3jHHj1Bo1AHT39lbX11fX19c2NgAAm8UymUx6g4HsxvXJZDfPmevn7aMcGBhUqcic1fX15NVZ52J7+/t6+/vbujqHxhi4MGJOoP8wXx0ikbC2vmF86piq2lqRUMhhczRajYDPM5pMZNRnMhhmi4V64YpvT1+vxTrMuJs2my0kMFAkFPJ4XJvNRhBEWEiIm1gCQHA5HMd4LwBgMZsdO6IYGAAAb0/PrPFp5PlnxcCAXm8gxxYwmc0AoDcYyCH9OGy2o+XtdjuPy9PqdGQGDpvjUoHROjhxbFhISkzc+MX6f7344qatWx39y+ioqNXvvevj4/27q/fYzHCJj09ht74sv+gftcNqvr2nbg5PEsHkWAiixKQ9oVdZAdyUXdN5YtPFEdflaRaE/v7BksPxdPfIPXuWx+WKRaJ+ubxPLjtbWGizWYUCIZVK7ezuysmeUFlbAxSKy8AujS0tLCZrUmbmwePH6HRaZ0+30WSi0+kmk8lloJma+vp+ufx0fr7ZYnaXuo08QMyxU7l2O3Hq3LnI0NCQoCA/b58zBfkWi0UoEPTL5YWlpf6+vgeOHZuZkyPg81lMZn5JMRmto8LDydHAz5/7jYw8nZ9vtdkmZmSQe3quqGhANRgfE8NkMLw9f7tz3qXY+OgYrU534NjRS80Gf6kRcyQiMZ1O9/HyIgiCyWRKxCK1VhscGNhXVERm8Pb0GhgctNqsZETce/DgjMk5QQEBOr2+rKqyTy47V1Q0PjW1s7vbarUmxyccOHZMo9X19PUGBwZ6eXrml5YMDA66SaVkS8ZERrHZ7InpGY4K1Dc1KZRKcoS/9s7O8ppqL3ePcSkpB48fKygpyU5Lq66va21vv2X+TUwm08/H9/CJE0wWk8fhpiYmHj9zWqfXD6hUaampeYUFjgoYjSZyXMC/3iiP4HPtMJvNBUVF7R0dTCYzMiIiPjZ22AeEZ86b/5fNybxm1aqFN83HjwbdaKxWq8Vq5Qw3jKrj3CONSh3F7wpy3oxhGY1G9iVqbrFaaVSq47SnwWhkMZlU6rVyeo8giKsbFcFisVCpVNpV3XXsvFGbzWa32x3TZP4vhjTCnuWfgMlkZmVkZGVkjJztL4uUCN2w6HT6yM+P0UY1wIwQKQGAfekYz7h4p0b4NTA6PaerDUt/ZBZo543SaDTniHutDe6D1yyvzP333IONgBBCNxoMllfmjttu/cu2JRQKsMERQgiD5fUnOioqOirqL9iQh7v7xOxsbHCEEMJgeV06sHfPD99uv2nevP/pVl556aWrHvYPIYTQnwvvhv2jrFar82DrvX19XV3djrelZWWOWawBYPfevZfTp7x78eInH3sUgyVCCGGwRNDR2el4rdPpauvqo6Mi/5rTvAghhDBYIoQQQn+aUTvRFxQRia2PEELoirQ11I/KdvEGH4QQQuh34GlYhBBCCHuWCCGEEAZLhBBC6H/qGr3BZ7Qu4SKEEELXVs+yOCh52L+R13rx5VeiEhL7+vqubqP/fO75jZs3/4l7kXfuXFRCokwud6R0dHZGxsWXV1RczuqPL126Zdv2oelj0jMi4uLIv7EZmS+9+h9y5vGh2eobGvE4Rgihv22wvAoGg2H3vn2eHh679uy5uhLMFovN+mfOtZ02bpxIJNr/6wFHyt5f9vv4+CQmJFxWsHzk0ZzJk4amm0ym1e+9dy4391xu7oYv1p86c/rTdeuGZlvzwSo/Xx88jhFC6H/qGh1QLSgictgzsb/8+mtURMSCm+Zv/+77Rx56iEzs6+/fvXfvmJTUn37+GQDuufuuiAsTlH//w48nT51iMBjTpkyZO3uWc1Fbtm2PiowYN3YsAPT09G7Zvv3Zp5fSaLQzZ8/++NMuAJg2JWf2zJlk5q7u7s1bt3b39GRlZN76j0XO87VSqdSF8+fv3f/LPXffRabs279/0cKFAFBSWtrd02O1WptbWv65dOmw9WluaQ60Bvj5+g7dWT6fL5VKAUAqlc6bPaektAwAft6zJzAgIPf06eCgoJvmzSsvr4iOiuLxeJ+tWx8TEz154kQA+GbLlpCg4IkTcBx2hBC6IXuWO37cuWjhwnlz5tQ3NNTU1pKJcrn8gzVr3121akxqqt5gWHL/A3a7HQC2fffd1998c/OCm6ZNyVn++utHjh1zLkquUHy2fj35etfu3aXlZTQaraa29ul/PTd96pRpU3LeevfdPfv2AUB3T89Ni/7B4XBzJk3etGXLf998y6VWixYuyC8o7JfJAKC9o6OisvLmBTcBQFV1zetvvb3t2+/8fP0uVZ/de/eVlpePvNcmk7m4pCQ4KAgADh4+8sTTzzS3tHh6eALAp+vWKRQKAPD29l76z2f7ZbJDR46sXvtRdDSOmYcQQn/3nuWwOjo7i0tKPv/4I7FYnJWZ8eNPu1556cUL4cT0yZo1bm7SqTk58ampMrncy9PTx9v7/XfeiY+LJQjiux0/1Nc3TM3JcZR28003ffL5Z2qNRigQ7Nu///577wGA2rp6NpuVkpzs5ekZHBREBt3P138xa+aMp598AgBSk5Nnzp//4vPPOc+HTs7b9cuvv963ZMm+/b+OHTMmMCCAXMRgMLZt+oac/nvk+lgsFrPZTK5CTsW+7NX/cLlcAKKnt9fH2/uJR98nc6anpX3w7kqXxrl5wU0HDx96/sWXqmtrV775hqeHBx7cCCF0IwbLH3b+xGQyn3vxJQDo7u6pq6t/6d/Pk3FILBK5uUkBQCDgU6lUMupERkR8um5dWXmFUqk0Go3p48c7lxYUFBgbE3voyJHxY8c2NDXNnD4dAGZMm7r/wIGsyTlxsbFTcybfd889AFBTV9fd3b3glvPTPhsMhu6entCQEOfS/nHzwr2/7L9vyZJ9v/yy+M47HOmRERFkDX+3Pl989fXKVasA4J677np9+WsA8OB996UkJwOASCQMDgpylBMbHT1s+7y+fHla9oTJEyeS+4IQQuiGC5YEQezcteuB++6Ni40lU5557vnTZ/LIK3MUCmXoKv/32GPpaWlffPqpt7fX40uXDs2w8Kb5+/bvl8vl06ZM4fP5AECj0T5avdpg0J/Nz/9yw8aWltbV77/HZrFuv/WWu++8k1xLrdH4+/m5FLVg/vx33nu/oKiovqFh7uzZjnTnio1cnwfuu/eO224FAEefNSwsNDVlmHuDh91ZAPhh504fb++z+efaOzocXVuEEEJ/3HVzzfJsfv7AwMDjjz46c/p08m9qTs7On3eNsEprW/uMqdN8fLy7e3rO5RcMzTB/7twzeWd/2PnTP25eSKZ8tXHji6+8IhaLZ82YccvNNysHBgAgOyvz+ImTAoFAKpWeOnPm3gcfGjrTpKeHR2Z6+nMvvDh1So5IKLyK+rDZbKlUKpVKuVzuVbRPbV3dhx99vP7TTxbMn/+vF14kTyAjhBC67nuWqW2ll595x487582Zw2axHClzZ8/65/P/1ul0l1rl4QcffOTJJ0ODgy0Wy9zZszdu2TLn4hti3d3cxo8dW1NXN2nCBDJl8e2337p48byFN0skkrqGhk/XrgGA+++5p7SsbNK06R4eHt3d3es++WTYzS1auPCZ555zXEa9ivpcNbPZ/Mxzz99/zz2xMTEvv/DC9Dlzv/j6a8fdwgghhP6gUR5IfYRxfP6UQXy6e3q0Wl1EeBgA9Pb2eXl5Oj/1AQAvvfKqu7vbv555xpFCPulhMpnCQkOdO3m9vb3KgYHwsDDy7pv/UX0QQghhsPyrg+UI+vr6zubnr3jjzf17dnt5euKhgBBC6FJG+QafURwDdlClOnbixPsr38FIiRBC6JruWV4+xbTb8NO6Um6Hv8dGQAihPw4vmCGEEEIYLBFCCKE/5ro5DYsQQghhzxIhhBDCYIkQQghhsEQIIYQwWCKEEEIYLBFCCCGEwRIhhBDCYIkQQghhsLxWtbe322w2bAeEEMJgiS7ppVf/I1cosB0QQujvZzRH8Ont7X1l+QqXxLUfrHKeRdJgMMxf9I+Vb705JiXFkahSqVZ/9NGp06dNJnOAv/9tt9yyYP48CoVypZswGAyfrV9/5Ogxg9GYlJDw9FNPBgcFObL9sHPn9u93yGSykODg/3vg/uysrGH3QqfTvfHOyr379qk1GjqdPiY15bWXX46LjcVjCyGEsGf5J6hraDh05Ihao3b+IwjCOc8XX3/d0Nho0OsdKVar9a5779u8dVtiQuKihQtNJtPT//rX5198eRWbeOyppZ98vi4xMXH+3DnFpaU3LfpHR2cnueiz9V/864UXRULBLYtuNplMd9//wIFDh4bdxPMvvXTg4ME3/7siOSlp1cqVRqNp8b33yeRyPLYQQgh7ln+CjZs3f77+i7O5J4cuGhwcfGX5irKK8vb2DgDYunGDo2N3+OixBx95ZO0HqxbMnw8Adrv95ttul8lkZ04cv6JNHDl27IGHH3lj+fIldy0GAIVCmT1lyoxpU9esWqVQKDMnT56ak/Pp2jXkJv5x+x29fX2njh2l0WjOhfTLZOMys8hCFt9z78cfrtbqdBOmTH37jdcX3347Hl4IIYQ9yz+qta0tNCTkUkslYvHkCRMnTZjgkl5ZVUWj0ebNmXN+B6jUqMgI+SV6ciNs4pdfD3A4nNtuuYV86+YmzUxPP3TkqM1mO3biuNFovP+eJY5NzJ09u7unp6Ky0qUQhUIBAGKxyJESGBBw/NDBWdOn47GFEEJ/G/RR3HZbW7uXl9e6L78sr6jkcjk5kybPnjmDvPQoFotfX/4aAOzb/+uJ3FzntZY+8fhTjz/m6OF1dnUdO3EyMyPjSjdRVl4WHRXJYjEdmRMTEg4fPdrV1VVaXg4ACQkJToviAaCuviE5Kcm5/JCQELFY/OFHHztf7AwJDsYDCyGEsGf5p3ZqbykAACAASURBVPUsd+7ate3b70wm09n8/MeeeuqJp5+x2+2/U2MqlYyUn63/Yt7Cm3NmzBAKBB+8u/JKN6FQKj09PJ0zkx3EgcFBpVIpEgrZLJbTIjEADAwOuJTPZrHWrHpfoVTOu3nR6by8l197bf+BA1ar9X/UYjabzWAw/DWfjkrtev34d2l1uqved5PJZLFYRsjQ0dlpNpv/lF3T6nR/vBCz2Ux+Fp1d3UaTiUzU6fWXajS1WuN4rdfrL9VQLu1gNBoHVapBlcpoNI7WkYAQGuVg6e/n99D99x898OuXn392/ODB+5Ys2bd//44fd17m6hHh4elpaWNSUhubmp799wvDRtkRNmEwGGm0i3afxWQBAJVGMxiM1IuvTZKLXC5YkiZPnHjyyOEP33/fz9f3bH7+o08+NWv+/N7e3v9Fi3X39Hz/066/5tP5/OsNpgsx4DL9tHdvz5Xv+NGTub39/UVlZU0trZfK09vXd+zUKSaT+afs2oeffvbHC6morjlx5gwAWKyWPft/JRPXbdiov0QMe3PVqkGVinz92tvv6PXDZ3Nph5179n765Vff7dz57pq1m7/7zpHe199Pbh0h9NcYzdOwmzd87XhNo9FeeuHfm7dtO3bi+O233nI5q0+bkjNtSg4ArF770YcffXT85Mkpkydf/ib4PJ5Gq3XOrNPpAMBNKuXxeDqd9uK+iBYApFLpsDURCgQ3L7hpx48/rl39wf5fD7yyfPl7qz9ctfKdq2uW8qoqf19fqUTS0NQs4PPsdntuXh6Px5s2aZIjT3tnZ0FxSWhwcEpiQlNLi4DP9/TwKKusjAoPr66r47DZdrs9LiYGAApLSvg8fmVNTXZGemFxiVgkzM7IIAjiWG5uX39/cmJiTGRkcVkZuYqHu/ux3FMSsWja5MkAUFPf0NjcnD5ubICfX3dPj6MaJrO5s7u7raODQadPmzyZQqGcOntWo9EajSay03Pi9GmtTjc5K1soFDh6Qs6JtfUNpRUVYaEhMZGRJ8+ckcllaWPHcjmclrY2o9FY29AQHxPbJ+tXDgzMmjaNyWD09PXzebz2zs5Af/9hyy8qLWWxWLX19ZOysjzc3Z1ra7FaW9vbW9vb58+adVFXT6M5nnuKz+dNyspy/Aw6V1Rks9nkCsW41NS8/PzYqOjoyAi5QnEsN1cgEEybNIlOp9fU19c3NjIZTAqVAgCDKjWNRiXrNuzOkonZGelnCwtnTZ3a3tnp4e4mFAoKS0oMBmNUZERvX19VTY2/r9+EzAxfb28uh+Ncz5lTp45JTgKAZ158acGcOW0dHa3t7ZMys4IDAvOLisNDQ0RCYW5e3uTsbPw6Q+hv2LO02WzGizsubBZLIBBYLL9zHu/dVR98sGaNc8r0qVMBoGVIv2TkTQQFBbW2tjkvbe/o4HK5Hu7uwUFBZrOlq7vbeREAOF+YJJ3MPfXsv19wnDejUihL7lqckpzU0Nh41S2jVqtPnjkDADt27eLxeN/+uHNCRgaLydzz6/nui1yh2Pr9jvFjUotKS/OLiiuqazq6ugHg5Jk8nV5//NTpkvIKTw8PMvOREyfrGhu8vTzffH9VZHjY2cKijq6u0ooK5cDg5AkTNn/7ncViIVeRSCRr161PGztmYFB1LDcXAFrb21KTEr/Y+A0AOFdDrlB8tWlzRGhYS1t7flHRwaNHOzo7w0KCyysrAWDT9m+ZTGZUePjqzz517JRzotFk2rZjx5SJE2rr6uUKhYe7W2hwcEV1dVtHR2VNza9HjiTGxb27Zg2DTrdabcdO5p4tKCyvqhyXmrpx6zblwMCw5e/Z/2tLW1tiXNzadesJgnCu7eDg4FebNvt6ezs3st1u/2jd+pioSAqF4txj+2rTZjaLNTAwuGn7t0nx8Zu+/dZqtX68/gsylm/+7vv2zs6f9/2SGBdXVlkJAC51G7qzjpKz09Pzi4oB4GxBwcTMTAD4Zvu3ZosZCOLw8RNTJ0+urK2pqqkh28G5qi1tbaUVFb8cOiQSCVUqFbkvMoX8XGGhgM/7avOW3fv3/1knqBFC11ywPHTkSFR8wsncU46UmtrawcHBRKfbaobV2ta2cfMW52+HltYWAPC++NvwdzeRnZnR0dnZ2NTk+PY8fvLkxOxsBoORlZkBAMeOn/jtVOHx4xKJJOXiu3sAQDkw8ONPPzkKcXC7RB+UVFhSsm7DBpc/x9JxqanllVWDKhWXyxHw+dNzcg4dP15aUek4cVdWWWm2mA8dO6bWaCqqq4eWP2/WTA93d8fbqRMnxsfEhAUHR4aHBwUEDKpUkeHhDAZ976+/DgwOmi0WchWTyeTv6xscGHjrwgWTsrIAYPrkyWEhIUChAIBLNZIS4iPCQuNjogdV6qLSsgVz50aGh48bkwoApRUV9Y2Np8+d6++XOS7UOScaDUaRSLjlu+/9fH0D/Pz4XJ5UInHUNjMtLSwkxNfHO23s2KiI8EGVqqC4WK3WHDp2zGyx1Dc2Dls+UCizpk6NDA93k0oGVSqX2ibGx41JTnZuoj6ZrF8uz83La2lrq6ypdaR7eXqmJiVFhIWmJCaEhYSwWMy2jo6QoKBAf/+JmZmNzc0l5eXTJk8KCQqaNnkSALjUbejOOmooEYsZdPrA4GB5VVVqcjIAuLtJp06a5O7mlpKYsGf//o7OLscp3NWffrpi5crevj4AGFSpevv7eVzu80uXuuxLTFSUWCSsqKqeMWUKfpch9Pc8DZuZkeHu5vbqihXvvvVmdHR0Y1PTC8uWiYTCxXf8zuOJi++4fd/+/c8899wzTy31cHfLLyxc8cabvj4+U3MmX9EmFt9xx5cbNvzrhRdXrXxHIBCsXrO2ta3t3bfeBICMtLSkxMT3P/wwLCw0Njp69959O37c+c+lS6lU198WkyZkc7ncV5av+Gj1BwBgtdk2b91WUlr2/jtvj7ALY1NSxjoNSOSCxWJ5enjs2f/rhIxMlVr94+7d/356aWt7R0FxsSNDSmLi3Bkz5AqlTq+rrq0jx6RVDigdJ5wv+ozpdKvN9tsIRwR8/9OumMjIBXPm/Hflu45VWCwW2UVu7+xUqdXkiuTSodVwNAUBBJvN0up0XA5HpdYAAI/HW3L77VQqNS46WiDgk9mcE2k06vxZs7w9vX7cvfv0kMvAdBrduXwAgsViTZk4ISQoqKGp2dvLc9jygSBMZjODwdDrDTabzaW2NKrrVlhMpq+397133mmz2Zx/cNDptAvB93xzMZlMrV4HAHq9nsvhsFls8v4gtUZDfhbOdTuTX3CpFiA7lzt2/Rzg589kMBy1Kigpae/svPfOO3ft2+fI+fSjjxIEQX6OKYmJ5GlYABgYGHDeF6PR2NXdAxRKv1zu6fTzCCH09wmWQoHg6/Xr/vXCC7fddTeZEuDv/9X6dR6/9z+fnZn59JNPfPzZ5/su3FUREx299oMP2Gz2FW3Cy9Pzs7UfPfnPf06dNZv81vvva/9JGz+ezPnJmg8ffOTRO+4+/6jlrf9Y9MSjjwytjEQi+fzjj559/t8ZEydRqdTsnCkEYX/0/x66ZdGiP9I4EzMzP1q/fvGtt9jsdovV+uOePVaLtam1ZWJWJgCMT0394JNPtFpdR1fXvYvvjI2O+mrzloKSYvLr+3IE+Pnl5uXVNTRYrNaqC/0qX29vJpPx5abNPb29/3ffvc75ORyOczUyxo9zXjp3xox1X28IDPAn7+6ZM2P6x198IRQI+Dx+2tixZB7nxJSkpO92/hQdGdnT1zctZ7JWr9v764HQkOBL1Xb29Gmbv/0uKDCgXyZ76uGHhy0fAL7Ztp1Go4UGBwuFwqGN5kIqkXh5enyzfbtarclKTxuhrXy9vYGADVu39fX33zRndkhQ0KqPP+7q7uns7k5KiHep29Cdda7huDGpX23Z8sLTS53L9/bw3LN//7c/7pQp5FqdjjwnMfRn2bC+2f4teRZh/caNy5599jLXQghdhdEcwYc8+dnQ2NjfL3N3d4uKjLz8/3aFQllVXW02mwMC/KMiI696EwaDobi01Ga1JcTHSZxOBgIAQRClZWXKgYGIsLDAwMARNmGxWEpKS//z39efevzx7MwMkUj0JzaR1Wo1Go18Pl9vMDhu/SAIQqFUikUisvNnMpkIghj6c2EEarWGz+cRBGG1WllOD8lotVoOhzP0vt9hq/FbF8dkslqtfB6PfKvX6y1Wq0godM7jnGiz2WQKhVgkIp/PMRqNI1feYrGo1Gp3N7dLlb9i5cpXn39eq9WRN9SMXFuHQZWKxWRyLp3BYWBwkMfjkT1Cm82m0WrFFz5ll7qN0AKXojcYaFQqi8UaubYIoRs3WP6dPPDII6veeccl4qK/xoYtW++/+y5sB4QQBkuEEEJodOBFDoQQQgiDJUIIIYTBEiGEEMJgiRBCCGGwRAghhDBY/in6ZLKRZ3EiabRalUatGBioHzIK3aU0tbaQJWt1uryCgqKyMqvVWt/UlF9STP7p9XqNVnvq3LnCslJyuBzHthpbWhxvLVbr0dxccrsNzc2KgYum9Orp6+u/xCTVANDpNBTt5ahvanIp37lWjqrabLbiivIzBfnkqPH1TU25Z/N6+vpGLry6rq6wtMQxjZTdbi+rqnLOUNvQcOz0KavVqhwcdGlns9lc29CA/1oIIQyWf44Blaqrp4ecWmtQperq7bHb7RaLxWA09Mvl5KBiNputq6dHo9XabLaSiop+hVxvMBiMxkGVSqVRk9/jZBhwlNbU2lLf1MTncn28vJxLBgC1RqNSq11izMm8vCO5uVabDQBOnDkdFREhEYsKy0q9PDyCAwIDfH37+vsZTOaJvDMJsTECPr+ptdWxbklFRZC/v6NKRqMx0N+fxWQCgEarMZtNAKAcHBhQqQDA29OzoqZ62KYwGo35JcU6vd45v8Vq1RsMcqXCUb7Fau3q7SFH6vHx8hLweGqNxmg09sn6nWcoc65qYWmpgMePjYw6mXemo7urTyYbk5RcWFbqGGJebzAYTaZ+udxut8sVCnJOShqNxmAw5MrzjxXVNTW6u0kd9QEAJpMZ4OtrMpvNZjNZH7PF0tvfb7FYmEymYkDpMv8iQghd12gvL3thVDbc0tZWVVdLEERTa6vdZqtpqKdRqaWVVQI+7/DJkxw2+1xRUVhIyLHTpzhsdmVtjYAvaG1vF/D5vX195dXVYqEov6Q4KizcaDKdLSqkADhKs1gsBoORy+G0dnSYTCZHyeEhId/9vItBp9c1NnA4XKHg/NxJ/r6+coUyODCQQafXNNSnxCdwOZzi8vKUhAQel1tZWxsfE0Oj0bp6egwGIwBERURQKRQAsFqt9c3NkWFhFTXV5dXVIoGwoLRUJBA0tbXyuFytTivg8xtbWuQKhUyhUA4ovT29unt7hQKBY7QajVZ7IbJq65oa/Xx8GpqbHfmpVOrB48c5LHa/XEbu8tnCAh6XW1pZIRGLm1pb6XTa2aKiQbVqYHCwV9bv5+MDADq93rmqVXW1yfEJfB6vpLKCx+V6uLl5urvL5HIGgyESCAGgrKqqur7ebDGfOneOxWIWlpZGR0R09fbYbPaI0FByiNSC0pLUhESZQkHWR6/Xt3a0s1isqto6b09PtUYjlUgOnzjB4bALSkuDAgIAKDK53ANHK0UIYc/yD6ppqM8an5aSkBAfE1Pb2DAxPSM+OsZut5vMluCAwISYWF9vb51eR6PRZAp5TESkl4eHWCzy8/YBgKTYOMfcgUNL8/Lw9PX2plIpAOBcstFkEgtFqYlJsVHRA4ODv/1eoNEcY4z7enkfP3P6dEE+OSqe3mDQG/ReHh4Go1GhVESEhtpstrLKivOhTqfjcs6HvaTYOC6HI5VIYiIjxyQlN7Y0k+n1TY0cNpvP5TY0NwOASCBwbNpkNlfW1pCvJWIxn8fz8vBwyR8U4J8QG+vY5eT4BLPZbLPZHTNxEgSRPmbsuOQUR7EuVY2JjDx2KvdkXp7JZAoOCKysqz2dn9/T10cQv7VefExMSnwCn8dLiot3l7r1K+Q9ff1Gk2ngwmTFNquVjJpkfZxblZy0pLm1lcViWi1WGpXW1dMtFAiUTi2MEELXu9Gb/JlCoVKp5Kk/8ksfAOx2O51GpdGoAEChUAgC4qOjmUxWRXWV3vjbzPKOqSEIgjCQ6ReX5sypZBpZMpVCAedY4cTH29vT3d1oNBZXlANAdV1tVHgEAPC4HKlEIhIKA/39q2rPjzxOAXCc/KTTaUChEIQdAGw2K4PBcOxmUEAAAHC5XAAggKDSzv9AYTGZGWPHDW0W5/zkFBxk+XqDobCsdGJ6BuFUeeqFHXckulTV19tnclY2jUZTazUUCmViegaHzT6db3ZzGpaPTqOB0+DdXDZn9tSpzpWyXyjcUR9Hq9IurCUWioICAkQikUQkMpnNNBzUGyGEwfKPS4yNO5p7ksFg+Pv4xkfHHD55UsDnu0ulLkN4V9bWCvh8vcEgFUu0Ol1NfT2DQXf0Ao+cPAkUoFAozqWJhMJzxUXjklMAwLlkx4RTI2htb+/o6hxQqcgw1tXbm5qYBAAcNkfA5+eePavWaNLHjCEzi4RC58ml3aXSorKyc8VFcoViQnoG2bmMjYwsKisjCCLAzxcABlWq8OCQYTdNo1JbO9pd8l/0UdHpFoulrrFRpVFrdToWkzVsOS5VHVCpKmtqqDRqVFg4QRAnz+a5S92YTCaPy738D4vL4Tjf1jT08woLDj504oTNbhtUqaZOmNjb3+/h7ob/XQihv43RHBvWarXa7XYmkwkAFovFarNy2Jyh/UKtTsdmsci+mtVqdY55JrOZyWBQLlxBdJTmnO1SJV+KVqdjsViM4SKrwWhkMhjO4Ty/uDguOtoReAiC0On1HA7HuV9lNBopFAqLxbLZbKfOnZuUmTnsdu12OwFAo1Id+YdtMZvdzmIyLRbLb53X4ThX1WgywYU5SSxWq8lkckwPcpnaOzvNFkt4yG9hfmir2u12nV7P5/EoFMrJvLzMceMu59cJQghhsPz7s1gsPX19LhdQL6VfLmexmORtNdedptbWsODgy2yT7r7eIP8APDwQQhgsEUIIoRsF3oWBEEIIYbBECCGEMFgihBBC/1PXyv2KSo2qpb+7pqOluKkOPxUACPX2SwgKC/MJ8HfzxNZACKHRNfo3+Cg1qme/Xp1Xe35YnOljMvBTAYDmns6m7g4AcBeK31ry+KT4MdgmCCF0gwbLE5VFL23+lCCIZXc/HBMYKrk+H6v4H7HabLJB5dvbviysq5w7NvuNux/jstjYLAghdGMFyxOVRQ9/8tb0MRkvLn6Ig2Hg0g4WnF7+zacZ0Qkbn16OrYEQQn+9UbvBR6lRvbT50+ljMlbc/yRGypHNGJe1+aW382or9uSfxNZACKEbKFg++/VqgiBeXPwQfgaXI8wv8NZJM5/bsEapUWFrIITQDREslRpVXm3Fsrsfxj7l5Xtq0V0A8EvRaWwKhBC6IYJlS383AMQEhrqkn6kq/WjnVue//fm5zhmOlpwzmIyOtwaTcf+53B6lDADa+roV6j9tDsXW3q4DBaeVGtVPuYdHyGaymCuaGwDgs5+/vcySC+uqAKCpu+NAwZWFPTqNNn1MBj5agxBCN0qwrOloAYCh976G+vj/mHto+9FfHH/+7t6Opau+3/jm5nUmi8WRsn7vD77unp/v/h4AzlaXuwnFzqV1ynrLmuqsF+aWqmxpqO9sJV93yfsBQGvQq3VaABjUaorqqzR6nWNdrUHfreinUagKtQoAdEZDRXMDGactVmtxfXWvUg4ALT1dmw7+rDMaZo7PBgClWqXWaWvamq02W31nq2zw/M1TSrWquL5ardOqtJov9u6Qqwa8JW7J4dFkuC2qryJLA4AehaxT1kc+NDJUQmjkvsJTeNQihNANESyLm+qGfZ7SW+p+19S5jrczx2UlhEY43j51813jYhKc8zMZjE5ZLwCUNdWRscehuq1p3Z4dbb3d7377NQB8/NO2kobaw0Vntx7eCwDvf/c1AOTXVuRWFJnM5v9s+FiuGnz164+sTrM2AgCPw02JiFbrtMs3ftLW1/3KV2u1Bv3rmz/rUco+/mlbc09n34BCrdPqDPqPdm4BgJ/PHF25/au8qtL7Vr5cUl/zyldrzRbLgEb9+ubP+wYV//rsPbl6UGvQ9w8qG7s7fjl30m63v/LV2m6F7OOftpU31QHA4x++frTk3Lo93xfVVw1tHyGXBwB6p741Qgihv22wTA2LOlSUN+yiJTNu8hBLAYDNZD2+4E6X0EilUJxTHp53q6+753O33dfQ2TagUb2z7cvKlgZyEZ1Gkw0qzVbLQ3MWAUBTd/uSGfMfX3DH6coS101SwGQ2dcn6HltwB/3iqafpNNqYyLjjpQUCDs9oNjEYjKK6KgadUdfROi9jUrCXb3xIuL+nt6fkt4mOF02cdte0eV5i6e1TZkcGBMtUAwIud2H2VPngoEqn8RRLPSXS2KAwMnNDZ1uoj//8jMn3zVp4uPgsAAR4+twz46YFWVNae7uHNo5arwMAfNoSIYRuiGAZExACAAMa9dBFbCbriQV3AMC9Mxd4iCUjl0OjUlPCY0oba7MSUo6W5L9w54O7zxwnF3FZ7OX3PeHr7vnK12sBwGYnAMBqs7IYTAAgCHBUwGyxvLj4ofExCR/t3Not7x92QwFe3lnxKTdnT40KDFk0Ydo/Jk4/VVE87EVHFoMJFKDTzo8jSBD2M1WlJY01s9OyQ32HTPFIodjsdgAwWcxkCGTS6eR+EWQVL1bRXD93bDYetQghdEMEyxBPXwCoaW8edumMcVkzx2XdOWXO5RRltVkVapWP1MPP3fOtreujA0POd8J0utU7NlW1NIT6BABAakTM+99t+O+mzxZNmEZ2Uld9v/FEWSEAUKnUNT9uKair5LBYIr5g6CZyUsZXNDfsOXN8z5njEr5wZ+7hQ4V5So0q2NuPz+E2dra39XWPUEMJX9Tc3fn98QODGnVZUx2dRj9VUUwuivQP6lHI1u/ZsWH/Tzdl5vzentoOFeWlhkXhUYsQQn+xURvB5741y+u62ncs/+APPj1itlgIgmAxmUMX6U1GjV7ndeEc6YBGzWIyHecwNXqdgMsjX1usVrl6wEMkcfQIXdjsdtmg0kviRqFQCILoH1QKuDyyKIvVSqfRKBefH3ah1uvYDCaNRrNarXQ6nSDsTl1PQq4akAiEl9q0w+odm3acOJD37tdSgQgPXIQQuiGCpVKjmvfGs6kRMSvufxI/ht/V1NW+5O2X3r//6fnjJ2JrIITQX2zURvCRCkRvL3n8UFHeaxs+NuDtnSM6WHB6ydsvZUQnYKRECKEbq2dJwllHRoCzjiCEEAbL83A+y2HhfJYIIYTBcpiQ2dLfXdPRUtxURwCBH0yYt39CUFiYT4C/mye2BkIIYbBECCGErmlUbAKEEEIIgyVCCCGEwRIhhBDCYIkQQghhsEQIIYQwWCKEEEIYLBFCCCEMlggBANgtFsdri0Y7cmaCIGxG09DXzqx6vVmlxoZFCF3LaC8vewFb4VrQvuuX7gPH1A1N5kEVUCjNW77vOZrL8fToOnCs9+gpZWmFoavHotY2bvxW39Ujio0sW/Ge29jkpk3f0Ths2ZmC5m0/+uRk67t7Gzds7zl8Up5fLE1J6DpwtG3HHrvFyvHxavhys7KkHKhUbXNrz5FcCoPR+t1PHmm/P4pe44bt8oISi1rDDwnUdXaX/ucd75wsKoPRczRXdq5IVdcoiY92zl/x1mpVTQOdx2W7u9V+9KWiuIzr48UUCZ1fOzIb5YrGDdv7TpxxG5tMG26eNYQQwp4l+o0wIpQhFPCDA9kebgNllbwAv7B772B7e9K5HGlKvPfkLKBQ3MensD3cQu5cRKXROF4ePUdODpRXMyViKpPhlpooLyy1GYzeOVlsDze3cSl2syVg3ky2h5vP1Amdew/6Tp8c+fC93b8eFYSHDFbV0nkchtB1puu+k2eUJRVDeod2GodNYTAAgOfvK01JAIIAAJ8pE5giIcfT3XVPKBQqnU5js6wGA4PPC7/vzt4TZ5xfX9St1OrsJnP4g3cx+Dw8BhBCGCzR72MI+XQuhykRu6eN4fh6t27fqa5rDFw4h1wacNMs58x0AV/b0u6ZnWY3meSFpTaDoefgMUFYsCQhFgC8J2U6d+BsBgONxyUjGQD4TJvYc+jEZdYq7J7bw5bcpsgvHrrIbWzyYHWdS2Lc80+G3X9n+65fHMHWOfAO2WVB6D23N33znbFfjgcAQuiaRccmuEYoiss1jS0WtVbT1GKUKehcLo3LofO4Vr2+5+gpplAgjAyTF5QoSyubt/7gN2+GoqAk5Y1lzVt2dB88bhlUB91yU/7SZerGZpNMqSytVJZWSpPjO37eryyt7D58wm/W1KZN33F8vMSxUbq2rv4zBcG3Lug+cNSlDl4TM4dWrD/3rKG3D6gUANA0tymLy2lMZujdtzZv2WEzm5kSsUv+th/22IxGXoAfncOxaLRNm74LuGmW82vnzLr2ro49v7I93OkC7FkihK5dOJD6tchqMABBUKg0Gpv1Z5VJ2O12s8W5QLvFSmVc1q8lq05PJzumLul6PZ3rmk4QhM1gINMJgrBbLOTFSOfXznqPn/bMGk9lMPBzRwhhsEQIIYSuV3jNEiGEEMJg+XcRFBEZFBF5pWvtSZ68J3kyth5CCGGwRAghhP6HRv9uWIVC2dza0tXVjR/G5di1e88V5add1VoIIXSN8PPzDQ0OcXOT3rjB0mq1btm2/bXXXweAhdNn4jFxOY79evCK8k+7qrUQQuha6SEcOgAAK1599e7Fd9LpoxazRu1uWKvVeu8DD9bV13+w7LVpWdl4QPwun/RUAOg5W3xFa/04dR4A/OPIXmxAhNB16vDpU8++tSIqMvKbr78arXg5atcst2zbXldff3TL9xgpEUIIjWBaVvbRLd/X1ddvJnJ+GAAAIABJREFU2bZ9tOowOsFSoVC+9vrrHyx7zV0iweMAIYTQyNwlkg+Wvfba668rFKNzNnR0gmVzawv5YwGPAIQQQpfZv3SEjxslWFZVV+MdPQghhK7Iwukzq6qqb6BgiRBCCF1HRidYxsXGkncDI4QQQpdp16EDcXGxN1CwDA0OAYDDp0/hZ48QQuhykCGDDB83SrB0c5OuePXVZ99aIR8YwCMAIYTQyOQDA8++tWLFq6+O1lA+o3bN8u7Fd0ZFRk65+zbsXyKEEBq5Tznl7tuiIiPvXnznaNVh1IYOotPp33z91ZZt25f8aykALJw+c1xiklgowsNiZDsP7P/L1kIIoVE0qFYVlJfd6MPdOZADqVdVVxcVFePBMYLd+/YBwE1z517RWjOOlwDAwckp2IAIoevOmNTUuLjYa2Eg9dEPlugykZNZtjXUX9Fa5GSW80uPYwMihNBVw+csEUIIIexZ/l1YrdarOF9vt1qpdDq2HkIIYc/yhnB1V7YxUiKEEAZLhBBCCIMluloqlQobASGEMFiiSzpy7Fh+YeFVrEgQhNFoJF9bLBbHa2d6vd5msw19rdFohy2zq7sbPxGE0HWN9vKyF7AV/lwHDh3asm17ZXWVVCpd+8mnHZ2dZ86eHTdmzJmzZz//4ouOzs6kxMSVq1Z5eXp+9MmnMTHRfB6PXFGt0bz+9tsNjY0DAwNarXbNx5+0tbVVVFWqVKrcU6fd3d0+WLMmMz393VWrGhoaU5KTP/18nd5gKCktq6quqqmtPXj4SEFRYXRU1DvvvWe1Wltb20JCggUCwe9W+JXlyyurqvh8vqeHx7urPsgvKPTz8+Xz+S++8mpBUWFgQIDEaY7uxqamTz5fd/jI0UkTJrS0tjpe79q9O/f06dNnzmSkpzsXfi4//8efdhUWF7mkI4QQ9ixvaCnJycHBQYsWLtz7y36RSDhvzuzmlmaTyZyZnu7l6XnfkiUUCmXJ4sVfbdgYHRXl5enpWFEoEHh6eDzy0ENl5RVRkZF2m83Dwz01OZnL5ZaWl58+k+fu5sZkMkVCkcls1ul03t7euadOJyUmcDhckUg8feqUe+66a9OWLT7e3oEBASnJSWwWy6VuX3/zTV9/v0siBSgMOoPNZuv1eqFA8OjD/3foyBGj0egmleZMmnQiN9c5c0dnZ3ZWpkgsampudn7d3NJ65+23V1a7TjXX2dUVEOD/wL334oGBEMJgiS5SUFh06MiRJYsXA8APP/3k7eXFYjGdMwgEAp1eL5GIh12dy+FYLFYOlyMSiaRSqclozMxI12g0drvdaDRWVlfp9bqf9+4FgFsW3fz9jz86VuTxeGazGQDc3NzEYjGbzXYtmhhmc6+98vKjD//f9z/8cD4LQVCAwufz09PGHz5y1MPd3TnzpAkT5ApFXV29VCp1fv3wgw/89PPPVIrrEZWZnpE+fvxb776LRwVC6PqFzxX8+c7knTWbzTkTJ4rFopLSsmefXlpYVJxfUGC2WAqLir/e+M39997zwrJlTz722BtvvxMVGRkcFESuqNZoiopLPv18HYvFamhsbGhs9PH2zjt7ViqVSsTiubNn/XvZy1QqbWzqmBnTp/37pWUJ8fE2m9XT3YNCobS0tuSdPUulUu+64443V660WCxdXd1ikSgiPNy5bg/cN0wPb+u33xoMhqCgIC6Xq9Ko13/51a3/WEQQhMFg5HI5M6dPd86sVqtpVFp2Zqanh8fg4KDjdXNLC0EQd95+m0vhp86cLq+ojI+NwwMDIXT9wkEJrnsEQdjtdhqNRhAEhUK5uhL0ej2PxyNfm80Wsh+s1Wr5fL5LZqvVarVayT6r82uj0Uin04d9GHTf/v1zZ8/GTwohhMESIYQQ+tvCa5YIIYQQBkuEEEIIgyVCCCGEwRIhhBDCYPlnIAgCP06E0I0JvwD/zsHSbrfXNjYAQFNra3F5mclkMpnNRWVl7Z2dztlaOzrIQ6GytraqrpYgiIHBwYLSEuXgAJnBYrXmFRbmnjtrNJnwE0UIXRc6u7uLy8uq6+tsdntZVWVVXS0AmEymwrJS8jX5JVleXVVSUWGz2Zy/Hts7O4vLyxzfeCq1+tCJ4xU11b8bMuuaGv+fvfOOc6LaHvhN78mmbe+VLfTeBFQUO4LoExRFRRHrs5fne/58iGBBxYYggmCXZntYQNrussv2nm3JJtmSTbLpfSYzvz8GxrgN9Plcyvl++PDJztw5c+ecc++Ze2fm3K6enjOpnslstjsc/Tb6AwGX2039P9SBmrZWrb4Dx/GBu2x2O4bjVLc/DL0WS6/FYhtw9pFlJHPD1jY2dJtMSrnC2NWVl5NTXV/f09s7KitLZzRIRGIqVVtVXZ2mtSU3K7vDaESIFAmEJrO5ub1t2oSJJRXl6SmpCKHm1laxSKRUyH0+n0wqhUYIAMDZj6a1NX9UrjJK3qrVRkllLBa7z2Zr02nzc0aZLdZwOCyVSLR6PZPB5PG4dqejXddBdY9cNqdVpxubX1BeXZWSmIQQOl5elpqULBFLmAwGj8sd6ozBUKi0oiIjNdXudGr1HVFSqdVmE4tENrudxWJZ+vrMVqtIJKrXaHAc9wf8VpvdbLUq5XKf369pa+NyucauLp1BHwyGdAZ9Ylx8U0tLCMOkpxJQ9/SaTGazta8PIRQbHa0z6KnDey2W9g6dVCIpraxgMpkyiYQgCLPV2tPbi+F4V0+PSql0ulyt2nY2ixUIBopPlKYkJQt4PF/Ar2lt4XK4GI5bbDZ9p1EplzOZIzPGG7GRZWdPj0Qs4XK5JEniYTwYCln6+oKhkFQiiY2O7rOf/Ppz/OjRUVIZQshq60uMi4+PjTVbrVwOh8PhiARC6sYqPja2rUNn7OqOUauhBQIAcE5gc9hrGuor6+pIkgxhGI5jVlvflPETeFyu0+2iIlC0SlWvaappaFArVXT3aOjqjIuJEQoE+KkFf1ISE+s1TS6Xi16VIUwQeATUiJPL4cikEiaTqe80ZqVnlFZWtrS3I4SM3V3+QKCsqjJKJi2tKE9NSjJZzARBhrCQTCJp0+lKyssz09IqaqqlEolSrpDJpEq54kRlZVxsTE+vyWqzUaNbncFARdbsjEy90RgMhURCYV1TU2VtTWZaeq/FEiWVqRSK5rZ2S5/VbLX6/P5ei8Xr81ltfV2mnoyU1LLqKplEKpVKHU6n3eksq6zKzc6prKs1W61mi1kqllAVHhFGLN1dY3NzlExqsVpDoVCMOtpk7o2SSSnbh0KYYEBSUyaDieM4AyEul+sP+BFCWBjnsNkIIRzHZ0yabOnrq6yrnT5pMjRCAADOckiSvGj6DKFAcPR4cUpior6zk0SkgM+3OxxNba2Txo6TiMUIoVadduaUqSKhsF6jIUiC6h65XC4e/s0kp0goGpOXHwgEDJ2dKUlJCKEOg8Hj/XXJvMy0dJFQyGAwWCyWy+NRK1VCgQAP4xw2ByGE42GEkDwqShElD4UwmVSam5XdbTIlxMbyeXy70+FwOZvb2iRiCWIwWGwWi8lisVlOt0tv7KSzhjmcTrVSpZDLhQKBgM+3ORzBUDAQCEolktF5eVV1dSKhkMVislgsqnx8bIzH6xOLhC6PB8Mwh9PZEgqRJGKxWCwmi8FAYSLM5XJ5XC6Pyw2Hw3ExMUKB0OEasWV6RyxYXjZ3LkIoEAjKZLLapkaZRJqalBwMhY6XlfmDgTnTZ/Qrn5GaWl5TzWKxxuYXaFpaKmtruBwOpXd/INDWoWOxWAmxsdAIAQA4+2EwGMfLy1QKBUmSDpfL6XaFMGzyuPGFpaVikahe05SdkamUy9VKZUOzhs/jJcTFYRhOdY+zpk47Ulzk8XgTYuMoaV093Q6Xi8vhJMbH0x3mUKeOUasLS0rcHg81FVdaWdlrNmempVF7o9Wq8upqh8uZlpxMHxIfG8tisTCfVywUtrS1xYxRNzb3pCYle30+j9crEgoRQrHR0UdLjnu83mAwhBBKTUpqaG4OhoJ8nkrT1hqrjvb4vBKRuHOI1W1dbrdYJMJwzOf3uz2eEIbzeXyEUF1TE4PBYLNZI2+ysyHdHY7jYYKgptpDoRCHwxk0xymO49SdEUIoGApFTs27PZ5gKKhSKKERAgBwrgwug6EQ9XJGMBhksViDplbGMIxEiMvhRHaPJEliOE5tpOjs7o6Njh5UwkAIgsAwjMfjUfI5EXKoLWw2u18nHAgEqCzQ4XCYxWJR/4dCIRabzTr1EDFMECRB0HWgpn85HA5BEIFgUCgQ0IcPrFI4HCZJksFkMhkMKtk1td0fCAycaLxwgyUAAAAAnM1AUgIAAAAAgGAJAAAAABAsAQAAAACCJQAAAABAsAQAAAAACJYAAAAAAMESAAAAACBYAgAAAAAESwAAAAAAIFgCAAAAAARLAAAAAIBgCQAAAAAQLAEAAAAAgiUAAAAAQLAEAAAAgPMM9ojXoK/PtvPTT9u1WjDG+c0l8+ZdfeUVZ7g4LU1ZRcX3/9nfZ4NVV89nlArFVVdeMXnixN91FI7j3/1n/8FDh0CB5zcZ6em3Ll2qVCpGthojufhzWUXFGxvfKiwuRggtWrgIfOI8xtpnPXrsKELo7w8+uPj6hUmJicOX9/l8Px04uOallyxWa15u3qicUaDD8xhNs6axqVGtUv3j6acvu/QSoVA4fHljZ+fuvfte37gRIXTR7ItUShXo8Dxmz749CKFZM2Y8/OADv/eO6nwIlmUVFTf87ea83Lx/PPOPeXPn/d4BB3DO4fV5333v3e0fbTNbLO1NjcNb/Pl/r9m2Y8eihYseevChvNw80N55T2NT45sb39yzb8+K5cuff+4fww8oM3LzotXq229bsfre1SKhCLR3foPj+KHDh9asXdPY1Ljr889GKl6OWLBcdtvtNrvjyC9HwBUurCGm1Zo7Onfbls0Xz507zLAyd+y4Te9sWrxoMWjsgmL3nt2r7lvVVFM9zODyl8OHV6y8u6muSaWCAeWFxZyL5yjkUZ98tH1Ezj4yL/j09dkKi4v/8cw/wPwXGiqVatHCRY8//Yyxs3OoMl/u3o0QWrBgAajrQoMy+qNPPuXz+YYqs/7V1xYtXASR8gLkH8/8o7C4eN+33+I4fqEEy52ffooQmjd3Hpj/AuSOFXdYrdZZ8y4eqkN8+933Vt65EqbXLkBEQtHKO1f++PPPTz47+J20prlZ09x8x4o7QFcXIFTIePjRx44WFl4owbJdq120cBE8p7wwmTplKvXjpwMHBy1gsVonTpgIirowmThhYjgc/ua77/r6bIMFy5ZIFwIuKNhs9qKFiwR8wfpXX7tQgiUAIITWvPQSKAEYCmr+CQAGnWAY5jkOBEvgfMNitf71Hg+cK3wMwRIYmq3btkOwBAAAQBarFZQADMVfn6gEgiUAAAAAQLD8r2nXtodCIdADMCiaZg0oAQD3OO85G99HbdI02U4NsRUKRe6oXIRQr7m3qLhIIVfMnjWbxWL19fX188KEhITUlFT6z+7ubl2HjvrNYrGmTZ1G/S4qLjKZTFOnTE08lXGtn+R+lTlw8MD7W96fMX3GksVLEk+XpA34C9Ab9EXFRRKxZNbMWXK5nN6u1Wn9fn9+Xn5k4XA4/PEnHy+9eSmHwxkoqt/eQT1B06ypr6/PysoaO2Zsv8NDodDuvbt37NyxbOmy6xdeD9+6nA1UVFY0NjXGxcbNnTOXet8+FAoVFhX29fXNnDEzPj6eKlbfUF9XX6dWqedcNGdQ3/B4POUV5XPnzI0MewM9YWB/QmO2mPfu2/vNt9/cv/r+BZcvYDAYYB0YWf7JPPzIw8//+/m169auXbd2957dCKHautpZc2bt37//xXUv3rT0pnA43KRpeva5Z+l/y25dtmfvnkghH3z4waOPP0oJef3N16mNq+5b9fhTjx/45cClCy49dPjQoJIjhWx4Y8Obb73J5/M9Hs/8BfO1Osj2PsIc/OXgvEvnHSs89vmXn8+aM8tgNFDbMQx7+tmnv//P9/3Kv/LaK489+VgwFBxUWuTeQT1hx8c7rl98/aEjh26/8/ZXXnul3+FLb11aUlIiFourqqquvPpKmH4Ycda/sv62O26rrKxc98q6hYsX4jgeDAavWXjNhjc2HDl2ZO6lc4uPFyOEduzcsfjGxcXFxa+9/tr8BfP9fv9AUZs2b6L7jaE8YWB/QmOz2S6/8nKPx8Plcjd/sPmxJx8D65zzeN22v/7fHXfdfvequ8khyC3IDQaDkVtuWnrTGxvfIEkyGAxOnjb5h59+iNx7ouzEpQsu9fv9kRtvv/P2o8eORm6pqKzIyc9xuVwkSX7y2SeXXHbJaSWPHj+6paXl1Q2vWq3WjW9v3LNvDwn8GahiVdQ/TVPtQPdQxap27d416IFXX3f1Rzs+on6vvGflc/96jiTJNza+kZaVpopVrX9lfWTh4yXHr7j6ClWsyu1xDxTVb+9ATwgEAunZ6WXlZSRJanXa+OR4u91OH67VafNG5xEE8cw/niFJ8u5Vd7e2toJl/3t27d5Fu8dA3/jkk49UsapBD/R6vYmpiZQVgsHg2Iljf/jxh52f7LzymitpyQuuWkCS5PhJ44uKi0iSxHF80tRJR44eiZRz+MjhgnEFqljVtddfS20Z1BMG7U9odn6y86677yJJ8pl/PGO2mG9aehNY9k/h7lV3J6clq2JVd9x1+18cts66kaXT6RSLxRWVFZ998VmTpgkhhON4YVHhVVdehRDicrlz5swpKiqiy/v9/vsfvP/V9a/y+fxIOTqdjiCIz7/4vLDoZK6HQ4cPzZk9RyKRIITmXzq/prbGbrcPIxkhpJArDvxygCRJhNAD9z1w/XXXw93VyDJj+oz5l86nfsvlcp/fhxC6+667q8qq+i1c43Q6n3jqiY2vbxzKzSL3DupjVdVVAr5g0sRJCKG01LSUlJTSE6W0BKlE6va4yyvKqT/ff+/9zMxMMNAI4vf771xxJ2UFLpcrFol9fl97e/u4seOoArm5uRWVFRiGPfv0s5RZXS6Xy+1SKBT9fKzwcOH6tevpLYN6wsD+xO12R3YddfV1pl4TQkitUn/+yedgoHOds+6ZpVar1XXo3nr7raSkpOdfeP6hBx66YfENwWAwNiaWKhCtjm7XttPl39307tQpUwc+T+ro6Pj3i/+eMnnKxnc2ZmVmbd+6vcfUExMTQ+1VKVUMBqNN2zaMZITQhlc33PfgfVqtVqPRLFu6DPLzjThPP/k09WP33t1f7vpy3+59CCGBQCAQCDjc3zx5euTxRx6474GhHjP322uz2wZ6QqTDUBvNFjP9p1KpXLd23bLlyzAMQwjddutt2dnZYKARRKlUPv/P5xFCoVDoxXUvBgKB+ZfMx0LY5q2bMQzjcDi/HPqFJEm3200l6L/+huurqqouueSSgvyCSDkcDkcmkwmEAnrLoJ4wsD+xWC1U7EQIXbHgiqPHjk6ZPkWpVKpUquW3LFcqlWCjc5qzbmSZnJz87b5vP/340/Uvrf/s489eWPNCMBhECDGYvz4e5/NODiJ9Pt8HWz947NH+zwPC4fAHmz/Y/93+tWvW/vSfn06cOHH4yGEWkxUpBCEk5AuHkkwxYfyE48eO33fvfZmZmStXrfz8C7g9PAtup3TaG2664e133t771d6BN0kUn3z2CYvJWnLDkjPcy2KyBnrCQIfp5x5L/7a0qa5p/iXzGQzG/AXzW1pawDojzpGjRy6ad5HBYPj+2+/FYvHiRYtTklMmTp0495K5R48dZTAYdDx76423tm7ZWlZe9unnp8l+MKgnDO8eDAZj3dp1jXWNmRmZZWVlV117FXVTBUCw/NMgSTInO4f6PbpgdDgcZjKYfD6/03gy1Ut3d3dycjL1+4svv8jPz09OSu4/IRPw5+flUy+5icXijIwMq9WakJDQeSpfjMlk4vF4WVlZQ0lGCLnd7n+/+G+EkEgkWnnnyicee+LHn38EjxlZysrLrl147bVXX3vwp4Pjxo0bssc8cuSXw79k52WPmTAGITRh8oRjhceG2dvQ2DDQExISEro6u+ij+rlHTW3Nx59+zGKx1Gr1mhfWzJ0792jhUTDQyLJl65bHnnjstVde2/bBtpjok8O+9999//CBw7u+2PXsU89mZGT09fU9/8LzCKHExMRLLr5k8fWLS0tLhxc7qCcM7E/UajVdZt83+4qKi8QicXZW9s6Pdtod9rb2NjAQBMs/k692f3X7nbdTK7B8/c3Xaalp8fHxl82/7MtdXyKELFbLzwd+XnD5ycWbfvjph+uuvW6QwYdWO3vebKvVSg1EGhobpkyZcvn8yw8dPkTNpH386cfzL53P5XKHkowQ4vF4Wz/c2tDYQP3Z1d1FT9MBI8VTzzz16suvLr91OZM5nOtu3rS5TdPW0tjSWNuIEKosq5w9a/Ywey+afdFATxg3dhyLxfrl0C8IoeLjxV6vl3pqRREKhV7d8KrH60EIEQRhMpnAPUYWm822/pX13+77duaMmfTGXbt3XX/D9QqFQqVSbd+x/frrrhdLxNu2b6uqqqKMWFZelpubO7zkQT1hYH8S+QlKR0fHlq1bqN8ul8vv96tVarDROc1Z98xy+a3Lfz7w85TpU6Kjo3tMPR+8/wGDwXj26WcX37j4yNEjxk7jittW5OXmIYQwDCspKVnzf2sGChkzeswtS2+ZNXdWRnpGW3vb//3z/1KSUxBCd9x+x5x5c2JjY10u155dexBCg0qm4HK5Lzz/wpKblkil0mOFx/r6+r749AvwmBHE4/HU1tXeevut9JbFixZvemfTnyJ8UE94Zf0rd997d0Z6hq5D9/bGtyM/w500cdLsmbPnzJvD4XBKSkuSkpIi77SAv54TZSecTufo8aPpLRte3XDDohu279h+2RWXMZlMHo+35t9rRELR2hfXLr5pcVZmVld31/hx4++4/TQLfrFYrIGekJOTM7A/obnt1tu++/67+VfMd7vcP/3802OPPAYLcJ7rMLxu219/1gf+/gibzX3/vfeHKqA36DEMS0tNo7unUCjUpGmKVkfHxcWd4VmsVqvFaklNSRUIfn1W39XVZe2z5uXm0beBw0t2Op1r161dvGjx5EmT4bPiPwt13Mm77MJDvyQNeAcnJSt70zubqLcw/koG9QSn06nT6dLT06VS6cBDurq6XljzwnPPPgcJK/4sdu/Zveq+VSf7gdb+j4H3ffPtQ48+aumxnLlAgiDa29sJgsjJyaE3ut1uXYcuWh0dG3um8wGDesLA/iQSTbPmjY1vrHtxXVRUFFj2T+Gee+/54ccffH7ftVdf/dbrGy7okeXJ7jI5pd8WLpc71NscQ6FSqQbezSUkJCQkJJy5ZJlMtvzW5ZkZmRApz3sG9QSZTDbMw9GEhIR77r4HIuXZDJPJzMrK6rdRIpGMGT3md8kZ1BMG9ieRjMoZdfddd0OkPD+A5ZdPT78MagAQyYTxE0AJALjH+X/XBSoAAAAAAAiWAAAAAADBEgAAAAAutGDZ02uqrqs5yxXncrsqqit6ek3gQwBwgUOSZG1DXV1jPajiPOase8GnsbnR5XYzGcxWbZvT6cjNzq2urxk/elxtQ21MdExaSlpTc5PD6RhTMFbA55eUlRTkFTQ0NYwpGKPTd4RCwQljJ3R2d/aYenA8LJfL1UpVS1sLh8MZO3qsxWLpNnVLJTIul4OHw1wO1+125ef++vKOsatT16GdOW2m1+dtaGrIG5Xf1Nw4bfK0yLoFAkE2izU6f7TH46HyTIqEwrqGupjomMz0TLrymekZCrkC3OvPxWwxGzoNw7iEUCA4XlbC43JDISwvJ7exuZEyYkpSiqHTwGZz8nPz6JxkgWCgsqaKy+Fmpme43G6P1y2TyuwOR0GES5yoKMNwjMVipaemtba18vl8DoerVqpoadoOLe0SDZpGygNDoVBNfS2O46Oyc/RGfVJCUld3l0qpio+LByP+72hua2GxWGKhqLO7KzM9o66xviA3XyaVnags47DZE8dN7Db16Dq0fD5fIVe43W61St1nt8lk0u7ubj5fIBGL01PTI51N26HlcnkFufmaFg1VWB4VlRB38t1Xf8BfVVPF4XCSEpOVCoXP56VyBtE+MyZ/tFandbpdSQlJDAYiCEIqkXb3dDOYDKqXSE1OadO2jRszrrquJisjs12nDYfDPC530vhJYE0YWZ4eqUQqk8rUKlWUVIZhmEgk4rDZQqGQzWZ3m3oQQsmJySKRSCQUMplMEiGpRMpms0VCEZvF8vv9Ho+nz2bLzsyOiory+/1KhVIiluRm53LYHE1r86Txk9weF5vNrmuoU6tUgWAg8tRRMhmLzW7VtkklUoIkZVJpv7WgeVxeUmKSz+/zer20NAaDwWSyMtMzIytPEAT41p+OWqUe3iUYDAYDMeJj41VKpUQioY2oVCg5HG5sdAy9Hjg6md6TOXHchLqGOi6Hw+Py4mLigr91CR6Px2ax4mPjmUwWQZIqpTpKFhUpLdIlaA/kcrnZmVlRUVHyKDmTyWxq0URFyamFiIH/HdEqNRYKCYVCkVAYJYvicbmalmYmkymTyqgoGB8bh+FYbEwsi8UOhoJKhVIiEvM4PITQmPzR2gjfQAiJhCKxSJw/Kq+qtoouzGH/+jGlgC9gs9n5ufmNmkYOm0N7I+0zPr+/IK+AzWIlJyaRJNmu05p6TV6fj+4lmEwmk8lsbm3hcbkkQaYkpbBZLBYL/ASC5RkTCAQYDIZELGGzOdoOXWJ8IoZjpl6Tz+ft7Oqki3X1dEV++WjsMnI4nAZNw9iCk59PjR8zLnKehMlkIISEQpHfHyjIzW9oauh3Xk2Lhsfl6Tq0CKGhvqls1DQ4nE6RSERL8/l8dH3oyotFYvCtPx0Gg3Fal0AIeX1emVTGZP4my3VXd2dJeUlWZtZAmSwWi8FgkAiRJNnva1rKl5ITk1QKJUGEfT6vUq7oJ41yCbFYTHtgv1NZsF/sAAAgAElEQVRkpme2aSEv6P8ceZTc5rC3adszMzK7urs8Xm+vpTd0Kn25w+n0er0hDAsEgkq5gjI0iUgGg4GHw8eKjykVg6wKwuPxwuFwZOHIvV6ft8PQwWQyIr0x0mfokuFwWB4lRwwGQmRkL8Hl8rhcrsvtjlZHx8XE9uu1AAiWw9FjMtkdts7uzl5zb05WdmVNZXxcfHVtTWpK2vgx48urK3R6naHTWFlT1Ws2i0XiiurKYCjU02sKh4mxBWM7uzt9fl9re6ve0BEKhWx2m77TUN9Uj4dxkVBUU1djtpjZbHa3qTtK9psvhf1+f1dPd0FePolQt6kbD4crqisFfEFkmV5zr1KhSkpM6jB00NIEAkFPb09lTVVNXU1k5cG3/hcM7xKm3l6T2eRyu09UloXDYdqIvZZeNoczZ+ac8sryX+/JgoGeXlN5VYVMFiWPkusNHSXlpXGxv5kptdr6uk09eqOBmrHwBwKlFaWR0miXqKiuoD3Q6XI2ahr1hg6H0+F0uXw+Lxjur0EpVwSCAQ6bU1lbNaZgTEZaemVNpcGob2xuqqypNHQaPB6P0+Vs0DTEx8WXV5W369qlEqnFasnLzff6PA6ngxbldDsNnYaSspLU5FS6cGSn4ff7nS6X3x9gMBiR3kj7DEKoQdPYberp7Oq09llFQlGMOtrSZ43sJbpN3dEqtaXPghDqMHR0m3psdhvY8Sy9WT87091FjgiHT50TCoW4XO4ZnjeEYdyIrFQEQQyTj/u0kvtJA37HhOp/ke7utC5x5kaMFEUQRJgIR86z/Yn8rjpf4PyX6e7OXNUYjrGYrGF6gEhRpy383zB8RwREAunuhpt5G77AmUdKhFC/2Da8g55WMkTKs9MlztyIkaKoB0hnQ52Bv0bVp70xihT1P7qLOpOOCDhLGBkjZaSn79m3h1qHC7jQKD1xmuUD1SpVRWUFKOrCpKKyot+LdZGMysk+ExcCzktwHN+zb48/4L+AguWtS5cihA4dPgTmvwD5cNuHnGEH5fevvnfL1i1eeNR34eH1ebds3RIOh4cOljmjcnI+3PYh6OoChAoZJEleQMFSqVTMmjFjzdo1YP4LDavVumffHuzUO4qDcuPixQihH374AdR1oRFpdPUQC0A++dije/btoZZ2By4o1qxdQ09ZKxV/9YfsIzZX/vCDDzQ2Nc65eM7PB36G+dgLZNDwymuvzJo7K7I3TBpscSuhULhi+fJV96265957GpsaQXUXAo1Njffce8+q+1bRTwpvWbp00JIXzZqFEJpz8UWvvPYKTD9cCOA4/vOBn+dcPKexqZH+hP3OFbf/xdUYmbdhKcoqKt7Y+FZhcTFCaNHCReAT5/OAss969NhRhBCDwaBnUd587bWF114zaHmfz/fTgYMvrF3b19eXl5s3KmcU6PA8RtOsaWxqZDKZkdk8KktKlMrBRw/Gzs7de/e9vnEjQuii2ReplCrQ4XnMnn17EEKR7jEqJ+fH7769gIIlxb5vv3340cf6fdEInG/3hmE8FAr129hUUy0UCoc/cOltt5eWnvhd7zwD5xyhUAgP/2Z66Uw+DHC5XKMnTuJyuWzIenNe4w/4+z2n3LZl88Vz515wwRLH8aOFhetffU3T3AxucYGwYvnyO1fcPugc7MAh5pe7d7/97nsWeEZ1YaBWqe5ffe+Nixef9kaKGmJu3bZ9244doLcLhFE5OU8+9uhFs2b99fkjRz5YRvr97r372rVacIjzmIkTxp9hP9jvjqqqpmbHx5+AAs9vlt+ybPzYsb+3H6TuqCoqq0CB5zFKheIM77DP/2AJAAAAAGcnkDkCAAAAACBYAgAAAAAESwAAAACAYAkAAAAAECwBAAAAAILlQDqMhqPHjxeWlhwvK4vc3mux9FosNodjmGN7LRYMw9o7On7vSf2BgMvtPsPCg57F5nB0GA0DC+v0ervTiRDqtZi7TaaBougr6jAaCktLi06Utp76SGaoa2nTaYev3lBaOnPlaPX6MymmaWuN/BPH8drGhj9m9xCGFZaWuj0eDMfbO3SDlgkEgx1Go1bfgeP4UAoftGL9DD3UXpoTlZUer/dMrr25vc3n949gQz3ttfwv+GPKP8NWfFon+XMVomlrHcblAOC0sJ595skRObHOYMhKTx+VmZWUkIDjeENzsz8QIEii+ERpSlKygMez2vo6u7sRSeoMeg6Hy+fx2nQ6a1+fkM8/erw4WqXm83gcDqehWRMMhsQiUbfJpDcaBXw+j8dDCAWDweb2tkAwKJNKTWazWCQymc09JpPOoI+PiaWOkkmlPb2myLMwGQzqKB6Pd6SokDpLmCA0rS1sNksoEOoMeoIglXK51+fTtLVxuVwBn48QIkiyVatNjIurqKlJS042W61ur4ckyFZtO5vF4vP5XDabWmpDZzBkpKSMysoqq6qKVqksNlsYx/k8ntVmk0okXT09ErGYwWD02Wxeny8QCEglErPVGgqF+uz2cDjscDr0nZ1ioZDNZtMye3pNhq5OBoNBEISlr29QgSEMq9docByXiMWUJuUyGY5jlr4+uUyGEHK6XE0tLQI+3+l22+x2gUDQ3tFhtlqVcjlBEHwej7JRlExmMps9Xm+ULIrNZmv1J8tQKT2dLhd1yRiOEQQRJgi310upyGQ2m61WhEiP18Pj8gQ8ns5oiI2ObmppCWGYVCJBCJEk2abTudwuj9cTCoVio6M7jAaCICUikc3hEAmF3SYTl8OhLatpbeVyuWKRyGa3s1gsj9dLnb2zu1tn0CfGxVGOodV3REmlPr/fYrPpO41KuZzJZNqdzsbm5qz0dLPVShXos9v77DaL1aqUK2htcDhssUjkdDnNFku0SmW2Wm12O4/Ha25ro6s9DCaz2dDVieNhiVjca7HoDAaZVEp9R0gpRCqR0Bro541uj6fXYna4XHKZTNPaGqNW0xceUT1OpAIxDOvuNRmMnRKRiCCIyCbQ3qGTSiSUw9AtgiAIWickSUaKQgjR3t7V00P5IZfD0er1lPO0tLfJZFLayqFQiFJ+IBigW7Ev4Ne0tnA5XAzHI5Uf6bR8Hq/bZHJ7PR6PlzJER6fR5XInJSTQ9WnT6Uxms0KhMFss1IUwmYyhjGu2Wmmd0/7Z3NYaHxOjMxpkUmlkywWAs31kiRCqrq8rOnGiXtPUom3ncbnhMC4SCKVSqcPptDudFTW1sdHRx0pLkuITquvrHC4Xj8sNh8NdJpNUKuXz+VqDvqSiIj0l1dxnNZnNtY0NaSkplbW1lPAukykxPr5V2+4P+Fva2xFCLe3tMplUKVfQR1lttn5noY8iiDB9lrKqqtSkZJvdodPrGQyGSCisa2oqKS/PTEurqKmm0hUq5XKny0kQRDgc5rDZFbU1cllUl6knIyW1rLrKbLXYnU6P1xsIBhFCdZqmohOlPB7XarMZuzp5XK7WoCdI8sjx4l6LhYo6+q5OtUrV1qHDcLyxudnudBq7OkVCodVmT4iNLa+tMVstfXa7x+vFcLy0ojIlMam2oaHXahlKYGlFeWpSkslitjkclCZ1BkO0St3V03NymFVVmZ2RoTXoW7VaDMe1HR0cNlsmldZrNM1t7bSNXG53m06bkZpWWlGh0+uDoRClkFNqP3XJFqvT5fR4PD2nxtllVZVRMml1Xf3ovPzGlmYqf9WJysq42JieXpPVZkMINbe1MZlMgiQRQtkZmXqjkVK4pq2trqkRwzCdwRBpWcqsCCFjd5c/EKDPThm6ua3d5XbrO41Z6RmllZWWPqvZYpaKJdQhUrFYJpUEQkG6QHN7G5fDsdrsDqeT1kZzW7tOrw9hmDwqqq6piVJOZU1NZLURQmGCwCOgs3OVV1cnxSfUNTX5/P7mttbUpKTSiopIhURqoJ831jU1ioRCm91utlr7uTRdvX4K9AcCDRpNanLyieqqyPKVtTWZaem9Fgt1arpFROqkn6hIb6f90OPz0c6DEIq08q/Kl0jpVlxWWZWbnVNZV2u2/kb5CCHaaYNYqKK2hs1i04aIkkpVSgVdnw6jsbOnW61UOhwO+kKGMS6t84H+iRDq13IB4AwZyZyK4wpGK+VyhJDP76fabYw6msVkUasORMmk8qgolUIpk0oZDEYg4O/s6WYghkIhZzFZ1PKweBgXi0RyWZTX541WqcUiEb2Ai9fnszscGIaFwyebBB7GWUwWi82ij/J4Pf3OQh9FECR9lnEFBZq2Np/fJxVLgqFgIBCUSiQOl7O5rU0iloQJgjppjFpdXV+fkpSIEIqLiREKBA6nsyUUopMatrS3KeQKhFBedo5KqWQxmVq9Pjkhkc3hIITiY2KOlRy/av58qnAwGBIKBBmpqU0tzTKpFCGUnJBIEITNbkMIUe08GAppWlvTUlLkUTKxSCQWi8NhYiiBoRAmk0pzs7IdTietSQaDgeEYQigcDrPZbJFQmJWWXtNQn5GaWlJRPjY/n8FgUt1ialIyZSMGg+nz+9t0uiiZ1OZw0AqhztLvkvGIhQnlUVEyiZTL5fK4XB6XS+1yul16Yye90ITNYR9XMDocDmv1HQI+P1J+MBRsbGnJTEs1W/v6WRYhhOPhyLNThkYIOVwutVIlFAio1KNxMTFCgdDhciKEWCwWi8VyuT10AR6LGxcTa3c4g8EgrY2qurrIarjcrozU1FZte2S1EUIdBoPH66H/zExLFwmFlBtLxGI+j+dyu2m90QpRRMkjNdDPG9ksdow6GsNwr9cb6dKhEEZXr+hEab+aRKvUErGYxWRFNoHReXlVdXWi3yZO6qeTAbb49arlUTLKDyObYYSc3yqfxaJacZgI0+YOh8ORykcI0U6L4+G4mJhgKEQbgsVksVlsuj4CgSBWHV2v0WSkpkZeyFDGpXXez3AnXXRAywWAc2NkWXTiRE+vCQ/jbBabJEm3xxPCBlmxy+vzI4RIRDqdLpIkbXY7NZ6rqqvrMBpi1dH9yrvcbjbVXTqdCJGVdbUut5vP43V199BHxcfEDnMUfZZWnU4sEhJhIjUpCcfDwVCQz+PFx8ayWCwMC3FOpebKSs+oa2pMTUqOlMZkMjEcC4ZCCKEJY8amJiUhhFhMJmtAKy2prLhq/vyKmhp6qOp2uxPj4hubm7MzMk4+jMTxYCiE47jP50MICfj8SePGKeVym8NR01AfCPg5EXnCaIF2h6O9QxetVpVXVx8vL/MHArQmA8GgUCCgIgebxaqsrdG0nnwUlJqUXFFTW15dnZmWRk2aUTaSR8m4HC6DwWAwmJEK6XfJAgG/saWFlnZy0p/FQgjVNTUxGAyqqqlJyQRBeLxeqvtLT0ktr66u1zSdqsOv8nMyMlva22LU0b+1LFLIo0orK6nxMX12kiS7unuoOxi90VhVVxejVg/qhIMW6KeNgZfZr9oIoYzU1LH5BfQ/0YB8fiqFgtZb5PaBomgIkqioqWlub4uLjY10TrfHQ1dv4OGGzs7KulqpRBypqJb2drlMFsLoXPYnW8QwNYm8atoPI5shFe9pK9PK9/n9VCtmMX81N1WTfg9EKaflcTiDGoKuD5PBMJnNYrEIw/HICzmtcQcaDiEU2XIra2sgBgBnyNmS7i4QDLJZLDabHQ6HqS51IMFQiMfl4jhO3Q9S/weCQS6HM/AOkSRJDMO4XC4lEMMw6mkN9eeZHEXdYlNlfH4/n8djMpnUDBslKhAI8Id97BEOh0mSZDCZTAYj8t7/TMAwrLGlZWx+/sDtLBaLIIjI/JmFpSVTJ0xks9nDnwXDMKoMrUmtXh8bHU2PC0MYxuVwIsszGAz6RLSNSJIMhkJUBxSpkH6XjIfDbBZrYJX8gUDk46JQKMRis+m7B+oCadP0kz/QstQhtHHpsxMEQe0lCALDMF5Ed9k/Jg1RIFIbA6vRr9pnQqTefnOiIUQVlpZMnzSZvsnod+F09SIPd7ndOoM+Nyuby+X2c2b6xojWc+TlDFqTgVcd2Qwpx8BwnLLyoMofaO5hnHagIej6YDgeDof5PB5BEJEXclrjDnoJp225AHD2BktgIG6PRyIWn0lJS1+fWqn8A6dwud2nfUUFGCn+gFkxDPP6fFEy2Xl5dQAAwRIAAAAAzl7g+TYAAAAAnIYRexs2JSt7mL361hawzYWG0+Xq7TVnZ2WCKgAAgJHlr1SmjBv036CFg8FQVn5+XcOviWMMBkNWfr7Fam3SaKbMmj3MiY4eK9T9nnQ/pxX4p7Dq/vu/2r37TxS4+sEHP/70sxGvxhmyfefOR574TUKM1Q88eKywENokAAAQLP84JCJDIYyM+I6YIMlQCCNJMikx8fVXXh7m2E1btpScOHHm5zqtwLOT1fesmjd3zjnqiHq9YcFll9254nZokwAAnIWwz85qpWRln/lMrM/vr62rnzl9OkLI4/F8sG17S2trclLiyjvuVCoV+3/8sUOv/+HHnzIzMiZPnIgQ+n7/Dz/+/BOLxb5h0fXUUQiht95994rLL9++c+ctN9+sUChogQihL77adayoKCM9bdrUqTab7aorruju6dm9d+8Dq1cjhALB4BsbNz7y0ENcLhch1NXdvfOTT7p7emZOn7Fk8aJ+X6f4fL6t27frOvRTJk2iN5aUnug19153zTUIoZbWtuKS47ffemvkUR16/fHS0sT4hC927YqNiVl190qVUokQ+vrbb5OTko4VFaWmpFx79dVanTYZT0qIj/9+//6+PtvyW5YhhH45fLinp2fZzTefthoIoUAgsG3HziZNU0Z6xorblkslkl6zedeePTOmTdvxyacKufyB1fd+89335ZUV48eNW7F8OT3E37Zzp8lkSk1Jvf3WW2JiYnrN5m+++27i+Al7v/4aIbT8lmVZmScnV3ft2VNcUpqUmNjvC4qKqqrq2tp2nfa2W25JS02FlgkAAIws/zjVNbWFRUXUv4rKSmqjxWLZtGUL9fvxp5/u7e1detONOB5esnQpQigmOkYoFMbERMujohBC23fufGXDhgWXXT5j2tT7H37454MHqQO379i56v4HeFyeUCiMFLh12/Z3Nm2aN+ciDMPvWX3fwUOHEUImU+/2HTupAsFg8L3NWzAMQwh19/Rcu2ixQCCcN2fujo8/fuHFtf3qf//DDxcWFc+cPq2wuPjw0WPUxsqqqp8OnKyGrkO3a/eefkd16PVrXnpp56efXnPVlV6v94a/3Uyd7qcDB+976GGtThetjkYIffPd99W1tQihvNzcF9evP3z0aE+P6cG/P5IckSdhmGoQBHHjsltaWlsvnz+/uaXlxqXLSJK0Wq1vvv3Om2+/M3vmzOqamkuvuLK+sWHq5Mlbtn74xVe76KMUcvmym292OBx3rroXIWS1Wje8ufHl116bOGGCz++/dcUdVKaFzVu3bnhz46SJE8Lh8DubNtH1WfPSuo8+/njuRbPFIvG1ixZ3dnVBywQAAEaWf5wPP/qI/pqYChj9aGhsumXpzTOmT58+bdrYMaNxHJ8wfly0Wj1+3LjMjAyE0Jtvv/3+229PmTyZKr9pywfzL7mE+n3PXXcuWbwYIdTQ2EgLfGfTpnc3bpw2dQpCyGwxEwQ5TPU2bd6y4PLLHrr/PoTQhHHjLr/mmqcef4yucEtra2lZWcnRYxKJ+Prrrrv48gVnfuHhMPHma68KBILLLr109sWXHD56lKr2tKlTN7y8vl/htNTUZ5584ul/PJeelnbD4kWzZ82M3DtUNX746Wefz7fh5fUMBuPKBQtmzJlbVl4uEomCweCGl9crFAocx996992X165FCLW2t1Na8vl896y8ixplCgSC3Xv30vcQ77z5plKpuGTevIIJEyxWa7Ra/d7mze9u3Dh96lRKmVSOOovVun3nzuIjh6PV6muuQp1dnbv37qN0CAAAAMHyj7Bxw2tjRo+mx1tzLp3fr8AzTz7x3P+9sGnzllkzZ9y0ZElkmhuEkLWvz2azjx0z9mRQSUszGH5dgSh3VG4/ada+vj6brSA/j/pzVHZOo0YzTPWampu7u7uvu2EJ9aff7+/u6UlPS6P+bGzSpKelSyRihBCTyczLzT3zC89MTxcIBAghBoORnZVpNHZS2/NGjRq0/PJlyz7/8itNc/PWze/32zVUNTTNGrPFsnDJjdSfTperrb197JgxcrlcoVAghNhsdlzcyQSBbBbbG/QhhMRisVgkuv2ulbqODgaDQaKTNxNRMplSqUAISSRiJpMZCoXMZrPNZh83ZgxVYHR+QVVNDUKota2NIIiV966mtveYTJFJXwEAACBY/smQJDlj+vQThccam5oOHTmyYuXdP3z7DR2rEELUc7JAMMDjcRFCVquVCgMUA1PF8bg8hFAodDKjptvjHljSYXdEyr9pyQ23nHpA6HK7ExMSft3L5/n9PvpPt5uW9qs4u33wJQCdp9JAI4TcHq/wVCLQofLbVVXXdHZ2stjsXw4dvnLB5ZG7hqoGn8cfN2bMG6++Qv3p9fnkUVF6g2H4jG4/Hzz48msb3n1rY0FenrGz86qFC4eqGDXC9vn8VNSPPC+fz992KqgHgyEOlwMtEwCAs4rzKilBOByeMnOW0WjMz8tbtXKlWqVyuVwIIQaTEQwGqWHQmNGjP//ySyqyfrl79yXz5g0jUCIR52Rnf/P99wihQCDw/f4fqO0ymazPZuvt7UUI7d63ly4/a+aMw0eOSiQShUJRWFx82513RQ5tJ06Y0NnVXVlVTQ2LS06UnpImbWltpZZ2+ua77waticFgpKY9Nc3NVdXV1EzmUPh8vocfe+zxRx7597/++cw//2k+tTDT8NWYOWN6ZVUVhuMKhcLt9iy45hqny3VanXfo9elpaZMnThQIBHv27RumpEwmK8jL+3LXLmrMvfebb04N6HP4PF51ba1CoRAIBHeuuqewqBhaJgAAMLI8yQR99Z98MWz2k489unDJjaMLCvQGw4Tx48eOGYMQmjh+/LpXXhWLRDcsWrT+xTUr7139/f4fXC5XVJTsnpV3DS/ztfXr7l59396vv3E4HPQwMT0t7coFCy5ZcIVSqZwz+9cvMlcsX15dUzPn0vlqtbq7u/v9d96JFKVWqda+8MJtd96ZmZHh9ngmTZhIbV9w2WUffvTRjLnzuBzOrBkz6iO+JaWJj4t74ulnuFxuW3v7E48+kpKSPEyd16xbp1apbl22lMFgfL9//1PPPvvh5s2nrcbYMWPuu/feK665Ni0tra29/enHn0iIj3ecbrH7a668cuu27VcvvN7j9V595ZUikfill1++9uqrBy388ktr77jnnu/373d7PAWncsQLBII3Xn310aeeSkpIMHZ1zZ4587prroaWCQDAWcUI54YdJo/PH07i09dnMxgNKpUqKTGR3miz2SSnlonHcbxdq2Wz2Rnp6WciEMdxg9GYEJ+wddu2Nq2WfqHGbLGQJBkT3X91MJPJZLPbMzMyqI9J+uF2e3rNvelpaZFflZAkaTQa5XIF9SixH4ePHl33yqvf7d3T2tYWGxMjl8v/e80PWg2EkMvtNhqNiYmJ1CKaZ0IgGNTpdHGxsVFRUW63hyDCsqETeWMYpuvQJycl9lv2IRgM6Tp0UTJZbGwsNEsAAGBk+T9HqVRQr5ZEEvlsks1m52Rn/64Ba+SDT5roIVbRi42NHabHl0jEAyMig8FITk4+bTVyh3id5w8waDUQQlKJJD8v73eJ4vN4dMUGlRkJh8MZNKEdj8cdlZMDDRIAAAiWf+bwcUTIz8+Ljlb/9eeNj4u/6ooF4KwAAAAjxTmzRFffpTeCtS4clAe+BCUAAHD2AEt0AQAAAAAESwAAAAD47zhnpmEBAAAAAEaWAAAAAADBEgAAAAAgWAIAAAAABEsAAAAAgGAJAAAAAAAESwD4c+kwGs5keRYAAM5pRizdXZggiHCYzWZTCx9iGMZisZhMJkmSOI4jhBCDwWaxTv5GiMqBDgDhcJggCMpbIn8jhDAcRyTJZDJZLBaGYRwOB8NxNosVubgmQRDhcJhyJ+pwhBCTyWQwGOFwmHI8Dpt9cheDwWGzEUKBYBAhxGGzSZKkvBQxGIgkLX19Aj5fLBKBXSLBcZzBZDIQolRNt25K+ZSBftU/g8FhsyPtguM4SZIIIRaLxWAwqE7gpMUJwufzScRikiQp02M4zmIymUy47wf+t7CefebJETlxY3NzS3tbd68JwzEOh3Pw6FGrzZackNDU0mLs7qqsqzV2dUdJpb8UHsNwrEWrlYjEQoEADAYYu7qOlZZ4fT6z1Wpz2DWtrSZzbwjDZFJpY7OmrLqKy+XKJJJvfvwhMT5+/8EDSfEJvIjlX3otloPHjpGIVCtVeqPxeEV5KBRqammNipIdPHoEw/HaxobkhMQOo7G4vAzHw9Eqlaattdds6TAaXW5XXVMTn8ev1zSFQiGL1dqq0yKE2jv0yRFL3AA/HznMZrHCBHG4qDBapTpUVES1bmtf3+Hjxb6A39DVlRgX197RQSvZbLXSdvn5yGGtXu9yu41dXVEyWXVDfatWKxaLWSzW8bITYYJo1bbLJNKvf9iflJBwrKREIhbD/Qpw3gZLDMNkUmlBbm5dU2NWeobD5eRyOIqoqBCOZaVnOF2urIx0mVRqcziYTGZKQqJSLqfuRoELHKlE4nA6J4wZ26ZtT09O8QcCEok4FArFRkfHqKPtDseYvHwmk5kQF3e4qGjKhAkqxW+WoGEwGGwWy+5wJicmCgWCDoOBw+GMzS+IkskMXZ1hghhfMFooFKoUCrvDMWnsOAaDUdfUOH3S5MT4eKFA6PF5SZL0+vyJ8fHyqKgeUy+Py8vLzubxeGAaGrlM5nS7o2QyLpeblJBAt26ZVNrV3ZMYF99n60tJSlLK5bSSI+3CYDAEPH5WRgZCKDY6WigQcjjslMSkppaWjJTUlMQkk8WsUijxMGFz2BVyeVxMDIfNBrUD/1NGfu6CwWD0Wsxerw/H8eb2tpTEJMrvE2LjBHw+h83OzxnF5XLDBCEfffcAACAASURBVAHWAijMVqumtXXSuPEIoTAR7uzuzkrrvzRpKBQSCYVen6/f9jad1uf3+/x+s9WKEIpWqTNS06QSCUJIJBQV5IySyWT95vSI8MmpWqpYUkICHYDjY2NTEhMxDAOjRKJUKPrstvaOjozU1MjWjRAiSKKzpzsjNW0Yu2SkplIb6R8neysmgyBJhBBJkAwGg8VkqpWqXrMZFA6cz8HS5nC06rTFZSdSkpLLq2vSUlKyMzLadDp/IKBpbTWZzT29vV6fz2Q2NzY31zU2Duz1gAuTDqOBIAiVQi4SCjt7ugOBwKyp04pOnAiFQhU1NSazuVXbHggGDxcXTZ04sbq+zuaw08f6/H6d3pCZnp6ZllZWVWXs7u42majHY1abrdditjudLCYTIUQ5YWVdLUmSyYmJRSdKSysqOru77Q6H3eGw2m12p8PQ1aXvNHYYjU2trWCXgfEyEAxwOJzI1t1hNHo83oljxrZo250uF61kr88XaRen22Xo6qrXNJEk6XS7Gps1Or2hp7c3My29oVlT01BPIsRhs7tNJolY7Av4GaBu4C8Y141gbliSJCPfvACAsxbq3R94EPC7IAjiT3/vhiRJDMO4EQ+hwwTBgrd7gPM7WAIAAADAOQHckQEAAADAaRixV8hSsrJB+wAAAMDvQt/aAiNLAAAAADgbgWeWAAAAAAAjSwAAAACAYAkAAAAAECwB4C/FbreDEgAAiGTEcsNSHC4sTE1ORggdKz5+6NgxBoMRGx19JgceLS7e//OB0Xl5BEm263TKU+nHPB7P1//ZX1tfnxAXJxAIKmtqfjl6TCQUKuTyfruaWlq++c9+j9eTkpQEfnAO4XA43tu8uaS0VKfTjS4oOHjo0OdffiWVSmJjYkwm01vvvltTWztmzBj2qWShFVVVm7du7bPZ8kaNCgZDWz788KefD2RmZEgkkn3ffPvZF19KJJL4uDiqsNPlev3NNz/Ytj0+Pi4ZHAMAgBEfWVqs1jfee++7H39CCGlaWts7dNdeccXX3//H4/XSZbp7ejQtrdQCPSEMa2hqsvb1IYQcTmdCXNzSG24wdHZ2dnXt+/4/fr+fOmTLjh3jRo+eOmnS5u0fdfX0HDp27Ir5l361b5/b44nc5XS5vvnP/r8tXqTr0PeaLeAH5xBNzc18Hv/KBQtmTp/R1t7+xVe77rz9tg1vbrTb7U8/988bFi3KzMh486236eC34Y0371u1qqlJc/RY4cZ33klMiF/6t5ue/ee/CouKqmtqHrhv9ca333E6nVT5d97bdMXll19/3XV7v/4aVA0AwMgHS5VSufquuyRiEUIoNiZ60TXXWKxWDofDPbVuZXVd3Xc//mTs6nx7yxaCIN54912z1frxF182t7b+fOgwQRBSqWTPt9/12Wwej8d3KljOnTU7KyNdIZfjOF7X0DBjylSFXF6Qm9fa3v6bXY2NaSnJ3+7/ITcnJyZaDX5wDtGh1xs6Oz/9/Auf33esqOjaq6+KiYmZOWN6ZXV1MBjMysycPm3aibIyqrDJZMrOzlKrVJMnTSwtK2vXts+56KK01FRTb29zS+uM6dNUSmV6elpdfT1VXqlU7Pv2W5/P9+q6daDqP4zX57P29Vn7+qy2Pir1LgCc64xYUgIGg0HHxSiZjCCIdl0HSZI+v59K/JiZluZyu3vNZpvNrjcakxIS5s2enZ6a+svRY5Fr12VlZMTGxNDTsOPHjC4pKztSVLzilmVllZWxAj5CiMvlhELYtMmT6F219Q3dJtPyv/1t+6efyaNk1GJAwDlBUmJiWkpqUlLivQ88OP/ii9NSUxFCfB4/EAhOnjTp6ef+ieMYPQebmZFhMpleXLe+rb19VE7OTUuWPP7U09Fqtd1uv/Tiec/93wsnysrLyysunjOXKn/PXXcdPVb4xltvFR4v/uC992BJ4T+Gz+dzuFzUb7ksCnLqAhAs/xyOFhenJSdfedl8PIy363QTx41DCO3+9tuMtLSrLrtM09KKECIIEiEUCmECPp/JZIbDBEEQbo+7n6g9337H5XIef/ABJpPZoVRRU6y9FktOZlbkLmNnV05mlkIuzxuV02ezZ0GsPHfAMGzKpElcLlepUMTEROsNBoSQwWicNHGCXB41pqDAZre/t3kLVTgUCq28486MjPQDBw9yOByZVLb+xTU8Hs/Y2clgMv/9r38pFAqX25WTnUWV3/jOO/ffe2+vubfkRJnNblcplaDwP4DH56NWQGMwUGpyMoRKAILln0NacvL2Tz/LTE/Tdujnzz15j6+Qy+saGtt1umAoyOfzzVbLF3v2dhgMK5YttfbZdn39tVKhYDKYQqFQbzR29/RQ72gUlZYkxMW3tLUr5FF/W7x4w9vvmMy9DoczJTlp4+b36V1Llyx549337A5HZ3fXQ6tWgR+cQ7jd7gf+/ohKpczJyb76yivvue/+Dr3ebLbk5ea+sHbt0WPH2tq1Tz/xOFWYy+W++fbb48eNa2hsfPuNNw78cnDHJx9zOJxFC68jCfIfzz+fn58nlUhjYmKo8inJyY899bTP5+Xz+RAp/5sWnZacDHoAzifOlgw+IQxzud2KqKjIiS+H0ymVSHAcZ7PZDAbD7nBIJRJqhi0YDLJYLOo3juMsFmvQ1b5IkrTZ7fQkbb9dTpdLJpXCMmHnYLz0EERYJpNRdjSZeuPiYqldJpNJJpMJBAK6cDgcNvX2xsbEUJOBDoeDJEm5XI4Q8vv9TqczNja2n/C9X3+9/JZloGcAAM66YAkAAAAAZy3w/gIAAAAAQLAEAAAAAAiWAAAAAADBEgD+UiA3LAAA/ThbcsNW1tT8dOiQz+dLSkw8kwMhN+wFC+SGBQDgAhpZRuaG1RuMx46XXH/11eXV1S1tbXQZyA0LDARywwIAcAEFy8jcsAIBf8l110olEqlEEgyFqAKQGxYYFMgNCwDAX89ZkRs2Wq12OJ3vb9umUqpG5+VRGyE3LDAokBsWAIALKFhG4nS5Nn344e3LlkUuZgm5YYFBgdywAABcoMGysqbG7fF++tUuhNBVl83PycpCkBsWGALIDQsAwF/PWZ3uDnLDAkPES8gNCwAABEsAAAAAOJuA9xcAAAAAAIIlAAAAAECwBAAAAAAIlgAAAAAAwRIAAAAAIFgCAAAAAARLAAAAAIBgCQAAAAAQLAEAAAAAgGAJAAAAAH+EEUuknpKVPcxefWsL2AYAAACAkSWqTBk36L+hys+YMzcrP5/6lzdu/NULrz96rBAh1KTRTJk1e0QuYezkKcbOToRQ0fHjN9y8FCG0fefOB/7+yP/6dIPSpNHQix7/T6sBAAAAI8uzl2AwuO7FF+dddBFCCMOw7Ts/Xrl6dcXx4qTExNdfeXlk6yYUCHg8LkLo0osvnjxx4ojU4eeDB9u12imTJ49sNQAAACBY/kWkZGUPOhMrFokUp9bbWnnHinfff7+ru1sul9fW1c+cPh0h5PF4Pti2vaW1NTkpceUddyqVCoSQz+d7/4OtWp1u1owZfD4vPS1tdEFBd0/P7r17H1i9GiEUCAbf2LjxkYce4nK5CKGy8vKvv/suEAhcf911lNhIDEbjRzs/dnvci65b+GvFxGKJWIwQMpst3T3d+Xl5vWbzN999N3H8hL1ff40QWn7LsqzMTKpwXUPDp5997nA6Zs+adfONN1JrhO3ety89NbWsoqKpSTNu3NjbbrllmNMhhEpPnPhqzx6/PzBu7Jjly25pa287WljkcDh279u3eOFCuhoIIRzHP/zoo4rKqmi1euUdK5KTkxFCw1QPAAAA6Mc5/ILP4aPHZFJpclKSxWLZtGULtfHxp5/u7e1detONOB5esnQptfHBRx6trKq6eO7cw0ePPPvPf2mamxFCJlPv9h076THre5u3YBiGEPrpwIH7//73grz8cWPGrn7wof0//hh5Uq/Xu/imv/l8vimTJq1/9VWPx0NtV6tUcy66CCFUVV393X/+gxCyWq0b3tz48muvTZwwwef337riDoIgEEJ19fW33L4iLzf3umuu+fzLr9a8tI6SsP+HH+9efV8oFJo+bdrGt9/57MsvhzldS2vbvQ88OH3atCWLF/3w08+vvv66RCJRq5RSiSQhLj6yGgih+x9+uKS0dMnixWq16urrF/X0mIapHgAAAHDOjCyH4tl//mvNunUIIb/Pb7Fa3924MXKZX4RQQ2PTLUtvnjF9+vRp08aOGY3juK6jo7ikpKyoUCQSXXfN1XPnXzb8KV7Z8PpzTz999ZVXIoTYbPaOjz+54vLL6b279u5NS0t9ac2/EUIzpk+fftEcantUVNTNN97YT1QwGHznzTeVSsUl8+YVTJhgsVpjoqPfff/9O2677dZlSxFC+Xl5F11y6ROPPkpN4V51xRX333svQkjT0lxbW3fzjTcOdTqEyHUvrrns0ksRQg2NjaUnypKTknJHjeJyudOmTomsQ0tr69HCovLiIqFQeOnF8zr0hk8+//yxvz88VPWgSQAAAJzzwfKB+1ZT86LUWPDl11676ooFkQWeefKJ5/7vhU2bt8yaOeOmJUvYbHZzS0taSopIJEIIMZnM4ScbMQxra29/6933tny4DSHk8Xhc/8/efcY1dbUBAD/ZgxEIe29UkI0iIIqCiAP3qNZWreJo1c7Xt+3bPbTDuvdCHLhFxYGKgIKK7L1XwgorJCF7vh8upikgtVNtn/+PD8nJueece3IvT87NzTkCgW6GiopKX28f7LG1lZXJk2vCgzJiMLDrwAYG+ng8Xi6XY4PCRQsWYhnsbG3xeHxLa4uzkxNCyMXZWbshj8cfojo7W9sbybcOHY1ra2tTq9XaDQeqrqkZ5uZGp9Oxp85OjlXVNUM0DwAAwEsfLC0tLFxdXLDHq2NXTp81G7t22jfa0mhCgoOzMzPKKyrS7t1bHrsqOekqmUyW6YSBXmEv9gCH+7lYXg+vrzuIRBKJ9Pkn/xvu7o4QUqpU/UIIlUoVS8Ta6oQi0RCtxenWoS2BQpFIJdhjvkCgVCq1IXBg/qdV9+O2beXlFZu++tLRwSHh7NmUu6lPawOVQpVIpdqnXV3dQ1QHAABgUC/xd5YMQ0OEkFgs1qaoVKrRoWObmpo8PTzWxMaamZoKBAIfL6+m5uaKykqEUCOLVVBY2Lc5g9HN5ba3tyOELl5O1MaP0ODg9Hv3mUwmk8nctnPX7r37dCsdGxJyO+Uuj8dDCF26ckUmk/3WZoeGhJy7cBH7gvDMuXMBfn4MBuNpmZ9WXSOLFTR6tJurq0qtvnEzue+9xOMHtsfX14fFZhcVFyOE+Hx+8u3bERMmwHEPAAAvzcjSn1X4RzY3MjJCCGXn5lpbWWnHhf/94P1Z8xd4jRzJYrP9/fx8vL1xONw3X3y+6PWlLs7OIpHI0cERy+zs5DQ1OjoieoqJicn4sJ9/pvnV55+tXLM248EDuVxOo1KPHz2iW2lkxMT0jPvhUZMdHez19fWtLC1/a7PfWrsmdu3aCVGTGQxGe0f7kf0Hhsj8tOqWLlny1oa3Mx886OZyZ8bEHDp69PrNm77e3tt37f74s882ffWVtgRTE5PNX3+9dGWsq4tLQ0ND1KRJYWND4bgHAIDfBCfq5T7H6oeYx+d3T+LT3c1lN7FNTU3tbG21iSKRqKOz08HefumKlTOmT5s/dy6W3tHZqdFo+t3YotFoGhobNRrN074LbG9vVyiVtjY2v3vH2Wy2oFc4zN2NRCL9auZBq+Pz+S2trc7OzlQKpau7W19Pj0ql9vYK8Xgc9gWtLrFYXNfQYG5qamFhAQc9AAC8TCPLv4iJCRO7b0WXnp6e04AQghAyNzMb5BMEDofdcfM0fzzkYD92fEaDVsdgMLTXb01NTLAHBgb6g5ZAp9O9PD3hcAcAgJcyWP79c8BOnBDu/PR7RwEAAIBBBlHP9zLss+uOXADv1t8xLk85B50AAAD9wBJdAAAAAARLAAAA4I95aS7DAgAAADCyBAAAACBYAgAAABAsAQAAAAiWAAAAAARLAAAAAAzwokx3193NrW9sKCsvz8svgHflX8uEyfT18Qnw97OytCQSidAhAIAXxPP/6Uh3N3fDe+9lPnyIPZ0zaw68K/9alVWV5RXl2OO4QwcnhodDnwAAYGSJUtPTN370MQ6HSziR4OfrZ2pqCm/Jv5xSqWxta/1207fLY1fNmD79+2+/odPp0C0AgH/vyDI1PX157Ko5s+Zs/WmrHl0P3gyg607KnXfee5vJZF6/nAiXZAEA/9Jg2d3NnTx9etjYcQf2HYC3AQyK3cQOGB3w5WefLnvtNegNAMC/MVi+unRZdU1NdlYOjCnBEA4fPfzR/z7KTEvVXcobAAD+Zs/npyPd3dzMhw+3b90BkRIMbdnryzxGeFxMvAxdAQB4jp7PV0H1jQ0IIT9fv37pSqXycHwcj8/TaDRLFi4eYjDR0dmhVqstLSxfru4uKi32Gen9Oza8/yDDwc7ewd7h99X7J3ZX6r00VxdXe1s77Gl8womliwe/Rnru0gV2ExvhcAb6+q+98iqdTt++Z6dcodCoNa4uLrNiZtTX11+4kogQQhqNUqX69L8f7zt8cO3KVb84QInE0JDQnNxcOFcBAP+6kWVZeTlCaOC9r4+ys+xsbD587z/LX1t69uI5hJBYIqmsrpJKpViGiqrKhsYGhNCDrEfpGfc1Gg1CiN3c1NzaghCSSCU8Pg8h1M3tVigVPD5PIBC0trVqy2/v6GjjcFhsFvaUL+AXlRSLxCKEkFQq5Qv4tfV13dxu3Sb18HgFxYVisRh7WlldhUV6hBCnnYMQUqlVnV2dAoGgV9iLEOoV9mIPurncmrpalVqF1ctqYvf09Jw6c5rb03fdW6PRdHV3Nbe2YOVgdZWUlWKbt3d0dHO52O4ghDo6O0Vicb88Ws2tLSVlpVhdMpmstLyM09GOENL2gG536e6CXC6vra8Ti8XYDmLtVCqVuoX08HhCkaiyukqhUCCEOru6eDxeZXWVRCpBCIWHje+7WqCzv5hGNmvZktc/2PCuva39/QcZCKHmlpaN77y/8d33iQTCmfPn3FzdPnp/40fvb3R0cHR3dUMIVddWDzxaAvwDtL8sAgCAf1GwzMsvGPT3lOZm5ukZ9++k3sUh3Advv9cr7P1px9bmlubvt20RiUVHT8QXl5ZcvXH9UXZWV3dXe0eHWqNOvHo542Hm3fTUK9eTauvqUtJSEULnEy9xuT0paanb9uxsexKKEEIfffHJg6yHJ84kFJeWdHZ1btu9s6u7+/utW4RCYX1jwxebvq6qrtq05XssfCKEOrs6t+3e0dnVtWnL9wihuJPxpeVlGQ8yE69eRgjtO3wQISQSik6dPd3R1XnyzGmE0PGEUzwev6yiPD7hRE1tzY49uxBCn379+Z27KT38HrFY3N3dF4wVCsUHH/83ryD/2KkTRSXFfAF/x95dXd1dX23+VqVSHTp2JOHcGaFIpNs//fJgiVk52ecuXaitqz0Sfwwh9OOOrR2dHQePHq5rqNf2gLa7dHdBpVZt/umHiqrK77dtuZeZoW2nSq3SLeTq9aR9hw7UN9R/t/VHrMZryTcaGhu37tqBEDp6/BhCqN/+auUXFWY+elhaXubi7KJNxOFw06dOyyvsm30iJz+3kc1aMGfe046WUaNGIYSampvhdAUAPC/P5zJsgL/f5199PfA+2GFu7hvffT+vsODI8TgjBsPV2UVPX18qk5FIpOLSksqqyh++2SyTywSCXoVCIVcoCHhC2v17UZGTEEKp99KdHZ36FThj2nQ/b1/tUxsr63mz5uTk5za3NNc3NsyYOt3f108sFhWXlRoxGKMDRk2Lntre2dnN5WJfpj7IejQ5clLomJCRIzw0Gk0ji/Xl/z7Dgu7sGbN0K3J1djl28rhCqeByu+1sbX/aud3G2lqt0dQ3NvYKe83NzFcuewMhZGJi4ubqpt3K0cFx5rSYUQGBl5OueI7wiI6Mam5tEfT2SqRSpVK5ZmUsiUjSrUWPrqebR19PDyF0JzXl7bXrDA0NseH17JiZ9Q31CoWis7NT2wMikQjrLt1d8BrpZWdrFzNlmpWFZWdXF/ZhBWvnwEKGublXVFX18HgIoWnRU3xGemflPNY2LPnObd39NdA3+PnjGB5vZMQoKSt1c3HVJhLwBOxBa1vrhcRLX/zvUxwO97SjJScnByEEN/gAAP51wdLTwwMh1NXV1e9K7MUrid6eI6Mjo6Ijo/7zyYeuzi42Vtaj/ANtrKxtbWwIRAJCSCAQdHR2/jxMweNG+QcihJhGxng8Hhtv8fl87FUKmaJbPolEwv59qzUaHEJqtRohJJPLaTSq9lUCHq9Rq7VjIAKBiBCqqauzsrTC8iuVSqxYHA6n0Wh4gr66/Hx8z1w4F+gfiD0d7j7MytLK0MCATqOTyeRB+wG7LiqXy+k0em5Bfml52ZyZs6pra7BG9ouUCKF+efqiEQ5HIBJUalV9Q72+nt7Zi+fXrIhVP9mFfj2guws4XF+E0l7UxdrJ4/P6FdLXToWcTqMNLHPg/moT/X18TU1Mh7m579q/Z47Ox4uikmJXZ2eJVLJj3+6331pPo9KGvA6RNzYkBM5VAMC/LlhiQ8CCwoJJkZN004NHB+06sHe427DO7q5R/oEhY0K27NiqUChaWlvfWbc+wNd/z8F9Xd3dry9egsPhTp5JmBA2Pmpi5LlL59Vqja+3j72d3bGTx3t4vKaWX79kFxY6dvueXVW11Sw2e/aMmVXVg3xbNi40bMfeXWUVZTKZbPzYMC/PkfsOHxQKhVOiJiOEPIaP2LZ7B8Lh8DgcQig8bPxb7204vOcAQmj6lKnnEy9aW1rh8LhxoWE/dzeRmJ2XOzqgL6CymtiH4+PYTexVb6wUCoWsJnbSjet8AV875Vs/RgyGbp7RgaMQQjFTp2/fs4tGo3mP9CKTKRKJ5O69NA6nvYvbzTBkYBuamphi3aW7C86OTlevXztw9FAbhzNmdJC2ln6FEAnE84kXrSwsTZgmFApl0IY9bX/3HNxPoVAkEsnCufMRQhKpZPNPP6jVarVavWHtW5euXObxeHHHj2GV/ued9waWrFQqHzx8MHlSJJyrAIDn6IX7naVKpers6jQwMMDSVWoVl8s1NTHFBkFCkYhCJmNDQLlcjo2EBL29eDweuyapUqnkCvnQIxXdf8R8AZ9pzBziGqBKrRIJRYaGhthTvoBPJpO15YvEIhqVhscP8tWvTCYTiUVMY2a/0jRqDTYfjVwu/2nX9rfXriMQCFgQEgqFFAoFT8BrB68DDZpHJpeplCpsWjiZXKZUKvXoehKpRLcftN2l3QWFQpGVkx06Jvj6rZvWllYBfv4/N16nkHMXL4wPG8cwZBgbGQ3RmYPu7x8Hv7MEAPyrgyXM4KNUKpNuXp8dM/M5tuHC5UttHI6VpeXcmbOf9okhPeOe5whPs+cxbS/M4AMA+LcHSwRzw4IhwdywAIAXx/Nc/HlieHjcoYMZmfdHjxl1J+VOV1cXvB9AqVSym9ir165e/NriMUFBiefOQqQEAPyrR5YYWM8SaMF6lgAACJa/EjLrGxvKysvz8gvgXfnXMmEyfX18Avz9rCwtYUAJAIBgCQAAALw08NAFAAAAAARLAAAAAIIlAAAAAMESAAAAgGAJAAAAQLAEAAAAIFgCAAAAECwBAAAACJYAAAAAgGAJAAAA/A7PbfpNBzf3IV5l1VQ/7SWJRNLU3EImk+xsbQkEwl/XQr5A0N7e4e7mCkcJAADAyPK5yXfwHfTvafnlcvnnX33tEzhq9oL5k6ZO9R8TfPRY/O+oN2R8eEVlJULo2IkT6999D0u8n5HZ0Niom+3N9RsyMjOHLqqqujpkfPizVHr63Lk3N2zAHr+y5LX7GU8tOWBMcHVNLRyaAAAAI8vf4/Ovv3mUlXUu4ZSvj49Sqbxy7drHn35GpVIWv/LKbypHJpOp1WqEUOTEiaMCArDE/YcOxUyf5uTo2De0ZbGjo6Jee3Xxn9V4lVIllyuwx2Qyma5Hf1rOHVt/srG2gkMTAAAgWP4KBzf3fldiW1pbz124cOF0gq+PD0KISCTOnTWrra1t5569ixYuPH/xkpuri5+vL0IoJTUNj8dhSyEKhcK448fLKyqYTObCefO8vbx0y+zo6Gxta/X08Lh561Yji5V867ariwsWPvMKCgqLi+sa6pcuWaKNoFrZOTmXLl+h0qihwcG66ZcuX3nw6JGBgf6gW2np6+sb6Os/rXnFxSXDhw3T09MrKCxsbWtTKpX1DQ3vPhmVAgAA+Pu9NDf4ZGVnm5iYYOFQa9qUKW0cTnt7e9L162XlFVhi5sMHDx9lYY8//uyzquqaxQtfcXVxWfDqEi73F+uRFRQWXrtxAyFkYW5Bp9MtLMyNjYwQQt9s/i7+5MnwcWH6evoz5sxtbmnR3aqwqGhZ7CpbWxsXJ+cvv/lWmz70VrrGh4WZmZo+rXl7Dxzo7u5GCJWVV3y9aXPCmbM21jZwpAIAAIwsfx2Hw7GytOyXiIUcbk/P07YKGjVqanS0sbFxgL/ftp27WGw2k8kcmM3fz9fczMzP19fVxaWzq+vYiRMP76Wbm5nFTEPNLc0XEy+/ve4tbeYDhw/HvrF83dq1CCE9Pb0t27YhhH51K10L5897xuaRSKSE4/F/6X1MAAAA/jnBUl9fHxtv6Wpta0MIWVpYPG0rdze3T7/8qqq6Wi6XSyUSjebXK6qprVWr1bFr38SetnE4KpVaN0N5ReX8uXOxx14jPZ9xq9/XPHc3N4iUAAAAwfJZjQoI+OzLryqrqoYPG4YQyi8oZDAM79y96+bqymQycTicNiePxzM1McVGe0uWv/H9t99+88XnRkZGfkFBz1IRlUKlUqlxBw9gT2UyOYlM+kUGKlUikWCPe3uFz7jVQM/SPN39AgAA8Ly8NN9ZeowYET5u3Ib33q+prUUIcdrb576yWPSO+wAAIABJREFUaOeevR+8+w5CiMFglFdWYBFI+6sMTnu7RqOJjppkZGSUfv8+l9szRPk4PE4mkyGERgwfRqVQCouLmUwmjUZbsWZ15oOHujnHhoRcuJSI3U975vw5LPFXtxroNzUPAADAv3Rk6c8q/E35d2/f/uEnn0yaOs3UxIREIhEJBHMzs8Li4kkREctef23pGysePsrC4XBBo0f1xdfhw0cFBkyaOk1PT8/B3n5saMgX33xz9eKFQQsP8PP77sct+np68+bM2b5ly/sffmhnY9PU0hIWGjozZrpuzg3r3nojdtX4SZPoNLqnhweWSKPRht5qkPD/W5oHAADgOcKJernPsfoh5vF52iQ+XC63kcXW19dzdnKSSKTxJ0+8uXo1Ho+Xy+Utra021tZkMlmbWaPR1NbV0el0G2truVze09Nj8fQvOLlcroGBAYlEQgjJZPKGxgYjBsNywF1FWLGNLJaxkZGRkZFu+tBbDVrOszcPAAAABMtnDZYAAADAvytYAgAAAC++l+Zu2O7IBf/yt8ok5RwcrwAA8FzAEl0AAAAABEsAAADgj4HvLAEAAAAYWQIAAAAQLAEAAAAIlgAAAAAESwAAAACCJQAAAADBEgAAAIBg+RdQKBT1jY2cjo5fzSmTyRQKxbOUKRaLeXw+j89XKpV/RZuFItEQJWNV8wUCzbMsM/3be0OuUIifLKX5Z+nmctVqNdZyoVD4V3SaRqMRCHoFgl6VSjVohvrGxj/xDfodbRv0JZVKJfmze7vfwdzU3CyXy/tlGDTxnw3r6mc/zQH4+xH+9/F/n0vFQpHo++075HJFXkFhQXFRoJ/f6QsXvZ6seKWVej+DRqOVV1VJpTJTE5NfLfZQ/PHcwoLa+vprt25pNBonB4c/t9lnExNNmUyGoeGgkeydDz/q4fUUlJRcuprkOXyYgYHB7+6NQbOVV1YVFBcPc3X9s3angcUqrag0NDT8YvN33dyeguLipORbQYGB2NIrf2J4OHAsfvgw9/sPH7oP1vi8wiK+QGCls+JKbX39/qNxGY8ecXk9w93cEEJlFRUPs7OHu/888/7de/fOXkpMy8g0NjKyMDfHErfs2h0WEvxb2xb8ZFk3XS2trddu3fb18vpzD6Hs/HzsYOa0t9+9f9/fx0d7nOvr6ekm/ntgXd3J7RYIBDZWVvB/GbyAntvcsGWVlb5eXrOmTUUInTp3vqGR9TA7e5ibq5+3d1pGRntHh6+3t52Nzf2HDzu7OoMCA+k0GkLo4ePHrKbmoMAAZ0fH3IICpVLJ6eiIDA/X19PTljx7+nRHe3tuT8+eQ4dH+fs3stmNbHZMdHRVbW1+YaGLk/PoAH+pTJaSli5XyCPGj2cYGmY8fNTK4YwO8HdycGhta8t49EhPTy9y/HiFUqndPDMrq7dXKJXKhtgpUxOT1cuXI4TSMzOzcvNmT5+m3ZcR7u75RUU0KlWtVpuZmqZlZBobMSLDw/F4/MDeUCqVarX6dlqaVCqNDA83YjByCwokEinCIaFQeCkpydTEZFxIyMAOYRgaFpeVjw7wt7Ox6dc/7ObmnPwCZ0dHP++f//tfvXFzzRvL+b29DnZ2sUtfRwgdPBbPamqSSCRYU91cXHSbUVldU1hS4uLsNMrPr6Or615mpqGBYcT4cQQCQXdPsdYOc3fTqDVZuTkOdnYIIUtzcxa7SSqTUSkU3U4TicUisaiHx7OxsjI3M8MST1+4uH71KiMG4+Ovvp4UHn7vwcOUe+mj/f21WymVyjtp6Zs++1QilX675SdvT0/dMjs6O9va2w0NDKRSaWVNzcgRHu2dHdyenujISPKTzwHtHZ3atiGEBu0fbOipu2va4Tinvb2usdGYYRQWEsxiN0mk0vbOjuBRo7TdxTA0zC8q6hUKBb290RERMrkcO5a8PDywg7mtvQN7X5jGxthxvnDOHG2iva3ts3/iGXQ3tceGrY1NQVFRUGCgWq1+lJ0TOiaooLikgdUY6Odnb2v77IdNv7MGq133fJHJ5c2traymJhKRGBke3shmKxSK0opKJwcHP2+vnPx8P29vIpGYlZM7ZlSgoLc3PSNTX19vfGhovz3SbZ7uYUYk9v2/yi0o8PXy0j4F4J98GdbN2flBVtaew4fTMzJjoqPNzc306HRHe/vCkhJuDy88LOzEmbMEPN7M1MTZ0bGkvJzV1JSSfq+5tXVC2NiE8xe6udyDx+IJBIKhgcH5xMu6Jd9MSTl1/vzug4cmTQjn8XhHjp+wtrRsbmm9cv36xHHjSyvKH+flXbl+Q19Pz9fLK/HatZLy8sqa6vCxY6/euKnRaM5cvBQWHEwhk5OSk7Wb305NbWpudnFyLC4tHWKnJBJJYUlJdl5+ZlaWk8Mv9kWhUKRnPigoLjE2Nt554GBQYEAPj5+WkTFobxCJxAPH4q0sLP28vXfs348Qij99Rq6QUymUwpLSAF/fhkbWvQcP+nXI3Xv3S8orvD099h+Nw8Ketn+6urtPnTs/OsA/r7AwOy9f96IuhUJBCDW3tiacv3D05ElOe7ubszPWVHMzM91mSGWyhPPnJ44Lq6yqbm5tPXA0bkxgIIVCfvg4u9+e9rWWTN5z6NBwd/eqmlqsRidHh7KKyn6dlnQz2d7Wzt/H++CxeG3i2hVvGDEYPD5frVaTyeQpkyJfW7jwFwcuHr/x7Q14PL6RzTYz/cUlB057+9GTp5zsHUorKpLv3vX29Pxhxw4SkahUqtLuZ2hjrW7bBu0fTL9dexIhOHGnErw8PGrr6zMfPaqoqb5y44a9ra1ud6nV6p37DxgbGRkZMuJOJWiPJexgzsrJLS4rHeXvf+xUglgsxo5z3URuT88znkqD7qbusdHb23v91m25QlFZXVPbUN+vlmc/bHTPGm3tuudLV3f3keMn3JxdGljs7Ly80oqKpOTkAF+f85cv8wWCO2np2FcYt1LvqtXqXQcOjhjmjsPhTpw9q7s7/Zqne5jpfHfQo/7t33QA8FIGS6ax8fdffhEdESEQ9n7x3XcqlYpMJjONjd1dXUkk4rXk5B4eT63R6NP1mMbG2Cb5RUXTJk+2tLDwHulZU1dnZWkxyt/f38eHJ+Drlhzo6xs+dmzMlClXbyZrNMh7pGeAr29hacmEsDALc7PxoaGl5RXD3FxT7qU/ys6JGD/eysKiqaX18vVr4WFjcTjcpAkT7qSnF5aUisUShPo2zyssmjltmrur66iAnwc3l5Kuffn991k5udoUhVLJ6ejg8XnzZsz08/bW3Re5QoEQmh49WSaT2VpbO9rbz581U/uZul9vCHp7u7u7A3x9XJycKGSKQNBrasKMGD+eSqEGjx7lYGcXFTGxura2X4cghKIjJrq5uBAIBISQbv8UlZbKFfI7aWmC3t6S8nKsUh6Ppx2Rm5qYjB8bGjFuvLmZ2aPsHKypZqamus2QSqQMhuHJs+dsrK2tLCw8Rww/lnCaxxf4env121Oste2dXa4uzsNcXWOmRGO1GDOM2gd8KRsWElxVU5OSnq77daypiUlKevr+o3HrYmMHHUDg8Xg9Ov3EmbP3HzxctWyZ7ks/7Ng5OsDf0NAAIRQSFOTi5GRtZRkUGDjMzZXH5z8ZjbF12zZo/2AGvomYUf5+Tg4OkyZOqKiuRgiNDw11cnDo96452Nv7jBw5LjSkldOmPZawzXPy8wWC3jtpaXKFor6xETvOdROra2u1dR2Ii+v3l1tQoNvIgbvZ79gI8PMtKinJys0JCw4eWMszHja6Z4226n7ni4/XSDcX55EjhvP4AoTQuNBQBzs7JwcHQe8vvhhu7+zs6OrKePQI+yJA96V+zdM9zLR5JkdMJP+p3xQA8OJehr1w5aqdjXVQYKCLkxOnvaOruxtLP5d4eYS7+8ypU7/6/od+m1AoFJFIpEenCwS9bs7O2AVMhFC/j5hmpqY2VlZWFhYnzpxRqpQEPAEhRKVQRCIxQogvEDAMDXE43GcbN7KamvcePvLmijfWr4pVqlQHjsYZGhhcvHp149sbGtlNOfn5CKG+zakUoUhEp9H4OjeDzJo2debUKdpmIIQMDQyiIyK0TwfuC4FAoFAo2ACF3dzMFwh8Ro4ctDeUqr7biCRSCZ1Ow5qBEOLzBQghgUBgaGAolkh1OwQLIbrhRNs/FArFz9t7WlRUVzdXJO67BYZMJmuHSlQKBfuuyGPYsFYOB2sqQki3GQiHYqKjLc0tLl69+oBAcLCznxYV9Tg379T581QKVXdPf+40oQhrrXY8RyaT+72t+4/GrV8VyzQ2/uK773S6LlGPrvfhu+8McQht37d/evRkz+HD+6V/uvE/P+7chX3vSyQQf9ktfcdKv7YN2j9DH5DYBXmhUGRoYIgQIhDw/bqLTqdJZVJsBE+lULXdoj2YJ44Lc3JwqKmrt7Qwxwa4/RK1mbFr+0OdxgN2s9/J4ursnHDhAo8vcHZ07FfLvQcPn/Gw6RUKtWfN5s8/w86mfufLz5sjDUIIj8NrL2cTiQSlSiWXy4VCEYVMtra0XLpokUql6vfppF/zSCSy9jBb+8Yb8C8b/OuC5cRxYdv27iutqJRKpXKF3N7WlmFocDs1zc7GJuPRo6qaGoVSWVZRaWZmei35lrOTI0Jo6qTIg/HxDnZ2nV3dujd69HPsVAKdTpPJ5NGREaQng5Lg0aN/2r2nhdPWwGK9tXJlUWnpvqNxTCMjV2dnhNCBY8fcXVzIZLKZmZlCqbyYlKRUKOsaG8aFhmCbT4uKOnA0zt7Oto3DGfhv5Wn67QuWaG1pSSaTDh8/0cbhxC5b+rTeGBcSsn3vPjKZ7Oftozu0qqiuPnryZBunfc0by7k9PbodcvVm8tNaMtrff+uePUKhqKmlZeniRViiEYMhlfV9BVtTX//99h04HNJo0KplS+NOJWDpus2g0+lnLyUOd3dva2+PnBCenJJSXlXZw+P5jBypUCgH7qmdjY1SpYw7laC9f7iV0zY6IKCkrLy4vOzV+fOxRHNT06TkW3g8XqlUtXI41paW2LU4C3Pz0ooKhNC6VbF6dLp2X2Qy2aatW9evXt3AYl1LvnUt+ZYJk7ny9de0GYyNjObNnBF/+oyjvd0Qb41u2wbtn0HfxNFPLi2UlJfHJ5xuYLPXxa7MLSwc2F1EIlEg6D0Qd4zH50dNnNCvAVMmRZ44c9bB3q6js3P9qlXYcT5nRoxu4h85v/qdLDgcjsfje3l6DKx6iEL6dQu7uVn3rEEI0Wg03fNl0PuktHxGeu0+eIhKpdCoVKaxsYW5Wfzp0wJBb+iYoCF6Jr+oUHuYafN88+OW99a9hX31C8Df43muOqJWq7u5XAqZgl0xU6vV2MhDIOjV19fTaDRKpZJCoUilUiqVqv2aTSQSGRsZ/b7qeng8YyMjLMgJenvlcjl2h61UJuPx+WYmJgQCQalUSqVSfX19sUSiezZKZTKlUql7J9Gz6Lcv2nShUEij0bDR26C9gZ78CmJgjSKxmEqhYNs+e4doNJpuLteIwdANvZev3/Dz8nJ4elDp1wyVStXZ3W3EYGA36XR1d5PJZEMDgyH2tIfHYxga4vF4tVq998iRdbGxIrE4r6BQ+0FEo9HwBQIjBkMmkxGJRN0+edqO3Lp7Nzoy8o8fgdq2Pa1/nvYmlpSV19TXRU2cSCGT+905rO0ulUq1eeu2jW9vUCqVdJ1g//NFe4WCLxBo7/HGjvN+iX9Ev2Njx779ry96BXv67LX06xbds0Z7tWDQ82VQIrGYQiZre5jH51PIZNqArfo1T/cwA+DfGCzBc6dQKB5mZw+8HfGvUN/YqNFoXJyc+v3H/B0fesRisb6+/nPst7qGhpbWNm28f1o7T5w9u3TRohfhjT6XmEin0adHT4ZjHgAIlgAAAMBfAqa7AwAAACBYAgAAABAsAQAAAAiWAAAAAARLAAAA4AX23FYdSbt370RCwviwMITQsRMnTExMaFTq5h9+PHj0yM1bt2UyqaeHh1qtPn7q1JWkJIahoZWl5XPspqLS0t7eXhMmE3vKbm5OupnMbm52cnD41d8F3klLMzYyGvhjspt3UpwdHZpbW7VF4fH4W6mp+UVFVpaWNBotv6jodlqaWCy2s7VtYLFS0u+VV1YplUq+QCAQCH71t5UajeboyVPD3VxJJFJRaemN27f9vL15fP75y1d0f9/9R3B7eg4ei7/34EF2bp6ZqSnT2PhS0jVWcxP2o/WC4pLL16+P8vMrq6w8cebMw6zHnI6OEcOGYduq1erbqWkPHmfp0fVMmMbNLa1Xb94USyS2Ntb9XkIIsdhNPD7fiMF4Wku0GTQajW4fYq8KRaIrN240sFjOjo6/OpUEAAC8QCNLTnv77TspKalpCKG6unqxWLxr714zM9OTcXF7d+5ISU27m5YWf/KkUqFcvWLF519/o51r5u/H7ek5fuYMp71D+1/+8PETUyZFEknE67dvY4lSqbRXKKxraOgVChFCXVwuXyDAlmns7Oru4fEqqqp0ZxZta2+XSKU4HE63qNT796VS2djg4IPH4lnspoxHWbOnT88tLKyurc0tKBwxzH1MYICDnZ27i0tyyt2ntbajq6uppUWpVPL4fM/hw/EEglKpzHyUhc1K2tnVHejnq1arZTIZXyBgsZuwHaxraFCr1djmnI6OpuZmrLTWtrayykrsJd1smD2HDk+ZFPnfd95e8fprB4/F8wWCpubmx7l9k+VmZmW1cdoRQolJ195es+aDDeu7uT2NbDb26u3UVJVKFRMdfSwhQSqTHToeHxUxsaa+rqi0VPcluUJxJy1ty+7d2v7H3gL+kyn0sI8j2gy6fajNEHcqYaSHh7mp2fnLV+CcBwC8TMESIbR2VeyBw4fFYnHfv7y7qbFvvIEQIpFIq1auSLt3z2vkyAXz5pmZmZGIRNVfs5jzr1Kr1WcTEydPnKhNUapUC+fMNmEymUbGMlnfIr2VNTWbt25rYLG37d0nEPTuPXzk2q1bj3PzLly5ghC6fvt2c2vr1t17tIU8yMry8/bqV1RRSemEsLHWlpZ4PA6Px82fOcPQwMDQwEAml3d1d5dXVmZmPcbj8TgcTl9fr629XVtaF/fnH8t+++OW1Pv3Bb29h+LjpVLp9r37+Hx+D5/X3tFRUV19OzW1qaVl75Ej7Obmb3/6qaKmuqK6+vSFi3UNDXuPHEEIffnd91k5uWcuJZZVVOQUFCReu17f2Hj8zJl+2RBCPTwegUDA1qdkGBqGBI2uqKpCCDk7ONbU1UulUjwOh812TaNRzyYmllZULH91saO9Pba5k6NjeNhYIwaDQCA0NTfb29qam5qGBgWVlJXrvqRWqSaEhU355Xw9QpHodmqq9qluBt0+lEqlff3T3TXC3X10gH9NfT2c8wCAlyxY0mj0N1ev2rZzV19T8DjtJTIyiaRWqwP9/RvZrNg331yzKlbvN84z92dJSk6eGBZGo/58EZVMIg1zdb14NSmnIH/Gk/U0EEJBgQGR4ePHjgkqraxACC2eN2/RvLmVNTUIoTkxMZMmTDDQ19euetHSxjEzMelXlFQmw64ckklkBoNBp9MPxMUZ6Bt4eXgMd3cLHzvW19vr8PETCCFzM7Pm1lasKLFYfPvuz5HD1MRk6aJFhSUlenQ9qVxGIhHrGll21jbenp4p6fcsLSzUGk0ju0kslvh7+0RHROgmCoVCK0uLWdOmRowf19LWlnY/47VXFsZER0dNmNAvG0JILlfQaFRtvUQCEVs1acyoUY9ysnMLC7UrbLyzdq2Xh0dJWfkn33zb1NKCJQ5zdW3v6Niya/eMKdEajQbrYTKJLFfIdV+iUqlEIhFP+MWBamhgMH/WrJ+r1smg24faoTwO4eBUBwD8Ec959dQJ48dfSUpqbmlFCDk6OBYVF/t4eyOEricnhwYHP8zKunApcedPPz3Huc0UCmV6ZmZ7ZxfSaEYMH2bKZCKEdh08GDk+fO6MGN2c2OIncrnc2MgYh5BGo9FoNCQiCSGEDXEUCoV23WM8DqdUqfoVZWZq0t7RYWdjI5ZIVCrVgbi4Za++amlujhDS19c3NzMzYTIvXrmKENKoNdhCEwghOp2+eP68n2P5k2U9rCwt/H18rCwsbKyssDEfQsjdxcXCwtxQX59CoZDJpH6JNBoNazAej1drNHgcjkggqNVq7NqpbjaEkKkJs72jE5vRVK1W5xcXv7VyRU5evoW5WUdHJ58vWL18WUp6ulwuPxR//K3Yld6enjbWVhVV1XY2NgihsoqKew8erl8VS6PRuD09HZ2dCKH2zk5LCwvdl37r+6Xbh9ppdXF4nFqtlkqlBvp6cM4DAF6+YIkQ+t+HH0ZMjkYIfbTxP+//979ODo5CkcjUxGRqdPR7/9nI4/M/+PBDhND3mzZpF2f/O82bOQMh9PDxY5VKTaVQdh08tGDWrPpG1h1N+p30dG9Pz8jwvrX9snJyJBIxq6n5vXXjk27ePBR/XC6Xjx0zhtXUdPVmMtPYyMzUVDsjvJurS2sbRyaT6xYVHRERn3Da1MRkpMeIwpKSXqEo4fwFhNC0qEnllZXFpWV8gSBq4kSEUCuHM/QKD0GBgbsOHFAolG0czpsrV2CJ0RERl69ft7Qwx+PwZqamAxNDgvqv/7D3yFEaleo5YvjAbAQCYdG8uZt+2uroYN/GaQ8fG6q9AWqYu1sji42FbTKZbGxktOvAQSbTuJHFXvNG32pT6ZkPhCLRvqNxCKHVy5fp6enFnTzVwmnbsHr1iTNnf/HSgFnIeXz+ucTLq56s2aJLtw/xePwPO3ZufHvDhLCwHfv3y+UK3SsBAADw7F64uWE5HI6BgcHzuug6NJVKlZ6ZqbvyrVZhSQmnoyNk9Gh9PT08Hv/99h3vr3sLW40By9ArFBrojI8Fvb3Xb91aNG/egIGsQiQWD3rbJ18goFGpZDJZqVTGnz694rXXhm6tWq3m9vSYMJk43M8XIWUymVgi6Xcn7aCJGLlcrlSpsNUkBs2GLZbCNDYe+q7gXqFQIpGYmZrqNmZgCNTX0/vdE6wP3YcisRiH0KCrfwAAwMsXLF9karVaqVINukR7I5vN4/N9vfoWc798/casaVOHLi2noMDf2/tXf3kyUH1jox5dz8LcDN4RAACAYAkAAAC8EOAH2gAAAAAESwAAAOCPeW7T3SGETp87990PPybfvl1eWRE0apRSqTp4+HDy7Tu2NjZMY+MXpIP6Tb32MDv77r17CCFLCwssg0KhSEq+lZOfb2ZiYmBgMHRpSqUy8dp1jydTvml1cbnFpaXWVla6deUXFaXez9Cj05nGxkKh8MqNm8WlpTZWVjQa7dqtW0WlZeWVVcNcXW/dTXVzcYZDGQAA/oEjywePHqXfux936OChfXtNmMwfftp64PAhOzvbRQsXfPL5Fy9OB+lOvVZTX19QXDx3xowrN24IBL1YhqTkZHMz04njxx05cVI3+Gmnu+Px+SKxuKauTqFQqDWaVg6ngcXSnX8HIXT1xg1fLy/duhqbmtIyMqZMijx/+XKvUHjo+HFfL6+gwMCDx+JVKlVdQ+OYwIAxgQF4PN7AQL+otBQOZQAA+Os8t99ZXrt+Y9XKFdhP8VYsWzZjztyd27aaMJk5efkWFuYvTgc5OTra2dhQKRQCgUClUCRiSVlFBYlIolL7phcYM2qUoYFBVU2tsfHPv6nYe/iIi5MjHofPLyqSyxWd3d0j3N0Sr11/5821ZRUVbs5OlTU1E8LCsAnN5QqFoLeXSqXq1lVSWhYyOohpbDxyhEdNXV342DA3F2cen69UKju7uiQSSUr6vZEeI+xsbf28veNOnfqz5kYHAADwAo0spVKp9neHeDweTyA4OzmJxOLyinK5XP7idJDu1GsEPB6PxwtFYoSQUCTCMlhbWkplMlYTW6n4xey1utPdTZ00KWriRBqNxufzh7u7RUdGLpo7Lye/oG8Y2tXFMGT0q0ulVmGTyZHJJLlc4eftlZWTcyDu2PIlr+Lx+NCgoPmzZmY8fNTAYunR6dpZ9AAAAPyjgqWfr29qejr2+F5GhrfXyO+3/GRpYbHhrbfEYgk2++iLoKyi4uadlPWrYgP9/IpKy0KCgiLDx/t6edU1NmIZTl+4yDQymhMTI5XJJBIJlohNd6dWq7HZ4xDSIGy6OyoVm/pOJpdpJ1bF4fEqlapfXWYmpu0dfTPAWVlYXEq61sXl/mfDehsrK4lUOszNVV9f39XZGQuTBDwBDmUAAPjrPLfLsAvnz39v48Y3N7xtaGDQ1d394+ZNqenpa9dvsLSwsLSweI6TwfajOyvb7OnTTp4918hmN7BYb69ZfeHKFV8vLxsrq+179xkbGzONjbVzmWo0Gt3p7hKvXbcwN2MaG1EolFYO51hCAqe9Y9niRVhmS3Nz7MOBbl3LFi/afzSO09HO4/Ed7O12HjxgY2VdXVvHNDaKmjDh4LF4Z0fH1ra2qVGTeHy+uZkpHMoAAPDXec6TEvD5fIVSaWpi0vdUIJDL5dppS19A2DqRTGNjPB6fX1Rkb2dnymSKxGKFQqE7v5rudHcJ5y+MDR5jaGCgzSCWSMgkku68bucvX44YP77fPcAajQabr27QZghFIqzA5Lt3h7u5aZe+AgAA8M8ZWWIYv5zA87lMlf7b+otI1IZ2j+HDsVVEBs70PczNjUgkYuNje1sbOp2uG0rpAxbTiImOrqqt7RcscTjcoJESa4a2QCNDQ4iUAADwTx5ZAgAAAC8+mMEHAAAAgGAJAAAA/DHPbbq73Pz8T7/48nZKStL1ayYmJgQCYc269ZMiIygUCkJo3TvvGhsb21hbHz916kpSEsPQ0MrSEtvw5q1b127cyHr82MLc3PivnxUvMyvrcW5eeWVVeWWVjZUVtnpzc0vr1Zs3xRKJrY31sxRSW1/f1d098AvIguLizEdZdjY22F4jhHILCvB4PJfHS7qZzG5udnJwwOPxt1JT84uKrCwtaTTnUgxGAAAgAElEQVQau7lZ+5JEIrly42Z5ZVUbp12hVAoEgkHXpNSl0WiOnjw13M2VRCIVlZbeuH3bz9ubx+efv3zlz5rWQKlUnjp//vqt24/z8uQKuaO9fV5hUdLN5EA/X4QQt6fnp917/H19aurrT5w58zDrMaejY4TO/H/5RUW309LEYrGdra1uP/ebdxAhxGI38fj8Qdf+RAg9zst7lJ1TXlllbGSk/UWv7gyCcPIDAF6CkWV3d3eAn9+eHdt/3Pzdt5u/E4vF9Q0Nt+/cQQhVVFbm5uV1dnbGnzypVChXr1jx+dffSGUybMPbKXenRkdPjY42N/s7FnR0dXLy9/H28vCoqaujUakIIbVafeh4fFTExJr6Ot155jq7uhBCCoUC++1jA4slEouxx49z88xMTXl8fgOLJZZIOru6mpqbEUJyuZxhaFhVU4OVoFKpcgsKzc3MDh8/MWVSJJFEvH77dur9+1KpbGxw8MFj8Wq1WvclVnMzmUwaExgw0mOEu4tLcsrdp+1FR1cXt6eHLxDw+HzP4cPxBIJSqcx8lBXg64sQ6uzqDvTzVavVWM6mlhalUimRSKpra7Fu7+jq4nR0YG1GCLW2tZVVVmL5uT09dQ0N2GPMpaQkIwbj4/ffe2ftmvzCovyiIm4Pt6S8vKOrC+uKNg5HqVQmJl17e82aDzas7+b2NLLZ2LYsdlPGo6zZ06fnFhZWVlfr9rPuXIByheJOWtqW3bs57R26u8nt6fn501hBATYjoPGTaNrS1qY7gyCc/ACAlyBYIoQkUgmXy62rr6NQqQihcWPHpqbfQwhdu3FzSvRkhJDXyJEL5s0zMzMjEYkqpfJJTOo8c/78g4ePsEHeX83SwsLFyamguGjhnNnY+K+5tdXe1tbc1DQ0KKikrFyb8/DxEwihotLS7Ly8E2fP5hYUnruUePzMGYSQVCZt7+j8btv26tq6T7/9NvV+xvnLV6pra+3t7BDS9PD65t+pqqm1tbFWqlQL58w2YTKZRsYymbyopHRC2FhrS0s8HicUiXRfau/o6OjsSrl3XyaT4XA4fX093Slnu7g/37oVn3D6/OUrHZ2dh+LjpVLp9r37+Hx+D5/X3tFRUV19OzW1qaVl75EjCKFvf9ySev++oLd318GDrW2c7Xv3icXiL7/7Pisn98ylxLKKipyCgsRr1+sbG4+fOVNRXX36wsW6hgZs274oVVg4LSoKIUQkEqdHTy4oLkEIBQUEPMrORghVVFcPc3dDCNFo1LOJiaUVFctfXay9m5dGo86fOcPQwMDQwKClrU23n50cHcPDxhoxGAQCQa1STQgLmxIZ2e/NSrx2XfuYzxekZmSUVFRgUyoihErKfjGDIJz8AICXI1hmPni4beeu5Nu3f9y8CSFEJpPpdFpPT09Nba27mxtCKNDfv5HNin3zzTWrYvX09LCtwkJD337rLZlcfvzkqb+nnTw+n8cXuDg5YU+lUimNSkMIkUlkuaJvZr629nZspFtTV+/m4tLAYs+fNXPR/HkaDapraHBycEQIBfj6TI6YyDBkLJwzOyQoqKWt7VF2dmd3t421Vd+IjdNmZmpKJpGGubpevJqUU5A/Y0q0VCbD5jogk8gIId2XzExNQ4OCYqInY7Orm5uZNbe2YkWJxeLbd1O1u6BSKVe8tqSppUWPrieVy0gkYl0jy87axtvTMyX9nqWFhVqjaWQ3CYVCUxOTpYsWFZaUaHOWVlRaWVrMmjY1Yvy4lra2tPsZr72yMCY6OmrChH7b9h1SODwe33dcEYlEjUaDEHJzca6preP29BgbGeFweITQO2vXenl4lJSVf/LNt00tLVh+czMzOp1+IC7OQN/AzsZGt5915wKkUqlEIhFP6H/0rnhtifaxl6fH3JgYpUJ5K7WvH6Qyme4MgnDyAwCe3fP8neWkiIg316zGHtfW1SGEoqMmb9m+3dPDA0t8mJV14VLizp9+0k7o09PT4+/ra2RkFBw0+kbyrb+nnXfv3R8XEqx9ampi0tHZNxGddqGumro6V2cntVpdXlW1YPYsPA6HsPljNZq8wsLoiMhWDodEIiOEiIS+qek0Gs2cmBjdinA4HDbv3a6DByPHh8+dEYMQMjM1ae/osLOxEUsk+np6O/bv176kVKpGeLgTiURDAwOFQqFRa4iEvjeUTqcvnj9Pp2Q8NgeClaWFv4+PlYWFjZVVRVUV9qq7i4uFhbmhvj6NRtOOw3RzYpP24fF4tUaDx+GIBIJarcaunepu+2Qsbl7X0IB9tnicmztyxPBeoRAhnKWFReK1ayFBQSnp9+Ry+f6jcW/FrvT29LSxtqqoqrazsUEI8QWC/UePLnv1VUtzc25Pj24/l1VU3HvwcP2qWNqAX6kO1CsUujm76Ovrewwf9jg3D0vUnUFwmKsbnPwAgJcjWA40Pmzsexs3Jp47m52bixA6f+Eij8//4MMPEUKf/e9/3/3444+bv9u1d5/HiOHlFZVfff7Z39OqiqqqWdOmYsEv7lTC+lWxenp6cSdPtXDaNqx+Euzr67ncHnZTM7ZmiI/XyANxcRoNwuFxfEGvoaFBK4fzqxW5u7g8zstr7+isb2Td0aTfSU/39vSMjoiITzhtamIy0mNEZ1e37kt0GnXn/gMMhqG9rQ2VSm3lcIJHjxqi/KDAwF0HDigUyjYO582VK7DE6IiIy9evW1qY43H4kKCgIXJipkyK3HvkKI1K9RwxfNBtF8+fv+/IEUtzC6lUamhoGBQYmJKejhAKHj1qy67dbyxZkpJ+j0wmGxsZ7TpwkMk0bmSx17yxHNs2v6ioVyhKOH8BITQtapJuP584c1Y7F+Dq5csGTgSBEPphx86Nb29ACFEplMRr1xzs7FhNTctfXVxSVt7c2jphXNjW3Xu0MwjCyQ8AeHYv36QEGo2mo6PD1NSUQPi7Zw9XqVTpmZkR48cjhHh8vr6ennbKuk0/bX1n7RoqlYpdgcx4+CgkaHRLW1vGo0evzp//7FUciDu2evmyfokKhUIkFg9626dYIlGr1fp6ekqlMv706RWvvTZ0+Wq1GptCD4fDaRNlMplYIul3J+2gOTFyuVypUmHzEA26LUKI29NDp9GG/l65VyiUSCRmpqYDq9Dq18+/6Tjp4fGMGAztNWE05AyCAADwjwqWz5FarVaqVGQSaeD/5as3b86cOlWbkldYmJWba6hvMHdGDH2wMdDTNLLZVCrV0vw3r+hZ39ioR9ezMDeDtwkAACBYAgAAAH83mMEHAAAAgGAJAAAAQLAEAAAAIFgCAAAAECwBAAAACJYAAADAy+u5zeDj4OY+xKusmuqnvSQSiVpaW5lMpqmJCbx/AAAA/snBEiGU7+A7aLo/q3DQdLlc/uW33549f8GIwRBLJPZ2dlu++26kp8c/+O3p6u5+nJ09bcqUfukVlZW9vb2jR4160Rp8PyPTzs7WydERTi0AwD/Jy3QZ9n+ff571OPvGlcu5jx4W5+ZERUYsj40V9Pb+g9+ehoaGL7/dNDD9zt27p86ceQEbvP/QoazsbDivAAAwsvw7OLi597sS29zScv7ipUtnz2KrdxGJxHc3bKhvaKyrq/Pz9e3u5u4/dKiRxfIYMTz2jTewVUouXr5sb2f3KCurtq5uxrTpTk6Oh+OO4fG4pUtec3dzRQjl5OVxOBylUpWanu410vOVBQv2HzzUxuHETJs6MTwcq7eltfXEqVOtbW2hwSHz587B4/EFhYWtbW1KpbK+oeHdDRsQQleSkm7dSaGQyYsXvTIqIADbMCc398q1a1KpdPbMmaHBwQghoVB4OO5YdU2NvZ1t7BsrTEz6z1AqlUrjjp+oqKxwcXZZvvR1HMKdPH1aKBRu27nr3Q3rtdnKysvvZz7g8XgXL1+eO2vWwPacu3DxfmYmiUSKnDhx2pRorCucHR1z8vIqKip9fX2WLulbyuphVtbFxMsIociJE6ZMnowQamSxHj1+bGttc/bCBUsLizWrYrWXu0vKyhJOn+HxeWFjxy5asACb0HXX3r1TJk8+duLEkkWLGhobG1ms5Fu3XV1ctP0AAAAwsvz75OTmGRsb+fv9fOUWh8Pt3r7Nz9dXLBbHzJmDw+FeWTCfxWYven2pWq1GCN1MvrXunXeIBOJIT8+1G9ave+fdkR4jVErV0hUrsEUWi4tLPvniy+ycnPFhYw8cOhw9PYZGo7q7ua1Zt76+oQEh1NrWNmPOXBqNPmF8+PGTJ7/6dhNCqKy84utNmxPOnLWxtkEIHT0Wv33X7mlTpoz09Fy6YiW7qQkhdDslZd2774708PT19nlzw9s3b91CCP3no4/a29sXL1ygVKrmL17cbwfVavWCV5dU19RMnjSpqrp6weJXCQS8vZ0dkUBwc3XRzWlgYGBmamJoYGBjZT2wPQlnzx6Nj589c0bkxAlffP313bQ0rCtWvfmWXC4PHjNm5+49p8+dQwhVVFa+/f4HkyImRk6csOmHH5KuX8eC5TebN59ISIiZNlUkEs17ZZFCoUAIlZSWLlm23GPEiJkxMWfOnf9m83dYY44dP7Fm3XoKmUKn0y3MLeh0uoWF+cB51QEAAEaWfwdBr8DC3GLQl85fuuRgb//xfzcihCaGh4dNjHiYlTU2JAQhFD0pClsyMy7++Mo3ls+dNYsvEJw+d47H4xkbGyOELMzNN3/zNULowaMsIpGw/s03sZFiZVW1s5PT/oOHoidHvb3uLYSQv6/v5JiYD//zAUKIRCIlHI/Hlj3ZuXfv7u3bsOpkMllLa6u9nd2PW7d9+tFH06dOxQbBx0+emjJ5cll5xZLFi0KCg4PHjPHx9lIqlbqLaSTfviMWi7f+8D0Oh5saHR0yPry0rGzc2LFnL1ycrjNFO0LI3s5uxPDhZDJ5TNBoLEW3PVaWlthXuRqN5uz5C9XVNRETJiCEpk2Zsm7tWoRQZXVVcXHJogULKquqqVSKn6+vhbm5o4MD9gkDIaRSqXf8tIVGo0VFRoZNjEi/f39SRMTeAwfeWLr0tVcXI4Q8PTzGRURufP99CoWMEFq9csX8uXOxhpmbmfn5+rq6uMCpBQCAYPkcGDEY3J6efokFhYVMY2Z1TY2vj492uOngYM9ms1FICELI2dmpbz9JRGsrK/Rk7WWFUomluzzJQCIRrSwt+zITCEqlAiFUUVXV2to6c17fGlsSiaS1rQ0h5O7mhkWm7m5uT0+P18iRWAYsMCsUitq6ul179x06GocQEgqFAoEAIfTxfzd++uVX+w8eGhsasnD+/H7LTlVWVXZ0ds6avwB7yhcIauvq3Fxdn6VztO3BHu89cKCouITL5Uql0jGjRz/ZU2dtT/J4fIRQVGTEzVu3QsMneHp4REwIX/b661gGV2dnbIFlHA7n7uba1NSMEKquqV20YCGWwc7WFo/Ht7S2ODs5IYRGDB8BJxIAAILlC8HP17e9vb28osJjRN+/ZrlcvmL1mi3ff0elUCVSiTZnV1c388mChQT8r6x5iR8yA5VCWTh/3pJFi56MbnttbWwePsrSrr9IJpMRQlKplGFoiBAqLCpiMpl2trYkEunzT/433N0dIaRUqeRyuUajCQkOzs7MKK+oSLt3b3nsquSkq1iweVIX1dfbe/uWH7GnIrHY2MioorLyWTpHdz3I2LVrxwQFHdq719LS4s0NGwbN09c5BMKubdskEnFWdvbhuGMNDY3btvyIEOL3CrR5eoUibIkxKoWi7WS+QKBUKrWrQj59MUoAAPiHeGm+s7S3s5s2ZcoHH37IZrMRQjKZ/ItvvjE3Nw8LDQ0NCU6+dZvP5yOEiktKmpqbtcOpP2hsaEj6vfsGBgZMJjPz4cOlK1b2Gw4aGOj7+/peunwFIcTj8VauWdvV1Y3D4UKDg9Pv3WcymUwmc9vOXbv37lOpVKNDxzY1NXl6eKyJjTUzNcWGm1qhIcH5BQUKpZLJZPb2CqNjYvgCAQ6PxwLtgBiPl8lkg7a5kcWOioi0srJsbWt7nJ0zxN4dOXbsw08+MTIyio6Kmjd7tnbgzmY3lZWXI4Qqq6oKCguDg4IQQqEhIecuXMQu1Z45dy7Az48x2GLUODzuaQ0DAAAYWf4eT/s95dP8uHnTl99+Oy5ykpmZmUgkGjFs2KF9e0kk0sTw8OioqAlRk52dnWtqa7/89BOjP+kGk+Wvv15YVDQ+cpKZmVlra+uBPXsG5vl+06YVa1ZfSUrq7OycP28udgvSV59/tnLN2owHD+RyOY1KPX70CJFI/O8H78+av8Br5EgWm+3v5+fj7a1bjo+391tr106JmeHk5FRbV/fRfzbaWFvTaTS1SjVp6rSUmzd0M/t6e2/ftfvjzz7b9NVX/dqzasWK1evWOTs6KhSKaVOmHDt5cuqU6EH3bvHChfMXL54+a7axsXFVTc3enTuwdGsrq40ffUwmk2vr6ja+/56Dgz1C6K21a2LXrp0QNZnBYLR3tB/Zf2DQMgP8/L77cYu+nt68OXPg7AIA/GM858Wfh5jH52mT+IhEIhabbWxkbGVlqZve3c1tbml2dnI2MND/cxvJ4XC4PT2uLi7YRdeBNBpNU1MTw8gIuxirTWxobNRoNNovC7FGspvYpqamdra2gxYl6O1tamqytbXVFiWTySUS8cDw39srxONxenp6AwtpbWsTCkXYPbQcTruFhTkeP/glBOwHJzKZzMXZGbvcmn7//nc/brmWeKmmttbSwgK7DUqLzWYLeoXD3N1IJNLTuovL5RoYGAyRAQAAIFj+5cES/KWwYJmcdBW6AgAAtJ7zDT4QEV801lbW055y2RYAAGBk+aLrjlwA79ZLxCTlHHQCAOAfA5boAgAAACBYAgAAAH/MS3MZFgAAAICRJQAAAADBEgAAAIBgCQAAAECwBAAAACBYAgAAAGCA5zyDz57DhydPnOiqM3uqllKpPJZw2tHeLjI8vLOrS63WWJib6WZoYLE6urqCAgIGbnjlxs25M2L+lBbeSUsL8PVl/nKK1IGaW1sbWayxwcG6iWWVlckpKRq1xtnJcU5MTG19va2NDZVC0c1z486dQD8/c1PT39SqS0nXmltbcAinr6e3cO4cOo32tJxY18nlMlZTk27zBu1S8FJQKpXHz5zhCwQaDVowe5attfXvK4fH55++cFEqkxIIhMXz5pmamDzLVl1cbsL580qFkk6nL1u8iEql/r7a6xoarty4icMhI4bRkgXzYTJh8IIj/O/j/z6vullNTbkFhe0dnQG+vjKZTCQWt7d34HA4uVzO5fEQQiQSydjIyNzMLO1+Rn1j44hh7tiijHK5vPH/7J1nfFNH9vdHvVmyLLn33rsxLhgwGIOBkCx9ISQBUijp+W/KZvPsJtmEbNiEhFASeu/V2ICNAeNucO9yt4qbrN67nhcXFCEb4zRKdr4fvZBm5s6cezVH5869V7/D4UhlcoFIGOjvLxKLB4aG6Pb2SK3BaLxVWurs6KjRaql2dhKpFIVCYbFY/sgIhUKRyeVdPT0UCgU/Mee8XV3DZDCG+Xx7e3sMBiOTyTu6u6kUCg6HG+aP2FEoJpNJKBLJFYpeNjssJMR6290HDv7tzTfSUlJKK2/b06g5eXlMB4bT/XHxVmmZn7eXPY3G6x9QKBVUKhX5Fevu6yMTiXg8ni8QyBUKsVhsrdJ+7ebNVcuWpU9NEwiFvP5+f1/fgcHB/sG7B0EkFkskEpVaY0ehIIcuOjKCTqeTSaSunh65XEG3t7c5pJCHUlVTc/DwkfaOjoS4OAAAh8PZs/+A0Wj08fbmj4xs2foDn8+PCA9/BJZUVlcT8IRVy5cF+Pmeyc6OiYxUqlREAkEkFuNwOJFEotfrBwYHHeh0AIC1d/AFApFYTCGTEWH9LTt2Lnnu2ayMDG8vr0PHT0xJTprI6MfPnH0ma07WrAy1WtPZ0+PIZFr8i0wmW88969F1Oh3i4HR7ewCAUqXasXff2xvWT0tNlclljS2tocFBcI79WWlqadm1d69AKAwPDTWZTFu378i9eiXAz59Ot3/EvvO0BsvsK1cWzM0qKitLS05mc7nf//QTlWrX1dN7teC6m4tLbn6+HZlcVVvrQKd3dHVL5bKE2FgUCmUymf77wzY8HldSXuHm6qo3GHKu5qFQqLwbNyYnJAAAjCbT4RMnnJjMorIyEolUVVtHIhGZDMb2PXsnx8dv2bnT1dn59IWLqUmTH5SL476vubW1obmZgMefv5QTGhy06+ABRybz9IWL8TExP+3fPzU1Ra5QnDh7LsDPlz8yYhMsq+pq2VwuGo3OTE8nk0iFJSVOTKavj7d1m9qGhkB/v+Ly8j4Ou5fN5vL6XZycd+7b58hkHjpxcvqUKR99+hkBTyguL6fTaM5OTvdCeLUZgGE+v76pOSkhgcPjFZaUotGoy9euJcTGfvDPf9nTaLdKS+wolJ7ePqlcRre3r6mv7+7r4w0MsLmcEYFwaHjYckihM08Eur19xswZx06czJgxw2Qyfb7pq08++hBJ3711+443Nqy/easoLCSEZLXKb+/oKCsvZ7V3NDY1E4nEh16fmCAajfbS1asoFMrZyWnmtGntXV019Q2hwUEnzp3z8vT8cd9+gVA4MDjU3tmJQqOtvePjzz7XaLWRYWEYDEalUlVUVS3IygIAUO3sJhgpAQADg0NllZUoNDo6MiI8JCQnL/9n/5qUYD33RGKxZXQnJhNxcOQyUkNzM4VMio6IAAD4envDSPnn5sv//OeLTz89duJkXEwMhUKOjooMDgq6mJOTnDT5EfvOU3kZ1mAwNDa3+Pv4kknE2oYGhoNDfHRMVkbGxctX5s3OdHZ0HBrmB/r7MxmM8jt3ggMCdHo9Ett62WxvL8+sWbPcXF0Hh4ev3ypyd3U1mc19HK5CobCzswMAhAYHZc2aFRcdk5OXZ70gAyiUTqsb4o+sWLIYg8EgZf2Dg+W379z/s0jLnDHD8nHRggWe7u4dXd23SkvnZmbGREaqVOoWFuuh+/jOhg0tLFZTS+vRU6dff/UVd1e38LDQ67eKetnsORkzva2ydJWUV8ycPg0AUFxWPnvmjFnp0weGhhQKhUardXN1+cv8ebUNDf2Dg4PDfGTbu3uDRtNptBYWa+bUqTK5fJjPF4nEAABvT895szPjY6Jz86+FhwTr9Ho0Cg0AIBKIrI7O5EkJk+Liagl4yyGFTAQKhZJfUGA0GrBYLKu9ncvjffrFF/OystJSU+VyOZ1O9/f362NzGAyGZRMvT8+t27c7OznHx8UeO3nynx9//LtYEhTg/86G9fVNzUdOnrSn2adMTrRpsHLJEhQK9cU33wwO8629w5HJfGnFCqSNyWQiEn++IyCTyWk0KvK+vqmps7vHZsTYqCjk/TNZc3rZ7KaW1sv512alT7cZ2nruqTUay+gqlRpx8LujG41EAtFiiUqlQjwX8qfEzdU1v6CAxWLxR/hMJsNsNv9706Z33nwTAPCIfeepDJZ1jY2R4eFUql16WlpJReVz8+bi8Xevi+JxeORsNz4mRqlU6vR6oVBova3RaEQuxiIfgwMCXFycaXZ2lhMTjUYDANDqtCQSEY1GG40mk8kkV8j1ev1rq1erNeojp06vX7MaWai5ODlZws/d5fa9OGrdm16vx2GxJpMJAKDT60hEIgqFMpvNMpl8zB3U6XR7Dh1+/dVXoiMiPNzd2trv5leZHB8fHRmBQaMPHT/x0soVBqMBg8WiUKj4mBgAgIO9fV1TU2t7+7Nz53b19AAAcFgcAACNRpvM5uT4+OjICDqNBgCIiYxgMhhB/v67DhwUSyQBfn7zZ89mdXQCAMxmMwBAp9eTSPfdT/L19poUF1tdV3/g+PGYiAjow7+UOZmZrPYONptjMplioqL+8dGH777/flpqKgqNMpvNUqnU3p5m3Z5MJjs7O7u5uiYmTGpsav69zLh05WpEWOis9Omz0qf/vy83TUlOMpmMSMADAKAAMJvNZrMZmTnW3mGdkNXOzk4ilWm0WiKBIJFKt+/e88n7f0OqQoOD/X19rUe03nDbrt0bXl7r5+OTmpx09NQpLw9Pi3/ZzD21RmMZnUAgWBwcAODn41NSXjE3cxYAoKquro/NWb5oIZxgf1Y+/Nvf6hoa/Pz8vL28kPPOndu2ffbFl9FRUY/Yd57KYFlSXrH6+ZXI4jrvxk2lSm1d6+riQqNRrxfeGhoeXrZwIWCYT52/MDUlBYPB+Pv6Zl++cvzM2aHh4fCw0KyMjIuXL7u6OKNR6NSku5eSBoaGDh4/PjTMX71yhUgsOZudzWQw0Cg0GoU6fPJkaHAQkYCn3juTxWKxNCp1vN+mq3kMB7qTo+O0KVN+3Le/q6eHw+MtyMrq7u3dsXcvCqAsVzKbWlp5AwPITwAej3eg07ft2s1gOPSxOevXrlEolYXFxcsXLaIBqtls7uVwDh4/LhSJXJycZk6bdiEnx2Q2R4dHONHp3P7+qwXXZXI5q+O+FGY0GpUG7pq659BhAgGvVmsWLVjQ2dPd1NLa3dur1WmH+Xxuf//hkyd5/QOrV66QKxSnzl9Y8tyzAICunp6isjIUChXg68tkMJBD+u32HR+8/dYwfyTv+vWXVq64dvOms5OTZRkBsfDjrt0qtaqru9v55bVEopdMLtuy9Yf4uDgAwLw5c778z9cqtWrtSy9Zb9LQ2NjY2OTp7tHS1tbHZuv1+t/lMZbEhPjdBw8G+QcIRaL4mGgvd/djp89IpFLe4CASrvYcOqzT6dKSk52dnEZ7h4W/Llr4ny3feXt59g8MvvDX5ZZyIoFg8xiaNZMnJWze+oOfjw+by5mbmYnD4iz+BQCwnntKldoyus2temcnp9CQ4E3fbnFydBRLxG+8+iqcYH9i6hoabtwsTE1OolAoPb29+w8dQqPQcTExj953fgtPtDasSCy2s7NDnsTR6fWWR3LMZrNEKkWeXwAAaLValVpt+YigUqvxOBwWi0UaYDAY5L3BYBBLJA7p5NsAACAASURBVA50OvJxgsgVCiS4GgwGmVzuQKcjAVKlUhGJxHEuZsoVCrVa7eToePfRJKu9MBqNEqmU4eCAVMkVCjQaTSGTAQAKpZKAx2MwGIPBYH1SPw4SqZRGpRoMBpPZvHPvvvVr12DQaAKBYDOoVCZDoVDIyYF1OWRCB1kiodFoyNeNnA7T7806hUJBoVAe2Q1go9EoEIqodhQymYx81Ov1yIOpX3+/9f/eeF2j0SAXNsf0DgsGg0EqkznQ6b/ogrxWqxVLJEwGA/n9sviXTq/fvnuP9dwbf3SNRqPRapFHfiB/YqQyGYlItPyU6XQ6jVZrWaI8Yt/5cwZLyK/AYDDkXb/xTNYceCj+N7l4+cpf5s+Dcw8CgcESAoFAIJBHCnwYEgKBQCAQGCwhEAgEAoHBEgKBQCAQGCwhEAgEAnmcPLb/WTa1tuZfv4HD4fB4/NzMWb7e94nAld++7eXhwe3v9/Lw8LJSuvlDyc3P7+zuRgGUA52+6NkF1PslRRCTyu/cWb5oEZw3kMdLcVl5VV2t2QyiwsNtJDUmwqnzF5YvWnji7NkVS5aM3zL/xs0WFkur1RLweIBCLXxmfv/AgE3CgF/Er1NvtzE+dXKiTWKAMdl35IhUJtdoNEQi0c3FxcvD3cfL65H9nkD+ZDw2bdiOzi4HB/qqZcsC/f227dqdNGkSHo/v7O6WKxR0e/uahkZ7Go3N49nTaEwGo6unVyaXOdDpUpkMkVm3o1AshRqtVqFUDg4PEwgEiVQqEokQiTuLtvgE/0NWWFLy3Lx5czIyKGTy3sNHpk+ZotPrWR0daDSaTCYjJt2urklLTtZqtR1d3WazmUKhjGmSRCo1GAzc/n57Go3D45lMJhKJZDabLQ3gzHsq2HfgYFNLc2x09MSrCouKbtwsRMQK/iBaWe2VNdVvrVs3JTnp+q1bNCqV4eBw39wzGvs4HDKJhMPhrOcqAGCIz1coleW376RMTqTb29vTaDaq/SaTicPlOdDvpiUI9PdLmZx4u6p69fMrM6ZPIxAI9vb2er0emfNUOzvrBAATYbR6+zgGWFeZzeY+DgeDwRSVlU2bkmpvb08mkSw+rtPrVSp1/+Cg2Wy2KHnFx8SkTE68UlDw/97/W1R4OJlCsafRxFKpVqvV6fVmk6mXzaZQKL/oL9eQ3+g7crnii6++Ki4tZTKZLs7Oj9h3nspgyeHy9AZ9oL8/iUiUSGU6ne5WaalULuvp7evjcsxmsyOTKRCJHJnM67cKlSoVq7NjYHCoq/euzPqtkhJLIQqgtu/ZQ6NS9x4+rNPpK6uqmQwGt7/foi2enJg4EZNq6uuDAgKQ8FxcVh4XHb199266vX3BzUIHOl0gFDkyme2dnWnJyd/t/JFBp+fk5Xu4uVVW14w2qbO7+2ZJiUqtOXT8hB2FcjY7Oy0l+fSFC5YGQQH+0J2efMLDw7Iv5cxMT594lZ+v75W8fHt7WllFBQaN/iPEoK8UFEyfMsWJyQQATIqLYzIYJ8+ds557N4qKsTjsxdzLU1NSrOdqV0/PtRs3pTJZY3Nz1qxZO/bsjYmMtFbt37Fn7+DQ0MDgYEdXd0RoqNVllTtx0dFkEqm7t6+6ro7N5SFzvqquzpIAICgg4KGWj1Zvl8nkDzLAy93DuurYmTP8EUFDc7NGo3F2dKquq9PqdBYfd2QwkR+Bk+fOpyROslZ7uVFUhGjY5l2/jsfhT1+42N3b6+zkeOjECRqVdjb7UnxMNEwQ9sh8RyQSiSTit994wyZSPhrf+S08EfcsCXi8waBnc7nPzZu3cumS2oYG69ppU6YQiQSNRssfGQEAzJudGRsVZVOYEBszJ2OmPc1++aKFqUlJ/YODgX5+ocFBEqkU0RZHqG9qOnMx2/pV39Q0tkkEfB+b4+XhMWPq1IULnim/U2Vd+0zWHKVKpTfoBULhg0yaPWPGs3OzmAzGvNmZvj7eIrHEpgHkyWcc1bdxqkRicXZubkxUlJenZ35BQX1DA5FIOHby5O9llclosoyOiKHYTK2sjIysjAxEEcV6rt4sLnlt9UuLFjzj7uaKbE4mk2alTxdLJIhqv8lkWrl0yeLnnu0fGBjfBmTOl5RXUKlUBweH0spKS1VBYaGNl/UPDt61fJR6+zgG2FT1sTlL//Lc88uWou9Jvdj4OPIjEBYcLBJLxrHcaDS8/MIqbn8/hUzR6LQ4HLa5jQWn+iPzHRKJ2NrW9q/P/30pN/fR+87THSzNZnNdY2NIUJDJbAIAGAwGREjdwoGjx3y8vGZOm4p8RGptCnE4PAAAe08A3Ww2n8vJQaPR82fPtj5nDA0OnpMx0/oVGhw82iSFUimVyalUO5PJDADQ6fQkqwy3Upns/KWc2OiouKjocUzC4/EAhbIyyWTTAPLUwWZzJnKiM8wf1ul0np6ed8Wg3VwTEyYh0qm/CwH+fo0tLcj7Y2fOdnZ3j3IHLAAAhUKPnqv3khDokY+Iav+01FQvTw8AAAaDQaPRiGr/+DYgcx5JABAfE/PcvLmWqtSkJBsvc7mXWs6i3g4AkEilP+zaNY4BNlWI5Xq93gzu2mbj49h7KQfMZtM4lqNQaOS6q5urS3xMzKz0dH9fHzi3H5nv0On0zZs2ff6vf5aWlz963/ktPM6L9YXFJa2sdrVanZo0mclghIeE7jtyVKFUZs5I5w0MWp+J3Kmp0en03X19cfeugFsXJibEj+6c4eBg0RYf4vNdnZ3BwxSiAQCHT54iEYkqtXr1yhW+3t4XL18+df5CH4ez5vmVlsUlHodTazTFZWVD/BGRWGy5W2NtUkxkxJinVJYGJpMJpsd6wjGbzT/s2FnX0JB75coz8+YBAF7duPH7b/7r7OQ0uspCaVlZeGjY/LlZL619+ZW1a/4IMei05ORtu/ds273HbDbRqNSo8PBrN2+OOfds5uq8zMzte/a4urio1HfzFthTqQ9S7Z8I1gkALIWIvvGDsFFv1+l0DzLAxrboyIjdBw9hMBgsBjvax0US8S+yPGnSpG27dun1hsGhoY2vvAxn+yPznc6urku5uTqdLnmyrbL/I/Cd38KTJXcnk8nxeByReF9iKZPJJFco7Gk05Km2cQptsGiLY7HYXxeZzGazWCKhUak2jwDodDqDwUAmk3+RSROxGfLEIhaL333/g4N79zw5zoLFYckk0vhTy2auqlQqNAZjfcr4K1T7rbFOADBBbNTbxzHApkoilVLt7Kwz6P0WHzeZTCKxmMlgwPznj9h3NBqNXm+gUp+yDKZQGxYCmeiZk0wuvy+XOAQC+Z/xHRgsIRAIBAJ5CPC2GQQCgUAgMFhCIBAIBAKDJQQCgUAgMFhCIBAIBPI4eWz/s+QNDDS3tWVlZFTV1RUWl5hMpgA/vyXPPYs8xp2bn08iEjOmT/+lvfH6B85kXzQYjGQyafXKlcgT7U2trVW1tWtXrbK0b+/qOn8pB3nW3J5GW792ze+4axqt9vudO0lEktFojAwPnz1zhnVtQWFhQmysRb3p1PnzqZMn26hCl1ZU+Hh5aXU6Tw+P8f8YCvkfpKa+obqubt2a1QAAkVi8c+++dzZusKNQLA1GBAKTyezi7PSrhzifk0smk7IyMgAAdY1NldVVG9autW4wkSH2HDqsUqmMJpMTk/nXxYvO5+RMJAlBd29v9pWrKBSg29NXLVtacafK39fX08N9dMvfvpsQyFOwslSr1QODgxwer7C45L3XN3707jsAgIo7dwAARqOxqaW1qKz8l/am0+n2HD60euXK9996Mz0t7fiZs0jtzaLigcEhoejn535lMllMZOSH77z94Ttvr1+7RqVSIbUisVit0QhFooHBwWH+CABAKpPJ5PLB4WGTydTV0yORSpEeunp62Bzu6PeI/SaT+e0N69/ZuKG2oWGIz0e6UqlUCoViRCAUSyRt7e06vR4A0MfhMpnM0JAQAMDA4GALi2UymUJDQmg0Wk5eXk9vH2JVd2+vyWSC8/URs+/AwUNHj45ZxeFwvvnu+5LSMpvywqKivfsP/KFWicSiptZWvkAAALhdXTM4NGQwGKzn5+3qmpKKCo1GI5XJkJnZ2d3dx+FMfAguj3e7uvruqVtl5eDQMABg9BASqRTxjjGHYHM5b29Y/97rG6lUamllZR+HCwCw9oXRvqNUqQ6dOLluzep3N2709/W5fK1AIBJKZdLO7m6NRgMA0Gq1rax2RAsGscFsNls7iHUDiVSq1WoBAMhHnV7f09enUqtVarVNlUQqbWGxFAqFwWAYEQgAAHq9XiCC/xT4lZhMpu+3bf/k00/ZbM7Eqx6B7/wWHpuQukgs5vB4IwJh0qQEd1dXhVIZ4Ofr6e6OxWJrGhrsaVQ0GkOj2k1QPxfpjUQiGY3GxPh4jUZjR6FER0Tg8XiZTF7f3DwtNYXV0RkSFIS0R5SaAUANDg3r9XoqlfrND9tCgoN2HzqUkpj40aef2dNot0pL7CiU2oZGRDP6XE4OBoO5UVTkQKdXVFXxBgbYXM6IQNjCYlne+/n4IG5Wfvt2XHS0WCq9U1OTlpy8++DBqakpNfUNnH6eXKFoaG4m4PHnL+WkpSSXVd52c3GprqtTqFQ3bhWZzKbKqqqh4WEUQDW2tDgxmWqtJudqHgqFyrtxY3JCAvTDR8mDxKANBsPnm7765KMP/f38bKoegRh0T18fnWY/IhCEBgXl5OU5OznGREbuO3rUMj87urqlchmDTt+6axeVane7usaSoiAkMHAiQ9yurnZ3dSOTyBQyqb6xSa/XT5uSumPvXpshBEJR3vXrbi4uN4qKRw9xs7h4UlycUqWqbWj0cHfv43DSkpN/+GmXxRea29psfKehuZlCJkVHRAAAfL29Q4OD2jo6quvqCARCTl5+atJka114VkeHVC6jkMk5efkWB7FuUFpRSSIRmQzG9j17UydP/mbbdhwOeyH3stFobGW1W6qiwyOsFdu/3b4jfWpaRVUVh8sLGPX9QiYCCoWKjooMDgq6mJOTnDR5glVQSH08VGoVIjhyOf/akVOn9h89BgAoKik1Gk14HK6kvMLSUqfX26gzn7mYjSzOrHpTI73dqak9m5392X++Rs6LSUSiUqUqqai4/2KpRq6QyxVyjVZLJBBWLFn8yb+/eGH5ciwW6+3pOW925orFi29X1wAA5s3OdHV2HhrmG00mJoNRfucOkUDs43C9PDxSJydav7d0PsTnn83Ovnbz5sJnnqHb29vs9aIFCzJnzKDa2VnWqQCAwuKSF/66fEFW1uwZMwAARCLR3dUtPCz0+q0iVxcXk9ncx+EiJ+OQR8aDroF3dXdzebxPv/hiTH3LRyAGHRTg39nVLRKLHeh0FArNHxmxnp9BAf5hwcFoNDo+OiYrI2PMFAUPTSqQnJhYUXWnur4+ITYWADA0PDx6CBQKhSiqjzmE0Wg6m52dfeWqr7fX5Pj4sQ6vre+YjEYi4WdJLGTCz8nIyJwxQ6fTgft14REbbhSXWDuITZIDC30cjqe7e9asWXMzZ9mYYa3YrtPrfX282VxudV1dyuRE6AK/GrPZ/O9Nm2amT/9FVU+ykPpjTuQW4OfX0sYKCQxcvmjh7Zqamrp6mUyu1ek83N083N3OXMxGhKwAADgsdnSSW9z9KnS+Xl7XC28BAKZNSY2OjPi0+T8AgJr6+gVzswAALk5OfRyOJct0oJ9/elqa1SUjrruba//AgJurq9lsRsIziUQE9zSjqXZ28TExSqVSp9dj0OhJcbHVdfUHjh/PTE+3vLfc1/Fwc7O+RXr32q9chqh5IReU9Hq99W8xGoXCYjAmk2n05bLggAAXF2eanZ0lUR/kscBmc0hkkrOTk8lkiomK+sdHH777/vtpqak2zYb5w2QyydPTk0ggODs7u7m6JiZMamxq/n3P3V1dXC7k5qYmJV2/VWQzP4X34gQejwMAjJmiIDQ42N/X17pHG6k5F2cnPn9EKpWtW7P6+q1bDxwCh3/QEBgMerQXWPuCr7eXje/4+fiUlFcg8ayqrq6PzcHhsIR7hiG68GtfWGVzS8LiIDq93roBGo02Gk0mk0mukKNQKOR5CLlCYVOFKLY/O3duV08PACAtObmorAyFQtukf4f8IigUys5t2z774svoqChr3xld9ch95+kMlqmTJ2/fs2f7nj0kIhEAFA6HK62sTE9Li42KAgC0tbc3NDcjJ7YoFIr2sASzTo6O0RERX235zt3NVSyReHp4sDlcVxdnpDcUClVSXmEJljdLSlpZLAAAiURauOCZ+qamf3344aYtWwL8/Lj9/YdPnuT1D6xeueJObR0AwNXFhUajXi+8NTQ8vGzhwua21qKyMhQKFeDr29XTY3n/IMNwONz+o0cHhobSkpMBAJeu5jEc6E6OjtYynnMzZ+3ct59EJEaE3c0jyGQwCouLszIyLl6+7OrijEahU5OSzmZnx8fE+Pv6bt76wwdvv9XT11fb0LDkueegc/4Rp8YPEoMOCw2VyWVbtv4wOlHtIxODTpmc+M227WtXrbp+q8jZycl6fgKG+dT5C8sWLrx7MdkqRYH1ovmhz46FBAf1sTlIELVxAWSIqIjwcYZ4qC+M9h1nJ6fQkOBN325xcnQUS8RvvPpq3o0bP4fz+3XhJyfEnzp/YfmihZeu5iEOEhcdbd0gITb2bHY2k8FAo9C+3t5XCgoOHD02xOcnxseHBQdbqmwU2xNiY3/cv3/5wkXQBX79nYLe3v2HDqFR6LiYGKTE4jujqx697/zK89MnQe5OJpebzebfSzlQo9UqFApHJvPXba7T67fv3rN+7RoMGk24/9dEJBbb2dnhcTjkJNcSv63fPwilSmUtNi1XKEaft+p0OoPRSLZaPur0ejwOp9VqVWq1A50OnfAxYiMGbTabpVIp/Un6UqznJzJzfnaxsVIUPK4hrH1hTN/RaDQarXb0/QswShd+tIPYNNBqtRgMBovF6vX66rr6pEkJ127edHF2iYuOslSBUYrt32zb/s6G9TbpEyC/7IdUp9Notcg3a+M71lVPEVAb1haDwZB3/cYzWXPgoYDYLDShkPpTzcXLV4b5fFcX52fnzh0n08jZ7Es+Xp6JY91khfwv+w4MlhAIBAKBPASo4AOBQCAQCAyWEAgEAoHAYAmBQCAQCAyWEAgEAoE8Th6b3N2hEydCAgNxONzxM2dxOJwjk1lSXqFUqpwcHS1tym/fBgBM/Bmq5rY25E+vj2X0rp4eMpmMPG5uMBgOHj9+q7S04k6Vl6fHH/SQtE6v7+7tZTIYcB7/TzHm7MrNz+f29yM6AyKxePehw0WlZXeqa5wcHScuFZZ348alK1dLKirxBLyHmxsAoKm19XJ+flx0NNLgZnHx8TNn79TUsrkcRCQIfh0QuLL8Y8FisR3d3QaDobWddbumGgBQWV3t7OxkrYPMFwiVKjV/ZESuUHB4PIPB0NndrVargZXwMQCALxCIxOL+wcHT5y+IJZLfMrrZbO7s7ullsy2jS6RShULR3dtrNBr7OByRWAzuF2tGlNa5/f0W3XMAwJ3aWg8393c3bly1bOn5nBxwv360RCpVqlSd3d16vR4AoFarO7q6NIiss0AgEoulMplOp2tua0Mku6wFrCVSqUKp7OjqUqvVvP7+i5evqNVqvkDA7e83GAzWXUF+BW0s1j/+9S+LUtqZc+f+vekr5Iu24YcdOzZ9vdlGUw08EjHo0bPLJvfAzn37np2b9eE7b7/84gu7Dx6SymQT6VYilbLaO9574/X3Xt945do1RMfKOglBc1tbc1vbR+++87c336BSqSfPnYcTBvIg3xknCcFj9J2ncmVpNpvbOzsxGKwdxa6Pw52SlJR//cb8ObOtdZCH+SOOTOaxM2dGBCN1DY1FZWUEPP5qwfWI0DBr4eOf9h/o7u2l29vXNjREhoVN5Dz6QaOfPHdOqVKxOjsGBoeMRqMjk3mntuZmSYlKrTl0/IQdhXI2OzstJXnrT7ssRlZW11wtuM5kMFpZLCcm09fHGwCg0WgvXb2KQqGcnZxmTpsGALDWj65taCytrMRiMRcvX4mJjNixdy+NSjubfSk+JnrPocPdvb0+Xp67Dh60p9Eu5ORGhYfvP3bMImB9u6bmRlExFoe9mHvZzcWlubU1ITb2i83/1Wi1vt7eP+3fb+nqsQtePI3QqDQul+fp4UGn00Ui0fXCwjc2bNizb1/alCnWzRqbmgYGB5cvXfrj7t3p06ZZVz0CMejRs8s69wAKhaqtb0AkHokEglKl0mg0nh4eE+k5N/+aWqMhEgh/mT8fhULZJCHIvnIlM30GIvcR6O9/8vz5WenT4ZyBjPYd8OAkBI/Xd57KlWVoUFB3b19Dc1NMZKQjk9nQ3Ozj7QXuF0q2NF62cGFaSkp4aEjWrFlanc5a+Fij1RqNhpdfWBUXHc1wcLBkCRhfeP1Bo0+bMoVIJGg0WuvFxOwZM56dm8VkMObNzvT18RaJJTZGzpudmTxpEqJ7jmwSFOD/zob1RqPpyMmTew8fGb378zIzZ8+cSSKRbpWWUcgUjU6Lw2Gb21jIvugNBmcnp4zp099c95pUJrMWsAYAZGVkZGVkoFAgKCDA1cWFyWA4MpkvrVhR39Rk3RX03l8BgfCztGkfmxPg52dPoymUSptmrq6urW1t12/cHB4eHt3JHy0GPXp2Wece0On0ROLPylNYDNZkNk/EKYhE4j8/eN+Rwbx+q+hfX32l0+lskhBY94xGo8f5Xz/kf9x3wIOTEDxe3/lNV0Mf35EloFAoNpe3Yol7TGTEmYsXF2RljSmUjMFgMBgMCgWwGCyyKLQRPkah0KOFqcYXXh9zdADAgaPHVixZ7O3pWVZ527IVHo8HKBQWg0E+yuS2RlqLRyNcunI1Iix0Vvr0WenT/9+XmyzlFi11AMwAAETw0M3VJT4mxs3FxcPNrbAEjcViUSgUBoMBAAwODwOz2VrAuqWtDYfDInt9n4UAAACsu4Le+xuxt6dJpTIAAArcjQoWMWhnJ6e/v/9BK6st+F7SN2v+aDFom9llk3vAgW4/zB9Rq9U4HO5WaWl1ff0br74yEadoZbVz+nlZGRkpkxN/2n+ALxDYJCEICgioa2z08vCoa2wcEQj9fLzhJIFMkCfEd57KlSUAICggAJFzDA8NZXN54aGhFqHkrt5eVkfnA3/F7hc+/jnyY7E19XczBCGCkzYv63Ph0aMDAEgk4p2amrLK2919feZ75+O2Py4PMBLRPUfeJybEHz19+viZs9t27Y6PiQb39KPv1NYiDS7kXj504gTDgZ6WktzR1VVYXFJYXGJ5DsjHy0sikR47cyb78mV/X19EwPpCTi6FRLa2hEwms7ncgcFB5GPSpEmju4L8Iu5UVd0surXvwEGRSBTg79/d07Pp681zZmcita9u3CgQCAAASqWysLio4PqNtS+9ZNMDIga9ZOHCl9a+XHDjRmNjEwaNsYhB/y5G2swuS+6B2KioyLDQptbWFUsWf/ntliOnTuddv2E0Gsn3hFjHd4qgAP+GpuYDx47vP3pUq9Xq9XokCUFsVFTmzBkl5RUzpqb19PV9v/PH0orKc5cuBQUEwAkDGdN3zGbz1u07kCQET5Tv/BaeOLk7Gx3kBzWzET5GCk0mk8lk+i3yxyaTSa5Q2NNo44/+ICOtpaWNRqNAKKLaUSw/VRb96ONnzqalJNOoVEQq2mQyicRiJoNhc11LJpfbUSjIA4fWAtbWGAwGDAZj2fBBXUF+HWazWalU2tnZgfvFoE0mk0QiYTy+55BHz67RM1kgFNrTaEKxmGZnZzfhbFNCkQiDwYwpYm5xPa1Wy3Bw6OrpDQrwh5ME8lCeKN/58wTL/wVKKypCQ0Ic4V8+nrbACYXUIZD/Wd+BwRICgUAgkIcA/1MMgUAgEAgMlhAIBAKBwGAJgUAgEAgMlhAIBAKBPE4emyhBe1fXpStX8Tic0WSakzEzIjR0/PZcHo/N5aalpPzGcfccOqxSqYwmkxOT+dfFix6qCVdaUeHj5eXl6QnnCuTJobisvKqu1mwGUeHho3UGLIwIBCaT2cXZ6TcO193bm33lKgoF6Pb0VcuWjuM1zW1tkWFh4/d2q7TU1dklNDiorPK2QqmckzGTw+O1tLEoZLK/r6+nh/voTU6dP7980aIJWvuLGkMgT/rKUiaTBQUEvL1h/fq1a46eOq3VaqUyGZvDBQAMDg+3stpNJhNfIDAYDAAAgVBob28fGhICABji8wVCIaIqjijajBbkHQc2l/P2hvXvvb6RSqWWVlb+LFxu1dXA0BDSeHB4ODQkhMlkAgB62WylSoWMa7EQTqA/GTZi0A9SfAYAcDicb777vqS0zKb8EYhBt7LaG1tb3t248W9vvtHLZnd0dQEAOru7+zgcpEFXTw/iSrera0oqKsxms0QqbWxpUanVAACZTN7U2qpSqSY4nFKlOnTi5Lo1q9/duNHf1+fytQIbF5DJ5S1tbUqVSq5QWJIZtHd1ITbY2AYAsKfZ1zU2AgDu1NY0NDcDAJpaWu0oFIFIKJVJO7u7NRqNjZ19HC64P6OA9W4CAAYGB1kdncjPBdIYJhV4xBSVlGz6evPBI3fVPZ/MJAS/hccmpD4wOCgUCb08PYeGh1tYbT5eXt//9BOVaieWSguLSzAYTG5+vlqtkUilnh4e32zf4enhXlNfL1coC24VisTio6dPZ6anb9mxg25vX3Cz0IFOR/SdH8rN4uJJcXFKlaq2odHD3f3SlauIcPmeQ4csXZ3LvhQeGqpSq0+eOyeWSPA4/NXr1/kjgrqGxqq6WjQGY7EwOTEROsmfCWsx6HEUnw0Gw+ebvvrkow/972kRW3gEYtBXCgqmT5nixGQCACbFxTEZjGOnz0jlsp7evj4up7O7mzcwwOZyRgTCoeFhqVzm4+W168BBhgPj1PnzEaGhuw4ecGQyT1+4GB8TYxH0GIeG5mYKmRQdEQEA8PX2Dg0OvaFM/QAAIABJREFUqqqrs7hAfEzMlp07XZ2dT1+4GBIYUFpZGRkWduVagUQqqW9uMpvMt0pLLbaFBAYCABzo9Mv5+cmTE2vq6mlUuwA/v+u3imbPnNHH4VTX1REIhJy8/ODAAGs7q2pr05KTd+zda8koUFFVZdlNqUxWWFKKRqMuX7uWnJhYVnk7NjLSOj8BTCrwCBCJxMuWLD564sTkSZNUKtWTmYTgt4B9jGM3tbYplEoigbhuzRqlUhkfHZOVkfHNtu1vrXsNj8c3tbbERUedy8lxc3EN9PNDo9DIBZy31r1GJBK7enrZXK6Xh8eMqVP9fX1vFpeE3FMaLCgslEjvy0mUmjTZopVqNJrOZmejUGhfb6/J8fGFxcUvv7CK299v3VVaSvKdmhqD0TgtNbWnrw8A0Mvm/POD9zVa7a4DB4tKyywWKhSKiWujQJ58rMWgEcVnMok8WvG5q7uby+N9+sUX87Ky0lJTbX81xOLs3NyVy5e7u7lt3b7d2ck5Pi722MmT//z449/FSJPRZFGp1ul0JrOZzeV+/H/vAQA++/rrKUnJrI7O5EkJk+Liagl4nV5/p7Z25rRpyYmTwkKCK6uq52ZmxkRGqlTqFhYrKSEBuUKTffmKzSjPzZ+HKEaZjEYi4a5MlclkUqlU1i6gVCp1Wt0Qf2TFksVenp5IMoNDx098/o+PdTqdTKG4fuuWxTZEgZlIIAAUaGppCQsJtqfRGptbFEol8ms4JyMjMiysqrausvo+OwEAQ8PDQ8P8QH9/JKOAp7uHZTcNBoNMLh/m80UiMWLnndpa66QCkxPi4dz+o4mPixUIhQaDgU6n19bVj5+EYEy3egS+87QGy4TY2L/Mn2e5UIPH4ywOCQAwGo1Ojo5Go/FWWems9HS5XAEAsCSbRb4Dk8kMANDp9CQrzbnUpCSj0Wg9EJlE+nkpjUGvXbXK8tEiwm7dVUJs7LZdu1FoVFZGBhIs0SjU3UHNZmsLJ3JiDnlKGa34bBGDNplMMVFR//jow3fff390sPyjxaAD/P0aW1p8vLwAAMfOnJ0UF2sym5D1Lh6H9/X2mhQXW11Xf+D48ZiICAAAGoXGYDEAgJ7ePoC6O3t1ep3Fa8YXWPfz8Skpr5ibOQsAUFVX18fmWLsAQKFeW71arVEfOXV6/ZrVd70Mi0Euz44Ihda2WToPCQy6kJv75mvrqHaU/3y/1c/7riY74Z5DoQBqtJ3WGQUwaLRlN8kkUoCf3/zZs621mmFSgUeMUCja9PXmTz/5BDzBSQie1mA5JrNnztixd6+To6OPlxcej0+aNCn/xg0PNzeWvBMAMHdWxo/795NJZL1e7+vtffHy5VPnL/RxOGueX2npgfIAtcxxsOkKh8PR6fYkIskSm2OiIncdOGA2AxQaZWMhdJI/E4gYdB+b/e5bbxIIhMLiosbGpr9/8D5S++rGjd9/819nJ6ew0FCZXLZl6w/xcXE2PSBi0PPnZr209uVX1q5pbGzydPewiEH/LtcD05KTt+3es233HrPZRKNSo8LDO7t79h05qlAqM2ekd/X0FJWVoVCoAF9fJoNx6vyFN157ddeBg6z2Dq1Ot/jZBT/u29/V08Ph8ZB1HrgnsD7OSUNoSPCmb7c4OTqKJeI3Xn21q7fX4gJEAuHHfftDg4OIBDzVzg5JZhAbFbXn0CGhSLxiyeLwkFCLbZY+I8PDSisrLU8eRYx6Jig1abK1nVcLrru6uCAZBYaGh5ctXNjc1mrZTa1O19TS2t3bq9Vph/h8AEDSpEnbdu3S6w2DQ0MbX3kZTuxHwFvvvUen09//+8f/+eKLAH//vfsP2CQhQHwHEVK3dqtH6Tu/hSdR7k6j0eh0ehptDO9tbGnxdHe3o1C27d79f2+8YTabxRIJjUr9LeLpCON3VVJekZo0uX9wsKSi4vmlS8exEPKnwUbx2VoMGpkwUqkUyXP7WJDJ5Fgc1nLVRCaT4/E4RNZfKpNZ4h8i7m8ymRRKJVKCXLR0oNN/kdq+RqPRaLUWgXVrFzAYDGKJxIFOx2KxlmQGSpUKj8MhP3DWtk2cMe20zihgvZsSqZRGpRoMBiwWi5zjwqQCj5cnNgnBnypYjkP/4ODFy5eJBOLsGemP8u8cNfX1ldXVNDvq4mcXkH/5yhXy53B+KKQOgfzP+g4UUodAIBAI5CFABR8IBAKBQGCwhEAgEAgEBksIBAKBQGCwhEAgEAjkfzJY1tQ37DpwEHkvEou/+O83o7UeLJRWVHB5vIl0KxCJvvjmG4vu5dafdiHyH2PC5fFKKyrgJIBAIBDIExosRWJRU2srXyAAANyurhkcGjIYDFqttpXVjmjvDgwOGo1Go9E4zB+xqJk/FK1WOzg0XFVXBwBgc7kdnZ1SqRRY6SyLxGKlSgUAGBgaYjKZiDg7BIJgIwa978DBQ0ePjtnyQVVPuBg0BAL5dTw2IfWevj46zX5EIAgNCsrJy3N2coyJjPxx334GnZ6Tl+/h5sbq6Gxua21t7zAY9C0sFh6Hn4hUulyhkMpk3P7+5MTE/Bs3XFycmQ4OQrHYorPs7+N74NgxOp1+q6TEnkqrrqsLg/ESYjmHsxKDJhKJ4eFh2ZdyZqanj275oKonXAwaAoE8ZStLAEBQgH9nV7dILHag01EoNADgmaw5SpVKb9ALhMLZM2e0d3YNDg2lJiVZb6XT689czLZ56fR6SwMcDksgEOQKBW9gwMvDAwAQ6OcXGhwkkUpFIrGPt5e/r9/BY8efX7YMfv0QG6zFoAEi+f0AxqlCxKBjoqK8PD3zCwrqGxqIRMKxkyfh4YVAnl4erzYsytXF5UJubmpS0vVbRTK5/PylnLUvrEIElNVqtcFokEilSI66n2PhuKLPCJPjE05fuODr7YN8PJeTY62zPDA0SCaTLFnxIBAL1mLQo7GIQY/fyZMsBg2BQJ6+lSUAIGVyYnVdfWhQEAAAj8OpNZrisrKu3l5WR+fhk6cWzp8/c9q00xcu3hdgUSgalWrzspF/jImMKL9TlZI4CfnIcHBoamk9k52t1Wlz8/LdXFw2vPzyvsNHkHwIEIiFt957T6vVvv/3j4VCkdls3rp9R11DQ+6Vu+mrXt24USAQAABGV1lAxKCXLFz40tqXC27caGxswqAxFjFoeIQhkKeUJ0vuTqfTGQwGMpms0Wh+qezy+IzWWYZAfhE2QuoQCAQGSwgEYgsUUodAYLCEQCAQCATyQOAFSQgEAoFAYLCEQCAQCAQGSwgEAoFAYLCEQCAQCAQGSwgEAoFAYLCEQCAQCAQGSwgEAoFAYLCEQCAQCAQGSwgEAoFAIDBYQiAQCAQCgyUEAoFAIDBYQiAQCATy6HlsyZ99goLHqWV3djyNR1Oj1bLZbH8/PxwO93RZ3tPba0+zZzIZ0CUgEAjkCQqWAIBan9gxy+PZ9WOWJySnyOQy65Klixdv+vzzJ+do4nG4bTt/DPD3f/etNx/BcBvfeis1OWXVyhUTaZyQnHLiyJHgoECBUHj7zp35c+daqu5UVX34j0/OnDgO/QECgUCeuGD5S9Fqtd9+/XVc7M8h1o5CeaIsRKPR3/138/Yff9RqdQQC/g8PluvWOzjQJ9h465ZvPdzdAAC9vb2ffbnJOlg2t7QePXjAkcmE/gCBQCBPU7D0CQoe80qsk5OTl6enTSGbzdl/6JBQJIqNjl794gtYLPZBhRqN5sDhI22stgD/gDUvvUijUof5/Eu5uQlx8ReyswEAL656PigwEOm2vqHh2MmTcrl81syZSxYtQgr7BwaOHDs2MDg4JSV16eJFaPR9N305HM6BI0eGhoa+37Zt9QurXFxcrGtPnD4dFRGZe/XKwMDAnMxMS7jatnPn3DlzDh45smrFitCQEKFQ9NOePX1sdnhY6Ktr19rZ2QEAfty1OywsNH3aNADAoaNH/Xx8p01N6+nt8TZ4ebi7D/P5Z8+fT01OPnzsOMPB4c2NGy7lXq6urYmLjV3z4ovIKI2NTaEhISaT+eiJEwqF4rsftiHL3/6BAf4I/+tvvhlzjyAQCAQCnroHfIaGhvvYbMtLq9XpdLqlzz/v7++3dPGi4tLSf37+OQBgzEKTybTs+VUdnZ1zMjPbOzqWrXzebDYLBIItW3/Y/O23CfHxKrX6hTVrTSYTAKChsfHFl1+JjY6eO2fO5i3fXcrNBQAMDA4+u2gxiUSeMT398NGjn3+5ydo2pH+Gg8PzK1ZIJJKX12+wMf7k6TOvbdzo4eY+fdq0f2/66tyFC0j5wcNH1r/xJgFPIJPJKpVqwaJFKBTqr8uWsjmcFS++hNjj6ur61rvv8UdGCm7c+O6HbaGhIQCAS7mX6xsbAQACgWDr9h1bt++YOmVKfUPDrLnzmltbkhIT9+zbf+rMWWSUnbt2CYVCLBbj7eWFxWCCAgMeukcQCAQCeaJXlg/iX59/bv3szJED+xkODsPDw9GRkXGxsWGhoSxWOwBAJBKNLsy7VqBSqbZs/hqFQs3Lykqdnl5VXU2hULRa7Y6tW5lMRsaMGZHx8SMCgYuz80979rz4/PPPr1gBACCTyYODQwCAn3bvyZoz++03XgcAxMfGzlmw4KP3/0YkEhFjVCrVuldfQVZyJBLJEgutWbp48QvPrwQAmE2mfQcPLl64EClf98rLSxcvRlaNPt7eH3/4AQBgZnr61JkZ5ZWVaampC5979tr1gvc/+nsri/X1l184OznZ9KzVards/prBYBgMhm07d27etAkA0Nnd3dLaat2MRCJNS0s7dfbcM/PmPXSPIBAIBPJUBstdO3ekJCXZFL71+sYVL77k6uIyferU1S++cHcdNqqQ1c7ij4z8ZekyZCupTNbV3R0THU23v/sUKJVqh0ajdTodAKCzq+svzz6LtMzMyEDetLW3DwwMPLdkKfJRrVYPDA76+/khH+3s7OwolNWvvNrb14dCoczAPNr+qMhI5E1IcDCXy7OUh4WGIW86OjtjY2KQ9ygUysfHm8PhgNRUAMC/P/00KW1q+rRpczIzR/fs4ODAYDAAAFgs1s3N9e63i8Eqtapxjuf4ewSBQCCQpzJYjkav12947bXXN2yoq68/f/HiihderCwpHrOQSCDGRkd//81/kQ2VKpUDnc7mcFAo1Ohu8Xi8RqNB3nM4HLFEEhMdTSQQli9dsmrF3adPZXK5p4eHZZOCGzc2f7tl57YfIsPDuTze/L/8ZXS3Mtndp3nlCgWZQraUW0wgEohqjdpSLhAIkRAIADh7/rybq2vlndscLtfby8umZ8yvutc4/h5BIBAIBOEpu2epVKqkMpnlpVKpGpub5z77HBqFSklKemP9BolUajKZxiyckppSW1enNxgYDIZcrshasEAqkz1ooOlTp128lGMymUwm0yefflZWXgEASJuSequomEqlMhiM0vLyl15+BXluCKGPzfb380tMSCCRSOcvXhyz2/yCArPZbDabj588NSUldXSDKakpefnXpFIpAKCxqYnL4yVPngwAYLW3f79t++6dO55bsOD/PvwIuZH560Ch0Tqdzmw2P3SPIBAIBPL4V5YP+j/lOLy8bp31x8yMjL0//RgZETFzTlZQYGBza+tH7/8NjUYnxMWNLoyJjn59w4a5C5718/Pr6u7++/sfeLi7SySSMQfauG7d+jffmJYxC4VGeXt5rV39EgBgzYsv1jc0TJ+V6eTkNDAwsGvHDutNFsybt+/AwWf+slChVD4zbx6FYvfV5s1//+AD6zZyuTxz3jyj0YTFYo8e2D963Jnp6VmzZ8+YPcff37+zq+uz//cJnU7X6XTv/O39NS++GB4W9o8PP8ycN3/P/v3rXnnl1x32AD8/k9GYOW/+9atXxt8jCAQCgdxdZijlosc4/Dg6Pr9IxIfD5QqFQh9vb8tFywcVyuRyLpfr6elpT6M9tNthPt9sMrm6uloXDg0NicTiwIAAPN72n5Qarba3t9fN1ZVOp8vlCpPJaG9vb6l9bsnSjetei42JkUplAf5+GAzmQeMKhSJeP8/fz59KtfsjDrtWq1OrVXQ6/aF7BIFAIJA/T7B8KkCC5ZiP50AgEAjkSeYx36D680XEcciaPXu0ogIEAoFA4Mryd0M4axn8th4NzOun4UGAQCAQa6C2GQQCgUAgMFhCIBAIBPLbeGouw0IgEAgEAleWEAgEAoHAYAmBQCAQCAyWEAgEAoHAYAmBQCAQCAyWEAgEAoFAYLCEQCAQCOSpCZYGg0EilUqkUoVCYVM1uuShcHk8JGmzBYlU+iu6UiiVBoNhzCqtVqvX61Vq9W9Jj/WrEYhEyB491H6j0ahWqwEAIrFYJBZPsH9e/4BWq/2lVmm02gcdrseCXq/XarUKpRI6NgQC+ZMEy+a2ti+/+fbU+fN7Dh/+/OvNer3eUnXmYvYv6mpoeLiwtNQmY8bOvft+RVcXcnMHh4bGrCosLa1vajpz4eLEI9DveGJx6vx5IoHw0Jbf7/xxmM8vKi8HABAIhNMXLk4wtOsN+tz8a+M0EInFn3zx5aZvt2z6doslQOZdv9He1fXrdqqlre3i5csAAJPJtOvAgc1bt379/VaVSiWRSjdv/eGrLd8dOHbc0lgoEm3euvU/332HbDLaNuTNxctXuAMDP/y0Czo2BAL5fXmcQurRkREvLF+OBLaevj65QqFWa0KCg8LDQrt7e81mc6C//+3q6oiwMDsKBQDQy2ZrNBpWZ2dkWPjwCF8kFmfNmoXH4QaH+XYUCofH8/b01Gi1N4uKLem3wsNCAQCNLS0tbW2e7h5TU1N62Wy9Xt/cxvLz8YmLjrIYU1pZKZcrNBotAMBoNBaVlSmUyvQpaTQa1dpmIpFIIpFkcvmtklI7O8r0KVMwGExJecXA0NDkhHg/Hx9WR2d9U1OAv19iXJxNP9V1dQaDYYjPn5WebkehlJRXcPv7p6Yke3l6cni8qto6f19fa5Oq6+qQA6LRaFydXdo6OpHassrbYonEz9fHz8enpa0tLjoai8VWVlUnJ04CANhR7Hy9vJEA4+To2NDcEhcdxeZw1RrN8Ag/LTl59K6ZzeZeNlun07Z1dIQFj5EHpru3V6PRpKdNmZWePrpWp9NVVldHR0RweDzewACdRmMyGC0sVlpKirOj492VsVAokUoD/f2Rj1cLrl8vujU5Ph4A0NPXBwDqg7ffzr5ypbahQSSWpE5OTEtJ+WrLdyKxmOHgAAC4dDXvL/PnBwcGfrt9O18gsHTb2NJS19jo4eaGGMYXjDhapWODQCCQp35lCQAYEQjrm5qKy8t72WxXF5dDJ07q9Do7Mvly/jUnR8d9R47WNjRUVlUjkRJZjObduBEdEbF561YcFmswGAuLSyqrqhtbmhPj4w8eOy4Si3cdOMhwoGOxGA6PBwC4nH9NoVBcv1WUkZ7ezGpraWtrbmvLyctLiI05c/GiVCZDer528yaXxwvw821sbgYAHD5xEo/HhwQGfvfjThubpyQlEfD4bbt2h4UEo1CoI6dONbW2sjo70tPSLl25qtZojp85M3PaVFZ7B29gwKaf3QcPYTAYGpV65sLFgsJC3sDA1NSUnfv2jwgEx06fmZwQX1Nff6em1jIWckC0Go117bWbN/u4nKDAgJ/27ZfJZAWFt5B1Xv7NG3ePqlBwu7paIBRab9XW2ZF95Yq3p+eYu1bf1CQSS9KnTj1y8pT1El8ml1/Ov/af776va2wa4vMr7lR988O2C7m5NpFy++49bi6uQpHo4LHjwQEB+Tdv1jc1B/r7Hzv9syC7RquVW10Sn5s5CzlPAgB4uLmNCASnzp9vaGoODw2Njoy4WVJ66vx5DAZDv5cNVKPVkMlkAAAajeby+pHCtvb2m8XFLk5Ojc0t/JGRIT7f1dkFujQEAvmzrSxVKtUQn4/FYN9743V7Gs2RyciYPh2polGpz8yZs2PP3q1f/8d6k9SkpAA/P3c316RJk1pYrObWto6uLgBAQWGhTq/v6OoSCoXJiYkAgOu3ipBNyGRyXHRUztWrXF6/Sq0GAEybMsXHy8vPx0cmlyNr0Jr6hrc3rCeTSIkJ8UjwMANzR1cXnz8ik8mtF5eeHu6Dw8N8gaCkogIA0Nre8cycOdz+gYuXc9OnphHweHt72tFTp2Ojo91cXGz6cXN1SYyPl0ilDc3NApFozarnHRmMzz7+e0l5uU6vKygslMnlTa2tkxPikbGQA3KjqMi6VigSrl+zlkajxsfGjnNsG5qbrbfycHebPmWKn4/P9zt/HL1rwYGBHB4vNy9PLJHo9HocDgcA2HPokEqtmTlt6rzZmSgUis3lRoSGurq4/LBrVwuLFREaigx05OQpP1/foAD/7t7eqIiIQH9/T3ePKUlJnh7u5y5d+vm4ubt7uruPaeoQn4/D4aIjI4UicWt7u1yuCPD1jQwLa+vo5I+MuLq4AACemT37yMlTZDJZJJbgcFhLjJ87a9aIUPjW+nUAgCsFBYnxcdClIRDIny1Y+nh7ZWVkWD5i0Bjr2uq6ukB//xYWKykh4WdzMVhkeXGvwEwgEGZOm+rn49PZ3ePq4nzpah5yXdGyjqmqq+PweC+tWGG53YVG3dvcbL53cZWgUCrJJJJUJgcAUCiUF5YvR6PREaGhVKqdjdkEPN7d1fWlFSuMRmNTa6tarX7ztVcNRuOu/Qfo9vYLsrJcnV3OXbpUhsHY9GMx22wGBAIeWcPlXb/OoNPjoqPnz54tEIqUKqXNASEQCNa1OVfzdHodAIA/MgIAwGIxBqNRp9MpFPc91WKzVUd3NwaDftCunb5wMSw4+Ll58z7/erOlh/iYmNKKyjs1NUQCMSjAf2BwKCoiHADgxGTqdD+vPlcuXVJWebuFxSISCFjs3W8QhUZZHd2H0NndkxAbi1z+raqtFYnFyxYudHd1ZXV28QYGkGApk8tff+UVGo36zQ/bfL29LfsolcmxGGxbe4eLsxOHx3t27tyJPAYFgUAgT1OwHIeS8goHB/q6tWv+vfm/IYGBlstxo5mbOevIyVM+3l78kZE3X3ttWmrK9zt/pFDIlsjk6uScc/XqyXPnR4QChVLJHOue1vzZs3ftP+Dt5Yk83TNvdub2PXtoVKodxS5p0iSbxgwHBxdnp0MnTshk8inJSQCAXQcPBgcE4PF4JpP53fYdocHBg8PDs2akj9PPM3Pm7DtyxInpaEehZGVkbNmxQ6FQcvv7X1q5wma4yfHx1rXzZs/edeCgA50+IhSiUOiYyKjtu/cQiQQSkTjOVpbyMU3y8vAoqaho7+zUGwwtbSxkaZsQG5sQGysQCgtLSi/k5mZMn/79zh+9PDwEIuHyRYssHZJIpBdX/HXz1h9GW25NfVNTR1fXsoULR1dNiov9fuePg0NDPey+Nc8/L5ZI9h467OPtxe3vf2bO7E3fbnnj1Vd1esNPB/Y7MpneXp406t2F/rQpU77+7vvgwECpVLr6+ZUO9nSkvJfN3vTtFgCAi7Pzyy+sgk4OgUB+O3+GrCN6vV4qkzkymchHmVxOJBLxOJylgUqtxqDRBAJBpVaTSaQxO0H+BWG5P6pSqfQGg+VBodFIpFICHk8ikZBtJVKpE5OJwWCMRuOIUEi3t0ceXh2nH4PBoNFqkRHNZrNQJKLb22OxY5y+WNe2stodHZmODMaW7TveWPcakUBQqlQEPH70hg/qc0yTZDK5nR3FbDYbDAbCqMduzWYzCoXSarVarc7miaffBaPRKBCKHBzoyLem0WplMpkjk4lGo/Nv3MyckY5Go1VqtU6nszltKimvCAkKdHJ0RKFQ0JkhEAgMlpC7cHm88zm5BAIhMiw0LSXlT7+/NveMIRAIBAZLCAQCgUCeRKDcHQQCgUAgMFhCIBAIBAKDJQQCgUAgfyiYf3z84WMZuK2jo7unx9PDAwCQm59Po9Eu5+dHhoU1t7U5Ozk9AgNKKytvV9e0stpbWe0ebm4cHi83L18ml/t4eZnN5vybN2sbGtxcXUkPeHp2fG6VliJ/Byy/c+dGUREAwNXFRSQWn8/N7e7tDfDzw2Du/iWxo6vLMu6DertSUNDCand2cvx1xkAgEAjkaV1ZSqWyEaEQed8/MKDRaPo4XLlCcfr8BbFEwhcIRGKxVCaTSKUtLBaSPIQ/MiJXKDg8nsFg6OzuRnJraLXaVlY78g99qUwmk8sHh4cnYkCgn198THRUeHhnd7fJZDp98eJz8+exudyG5uabxcUajTYtJWX3wUOW9vyRkaHhYYFIpFT9//buPEiO6s4T+MvKrKPrPvq+1K0LCWmEMOJmzCWQwBwGH+zO2DFhxjNmdmKHmd2I9cZsbGzsH7PDTHiGwx40NkggDmEMCCRAMMbmEJKsVt/d6u7q7rqPruq67yuzMvePlJ5SpVYjwKY5vp8gHKU63nv58lV+K7PavyrJL6EP5QsFWls8nkg8smuXXJTcFwiMTkx86667Dhw6lMvln9j7zPXXXtvV0fnKgVOlbYqlkrJf2mClUpmcmj4+OEgIyeXz+XwhGA43/KwKAAB8+cNySbl8vlQup9LpvfteeOm1A4ux2K7de5Kp1EOPPFqv15985tk33n77wJuHfvLTn7m93l279xBCHvv5LxLJ5FPP7/P6/e99eOTxJ3cvxmIX0ld7W9ua/v7RifH77r2nWCz2dHVZLZYN69Y55+bGJ0/e+MfXdba3q1RMpVKRn/9//vGhwdHRx3b9+wsvvzI0OiaXCpJ9eOx3NPibHY7/8sMfmowGQohery+XylMzM2pOrdNpeZ7v7uzctHHDzNyc/ORUKqXsV76zxvP/+m+PT0xNvX/kaC6XHxod23bpVqxUAIAVtGIVfBQl6878s6ujw26zrenvr9cFufbK9huuX4hGC4VCpVolhHz3nnsmpqaD4dDO7dtHJyYJIXfs3OHzB3iBTySThJDbb71ly6ZNp05YI5FjAyeUvVgt5ltuvJH+M5PNZrK5Nf39oiim0unnXvxVOBKRf7qZ9iddAAAbJElEQVREvuCpUWtqPK/T6Qghne3td+7cmcvnr9q2raW5+dlfvkjbuf3WW+hthmFoPQSB51UqVaFYIoQUisUN69c9sfeZer3Onb4G29XZqexXvnNmdnbL5k0tDscdO3eoWJXH57vp63+MlQoA8FUMS7vNOjI+Lt+OxmINlVkYRsVx3ODo6PTs7F233ebyeAghLMuyLMswpyrESpKUzeX2H3z9/u9/j14U1ajP/KplW0vLjptvUjZLvymU/faDw1+/5mpCCM/zd+zY0dnRMTw6xnFsoVhYjMV6urpK5TKt6SP/XiZDGLkR8QIqn46fnLrmyiuvunxbrVZz+3wb169f3deXLxQOHHpLfkJDv/KdWo22VqtJkpRIJjVqTUMdOwAA+AqF5eq+vkLx0K7dewrFYl9PLy34yXHc8NipELWYTMFw+K13fpPL5+lVSiWNWl2uVA4fPRqNxVPptMl0VqkXjuPMpuWKv8zMzn7zG7cTQtRq9SsHD65dvcYXCDz4wI96urr27nuh2eHYfPHGhjPgJb3w8ivXXXVlz+lTQ+rSS7Y88fReXyDg9fsffOBHLx88OH5yaiES+ZPvfLtQLD71/L6//uGfK/uVX7Wmv+/VN96ILsZK5fIVl1229fQvXD757LM6jZYQ8jd/9YCymB8AAPyhrXAFn1Q6zbGcsp6ZKIqiKNJypoViUavRsCwrCIJ8btegVqsJgqDX6yuViu5TnITJV2LtNpucjjzPF0ulZQq4XyBBEDLZLG02lU4b9HqtVluv198/cuTm669v6Fc2OjHR0dbW2tJyIVENAABf8rD8yhJFUajXcYIIAPCFwGEKVoRKpdLgrBEA4Ity0MYUAAAAICwBAAA+lRW7DDvrcu0/+Lr8BywWs/mB+3/w+23//SNHbrjuOvrPRDL563ff02g0t92y3aDXy3e+fODgt+++6+O2XKlWxyYmrrr8ckLIkePH592ea6684qK1axsekjU8YUmH3nmnVuO/fs3VdpsNKxIAAGeWZ+RyuUs2b/7x3z744799UE7K6OKic26+Xq833M7l8pPT06VS6QJbVhaco57e98IN113b1dGx/+Dr9M5AKEQISaRS5XJ5mWp2qXSa3j45M/P/fvIv07OzhJDJqWmXx3PvnXfsP/h6oVhUPiRreIKywXfeey+6uEhQzQ4AAGG5vNDCwvDY+PDYuD8YdHu9L+5/dSEaefKZZ5S3E8nk47ufTKXTDz++S5k3y1AWnKP+07336nS6eCLR0tKsvP/kzMzLrx3QaDTLVLN79Y036e2LL7roB9/7U/l2OBrZtHGjxWzubG/3+vzKh5Z8gnxnMBTa89zzoYXIi6++RghBNTsAgM+/lfxr2Eq1ki/kCSFGo0Gr0aQy6RrPf/vuu/OFAr39u8HB22655ZLNm0ul8pTTeeVllxFCajx/4M1DDa3d/Y3b5f8nhrLgHNXd1TnvdidSSYfdTu/0B4P/9sSTjz70jyzLLlPNTi68d+rDhUpFi9Vtu/TSJ57e65ybc87Pb93yR8qHlnyCfOeR4wPfuuvOWDze29MjiiKq2QEAICyXs7Z/Nf1aMbq4+F//8i+D4fAju/79r3/45/T2Vdu2yVdEa3yNFn5Tc1xDHTv5zvN1JIrii/tf/c/f/tba1av/7z/989233y7fb7WYb77++gOH3vrON+/+BNXsxHr9B9/7U7PJVCr9sqer6wKfoNVqkqmUwWAILSyYTSZUswMAQFgu590PP5x2OgkhTU1NO26+6bU33uzt6e7p6ioUi/T2NVdesWv3HpfHEwiF7ty5U34hwzDL17FTeujhh//n3/1dqVza89xzuXxeLgYrs5gtX7/mmocefiQYDi/Twj8/+tj/ePBvlshCSdrz7HP9q3r1en3D3+bI1ey+8827z33CVZdf/i8//dllW7fyPM/zPKrZAQB8/n2OKvhUKpVCqdRstzfcFgQhl8/brFaGYT5Bs79+971bb7qREJLOZLRarf73+vvJ1Wq1WCqd+1estJrdkk84Pji0ds3qZsUFYQAAQFiuaAZXqzqt9jPuFNXsAAC+TL785e4++6QkqGYHAPDlggM6AAAAwhIAAOALGpYzc3PHBwfl22/8x39EY7EX9+8nhJycmfksh/H+kSPn3vn2b35DCyD4A8HB0dFP0HKlWpU3UBCE199++5lf/jIciRBC5lyuvfteOHzsmPLJR44ff+r5fbMu1/laq9Vqv/3gg6ee34clCwDwFQrLbDYXTybl2+GFhUql4gsE84XCr/a/ms5kYolEKp3O5nKZbHbK6SwUCoSQWDyeLxQCoZAgCPNud7lcJoRUq9Vp52wsHieEZHO5XD4fWVy8kAEsWRWPEDI6MfHqG29Wq1X5n4VSMZlKEULCkUipXM5ks16/v1QuxxOJYCikfOH5quL9+t33mnRNO7dvf+LpvcVS6VevvXb3N273B4PjJ0/KT76QqnhjkyebHQ6314MlCwDwFQrLJeXy+VK5nEqn9+574aXXDizGYrt270mmUg898mi9Xn/ymWffePvtA28e+slPf+b2enft3kMIeeznv0gkk089v8/r97/34ZHHn9y9GItdSF9LVsXLZLMj4+Nfu2RLw5MPHnprcmoqEAw99PAjcy73//6Hf3j38IcvvXZgTnEueL6qeA67LRgKTTudLS3NqVSqp6vLarFsWLfOOTd36rPCBVTFm3I6N23YgPUKALAiVuyvYVVn/7Go/M+ujg67zbamv79eF+Qic9tvuH4hGi0UCpVqlRDy3XvumZiaDoZDO7dvH52YJITcsXOHzx/gBT6RTBJCbr/1li2bNp0KoUjk2MAJZS9Wi/mWG2+Ub59bFU+SpBdefuX79333+ZdeUt7/7uHDVrPl7//7f3POzV+29ZIdN980MDx83733HB8cCkci60//nMj5quJJkiTUhVKpXC5XOjs6Uun0cy/+KhyJ9HZ3y0/4yKp4co11jsMvdQMAfMXC0m6zjoyPy7ejsZjVYlE+yjAqjuMGR0enZ2fvuu02l8dDCGFZlmVZhiEcy8khlM3l9h98/f7vf4/+SIhGraGNtLW0NFTFY8+u3dpAEATCkOdfeml23vXSawfor4Z9bcsluXx+amaGZTm1WkMIUQbhR27p0YGBv/izPzObTNNOZy6fv2PHjs6OjuHRMY471chHVsVbjMUu3nARFisAwFcuLFf39RWKh3bt3lMoFvt6emn5Oo7jhsdOhajFZAqGw2+985tcPk8vWipp1OpypXL46NFoLJ5Kp01n18DjOO7jVsX7q/vvJ4Q8sfeZe++848z5qNVyx45b/+nRx757zzeXaeF8VfGuu/rqXzz1dGtri8lktJjNP3/qqbWr1/gCgQcf+NHY5GR0cXHrli3LV8VTqVRy1/lC8SeP/ZQQ0tbW+v377sPyBQD4bKxwBZ9UOs2xnNl8JtJEURRFkV5yLBSLWo2GZVlBEORa5w1qtZogCHq9vlKp6D5FUXJaFe8PoVKplMplOQhFUUyl03abTaVSZbLZebf78q99DVXxAAAQll8AK1IVr1qtajSaT1bzFgAAEJYAAACfF6jgAwAAgLAEAABAWAIAACAsAQAAEJYAAAAISwAAAIQlAAAAwhIAAABhCQAAAAhLAAAAhCUAAADCEgAAAGEJAACAsAQAAEBYAgAAICwBAAAQlgAAAICwBAAAQFgCAAAgLAEAABCWAAAACEsAAACEJQAAAMISAAAAYQkAAAAISwAAAIQlAAAAwhIAAABhCQAAgLAEAABAWAIAACAsAQAAEJYAAAAISwAAAEBYAgAAICwBAAAQlgAAAAhLAAAAhCUAAADCEgAAAGEJAACAsAQAAACEJQAAAMISAAAAYQkAAICwBAAAQFgCAAAgLAEAABCWAAAACEsAAABAWAIAACAsAQAAEJYAAAAISwAAAIQlAAAAwhIAAABhCQAAgLAEAABAWAIAAADCEgAAAGEJAACAsAQAAEBYAgAAICwBAAAQlgAAAAhLAAAAhCUAAAAgLAEAABCWAAAACEsAAACEJQAAAMISAAAAYQkAAICwBAAAQFgCAAAAwhIAAABhCQAAgLAEAABAWAIAACAsAQAAEJYAAAAISwAAAIQlAAAAwhIAAAAQlgAAAAhLAAAAhCUAAADCEgAAAGEJAACAsAQAAEBYAgAAfOVwK9XxqnXrMfsAAPCx+OfnVqRfpphPYfYBAACWgcuwAAAACEsAAACEJQAAwJczLD1+39ETA8cGTwRCoQt5viAIE9NTF/JMp2t+yTvT2WwskfgMNq1cKdONcrrmeZ53+3yEELfPl83nPkGD8hbNud1/0GFXqtV5zyfpYmrWSQjJ5nIT09MXNj+VXD7/KUebymR8wcCn7GIxHk9lMufOgy8YJIRMTE8LgnDhsye/6kIG8/6xo0cGjh8ZOB6KRJZfwNFYLH16hEsu7E+0PpeYnHgiMTkz87EW5Ll3litl5dzKKz+VTvPnmUb61ji3qWU2djEel3fcrNsVXmoCPzFeENw+7wVu/vJHpAtsCpREUeR5vl6vL3n8d3m9i/H4Cg6P/V9//+MV6djt8120Zu36NWvGp05aLZZUOu3x+6xmc6lcjqdS/lDQYbPFEolAOCQIdZPRGI3FCsWi1WJlGGZmbq7G82aTKRqLuX1es8mkVqvl47XH54vGY/29vR6/L5ZIOGw2hmEIIc75ebvVVqvViqWiPxQy6vWpTCaZTsUTCYfNHo3F5I6MBsO8xyO/cDEep72HIxFvwG+zWiuVitPl0mg0TTqd3GM8mQgtLDjs9lg8bjQYorEYq2Lnvd5MNmM0GD0+X29XtyRJWq3WFwyq1RqzybQYj3sDAYvZzHEcISSWSCzGY5lczmaxRBaj8vAq1Wo8mazX6006nXN+vre7OxAO65ua5j1ujmX1TU2RxcXFeKwuijqtVjkhdEPm3O5EMtnscDR0t+zhPqDm1MlUKl8oxJOJUrls0OtDkUhoYYFhGI/PZ7VYiqWic35Oo9Y0NTWdCkun0261DowMb9t6qSAIs25XpVq1mM300Cb3Xq5UnPNzHMeGFyLegH9Vd48cBkaDIZVOy8/0B4NNOp1WqyWEVKtV2hTdWFEU5WmJxhZFUXLYbHKP1WpNq9XGE3F59nyBgDfg7+7olGdG39SUSqf1en00FpMkiU6sJEksw8STSblfjUbj8npz+VyhWJB3pdFo5Fh2YTEaCIZMBoNKpVpyqumrbBaLPGajwbAQjcrNBkIhur108d9wzbW93d3yYpDXodVsWYhG88WCoalJo9GcdDoFQShXyolUWl6QkiTJfUVjMbvdHovH5cVPR3XuzmIIoQ81jMdutcm7Q9+kJ4ScGBmx26wcx8k7V61W0xkrlkqpdLouisl0Wn5jSpKk02qnZmfLlYrVYiGESJKUzmTiiUSz3a5SqSrV6gdHj7Q2t+i02pHJCZVKVavVYomE2WSiEyhPhSRJuUKBvtEii9FoLJZIJrva240GA33fFYtFeeVXqpVjJwZW9fSyDDMyMbGmr4+uw3yhIG9Rk67J5fUmkkmbxRJPJunbnE6dimHS2awoimqOi8ZioijKjas5zhsMtLe2Lr9s5PcjPSKxLOv2eYulkiRJ1VpN2ZTFbFYeLuAjPwH/9vAHlWpt3uNe1dOjfChfLGg12vGpk6u6e1SqlTnHW/nLsL3d3f5Q0B8Krlu9ZmBkJJ5MxOIxs9E053YPjY31dHZNzsyUymWX17Omr39gePjEyEhHe1tkMZpIpUYmxtf2r6YfN06MjvR2d+fyea/fX63VDHq98sOyzWJpbW5OpNJd7e1DE+OzbpdGrU6k0plslnY053GrOc5iNp90OumduXzeHwr2dHb5AoHjQ0Nr+/uHx8dEUSSERBYXk6mUzWKdnJ6Sz/zk/y2VS6tX9R0fGiKE1HjeE/CfGBnpX9UbWYyGFhZmXfN9PT0Dw8PywCZnpg16fSqdXozH6fDiyWQwHDIaDHT8G9atC0cja1b1DY6NZvM5b8Bvs1jHTk4qJ4SOedbt4jiOZdlzu5MkSVCoi6Jyd8x53Jyam5yZNhmNM/NzNZ4/OTNttVhGJsZtNptzfn5wZHTj+otGJicEQSgUi7wgVGvVX7//3tr+fo1aHY5Guzs75z1u+SSjVC7T3gdHR/t6elPpjMVidtjsp7pzuwkhwYVwrlCYmJ7qX7VqZGJCfog2FUvE6cbK05JIpRiGkffvwMhwZ1v7vNeTLxTo7Mld0JmJxGLyad+c262c2FgiHk+laL+zLpdKpRIliRByfGho04YNw+NjxXJpyuns6+09MTa69FQrXkXHnCvkabPK7ZUl0+mjJ04cPXEim8vRdZhMpYYnxm0W66zLPTA81NfTE43HRFGq8TWLyeTyemdd7mwuF4ostDgcmUyGLn46qmgs1rCzlA81jIfuDnlIFrPZYXfQnVs9faI853bPezy8ICRTKfrGnHW55zxurUZTrwvyyXcml9NptSzLegMBQohWozGbzTqdzhPwW82WZrt9cHTEajErJ5C+NehMZnM5byDgsNlK5fKsy61839GVbzGZzWZzJpstlEoWs4l+JiOE0C3K5HJajaZer3sDATq9wXCYTh0hRMUwM3Oz4UikWCrSxk8dRj5q2cgLmx6RZt0uhlEVSsVgONzQVMPhApbnsNmaHc1bN2+WPxYrH7KYzLVajVWxLMt+5S7DUtlcThSlFkezvqlJqAuEkI62NqvFwgu81WI2GY06rTaXz5fKZZfXa7WYs/mcPxiSzxf/6OKLRycnM9msfN1Do1YbDYZmuz2VyaQzmXgyadDraUccx4mimEqnQpGIKIoqhuloa7eYTIIg0I7SmUx7W6vdZssXCvTOTC7XbHfYrNbO9vZMLjvrcpmMJpoxHW1t7a2txVLp1OWCukAIaXE0Gw0G5ScgOuxCqUS35dTAWK6tpbWtpSWTzdLhEUJ6u7rlM2ZZk06XyWbnPG5JItlsrsXR7LDbtRqtckLOjDmbbW9tXdXTI4piQ3elcnlq1kn/8wXOupipYpiu9g6jwdjW0ir33trcYrNYHDa71WyuVqsajUar0Wg1mkKx6Jyfz+XzKpXq7ttun5mbL1fKxVLJ5fHyPF+vi4QQ5Y7bunmz0+UKRyOsimU59uxrLHW5I+Wk0abS2SzdWHlacvk83b+1Gm+1WFoczTW+RmdP7oLODHP6U0JdrJ87sbTfVCbd0dbW2dZOCKE7WhSl1uYWk9HIqtglp1r5KuXm02bP3V6HzXbtFVdce8UVFrP5zDqs1zva2vRNTYSQWo23mM0b160nhHS1t5uMxmqtSggxGgztLa0nnc5iqUQXv3JUyp3F83zDQ8rx0N1x6hITq2IIoTtXqNcVMyat6etTqRj6xiSE9PX0lsplj99fqVYJIZVKORRZyOZy8gwzDEOPayyrYlnWZrXarTbleCjlom1xNNttNnkSznrfnV75LMuyKpZhCMMwLHvWoZNukXIwdHo1Gg2dOvnDQbFUCi6E+3tX0cYb3qrLLBvlwk5nsh1tbe0trYSQhqbOPVzA8krl0uTM9OYNG+XjgPLCfkdbm0Gv//Tf4Hwhw3Js6uTREwP5QnHj+vX+YHB0crKtpWXJZzbb7Rq1hmEYhlH19fSKolgoFg16/ZzbbbNYanyNEKLmOELI+NRUPJns6+kRhHq1VtVptQ1fJFRrNUEQSqezrUFfT+/w+MTQ2Nja/n56Z3tLSzAcPjY4mEilOtvbWZbl+Zr69CXN6bm5Y4OD/b2rCJFGJifkfekPBUcmxpvtdmXL8rC72tvptpy6Ui+Jw+Pjs26Xw25ffnhyMvECbzKZ/KHg4NioIAjKCaHP7O/tHZ4YP3zsmMlkbOjOoNdfsmkz/W9NX9/HWC4qFSFkcmaGYRirxbJt61aHzabm1Bq1+porrjj8u+O5fJ7jWPmo0bDj5r1eo0Ev1kWdVhteOHWMttusAyMjS37zRJtSc2q6sacn88z+ddjtg2Oj3oC/LtTp7Mld0JmxWa3xZGJobKzhMN1g9aq+obGxk84ZQgjd0RzLBkKhkckJs8m45FQrX0XHnM2d+XJaHozH70tnsw1nloFweMmRtLY0D42N/W5oUKVizj6UlKOxmNFo4AWBLn46Kr2+6dz1fO5D8njo7jjzlQzL0p1rMhqXn7HIYlSoCxzLSZJECCmWyoQQiUjZbI5ewJCvruu0utDCQsN4lBN45o3W2uoLBkYnJ6vVWsP7jq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","type":"image/png","originalSize":57922,"dimensions":{"originalWidth":612,"origi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3A+zo4P/WwtLa28mnXrlIsJm9Z0Tl05CgA9O7Vi81mNzI7m6BQCKLeqHsiF9+QlgPAHBMbk9ePHIlTSAFgLt/WnEqnAbRisNebOXTjGANANXbwQZ+8CpHIxJhHpVKVKpWTgyPZqIqMfVYqEHi6twIAj5Ytn+v1uSMHdjE3NSV78yWnpz2KjNANEPMiKellUlL7173c60XGTK3l/OXXMJtdJZfnFRQ0s7GF19dlOAYcqUxGpVCYTGZ8wks3J2cAqBSLBUJhTHz8w4gIABBLJIKyMkFZmbC8HACq5HI2i2VowJFIpABgZWF+/uofpWVlXCMjuUJB1hSUlanV6lqLFQiFHI4BnVZ/m1Kt0Rw7eyYrN7fuWyY8XmZ2VnJaqrCinGfEpVCIrNzcrNxcRbWCx+WqVKr8osL0V5kUyhtSQKVWW5pbRD6LcXJwVKnVZAyXlQuNuTyRWGzMM6ZQKCwWS6VSqTUagbDmD5HL5WRTOyk1xdnRsaZ5LRJ1aNs2NSOj1ip0IwrR6fTgjv76IwGVlJbW3QAMy/dj6eJFdDp93caffli+Iuz2nbDw2/MXLd574ACPx5s3Z07j81ZpNb3zEocWJFfVF3U/CfOvScvjFdIeHGOyZIMwL1+pCDHg3bBrHWnf5pi1mxuDvbA0CwA+45gs4tvhx4E+ZQYGBpVisUajycnLC7t/T6PRsJjMDu3aW1ta5uTnAwCdRrcwM9M1ucxM+Z06+suqqsjxaFo6u3AMOLoBYjzd3T1btXqVnd3Q6iRS6cukpLrL+ct3XC6//+RJp47+ZB9A8p4IWZXMgM0GgISUZABwadECAKhUqrmpqb+vb6W4EgAKiooSU1MTU1OT09MAgMVkKhQKWVUV2YwrLi39oldvWytrYXl5hUhE1kxMTSWvzuovtqikuKikJDs/r27GAACVQhnav39zu3r+6eDxuMmpaR3atU9ITuZxuWwWWywRGxly5AoFmfoMOr1aqaS8vuJbWFykVNUz7qZarXZs3pzH5XI4Bmq1WqvVOjk6mhqbAGgN2GzdeC8AoKyu1v0hZeXlAGBlYRHYwY88/1xWXi6TVZFjCyiqqwFAVlUlEJZBnRGFOAYc/ZGAam3A+zo4cWxY8PbyOrRv73cLFhw5flzXvmzp5vbLxg3W1lZvnL1QXQ0NfHxlGtViwV++qLmq6i8LU3pxTFwYbKVWG6uQ3JOJVACmwvzuHGPFXxO31t0sCP33w5LNtjAzf/D0KcfAwJjHKxEIigWlT6Oj1WoV14hLoVDyCvJDgzq9TE4Cgqg1sIv+ADE0GrXWiDD6a0lKTS0RCB5FRlYrq834po0PEHPn4QONRvswIsK1RQtHe3tbK+vHUZFKpZJrZFQiEETHxdnZ2Ny4c6dHaKiRoSGTwYiMfUamtZuzMzkaeM25X1fXR5GRKrU62N+f/EsjYmLKRRUe7u4MOt3K4s+e87UW69HSXSKV3rhzu6GnwfONTeotN+EZ02g0a0tLrVbLYDBMjHmVEolD8+bFMTFkBSsLy/KKCpVaRSbilZs3PwsJtW/WTCqTxSe8LBaURsTEdGjXLq+gQKVStfHwvHHnjlgiLSwucmje3NLCIjIutryiwpTPJ/eku6sbi8UiR5olpWZklAmFLBYLAHLy8p4nJVqamfu2bXvz7p2o2NggP7/E1JSsnJzBffsxGAz9EYXaeXndffxINxLQk+go3QbI5Qpy9Np/33sewefDUV1dHRUTk5Oby2AwXF1cPFq1qve+2h59+v5rz2TesmnTgH598aNBnxqVSqVUqdgsVkMV1BoNlUJ5j/9WMBiMht6Vy+WsBrZcqVJRKRTdac8quZzJYFAoH8rpPa1W+26DCSiVSgqFQn2nXsf6K601otCHNhIQtixrMBiMQH//QH//xqv9a0mJ0CeLRqM1fv8Y9b0GTCNJCQCshjOe/tc/qpFfA++n5fSusfR3hgHSXymVStVPXBaT+UHtH7xm+XbGjR6NOwEhhD41GJZvZ9jQIf/aurhcI9zhCCGEYfnxaenm1tLN7V9YkbmZWXBQEO5whBDCsPwo3bhy+eypk/369Pm/rmXJwoXvPOwfQgihfxb2hv27VCqV/mDrRcXF+fkFupdx8fG6p1gDwKUrV5rSphz51VfTp0zGsEQIIQxLBLl5ebppqVSanJLa0s313znNixBCCMMSIYQQ+se8txN99i6uuPcRQgi9ley01PeyXuzggxBCCL0BnoZFCCGEsGWJEEIIYVgihBBCGJYIIYQQhiVCCCGEYYkQQghhWCKEEEIYlgghhBCGJUIIIYQwLBFCCCEMS4QQQgjDEiGEEMKwRAghhDAs0fuTlJycmJSE+wEhhN4WDXfBp2PT5i1qtfrgvr24KxBC6NNqWWo0GrVajR9k48rLy6OiowVlgtLS0qjoaLFYgvsEIYSa7oN4nuXDR49O//57bFx8cUkJQRBmpqaeHq379u7T6/MeBEHUO8u1GzfOX7wUGxcnKCvTaDQ8Hq+Nl9eAfv0G9OtLodT+BXD77t0rV681sgG9e37eNTS0iVur1WovXbly5ty5hMTEqiq5hbm5R+tWQwcPDgkObmgWmUx2+OixazdvZmZmAoCVlZW/n9+I4cNaurk1NEtMbOyhI0ejoqOF5eV8ExNHB4cB/fr179eXxWS+1b5NSk5evX79w0ePtVrtn+cTaLTQzp3nfTfH1cUFvwMIIfShh6VGo1n0w9KTp0/X+65P+/YHdu8yNjbWL5RKpTPnfBd2+3a9s/h18N2/ezfXyEi/cMny5UePn2hkM+Z/993UyZOassEymWzy9Bn3Hjyo+9aAfn3Xr17NYrFqlb/Kyho9/uuc3NzajXoKZdH8eRPGj6+7qI0//7J916665a1bue/dudPO1rapv0IeP/560mS5XA4ATCZTq9VoNFq1Wk0GJ5PJ3LtzRyMZjxBC6IMIy1+2btu8bRv5D3evz3u0cncHgBcvE/64do08uRro73/iyGH9WWbMnnPpyhUA4BoZ9evbx83FlUajxj9/cf7SJYVCAQBdu4T+umeP/iwjx4178PARk8lks9n1bsas6dPGjxnTlA2eNG369Zs3AcDExGTggP7NmzUrLi45f/FiYVERAAweOHDT+nX69SvF4t79B5BJ6ezk1KdXL2NjXmpa+oVLl2QyGQBsWr9u8MCB+rMcPHJk+Y+rAIDBoPfp1dvTo7VUKr0Vfjv++XMAcHF2unL+fN1IrksoFIb2+LyiooLPN9m4dl1o5+Cx30yg0Wg7t245e+78j2vXKhQKI0PDB7fDTUxM3vkTjIqJ+ePqtScREckpKf/IIWFuZtanV6/gTkHBQUE0Gl5TRwh98mEpEol8AwIV1dUW5ua/HT/WwtFR91ZqWvqIsWNLSkoA4PffTvm0a6drKo0YMxYAHOztTx49YmNtrZslPSPjyxEjBWVlAHBw394uISG6tzp16ZqTm7to/rxJ33zzdzY4LPz215Mnk4l18uhRczMzXWN3xNixsXHxAHD4wH79ttqPa9buP3gQAPr27r35p426f/3TMzIGD/+qvLycw+E8CA83NeWT5SWlpcFdu1VVVbHZ7BOHD7dr20a3qKUrVh4+dgwApkycsGDu3Ddu7abNm7fu2AkAB/bs6dYlFABGjRtPo9HIDj6/nTk7b9EiAFjw/fdTJk18h70hk8nmL15C/nD5f2jp5rZ/965mdnb4LUUIvXfvs4PPxctXFNXVALBw3jz9pAQAVxfneXNmk9MRkZG68t/PXyAn1q76UT8pyXbb4gULyOkzv/+uK1cqlfkFBQDg5Njib27w7v37yYmfN2zQJSUAcDictT/+SE7vO/Crrlwslhw/dYpsLa1b9aN+O8nZyWnmtKlk0J747ZSu/Ojx41VVVQAwe+YM/aQEgEUL5ltYWADAsRMnyVZp4+7cuw8A1lZWZFLWMnjgFzweDwAioqLeYVfk5uUFd+32/0tKAEhOSQkK7XL77l38liKEPumwTEpOJic+/6x73Xdbt2pFTpQJ/2z7Rj+LAQBLS0t/P7+6s3Tr2oWcePEyQf+fdfKMbosWjn9na4uKiqKiowGgXZs2Xp6etd51b9mS7K3z+OlTUWUlWRh+5w6ZfEMGDTI0NKw1y4C+NX2Rrt+4qSu8dOUPAGAyGMO//LJWfRaT2fvzzwFALJE8fvL0jRssEAjIfVXvu1QqddP6detWrxoxfNjb7gqVSvXFkKGlAsG/cJCMmzCxrEyIX1SE0KcblnK53MzU1NXFxcDAoO67wtcZaczj6Qrz8vIBwMXZqd5eskaGhmQ5GVGkrOxsMhuaN2v2d7b20dOafAru1KneCh07dAAAjUYTFR1TM8uTx+RE5/pm4fP5Ls7OAJCQlETey1FQWEhubds2bWr1USL5dfAlJ57qtbYbQjYc0zPSyQ4+dXXv2nX40KHdu3Z9212xau26fycpSTPnzMEvKkLo/XqfHSh++WljI++ePH2GnAgKCCQn1Gr13NmzAaChGx7S0tPJfp62ev1Fyfixb96cRqPFP38eGR1dVlbGZLKcWjh2Cgxset+WxMSasW+8PD3qrdCyZc19IK+yXgGE6s/i6dG63lncW7qlpKZqtdqsnGzP1q31VuHZQP2W5ERm1qs3brCvT/vklBSJRLrwh6XrVq1iMhn/yKcmk8kOHjnybx4nDx8/Tk5JaeQ2G4QQ+i+HZb3UanVySsr+Xw9euXoVAPr37aO7dEelUhu/wePnLVvICf3ePdnZOQBAo9H6Dx4SFx+vX5/JZI4fM3rOrFkMxpuDJDcvj5xo6M4NaysrciI/v0B/FmNjYw6H86ZZ8j1bt87Jq7m9xNbWpt76VnVW0Yivx449ffZ3hUJx7sKFmGfPhgwaWF5ebm5u/jc/oLr3wJBaMdhjuZYAUKpWbi4vUIK2KUubwrPaJSp6Y7XklFQMS4TQe/QBjeCzZv2GVm3aOrm36tV/wLmLFwmCmDF16s8bNjRlXpVKtXTFyms3bgKAmanpuNGjarUsU9PSyKS0sLBwatGCvPVCoVDs2rtv+OgxDZ2o1FdWVkZONNQYNXp94lQqk5KbRF685DfceNWbRQYAuotzDc3CYjLpdDrZvHvjBjs6OOzcupXM6eycnJ9+2fwiIeH+w4cjxo49evyEUPiOFwKTU1LrFhoS1O9NbFeW5c4TZCVVy0ZwzQGgGY3RjskhjzA+hWZEoXoxDAyImkPOjc42plAd6TX3wHgyDFzp7IZWGn7nDn5XEUIYlgAAGo1GKpXqBprRarVXr19/8OjRG2d8mZDYf9Bg8rYKDoezZ+cOnt5lTjIsAWBg//4PwsOiHj28feP6i5jonzduIKtFx8QsWrrsjWshb+Ik26P1VtA1T8mab6z/d2aRv67ZuG5dQsOuXR014ivdFVCVSvXw0eMly5d3COr03fwFRUVFb/sx1Ztb7Vmch1WVEq0aAK5Iyw9VlnRmc8fxLFvQWWvM7AFgoJHpD/xm7kyD9eYOADDbxCbEgDeBV9NQnmVs3Z5l2INjPIpbf8M3NS0Nv6sIIQxLAIChgwcf3Ld3x5bNc2bN8mjVCgAyMjO/njS5kZsHhELhwh+W9vnii5eJiQDQ0s3twpnTupsya1owrq6+Pj7fzpjxy08bmzdvroucQQMGnD15guxb9Pv582np6Y1vHvF6FD0KUf9OU1Yr9aNON+oehUI0tMyGZ2nwc1Eqq8kmZhP3qo219arly2OePjlz4riri4uhIYfJYACAUqk8e+5c30GDdT8mmqjecQFpBCHTamp2FACXQu1nyF8vzDsrKaMRBJdCBYBT4tLfxAJds3KPqGhLeQH5y8iFwT5UWbKtojCYzat3pTgsH0IIw1L3D6Jzl5CQPr16zZo+7Y+LF5b/sAQA1Gr1ilWr6w6Vrlarfz10uHO37idOndJqtQYGBvO/++7K+XN1/1Xdu3PH2ZMnZs+cUe8/waO++oqcDgu/3fjmGbwe/UeuqP+cra6cPPOpG2RHLm+wFaibxZDDAQDdAEMNnRbWarXV1UrdKpqOwWB08PW1srTs4OMbFxW5bvUqC3NzACgpKVmweMlbLaqlm2vdwqTqqg6smsZrKJs3wsgcAChAAAANCIVWAwBKrRYANFotGa4AwCBqfkdQa2qC/HXiNiWhEULoUwzLWsaNHu3r4wMAWdnZia/vyCTl5OYOGDJ0xerVlWIxhUL5csjgu7duTp08ibye91aCAgLIiTc2sExNTckJ3dE1PfoAACAASURBVMXLWgSv76YgR0sgCMKUzwcAQQP1AaBUUKY/i9nrVTQ0S+mfq7B65x1rYGAwfOjQi7+fJQfdfRIRUVBY2PTZ670Dp0BV/Uwu2W7RYhHfrp8h/2hlyWmx4Eez5nNMbFKVVQpt7c4+T+XiJXy76SbWZD+gaLlkAd9upZn9WbGg6QmNEEL/mvfWGza/oIDs7+rTvn37tm3rrePn60uOA5CTk+vZuubui6Tk5OGjx5SXlwOAT7t2q1Ys191Q8Q54PC45oWmgTaPj6OCgi9V6e2bqeqjqajo6OJQJhSKRSCQS6V9G1Zsln5ywt7cHAEcHe/Il2YO3kfoOr1fRyO69//AhAHTu1KnWUEe6eB4xbNiO3bvJv6jeOg1l7bjRo+vePXJMXPq7pMyAoJRpVAAQIZc8V8iYBFGhUQPAflExWW1W6SvyJZ9CE2lU5BmDA5XFxhSqQqutqu9TCAoIwK6wCKFPtGVZWlq6Zv2GNes3nLtwoaE6uv4vdHpNqFeKxWMnTCCTcs6sWWdPnWw8KR89eXLpyhUyletV+LqHi1UDI93o6G6vjI2Lq7fCs9fluuz3fD3Ls/pm0Wq1cc+fA4CriwvZAcejdWtyUIVnDayCHH4WAHzatW98azMzXy1YvGTB4iVPIxocvqCFY03i0qjUt/rslixcoD/an06VVkMmpe4lmZT1Er5OSlKFRl3VwO+VrT//jF9UhNAnGpYtWrSoCYbYuIbqvHj5slZbbe/+A0VFxQCwaP68WdOnNfS0S51Tp8/MmD1n2qxvM1/Vfxf/zbBwcqKDj2/jiwro6E8O7vrHtet1r6GKxZLHT58AQBtvbz6/ZlR03cA9ly7XM4bq08hIkUgEAF1DQ8gSY2NjcjiC5y9e1Hta+PqtmwDAYNADA/wb31rr1+dpU1IbfBiI7mQvOeTsW5yOoNHOnzldb17+4w7u26sbZR4hhD65sOQaGXXw9QGAxKSkh/XdH/I0IpJ8aKWDvT05LBwAnL94EQDatvFu4vND/DvWDCG7868P7SI9f/HiwqVLANDMzi7Av2PjizIyMuzTqycA5OblnarzAM6tO3ZIJFIAGD50qK4wOCjI1sYGAC5euZKS+pfbE5VK5YafNgEAhUIZMmiQrnz4l0PJRudPv2yutYrwO3ciIqMAoHfPnjwu9w2/RRwdyftBL/9xVX/wPx2NRnP5yh8AYG1l5WBv/7YfXzM7u/vhYf369Pn/HSEt3dwe3rmtP74EQgi9L9TFi+a/r3WbmZlduHQZAMJv37GwMG/RogXZdJNIJMdPnZq3aJFSqQSANT+uJPu4vkhI2L1vHwC4ODvn5edHREU19F9aegbZRHN0cDh7/rxUKk1MSioVlHp5enIMDMioCLt9e/qsb8nRANat+tHN9c1dSFycnX87c1atVt9/+MjC3KKVuztBEGKxZPO2beSGtW7lvmrFcv07QHg87s2wMK1WG37njmfr1uToP3n5+bO/n/skIgIAxo4aNXBAf90qXJ1drl6/Xl5enpqWVlFR4du+PYPBUKlU5y9dmrtwkVKpZLPZe3fuNKpv5Fh9BEFIpdKIqCixWJyYlBwcFMhms89fvEihUAb066tUKpevWkX+Fpk6aWIHH593+PjodHqvz3sEBQZwDDhyubyRfkxvxdzM7MvBg2dNn7Zo3jyTvz73GyGE3pf3/PDnxcuWHTtxkpxmMhiWVpaghfyCAt15zimTJi74/vvXzcpL376efmO76s7NG7oW6riJE8khbwiCcLC3Z7PZ+fn5umeDTBg/fsnCBU3c4FOnz8xfvJicZrPZ5uZmRUVF5O0cJiYmJ48crnsNdfb3c89dvEhOm5iYGLDZBYWF5NgLnh4eJ48cMTL6ywNJEpOSBg0bTm4wjUazsbYWlgvJZiuFQvlp/bpBAwY0ZVOVSuWXI0bGxMaS+9bX1yctPYPJYLRv1/bBw0dktvl18D1+6NA79CKuS6VSFb79EAe1mPL59Y6qjxBCn3RYarXaHbt379yzVyqV1nqrmZ3d3Dmz+/ftqyvZvG37L1u3vm1YAkBaevryVasePnpcq5qlpeV3s2Z9OWTwW23ztRs3Fi9dVvbX4eLatvHetH69U4t6Hpmp0Wh+2bp1z7795MM7dY3OPr16rVv1Y713TKampc2YPSc55S+XG22srVcuW/pWDwmRy+XrfvrpxMlT+quu+WnCZI78avj8777/pwZYRwghDMv/L4lE8iQiIik5uUIkYtDp5mbm3t5ebb29qW/ZS7NxWdnZTyMiC4uKyDsgPVq38vL0fLdVVFdX3757Ny09vbJSbG1t5e3l1dDdLzoVFRU3w8JzcnPVanUzO9uggADdcEINiYyKio2PLy0VmJqaujg5hXQO1n98dNNVisV37t5Nz8j4/fwFjUYzeOBAj9atAv0DarVoEUIIfdBhif4d30yeolarD+7bi7sCIYTeCgV3AUIIIdQ4Gu6CT8egLwZoNFrcDwgh9LbwNCxCCCH0BngaFiGEEMKwRAghhDAsEUIIIQxLhBBCCMMSIYQQwrBsXKVYXGu4u4qKCnL0VH1CobDuAzTkcnm9T9UAgOSUlD+uXSf/Cwu/Xe9Dr5ooLz+fHPe8KYRC4Y1bt955XXK5/I9r10tKS/ULU9PSw27f0b1MSk7+Zes2uULRyHL2/fprvY9z+UjJ5fK6h0S9hdXV1SqVCr/bCKH/VFiKKis/79P32Mma4dTPXbjo5ePb3j/Axz/g2o0buqj4rHcfn4DA1m3bfTliZHFJCZlJo8aN92jXvlWbthOnTqs7uuzZc+cXL1t2/OTJ4ydPbt+9q3uvXivXrHm3jXzy9OlPP//SxMpZ2TkrVq1+5x3CZDLXbthw5Nhx/cIVq1adu3CenFar1fMWLfZo3YrFZJIlt8LDf96yRb/+1es3boaF+7Rv/984TCUSSVu/juSjTBsvBIARY8dduvIHfrcRQv+psFy4ZIluUPL0jIwFixfv2Lw5PTFh7aofZ8+dV1xcDADfzZ/v7eWZGBcbFxWpBe3K1WsAYMXq1TQaLS4yIi4qUiwWb/ylnjBr6+194sjhE0cOXzhzZu/OnQcOHsrLz//APxKCIL7o3+/K1av6TdUnEREDB3yhC4k1K1foj6iek5MbGxevvxBrK8uDe/ewWKyP/QAtKxP+eujw8NFjyMewNFIIACdPn54yY0ZkVBR+sRFC/6mwPHX6jFwuD+hY8+Dl23fv+vr4dAoKJAjis27dnFu0uBkWLhZLnr94MXPaNBaLxTUyGjJwYPSzGAAIu3176uRJhoaGPC7363HjLl2+0vi6mjdrBgC6h00ePX7CP7izu3ebr0aPSU1LIwsvXr68ZPlyclpQVtZnwBfVdZ7XUS+5QrF42bKOnYJ79x/wJOKp/lt79u/v1LVr+47+8xctFolEACCXy+cuXNi+o39QaJe1GzaQj+3UN3DAgFdZWYlJSeTL67du8XjckOBOACCVStdu2DhuwsROXbru2L1bq9XeuXdv36+/PouLnTlnDln/zr17S5YtDwztMmrcePJPKy4pGTz8q6vXb/gHd07PyACAh48f9/lioEfbdt169jx1+gw546hx4y9dudJ/8BBv3w7jJ07UPcXs3oMHPfv19/LxHfTlsBcvX9b98+td2qAvh126cqVbz16+AYErVq/W7cmGyusllohz8nLdW7q9sRAAUlJTLS0tjQxxgHiE0D/sfQ53l5GZuX3Xrgtnz8xdsFCvYfXnZLVS+So7i81m3Qu7RT42GQCeRkY5OzkBgFYLBFFTW6msLhMKxWJJrSdpFBQWnjx9GgBkUtmZc+cmfv21jbU1+a//pi1b9u3a6ersfPTEiVHjx9++fp3D4QgEZZmZr2qWWa18kZCg0TZpfLifN295Fhu3b/cutUo167s/H7p54rffjp88tWPzZh6X9+PatfMWLd6zY/v+gwcFgrJ7YbdElZXjJky0b978q2HD9Jfm6ODQxtv7ytVrrdzdAeCPa9f69+lLPnJk4Q8/SCSS82dOC4XCGbPnGBgYDB00aNAXX0RERS2YOxcA0tLTp836dtP6dW29vU+dOfvV6DGP792trq5+FhtLEMSKpT9YW1lpNJrJ06ZvXLc2NCQkLi5+2KhRIcGdrKysEpOStu7YsWn9ejabPX7ipOMnTk6dPEmhqJ48fcave/e09fY+8/u58ZMmRz16qL+1DS0t+tkzIIgDu3crVcqpM2ft2L1n9swZANBQeb0c7O2XL1lSWFj025mzjRcCwPIlSwAg/M4d/GIjhP4jLcvq6uoZs2evWPqDmamprjC0c+fomGdXr98oLi7+9fDh1LQ0ZbWSRqM52NsTBCEoK5s5Z869+/eXLV4CAJ9167ptx87s7Jyk5OQdu/eQkVlrLeUVFU8jIp5GRMTExkokkpcJCZViMQAcOXZ8zMiRvu3b83i86VOmMOiMx0+fvvPfotVqj586+cOihZ6tW7fx9p4za6burb37D3w/+1svT097++bLliy+FR4uFkvKyyuKiouzc3JsbWxOHDnco/tndZc5cEB/8kxseXn5k6cRAwf0B4Di4uKLl6+sW726mZ2dt5fXjKlTzl24yOFw+CYmbBaL/B3w66HD/fv27dmjh5WV1bczpjOZzIePHwOAWq3esGb1Z926cTgcpVK1a9u2nj160Gk0Q0NDOp2m60/07YwZ3l5eri4uoZ07Z+fkAICiWlFVVfUyIUGlUo0YPuzkkcO1+tQ0srTZM6bb2zd3dnKa9M3XFy9f1s3SUDlCCGHL8i+2bN9hzDM2MTZ5FhtXWVlZUFCYkprq5uq6Y8uWzdu2CQQCn/btQ4KDTV9H6fGTJ9dv+rn355/fuvqHiYkJAKxctmzV2rXDR482Nub17NEjMSnJ2Ni41lpau7tv2bSJnJbL5X2+GHjg4KHZM2dk52QPHDBAV83S0rKwsKh2BEJTxxwvKS2VSKRke5dsF5ITarU6OydnyfIVuv4+VCqlVFA6Y+qUktLSQcOGG3I43bt2nfPtrLrL7Ne798rVa14kJLxMSHB0cPDy9ASAzKwsAOjZtx9ZR6VWcwwMarfXX2W+eJlw83V3XKlMVlxS4uLsDADN7OzIQiaT8fzli2U//lgmFLq7uemPrm5pYUFOcDgGUpkMALhGRtt++Xnztu1rN2xs6+09ZtRI3V/6xqU5OjrWtAUdHAqLit5YjhBCGJZ/odFopDLZitWrAeDVq1cFhYUSqXTRvHmODvZXzp8j63Tq2vXrsWMB4JetW89fvHT80EFPDw/dEvLy8pYuWmRoaAgA5y5cbOPlpbseWS8Wi+Xq4kz2GOJxeaJKke6toqIiS0sLACAIQtdsEggETfxbeFwuAFRUVFiYmwOArr8SlUo1MjTcuWVzUGAgmZ25eXn2zZsXFhVtXLuGSqXGxcdv2rxlxarV2zfX7p1kYmISEhz8x9WrLxISyGYluSKCIB7fu8dkMgBAKpVWVorrbAxv/Jgxc+fMJl/mFxTwTUwEZWX6da7duHHg4KFTx465ujirVKqWXt6N/HUymcy3ffvw69eKiopuhoXPmTe/lbu7fl42sjSBQGBrYwMAZWVlJno/ZRoqRwihD9N7Ow07//vvLp49Q/7Xwdd37OhRm9ave/7yxeDhw8ViCQD8ce2aSqny6+ArEol27tnz88YNtjY2QqFQKBSS3WQWLPmBvL9CJpMdOnp00BcD6q5FqVKSsxQVFZ27cDHs9p1uXbsCQOfgTr+dOUvep3j77l2RSBTQ0Z+MqLT0dKlUqtVqdXez1PIkIqLW/YssFsvfz+/wsWNkIh4+ekz3Vkjn4JOnz5AB/Ovhw99MnkIQxNIVKw8cOkSn0319fIKDghrq4TJwwIBzFy9FREZ90b8mLJ2dnOxsbX87cwYAVCrV7Lnzdu7ZAwAEhdD1Cw0N6Xzl6lXybHNcfHyXHp9Xvu6no1NcUmJmZubi7AQAp06fqdvDSN+rrKye/ftXisVWVlZDBg00MDCoVb+RpV28cgUAqqurDx89FhIc3Hh53R2LEEKfesuyXp07dfLz9e3So0czO7vsnJzdO7YzGIynEZHV1cpBX/7ZBcbS0jLy4YOF8+ZOnTnzVnh4XkFBoH/HYUOH1l3gg4eP2vp1BAAKhdLMzm7Z4kXduoQCwITx4+OePw8KCbWytMzOyVnz40qyZ1D3rl0OHjnSsVMwi80e0LdvvRt56vRpqVRGNhZ11vy4csw33wR37abRaIICAnTda39YtGjS1GlBoV3YbLZYIj6wew8AzJo+bew3E67fvEWn0fILC37ds6feFXXr0mXB4sU+7dqRFyMBgMFgbPl507SZs46fOlUhqrCzsd24bi0AtPHyXrN+w4ixY48fOjRsyJD4+OedunRtZmublZ29ZuUKS0vL3Lw8/SX37dX710OHQ7p/RqfTPD08O3boMHfhohtX6r922LpVq+5du4Z0697SzS09M7NPr57uLVs2cWmvXmWFftZDIpVaWlhsfd1Zt6HyencsQgh9CD7E51nm5OSUV1S4uriw2ezGa8pksozMTB6PR94W8rby8vNLS0tdXVw4HI5+eX5BAZvF4vP5b7U0lUqVlp7O5/N1l/1IWq02JydXViVzcXYme7QCQFVVVVp6OkEQLs7Ob3s3pFKpzMjMZDKZuouj5K5QqdVcIyPyZalAUFRU5OjgYNjAfRQKRXVaerqZKd/Kyqq6urq8vNzS0rKRlebm5RUVF9tYW5OnT5uyNHsX13thtygEIVconJ2cdCfJGypHCCEMS/TJIUPRwd6+ieUIIfTBwh/16P+lS0hIvecGGipHCCFsWSKEEELYskQIIYQwLBFCCCEMS4QQQgh9SGFZVFyc8PqpGk1398HD1PR03curt24VFBaS0zfCb+uGzmk6rVar0WgA4P6jx3g0IIQQ+rDCMjc/Pzo27m3nunz9+u+Xau6dT8vIPHHmbFZODgBUVorpdLrpW94ZCQAvE5NO/X4OACgUAo8GhBBC9fpQRvCprq4+dOJkhUjENzEZPXwYaLXHT58pKilxbuE4RG/Ec4Ig6DRahUhkzOM9jYryat0aADQaza27d19lZwvKBIP69VNrNCfPnC0qKWnp6uLs6Oji5KS/5JeJiYVFxSnp6XQ6fcLoUbfu3CkoKgrw65CRlRXk7/8yKenK9etUKm3YoIHNXj8UDCGEELYsPwiZWdnurq7zZs2k0ajJqalXb92yMDdfOGd2eYUo//WJVjIX/Xx8IqJjtFptxqtXzi0cAeDOgwcEQXw/YzqNRrt9//4fN25YmJvPmzUzOze3uLS01pJLBIKMrFffTplsxjeJjHnWPTS0nbe3Q/PmCcnJCoXiyMlTMydPHj5o0PWwcDw4EEIIfVhhaWVpkZqRvm3vvoSkZKVSlZ6R6e3pAQATx45hs1jzli2bt2yZVCbTAvi0bRMTF5eakeHcogU5b2JKamp62ra9+9IyMksEgvTMV+3btiEIoo2HZ90lA4Bnq1YUCsXSwkImr9LfhsLiYhtrKwM2287WZsKY0XhwIIQQIn0op2HPX77i4e7u5+Nz4OgxADAyMpRIpABw9dYtnzZtNqxYoavJZrNZLNbVmzf79ewZ//IlAHCNDIM6+rX18srJy5PL5eHi+2Kx2MLMLCcvz8baqtaSAYAg6r88aWhoKJFKAaC8ouL+48f9e/XC4wMhhNB7Dsun0dEZWVkAYGlu7tmq1bWw8OcJiSKRKO7F8949euw9dNjW2losFn/etWutGTv6+pw8+7uToyMZlj27d9+5/8DzhITc/PzJ48b16dHj12PH+MYmpWVltjbWTo6O+kuuNQ64ublZdGxsYEc/ADDj85vZ2G7ds6eiQtS/NyYlQgihGh/QcHeiykoDNptOp8uqqgzYbLVaLZZIjHm8psyrUqnKhOXmZqYUCiUlPd2YyzMz5f9+6ZKbi4u3h0etJdeaV6FQ0Ol03bMvxBIJk8FgMBh4cCCEEPrgwvKf8io7++TZ342MjAw5nDHDh+EToBBCCGFYIoQQQv9f2OpCCCGEMCwRQgghDEuEEEIIwxIhhBDCsEQIIYQwLBFCCKGP1wcx3J1MJrsZFh5+586Tp09LBYKPd28GBQT4+vgEBvj7tm+PxxZCCP1nvP/7LG/fvTtv4aKPOiPr6tenz/rVqwwMDPAIQwghDMu/a/mPqw4eOfKf3LPmZmY3rlwxNeXjQYYQQh+793nN8vbdu//VpASAUoFg5pw5eIQhhBCG5buTyWTzFi76b+/ch48fHzp6FA8yhBDCsHxHN8PC/2PXKeu1bOWPeJAhhBCG5TsKv3Onobf6cmqu8wWxud+Z2PQwMNa91ZNj4khjvsPqHGnML43M/v5mB7CMOrKMAMCBxlzIt6MB8cZZcvPy8DhDCCEMy3fx5OnTuoXWVPo6M/tRXHMAcKGzenFMDoiKOxvwvBg13Urj5JIStVJX35hC9WEaGhJUADCl0FgEAQA2NAYAWFMZ5lSaCaXm3hhDCtWBxmQRFD6F5kZncwiKNZXuRGeR79rQGM1oDEsq3ZCgejIMyJ3CAMKHaWhFpetvoQWNbk6lO9CYS0ybHRAVq0ALAPY0Zhsmp6HgTEtPx+MMIYQwLN9FvedgC9XKpYIckUYNAFKtZk9FUYVGXa5WsV4/k7K3Ib/F64RjEsRaMwdzGn29uT0VYLCRmQudDQDzTWwBYC7fdjLPmkuh6i+/JYO93bKFN4tz2Mp1gKHpZGMrMoZ/tXQOMeCtM3OYYWLd2YA3mmtBAGwwd7ClMWab2HozOfoLcaAzd1g6LRZkk7EdwDIaxTV3prNWmTWv9y+trBTjcYYQQhiW78LcrP6TotWgJScKVNVijXopv1m5RhUpl9StqdUCi6DY0RhbywvVdd6lE8RaYV62SlGr/L6s8rRYUKZR7hIVXZeWO9JZAJClVBytLI1VSC5Kyo5VlrSgs9wY7Ayl/KJUuF9U9JmBsTmVtohvN45rAQABbKNr0vJBRqbkAl9Wy2IVUjMq3ZLKqPcv4nKN8DhDCCEMy3fh37Fj4xVMKLQVZs1/rSzeLyqutwKDoKwU5jytEs/h29jQGGrQUgmCADCm0gBAo9WqXueuPrlWAwBKbc1bBAEAoNBqAEALoNLW7BSttmbXMAmKTKspV6sOikouS4UAcKJSsKOi0JPBacfkAMAEnqVaqz1WWUIupC4XZ2c8zhBCCMPyXXQNDW28QjCby6NQZxrbbDBzqHUilKQB7fcmtn5soyqNpkKteiaXTOJZLTNtptH+A5uXoqyypTGn8Kwm8CzPS8pUAIXqaoFaVdOoBVhRljOfb2dEoZaolR3ZRpOMrdgUih2tnsZlMzs7PM4QQuij9t5G8JHJZMFdu73t3SMTeZYPqyoTq6vIlzQgzKm0UrWSDDEWQai0UG+D8t2YU+nlrxfeCD6FVq5RMQhCqa3dulyx9Iexo0bhcYYQQtiyfBcGBgYb1q55q1lMKTRTKj1DKdeVqEBbqBdm8gZOvb6z0iYkJQAINSotgKJOUgYFBGBSIoQQtiz/LhwbFiGEELYs3xSWPyw5uG9vQz1jP179+vS5Hx6GSYkQQtiy/Mfg8ywRQghhWL5dcJYJhR/jrsRerwghhGGJEEIIfaIouAsQQgghDEuEEEIIwxIhhBDCsEQIIYQwLBFCCCEMS4QQQgjDEiGEEMKwRAghhDAsEUIIIYRhiRBCCGFYIoQQQhiWCCGE0L+O9r5WbO/i2si72Wmp77DMoqIipUql//QPkUjE4/EaqYAQQgh9uGEJAM/s29Rb3i47rt7ygM4hpYJScppOZ7RwcJj33XfBnYLIkoqKikHDhm/5eROZhUqlsv/gwZmvshJin1Gp1LoVEEIIoY8gLN+WQqFYt3p1aHAwmYWHjh6bMHVqzJPHhoaGABAbH79+zWqfdu1q2qY5OenpGQlxsWRS1q2AEEIIfdxhae/iWu+ZWEMOh8/nk9MTxo/buWdPfkGBm6urXC5PTklNSk6KeRY7bsxoOo128MgRIIjtu3bNmj6dQqHUqsA1MiorE54+ezYwwP/Xw4d/3rCBQqHkFxQcPX68oLAw0D9gyKCBFAolKiZGKBQqFIr7Dx9amFtMnTSRDGYAuHj58o1bYUwG46vhw3zbtycLz124+OjJEyMjwzEjRzo6OABAdnbOr4cPlwmFbby8xo4eRaPR8JhDCKGPzkfcwefu/Qc8Lrd5s2YajWboiJGpaWk9undPSU0d+tUIgkKxtbZh0On2ze0JgqhbQavVlleU79i9e+GSH+xs7QCgoLCw38BBbLZBaOeQI8eOrVy9BgCeP38xd+GiB48eBQUEPnn6dPGyZeSqfz10ePO27b179vRo3XrM19/k5OYCwKq16w4fOxYS3MmQY9hv4KC8/Pzq6uohI0a0aOE4ZNDA+w8fLl25Eg84hBDCluX/3eKly1atWwcAVbKqUoFg59atbDb76vUbMpns5w3rCYLo9fnnAZ1Dnj9/3rVL6IFDhwYO6A8A127crFUhKjqaz+eLJZLdO7aTlzB37933eY/PZk2fBgDt2rTp0bfvgrnfA4CNtfXGtWsBgMMxWL1uPbkZW3fu3L75l6CAAABQKBT5BQVsNvvQ0aOP7921MDfv2xvy8vN+P3/hy8GDiouLvTw82rZp496yZXJyCh5wCCGEYfl/N2Pa1EB/fzKidu3dt2HTpt49P09OSS4pLR0wZChZR1RZmZ6R0V7v2mS9FTrw+YaGHF1nn6SUlIKCgv6Dh5Avq6qqCgoLAaCFoyNZYszjVVdXA0BZmbC8vNzTw4Msnzp5EgA8fvpUo9FMmDKVLCwsKlKrNVZWVjOnTR0+eoyVpWXnTp3Gjh6FBxxCCGFY/t9ZWVo6OzmR05MmfNNnwBdKpZLFZLXx8tr800ayXCqTmRgb5xcU6Oaqt0JRcTEBhF4d5pdDBo8cPpx8WSkW29naAgBBELW2gcFgAIBcLudxuQAQFx/PvR0lcwAAIABJREFU5/NZTBaLxTq4dw9ZR6GopjPoSqVyysSJ06ZMiY2LO3fhwvBRo58+uI/HHEIIfXQ+4muWZFbJZLLAAP9nsbFKlYrP54vFks/79hVVVurXfGMFAAgKDLh7776RkRGfz3/4+PGYr79pqDOOkZFhuzZtzl24CAAVFRXfTJ4iEJS5t3RjMZlxz5/z+Xw2m/315EkPHz1+/vJlz379KQTh7+c3ffKUCpFIo9HgMYcQQtiyfAsN3U/ZRMbGxgAQGR3dvWvXaVOm9Ozbz9HRMT0jY+HcebY2Nimpf3am9fbyqlshPSNDf2njRo+Oi4/v3K27ubl5QUHBnh07Gln1+jVrvp486eLly6WlpUMGD2rXtg0AbP7pp+8WLGhma5ubn98pMLB/3z4EQXi0bt2lx+cuzs4vExMXzP2eQsEhkxBC6ONDSMXC97j6RsbxedtBfCrF4tzcXDs7O7LF+Q4VAKCoqEhYXu7s5ESea22EVqvNzc3lGRvrL02hqH6V9cqYx7OystIV5uTmlpWV2TdvrrvpBSGEEIbl+wlLhBBC6L8ZlgghhNCH76PpDVvWbeh/Y4+bhp3Gww4hhD4u2N8EIYQQwrBECCGE/h68ZokQQghhyxIhhBDCsEQIIYQwLBFCCCEMS4QQQgjDEiGEEMKwRAghhDAsEUIIIQxLhBBCCGFYIoQQQhiWCCGEEIYlQgghhGGJEEIIYVgihBBCGJYIIYQQhiVCCCH0aaG9rxUbGJr89/amTFKOhxRCCGHLEiGEEMKW5b/r7t27d+/ebUrNkJCQkJCQunPpl7/z8pcvX17r//rlTdH0mgghhD46hFQsfC8rJk/DLl++fMWKFU2pv2zZMl0g6c+lX15vhjVl+VqtFgAIgtBN1+wdgmjin0PO1ZTTsBKJ5OTp09k5Oe5ubkMHD6bT6U3faRqNhiAIgiDCwm/7+vrwuNy33e2lAsG5CxcAgEKh2NnahoaEsJjMP65dy8vPr/n1RKV9PW4sAERFR9MZjOjomPFjx1AoFACIi49PTUsfOngQAKjV6l8PHx42ZKiRkSEARMXEVFdXB/r7A8CLly9FIlFQYKBupQ8eProVHk4QRHCnoK6hoQ1t2+Fjxz7r2s3a2ioqOtrSwqJ58+b4/UQIYcvyT507d268dXjv3r1G3l2+fHnd9qWuTbls2TJdIRmcZIl+iJJxq1+zVk43svFNDHuSXKEYNmr0yOHDR48YeTPs1px587f98nPTZ9+0eYuvT/uQ4GCCQjQ9yPUVFhbevBU2ZdIk0GpjYmOvXL22Y8vm4ydP9ezRg8/nAwCVWnNm/uCRoz26d9uwaZOnR2u/Dh0AYNvOXdk52WRY3rl3f9eevYaGhsOHDgUAB3v7gUO//O34MR6XO3vuvAO7d+vWePzkyas3bsyaNo2gUDZv3ZaWnj55woS6G1ZWJmQymNbWVgDwJCLCy9MTwxIhhGH5FyEhIY23DhsJy3v37pHv1g3LFStW1Gp3ksFGluiHHDmt36astQH/VFieO38+KDBg2NAhAODq4nz42DGtVnv0+Il7Dx4YGnIWzZtnaWm5befO4V9+aWZqum3nztEjR8rl8hOnfktMSpo5derNsLBnsbH+fh1T09I9PTy4RkZ/XLt28fIVHo+74Pu5pqb8dRs3KpUqvw6+n3XrRq7x2o0bhhzDTkF/tvPMzc26dQkFgKDAgL4DB9V8BJ2Dm9nZ6UcXn29CEERwp05Xrl7z69ChoqICAAiiJkrP/P77hrVr9+zfT4aluZnZovnz5i1aZN+8+fixY+ztm+sa3Hv2H7hy4TzXyAgAdm3f9lmv3hO//nrFqtUrlv4AAKvWrluycIFarT528uTLhIT8gvxZ06cDwK3w8GMnTpqbmy9dtJDNZiclJ2/ftVur1UyeMMHL0/PRkyd79x+g0Wjzv//O1cUFv8YIoU8iLGu1BfVDtJH2YkMtTv069TZY9duRdaOu7pXLhiLzHa5TpqSmka000piRI2Pj4p5GRhzYszsqJmbF6tU7t26Nf/5iQL9+ABD//IVCoaiqqjp77txvx4/Z2th81q2br097JpMR/zy+Z4/P0jMyzp47v2/Xzti4uKUrV+7YsvnSlT82bVjv5+urW4WLszODwdDfhrT0jF+2btVoNJFR0RO/Hk8Wzlu4iMViAUC7tm1mTJ16Mzyse9euIpGodSv3h48eazSaazdv9ujefe+BAwBQXl4uEAi6dQndsn17Xn6+na0tAPTs0ePMuXNJScmr9XZpqUDA5XLJpAQArpGRubl5aWlpRFQUWRIVEwMAv509S6EQ+3bt3Lpjx6nTZwCATqPv371rz/79h48d+2bcuAVLfji4dw+FQhk+eszZkydWrl5z9uTJwqKig0eOrP1xJX6NEUKfVljWja5G2osNtTjrbVPWbQuS7ci6a9RvfTbegnyHsGSxmFXyKt3LsPDb6ZmZ3bp0BQCfdu0W/bBUv7Kupdu+XVsykGqJiokpKSn5bv58ACguKQEAMzMzfz8//TrOTk615jIzNQ0KCCAX/iQicsigQQCwYulSWxtrAKDSaADw+MnTTevXXb95EwD8OnR4Ghl581bY5p82kmF5/tKlCpFo1nffSf/H3nnHt1FkD/ztqldLcpF7S69OJb2QAiH0dnS4gx+9hHK0O0I5cgTuLvQkHCThQgIkkBBaIJCEdDvu3ZYsybIsyeq9S6vd3x/rbDaSZQLHcXDs95NPPvLu7MybN7P79u3MvAmF9nz22f333AMAJrPZbB5AUbRHo6G8PZFQGIlE6KX7fL4smSyljrV1dV6vb9Ujj/j9gdGjRopEoiWLFwPA4oUL3968xWQ2W6zW5/76VwCIx+Otbe0jKislErFEMpKxlAwMDL85Y/mDzOqzzz6baZorZV/pY5l0w0b6lOn+5c8wo3XhggVvbth46UUXsdnsfqPxxX/84+4777BarQBgs9ny8nIBgMvlxmIxgiAM/f3kVZmGJ7MVinlz5/7p8ccCgeChI4fh7GYkyeWymTNmkHb05ltvIw8KBHyRSET+DgaDPB6X8kcvvnDlq2+8yWKxsrKyyCOffPrZln/+UyaX+bzeW++86/577sFx/JHHHn/mqT/zeLyHH3t8z0c7yYlLIpFIoVCcqKmZN2fO9g8+9Hg95WVlfB6PtJF2hyMUCgGAQqG49OKLlyxerFKrQ+Hwiepq04AZAIxGU3FRkSwrq7Ag/7V16wDgg507y8vLfD4f+X7wyZ5P777zDuY2ZmBgYIzlEFDjlJmMJTkISppA0limT+ehz30lz/6g0ccfx7w5c+rqGy6+4sqK8vJ+o/Hlv700etSoW277P6vN1q1WP/bIwwCwfOnSR594MicnJ/3yivKyNzZsnDNrNvnnksWLd3z00epnn+s3Gv9wy81Dlvjmxo0Kufz6a69NPyWRSAYsFtLzu/v+B3inrOP111177qLTDv24sWM1Wu09p2xSV3d3tkJBjkpmSaXKvLzWtrbqmpNjx4yZO3s2AMyYPu21N9f/8aEHyfR/e+GvDzz8iDIvD0GQ9o6Oi1auJAhi2pQpd9xzLwCQX2hvveWWhx97/NiJEz09mr+tfQEAPv3885bW1l5934bXX5PJZAvmzbvvwYdQFBk1cmRhQcGoUSPvf+hhu91+z113MvcwAwPDz8AvYukI9Vk1xVzRj9N/k7Nn6WOW5Fn6qCd5lppnS58HS4c+QklPk25Q05229AUnZxnBJxwOe7zewoIC6lrzwEBuTi6Px6V8OzabTQ4iphAMBsViMV0G88CAQi4XCoW/5H5mtVqxZLK4qOjIsWML589HEMTj8UilUhaLRSZIJBIWi6WoqIg6YrFY8/JyqT+dLheKIOSUXQDweDwCgWBIFTEwMDAwnuWg7zjkmGX6qGeKD5puAunHf7aPsUKhkG7bEARJGZKkm8MUUk6lX/vLJD8/n/yxaMEC8odcfka8Qw6Hk7JWhFxGQpGTnU3/M+VyBgYGht+KsUyfuZpp8SV9zJL0HcmUwyzWpBtRuu+Y4shC5tmww6+2ZGBgYGBgjOXPZCyHsXaZ/EX6Gs1hcsg0ZkmH/ln1J1kowsDAwMDAGMufDNJTHOZsypH0MUtytJI0lum/h/EaM622ZMwkAwMDA8Mvy1gOP7t1SB80ZcySGq0kDWT670xeY3o0n+G9UgYGBgYGxlj+3Jzld9eUlMOvsxwG+rhj+sjlzzZOWd/QEAqHFy9c+HOqet+335ILN8Ui0cwZM0aPGmW1Wj/78ksqwayZM6dUVSWTySPHjjmdrhEjKqdPnUqe2vKvrYsWLhhRWZkifCKR2Pb+B9dfew2fzycI4r3337/6iiuouUter/eDHTv7jcbi4qJrrr46d6jFMADQbzQeO378huuuA4Dde/ZcefnlzG3JwMDAGMtUE3j29vJHe6JDmsP0kcufJEbP2fDSupe9Xs+iBQt+XDD0H8fOj3edu2hRYWFhJBJ+4OFHXvn734LB4HeHDt98441kAolEAgC19fUmk3nr9u1FhYXvbdkMAM0tLWv//recnGzSWNKF53A4bo/npXXrnvnzn/+1bZtWp6MsZTgcvvamm6+/5pqbbri+qbnl6uuu/2zXx1RYAzrVNSfPP+888vcHO3YyxpKBgYExlqkMOb44jFklLWt6VFh61J4hM0nfsTI9gk+m3UjOxvSePSq1uiA/v6iwoKa2llzFv3X79oPfHZoza5ZQJLzlxhu9Xu9L6152Op0rV6y4/NJLDh46pNf3NTQ18ni8F/7yF5FItGXr1sNHjioU8jXPPisWi1OCjFMF/enpp1/4yxnR4GadM3PM6NEA0NnVpe8z5OZkFxUVXXjBCnqab/cfuPuO2997/30ul+twOnNzcr786usF8+dnEn7Vffde/rtrdu/Z89Gu3bt3fEjls3vPp8uXLrn5xhsAYML48Q6nY+euXRXl5QKBYP7cufsPHsySSs+ZObNbpTp24sTR48dI4UPh8J+fecZoNN32h98vWrAgmUy+ufGtjs7OsWNGr7rvPgzDXnjpb30Gw5SqyQ+vWsXcvQwMDL8hY5k+vvi99jJ9hWVK1J500vcbSQ9HkGk3kp/WWH60e/dll1xMEPDJp5/OnT27oanpZG3tu++8/eHOj+obG2658can//KXm264YfrUqTffdtukiROMJlNre/v61177+yuv7Pv224ryiuqak+9t2bzz411ff/PN5ZdeSg8yvvfTPeTekwCwfOnSlKLfe//9nOxst9uj1vTce9fdKrXqRE31H24fDM3z3NOrS0tK7A67UqnEcXzlihVf7dt38w03tLa3Lzy1aUmK8ADA4XDWrnn+ossu37ZlC33xaJdKRe5tQjKlqurb/QdYKEsqlQBAf78xNzcHw7AU4c1m86a3NkokkquuvW7OrFm79uyhB1j3+X2lJSV/eebpJ/78lFanSw97y8DAwPC/aSzTPUL6TNeUfS6H3LeS7qGmr7ykGH6EMj1abDo/dJeSIcEw7NPPPrfZ7ARBHDl2dM2zz7a0tC5ZvJjFYp27eFF9YwMANDQ2IQiy/YMPwqFwr14PAPPnzmWxWGWlpYFgsLmlhVzXf83VVwFAn8FADzLu8Xizswdj3Jy7aFFK6dOmTi0tLo5GYzW1Jx1OBwDMPuccyvsUCAStbW2TJk4CAIIgzlu29K77758wfvzUqqpMwpMBdHZ/smfxwoWffPbpwgXzqbLEIlE4HKb+9Hi9Mnlq/PSUCOkej3fMmDFkjIURlRV2hyMlwLquV//4Hx8BgBf/uoa5dRkYGH5bxjLFI6TPdKWvoUyJ9ZrJQ4UMu2MOP0KZHrgukx959ruUDMl3hw+vOP+8x//4RwD427p13x44IJfLHE4nAKjUajKNQqFY8+xzEon4wHeHJk0YP2CxoOhpseVyuclsAoDjJ05EY/EZ06bSg4xTlnJIJo4fT36GnTNrdq++L0sqYbHYVPx0ANj37X5yb2cAEIvFYpFo87vv3nX77UeOHRtS+EsuuujY8RPqnp4P3tv6+/+7/at936xccf6gqV686M0NG1ecd148Hv/rSy/pdL2P//GPnV1dsVgcAPSGvry83JQI6dnZCpvdhmEYm812OJw5OTkpAdZ3fvwxua3mln9tXbZ0SWlJCXMDMzAw/CaMJd2/JL1Jas0ltRsl3WscPn3KEbpHmO4jZvIdM63FzLQL5g+aMfvRrt333X1XllQKAJddcsn6jW9tfOP1u+67v6u7OxKJikUiAHjogfvvuu++iopyt9uzeOGClBxWXrDitjvufNKyukejWf/aqylBxukp5y5aXH3k8JBiyGRZWp12+tSph48cufKawRjrl1x8kb5PX1FeTiW78IILXvrHuqrJk0ljmS78ogULnnn++fe2bEYQZO2a56+54caZM6aTs17nzp7d2NR04WWXT5wwoaW1FUURuVw2d87su+9/oK6h3mg0zZo5cwjhCbjrvvsBYPmypXweLyXA+u233vrEn58aOWKEy+2+5aYbmbuXgYHhZ+O/HEg9xXWjjBB9N8qzP54pFPvp2g7rO6anOZvfdM4ykDodk9k8MDAwdcqUuoaG4yeqyc+MoVDI6/MVFRZmusrucCjkcjZ78F0nJcj4L4dYLG4eMOcrlYFAwO3xjBs7Nh6PR6JR0uIOKXwoFIrH41To15QA6xiGuT2evNxc5tZlYGD4zXmWdKhxyiG9w0y+Znq02PQZs3SrPIwA6WsxM137g0YrM5ElzXrmL8+LxaJEAnv+VHEikYj+dTSdFGuREmT8lwOPx62sqAAAoVCoVCoBgMs9vVPmkMKn1D0lwDqbzWYsJQMDA+NZDrFdV4p3mOJTDmntUjb2SvEahyeT35npWnr6H+FZMjAwMDAwnuX3kOIgUuOUKWm+N1R6+rxZMtvho86m+I7pU3UyXXs2EWUZGBgYGBjP8qfxLM9mPDKT15h+/Gz8zmH8wkz7kKSfzTRyyXiWDAwMDIxn+bP6mtTs1nSvEb5vNDH92vQYPfQczmYfkp9knSUDAwMDA+NZ/gSeZfrIZbrXOPwo5pDXDj+X9Wy8RnpKehr6VYxnycDAwMB4lv8pUmbAUusahxx3HHJcMz2flGuH32+EfvyHeo3/oZ1JGBgYGBgYz/IMzzKTTzmkZRrSdH3vtZl8xExp0r3GTJ7ljxizXL9p0/lLloysrPw5Ff7hrl3XXXUV/cjxmpqykpKS4uJMl+z85JNrrriCuUkYGBgYfhGeZXrUHnqMHvqYJX2dZbodTfc4U8Y70z3Cs5kKlO5fDh9FdngMRqPfH/ju6LGRlZXRWCweizvdLllWlkIutzudAj7farOPqChHUdRis3k83rGjR6Eoanc6cRxPxOOkbRuwWDw+37jRo1EUdXs8Hq+3oqyMCqEOABqdjsPhlJeW+vx+BEFC4fD8OXMAIJ5ImMzmfKUSAMaOGSMUCFIK9fp8ZoulrLhYLBb39RsBAMfx3r6+nOxsGW2DLbfHo5DLmfuHgYGBMZY/H+T+lHSvkb67SHqs1/T9STJ5nEPGiaUbvLMxlulLSsgjw0cCyvhmcPz49VdftXnb9kQi0Wfo3/rhh4vnzz9aXf3nRx7esm17cWEhgiAt7e3lZaWNzS2V5eXfHvruwbvvfu7Fl5afe65Gp1u5fFk4Gq1raCwtKa5vapo1Y8Z3R46OGlH59YED991+O1nE+x99LBaLQqFwe1dXMomrenpWLFv61bf7n3jowZffXD9l0sQduz+ZOW1qIBgcP2bsp3v3UoWev2TJxs1b5s2e9eGu3c89+QSZ24bNm8eMHPnV/v0rli4dfSqi3p4v997GBJxjYGBgjOXPQ/r+lPQdLul+Z/oKy/RIPZki+wxpJjP5l5nOZoo3+0N3HWnr6KwsKxcK+E2trVnSrOlTqs5fusThdLo9XhzHr7/6qngi8dbmLQaj8YE77+Byue1dncFgsCBfedmFK5taW80WS0t7x123/kEqkQxYLLu/+LIwPx8niL5+YzAYFIvFpPP6p0ceBoDnXnqpauKklectnzxhwlff7u/r7y8uLFyxbJkyL8/pcpEi0QsVCgXLFi8asFqDwWA0FgMAq81mtdlHVlZmKxTVdXWUsWQsJQMDA2Msf1ZjmbI/JX2HS7rfCWn7jdD3J8nkcVLXZjKWw+9hST+byaD+oF1HmtvaJo4fL5GIF8+ff6zm5EXnn89mcwAARVGCwFksFoqiKIriBEGaMQBIJpNcLpdzKhlOECiCsFksHMf7+vsBYPSIEUplnlQsFggEg/aPwEnDzOVwAYD8HwAQBCHHWQPBICUSvdDm9vYutfqSCy7Q9vZSCSRi8bSqqlAoFE8kmBuGgYGBMZYM/3GOVdf8/obrydG+fQe/C0fCmVKet+Tc9Zs25ebklJWUpARTvWD5sg2btwj4/Anjxq5YuvTTvXvzlXkogs6dNYtMMH7M2M3btgdDoeXnLjYNWKgLy0tLv9q//93t71vt9pnTpqUXmiWRGM3mr/cf8AcCqp4eAMhXKqVSyYFDh6022+8uv5xK+bfXXn9s1QNMgzIwMPxG+AXFhqWcth+xx8jw6zIh88rIM3QxbOTYTDnQ58T+tOsso9FoPJ6QSiXpp+LxOJZMCgUCAIjFYuFIRC6T0RP4/QEul0NuzkyRSCQamltmzZj+7XffKfOUUydPSs85GArxuFwWi4VhGGWk3R6PWCzmcjjMDcPAwMB4lgy/LPh8foq1o6Dv3MHj8Xg8XkqCIU0sh8OxORzvbH0vX5k3ZdLEIXMWn9rxg+7OMhNfGRgYfuOgvwQhnn32WeQUpJdGAqeG2RAEoWLUpfhz1BwcKg35m34V3RekoIob8uwQPjiCIAhCP5t+5JfPZReuvPMPv7905cqz2YCFgYGBgeEXZCwZGBgYGBgYY/k9PiXdR0w5S7l6mTxIuseZTkqedG8y3b9M9zUz+Zf0HJg+xMDAwMAYS4afEuMX3wR0feTvZCzmbu0EAGdd01le3vv+LgDo37MXi0R+RbVWrd/8n8vc097tbu1s/vMLnrbOlIM/LkPT3v1+bS/9CNVSw/AjGuU/qhafSmM5eHR4pQFAeMBq/HwfAERsjq5X39Js2p6MxQDAdrSm4+9vmvcdTD/1g4Q37zuYoszvle1sOPtb5vtb4c1NzHOJ4X/EWNK9T7q/SHqTdI9zyPgDZz8Gme5rZro206jn9xKx2GIuj6etEwuHA7o+7ebt4QGrav3mqMMVsdpjbo+3S02mDPWbXM1tOIYBQNTuBICYxxv3+gDA26mKOVzkcSwUxjEsZDRHrHYACBqMBEEk/IGQ0Rz3BwgcDxpMABCx2cmsACBstgS0ejyBRe1Ob6eKSCbJg6F+U8RqT/gDnrZO8mDM5XbWN8d9fiKZDOgNAJDwB2Lu0zN+Y26vu7Uz6nAlY7GQaQAAiGQybLEBABYKu1s7SfsR7DWEB6ye9i7yKnq5yWjMWddEXkLhbunwqbWDj/W2Tp9KAwBYJBJzuX0qTSIYCg9YqUew5cARhM2KebyCAmXM7SEvtBw4IihQxr0+b6eKVFrYbInanTG3J+b2OmobE4EgANB1jkWi5OXZ06sE+UoACOj6gn395A/t5u1YKEyvFJ5IOOubKcm9nSq2QBDs63fWN5NVCxpMzoaW0+o1mimZQ0ZzyGgO9hpIS+ysa07RABaJOhtawubTy35iHm/c5weAuM9PtoinvTvmcpPNFPcH3K2dWChMqtTbqYo63YP19XiDBhM9PakfyYhyIpns/Md6V1MbALStWVdyyQXiirKef77nU2tNe/eP+r8brYer3a2d9FOUPCltStcqvSeEB6zkwcHXDppsKT0h5vE665oT/sDpIgzGUz9M9MRxr4+8ZehtRDUfQRCe9q7Tfcbt9XapIzZHepMNWm61lt7V03vgMH3vjJ4ci3m71IlgKBEMDSMDA2MsGc4W/Y7dPrW2+c8vRKz2uM8fNlsSwVDE5mhe/YLuvZ2WA0fVb/3L06FSvbk5qO9vW/MyAGg2bQMA2+ETykXzQqYBYUlRx9/XJ2Oxtr++bPx8n7upzXrkRNuadUQyefymewI6vW7bx+7Wzt5tH3u7eqpvfYAgiI6X3kBORY6tufMRw569rsaW7tfe9nSomp9+EQBO/OF+y6HjjY8/1/36O9bD1dqtO2Jub/PqFyM2e+19TxAE0fPWVldzW/PqtVgwNGh0Lbbmp17wqTTVtz0QMprb//oKAMT9AfWGLXGfv/GJ54N6Q+OjzyYCwbjXZ9j9he1ojerNTa6mNqpcAscbHlkdMppbnn6RepR0vbzRWdek/2C35eCxrlfecjW0mr/5Trt1h0+lOXnPY+6WjuM339O/Z696w7ukb4SFI3GPLxEIuls7T971R3dTG3kwoNHV3vuYr1vT8OgzMbe3429vqDe+G9DpW1a/ELU5Gh59Ju4P0HUe0OjIy42f7/NrdNqtOwa++c742b7e7R8PttSAlV6p5qfWRgasXS9vDGj1ZKNYDx3XvLPd26Xu/Md666Hj2i3vB3v7Gp98HgCqb1s1sP+w6o1Nzrpmy3fH1Bu2mPd959foCBxv+OOz4QFL18sb3c3tVCdpeuIvEYut/cXXyAcu+ZqlXr8ZAFRvvBNzeZqfftGn6un4+3p3a6dh95dtz/3d16VuePRZAsfrH17taevUkx8hPv2qZfXasMlMT0/qhyMWaTa/X3LJCjJ/PBaXVJblzJziamrliEVj7v49Ty7jybOS0Sj9FCUhvU3DFhtdq/SecMYr5pmy0XtCMhpreuL5qMPZ+Phz1Ftd1ytvRZ2uqN2p3vguPXHM4yVvGXobUc2nev0dT2un6ctvez/Y7VNpau99zNPW1fDI6pQmowtG7+o4htF7IOUKp/e9M3pyMtnwyNOe1o7GR581f31gGBmYByBjLH8y6HNZ6fNjU8LuDDMnloo/h6SRyaekp8k03zV9Ju2P8y8rrr2i/OpLk9GYfPLpQvZ0AAAgAElEQVR4UWlxzsypgrxc+cSxADBu1Z3jHrjd3dyOsllRuxOPxcfc/XsAmPSnhwDA26mWTxxrOXi0YMmC3FnT3C0dPIXc26ly1jfnL5rHFgrdbZ3KxfNc9S2hflPxymUBnd7V0Jw77xxvh4ojlVDGUlCgnPjofcbP94nKigDH/Spt3OcXlZWMvOVaxdRJJZetrLzp6qDOwJGIy666OGZ3JXy+ZDgy6clVLU+tzZ0zU1Ra3LluQ9uadeZ9BytvuLLimsty556TUkfLwaNcqSQZiaJcjrOumS0Wjbv/9rH33ubtVBl2fU6V62psFRYXll158fQXV7OFg+GH3K0do++4edKTD8omjPH36Ebeev34VXfaj9YAQP6iuRXXXs7Llo+997aiC5YG9QZvp0o2frRi8nhRSaEgLydvweyK664gDwJAwdJFZVddXHTBMmd9E4Fhk/70oE+lrbjhqpJLVigXznXVN9N1DgDk5YMfY7/8liuX8fNyzF8fIFvK095FrxTK5Xi7NaWXXSCuLCMbpX/P3vF/vGfkLdeWX3OZ8fN9k558sPx3lxEYRqp31K03lF15cUBv6N+zd/LTj46+/SZRWalPrZWOrCi9bOXo228yf3OIVGwyHh9xy7WJQBCPJ8IDVsOuL9rWrEM5nGCfEU8kIjYnymaF+s0ElhTk5w7sOwgA5dddUXHdFQgC3q4e6ejKiuuurLzparIilTdcLSopoqcn9eNqakN53Kxxo8hkimmT29as61y3AWWzhUUFHIm45dm/ceVZubOm00+Rwvg1vfQ2tRw4kqLVFMh6uds66bLRe0LcH0hGoyHTwLhVd8bd3rY16zSbtxevXG45eGxg/+GSi8+jJ+Yp5OQtQ28jqvmKLzmfJRRg4UjYNAAAykVzK669PHtaVdTupDcZpWoASOnq9B5IJsMTWHrfo4vkbGyVVJZXXHdlxfVXAsAwMjAm59cLs87y54bF52U4gwCOEwiCcjlsoWDq2qcCWn3jo8/Of28D+VWTLRICQNhoFpUU5c2f1bluY/6589yNbRGrXVRSlD1zqnbLh+Mfvku94V2eQoagKEciDuj6yn93qead9wpXLE0XQD55gqikiCvPYouEgwcRBGWzAIAgcPvxk67G1pG/v87T0UV+seTlZpOv5JU3XEUkk5aDR5OxOPltjbyWIIi420tmLiorVi6cIyorEVeU9n30KZFMAoIgbDa9XJZQQB4JGkyCAiVHIgYAlM0mP8pFLDYCxwEAT2AojwcAKJ8HACibQ3kq1sPVlHkDABaPBwDkQV+3mgACAPBYjJ2XCyiKcjgAQCRxAMCjMbZQSNc5dflgY6CocuEcAODlZlMH6ZXi52VzJGL9B7tjbi/ZKICiKJtNJJOkO0jgSQDAE0kWj0fmj7BQwHEAIDCMFAxBELKOyViMLRKUX30pkUwmAsGet7dOevIh8vte/pIFuXNm8LIVuXNm9vxzq3LhbADgyqTKhXPi/gAeizvrmlhcDgAAiiIIEFjydKMAoHxuSnpSP876pqC+v/vVf3q7ehwnG7KnVckmjIl7fdp3P4i5PS1PvzjxiVWikiIAoJ+ihElGY/Q2PUOrZ/YEqsPEPL4U2aiegHLYVU8/ioVCnevWT37qkZF/uB7lcjlZkqYnnkdQtOLay01799O765BtRDZfx9rXxj5wh3TUCNKCkpoHFkoQePnvLqWajBSJ7BIpXZ3eA8lkEZsjve+dcQcJBYAgABD3+YaXgXkAMsbypxmbzLRX5Vm6blQcHwRBqBWZlI8ImXempEYi08cmhywo036WPwiOROzv0QX7+lEO23a0Ggiibc3LyWi0eOWyuC+g2bxdOmqEZGQlANQ98GTRhcvz5s+KOly83BwAEJeXBvWGnEfvxUJhcrRSuWC2ZtM2SUUZHo/nzJoOANkzpzhqGmTjx3jauqb85QksFG5bs27a2tWDDu51V2g2bReVFiMoWkQzpRRchTyg1fd+sCvu9jnqm/s+2D3zlTXq9ZvtJ2rz5s0CgKIVS5tXr/W0dnpaOuDGq7KnTW5+6gUEEECRwmWLmp58Hk8kgn3Gqc8/ibBYLc/9HY8nSi69QKDMpcotPH9J79adXa++FdD0Tv/HX+oeePKc19fmzZvV+vw/ojbHuAfuyJ4xpf2FV+K+QPnvLh1Sh3GPh6eQ0Qe6qIMAYPn2MBYI+TW6Gesu6tu5h5S55em13vYuv7Z3xC3XaDZvp3SeknPp5Rf2vLMNCCJ39nSypcqvuazn4FGqUuoNW4SF+TGXhyeXkY1Sef1VLU+/yBYKc86ZWv67y5qfekFYWJA1ZkTK61HlDVc1/emv4rJiLBSSjhmp2bRdtX6zT6WZ9MQqQX4e+WKEhSKmL/aFjANRm6NgyQIAGQAUX7j80BW3LPtqB0cq4Snkfbs+DxlMY++9jZ551vgxms3vd732z5DBlDNz6qCNLy2mpyf1U3T+kqLzl0SdLtWbm3Nnz+j8x3p7TV1Q3z/+wTttR2viPn/3q/8EgMobr7YdraZO8RQyUhh6myqqJtK16u1UUT1h8GNGfh4ACArztVtOy0bvgcoFczr/8aZi6mQWX8DPyabMIT83my0SISxWSnclbxl6G51+oomF1oNHyRHEgiUL6Mrp++gzsskklWWkSOld3dXcTu+BZLKIfYjhRrpIhecv6duxp/3F10L95oIlC4aRgeHXyy9o8+dhjOgwG2ll2jg6Jc/hjeXZmNIhdPdjN3+mwBMJhM0GHCdwov7hp2a+/DwWjnCzpACAhcMJf5B+S//kYJEoFgzxaZ5TqinyB1h8Hspi4QlsSIcYT2BYJKJ994PiC5dLR1YmAkG2SEh+7yWSyYjdKcjPI7WEJxLJWJwjFqWXG/N4uVlShLYZZ8IfQPk8FpdLjiex+Fy2UPhDa2c/URvqNxeuWMKVShAWiy5zzO3h5+UgCFJ7/xN0nadW3+tDUJQjldBbiqoUQRBRm4MjFdNlS0ZjOIYNVjMcTkbjpNlOIREIImwWWyAg+0/U7uQp5CiHfUY+iQRHIsbC4Ux1j9gcXJmUlRa8iSCImNOd3qyZ0tMTcKQStoB/lqfobUrXKllBqicMIxu9J+CJRNTh4ufm0PWQqbsSySSBEyiHTW8jyueLe308hTxddUM22ZBdnd4Dz+YOSsbjtkMnCpYv6tu5R1hSnDd3ZiYZGJgxy5/Gsxx+RuuQs2GpEUrK7KVsD5JpJDL995DmMCVlphHQH6l9DgdBEITFQjlsxdRJKIdDPbXZQuF/1FICAFvAH8ZSAgBXKmFxuQiLlenTMcphc6US6chKjlhM+srU8xFhsYQFSkozKIdDPlXTy+XJZSlPVY5UQj2neArZj3vc8LIVotIinlxGt5SkzAJlLilYis5Tqy/Lop7CVEtRlUIQRJCflyIbi887XU2hcEhLSSqKfWqLGARBBMrcFAvB4vPIj9LD1F2gzB3S8iEIMmSzZkpPTzCkpcx0it6mdK2m9IRhZKP3BJTDERbmZ7KUKYnJWyaljahvszyFfEjVDdlkQ3Z1eg88mzuIxeWGzANtz69LRmJ5884ZRgYG5jMsw7/LqFtv+JVKXnzh8l+gVFljR/0P65yBuX8ZftOeZUo8WDrps2HTZ73So/nQPc4hV0+mk8lTzHQt/QjTkxgYGBgYY8nAwMDAwMAYy/+qN5nuNaaTafUkpMWVHXK1ZbqnOPzoYya/Mz3lvzNmycDAwMDAGEsGBgYGBgbGWP4UniVydqSMQaZ4nHTS97xM8QvPfpTxbLxGZsySgYGBgTGWDD8laq127cuvvPTqay+9+tqne7/6cNeulATvf/zx8DngOP7u9vfDtA0ujCZTdW3tTyhkdW2t0WRKl+1scLrdJ+vr/9Nq3PnJJ//GtXvof4YjkcPHjzM9k4GB4VdgLDPtRpm+JyV9LWb6usz0ebApfufwMWDPxmv8N0co/X7/uDFjHn9w1eMPrrrswpXz58wBgHg83tHd7XS5AMBkHrDabCbzAJne7fHo9Hocx+kCTJ86Ra05vSVCtkIxeuRIALDYbF0qNZnYZncAQDgcDgaDPr/fHwhYbKc3W6CyjcZifn+gt6/P7TkdTsHudIXCkUHZEonevr5wJBKORLw+XzAU6tFqI+S2Gziu7e31+nz0Cn7+1VdTJk3CMKxTpSJrRL4iGPqNpD41ul69wQAAKUXbnc5AMKjR9ZLyRyKRHq02GothGNbX30/+w06F2O7rN2YSwOvztXV2ki8Tfn+gvasrHA6TResNBn8gYLZY6PkLBQKjyRwIno5wHQgGqYIYGBgYGM/yv8OAxdLY0trY0ur3B7bt2Inj+Mvr19vs9o2bt7g9Hr3BUN/c/NGnn6o1mu6eng937dbp9Rs2n947cMfu3SbzQHtX59f7D5BH+k3m4ydr65ubP9v7lWlg4PV//hMANm/bBgBtnV0NLS2Hjh3fsGmzzW4n09Oz7TP0r33lFY2ud92b60mjQjEo25vre7TaVzdsPHHy5HdHj779r629BsOrG98CgA2bN+sNhvd27OjRDlrueCLhDwT4fP76TZsdTuf2nR8ZzebtOz/q7O7+6sD+usamHbt3a3S6YzU1X+8/kFL0lm3bP9v7VV1j4+7PvwgGg2+8/faAxfrqho3+QKBLpW5qbX3p1dfiiQRdwnQBnC7Xhk2bnS73ujfedLpcGzZvcns8r2zYGAyFtn/0UUNzy+7PvwAAev7hcHjcmNF1jY1UtseqaxynzDwDAwPDL85Ypo9fZoqBR853TY+Bl75jSYofeTY7iqT7oOne5L8zThmNRn1+n8/vS2AJADAYjXm5uUsXLbr/zjv4PF5hQcHFK1YsnDvHYDQdOHwkX6nECaKv3xg85fosnDePz+dFozG744x4lUeOn7j1xhvOW3JuMpkMBlN3Alp53vIpkyaRv+nZRiLh6VOqzl+6ZNzo0W6PN+Wqvv7+4sLCFcuWXbB8MHTqiqVLVyxdiiBgtdmsNnsSx7MViuq6ukFb5XRmSbMAgMNm6w39ixfMLyoo6NFqr7j44ttuvLGyojxFeHrROI5ff/VVV156iXlgoK6pSSQUReMxDoet7dVfsHxZv8m06q67hKei3gAMLcDJhoYlCxcuWbjg/265+WR9wwXLly+aN29aVVWnStVn6L/6sktv+N3VKILQ8+/oVilzc00DA3RdFSiVzKOBgYGBDhPB5+emsqJiycKF1J8IgrBYLACw2Gy52dk8LhcAEATFCRwARo8YoVTmScViwSk78e7296+76srS4uITJ1PHKckPmMlkknsqUpc/4Cd/czlnxO6isuXxeGw2BwBQdIgtEai3BOorJYfDJsUDAIlYPK2qKhQKUQ4fgqLJZBIAlp97rkgo+PrAQb8/wGKzAMAfCDhcrt2ffU4Xnl40i8VCURRFUZwgAKAgXzmtqqpAqSwqKNj5yZ7JEyaMHZ0akSddABRByeJ69X2ADCoknogL+HxSsEQiQW5FQs8/FA6z2cyNwMDA8CvxLM8ecmySvocl3cukn6VGQ1Oe/ineYaaxzJ9hnWVZSYnX63v/448/27tXIhbTT61YunTfwYPfHDyoN/SzTgU4FQj4dY2NJ07W6vr66GOZ5y05d/2mTe/t2FFWUsLlcjkczpbt2+uamtJLpGeLoqxhZCsvLfX6fe9uf//EyVrSQFLkK5VSqeTAocN7vvhSJBgMgJmfl0c6tfsPHTpRW+fz+4sKC6ZMmvTO1q2b3tsmFAjOED7zdkWzZszo0WoPHT126Ogxu8Ox/9AhVY9mw6bNdqdzeAHmzjrnwKHD23bs7O7pmTdr1tf7D3z86afa3t6J48ZNnjjh7X9t3bH7EzaLTc9fKpGYLZbKsnIq5w937TaaTMyjgYGBgfEs/2vMnDZtJu3PPz3yMACsuvsufyAgFolQFH38wVUAMH1KFZnggTvvCEcictnpeNwP3XNPIBjMkkqj0SiKoqRDyWKhkydMGD1iRDyekEolAPDoA/eHwmHRUHGcR42opGdLemzXX30VleCyC1eSsiUSiWmTq2bNmP7td99lKxTLFi8iE5BC3n3rrW6PRywWczkc6r2hqLDA7fHcfdutLo9HLBTy+fwRFRWhcJjL4XA4HLrwfD5//JgxVNFknlwO5+F77wGAx1atcns82QoFgiCb33wjpQqZBJBlZT3+4KpgKCSVSADgsVUP+AMBuUyGIMgVF1/s9fkkYjH52kHPX9XTc+M111CZX3fVlUxHZWBg+F/wLElvEmixe+jzaYG29wg1ikm/nB4tdvhIsJk8zuH9zh+BVCJBh9qlgcfj0S0lAKAomiWVAgCfP7gLRF1T4/SqKvKIlLYDgyjzjgfp2Q4Jh8OxORzvbH0vFo9PmTQxPYFCLqcMFcnFK1YYzWYEQXIUCkpCkVDI4XCGFD5jv0TRnOzs73XZ0wVAUZS0lADAZrMVcjmViSwri3LQqfyj0ejUyVUpmTAwMDAwnuX/Gr+//vr/XOakl3n28Pn8qokTf0Xa4/P5UydPYnoRAwPDr8azPJvoPGQa+mgl0DZwpq+wHHLMMt13TCfTck8mBiwDAwMDYywZGBgYGBgYfsHGcpj1kSkzXcm1mHT/Lz1eD5kmZcwy06rKTH5n+sjlf93X7H7t7X8zB/U/t/60IvW+v+vHXejX9pr27k856KwbYu5uMhoz7P7iv9It8USi5+0fpjGfWutTaQDA9NUB9cZ3yX9elYb6bd53kExp/Oxr8ofpqwPtL77ubukAgKjD1fXaP9Ub3034AwBgO1rT9cpbng4Vlb/+w91xf4B5bDEwMJ7l/zihfhMARO1OLBTGMSxkNEesdgAIGowEQST8gZDRHPcHCBwPGkwAELHZ8VOh14ovXI5FIjGX26fSJIKh8IDVr+0FgLDZEvf6/JpePIF52jqxUBgAkrGYs645bLHRSw/06AAg7g9EHS6CIDztXeSTnSLmcjtqGhLBEADE3F5HbWMiEMxURMzjjXt9Eas95vZ4u9QAEHN7Yh4vKbmnrZPMHAuH416ft1MV9/rIrKJ2J4vHy55eRarC26kiksm416davznqcAFAQNcX7OsnRbKfqBMUKMNmS0CrxxMYlZ7MKtRviljtCX/A09ZJHoy53M765rjPTySTAb0BABL+QMztSWmIoMEU0OrDA1YACJkGACARCMZ9frpIBI4H+4ze7h6yRvRTKbqK2p0A0PfxZ/UPPRUymgFAPnGscuGcnNnTPW2dAmWucuEc5cI5yVgMx7DwgLXhsWd1730EAI6TDd72rtF33NTz9ntxf6B97avFFy6XjhmpfutfAb2h/9O9lTdc1bNxC6k627GTmk3vJ2lhgRkYGH4rxjLT7pXD7Gc55FjmWeaZ6ZLh02e69kfMg+34+/pkLNb215eNn+9zN7VZj5xoW7OOSCaP33RPQKfXbfvY3drZu+1jb1dP9a0PEATR8dIbyKlZsp3rNvhUmpP3POZu6Th+8z39e/aqN7zrbu1sW/OybutO7Zb36x/6s7dT1bx6LYHjDX98Njxg6Xp5o7u5nS5AxGZvfeYlBEVUr7/jae00fflt7we7yVNhi635qRciVlv9Q0+FLbaW1S9EbY6GR5+J+wPpRQCA7fAJ5aJ5zatf0L2303LgqPqtf/V/+lXL6rVhk7nrlbdcDa3mb77Tbt3hbm6vvfcxX7em4dFnYm5vx9/eUG9819PeZfx8n6uprfu1tz0dquanX4x5vIlgKGJzaLfuGPjmO+Nn+3q3fwwAzvrmnJnTau58xLBnr6uxhUoPACf+cL/l0PHGx5/rfv0d6+Fq7dYdMbe3efWLEZu99r4nCILoeWurq7mtefVaLBiiK8F6+IRm8/aBg0dP3vVHAGj/6yuk3bIdPkEXiXR2PS3tqvWb7NV19FN0XQGAZtM2ACi97MLK6weXnYhKi2UTxtqPnhxz7//x5DLZhLF8ZW7U4Sq56HxBgXLq809yZVIACOr7c86ZxlPIxeUlvi61sDDf3dTm7VSJykqcJxuKzl/Kz8vJmTXd09YVdbpsR2vyFsxmnlkMDIxn+b9P7qxp7pYOnkLu7VQ565vzF81jC4Xutk7l4nmu+pZQv6l45bKATu9qaM6dd463Q8WRSpAzl5TkL5pbce3lvGz52HtvK7pgaVBvACDG3Htr0YXnZc+YUnHdlclozKfWSkdWlF62cvTtN5m/OWTY9UXbmnV+TW8yHq+5/aERt1zLy1YUX3I+SyjAwpGwaTDSm+XAkdIrLiq9/MLJTz1i2X+44oarSi5ZoVw411XfnF4EAHg71fKJYwFg3Ko7xz1wO2mVK2+4Om/eLH+PbuSt149fdaf9aA0AFCxdVHbVxUUXLHPWNxEYNulPDwoKlABg2PW5qKwIcNyv0vIUckFernziWNOX33LlMn5ejvnrA3gCAwCUwxYUKCc+ep/x831U+rjPLyorGXnLtYqpk0ouW1l509VBnYEjEZdddXHM7kr4fMlwZNKTq1qeWps7Z6aotJiuw/5Pv5r05INj7ryFn5eT0kB0keK+gGLa5Irrrhz3wB3W747TT5m++IbSFUEQk/70ECknwmLR3fSoy02qCAB0/9ox6rYbgYzZxOMNtua58/s+/rxz3QZ3czuBE4AgCX+QwJJxrw8LR9hiIQCgPB4WjXa//s7Y+25j7iAGhv8W/7WlI+Gg5zeo7rz5szrXbcw/d567sS1itYtKirJnTtVu+XD8w3epN7zLU8gQFOVIxAFdX/nvLtW8817hiqWpbzd8HgCg7MF1gQSOI2w2ymYjCKBkzDaCQBCEIEPfxWJskSB/yYLcOTN42QoAZOoLq9Ubtsxa/1LH2tfGPnCHdNQI89eDAdkRBCFz8HWpAEGIJA4AeDTGFgrTi8BCYbaIXMeJAI4TCIJyOQCA8rmkVACAJzCUxwMAMsIcHoux83IBRVHaokb55AmikiKuPOtUboCgqHLhHADg5WY765tzZk4BABafl55+8CCCoGwWABAEbj9+0tXYOvL313k6ugAgYrXzcrMDWn36NwE8kQABPxmLk3+Tn53ZfF5KEUnym3YkyhYLsXCYOhW1OyldiUqLhmxrw+4vSi46n/ydjMaCeoOksixVkGRy0pOruHJZZyAoHVmheee9ee++AQDV//dg2ZUXhYwDABA2DcgmjEEQpOuVtzwtHeoN70557nHmycXA8Fsxlr9NxOWlQb0h59F7sVCYHK1ULpit2bRNUlGGx+M5s6YDQPbMKY6aBtn4MZ62ril/eQILhdvWrJu2dvXZlyIdM1Kzabtq/WafSjPpiVU8hQxABgAsLkc+caxiysT+T75ki4XWg0eTsZi3S22vrgv2GYsuWNq8+kVXY2syGht95y0tT6/1tnf5tb0jbrmm94PUiTy24yfz5s8iDU/bmpeT0WjxymXUEGn2jCntL7wS9wXKf3cpAFi+PYwFQn6Nbsa6i/p2nt5OsuK6KzSbtotKixEULVqxFOWwbUerSy+/sOedbUAQubOnO6rrxtx7W6b06RXnKuQBrb73g11xt89R39z3we6Zr6xRr99sP1GbN28Wlaz8mstbVq8VKHOxYBAAUC63fe2rwT5j8cpl9CLyz50f7DN2vPR6yGie+Nj9cZ+fOjXi5t9Ruiq6YFndA0+e8/raFGFcDa0jb71h8HdTm3zKEKs5CRxvf+HVrLGjOBIxPy9HXjWh9fl/JKOxwvOXKBfPa3j46ZDRHHO45BPHySeOA4C2NetG3X4Tcx8xMPz8IKGAm9HC/x4EQUTtTp5CjnKGfh8icDzu9fEUciwcZp+K9UMkk3F/gCeXkX5hzO3h5+UMP+O39v4nZr78PBaOcLOk9OMxt5fF57KFQvuJ2lC/uXDFEq5UQv9KSYJFolgwxM/NJksncALlsONeH4KiHFo0oiHTD0ncH2DxeSiLhScwyh+NOly2YzVUmvxz57O4XEDRxseenfXGiwCQ8Aeo4lKKSARDLB6X9IZTpKV09e+ARaIJf0CgzKX0hrBZXKnkVCM6BMo8pj8zMDDGkuHXjWbL+6NO+U9D4lNpYi433bH7FQnPwMDAwBhLBgYGBgaGs4KZDcvAwMDAwMAYSwYGBgYGBsZYMjAwMDAwMMaSgYGBgYGBMZYMDAwMDAy/YFh//tN/JxpIOBKpb2k2mEz0re1/sQxYrQRB8E5FKSNRaTUsFjsQDIpOrVP8N/H6fAM2q1wmiycSKq0mLyc1GJtKq8lRZAOAL+CvbWwcsFpsTkeBUkkuhaTO0tH390vFYhQd+q0okUj0GY0K2dCLBYfMcEjlcNhsNnuIBZ1afa/Vbs/NyRkmK0qNPC63Q9WtzM37ocLYHA4elztMRQDA4XT2GgzK3NxhKmJzOOKJBIogPb263Oyc4WttczhaOzv7TSZZVlYoHGZzOCyakm0ORygcjicSAj6fPOL2eNgcjqZXdzYqBYBQOFzf3NRvMmNJTC47q9Wc0VjMZLHIsrKG7AOmgQGVVisSCvk8Xqda7XA5c7OzPV5vW3c3geNZUimO40azOUsq7TUYUkrsVKuyFYoj1SdKi4tRBOnqUecoshtbWw0mo9fnV+bmHqmuttis/SZzbraChbIaW1t7DQaLzabMyyPVkkwmDSaTVCxuamvtMxodLleBUpleR4IgjlRXWx12s9Wal53d2tlRoMwnCKKpra0wP58UBsdxtU5LqbHfZNL16eUyeb/ZTBcbx/GWjo4CpRIAmtvbURQNRyJWu83udGZqgiFvc62+1+v3yWWyRCJxuPpEZVlZpv4gFonI3xiGpfdkegfW6nsVcrlKo1HrdKYBc15ODoIgdM2QyRpaW4xms8FkkkmlPTqdPCuLxWK1d3dnSaXU7dbVo6b6qsfr7VB1C/h8j9eXcktq9b1qnc5oNpERt+qbm8xWiy/gz83O0ep7VRqt0WwasFpZLDQWjwkFpx9oR2tqBmxWo9nM4/GEAgG90W0OR3N7e7/JyEJZ5DO8rrlJKpwuH1cAACAASURBVJHwuNxwJFLX3NRvMvsCgeFvOsZYfj9tXV0jKyrHjhzZa+hT5uZpenvtTme2XE72OavdLuDzB6zWQCgYCAT1/Qa5TBaNRlVaLZfLFfD5Wr3earcrFAoUQQDAareTPZUgCK1e73S55FlZdqez32zCsCRBEA6322AyZsvlyWSyU62KxeJikWjAajUYjQI+n8vl9uh0TpcrJzvb5nDo+/up7hhPJNRardPjloolgVCIfkql0Shk8ng8HgqHDCaTWChks9m6Pn0oHCYIAksmSWm5HA5VEIKiHSoVhmFSiaTX0EdWmVr1z+Vy65qbR1ZUaPv0fC4vFo/bnU6hQKDWaaOxWJZUmkwmyWr6AwEMS8ycMtXudOI47g8EAqGgSCAIRyIoivj8fgCEw+EkcbxHp5WIJTiOc9hsq90eCoftTieKoppeHZvFEggEBEEIhUIAsDudNofd6/fLs7Ksdrvd6ZRKxCkZEgRBKcpis5K1zpJK27o6C/ML6A8Ot8cjEAgsNls8kVDm5qq12tLi4pQqW2xWq93udLnkMlk8Hg9HIsFQSJYls9rtUonE7nRyOZxeg4FszR6dtjA/3+3xCIVCq93OYbNJtSTxZHVdbXlpGQtFE0lMpenhcrhcLpfSOfnsq2tqUshlbDabTJDAMKpLYMkk2SgSsZjLZidxfMBqFQqFGIY5XK5kMukPBOjtTlLTUL9o7tyCvLw+o1Gt06Ioyjql1WgsWl1XW1ZSKuDxXB6Prk8vlUhqmxpRFM2SSERCYY9OFw6Hk8mkLxAge7tYJErpeMdO1syaNr2yvFyj783Py7M57KS23V6vy+N2OJ3ZcoXP7x9sRz5fq9f7A/5gKCjPyqI6DACQfSAvJ7erRz1tclV1XS2LxQYgRAKh1W7X9PbOnj69tqmpUKmsbqgPBIOlxcXxRNzhcslpRrdTpSotKqpvaU5iyQKlslOt5vN48Xh8etUUp8sly8rqNfQtmjtPwONp9X1Wu71AqZw4diyXy23v6iwtKiZzKMzP79ZoCpTK8WPGBEMhAscb21rpdURRlCCIPqNxwezZ4XA4nkjgBBEMBgdsVqlESsnT1tU5YLWOKK8AgGg02qFSVU2Y2NDSXKDMo4uNIEinWlVSWBSJRk0D5uLCQgTAZLGQdTz725x8NFWWlfUaDL2GvvFjxlAPK7vTSbYFgiDVdbVktoFQMBwe7MksFot6HPXotKXFxQDgcrtD4TCO4w6Xc/b0GWKRyGSxmC0WumbIW7Jb07Ng9hyFTNbV01NRWtre3SUSCi1Wa0VpKVlHo9nc0NI8cdw48s8TdbWzp00/2dgwcey4lFtS09s7cdy4yrLyxrZWeZYUAJleVeXz+x0u15iRI0uLish3iEg0CgSQPYekp1e3aO68woKC2sYGoUBANbpYJKpralw8b15ZcUltU2OBMj+RSGj0veFIpDA//0h19azp00aUl1vttnAkcpZve8xn2KGpKC1tams9dOJ4Xk5uT6+Ow2ZnSaUdKlWPTgcAPTodhmGNba1sFttgMpYUFvX1959saBhZUdHY2uLxek2WgdzsbK/Xe+rGOOWc+f08LjeZTOr7+xtaWkoKi9q7ux0up91hl4olPTrdycbGyrJyu8tptdvbujorysqa2trUOi2bzWaxWKaBAbVWU15SUtvYSGZY19RYmJ8fiUSi0WjKKQCQZ2Xl5eQ43Z6i/PyGtla1TosgaDAcMprNlLTRWIwqqLaxobykxOqw6/r6YvG4SChs7+4+3RgoKhGLQ+GwecBSWlJS39wky5KardbiwkJNry4Sjai1ukg0Gg6HAcBgMp1sbBiwWcVicWNbqzxLptbqhAJBdV19W2cX6dCQfRpFkO4etdliCYVDp/K0jCgrr29pjicSvf0GMk/yVnR7PHank0yWniGlKIfLRdWaxWJFojF642p6exMY1tvXJxGJ8/PyOlQqANAbDPQq+/x+fX9/tlwejkTkWVkSsVir7x1RXlHb2Kjt0ycwrEutDobDVGsCQDwe7zMaye5BqYXL4UilUgDo7TfUNzWPGz2mqb0tFo9TOidFypJKsxXZVAK783SXoBrFard7fL6TDQ0Txo5tbG2xOx1GswlF0fR2B4DKsvIDR4/UtzQXFxbKpFk5CgWl1SyJVCqVen0+j8/X1NY6sqLS5nCQadRanVqn5XK54UjEOGCmens4Ekkvhc/nA8A5U6ehKErvY1wOx+n2eH0+qkS1VouiKE4QAEDvMFQf4HG5c2ee43K7pRKp0+0qLigszM93uFwL58yJxmIsFioQCObNPIcsNy8n12yxpN+zypzccCTsDwQAIEehcHrcB48eZbFYXA7H4/PVNjY2tbcV5ivdXk9RQQEA5OXkhE9tKGZ3OSVisdvrIR3EUZWVOdnZ9DomcTwYCuE4bnc6aurr27u7sqTScaNG9xmNTpeLMg8mi0UilnC53EF/3estUCqFAgGWTKaLPaK8os/Yr9XrR48YaXc6fH4/9VnrB93mQoEwEAw6XM683DMeVlRbEARIpVIUQRrbWvk8HtWT6Y8jKjeD2ZSbk2NzOEqKigFALpONHTWKrhm5TBYMhRIYFo3GahrqD584kZudLZfJeFzuyYaGGVOmULWwO535p9xQHMe5HA6HwxEJhAkMS7klAaCprfVw9Qkuh0sdKS8ptTnsAGAwGgmCGNJp9vp8NQ31h08cz89T0hs9FAqR7zcIgpQUFbk9HrVOd87UaS6PGwAIIAR8AQBUlJY5XE5mzPLf++To9y1buGj+rNmNrS1ujzdfmaeQywPB4OB3jCQGAAVKZSwez1Fky2Wywvx8r9+n1molYolAIMjPzetQqULh8OBtfOqLRzQaMVkGfH5/Ek/KsqQSsZjP45FZybKyElgCS2JikUieJQuFQ3k5uWKRCEVRr8+Xn5dXVlKC43g4EtHq9bJTwdtisbgsKys/TxkMh1NOAQCbzcZx3O1xmywWHMc9Xl+BUpmfm0dWkJQWx3GqoHg8QT4CPF6vx+t1uFwpn3BHjxjR3aPm8XgsFJXLZAqZPBQOa3v1iUQimcQBwDRg1hkMAFBSWDRzytSVS5fxuVzyeQEAYpEogSXycgdj1MViMQFfkCWVhsJh44C5orSMzNPr8/X06qjtxcg82Sy2MjdPmZsbCoXIZOkZUoricDhUran2okGMKC/3+n30ZnWfWWWvz5ebnaOQy4UCAZvNjkSjlHpHlJd396izpFJ6a1LvRARBJPEkpRYAhIWyEATBcZzL5fK4XB6Xm8QwSueDn1BYKAJwOkEySXUJqlGonnmq4YjSomK6YCl9ePmixVUTJtY0NLBYKIvForTKYrFYKIt8gZs0fnxze7vX5yPTAADZSejfprAk5g8EUkqhdn9r7+oKBIOUtlEEKVDmZ0kkGIZRJbq9ngKlslCZT36/pXcYsg8gCFLf0hwMh+bMmIEiKIZh8Xicy+Xq+vrUOu3iefPpQQ0RBElgiSFv2xlTpja0tgCAx+ebUTVlyYIFbq+HfBWYXlV13uJzC5T5XA43Go1iGOb2eqhHMwtlAQCfx4tEo+QHT4vNSq+jzW5XaTSkzZszc+bSBQs71SoAKC4sqCg9/RDvUqudbpfD6XQ4neT7JdX30sUuLS42WSwerzc3+4zvrunaHv42H1Fe3q3p4XA45KdOqldTbUHgONkJC5TKRAKjckjtwKfKEgoEYqEwGAqRH887VN10zRhMRpVG4w8E+HzenBkzL1y+XNOrA4AxI0fJZTLOqX0IutSqeDzucDp1fX1k9ZM4DgCJJMZhs9NuSZg2uercefOj0WgyOSiMz+8Xi8Q+v1+r18+omjJki8uysubMmDlnxkyHy0lv9FAkEgiGSGV6fT6pRDJgtXT1qOPxhN3pxHGC7IpOt4vup/6q+W8GUj9We1IiEmdJpRWlpY2tbeRblVqraWpvI99eASA/N/fYyZMOl6swP78wP5/FYiXCoUQiYbXbxWJR4tTGyE3tbdMmTQaAUDhCvtf4fP4hC82Wy5vb291ez8yqKf5AkHJzG9ta47H49ClVXA4XQRAEGXzI5mRnN7a22p3OqZMmca1nnBoc9sOwWDyOYVg4HB4zYmRDSwsAQVp3Ulr6t7u83JyGlhav3zd14qROtToWj/F5Obo+vUKuID8f5WZnHz5xfOGcufQbWywatC4AMKpyBPnJFEURVlqoVZVGM6pyhHHAHAyFxCKRQi7X9ekBIDcnx+P1Uen9gQBpBcn7k8zz0Injja2tXr9v3jmzzFZLeoY9Oi2lqInjxlK1BgAWinp8PrfHTX4co95bqWbV9OrKS0qoKgNAfl7e0ZM1wVAoFouTngql+eKCwhO1tRefv8Jis9Fbk8/jOVzOhpYWBEHoagkEg7FYjLSL7d3dCIKwaRubnB5yYLFOJ2Cz0huFdF+ohiPT0wWjt5TL7fZ4PThOFCiVbBbLNDBAaTUciQSCwXgC4/OgR6fLz80LhkMSkdg0MAAA5SXF9c3NBEFkSSUABNnb6aWQUo2qqDx0/LhQIEAQBEEQStv0AX6qxML8goaWFg6HLRQI6Jqh+oDT7R6w2giCaO3sGFFe3tDawmKxJo+fcODI4ZKiooaW5hlTptLHPoUCQXqDAgCPyy0tKtb06ng8bk1DQ35ebigczpJKEQSofl41YcKRmhpZVlZ3j3ruKW+Vy+UQBDF5/IRjJ2sUMrkv4F80Z+6oigRVx0njx5cUFQ16lg0N0Vh0/Gjy9eWM0MTnLV4MANFoLDcnp6mttWripPburmAwVJRfkC42C0W5HC75HkknXdvD3+ZiodBis82ZMbOrR53Sq6k0gWAwGk/tyUM+jrLl8kAgUFZScujEcbfX4/cHpk2eXFZcQtdMZVk5Wc2ahnocx0sKi1IVAUA22dGamhHl5U1trdMmV/G5vKa2Vi6Hw2KxyKHihtYWygo2tbVyOFyBgM9isfT9Bn/AHw5H5s+e/dWBA3JZVnV9vVgkJB19+lWnfGtBLBajN3p+Xp7D5TxeW+vxeRMJrKy4pLS4eNK48cFQqKWjfXRl5cFjR1koS99vuPKii+k3zq+X/2a4OwzDEhhGft9LJBIIgpD3WyKR4NAeduT4H4fNJocoyOZMYFgymeSfORQ/+OIWj/O4XAzDhpxyQj4LuBxOypyXZDIJCMJCUYIgYvE4PWfyHZyUJOUUNU2GxWLhOO7yeJLJZDAU4vP5pUVFlLQpidlsNoIgGIYRBMHhcGwOh1QiJr9apEMQRCKR4HK5yWQy3Tp+L+n9nqwsQRAI+Q3llEtxvPbknBkzKaOSCUpRVK3JcdCSoiLS70ypLNWsZIuTVR7MCscJHKfODqnelNZM4jgQBIvFoquFLnMkGhWk6ZzOkAmoRjn1feKMhqMES2mpaCyGoiiXwxlUCwClVRzHSZFwHCcf4mQa8iCGYcFQyGAyVk2YSPX29Oonk0kcx8mzlLbpvZrejhiGsVgsctgvpcOk9wEMwxAEydTQPTpdfl4el8tNb1A6OI5Ho1GBQDBkqP1wJMLn8fyBwP+3d6fPbZwJYocbBwHe4E3qoijJ1s7sTDLeyqz3rE1SlUpV8iV/TqqS/yff9nOqUpnKrsf2mjooHpbE+6ZIAgRIACRI3PnQVhumZFmemR17Zp6nXFNjHt1vv93AjzheOHzDUfbk5Kpanb51q91u12q16E00ncf4m2m32/VGI9XV9Z7D/raL7Ttv5t92VUcHEs1n5xbevDuq1+svVld/8bOfhZdQOpWKvX7KpHNmfjPh7oqlUvbk5MP797f39mbu3Pm+G3nHb1076fV6vdVu1+v17nT62oS0Wq1qrdqV7Aqfm3nHXZxY/ilqtlrbuztdya7wlfwfg0qlkk6n36eyuXz+2rNV76lULv/438/8Y1Ov1y8qlaHfy9/a738N/Kue0PL5+UB//7/eYf4BXYemQiwB4I+QDyUAgB9rLDe2t8K3le/s7xXL19+Ms7y+9o7fXdlY/3R29tdffBG9D6hjs9vh1t69hd+h38mOKpeXn87Ofjo727mS5B2Oc7nPHj36dHa2VC4XTk+jNzq91eXVValcXt/a3NzZDoKgXq//6tefuPQB3t8P9m7Y/Onp2Mho0NNzViw2m61kItnT3X2cy/X29BwcHh6f5D68d//w+DgWj8WCWC5/8mcPPgjfc3GUzZZK5b//q7+qVqu/+vUn//k//MezYnFsdPQomx0fHT0rFtPpdGZg8Dibq9cbk+PjE2Njmzvb9Xrj4YMH9Ubj5erq6PDw7Zs3o5GUyuWDw8PM4OCNycn1ra1Wq/XBvXuvjo/K5fNEIvHwwYPVjY2e7u7+vr5avT41MXGUzU5NTBzncuFL6PnC6drmZiKRuH/37vnFxebOzp1bt8JP3Gi32z/58MMgCMK1/OG/Hh4f5U/P7k9P93YsGvl0dvbvP/64t7d34fmXmzs7/X195xfnsVj83vR0tKNiqXRVvarVah/ef7D44vl/+od/X6/X17e2Do4O79+d6e/rq1arE+PjG1tbmUzm9o0b0WAOj47yp4VUV+q8cnH/7sz23l6+UAiCIBrJeaUS7c5NAuBH9MgyCIInC/Ofzn6xubMTj8dfrK68Ojo6vzh/9Gxu+vbtUrnc+aEEH95/MDs3V6vXzy8uDo+PZ6angyBIp9PpdPqqehUtVH80N3fv7vTh8dFJoVAql+7fvftsaalz+X+0/DxckBSanXt6++bNx/PPOlcQj4+M9vT05PL571xCXqvVhjKZ/GnhOJeLPohgeW0tXLx/UigE37KWv3MqYrEgbGe4hjf6fIDdg/1oR52r0d9cER9+DsDcwsKNqclwBqLBDA4MjA6PBN9cW91ut6ORdH4cgZsEwI8rlr/8xUd//1d/ff/u3d6enkrlcvdg//bNW6murv6+vrGRkeD1hxKMj4719vQ0mo18obC8ttadToeL3EvlcqPRjMcTweuF6sVyaWdvP3xP81Am09fbO9Dfd1LIR2vho+Xn0Vvdm81mqis10N8/nBnqXEHcarc3d7b/9i//8juXkCeTifHR0YnRsYvKRbSevVA8Cxfvh59O921r+SPRv4bLe6PPBzjJF6Idda5GD95YER9+DkDpvBzNQDSYIBZLJBPBN9dWV6vVaCSdH0fgJgHw44plp7HRkcbrNZcLz5/n8vnw65Pj4zt7e8+WlibHx29MTv7yo48ePniwub39+Nmz//2r/9udTnUuVJ+5M91qtc4vLvp6ewtnZ/NfLjWbrQ9m7jUazWqt2p1Oh8vP/+XJ42K5HK7WTyQSsVgwt7hwUih0riD+1a8/SXV1PVmYD5eQv34p8S1LyFut9pP5Zxs727embny1nr1e+2Bm5uniwieff16v1+cWF8K1/J98/vnV1WW0uvy0WAzHEATBw/sPfvXrT57MP9vc2Xl4/0Gr3Xq6sLCysf7wwYNrq6dD+ULhyfyzFysrNyYnu9Pd4Wr3IAg6ZyAaTH9v78Grw+D12uq7t+8E3/wshWh3N6am5r75kBeA4EeydKTVaoX/HYNwKXGtXk91LFJutVr1ev3aWt2rajWZSJxfXAxlMtFC9SAIarVaIpkMP70iWgjcuRY+XH5erdWilcsvVlamb9+eW1r8h7/+m7d+oMF3LiFvNpuxWCz8lINoPXu0eD96CHttLX+j2excPd15mJ2fD/BtS6SvrYiPVtF1zkDnYN5cZheN5IunT97n4wgAxPKHVK/XD44OZ+78MO8uOS0Wj7PZ6Vu3er/9v7T1+1xCHvwWnw/wB7E7ALEEgD82PpQAAMQSAMQSAMQSAMQSAMQSAMQSAMQSAMQSABBLABBLABBLABBLABBLABBLABBLABBLAEAsAUAsAUAsAUAsAUAsAUAsAUAsAUAsAQCxBACxBACxBACxBACxBACxBACxBACxBACxBADEEgDEEgDEEgDEEgDEEgDEEgDEEgDEEgAQSwAQSwAQSwAQSwAQSwAQSwAQSwAQSwBALAFALAFALAFALAFALAFALAFALAFALAFALAEAsQQAsQQAsQQAsQQAsQQAsQQAsQQAsQQAxBIAxBIAxBIAxBIAxBIAxBIAxBIAxBIAEEsAEEsAEEsAEEsAEEsAEEsAEEsAEEsAEEsAQCwBQCwBQCwBQCwBQCwBQCwBQCwBQCwBALEEALEEALEEALEEALEEALEEALEEALEEAMQSAMQSAMQSAMQSAMQSAMQSAMQSAMQSAMQSABBLABBLABBLABBLABBLABBLABBLABBLAEAsAUAsAUAsAUAsAUAsAUAsAUAsAUAsAQCxBACxBACxBACxBACxBACxBACxBACxBACxBADEEgDEEgDEEgDEEgDEEgDEEgDEEgDEEgAQSwAQSwAQSwAQSwAQSwAQSwAQSwAQSwBALAFALAFALAFALAFALAFALAFALAFALAFALAEAsQQAsQQAsQQAsQQAsQQAsQQAsQQAsQQAxBIAxBIAxBIAxBIAxBIAxBIAxBIAxBIAEEsAEEsAEEsAEEsAEEsAEEsAEEsAEEsAEEsAQCwBQCwBQCwBQCwBQCwBQCwBQCwBQCwBALEEALEEALEEALEEALEEALEEALEEALEEAMQSAMQSAMQSAMQSAMQSAMQSAMQSAMQSAMQSABBLABBLABBLABBLABBLABBLABBLABBLAEAsAUAsAUAsAUAsAUAsAUAsAUAsAUAsAQCxBACxBACxBACxBACxBACxBACxBACxBACxNAUAIJYAIJYAIJYAIJYAIJYAIJYAIJYAIJYAgFgCgFgCgFgCgFgCgFgCgFgCgFgCgFgCAGIJAGIJAGIJAGIJAGIJAGIJAGIJAGIJAIglAIglAIglAIglAIglAIglAIglAIglAIglACCWACCWACCWACCWACCWACCWACCWACCWAIBYAoBYAoBYAoBYAoBYAoBYAoBYAoBYAgBiCQBiCQBiCQBiCQBiCQBiCQBiCQBiCQBiCQCIJQCIJQCIJQCIJQCIJQCIJQCIJQCIJQAglgAglgAglgAglgAglgAglgAglgAglgCAWAKAWAKAWAKAWAKAWAKAWAKAWAKAWAKAWAIAYgkAYgkAYgkAYgkAYgkAYgkAYgkAYgkAiCUAiCUAiCUAiCUAiCUAiCUAiCUAiCUAIJYAIJYAIJYAIJYAIJYAIJYAIJYAIJYAIJYAgFgCgFgCgFgCgFgCgFgCgFgCgFgCgFgCAGIJAGIJAGIJAGIJAGIJAGIJAGIJAGIJAIglAIglAIglAIglAIglAIglAIglAIglAIglACCWACCWACCWACCWACCWACCWACCWACCWAIBYAoBYAoBYAoBYAoBYAoBYAoBYAoBYAgBiCQBiCQBiCQBiCQBiCQBiCQBiCQBiCQBiCQCIJQCIJQCIJQCIJQCIJQCIJQCIJQCIJQAglgAglgAglgAglgAglgAglgAglgAglgCAWAKAWAKAWAKAWAKAWAKAWAKAWAKAWAKAWAIAYgkAYgkAYgkAYgkAYgkAYgkAYgkAYgkAiCUAiCUAiCUA/H4lf6gd3/3wodkH4HvZWVv9QfYbuygXzD4AvIOnYQFALAFALAHgjzOWmzvbnz2a/fzxo939/ff5+Uajsfji+fv85PL62lu/eFosZk9Ofg+Hdnl1GR3U8vpavV7f2N4OgmBje7tYLv0GGwyPaHVj41912FfV6trmb7KL5yvLQRAUS6XFFy/eb36uSuXybznawtnZ9t7ub7mL41yucHb25jxs7+0FQbD44kWj0Xj/2Qt/630G80+ff/bp7Befzn6xf3j47gv4KJs9fT3Ct17Yv9H1+ZbJyZ2cLL18+b0uyDe/eHl12Tm34ZVfOD2tf8s0RjeNNzf1joM9zuXCE7eysX7wtgn8jdUbjY3trfc8/HffI73npujUarXq9Xqz2XzzW81Wa//Vq8urqx9weIn/+T/++w+y443t7T978MHDBw8Wnn85lMkUTk83d7aHBgcrl5e5QmFnf290eDh7crJ7sN9oNAf6+4+y2fOLi6HMUCwWe7m6WqvXBwcGjrLZje2twYGBrq6u8P56c3v7KJe9Nz29ubOdPTkZHR6OxWJBECyvrY0MDddqtYvKxc7+fn9vb+HsLH9ayJ2cjA6PHGWz4Y76+/rWNjfDXzzO5aK9Hxwebu3uDA8NXV1dLa+vp1Kpnu7ucI+5/Mn+q1ejIyPZXK6/r+8om03EE2tbW2fFs/6+/s3t7elbt9vtdjqd3t7b6+pKDQ4MHOdyW7u7mcHBZDIZBEH25OQ4lz0rlYYzmcPjo3B4V9VqLp9vNps93d3La2vTt2/vHhz09vSsbW4kE4nenp7D4+PjXLbZanWn050TEh3I6sbGST4/Njp6bXfvvLvf7Up25QuF8vl5Ln9Subzs6+3dPzzcf/UqFottbm8PZTIXlYvltdVUV6qnp+erWC4vjwwNzc49/eVHf9FoNFY21q+q1czgYHTXFu798upqeW01mUwcvDrc2t25e/tOGIP+vr7C6Wn4kzt7ez3d3el0OgiCarUabSo62FarFU7LUfa41WqPDg+He6xWa+l0OneSC2dve3d3a3fn9o2b4cz09vQUTk97e3uPstl2ux1NbLvdTsRiuXw+3G8qlVrf2iqVS+cX5+Gp7O/vTyYSr46Pdvf2B/r64vH4W6c6+q3hTCYcc39f36ujo3Czu/v70fFGF/9/+Nu/m759O7wYwutwaDDz6uiofHHe19OTSqW+XF5uNBqXV5cnhdPwgmy32+G+jrLZkZGRbC4XXvzRqN48WbEgiL51bTwjQ8Ph6ejt6Q2C4NHc3MjwUDKZDE9uV1dXNGMXlUrh9LTZauVPT8MbZrvd7k6nn6+sXF5dDWUyQRC02+3Ts7PcycnYyEg8Hr+qVv/5s08nxsa70+m5pcV4PF6r1bInJ4MDA9EEhlPRbrdL5+fRDe3w+Ogomz3J529NTfX39UW3u4uLi/DKv6peff5o9u6d6UQsNre4+GBmEXttVQAAC21JREFUJroOy+fn4RH1dPesb22d5PPDmUwun49u5tHUxWOx02Kx1Wp1JZNH2Wyr1Qo33pVMbu3tTk1MvPuyCW+P0T1SIpHY2N66qFTa7Xa1VuvcVGZwsPPugu/8C/hXn/zzVbW2trlx986db9w7XV1t7uyk0+n+vr4/3adhp2/f3tnf29nf+/D+g9m5uVz+JJvLDvYPrG5sPJmfv3Pz1tLLl5XLy/WtzQcz92afPn00N3djavLw+OikUJhbXPjg3v3jXC7c1KNnc9O3b5fK5a2dnWqt1tfb2/nH8nAmMzE2dlI4vTU19WRxYWVjPdXVdVI4PSsWox2tbm50JZOZwcEvl5ejL5bK5Z39vTs3b23v7n7x5MkH9+49XZhvtVpBEBweH+cLheHM0NKL5+Ejv/B/K5eV+3dnvnjyJAiCWr2+ubvzaG7u3t3pw+Oj/VevVtbXZu7cmX36NBzY0ssXfb29hdPT41wuGl4un9872O+8Mn7y4YcHR4cP7s48nn9WLJe2dneGM0PzXy51Tkg05pWN9WQymUgk3txdu91udGi2Wp2nY3VzI9mVXHr5YqC//+Xaaq1e//Lli6FMZm5xYXh4eHlt7fHcs58+/LO5pcVGo3F+cVFvNKq16v/5p//3wb17qa6ug6Oj2zdvrm1uhA8yKpeX0d4fP3s2c2e6cHqWyQyODo98tbuNjSAI9l4dlM7PF188v3f37tziYvitaFPZk1x0sOG0nBQKsVgsPL+zc09vTk6tbW2Wz8+j2Qt3Ec3MYTYbPuxb3djonNjsSS5XKET7XVlfj8fjrXY7CIIvnjz52U9+8nRh/uKy8nx5eWZ6+tH8s7dPdcdvRWMunZejzXYebyh/evrZo0efPXpULJWi6zBfKDxdXBjODK2sb8w+fTJz585RLttqtWv1WmZgYH1ra2V9o1gq7R++Gh8dPTs7iy7+aFRH2ey1k9X5rWvjiU5HOKTM4ODoyGh0cquvHyivbmysbW7WG418oRDdMFfWN1Y3N9KpVLPZCB98n5VK3el0IpHY2t0NgiCdSg0ODnZ3d2/u7gwNZsZGRh4/mxvKDHZOYHTTiGayWCpt7e6ODg9XLi9X1jc6b3fRlZ8ZGBwcHDwrFs8rlczgQPQ3WRAE0RGdlUrpVKrZbG7t7kbTu3dwEE1dEATxWOzl6srB4eFF5SLa+Fd3I9912YQXdnSPtLKxHovFzysXewcH1zZ17e6CdxsdHh4bHfvo5z8P/yzu/FZvT8/I8NCf6NOwkWKp1Gq1x0fHent6Gs1GEAQ3JieHMpl6oz6UGRzo7+9Op0vlcuXycn1raygzWCyXdvb2w8eL/+bP//zZ0tJZsRg+75Hq6urv6xsbGSmcnZ2eneXy+b7e3mhHyWSy1WoVTgv7h4etVisei92YnMoMDDQajWhHp2dnU5MTI8PD5fPz6ItnpdLYyOjw0NDNqamzUnFlfX2gfyBqzI3JyamJiYtK5auni5uNIAjGR8f6+/ri8a+nNxr2eaUSHctXA0skJ8cnJsfHz4rFaHhBEEzfuh0+Yg71dHefFYurmxvtdlAslsZHx0ZHRtKpdOeEfD3mYnFqYuLunTutVuva7iqXl89XlqN/tne/8WRmPBa7NXWjv69/cnwi3PvE2PhwJjM6PDI0OFitVlOpVDqVSqdS5xcXy2trpXI5Ho//t//yX1+url1eXV5UKuubW/V6vdlsBUHQeeI++vnPl9fXD44OE/FEIpn45nPszXBHnZMWbeq0WIwONpyWUrkcnd9arT6UyYyPjtXqtWj2wl1EMxN7/VdCs9V8c2Kj/RbOTm9MTt6cnAqCIDrRrVZ7Ymx8oL8/EU+8dao7f6vz8KPNvnm8o8PDf/fxx3/38ceZwcGvr8Nm88bkZG9PTxAEtVo9Mzj40w8fBkFwa2pqoL+/WqsGQdDf1zc1PvHl8vJFpRJd/J2j6jxZ9Xr92rc6xxOdjq+eYkrEY0EQndxGs9kxY+0HMzPxeCy6YQZBMHNnunJ5ubmzc1WtBkFwdXW5f/iqWCqFMxyLxRLxRCKRCLecSCSGh4ZGhoY7xxPpvGjHR8dGhofDSfjG7e71lZ9IJBLxRCwWxGKxROKrXYSiI+ocTDS9qVQqmrrwj4OLSmXv1cG96bvRxq/dVN9x2XRe2KdnxRuTk1PjE0EQXNvUm3cXvFvlsrL08sXPf/LT8H6g84n9P93XLIMgmH/+5WePZsvnFz99+HBnb+/Z0tLk+Phbf3JsZCTVlYrFYrFYfObOdKvVOr+46OvtXd3YGM5kavVaEARdyWQQBAvPn+fy+Zk7dxqNZrVW7U6nr72QUK3VGo1G5XXbrpm5M/10YfHJ/PwH9+5FX5waH987OPj88eOTQuHm1FQikajXa12vn9J8sbr6+ePH96bvBkF7bmkxfDVoZ39vbnFhbGSkc8vhsG9NTUXH8tUz9e3W04WFlY310ZGRdw8vLFO9UR8YGNjZ33s8/6zRaHROSPST96anny4ufPL55wMD/dd219fb+4uf/Tz658HMzPe4XOLxIAiWXr6MxWJDmcwvP/podHi4K9mV6ur6248//uRfviiVy8lkIrzXuHbi1ra2+vt6W81Wdzp98Oqr++iR4aHZubm3vvIUbaor2RUd7OvJ/Pr8jo6MPJ5/trW702w0o9kLdxHNzPDQUC5/8mR+/trd9DX37848mZ//cvllEATRiU4mErv7+3NLi4MD/W+d6s7fisZcLH394nQ4mM2d7dNi8dojy92Dg7eOZGJ87Mn8/L88eRyPx755V3J5lM329/fVG43o4o9G1dvb8+b1/Oa3wvFEp+Prl2QSiejkDvT3v3vGDo+PGs1GMpFst9tBEFxULoMgaAftYrEUPYERPrvene7ef/Xq2ng6J/DrG9rExPbe7rOlpWq1du12F135lcvL8vl5rf71i6BPFubD/xMd0ZuDCZ/Hi6Yu/Mr42Fir1U4kEl9v/Oqqc5DvuGy+eY9058n8/NLLF0EsuLapzruLucUFLXy3k3y+fH7ene4eHx0NX8L8X//4j+GtplgqbWxvr6yvXf1wL1v+WD6UIHxpN/3NtnUKXw8I41er1RLJZCIeb7VaV9Vqb8/XdwS1ej3V1RW+/N5utzsfQETvKUgkEq1W69tewKvX67FY7Np32+12o9kMA3l1ddX9+hWI5bW10ZGRocHBcEf1ej3aY6PRuLaRaNidxxIEwaezX/zNL/8yvLd69/CazWa73Y7F4/Vabedgf3x0bHN765cf/UW05Ws/HMRib+7ud/IOkW97Dabdbtfr9VQq1Ww2wzvfzr1XLi+70+l4PB5999qkvXVTlcvLvVcH0cFG0xue37XNzcnx8S+XX/67f/uLeDwezV64i2hmmq1W0G53PhD5trOfSCTCvwnCE10ql7d2d3764cNUKtV5Et/6W28efnQuvnPXb44kmUy+eTddbzSazWZ3Ot158b91VNeuumvXRiKRiE7HW0/ud87YVbWaTCSiC7Vaq6VTqeiyD58dCTfeefjvGGq403bHxR/d7qIrPx6LtVqtzlFt7+3NvH59Kzqia4O5NnXfdrOKx756PPk+l010YR/ncs1m8/zioru7+9bU1LVNdd5d8H212+13/4H7pxjLP1ClcjmdSqV/uw7l8vnwL6nv5eDo8OKi8mBm5vveC/8hesfBXl5d7ezvjY2Mdj6O/x2q1+sXlUr4NhZ4a+C3d3e6kl3Tt2+bjT9iYgkA38GHEgCAWAKAWAKAWAKAWAKAWAKAWAKAWAKAWAIAYgkAYgkAYgkAYgkAYgkAYgkAYgkAYgkAiCUAiCUAiCUAiCUAiCUAiCUAiCUAiCUAIJYAIJYAIJYAIJYAIJYAIJYAIJYAIJYAIJYAgFgCgFgCgFgCgFgCgFgCgFgCgFgCgFgCAGIJAN/D/wetk84dkI52FAAAAABJRU5ErkJggg==","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"c67a6343-4991-419c-b3ea-0219b251b06f","timestamp":"2026-08-24T21:49:36.372Z","tool_use_result":{"type":"image","file":{"base64":"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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TfAyQo4DXj6Vk7yoLX2E3t","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"eb55b66f-2edb-4215-9d0a-6f79bbae4cf5","timestamp":"2026-08-24T21:49:48.258Z","tool_use_result":{"oldTodos":[{"content":"Cartographier la fiche web + modules backend (formes de retour exactes)","status":"completed","activeForm":"Cartographiant la fiche web + modules backend"},{"content":"Lire la référence lou-ka pdfgen.py (approche + style)","status":"completed","activeForm":"Lisant la référence lou-ka pdfgen.py"},{"content":"Écrire immoka/pdffiche.py (canvas, pagination dynamique, sections conditionnelles)","status":"completed","activeForm":"Écrivant immoka/pdffiche.py"},{"content":"Endpoint PDF + tester fiche riche et fiche pauvre (pdftoppm, taille, temps)","status":"in_progress","activeForm":"Testant l'endpoint PDF riche/pauvre"},{"content":"pm2 restart + healthchecks + commit/push origin main","status":"pending","activeForm":"Redéployant et poussant"}],"newTodos":[{"content":"Cartographier la fiche web + modules backend (formes de retour exactes)","status":"completed","activeForm":"Cartographiant la fiche web + modules backend"},{"content":"Lire la référence lou-ka pdfgen.py (approche + style)","status":"completed","activeForm":"Lisant la référence lou-ka pdfgen.py"},{"content":"Écrire immoka/pdffiche.py (canvas, pagination dynamique, sections conditionnelles)","status":"completed","activeForm":"Écrivant immoka/pdffiche.py"},{"content":"Endpoint PDF + tester fiche riche et fiche pauvre (pdftoppm, taille, temps)","status":"completed","activeForm":"Testant l'endpoint PDF riche/pauvre"},{"content":"pm2 restart + healthchecks + commit/push origin main","status":"in_progress","activeForm":"Redéployant et poussant"}],"verificationNudgeNeeded":false}}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNK1aNX6tfrfoxfNaVAh","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015kkNDdmXBiByYsH1Ny7bC9","name":"Bash","input":{"command":"pm2 restart immo-ka-web && sleep 3 && curl -s -o /dev/null -w \"site local: %{http_code}\\n\" http://localhost:8096/ && curl -s -o /tmp/hc_riche.pdf -w \"pdf riche: %{http_code} %{content_type} %{size_download} octets en %{time_total}s\\n\" \"http://localhost:8096/api/listings/domicilia:17516934/pdf\" && curl -s -o /dev/null -w \"pdf pauvre: %{http_code}\\n\" \"http://localhost:8096/api/listings/remax_quebec:17719804/pdf\" && curl -s -o /dev/null -w \"pdf 404: %{http_code}\\n\" \"http://localhost:8096/api/listings/inexistant:0/pdf\"","description":"Restart PM2 + healthchecks endpoint PDF local","timeout":180000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1890,"cache_read_input_tokens":156265,"cache_creation":{"ephemeral_5m_input_tokens":1890,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"2f45433e-6852-4e46-8361-1a17a0eaf86f"}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015kkNDdmXBiByYsH1Ny7bC9","type":"tool_result","content":"Use --update-env to update environment variables\n[PM2] Applying action restartProcessId on app [immo-ka-web](ids: [ 9 ])\n[PM2] [immo-ka-web](9) ✓\n┌────┬───────────────────────┬─────────────┬─────────┬─────────┬──────────┬────────┬──────┬───────────┬──────────┬──────────┬──────────┬──────────┐\n│ id │ name                  │ namespace   │ version │ mode    │ pid      │ uptime │ ↺    │ status    │ cpu      │ mem      │ user     │ watching │\n├────┼───────────────────────┼─────────────┼─────────┼─────────┼──────────┼────────┼──────┼───────────┼──────────┼──────────┼──────────┼──────────┤\n│ 8  │ fabri-ka-ngrok        │ default     │ N/A     │ fork    │ 79941    │ 7D     │ 1    │ online    │ 0%       │ 369.1mb  │ simon-p… │ disabled │\n│ 7  │ fabri-ka-sync         │ default     │ N/A     │ fork    │ 23012    │ 65m    │ 47   │ online    │ 0%       │ 432.7mb  │ simon-p… │ disabled │\n│ 6  │ fabri-ka-web          │ default     │ N/A     │ fork    │ 22706    │ 65m    │ 66   │ online    │ 3%       │ 160.5mb  │ simon-p… │ disabled │\n│ 0  │ hfmarketdata-api      │ default     │ N/A     │ fork    │ 15999    │ 14D    │ 2    │ online    │ 0%       │ 30.5mb   │ simon-p… │ disabled │\n│ 1  │ hfmarketdata-ngrok    │ default     │ N/A     │ fork    │ 58051    │ 14D    │ 0    │ online    │ 2%       │ 513.6mb  │ simon-p… │ disabled │\n│ 4  │ immo-ka-ngrok         │ default     │ N/A     │ fork    │ 79925    │ 7D     │ 1    │ online    │ 0%       │ 307.1mb  │ simon-p… │ disabled │\n│ 11 │ immo-ka-sync          │ default     │ N/A     │ fork    │ 61793    │ 2h     │ 13   │ online    │ 0%       │ 325.7mb  │ simon-p… │ disabled │\n│ 9  │ immo-ka-web           │ default     │ N/A     │ fork    │ 92213    │ 0s     │ 57   │ online    │ 0%       │ 656.0kb  │ simon-p… │ disabled │\n│ 14 │ ora-ka-ngrok          │ default     │ N/A     │ fork    │ 74008    │ 11D    │ 0    │ online    │ 1%       │ 246.7mb  │ simon-p… │ disabled │\n│ 13 │ ora-ka-sync           │ default     │ N/A     │ fork    │ 73996    │ 11D    │ 0    │ online    │ 0%       │ 1.2gb    │ simon-p… │ disabled │\n│ 12 │ ora-ka-web            │ default     │ N/A     │ fork    │ 68503    │ 11D    │ 9    │ online    │ 0%       │ 2.9gb    │ simon-p… │ disabled │\n└────┴───────────────────────┴─────────────┴─────────┴─────────┴──────────┴────────┴──────┴───────────┴──────────┴──────────┴──────────┴──────────┘\nsite local: 200\npdf riche: 200 application/pdf 700178 octets en 32.321580s\npdf pauvre: 200\npdf 404: 404","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"5a9d35cf-59d6-4b83-8e35-07a75099b928","timestamp":"2026-08-24T21:50:30.168Z","tool_use_result":{"stdout":"Use --update-env to update environment variables\n[PM2] Applying action restartProcessId on app [immo-ka-web](ids: [ 9 ])\n[PM2] [immo-ka-web](9) ✓\n┌────┬───────────────────────┬─────────────┬─────────┬─────────┬──────────┬────────┬──────┬───────────┬──────────┬──────────┬──────────┬──────────┐\n│ id │ name                  │ namespace   │ version │ mode    │ pid      │ uptime │ ↺    │ status    │ cpu      │ mem      │ user     │ watching │\n├────┼───────────────────────┼─────────────┼─────────┼─────────┼──────────┼────────┼──────┼───────────┼──────────┼──────────┼──────────┼──────────┤\n│ 8  │ fabri-ka-ngrok        │ default     │ N/A     │ fork    │ 79941    │ 7D     │ 1    │ online    │ 0%       │ 369.1mb  │ simon-p… │ disabled │\n│ 7  │ fabri-ka-sync         │ default     │ N/A     │ fork    │ 23012    │ 65m    │ 47   │ online    │ 0%       │ 432.7mb  │ simon-p… │ disabled │\n│ 6  │ fabri-ka-web          │ default     │ N/A     │ fork    │ 22706    │ 65m    │ 66   │ online    │ 3%       │ 160.5mb  │ simon-p… │ disabled │\n│ 0  │ hfmarketdata-api      │ default     │ N/A     │ fork    │ 15999    │ 14D    │ 2    │ online    │ 0%       │ 30.5mb   │ simon-p… │ disabled │\n│ 1  │ hfmarketdata-ngrok    │ default     │ N/A     │ fork    │ 58051    │ 14D    │ 0    │ online    │ 2%       │ 513.6mb  │ simon-p… │ disabled │\n│ 4  │ immo-ka-ngrok         │ default     │ N/A     │ fork    │ 79925    │ 7D     │ 1    │ online    │ 0%       │ 307.1mb  │ simon-p… │ disabled │\n│ 11 │ immo-ka-sync          │ default     │ N/A     │ fork    │ 61793    │ 2h     │ 13   │ online    │ 0%       │ 325.7mb  │ simon-p… │ disabled │\n│ 9  │ immo-ka-web           │ default     │ N/A     │ fork    │ 92213    │ 0s     │ 57   │ online    │ 0%       │ 656.0kb  │ simon-p… │ disabled │\n│ 14 │ ora-ka-ngrok          │ default     │ N/A     │ fork    │ 74008    │ 11D    │ 0    │ online    │ 1%       │ 246.7mb  │ simon-p… │ disabled │\n│ 13 │ ora-ka-sync           │ default     │ N/A     │ fork    │ 73996    │ 11D    │ 0    │ online    │ 0%       │ 1.2gb    │ simon-p… │ disabled │\n│ 12 │ ora-ka-web            │ default     │ N/A     │ fork    │ 68503    │ 11D    │ 9    │ online    │ 0%       │ 2.9gb    │ simon-p… │ disabled │\n└────┴───────────────────────┴─────────────┴─────────┴─────────┴──────────┴────────┴──────┴───────────┴──────────┴──────────┴──────────┴──────────┘\nsite local: 200\npdf riche: 200 application/pdf 700178 octets en 32.321580s\npdf pauvre: 200\npdf 404: 404","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNK5Ym9FuQGFMonAPSxk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01G7JYA4tyMVjoovZ6yKMzD4","name":"Bash","input":{"command":"curl -s -o /dev/null -w \"public site: %{http_code}\\n\" https://www.immo-ka.com/ && curl -s -o /dev/null -w \"public pdf: %{http_code} %{content_type} %{size_download} octets\\n\" \"https://www.immo-ka.com/api/listings/sutton:13714330/pdf\"","description":"Healthcheck site public + endpoint PDF","timeout":120000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2487,"cache_read_input_tokens":158155,"cache_creation":{"ephemeral_5m_input_tokens":2487,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"d781a40b-a1d5-457c-a38f-7b5ae2d78cc9"}}
{"type":"permission_request","requestId":"63030f132ff94d1c","tool":"Bash","input":{"command":"curl -s -o /dev/null -w \"public site: %{http_code}\\n\" https://www.immo-ka.com/ && curl -s -o /dev/null -w \"public pdf: %{http_code} %{content_type} %{size_download} octets\\n\" \"https://www.immo-ka.com/api/listings/sutton:13714330/pdf\"","timeout":120000,"description":"Healthcheck site public + endpoint PDF"}}
{"type":"permission_resolved","requestId":"63030f132ff94d1c","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G7JYA4tyMVjoovZ6yKMzD4","type":"tool_result","content":"public site: 200\npublic pdf: 200 application/pdf 340834 octets","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"8c384407-a852-4378-a5f0-d1dca78ce04f","timestamp":"2026-08-24T21:50:42.054Z","tool_use_result":{"stdout":"public site: 200\npublic pdf: 200 application/pdf 340834 octets","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNK6R54Yc8x4nMNPBc8k","type":"message","role":"assistant","content":[{"type":"text","text":"Déployé et vérifié en public. Je committe et pousse."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":248,"cache_read_input_tokens":160642,"cache_creation":{"ephemeral_5m_input_tokens":248,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f98e47db-d2d9-44c8-90fa-2861b066f147"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNK6R54Yc8x4nMNPBc8k","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01B8zPjVTNdv28He52eky8Yq","name":"Bash","input":{"command":"git status && git diff --stat","description":"État git avant commit"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":248,"cache_read_input_tokens":160642,"cache_creation":{"ephemeral_5m_input_tokens":248,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"245b4efd-0d93-4086-ab79-ad0caf8ae23c"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01B8zPjVTNdv28He52eky8Yq","type":"tool_result","content":"On branch main\nYour branch is up to date with 'origin/main'.\n\nChanges not staged for commit:\n  (use \"git add <file>...\" to update what will be committed)\n  (use \"git restore <file>...\" to discard changes in working directory)\n\tmodified:   immoka/web.py\n\tmodified:   requirements.txt\n\nUntracked files:\n  (use \"git add <file>...\" to include in what will be committed)\n\timmoka/pdffiche.py\n\nno changes added to commit (use \"git add\" and/or \"git commit -a\")\n immoka/web.py    | 16 ++++++++++++++++\n requirements.txt |  2 ++\n 2 files changed, 18 insertions(+)","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"fda302be-37ab-4c22-b10d-66557a49bc64","timestamp":"2026-08-24T21:50:46.094Z","tool_use_result":{"stdout":"On branch main\nYour branch is up to date with 'origin/main'.\n\nChanges not staged for commit:\n  (use \"git add <file>...\" to update what will be committed)\n  (use \"git restore <file>...\" to discard changes in working directory)\n\tmodified:   immoka/web.py\n\tmodified:   requirements.txt\n\nUntracked files:\n  (use \"git add <file>...\" to include in what will be committed)\n\timmoka/pdffiche.py\n\nno changes added to commit (use \"git add\" and/or \"git commit -a\")\n immoka/web.py    | 16 ++++++++++++++++\n requirements.txt |  2 ++\n 2 files changed, 18 insertions(+)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QJE65asUts3ThdGLtcFs3u","type":"tool_result","content":"The file /Users/simon-pierreboucher/.claude/projects/-Users-simon-pierreboucher-apps-immo-ka/memory/MEMORY.md has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f099ccfc-def8-4d0c-af09-c3cae40818be","timestamp":"2026-08-24T21:51:39.599Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/.claude/projects/-Users-simon-pierreboucher-apps-immo-ka/memory/MEMORY.md","oldString":"- [Vérifs Playwright immo-ka](project_playwright_checks.md) — check-order avec BASE=http://localhost:8096 ; playwright installé --no-save ; uid test domicilia:17516934","newString":"- [Vérifs Playwright immo-ka](project_playwright_checks.md) — check-order avec BASE=http://localhost:8096 ; playwright installé --no-save ; uid test domicilia:17516934\n- [Fiche PDF immo-ka](project_pdf_fiche.md) — uids test riche/pauvre/vente ; piège /tmp/inspect.py → python -P ; pdftoppm pour inspecter","originalFile":"# MEMORY\n\n- [Vérifs Playwright immo-ka](project_playwright_checks.md) — check-order avec BASE=http://localhost:8096 ; playwright installé --no-save ; uid test domicilia:17516934\n","structuredPatch":[{"oldStart":1,"oldLines":3,"newStart":1,"newLines":4,"lines":[" # MEMORY"," "," - [Vérifs Playwright immo-ka](project_playwright_checks.md) — check-order avec BASE=http://localhost:8096 ; playwright installé --no-save ; uid test domicilia:17516934","+- [Fiche PDF immo-ka](project_pdf_fiche.md) — uids test riche/pauvre/vente ; piège /tmp/inspect.py → python -P ; pdftoppm pour inspecter"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNKAfLR84hE4wbGXpVyL","type":"message","role":"assistant","content":[{"type":"text","text":"**Mission 2 livrée** — la fiche PDF d'immo-ka est en prod (commit `5fea46d` poussé sur spbgit).\n\n**Ce qui a été créé**\n- `immoka/pdffiche.py` (nouveau, ~950 lignes) : transposition de l'approche lou-ka `pdfgen.py@6ff906e` — reportlab canvas, pagination dynamique (`page=[1]`, `saut_page()`, `place(y, besoin)`, aucune section coupée), `_safe()` autour de chaque enrichissement (section omise si pas de données, jamais de « N/D »), style « éditorial sharp » aux couleurs immo-ka (crème `#f5f3ee`, encre `#141814`, cerise `#e23744` — carrés accent à ombre dure, tuiles KPI, tableaux zébrés à en-têtes Courier soulignés, pilules verdicts).\n- Endpoint `GET /api/listings/{uid}/pdf` dans `web.py` (seul ajout, frontend intact).\n\n**Sections couvertes** : héro prix + pastille verdict Vrai-Prix (masquée si |Δ| > 60 %, comme le site) + type, adresse, chips specs, photos (1 grande + 3 vignettes, compressées Pillow 1000 px q78), bandeau baisse de prix, description | inclusions 2 colonnes, caractéristiques (mêmes exclusions `DETAIL_HIDDEN` que le site), pièces, analyse Vrai-Prix (jauge low–high avec marqueurs ESTIMATION/PRIX DEMANDÉ + tuiles rôle foncier), historique du prix (dates en toutes lettres « 13 août 2026 »), inondation, qualité de l'air, Hydro-Québec, commerces et transport, essence, quartier (6 tuiles démo + barres proximité + pastilles chaleur/crime), références (courtier, agence, URL originale, QR vers la fiche).\n\n**Tests** (pdftoppm -r 72, chaque page inspectée) : riche `domicilia:17516934` → 684 Ko / 3 pages / ~30 s au 1er appel (photos + enrichissements non cachés) ; pauvre `remax_quebec:17719804` → 5 Ko / 1 page propre ; vente `sutton:13714330` → 333 Ko / 4 pages / 1,1 s avec verdict « ▼ 1 % sous l'estimation ».\n\n**Déploiement** : pm2 restart `immo-ka-web` ✓, local 8096 → 200 (PDF `application/pdf`, 404 propre sur uid inexistant) ✓, public https://www.immo-ka.com → 200 ✓.\n\n**Dérogation à signaler** : la contrainte « pas de dépendances nouvelles » supposait que reportlab/Pillow étaient déjà là — le repo n'utilisait que fpdf2. J'ai installé et épinglé `reportlab` + `qrcode` dans requirements.txt (l'approche reportlab canvas + QR étant explicitement demandée)."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":455,"cache_read_input_tokens":164028,"cache_creation":{"ephemeral_5m_input_tokens":455,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f4b24a32-6973-4ae4-98cc-8ff036d24ee8"}}
{"type":"claude","event":{"type":"result","subtype":"success","is_error":false,"api_error_status":null,"duration_ms":2305348,"duration_api_ms":1077710,"num_turns":81,"result":"**Mission 2 livrée** — la fiche PDF d'immo-ka est en prod (commit `5fea46d` poussé sur spbgit).\n\n**Ce qui a été créé**\n- `immoka/pdffiche.py` (nouveau, ~950 lignes) : transposition de l'approche lou-ka `pdfgen.py@6ff906e` — reportlab canvas, pagination dynamique (`page=[1]`, `saut_page()`, `place(y, besoin)`, aucune section coupée), `_safe()` autour de chaque enrichissement (section omise si pas de données, jamais de « N/D »), style « éditorial sharp » aux couleurs immo-ka (crème `#f5f3ee`, encre `#141814`, cerise `#e23744` — carrés accent à ombre dure, tuiles KPI, tableaux zébrés à en-têtes Courier soulignés, pilules verdicts).\n- Endpoint `GET /api/listings/{uid}/pdf` dans `web.py` (seul ajout, frontend intact).\n\n**Sections couvertes** : héro prix + pastille verdict Vrai-Prix (masquée si |Δ| > 60 %, comme le site) + type, adresse, chips specs, photos (1 grande + 3 vignettes, compressées Pillow 1000 px q78), bandeau baisse de prix, description | inclusions 2 colonnes, caractéristiques (mêmes exclusions `DETAIL_HIDDEN` que le site), pièces, analyse Vrai-Prix (jauge low–high avec marqueurs ESTIMATION/PRIX DEMANDÉ + tuiles rôle foncier), historique du prix (dates en toutes lettres « 13 août 2026 »), inondation, qualité de l'air, Hydro-Québec, commerces et transport, essence, quartier (6 tuiles démo + barres proximité + pastilles chaleur/crime), références (courtier, agence, URL originale, QR vers la fiche).\n\n**Tests** (pdftoppm -r 72, chaque page inspectée) : riche `domicilia:17516934` → 684 Ko / 3 pages / ~30 s au 1er appel (photos + enrichissements non cachés) ; pauvre `remax_quebec:17719804` → 5 Ko / 1 page propre ; vente `sutton:13714330` → 333 Ko / 4 pages / 1,1 s avec verdict « ▼ 1 % sous l'estimation ».\n\n**Déploiement** : pm2 restart `immo-ka-web` ✓, local 8096 → 200 (PDF `application/pdf`, 404 propre sur uid inexistant) ✓, public https://www.immo-ka.com → 200 ✓.\n\n**Dérogation à signaler** : la contrainte « pas de dépendances nouvelles » supposait que reportlab/Pillow étaient déjà là — le repo n'utilisait que fpdf2. J'ai installé et épinglé `reportlab` + `qrcode` dans requirements.txt (l'approche reportlab canvas + QR étant explicitement demandée).","stop_reason":"end_turn","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","total_cost_usd":5.9079694499999995,"usage":{"input_tokens":122,"cache_creation_input_tokens":560933,"cache_read_input_tokens":7108709,"output_tokens":72972,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":560933},"inference_geo":"","iterations":[{"input_tokens":2,"output_tokens":1062,"cache_read_input_tokens":164028,"cache_creation_input_tokens":455,"cache_creation":{"ephemeral_5m_input_tokens":455,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"modelUsage":{"claude-fable-5":{"inputTokens":133321,"outputTokens":84793,"cacheReadInputTokens":7108709,"cacheCreationInputTokens":560933,"webSearchRequests":0,"costUSD":5.9079694499999995,"contextWindow":200000,"maxOutputTokens":32000}},"permission_denials":[{"tool_name":"Bash","tool_use_id":"toolu_01T82XRMuXigYck15ihjiLtK","tool_input":{"command":"python3 - <<'EOF'\nimport json, urllib.request\ndef get(u):\n    with urllib.request.urlopen(\"http://localhost:8096\"+u, timeout=30) as r:\n        return json.load(r)\ndef shape(o, depth=0, key=\"\"):\n    pad=\"  \"*depth\n    if isinstance(o, dict):\n        for k,v in o.items(): shape(v, depth+1, k)\n    elif isinstance(o, list):\n        print(f\"{'  '*depth}{key}: list[{len(o)}]\")\n        if o: shape(o[0], depth+1, key+\"[0]\")\n    else:\n        s=str(o); print(f\"{pad}{key}: {type(o).__name__} = {s[:60]}\")\nd=get(\"/api/listings/domicilia:17516934\")\nprint(\"=== LISTING keys ===\"); print(list(d.keys()))\nprint(\"=== vraiprix ===\"); print(json.dumps(d.get(\"vraiprix\"), ensure_ascii=False)[:400])\nprint(\"=== price_history ===\"); print(d.get(\"price_history\"))\nprint(\"=== poi[0:2] ===\"); print(json.dumps(d.get(\"poi\")[:2], ensure_ascii=False))\nprint(\"=== quartier ===\"); shape(d.get(\"quartier\"))\nprint(\"=== details keys ===\"); print(list((d.get(\"details\") or {}).keys()))\nprint(\"=== features ===\"); print(d.get(\"features\"))\nlat,lng=d[\"lat\"],d[\"lng\"]\nfor name,u in [(\"inondation\",f\"/api/inondation?lat={lat}&lng={lng}\"),(\"air\",f\"/api/air?lat={lat}&lng={lng}\"),(\"gaz\",f\"/api/gaz?lat={lat}&lng={lng}\"),(\"commerces\",f\"/api/commerces?lat={lat}&lng={lng}\")]:\n    try:\n        o=get(u); print(f\"=== {name} ===\"); print(json.dumps(o, ensure_ascii=False)[:700])\n    except Exception as e: print(f\"=== {name} === ERREUR {e}\")\nimport urllib.parse\nadr=urllib.parse.quote(d.get(\"address\") or \"\")\ntry:\n    o=get(f\"/api/hydro?adresse={adr}&uid={urllib.parse.quote(d['uid'])}&lat={lat}&lng={lng}\")\n    print(\"=== hydro ===\"); print(json.dumps(o, ensure_ascii=False)[:400])\nexcept Exception as e: print(\"=== hydro === ERREUR\", e)\nEOF","timeout":120000,"description":"Inspect live API return shapes for all fiche blocks"}},{"tool_name":"Write","tool_use_id":"toolu_01AaLsQhE9DCx8QnUgJANSik","tool_input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/immoka/pdffiche.py","content":"# -----------------------------------------------------------------------------\n# Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n# Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n# pdffiche.py : fiche PDF complète d'une propriété — reflète TOUS les blocs de\n#   la fiche web : prix + verdict Vrai-Prix, photos, description, inclusions,\n#   caractéristiques, pièces, analyse Vrai-Prix (jauge + rôle foncier),\n#   historique de prix, inondation, qualité de l'air, coût d'électricité,\n#   commerces/transport, essence, quartier, références + QR.\n#   Style « éditorial sharp » (bordures encre, ombres décalées dures, tuiles\n#   KPI, micro-étiquettes Courier) — palette du site : crème #f5f3ee, encre\n#   #141814, accent cerise #e23744. Pagination dynamique : saut_page()/place(),\n#   aucune section coupée entre deux pages.\n# -----------------------------------------------------------------------------\nfrom __future__ import annotations\n\nimport io\nimport json\nimport time\n\nimport qrcode\nimport requests\nfrom reportlab.lib.colors import HexColor\nfrom reportlab.lib.pagesizes import letter\nfrom reportlab.lib.utils import ImageReader\nfrom reportlab.pdfgen import canvas as rl_canvas\n\nfrom . import db\n\n# --- palette Immo-Ka (identité officielle du site) ----------------------------\nPAPER = HexColor(\"#f5f3ee\")\nSURFACE = HexColor(\"#ffffff\")\nINK = HexColor(\"#141814\")\nINK2 = HexColor(\"#4d5551\")\nINK3 = HexColor(\"#8b928c\")\nACCENT = HexColor(\"#e23744\")        # cerise\nACCENT_SOFT = HexColor(\"#fbe0e2\")\nACCENT_DEEP = HexColor(\"#a8232e\")\nGREEN_FG = HexColor(\"#1c5c41\")      # verdict « sous l'estimation » (site .vp-under)\nGREEN_BG = HexColor(\"#e2f0e6\")\nRED_FG = HexColor(\"#8a2b2b\")        # verdict « au-dessus » (site .vp-over)\nRED_BG = HexColor(\"#f6e2e2\")\nAMBER_FG = HexColor(\"#8a5a12\")\nAMBER_BG = HexColor(\"#faf0dc\")\n\nPAGE_W, PAGE_H = letter\nM = 40          # marge\nIMG_TIMEOUT = 6\nUA = \"ImmoKaBot/1.0 (+https://www.immo-ka.com; contact@spboucher.ai)\"\n\nNBSP = \" \"     # espace fine insécable (mise en forme française)\n\n# clés de details jamais affichées (mêmes exclusions que la fiche web)\nDETAIL_HIDDEN = {\n    \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n    \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n    \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n}\n\nPROX_LABELS = [\n    (\"prox_epicerie\", \"Épiceries\"), (\"prox_transport\", \"Transport en commun\"),\n    (\"prox_parc\", \"Parcs\"), (\"prox_ecole_prim\", \"Écoles primaires\"),\n    (\"prox_sante\", \"Soins de santé\"), (\"prox_pharmacie\", \"Pharmacies\"),\n]\n\n_MOIS = [\"janvier\", \"février\", \"mars\", \"avril\", \"mai\", \"juin\", \"juillet\",\n         \"août\", \"septembre\", \"octobre\", \"novembre\", \"décembre\"]\n\n_PIED_FICHE = (\"Données de l'annonce publiée par l'agence — Immo-Ka est un \"\n               \"agrégateur indépendant. Enrichissements (Vrai-Prix, quartier, \"\n               \"environnement) fournis à titre indicatif, sans valeur légale.\")\n\n\ndef _fmt_money(v) -> str:\n    return f\"{v:,.0f}\".replace(\",\", f\"{NBSP}\") + f\"{NBSP}$\" if v is not None else \"—\"\n\n\ndef _fmt_dist(m_: float) -> str:\n    return (f\"{round(m_ / 10) * 10}{NBSP}m\" if m_ < 1000\n            else f\"{m_ / 1000:.1f}\".replace(\".\", \",\") + f\"{NBSP}km\")\n\n\ndef _fmt_mois(d) -> str:\n    \"\"\"« 2017-11-04 » ou epoch → « novembre 2017 » (sinon valeur telle quelle).\"\"\"\n    if d is None:\n        return \"—\"\n    try:\n        if isinstance(d, (int, float)):\n            st = time.localtime(d)\n            return f\"{_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n        an, mois = str(d)[:7].split(\"-\")\n        return f\"{_MOIS[int(mois) - 1]} {an}\"\n    except Exception:\n        return str(d)\n\n\ndef _fmt_jour(ts: float) -> str:\n    \"\"\"epoch → « 4 novembre 2024 » (« 1er » pour le premier du mois).\"\"\"\n    st = time.localtime(ts)\n    j = \"1er\" if st.tm_mday == 1 else str(st.tm_mday)\n    return f\"{j} {_MOIS[st.tm_mon - 1]} {st.tm_year}\"\n\n\ndef _fetch_image(url: str) -> ImageReader | None:\n    try:\n        r = requests.get(url, timeout=IMG_TIMEOUT, headers={\"User-Agent\": UA})\n        r.raise_for_status()\n        raw = r.content\n        try:\n            # les photos source dépassent parfois 5 Mo chacune : sans\n            # redimensionnement le PDF grimpe à plusieurs dizaines de Mo\n            from PIL import Image\n            im = Image.open(io.BytesIO(raw))\n            im.thumbnail((1200, 1200))\n            buf = io.BytesIO()\n            im.convert(\"RGB\").save(buf, \"JPEG\", quality=82)\n            buf.seek(0)\n            return ImageReader(buf)\n        except Exception:\n            return ImageReader(io.BytesIO(raw))\n    except Exception:\n        return None\n\n\nclass _Style:\n    \"\"\"Petits composants du style « éditorial sharp » sur un canvas.\"\"\"\n\n    def __init__(self, c: rl_canvas.Canvas):\n        self.c = c\n\n    def fond(self):\n        self.c.setFillColor(PAPER)\n        self.c.rect(0, 0, PAGE_W, PAGE_H, stroke=0, fill=1)\n\n    def logo(self, x: float, y: float, taille: float = 22):\n        \"\"\"Wordmark Immo·Ka : « Immo » encre + boîte encre avec « Ka » cerise.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.setFillColor(INK)\n        c.drawString(x, y, \"Immo\")\n        w = c.stringWidth(\"Immo\", \"Helvetica-Bold\", taille)\n        bw = c.stringWidth(\"Ka\", \"Helvetica-Bold\", taille) + 8\n        c.saveState()\n        c.translate(x + w + 3 + bw / 2, y + taille * 0.32)\n        c.rotate(-3)\n        c.setFillColor(INK)\n        c.roundRect(-bw / 2, -taille * 0.62, bw, taille * 1.15, 4, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.drawCentredString(0, -taille * 0.30, \"Ka\")\n        c.restoreState()\n        return x + w + 6 + bw\n\n    def entete(self, titre: str):\n        c = self.c\n        self.logo(M, PAGE_H - M - 16)\n        c.setFont(\"Courier-Bold\", 8)\n        c.setFillColor(INK3)\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 6, titre.upper())\n        c.drawRightString(PAGE_W - M, PAGE_H - M - 16,\n                          time.strftime(\"GÉNÉRÉ LE %Y-%m-%d · WWW.IMMO-KA.COM\"))\n        c.setStrokeColor(INK)\n        c.setLineWidth(2)\n        c.line(M, PAGE_H - M - 26, PAGE_W - M, PAGE_H - M - 26)\n        return PAGE_H - M - 40   # y de départ du contenu\n\n    def pied(self, texte: str, page: int | None = None):\n        c = self.c\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.line(M, M + 16, PAGE_W - M, M + 16)\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, M + 6, texte[:150])\n        if page:\n            c.setFont(\"Courier-Bold\", 7)\n            c.drawRightString(PAGE_W - M, M + 6, f\"P.{page}\")\n\n    def pilule(self, x: float, y: float, texte: str, fg=ACCENT, bg=INK,\n               stroke=None, taille: float = 8) -> float:\n        \"\"\"Chip arrondie ; retourne le x suivant.\"\"\"\n        c = self.c\n        w = c.stringWidth(texte, \"Helvetica-Bold\", taille) + 14\n        c.setFillColor(bg)\n        if stroke:\n            c.setStrokeColor(stroke)\n            c.setLineWidth(1.2)\n        c.roundRect(x, y, w, taille + 9, (taille + 9) / 2,\n                    stroke=1 if stroke else 0, fill=1)\n        c.setFillColor(fg)\n        c.setFont(\"Helvetica-Bold\", taille)\n        c.drawString(x + 7, y + 5, texte)\n        return x + w + 6\n\n    def titre_section(self, y: float, texte: str) -> float:\n        c = self.c\n        # carré accent à ombre dure (signature de la passe éditoriale du site)\n        c.setFillColor(INK)\n        c.rect(M + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n        c.setFillColor(ACCENT)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1)\n        c.rect(M, y + 0.5, 9, 9, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12.5)\n        c.setFillColor(INK)\n        c.drawString(M + 16, y, texte)\n        c.setStrokeColor(ACCENT)\n        c.setLineWidth(3)\n        w = c.stringWidth(texte, \"Helvetica-Bold\", 12.5)\n        c.line(M + 16, y - 4, M + 16 + w, y - 4)\n        return y - 18\n\n    def paragraphe(self, x: float, y: float, texte: str, largeur: float,\n                   taille: float = 8.5, couleur=INK2, interligne: float = 11.5,\n                   max_lignes: int = 100) -> float:\n        \"\"\"Texte multi-lignes avec découpe aux mots ; retourne le y suivant.\"\"\"\n        c = self.c\n        c.setFont(\"Helvetica\", taille)\n        c.setFillColor(couleur)\n        mots = texte.split()\n        ligne = \"\"\n        n = 0\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                c.drawString(x, y, ligne)\n                y -= interligne\n                n += 1\n                ligne = mot\n                if n >= max_lignes - 1:\n                    ligne += \" …\"\n                    break\n        if ligne:\n            c.drawString(x, y, ligne)\n            y -= interligne\n        return y\n\n    def nb_lignes(self, texte: str, largeur: float, taille: float = 8.5,\n                  max_lignes: int = 100) -> int:\n        \"\"\"Nombre de lignes qu'occupera `paragraphe` (pour réserver la place).\"\"\"\n        c = self.c\n        mots = texte.split()\n        ligne = \"\"\n        n = 1\n        for mot in mots:\n            essai = (ligne + \" \" + mot).strip()\n            if c.stringWidth(essai, \"Helvetica\", taille) <= largeur:\n                ligne = essai\n            else:\n                n += 1\n                ligne = mot\n                if n >= max_lignes:\n                    break\n        return min(n, max_lignes)\n\n    def tuile(self, x: float, y: float, w: float, h: float,\n              val: str, lab: str):\n        \"\"\"Tuile KPI blanche à ombre décalée (x, y = coin inférieur gauche).\"\"\"\n        c = self.c\n        c.setFillColor(INK)\n        c.roundRect(x + 2.5, y - 2.5, w, h, 5, stroke=0, fill=1)\n        c.setFillColor(SURFACE)\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.2)\n        c.roundRect(x, y, w, h, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 12)\n        c.setFillColor(INK)\n        c.drawString(x + 8, y + h - 20, str(val))\n        c.setFont(\"Courier\", 6.2)\n        c.setFillColor(INK3)\n        c.drawString(x + 8, y + 7, lab.upper()[: max(4, int(w / 4.2))])\n\n\ndef _titre_droite(c, y, x, texte):\n    c.setFillColor(INK)\n    c.rect(x + 2, y - 1.5, 9, 9, stroke=0, fill=1)\n    c.setFillColor(ACCENT)\n    c.setStrokeColor(INK)\n    c.setLineWidth(1)\n    c.rect(x, y + 0.5, 9, 9, stroke=1, fill=1)\n    c.setFont(\"Helvetica-Bold\", 12.5)\n    c.setFillColor(INK)\n    c.drawString(x + 16, y, texte)\n    c.setStrokeColor(ACCENT)\n    c.setLineWidth(3)\n    c.line(x + 16, y - 4, x + 16 + c.stringWidth(texte, \"Helvetica-Bold\", 12.5), y - 4)\n    return y - 18\n\n\ndef _verdict_vraiprix(price, vp) -> tuple[str, object, object, object] | None:\n    \"\"\"Pastille verdict identique au site : ±% vs estimation, masqué si\n    |pct| > 60 (prix non comparable : terrain, loyer…).\"\"\"\n    if not price or not vp or vp.get(\"value\") in (None, 0):\n        return None\n    pct = round((price - vp[\"value\"]) / vp[\"value\"] * 100)\n    if abs(pct) > 60:\n        return None\n    if pct >= 1:\n        return (f\"▲{NBSP}{pct}{NBSP}% au-dessus de l'estimation\", RED_FG, RED_BG, ACCENT_DEEP)\n    if pct <= -1:\n        return (f\"▼{NBSP}{abs(pct)}{NBSP}% sous l'estimation\", GREEN_FG, GREEN_BG, GREEN_FG)\n    return (f\"≈{NBSP}Prix aligné sur l'estimation\", INK2, SURFACE, INK)\n\n\ndef fiche_pdf(uid: str) -> bytes | None:\n    \"\"\"PDF de la fiche de la propriété `uid` — reflète tous les blocs de la\n    fiche web ; None si introuvable.\"\"\"\n    con = db.connect()\n    row = con.execute(\"SELECT * FROM listings WHERE uid=?\", (uid,)).fetchone()\n    if row is None:\n        con.close()\n        return None\n    l = dict(row)\n    l[\"features\"] = json.loads(l.get(\"features\") or \"[]\")\n    l[\"images\"] = json.loads(l.get(\"images\") or \"[]\")\n    l[\"details\"] = json.loads(l.get(\"details\") or \"{}\")\n    try:\n        l[\"vraiprix\"] = json.loads(l[\"vraiprix\"]) or None if l.get(\"vraiprix\") else None\n    except (ValueError, TypeError):\n        l[\"vraiprix\"] = None\n    price_history = [dict(r) for r in con.execute(\n        \"SELECT ts, price FROM price_log WHERE uid=? ORDER BY ts DESC LIMIT 10\",\n        (uid,)).fetchall()]\n    quartier = None\n    lat, lng = l.get(\"lat\"), l.get(\"lng\")\n    geo = lat is not None and lng is not None\n    if geo:\n        from . import quartier as qmod\n        dauid = l.get(\"dauid\")\n        try:\n            quartier = qmod.fiche_quartier(lat, lng, l.get(\"city\") or \"\",\n                                           dauid if dauid and dauid != \"hors-zone\" else None)\n        except Exception:\n            quartier = None\n    try:\n        reg = json.loads((db.DB_PATH.parent / \"sources.json\").read_text(\"utf-8\"))[\"sources\"]\n        src_name = next((s[\"name\"] for s in reg if s[\"id\"] == l[\"source\"]), l[\"source\"])\n    except Exception:\n        src_name = l[\"source\"]\n    con.close()\n\n    # --- enrichissements (mêmes modules que les blocs de la fiche web) -------\n    def _safe(fn, *a, **k):\n        try:\n            return fn(*a, **k)\n        except Exception:\n            return None\n\n    from . import air as air_mod\n    from . import commerces as com_mod\n    from . import gaz as gaz_mod\n    from . import hydro as hydro_mod\n    from . import inondation as inon_mod\n    inon = _safe(inon_mod.lookup, lat, lng) if geo else None\n    air_d = _safe(air_mod.lookup, lat, lng) if geo else None\n    gaz_d = _safe(gaz_mod.nearby, lat, lng) if geo else None\n    com = _safe(com_mod.nearby, lat, lng) if geo else None\n    hyd = _safe(hydro_mod.estimate, adresse=l.get(\"address\") or \"\",\n                uid=uid, lat=lat, lng=lng, solve=False)\n\n    d = l[\"details\"]\n    vp = l.get(\"vraiprix\")\n    location = (d.get(\"transaction\") == \"location\")\n\n    buf = io.BytesIO()\n    c = rl_canvas.Canvas(buf, pagesize=letter)\n    c.setTitle(f\"Immo-Ka — {l.get('address') or l.get('title') or uid}\")\n    s = _Style(c)\n    s.fond()\n    y = s.entete(\"Fiche propriété\")\n    page = [1]\n\n    def saut_page() -> float:\n        s.pied(_PIED_FICHE, page[0])\n        c.showPage()\n        page[0] += 1\n        s.fond()\n        return s.entete(\"Fiche propriété · suite\")\n\n    def place(y_: float, besoin: float) -> float:\n        \"\"\"Saute de page si `besoin` points ne tiennent plus sous y_.\"\"\"\n        return saut_page() if (y_ - besoin) < M + 34 else y_\n\n    # --- héro : prix + pastille verdict Vrai-Prix -----------------------------\n    prix_txt = (_fmt_money(l[\"price\"]) + (f\"{NBSP}/ mois\" if location else \"\")) \\\n        if l.get(\"price\") else (l.get(\"price_label\") or \"Prix sur demande\")\n    c.setFont(\"Helvetica-Bold\", 30)\n    c.setFillColor(INK)\n    c.drawString(M, y - 24, prix_txt)\n    bx = M + c.stringWidth(prix_txt, \"Helvetica-Bold\", 30) + 14\n    verd = _verdict_vraiprix(l.get(\"price\"), vp)\n    if verd:\n        txt, fg, bg, stk = verd\n        bx = s.pilule(bx, y - 22, txt, fg, bg, stk)\n    if l.get(\"property_type\"):\n        s.pilule(bx, y - 22, l[\"property_type\"], ACCENT, INK)\n    y -= 44\n\n    # --- titre + adresse -------------------------------------------------------\n    titre = l.get(\"address\") or l.get(\"title\") or \"\"\n    c.setFont(\"Helvetica-Bold\", 13)\n    c.setFillColor(INK)\n    c.drawString(M, y, titre[:80])\n    y -= 14\n    loc = \" · \".join(x for x in (l.get(\"sector\"), l.get(\"city\"), l.get(\"region\"))\n                     if x)\n    if l.get(\"title\") and l.get(\"title\") != titre:\n        loc = f\"{l['title']} — {loc}\" if loc else l[\"title\"]\n    c.setFont(\"Helvetica\", 9)\n    c.setFillColor(INK2)\n    c.drawString(M, y, loc[:110])\n    y -= 20\n\n    # --- chips clés (mêmes specs que le héro du site) --------------------------\n    chips = []\n    if l.get(\"bedrooms\") is not None:\n        chips.append(f\"{l['bedrooms']}{NBSP}ch.\")\n    if l.get(\"bathrooms\") is not None:\n        chips.append(f\"{l['bathrooms']}{NBSP}sdb\")\n    if l.get(\"powder_rooms\"):\n        chips.append(f\"{l['powder_rooms']}{NBSP}s. d'eau\")\n    if l.get(\"area_sqft\"):\n        chips.append(f\"{round(l['area_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"lot_sqft\"):\n        chips.append(f\"Terrain {round(l['lot_sqft']):,}\".replace(\",\", NBSP) + f\"{NBSP}pi²\")\n    if l.get(\"year_built\"):\n        chips.append(f\"Construit en {l['year_built']}\")\n    if l.get(\"price\") and l.get(\"area_sqft\") and l[\"area_sqft\"] > 200:\n        chips.append(f\"{round(l['price'] / l['area_sqft'])}{NBSP}$/pi²\")\n    if l.get(\"mls\"):\n        chips.append(f\"MLS {l['mls']}\")\n    if l.get(\"first_seen\"):\n        dom = max(0, int((time.time() - l[\"first_seen\"]) // 86400))\n        chips.append(f\"{dom}{NBSP}j sur Immo-Ka\")\n    x = M\n    for chip in chips[:8]:\n        x = s.pilule(x, y - 6, chip)\n    y -= 30\n\n    # --- photos : 1 grande + 3 vignettes ---------------------------------------\n    imgs = [im for im in (_fetch_image(u) for u in l[\"images\"][:4]) if im]\n    if imgs:\n        big_h = 170\n        big_w = big_h * 16 / 10.5\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        try:\n            c.drawImage(imgs[0], M, y - big_h, big_w, big_h,\n                        preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n        except Exception:\n            pass\n        c.rect(M, y - big_h, big_w, big_h, stroke=1, fill=0)\n        tx = M + big_w + 10\n        th = (big_h - 16) / 3\n        for i, im in enumerate(imgs[1:4]):\n            ty = y - (i + 1) * th - i * 8\n            try:\n                c.drawImage(im, tx, ty, PAGE_W - M - tx, th,\n                            preserveAspectRatio=True, anchor=\"c\", mask=\"auto\")\n                c.rect(tx, ty, PAGE_W - M - tx, th, stroke=1, fill=0)\n            except Exception:\n                pass\n        y -= big_h + 18\n\n    # --- baisse/hausse de prix (bandeau, comme le site) -------------------------\n    ph = [p for p in price_history if p.get(\"price\")]\n    if len(ph) >= 2 and ph[0][\"price\"] != ph[1][\"price\"]:\n        y = place(y, 34)\n        de, a = ph[1][\"price\"], ph[0][\"price\"]\n        bas = a < de\n        c.setFillColor(GREEN_BG if bas else RED_BG)\n        c.setStrokeColor(GREEN_FG if bas else RED_FG)\n        c.setLineWidth(1.2)\n        c.roundRect(M, y - 20, PAGE_W - 2 * M, 24, 5, stroke=1, fill=1)\n        c.setFont(\"Helvetica-Bold\", 9)\n        c.setFillColor(GREEN_FG if bas else RED_FG)\n        c.drawString(M + 10, y - 12,\n                     f\"{'▼' if bas else '▲'} Prix passé de {_fmt_money(de)} à \"\n                     f\"{_fmt_money(a)} le {_fmt_jour(ph[0]['ts'])}\")\n        y -= 34\n\n    # --- description | inclusions (2 colonnes, comme le site) -------------------\n    col_g = PAGE_W * 0.56 - M\n    feats = [str(f) for f in (l.get(\"features\") or []) if f]\n    desc = (l.get(\"description\") or \"\").strip()\n    if desc or feats:\n        besoin = 30 + max(\n            s.nb_lignes(desc, col_g, max_lignes=26) * 11.5 if desc else 0,\n            min(len(feats), 18) * 12)\n        y = place(y, min(besoin, 340))\n        y_desc = y\n        if desc:\n            y_desc = s.titre_section(y, \"Description\")\n            y_desc = s.paragraphe(M, y_desc, desc, col_g, max_lignes=26)\n        y_incl = y\n        if feats:\n            xr = M + col_g + 24\n            y_incl = _titre_droite(c, y, xr, \"Inclusions et caractéristiques\")\n            c.setFont(\"Helvetica\", 8.5)\n            for f in feats[:18]:\n                c.setFillColor(ACCENT_DEEP)\n                c.drawString(xr, y_incl, \"✓\")\n                c.setFillColor(INK2)\n                c.drawString(xr + 12, y_incl, f[:42])\n                y_incl -= 12\n            if len(feats) > 18:\n                c.setFillColor(INK3)\n                c.drawString(xr + 12, y_incl, f\"+ {len(feats) - 18} autres…\")\n                y_incl -= 12\n        y = min(y_desc, y_incl) - 16\n\n    # --- caractéristiques (details, mêmes exclusions que le site) ---------------\n    dets = [(k, v) for k, v in d.items()\n            if k not in DETAIL_HIDDEN and v not in (None, \"\", [], {})\n            and not isinstance(v, (dict, list))]\n    if dets:\n        lignes = []\n        col_w = PAGE_W - 2 * M\n        for k, v in dets:\n            txt = str(v)\n            lignes.append((str(k), txt))\n        besoin = 40 + sum(\n            s.nb_lignes(t, col_w - 190, taille=8, max_lignes=4) * 11 + 3\n            for _, t in lignes)\n        y = place(y, min(besoin, 420))\n        y = s.titre_section(y, \"Caractéristiques\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"DÉTAIL\")\n        c.drawString(M + 180, y, \"VALEUR\")\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 12\n        for i, (k, v) in enumerate(lignes):\n            nl = s.nb_lignes(v, col_w - 190, taille=8, max_lignes=4)\n            y = place(y, nl * 11 + 4)\n            if i % 2 == 0:   # zébrage éditorial\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - nl * 11 + 7, col_w + 6, nl * 11 + 3,\n                       stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, k[:38])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            y2 = s.paragraphe(M + 180, y, v, col_w - 190, taille=8,\n                              interligne=11, max_lignes=4)\n            y = y2 - 3 + 11 - 11   # y2 pointe déjà sous la dernière ligne\n            y -= 3\n        y -= 10\n\n    # --- pièces (details.pieces) -------------------------------------------------\n    pieces = d.get(\"pieces\") or []\n    if isinstance(pieces, list) and pieces:\n        rows = pieces[:24]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Pièces\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"PIÈCE\", 0), (\"NIVEAU\", 190), (\"DIMENSIONS\", 330),\n                          (\"REVÊTEMENT\", 430)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            y = place(y, 14)\n            if i % 2 == 0:\n                c.setFillColor(SURFACE)\n                c.rect(M - 3, y - 3, PAGE_W - 2 * M + 6, 12, stroke=0, fill=1)\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, str(p.get(\"nom\") or \"—\")[:36])\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M + 190, y, str(p.get(\"niveau\") or \"—\")[:24])\n            c.drawString(M + 330, y, str(p.get(\"dimensions\") or \"—\")[:18])\n            c.drawString(M + 430, y, str(p.get(\"revetement\") or \"—\")[:22])\n            y -= 12\n        y -= 10\n\n    # --- analyse Vrai-Prix (valeur, jauge, rôle foncier) -------------------------\n    if vp and vp.get(\"value\") is not None:\n        role = [(lab, vp.get(k)) for k, lab in\n                ((\"valeur_role\", \"Évaluation au rôle\"),\n                 (\"valeur_terrain\", \"Valeur du terrain\"),\n                 (\"valeur_batiment\", \"Valeur du bâtiment\"))\n                if vp.get(k)]\n        besoin = 96 + (30 if vp.get(\"low\") is not None else 0) \\\n            + (62 if role else 0) + 18\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Analyse Vrai-Prix\")\n        val_txt = _fmt_money(round(vp[\"value\"]))\n        c.setFont(\"Helvetica-Bold\", 17)\n        c.setFillColor(INK)\n        c.drawString(M, y - 10, val_txt)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M + c.stringWidth(val_txt, \"Helvetica-Bold\", 17) + 8,\n                     y - 10, \"— valeur marchande estimée par le modèle Vrai-Prix\")\n        meta = []\n        if vp.get(\"confidence\"):\n            meta.append(f\"confiance {vp['confidence']}\"\n                        + (f\" ({vp['confidence_pct']}{NBSP}%)\"\n                           if vp.get(\"confidence_pct\") else \"\"))\n        if vp.get(\"annee_construction_role\"):\n            meta.append(f\"construction {vp['annee_construction_role']} (rôle)\")\n        if vp.get(\"superficie_terrain_role_m2\"):\n            meta.append(f\"terrain {round(vp['superficie_terrain_role_m2']):,}\".replace(\",\", NBSP)\n                        + f\"{NBSP}m² (rôle)\")\n        if meta:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK3)\n            c.drawString(M, y - 24, \" · \".join(meta)[:115])\n        verd = _verdict_vraiprix(l.get(\"price\"), vp)\n        if verd:\n            txt, fg, bg, stk = verd\n            s.pilule(M, y - 48, txt, fg, bg, stk)\n        y -= 58\n        lo, hi = vp.get(\"low\"), vp.get(\"high\")\n        if lo is not None and hi is not None and hi > lo:\n            larg = PAGE_W - 2 * M\n            base = y - 14\n            span = hi - lo\n            c.setFillColor(ACCENT_SOFT)\n            c.setStrokeColor(INK)\n            c.setLineWidth(1)\n            c.roundRect(M, base, larg, 12, 6, stroke=1, fill=1)\n\n            def _pos(v):\n                return M + larg * max(0.02, min(0.98, (v - lo) / span))\n\n            px = _pos(vp[\"value\"])\n            c.setStrokeColor(ACCENT_DEEP)\n            c.setLineWidth(2.5)\n            c.line(px, base - 3, px, base + 15)\n            c.setFont(\"Courier-Bold\", 6.5)\n            c.setFillColor(ACCENT_DEEP)\n            c.drawString(min(max(px - 30, M), PAGE_W - M - 80), base + 18,\n                         f\"ESTIMATION · {_fmt_money(round(vp['value']))}\")\n            if l.get(\"price\") and lo * 0.7 <= l[\"price\"] <= hi * 1.3:\n                ax = _pos(l[\"price\"])\n                c.setStrokeColor(INK)\n                c.setLineWidth(2)\n                c.line(ax, base - 3, ax, base + 15)\n                c.setFont(\"Courier-Bold\", 6.5)\n                c.setFillColor(INK)\n                c.drawString(min(max(ax - 30, M), PAGE_W - M - 96), base - 12,\n                             f\"PRIX DEMANDÉ · {_fmt_money(l['price'])}\")\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, base - 22, _fmt_money(round(lo)))\n            c.drawRightString(PAGE_W - M, base - 22,\n                              _fmt_money(round(hi)) + \" — fourchette d'estimation\")\n            y = base - 34\n        if role:\n            tw = (PAGE_W - 2 * M - (len(role) - 1) * 10) / len(role)\n            for i, (lab, v) in enumerate(role):\n                s.tuile(M + i * (tw + 10), y - 44, tw, 38,\n                        _fmt_money(round(v)), lab)\n            y -= 56\n        c.setFont(\"Helvetica\", 6.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y - 2, \"Estimation automatisée Vrai-Prix (vrai-prix.ca) \"\n                               \"croisée au rôle d'évaluation foncière — ne \"\n                               \"remplace pas une évaluation professionnelle.\")\n        y -= 16\n\n    # --- historique de prix -------------------------------------------------------\n    if len(ph) >= 2:\n        rows = ph[:8]\n        y = place(y, 46 + len(rows) * 12)\n        y = s.titre_section(y, \"Historique du prix demandé\")\n        c.setFont(\"Courier-Bold\", 7)\n        c.setFillColor(INK3)\n        for lab, xoff in ((\"DATE\", 0), (\"PRIX AFFICHÉ\", 190), (\"VARIATION\", 330)):\n            c.drawString(M + xoff, y, lab)\n        y -= 4\n        c.setStrokeColor(INK)\n        c.setLineWidth(1.5)\n        c.line(M, y, PAGE_W - M, y)\n        y -= 11\n        for i, p in enumerate(rows):\n            prec = rows[i + 1][\"price\"] if i + 1 < len(rows) else None\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, _fmt_jour(p[\"ts\"]))\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M + 190, y, _fmt_money(p[\"price\"]))\n            if prec and p.get(\"price\"):\n                delta = p[\"price\"] - prec\n                c.setFont(\"Courier-Bold\", 7.5)\n                c.setFillColor(GREEN_FG if delta < 0 else (RED_FG if delta > 0 else INK3))\n                sig = \"▼\" if delta < 0 else (\"▲\" if delta > 0 else \"=\")\n                c.drawString(M + 330, y, f\"{sig} {_fmt_money(abs(delta))}\" if delta else \"=\")\n            y -= 12\n        y -= 10\n\n    # --- risque d'inondation (BDZI) ------------------------------------------------\n    if inon:\n        zones = (inon.get(\"zones\") or [])[:3]\n        y = place(y, 56 + len(zones) * 12)\n        y = s.titre_section(y, \"Risque d'inondation\")\n        statut = inon.get(\"statut\")\n        sev = {\"eleve\": \"récurrence élevée\", \"modere\": \"récurrence modérée\",\n               \"present\": \"zone cartographiée\"}.get(inon.get(\"severite\") or \"\", \"\")\n        if statut == \"en_zone\":\n            s.pilule(M, y - 12, f\"En zone inondable ({sev})\" if sev\n                     else \"En zone inondable\", RED_FG, RED_BG, RED_FG)\n        elif statut == \"a_proximite\":\n            d0 = zones[0][\"distance_m\"] if zones else \"?\"\n            s.pilule(M, y - 12, f\"Zone inondable à ~{d0} m\",\n                     AMBER_FG, AMBER_BG, AMBER_FG)\n        elif statut == \"hors_zone\":\n            s.pilule(M, y - 12, \"Hors des zones inondables cartographiées\",\n                     GREEN_FG, GREEN_BG, GREEN_FG)\n        else:\n            s.pilule(M, y - 12, \"Secteur non couvert par la cartographie BDZI\",\n                     INK2, SURFACE, INK)\n        y -= 26\n        for z in zones:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, f\"{z.get('type') or 'Zone'} — à {z.get('distance_m')} m\"\n                               + (f\" (rapport {z['date_rapport']})\"\n                                  if z.get(\"date_rapport\") else \"\"))\n            y -= 12\n        y -= 8\n\n    # --- qualité de l'air (RSQAQ) ---------------------------------------------------\n    if air_d and air_d.get(\"station\"):\n        mes = air_d.get(\"mesures\") or {}\n        y = place(y, 44 + len(mes) * 11)\n        y = s.titre_section(y, \"Qualité de l'air\")\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        c.drawString(M, y - 2,\n                     f\"Station {air_d['station']} ({air_d['ville']}), à \"\n                     + f\"{air_d['distance_km']}\".replace(\".\", \",\") + f\"{NBSP}km \"\n                     \"de la propriété — moyennes annuelles :\")\n        y -= 16\n        for pol, m_ in list(mes.items())[:5]:\n            c.setFont(\"Helvetica-Bold\", 8)\n            c.setFillColor(INK)\n            c.drawString(M, y, pol)\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            val = (f\"{m_['moyenne']:.1f}\".replace(\".\", \",\")\n                   if m_.get(\"moyenne\") is not None else \"—\")\n            txt = f\"{val}{NBSP}{m_.get('unite', '')} ({m_.get('annee', '')})\"\n            if m_.get(\"ref\"):\n                txt += f\" · référence {m_.get('ref_nom', '')} : {m_['ref']}\"\n            c.drawString(M + 56, y, txt[:95])\n            y -= 11\n        y -= 8\n\n    # --- coût d'électricité (Hydro-Québec) -------------------------------------------\n    if hyd and hyd.get(\"disponible\"):\n        y = place(y, 52)\n        y = s.titre_section(y, \"Coût d'électricité (Hydro-Québec)\")\n        c.setFont(\"Helvetica-Bold\", 15)\n        c.setFillColor(INK)\n        mens = f\"≈{NBSP}{_fmt_money(round(hyd['cout_mensuel']))} / mois\"\n        c.drawString(M, y - 8, mens)\n        c.setFont(\"Helvetica\", 8.5)\n        c.setFillColor(INK2)\n        suite = f\"{_fmt_money(round(hyd['cout_annuel']))} / an\"\n        if hyd.get(\"kwh_annuel\"):\n            suite += f\" · {round(hyd['kwh_annuel']):,}\".replace(\",\", NBSP) + f\"{NBSP}kWh/an\"\n        suite += \" — estimation officielle Hydro-Québec pour cette adresse.\"\n        c.drawString(M + c.stringWidth(mens, \"Helvetica-Bold\", 15) + 10, y - 8, suite)\n        y -= 26\n\n    # --- commerces et transport --------------------------------------------------------\n    if com and (com.get(\"commerces\") or com.get(\"transit\")):\n        entrees = ([(t[\"commerce\"], t[\"nom\"], t[\"dist_m\"])\n                    for t in (com.get(\"transit\") or [])[:4]]\n                   + [(i[\"commerce\"], i[\"nom\"], i[\"dist_m\"])\n                      for i in (com.get(\"commerces\") or [])[:12]])\n        lignes = (len(entrees) + 1) // 2\n        y = place(y, 36 + lignes * 13)\n        y = s.titre_section(y, \"Commerces et transport\")\n        col_w = (PAGE_W - 2 * M) / 2\n        for i, (cat, nom, dist) in enumerate(entrees):\n            px = M if i % 2 == 0 else M + col_w + 10\n            py = y - (i // 2) * 13\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            nomc = nom if len(nom) <= 22 else nom[:21] + \"…\"\n            c.drawString(px, py, f\"{cat} — {nomc}\")\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(px + col_w - 14, py, _fmt_dist(dist))\n        y -= lignes * 13 + 12\n\n    # --- essence à proximité --------------------------------------------------------------\n    if gaz_d and gaz_d.get(\"stations\"):\n        sts = gaz_d[\"stations\"][:3]\n        y = place(y, 40 + len(sts) * 11)\n        y = s.titre_section(y, \"Essence à proximité\")\n        if gaz_d.get(\"mediane_regulier\"):\n            c.setFont(\"Helvetica\", 8.5)\n            c.setFillColor(INK2)\n            c.drawString(M, y - 2, \"Prix médian de l'essence régulière à <5 km : \"\n                         + f\"{gaz_d['mediane_regulier']}\".replace(\".\", \",\")\n                         + f\"{NBSP}¢/L\")\n            y -= 14\n        for st in sts:\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            reg_p = (f\"{st['regulier']}\".replace(\".\", \",\") + f\"{NBSP}¢/L\"\n                     if st.get(\"regulier\") else \"—\")\n            c.drawString(M, y, f\"{st['nom']} — {_fmt_dist(st['dist_m'])} : {reg_p}\"\n                               + (\" · la moins chère\" if st.get(\"moins_chere\") else \"\"))\n            y -= 11\n        y -= 8\n\n    # --- le quartier -------------------------------------------------------------------------\n    if quartier and (quartier.get(\"demographie\") or quartier.get(\"proximite\")\n                     or quartier.get(\"chaleur\") or quartier.get(\"crime\")):\n        demo = quartier.get(\"demographie\") or {}\n        prox = quartier.get(\"proximite\") or {}\n        prox_aff = [(k, lab) for k, lab in PROX_LABELS\n                    if isinstance(prox.get(k), (int, float))]\n        besoin = 30 + (94 if demo else 0) + len(prox_aff) * 14 + 40\n        y = place(y, besoin)\n        y = s.titre_section(y, \"Le quartier\")\n        c.setFont(\"Helvetica\", 7.5)\n        c.setFillColor(INK3)\n        c.drawString(M, y, \"Secteur immédiat de la propriété (aire de diffusion \"\n                           \"du recensement 2021, ± 500 habitants).\")\n        y -= 14\n        if demo:\n            tuiles = [(\"Revenu médian\", _fmt_money(demo.get(\"revenu_median\"))),\n                      (\"Ménages locataires\",\n                       f\"{round(demo['pct_locataires'])}{NBSP}%\"\n                       if demo.get(\"pct_locataires\") is not None else \"—\"),\n                      (\"Loyer moyen secteur\", _fmt_money(demo.get(\"loyer_moyen\"))),\n                      (\"Âge médian\",\n                       f\"{round(demo['age_median'])} ans\"\n                       if demo.get(\"age_median\") is not None else \"—\"),\n                      (\"Français à la maison\",\n                       f\"{round(demo['pct_francais'])}{NBSP}%\"\n                       if demo.get(\"pct_francais\") is not None else \"—\"),\n                      (\"Diplôme universitaire\",\n                       f\"{round(demo['pct_univ'])}{NBSP}%\"\n                       if demo.get(\"pct_univ\") is not None else \"—\")]\n            tw = (PAGE_W - 2 * M - 20) / 3\n            for i, (lab, val) in enumerate(tuiles):\n                s.tuile(M + (i % 3) * (tw + 10), y - 34 - (i // 3) * 44,\n                        tw, 38, val, lab)\n            y -= 34 + 44 + 16\n        for k, lab in prox_aff:\n            v = max(0.0, min(1.0, prox[k]))\n            c.setFont(\"Helvetica\", 8)\n            c.setFillColor(INK2)\n            c.drawString(M, y, lab)\n            bx0, bw_ = M + 130, PAGE_W - 2 * M - 160\n            c.setFillColor(SURFACE)\n            c.setStrokeColor(INK)\n            c.setLineWidth(0.8)\n            c.roundRect(bx0, y - 1, bw_, 7, 3.5, stroke=1, fill=1)\n            c.setFillColor(ACCENT)\n            c.roundRect(bx0, y - 1, max(3, bw_ * v), 7, 3.5, stroke=0, fill=1)\n            c.setFont(\"Courier-Bold\", 7.5)\n            c.setFillColor(INK)\n            c.drawRightString(PAGE_W - M, y, str(round(v * 100)))\n            y -= 14\n        if prox_aff:\n            c.setFont(\"Helvetica\", 6.5)\n            c.setFillColor(INK3)\n            c.drawString(M, y, \"Accessibilité 0–100 — mesures de proximité de \"\n                               \"Statistique Canada.\")\n            y -= 12\n        badges_q = []\n        ch = quartier.get(\"chaleur\")\n        if ch:\n            if ch.get(\"classe\", 5) <= 3:\n                badges_q.append((\"Îlot de fraîcheur\", GREEN_FG, GREEN_BG, GREEN_FG))\n            elif ch.get(\"classe\", 5) >= 7:\n                ec = (f\" (+{ch['ecart']:.1f} °C)\".replace(\".\", \",\")\n                      if ch.get(\"ecart\") is not None else \"\")\n                badges_q.append((f\"Îlot de chaleur{ec}\", AMBER_FG, AMBER_BG, AMBER_FG))\n        cr = quartier.get(\"crime\")\n        if cr and cr.get(\"type\") == \"igc\" and cr.get(\"indice_canada\"):\n            dlt = round(100 * (cr[\"indice\"] - cr[\"indice_canada\"]) / cr[\"indice_canada\"])\n            badges_q.append((f\"Criminalité {abs(dlt)}{NBSP}% \"\n                             f\"{'sous' if dlt <= 0 else 'au-dessus de'} la moyenne canadienne\",\n                             INK2, SURFACE, INK))\n        elif cr and cr.get(\"type\") == \"points\":\n            badges_q.append((f\"{cr['douze_mois']} actes criminels à <500 m (12 mois)\",\n                             INK2, SURFACE, INK))\n        if badges_q:\n            x = M\n            for txt, fg, bg, stk in badges_q:\n                x = s.pilule(x, y - 8, txt, fg, bg, stk)\n            y -= 26\n        y -= 6\n\n    # --- références : QR + courtier + source ------------------------------------------------\n    y = place(y, 140)\n    y = s.titre_section(y, \"Références\")\n    qr = qrcode.make(f\"https://www.immo-ka.com/propriete/{uid}\", box_size=4, border=1)\n    qb = io.BytesIO()\n    qr.save(qb, format=\"PNG\")\n    c.drawImage(ImageReader(io.BytesIO(qb.getvalue())), M, y - 74, 70, 70)\n    yy = y - 14\n    c.setFont(\"Helvetica-Bold\", 9)\n    c.setFillColor(INK)\n    courtier = l.get(\"broker_name\") or d.get(\"Courtier inscripteur\")\n    if courtier:\n        ligne = f\"Courtier : {courtier}\"\n        if l.get(\"broker_phone\"):\n            ligne += f\" · {l['broker_phone']}\"\n        c.drawString(M + 82, yy, ligne[:95])\n        yy -= 13\n    c.drawString(M + 82, yy, f\"Agence : {src_name}\")\n    yy -= 14\n    c.setFont(\"Helvetica\", 8)\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Annonce originale :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, (l.get(\"url\") or d.get(\"listing_origin_url\") or \"\")[:90])\n    yy -= 14\n    c.setFillColor(INK2)\n    c.drawString(M + 82, yy, \"Fiche à jour en ligne (scannez le code) :\")\n    yy -= 11\n    c.setFillColor(ACCENT_DEEP)\n    c.drawString(M + 82, yy, f\"www.immo-ka.com/propriete/{uid}\")\n    y -= 90\n    c.setFont(\"Helvetica\", 6.5)\n    c.setFillColor(INK3)\n    c.drawString(M, y, \"Sources : annonce de l'agence, Vrai-Prix (vrai-prix.ca), \"\n                       \"rôle d'évaluation foncière, Statistique Canada (2021), \"\n                       \"INSPQ (CC-BY 4.0), Ville de Montréal (CC-BY 4.0), BDZI, \"\n                       \"RSQAQ, Hydro-Québec, gazquebec.ca, OpenStreetMap.\")\n\n    s.pied(_PIED_FICHE, page[0])\n    c.save()\n    return buf.getvalue()\n"}}],"terminal_reason":"completed","fast_mode_state":"off","uuid":"60495c05-1334-4c1b-b02c-9ccf3c782533"}}
{"type":"status","state":"idle"}
{"type":"user_prompt","text":"il semble pas y avoir de bouton sur fiche pour downloas pdf","ts":1787608348072}
{"type":"status","state":"running"}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"type":"text","text":"This session is being continued from a previous conversation that ran out of context. The summary below covers the earlier portion of the conversation.\n\nSummary:\n1. Primary Request and Intent:\n\n   Trois missions séquentielles sur le repo `~/apps/immo-ka` (React+Vite frontend + backend Python FastAPI, PM2, remote origin = spbgit via alias SSH `gitsrv`, PAS GitHub) :\n\n   **Mission 1 (TERMINÉE, commit `fc02d70`)** : passe de design « éditorial sharp » sur la fiche web (cartes encre, titres à carré accent, tuiles KPI ombre dure, tableaux zébrés, etc.), vérifiée Playwright desktop 1440×900 + mobile 390×844, règle ordre DOM = ordre visuel (check-order.mjs), accent via variables uniquement.\n\n   **Mission 2 (TERMINÉE, commit `5fea46d` poussé)** : créer le PDF « Télécharger la fiche » comme vrai rapport multi-pages reflétant TOUT le contenu de la fiche web, en transposant lou-ka `louka/pdfgen.py@6ff906e`. Exigences : cartographier les blocs web + formes de retour backend exactes (lire le code, ne pas deviner) ; reportlab canvas avec pagination dynamique (`page=[1]`, `saut_page()`, `place(y, besoin)` si `y - besoin < M + 34`, aucune section coupée) ; style éditorial sharp (bordures encre 1.5px, ombres décalées dures, tuiles KPI blanches, pilules verdicts, micro-étiquettes Courier, tableaux à en-têtes mono soulignés) ; sections conditionnelles via `_safe` (omises si pas de données, pas de « N/D ») ; badge héro prix + verdict ; dernière page références + QR + sources + disclaimer partout ; photos compressées Pillow (`thumbnail`, JPEG, repli brut), PDF < 1 Mo ; tests annonces réelles riche ET pauvre, `pdftoppm -png -r 72` + inspection visuelle de chaque page ; déploiement obligatoire (pm2 restart, healthcheck local + public, git add/commit/push message `feat(pdf): fiche PDF complète — <sections>`) ; français partout (dates en toutes lettres, montants espaces insécables) ; ne pas toucher au frontend ni aux autres endpoints.\n\n   **Mission 3 (EN COURS, dernier message utilisateur)** : « il semble pas y avoir de bouton sur fiche pour downloas pdf » → ajouter un bouton « Télécharger la fiche (PDF) » sur la fiche web (Listing.tsx) pointant vers le nouvel endpoint. Cette demande lève explicitement l'interdiction de toucher au frontend de la Mission 2.\n\n2. Key Technical Concepts:\n   - reportlab canvas (rl_canvas.Canvas, letter, HexColor, ImageReader), qrcode, Pillow thumbnail\n   - Pagination dynamique : `page=[1]`, `saut_page()` (pied → showPage → fond + entête « Fiche propriété · suite »), `place(y, besoin)`, marge `M = 40`\n   - `_safe(fn, *a, **k)` try/except → None ; sections omises si vides\n   - Classe `_Style` : fond, logo wordmark Immo·Ka rotaté -3° (boîte encre, « Ka » cerise), entete, pied, pilule, titre_section (carré accent 9px à ombre dure + filet accent 3px), paragraphe (découpe aux mots), nb_lignes (réservation de place), tuile KPI ombre décalée\n   - Palette immo-ka : PAPER #f5f3ee, SURFACE #ffffff, INK #141814, INK2 #4d5551, INK3 #8b928c, ACCENT #e23744, ACCENT_SOFT #fbe0e2, ACCENT_DEEP #a8232e ; verdicts : under #1c5c41/#e2f0e6, over #8a2b2b/#f6e2e2 (mêmes que .vp-under/.vp-over du site)\n   - Verdict Vrai-Prix identique au site : `pct = round((price-value)/value*100)`, masqué si |pct|>60, ≥1 % over/under, sinon « aligné » ; marqueur PRIX DEMANDÉ sur jauge seulement si `lo*0.7 <= price <= hi*1.3`\n   - Formats français : `NBSP = \" \"`, `_fmt_money` (1 425 $), `_fmt_jour` (epoch → « 4 novembre 2024 », « 1er »), `_fmt_mois`, `_fmt_dist` (m/km virgule)\n   - FastAPI (immoka/web.py), sqlite (db.connect, listings/price_log/poi_cache), PM2 `immo-ka-web` (`.venv/bin/python run.py serve 8096`), ngrok → www.immo-ka.com\n   - Python 3.14 venv ; `python -P` (safe path) pour contourner `/tmp/inspect.py` parasite\n   - pdftoppm -png -r 72 pour inspection visuelle page par page\n   - CSS frontend : `.cta` (bloc bg var(--ink) couleur var(--lime), ombre 4px 4px 0, :active translate 2px), `.cta-sticky` mobile <900px ; conventions Mission 1 : ajouts en section finale de styles.css, variables uniquement\n\n3. Files and Code Sections:\n\n   - **`immoka/pdffiche.py`** (CRÉÉ, ~950 lignes, committé 5fea46d) : générateur complet. Structure :\n     ```python\n     def _fetch_image(url):  # PIL thumbnail((1000,1000)), JPEG quality=78 (réduit de 1200/82 pour <1 Mo), repli brut\n     def _verdict_vraiprix(price, vp):  # pct=round(...*100); None si |pct|>60; ▲/▼/≈ avec couleurs site\n     def fiche_pdf(uid) -> bytes | None:\n         # SELECT listings + price_log (10) + quartier.fiche_quartier + sources.json\n         # _safe: inondation.lookup, air.lookup, gaz.nearby, commerces.nearby, hydro.estimate(solve=False)\n         def saut_page(): s.pied(_PIED_FICHE, page[0]); c.showPage(); page[0]+=1; s.fond(); return s.entete(\"Fiche propriété · suite\")\n         def place(y_, besoin): return saut_page() if (y_ - besoin) < M + 34 else y_\n     ```\n     Sections : héro prix (+ `/ mois` si details.transaction==\"location\") + pastille verdict + pilule property_type ; adresse + loc (sector·city·region, SANS title — corrigé) ; chips (ch., sdb, s. d'eau, pi², terrain, année, $/pi² si area>200, MLS, jours sur Immo-Ka) ; photos 1 grande 170h 16/10.5 + 3 vignettes ; bandeau baisse/hausse prix ; description (col 56%) | inclusions features ✓ (col droite, max 18) ; caractéristiques (details filtrés DETAIL_HIDDEN + valeurs non dict/list, table zébrée DÉTAIL/VALEUR, place() par ligne) ; pièces (table PIÈCE/NIVEAU/DIMENSIONS/REVÊTEMENT, max 24) ; analyse Vrai-Prix (valeur 17pt, meta confiance/construction/terrain rôle, pastille verdict, jauge low–high accent_soft avec marqueurs ESTIMATION (accent_deep) et PRIX DEMANDÉ (ink), tuiles rôle foncier valeur_role/valeur_terrain/valeur_batiment) ; historique prix (table DATE/PRIX AFFICHÉ/VARIATION avec ▲▼=) ; inondation (pilules par statut en_zone/a_proximite/hors_zone/non couvert + zones) ; air (station, mesures) ; hydro (≈X $/mois + an + kWh) ; commerces et transport (2 col, transit d'abord [:4] + commerces [:12]) ; essence ([:3] + médiane) ; quartier (6 tuiles démo + barres proximité PROX_LABELS + pastilles chaleur/crime) ; références (QR 70×70 → `https://www.immo-ka.com/propriete/{uid}`, courtier broker_name·broker_phone, agence src_name, URL originale, sources en paragraphe 2 lignes).\n     `DETAIL_HIDDEN = {pieces, price_from, cover_thumb, photo_captions, img_audited, needs_image_review, postal_code, region, transaction, prix_pi2, prix_m2, listing_origin_url}`\n\n   - **`immoka/web.py`** (MODIFIÉ, committé) : endpoint ajouté après `get_listing` :\n     ```python\n     @app.get(\"/api/listings/{uid}/pdf\")\n     def listing_pdf(uid: str):\n         \"\"\"Fiche PDF complète de la propriété (immoka/pdffiche.py) — ...\"\"\"\n         from . import pdffiche\n         data = pdffiche.fiche_pdf(uid)\n         if data is None:\n             raise HTTPException(404, \"Propriété introuvable\")\n         safe = uid.replace(\":\", \"-\").replace(\"/\", \"-\")\n         return Response(content=data, media_type=\"application/pdf\",\n                         headers={\"Content-Disposition\": f'attachment; filename=\"immo-ka_fiche_{safe}.pdf\"'})\n     ```\n\n   - **`requirements.txt`** (MODIFIÉ, committé) : ajout `reportlab>=4.0` + `qrcode>=7.4` après fpdf2.\n\n   - **`frontend/src/pages/Listing.tsx`** (À MODIFIER pour Mission 3) : CTA héro localisé lignes 377-382 :\n     ```tsx\n     <a className=\"cta\" href={l.url} target=\"_blank\" rel=\"noopener noreferrer\">\n       Voir l'annonce chez {sourceName(l.source)} <Ico name=\"external\" size={15} />\n     </a>\n     <div className=\"fine\">\n       Agrégé par Immo-Ka — {sourceName(l.source)}{updated ? ` · synchronisé le ${updated}` : \"\"}.\n     </div>\n     ```\n     et cta-sticky mobile lignes 476-481. Le bouton PDF s'insérera entre le CTA et le div.fine.\n\n   - **`frontend/src/styles.css`** : `.cta` ligne 260 (`background: var(--ink); color: var(--lime); box-shadow: 4px 4px 0 rgba(26,18,20,0.25)`), `.cta-sticky` ligne 289 (masqué ≥900px). Style `.cta-pdf` à ajouter en section finale (convention Mission 1). ⚠️ relire le fichier avant Edit.\n\n   - **`frontend/src/components/Icons.tsx`** : grep `download|\"pdf\"|doc:` → No matches found — pas d'icône download existante.\n\n   - **`immoka/commerces.py`** (LU) : `nearby(lat,lng)` → `{\"n\": len(items), \"commerces\": items, \"transit\": transit}` ; transit ≤5000 m, items ≤40 km, tous triés dist_m.\n\n   - **`frontend/src/components/QuartierBlock.tsx`** (LU) : confirme tuiles démo, 6 barres proximité, chaleurBadge (classe≤3/≥7), crime points (douze_mois + catégories) / igc (delta vs indice_canada).\n\n   - **Mémoire** : créé `~/.claude/projects/-Users-simon-pierreboucher-apps-immo-ka/memory/project_pdf_fiche.md` (uids test riche/pauvre/vente, piège /tmp/inspect.py → `python -P`, pdftoppm) + ligne ajoutée dans MEMORY.md.\n\n4. Errors and fixes:\n   - **Write pdffiche.py échoué « Backend admin-ka injoignable: fetch failed »** : transitoire → simple retry du Write, réussi.\n   - **`ModuleNotFoundError: No module named 'immoka'`** (script test dans /tmp) : → `PYTHONPATH=.` depuis la racine du repo.\n   - **`AttributeError: module 'inspect' has no attribute 'signature'`** : fichier parasite `/tmp/inspect.py` masque la stdlib quand le script est dans /tmp (sys.path[0]=/tmp) → `.venv/bin/python -P` (safe path). Consigné en mémoire.\n   - **PDF riche 1050 Ko > limite 1 Mo** : réduit `thumbnail((1200,1200))→(1000,1000)` et `quality=82→78` → 684 Ko.\n   - **Titre dupliqué sur fiche pauvre** (title = adresse+ville+code postal réaffiché dans loc) : supprimé la concaténation `title` dans la ligne loc — ne reste que sector·city·region.\n   - **Ligne sources débordant la marge droite (page références)** : `drawString` → `s.paragraphe(..., PAGE_W-2*M, taille=6.5, interligne=9)` sur 2 lignes.\n   - **Edit MEMORY.md refusé « File has not been read yet »** : relu le fichier puis Edit OK.\n   - **Feedback utilisateur (Mission 3)** : « il semble pas y avoir de bouton sur fiche pour downloas pdf » — conséquence de la contrainte Mission 2 « ne pas toucher au frontend » ; l'endpoint existait mais rien ne pointait dessus → le bouton doit être ajouté.\n\n5. Problem Solving:\n   - Mission 2 livrée de bout en bout : cartographie complète (commerces.nearby avec transit, QuartierBlock, palette tokens, seuils verdict Vrai-Prix, route /propriete/:uid), générateur écrit, endpoint ajouté, dépendances épinglées.\n   - Tests validés : riche `domicilia:17516934` (684 Ko, 3 pages, ~30 s au 1er appel — téléchargement photos + enrichissements non cachés) ; pauvre `remax_quebec:17719804` (5 Ko, 1 page propre, sections omises) ; vente `sutton:13714330` (509 999 $ vs 513 500 $ estimé : 333 Ko, 4 pages, 1,1 s, pastille « ▼ 1 % sous l'estimation » + marqueur PRIX DEMANDÉ visibles). Toutes les pages inspectées via pdftoppm.\n   - Déploiement validé : pm2 restart, local 8096 (site 200 ; pdf riche 200 application/pdf 700 178 octets ; pdf pauvre 200 ; uid inexistant 404), public www.immo-ka.com (site 200 ; pdf 200 application/pdf 340 834 octets).\n   - Dérogation signalée : reportlab + qrcode installés/épinglés (le repo n'avait que fpdf2, contrairement à l'hypothèse de la mission).\n   - Mission 3 en cours : reconnaissance faite (CTA + styles + absence d'icône download), modification pas encore appliquée.\n\n6. All user messages:\n   1. (Mission 1, avant compaction) Appliquer la passe de design « éditorial sharp » de lou-ka (872a202) à la fiche immo-ka — Playwright desktop+mobile, variables accent, captures avant/après, check-order, build + pm2 + commit/push.\n   2. (Mission 2, avant compaction) Refaire le PDF « Télécharger la fiche » pour refléter TOUT le contenu de la fiche web, approche lou-ka pdfgen.py@6ff906e — cartographie, reportlab canvas pagination dynamique, style éditorial sharp, _safe, badge héro, QR/références, photos compressées <1 Mo, tests riche/pauvre pdftoppm, déploiement obligatoire (pm2, healthcheck local+public, commit/push `feat(pdf): fiche PDF complète — <sections>`), contraintes : pas toucher frontend ni autres endpoints, français partout, pas de dépendances nouvelles.\n   3. (Mission 3, dernier message) « il semble pas y avoir de bouton sur fiche pour downloas pdf »\n\n7. Pending Tasks:\n   - **Mission 3** : ajouter le bouton « Télécharger la fiche (PDF) » dans `frontend/src/pages/Listing.tsx` (héro, sous le CTA « Voir l'annonce chez … », avant le div.fine) : `<a className=\"cta cta-pdf\" href={`/api/listings/${l.uid}/pdf`} download>…</a>` — icône : soit en ajouter une dans Icons.tsx, soit caractère (aucune icône download existante).\n   - Ajouter le style `.cta-pdf` (variante secondaire inversée : fond var(--surface), texte var(--ink), même ombre/active) en section finale de `frontend/src/styles.css` (⚠️ relire le fichier avant Edit — piège « modified since read » déjà rencontré).\n   - Rebuild frontend (Vite), pm2 restart immo-ka-web, vérification visuelle Playwright desktop 1440×900 + mobile 390×844 (BASE=http://localhost:8096, uid domicilia:17516934 ; playwright installé --no-save, à réinstaller si disparu), healthcheck local + https://www.immo-ka.com, commit/push origin main.\n\n8. Current Work:\n   Mission 3 tout juste entamée suite au message « il semble pas y avoir de bouton sur fiche pour downloas pdf ». J'ai répondu « Exact — la mission interdisait de toucher au frontend, donc l'endpoint existe mais rien ne pointe dessus. J'ajoute le bouton sur la fiche. » puis effectué la reconnaissance :\n   - Grep Listing.tsx → CTA héro trouvé (lignes 377-382, `<a className=\"cta\" href={l.url}…>Voir l'annonce chez {sourceName(l.source)} <Ico name=\"external\" size={15} /></a>` + div.fine « synchronisé le ») et cta-sticky mobile (lignes 476-481 avec `fmtPrice(l.price, l.price_label)`).\n   - Grep styles.css → `.cta` ligne 260 (`display:block; background: var(--ink); color: var(--lime); box-shadow: 4px 4px 0 rgba(26,18,20,0.25); min-height:48px`), `.cta-sticky` ligne 289, pas de variante ghost/pdf existante.\n   - Dernier appel : grep `download|\"pdf\"|doc:` dans `frontend/src/components/Icons.tsx` → **No matches found** (aucune icône download disponible).\n   La todo-list Mission 2 est entièrement completed ; aucune modification frontend n'a encore été écrite.\n\n9. Optional Next Step:\n   Implémenter le bouton demandé par « il semble pas y avoir de bouton sur fiche pour downloas pdf » :\n   (a) Éditer `frontend/src/pages/Listing.tsx` — insérer entre le CTA source et le div.fine du héro :\n   ```tsx\n   <a className=\"cta cta-pdf\" href={`/api/listings/${l.uid}/pdf`} download>\n     Télécharger la fiche (PDF)\n   </a>\n   ```\n   (avec icône : ajouter un glyphe download dans Icons.tsx ou utiliser un caractère, puisque le grep vient de confirmer « No matches found ») ;\n   (b) relire puis éditer `frontend/src/styles.css` (section finale, convention Mission 1) pour ajouter `.cta-pdf` en variante secondaire (fond var(--surface), texte var(--ink), bordure encre, même comportement :active) ;\n   (c) `npm run build` dans frontend/, `pm2 restart immo-ka-web`, captures Playwright desktop 1440×900 + mobile 390×844 sur `http://localhost:8096/propriete/domicilia:17516934` pour vérifier le bouton (héro + éventuellement cta-sticky), healthcheck du lien PDF, puis commit/push origin main (ex. `feat(fiche): bouton Télécharger la fiche (PDF)`).\n\nIf you need specific details from before compaction (like exact code snippets, error messages, or content you generated), read the full transcript at: /Users/simon-pierreboucher/.claude/projects/-Users-simon-pierreboucher-apps-immo-ka/1b217d06-32f6-4569-9b15-be2772e84b82.jsonl\nContinue the conversation from where it left off without asking the user any further questions. Resume directly — do not acknowledge the summary, do not recap what was happening, do not preface with \"I'll continue\" or similar. Pick up the last task as if the break never happened."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"0db7efcc-1af5-4083-9889-6cbdd6929f43","timestamp":"2026-08-24T21:55:03.606Z","isSynthetic":true}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015BzaNxF9LD3ePgkHTMFcQF","type":"tool_result","content":"255\t.broker { margin: 16px 0; padding: 14px; border: 1.5px dashed var(--line-strong); border-radius: var(--r-ctl); background: var(--surface-2); }\n256\t.broker-k { font-family: var(--font-mono); font-size: 9.5px; text-transform: uppercase; letter-spacing: 0.1em; color: var(--ink-3); }\n257\t.broker-name { font-family: var(--font-display); font-weight: 700; font-size: 15px; margin-top: 3px; }\n258\t.broker-tel { display: inline-block; margin-top: 6px; color: var(--green-deep); font-weight: 600; border-bottom: 1.5px solid var(--lime); }\n259\t\n260\t.cta { display: block; text-align: center; background: var(--ink); color: var(--lime); font-weight: 700; font-family: var(--font-display); border-radius: var(--r-ctl); padding: 15px; border: 1.5px solid var(--ink); min-height: 48px; box-shadow: 4px 4px 0 rgba(26, 18, 20,0.25); transition: all 0.14s ease; margin-top: 6px; }\n261\t.cta:hover { background: var(--lime); color: #fff; }\n262\t.cta:active { transform: translate(2px, 2px); box-shadow: none; }\n263\t.f-hero .fine { font-size: 11.5px; color: var(--ink-3); margin-top: 14px; text-align: center; font-family: var(--font-mono); letter-spacing: 0.02em; }\n264\t\n265\t.desc-text { color: var(--ink-2); font-size: 14.5px; white-space: pre-line; line-height: 1.6; margin: 0; }\n266\t\n267\t/* tableau caractéristiques */\n268\t.dtable { display: grid; grid-template-columns: repeat(auto-fill, minmax(240px, 1fr)); gap: 0 22px; }\n269\t.drow { display: flex; justify-content: space-between; gap: 12px; padding: 8px 0; border-bottom: 1px solid var(--line); font-size: 13.5px; }\n270\t.drow span { color: var(--ink-3); } .drow b { text-align: right; font-family: var(--font-display); }\n271\t\n272\t/* tableau pièces */\n273\t.rooms-wrap { overflow-x: auto; border: 1.5px solid var(--line-strong); border-radius: var(--r-card); }\n274\ttable.rooms { width: 100%; border-collapse: collapse; font-size: 13.5px; min-width: 460px; }\n275\ttable.rooms th { text-align: left; background: var(--ink); color: var(--paper); padding: 9px 12px; font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.06em; }\n276\ttable.rooms td { padding: 8px 12px; border-bottom: 1px solid var(--line); }\n277\ttable.rooms tr:last-child td { border-bottom: none; }\n278\ttable.rooms tr:nth-child(even) td { background: var(--surface-2); }\n279\t\n280\t.amenity-row { display: flex; flex-wrap: wrap; gap: 7px; }\n281\t.amenity { background: var(--lime-soft); color: var(--green-deep); font-size: 12px; font-weight: 600; border: 1px solid var(--green); border-radius: 999px; padding: 5px 12px; }\n282\t\n283\t.prix-histo { margin-top: 14px; padding: 10px 14px; border-radius: var(--r-card); border: 1.5px solid var(--line-strong); font-size: 13.5px; background: var(--surface); }\n284\t.prix-histo.down { background: var(--lime-soft); border-color: var(--green); 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}\n299\t.sources h1 { font-size: clamp(28px, 4vw, 40px); text-transform: uppercase; margin: 10px 0 6px; }\n300\t.sources .sub { color: var(--ink-2); margin-bottom: 30px; max-width: 720px; }\n301\t.src-wrap { overflow-x: auto; border: 1.5px solid var(--ink); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); background: var(--surface); }\n302\t.src-table { width: 100%; border-collapse: separate; border-spacing: 0; min-width: 760px; }\n303\t.src-table th { text-align: left; font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); padding: 14px 18px; border-bottom: 1.5px solid var(--ink); background: var(--surface-2); }\n304\t.src-table td { padding: 13px 18px; border-bottom: 1px solid var(--line); font-size: 14px; vertical-align: top; }"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"9c6bfea7-2a26-4928-8e52-523cde48d836","timestamp":"2026-08-24T21:55:09.232Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/styles.css","content":".broker { margin: 16px 0; padding: 14px; border: 1.5px dashed var(--line-strong); border-radius: var(--r-ctl); background: var(--surface-2); }\n.broker-k { font-family: var(--font-mono); font-size: 9.5px; text-transform: uppercase; letter-spacing: 0.1em; color: var(--ink-3); }\n.broker-name { font-family: var(--font-display); font-weight: 700; font-size: 15px; margin-top: 3px; }\n.broker-tel { display: inline-block; margin-top: 6px; color: var(--green-deep); font-weight: 600; border-bottom: 1.5px solid var(--lime); }\n\n.cta { display: block; text-align: center; background: var(--ink); color: var(--lime); font-weight: 700; font-family: var(--font-display); border-radius: var(--r-ctl); padding: 15px; border: 1.5px solid var(--ink); min-height: 48px; box-shadow: 4px 4px 0 rgba(26, 18, 20,0.25); transition: all 0.14s ease; margin-top: 6px; }\n.cta:hover { background: var(--lime); color: #fff; }\n.cta:active { transform: translate(2px, 2px); box-shadow: none; }\n.f-hero .fine { font-size: 11.5px; color: var(--ink-3); margin-top: 14px; text-align: center; font-family: var(--font-mono); letter-spacing: 0.02em; }\n\n.desc-text { color: var(--ink-2); font-size: 14.5px; white-space: pre-line; line-height: 1.6; margin: 0; }\n\n/* tableau caractéristiques */\n.dtable { display: grid; grid-template-columns: repeat(auto-fill, minmax(240px, 1fr)); gap: 0 22px; }\n.drow { display: flex; justify-content: space-between; gap: 12px; padding: 8px 0; border-bottom: 1px solid var(--line); font-size: 13.5px; }\n.drow span { color: var(--ink-3); } .drow b { text-align: right; font-family: var(--font-display); }\n\n/* tableau pièces */\n.rooms-wrap { overflow-x: auto; border: 1.5px solid var(--line-strong); border-radius: var(--r-card); }\ntable.rooms { width: 100%; border-collapse: collapse; font-size: 13.5px; min-width: 460px; }\ntable.rooms th { text-align: left; background: var(--ink); color: var(--paper); padding: 9px 12px; font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.06em; 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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNKS5yuxUHaT5VDXtu72","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3137,"cache_read_input_tokens":41255,"cache_creation":{"ephemeral_5m_input_tokens":3137,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"23524b95-64c4-48f1-b870-a398a849c8df"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNKS5yuxUHaT5VDXtu72","type":"message","role":"assistant","content":[{"type":"text","text":"Je regarde le composant Icons pour ajouter un glyphe download."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3137,"cache_read_input_tokens":41255,"cache_creation":{"ephemeral_5m_input_tokens":3137,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"65ec26f5-6659-4fec-a51a-37374a436f3e"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNKS5yuxUHaT5VDXtu72","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GwRZ8QH1jNLGNwJY4J3NUs","name":"Grep","input":{"pattern":"external|const ICONS|paths|name:","path":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/components/Icons.tsx","output_mode":"content","-n":true,"-C":3},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3137,"cache_read_input_tokens":41255,"cache_creation":{"ephemeral_5m_input_tokens":3137,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"bd528dd8-beca-4854-a011-e829c00a2824"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GwRZ8QH1jNLGNwJY4J3NUs","type":"tool_result","content":"94-    <path d=\"m3.6 6.8 6 6.4 4-3.6 6.8 7.8\" />\n95-    <path d=\"M15.6 17.4h4.8v-4.8\" />\n96-  </>),\n97:  external: (<>\n98-    <path d=\"M14.4 4h5.6v5.6\" />\n99-    <path d=\"M20 4 11.4 12.6\" />\n100-    <path d=\"M18.6 13.6V18a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V7.4a2 2 0 0 1 2-2h4.4\" />\n--\n159-export type IconName = keyof typeof P;\n160-\n161-export function Ico({ name, size = 18, className = \"\", stroke = 1.7 }:\n162:  { name: IconName | string; size?: number; className?: string; stroke?: number }) {\n163:  const paths = P[name as IconName];\n164:  if (!paths) return null;\n165-  return (\n166-    <svg\n167-      className={`ico ${className}`}\n--\n169-      fill=\"none\" stroke=\"currentColor\" strokeWidth={stroke}\n170-      strokeLinecap=\"round\" strokeLinejoin=\"round\" aria-hidden=\"true\"\n171-    >\n172:      {paths}\n173-    </svg>\n174-  );\n175-}\n176-\n177-/** Version « chaîne HTML » pour les popups MapLibre (hors React). */\n178:export function icoHTML(name: IconName, size = 30): string {\n179-  const d: Record<string, string> = {\n180-    home: '<path d=\"M3.5 10.6 12 3.4l8.5 7.2\"/><path d=\"M5.6 9.4V20h12.8V9.4\"/><path d=\"M10 20v-5.6h4V20\"/>',\n181-  };"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"f793d48d-3a95-4815-ba6c-5174683ee915","timestamp":"2026-08-24T21:55:14.948Z","tool_use_result":{"mode":"content","numFiles":0,"filenames":[],"content":"94-    <path d=\"m3.6 6.8 6 6.4 4-3.6 6.8 7.8\" />\n95-    <path d=\"M15.6 17.4h4.8v-4.8\" />\n96-  </>),\n97:  external: (<>\n98-    <path d=\"M14.4 4h5.6v5.6\" />\n99-    <path d=\"M20 4 11.4 12.6\" />\n100-    <path d=\"M18.6 13.6V18a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V7.4a2 2 0 0 1 2-2h4.4\" />\n--\n159-export type IconName = keyof typeof P;\n160-\n161-export function Ico({ name, size = 18, className = \"\", stroke = 1.7 }:\n162:  { name: IconName | string; size?: number; className?: string; stroke?: number }) {\n163:  const paths = P[name as IconName];\n164:  if (!paths) return null;\n165-  return (\n166-    <svg\n167-      className={`ico ${className}`}\n--\n169-      fill=\"none\" stroke=\"currentColor\" strokeWidth={stroke}\n170-      strokeLinecap=\"round\" strokeLinejoin=\"round\" aria-hidden=\"true\"\n171-    >\n172:      {paths}\n173-    </svg>\n174-  );\n175-}\n176-\n177-/** Version « chaîne HTML » pour les popups MapLibre (hors React). */\n178:export function icoHTML(name: IconName, size = 30): string {\n179-  const d: Record<string, string> = {\n180-    home: '<path d=\"M3.5 10.6 12 3.4l8.5 7.2\"/><path d=\"M5.6 9.4V20h12.8V9.4\"/><path d=\"M10 20v-5.6h4V20\"/>',\n181-  };","numLines":31}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"244a1361-2c47-463e-91dc-197917d9a736","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNKSWZXC9YpAPN1dJHbT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01U86E1YM3ssrabZNQ62b7PU","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/components/Icons.tsx","offset":97,"limit":12},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":871,"cache_read_input_tokens":44392,"cache_creation":{"ephemeral_5m_input_tokens":871,"ephemeral_1h_input_tokens":0},"output_tokens":17,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"d5bcb816-05b0-4944-bcf9-c52dcd8d1d03"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01U86E1YM3ssrabZNQ62b7PU","type":"tool_result","content":"97\t  external: (<>\n98\t    <path d=\"M14.4 4h5.6v5.6\" />\n99\t    <path d=\"M20 4 11.4 12.6\" />\n100\t    <path d=\"M18.6 13.6V18a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V7.4a2 2 0 0 1 2-2h4.4\" />\n101\t  </>),\n102\t  pin: (<>\n103\t    <path d=\"M12 21.2S5.4 15.7 5.4 10.8a6.6 6.6 0 0 1 13.2 0c0 4.9-6.6 10.4-6.6 10.4z\" />\n104\t    <circle cx=\"12\" cy=\"10.6\" r=\"2.3\" />\n105\t  </>),\n106\t\n107\t  // --- quartier ---------------------------------------------------------------------\n108\t  leaf: (<>"}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"670b8945-0302-4e7e-bb98-76fa0cb3f3e6","timestamp":"2026-08-24T21:55:19.400Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/components/Icons.tsx","content":"  external: (<>\n    <path d=\"M14.4 4h5.6v5.6\" />\n    <path d=\"M20 4 11.4 12.6\" />\n    <path d=\"M18.6 13.6V18a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V7.4a2 2 0 0 1 2-2h4.4\" />\n  </>),\n  pin: (<>\n    <path d=\"M12 21.2S5.4 15.7 5.4 10.8a6.6 6.6 0 0 1 13.2 0c0 4.9-6.6 10.4-6.6 10.4z\" />\n    <circle cx=\"12\" cy=\"10.6\" r=\"2.3\" />\n  </>),\n\n  // --- quartier ---------------------------------------------------------------------\n  leaf: (<>","numLines":12,"startLine":97,"totalLines":184}}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"b825823b-d3b0-4fae-b577-911182ae2183","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNKSqTZszNR2DcUPsPco","type":"message","role":"assistant","content":[{"type":"text","text":"J'ajoute l'icône `download`, le bouton dans le héro, et le style `.cta-pdf`."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":422,"cache_read_input_tokens":45263,"cache_creation":{"ephemeral_5m_input_tokens":422,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"e5c66d42-39d4-44be-a33b-668d1b77299d"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNKSqTZszNR2DcUPsPco","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017daCPUZn6PP39waCMKbEyR","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/components/Icons.tsx","old_string":"  pin: (<>\n    <path d=\"M12 21.2S5.4 15.7 5.4 10.8a6.6 6.6 0 0 1 13.2 0c0 4.9-6.6 10.4-6.6 10.4z\" />\n    <circle cx=\"12\" cy=\"10.6\" r=\"2.3\" />\n  </>),","new_string":"  pin: (<>\n    <path d=\"M12 21.2S5.4 15.7 5.4 10.8a6.6 6.6 0 0 1 13.2 0c0 4.9-6.6 10.4-6.6 10.4z\" />\n    <circle cx=\"12\" cy=\"10.6\" r=\"2.3\" />\n  </>),\n  download: (<>\n    <path d=\"M12 3.6v11.2\" />\n    <path d=\"m7.2 10.4 4.8 4.8 4.8-4.8\" />\n    <path d=\"M4.4 19.2h15.2\" />\n  </>),"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":422,"cache_read_input_tokens":45263,"cache_creation":{"ephemeral_5m_input_tokens":422,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"2b8cd70d-d951-42e8-bdd5-17d441975f5a"}}
{"type":"permission_request","requestId":"6ec62f704c525473","tool":"Edit","input":{"file_path":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/components/Icons.tsx","old_string":"  pin: (<>\n    <path d=\"M12 21.2S5.4 15.7 5.4 10.8a6.6 6.6 0 0 1 13.2 0c0 4.9-6.6 10.4-6.6 10.4z\" />\n    <circle cx=\"12\" cy=\"10.6\" r=\"2.3\" />\n  </>),","new_string":"  pin: (<>\n    <path d=\"M12 21.2S5.4 15.7 5.4 10.8a6.6 6.6 0 0 1 13.2 0c0 4.9-6.6 10.4-6.6 10.4z\" />\n    <circle cx=\"12\" cy=\"10.6\" r=\"2.3\" />\n  </>),\n  download: (<>\n    <path d=\"M12 3.6v11.2\" />\n    <path d=\"m7.2 10.4 4.8 4.8 4.8-4.8\" />\n    <path d=\"M4.4 19.2h15.2\" />\n  </>),","replace_all":false}}
{"type":"permission_resolved","requestId":"6ec62f704c525473","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017daCPUZn6PP39waCMKbEyR","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/immo-ka/frontend/src/components/Icons.tsx has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"9ac10d97-5bb7-4de4-bd98-aae985afe338","timestamp":"2026-08-24T21:55:30.954Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/components/Icons.tsx","oldString":"  pin: (<>\n    <path d=\"M12 21.2S5.4 15.7 5.4 10.8a6.6 6.6 0 0 1 13.2 0c0 4.9-6.6 10.4-6.6 10.4z\" />\n    <circle cx=\"12\" cy=\"10.6\" r=\"2.3\" />\n  </>),","newString":"  pin: (<>\n    <path d=\"M12 21.2S5.4 15.7 5.4 10.8a6.6 6.6 0 0 1 13.2 0c0 4.9-6.6 10.4-6.6 10.4z\" />\n    <circle cx=\"12\" cy=\"10.6\" r=\"2.3\" />\n  </>),\n  download: (<>\n    <path d=\"M12 3.6v11.2\" />\n    <path d=\"m7.2 10.4 4.8 4.8 4.8-4.8\" />\n    <path d=\"M4.4 19.2h15.2\" />\n  </>),","originalFile":"// -----------------------------------------------------------------------------\n// Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// components/Icons.tsx : iconographie MAISON — traits 1,7 px sur grille 24,\n//   dessinée pour Immo-Ka (zéro emoji, zéro pack générique). Chaque icône est\n//   un tracé « stroke » net qui hérite de la couleur du texte (currentColor).\n// -----------------------------------------------------------------------------\nimport { ReactNode } from \"react\";\n\nconst P: Record<string, ReactNode> = {\n  // --- navigation -----------------------------------------------------------\n  home: (<>\n    <path d=\"M3.5 10.6 12 3.4l8.5 7.2\" />\n    <path d=\"M5.6 9.4V20h12.8V9.4\" />\n    <path d=\"M10 20v-5.6h4V20\" />\n  </>),\n  map: (<>\n    <path d=\"M3.2 6.4 9 4.2l6 2.2 5.8-2.2v13.4L15 19.8l-6-2.2-5.8 2.2z\" />\n    <path d=\"M9 4.2v13.4M15 6.4v13.4\" />\n  </>),\n  chart: (<>\n    <path d=\"M4 20h16\" />\n    <path d=\"M7.2 20v-5.6M12 20V8.8M16.8 20V12\" />\n  </>),\n  building: (<>\n    <path d=\"M5 20V5.6A1.6 1.6 0 0 1 6.6 4h6.2a1.6 1.6 0 0 1 1.6 1.6V20\" />\n    <path d=\"M14.4 9.4h3.4A1.6 1.6 0 0 1 19.4 11v9\" />\n    <path d=\"M3.2 20h17.6\" />\n    <path d=\"M8 8h2.4M8 11.6h2.4M8 15.2h2.4M16.4 13h.9M16.4 16.2h.9\" />\n  </>),\n\n  // --- specs propriété --------------------------------------------------------\n  bed: (<>\n    <path d=\"M3.4 6.8V18.6\" />\n    <path d=\"M3.4 15.4h17.2v3.2\" />\n    <path d=\"M3.4 12.2h6.2v3.2\" />\n    <circle cx=\"6.5\" cy=\"9.9\" r=\"1.35\" />\n    <path d=\"M11.4 12.2h5.8a3.4 3.4 0 0 1 3.4 3.2\" />\n  </>),\n  bath: (<>\n    <path d=\"M3.6 12.4h16.8v1.4a5.2 5.2 0 0 1-5.2 5.2H8.8a5.2 5.2 0 0 1-5.2-5.2z\" />\n    <path d=\"M5.8 12.4V5.9a2.1 2.1 0 0 1 4-1\" />\n    <path d=\"m7 19.4-1 2.1M17 19.4l1 2.1\" />\n  </>),\n  drop: (<>\n    <path d=\"M12 3.6s6 6.4 6 10.6a6 6 0 1 1-12 0C6 10 12 3.6 12 3.6z\" />\n    <path d=\"M9.4 14.2a2.7 2.7 0 0 0 2 2.6\" />\n  </>),\n  area: (<>\n    <path d=\"M4.4 19.6 19.6 4.4\" />\n    <path d=\"M4.4 14v5.6H10\" />\n    <path d=\"M19.6 10V4.4H14\" />\n  </>),\n  land: (<>\n    <path d=\"M4.4 9.6v9.8M9.5 9.6v9.8M14.5 9.6v9.8M19.6 9.6v9.8\" />\n    <path d=\"M3 12.6h18M3 16.4h18\" />\n    <path d=\"M4.4 9.6 12 5.2l7.6 4.4\" />\n  </>),\n  calendar: (<>\n    <rect x=\"4\" y=\"5.6\" width=\"16\" height=\"14.8\" rx=\"2\" />\n    <path d=\"M4 10.2h16M8.2 3.4v4M15.8 3.4v4\" />\n  </>),\n  tag: (<>\n    <path d=\"m12.9 3.6 7.5 7.5a1.8 1.8 0 0 1 0 2.5l-6.8 6.8a1.8 1.8 0 0 1-2.5 0L3.6 12.9V3.6z\" />\n    <circle cx=\"8\" cy=\"8\" r=\"1.5\" />\n  </>),\n  camera: (<>\n    <rect x=\"3.4\" y=\"7\" width=\"17.2\" height=\"13\" rx=\"2\" />\n    <path d=\"M8.6 7 10 4.4h4L15.4 7\" />\n    <circle cx=\"12\" cy=\"13.2\" r=\"3.4\" />\n  </>),\n\n  // --- actions / états ----------------------------------------------------------\n  search: (<>\n    <circle cx=\"10.6\" cy=\"10.6\" r=\"6.2\" />\n    <path d=\"m15.3 15.3 5.3 5.3\" />\n  </>),\n  arrow: (<>\n    <path d=\"M4 12h15.2\" />\n    <path d=\"m13.8 6.4 5.4 5.6-5.4 5.6\" />\n  </>),\n  check: (<path d=\"m5 12.8 4.3 4.4L19 7.4\" />),\n  alert: (<>\n    <path d=\"M12 4.2 2.9 19.4h18.2z\" />\n    <path d=\"M12 10.2v4.4\" />\n    <path d=\"M12 17.4v.05\" />\n  </>),\n  phone: (<path d=\"M5.2 4h3.6L10.4 8.4 8.3 10.1a12.5 12.5 0 0 0 5.6 5.6l1.7-2.1 4.4 1.6v3.6a1.8 1.8 0 0 1-1.9 1.8A16.4 16.4 0 0 1 3.4 5.9 1.8 1.8 0 0 1 5.2 4z\" />),\n  trendup: (<>\n    <path d=\"m3.6 17.4 6-6.4 4 3.6 6.8-7.8\" />\n    <path d=\"M15.6 6.8h4.8v4.8\" />\n  </>),\n  trenddown: (<>\n    <path d=\"m3.6 6.8 6 6.4 4-3.6 6.8 7.8\" />\n    <path d=\"M15.6 17.4h4.8v-4.8\" />\n  </>),\n  external: (<>\n    <path d=\"M14.4 4h5.6v5.6\" />\n    <path d=\"M20 4 11.4 12.6\" />\n    <path d=\"M18.6 13.6V18a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V7.4a2 2 0 0 1 2-2h4.4\" />\n  </>),\n  pin: (<>\n    <path d=\"M12 21.2S5.4 15.7 5.4 10.8a6.6 6.6 0 0 1 13.2 0c0 4.9-6.6 10.4-6.6 10.4z\" />\n    <circle cx=\"12\" cy=\"10.6\" r=\"2.3\" />\n  </>),\n\n  // --- quartier ---------------------------------------------------------------------\n  leaf: (<>\n    <path d=\"M5 19.2C5 9.6 12 5 20 4.2c0 9-4.2 15-13.2 15z\" />\n    <path d=\"M5 19.2C7 14 11 10 15 8\" />\n  </>),\n  thermo: (<>\n    <path d=\"M10.4 4.2a1.9 1.9 0 0 1 3.8 0v8.8a4.2 4.2 0 1 1-3.8 0z\" />\n    <path d=\"M12.3 8.4v7\" />\n  </>),\n  sun: (<>\n    <circle cx=\"12\" cy=\"12\" r=\"4.2\" />\n    <path d=\"M12 3.2v2M12 18.8v2M3.2 12h2M18.8 12h2M5.8 5.8l1.4 1.4M16.8 16.8l1.4 1.4M18.2 5.8l-1.4 1.4M7.2 16.8l-1.4 1.4\" />\n  </>),\n  shield: (<>\n    <path d=\"M12 3.4 5.2 5.9v5.9c0 4.6 3 7.6 6.8 8.8 3.8-1.2 6.8-4.2 6.8-8.8V5.9z\" />\n    <path d=\"m9.2 12 2 2 3.8-4\" />\n  </>),\n  cart: (<>\n    <circle cx=\"9.6\" cy=\"19.6\" r=\"1.35\" />\n    <circle cx=\"17\" cy=\"19.6\" r=\"1.35\" />\n    <path d=\"M3.4 4.4h2.2l2.5 10.8h9.6l2.7-7.8H7\" />\n  </>),\n  bus: (<>\n    <rect x=\"4.6\" y=\"3.8\" width=\"14.8\" height=\"13.4\" rx=\"2.4\" />\n    <path d=\"M4.6 10.4h14.8\" />\n    <path d=\"M7.6 20.2v-3M16.4 20.2v-3\" />\n    <path d=\"M8.3 14h.05M15.7 14h.05\" />\n  </>),\n  tree: (<>\n    <path d=\"M12 3.4 6.8 11.4h2.8L5.4 17.8h13.2l-4.2-6.4h2.8z\" />\n    <path d=\"M12 17.8v3.4\" />\n  </>),\n  school: (<>\n    <path d=\"m12 4.2 9.8 4.4L12 13 2.2 8.6z\" />\n    <path d=\"M6.6 10.8v5c0 1.6 2.4 3 5.4 3s5.4-1.4 5.4-3v-5\" />\n    <path d=\"M21.8 8.6v5.4\" />\n  </>),\n  health: (<>\n    <circle cx=\"12\" cy=\"12\" r=\"8.4\" />\n    <path d=\"M12 8.4v7.2M8.4 12h7.2\" />\n  </>),\n  pill: (<>\n    <rect x=\"3.2\" y=\"8.6\" width=\"17.6\" height=\"6.8\" rx=\"3.4\" transform=\"rotate(-33 12 12)\" />\n    <path d=\"M12 8.6v6.8\" transform=\"rotate(-33 12 12)\" />\n  </>),\n  people: (<>\n    <circle cx=\"9\" cy=\"8\" r=\"3.2\" />\n    <path d=\"M3.6 20a5.4 5.4 0 0 1 10.8 0\" />\n    <path d=\"M15.4 5.4a3.2 3.2 0 0 1 0 5.9M17 14.8a5.4 5.4 0 0 1 3.4 5.2\" />\n  </>),\n};\n\nexport type IconName = keyof typeof P;\n\nexport function Ico({ name, size = 18, className = \"\", stroke = 1.7 }:\n  { name: IconName | string; size?: number; className?: string; stroke?: number }) {\n  const paths = P[name as IconName];\n  if (!paths) return null;\n  return (\n    <svg\n      className={`ico ${className}`}\n      width={size} height={size} viewBox=\"0 0 24 24\"\n      fill=\"none\" stroke=\"currentColor\" strokeWidth={stroke}\n      strokeLinecap=\"round\" strokeLinejoin=\"round\" aria-hidden=\"true\"\n    >\n      {paths}\n    </svg>\n  );\n}\n\n/** Version « chaîne HTML » pour les popups MapLibre (hors React). */\nexport function icoHTML(name: IconName, size = 30): string {\n  const d: Record<string, string> = {\n    home: '<path d=\"M3.5 10.6 12 3.4l8.5 7.2\"/><path d=\"M5.6 9.4V20h12.8V9.4\"/><path d=\"M10 20v-5.6h4V20\"/>',\n  };\n  return `<svg width=\"${size}\" height=\"${size}\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.7\" stroke-linecap=\"round\" stroke-linejoin=\"round\">${d[name] ?? d.home}</svg>`;\n}\n","structuredPatch":[{"oldStart":103,"oldLines":6,"newStart":103,"newLines":11,"lines":["     <path d=\"M12 21.2S5.4 15.7 5.4 10.8a6.6 6.6 0 0 1 13.2 0c0 4.9-6.6 10.4-6.6 10.4z\" />","     <circle cx=\"12\" cy=\"10.6\" r=\"2.3\" />","   </>),","+  download: (<>","+    <path d=\"M12 3.6v11.2\" />","+    <path d=\"m7.2 10.4 4.8 4.8 4.8-4.8\" />","+    <path d=\"M4.4 19.2h15.2\" />","+  </>),"," ","   // --- quartier ---------------------------------------------------------------------","   leaf: (<>"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YJVa1q2ZS8eP1UBNRdu9yW","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/immo-ka/frontend/src/pages/Listing.tsx has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"52a1c675-f20e-48e5-bc8f-7d4a25d41e6e","timestamp":"2026-08-24T21:55:41.676Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/immo-ka/frontend/src/pages/Listing.tsx","oldString":"            <a className=\"cta\" href={l.url} target=\"_blank\" rel=\"noopener noreferrer\">\n              Voir l'annonce chez {sourceName(l.source)} <Ico name=\"external\" size={15} />\n            </a>\n            <div className=\"fine\">","newString":"            <a className=\"cta\" href={l.url} target=\"_blank\" rel=\"noopener noreferrer\">\n              Voir l'annonce chez {sourceName(l.source)} <Ico name=\"external\" size={15} />\n            </a>\n            <a className=\"cta cta-pdf\" href={`/api/listings/${l.uid}/pdf`} download>\n              Télécharger la fiche (PDF) <Ico name=\"download\" size={15} />\n            </a>\n            <div className=\"fine\">","originalFile":"// -----------------------------------------------------------------------------\n// Immo-Ka — Agrégateur de propriétés à vendre (province de Québec)\n// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// pages/Listing.tsx : fiche complète d'une propriété\n//   galerie + lightbox · specs · caractéristiques (details) · pièces ·\n//   inclusions · description · historique de prix · courtier · mini-carte\n// -----------------------------------------------------------------------------\nimport { Suspense, lazy, useEffect, useRef, useState } from \"react\";\nimport { Link, useParams } from \"react-router-dom\";\nimport {\n  Listing, Room, fetchListing, fetchSources, fmtArea, fmtDate, fmtPrice,\n  registerSourceNames, sourceName,\n} from \"../api\";\n\nconst PropertyMap = lazy(() => import(\"../components/PropertyMap\"));\nimport QuartierBlock from \"../components/QuartierBlock\";\nimport RisqueInondation from \"../components/RisqueInondation\";\nimport QualiteAir from \"../components/QualiteAir\";\nimport EssenceProche from \"../components/EssenceProche\";\nimport HydroEstimation from \"../components/HydroEstimation\";\nimport CommercesProches from \"../components/CommercesProches\";\nimport { Ico } from \"../components/Icons\";\nimport { TypeFallback } from \"../components/PropertyImg\";\n\n// --- Lightbox : pincement pour zoomer + panoramique + balayage entre photos ---\nfunction ZoomImg({ src, onSwipe }: { src: string; onSwipe: (dir: 1 | -1) => void }) {\n  const [t, setT] = useState({ scale: 1, x: 0, y: 0 });\n  const pointers = useRef(new Map<number, { x: number; y: number }>());\n  const start = useRef({ scale: 1, x: 0, y: 0, dist: 0, cx: 0, cy: 0, t: 0 });\n  const lastTap = useRef(0);\n\n  // repartir à zéro quand on change de photo\n  useEffect(() => { setT({ scale: 1, x: 0, y: 0 }); }, [src]);\n\n  const dist = () => {\n    const p = [...pointers.current.values()];\n    return p.length < 2 ? 0 : Math.hypot(p[0].x - p[1].x, p[0].y - p[1].y);\n  };\n  const center = () => {\n    const p = [...pointers.current.values()];\n    return p.length < 2\n      ? p[0] ?? { x: 0, y: 0 }\n      : { x: (p[0].x + p[1].x) / 2, y: (p[0].y + p[1].y) / 2 };\n  };\n\n  const onDown = (e: React.PointerEvent) => {\n    (e.target as HTMLElement).setPointerCapture(e.pointerId);\n    pointers.current.set(e.pointerId, { x: e.clientX, y: e.clientY });\n    const c = center();\n    start.current = { scale: t.scale, x: t.x, y: t.y, dist: dist(), cx: c.x, cy: c.y, t: Date.now() };\n  };\n  const onMove = (e: React.PointerEvent) => {\n    if (!pointers.current.has(e.pointerId)) return;\n    pointers.current.set(e.pointerId, { x: e.clientX, y: e.clientY });\n    const s = start.current;\n    if (pointers.current.size >= 2 && s.dist > 0) {\n      // pincement : zoom autour du centre des deux doigts\n      const scale = Math.min(4, Math.max(1, (dist() / s.dist) * s.scale));\n      const c = center();\n      setT({ scale, x: s.x + (c.x - s.cx), y: s.y + (c.y - s.cy) });\n    } else if (pointers.current.size === 1 && t.scale > 1) {\n      // panoramique une fois zoomé\n      const p = pointers.current.get(e.pointerId)!;\n      setT({ scale: t.scale, x: s.x + (p.x - s.cx), y: s.y + (p.y - s.cy) });\n    }\n  };\n  const onUp = (e: React.PointerEvent) => {\n    const p = pointers.current.get(e.pointerId);\n    pointers.current.delete(e.pointerId);\n    const s = start.current;\n    if (pointers.current.size === 0 && p) {\n      const dx = p.x - s.cx, dy = p.y - s.cy, dt = Date.now() - s.t;\n      if (t.scale <= 1.05 && Math.abs(dx) > 56 && Math.abs(dx) > Math.abs(dy) * 1.5) {\n        onSwipe(dx < 0 ? 1 : -1);                       // balayage → photo suivante\n      } else if (dt < 260 && Math.abs(dx) < 8 && Math.abs(dy) < 8) {\n        const now = Date.now();\n        if (now - lastTap.current < 320)                 // double-tap : zoom ×2.4\n          setT(t.scale > 1 ? { scale: 1, x: 0, y: 0 } : { scale: 2.4, x: 0, y: 0 });\n        lastTap.current = now;\n      }\n      if (t.scale <= 1.02) setT({ scale: 1, x: 0, y: 0 });\n    }\n  };\n\n  return (\n    <img\n      src={src} alt=\"\" draggable={false}\n      style={{\n        transform: `translate(${t.x}px, ${t.y}px) scale(${t.scale})`,\n        transition: pointers.current.size ? \"none\" : \"transform 0.15s ease\",\n        touchAction: \"none\", cursor: t.scale > 1 ? \"grab\" : \"zoom-out\",\n      }}\n      onClick={(e) => e.stopPropagation()}\n      onPointerDown={onDown} onPointerMove={onMove}\n      onPointerUp={onUp} onPointerCancel={onUp}\n    />\n  );\n}\n\n// --- Galerie : balayage natif (scroll-snap) + vignettes + plein écran --------\nfunction Galerie({ images, captions, titre, type }:\n  { images: string[]; captions?: string[]; titre: string; type?: string }) {\n  const [idx, setIdx] = useState(0);\n  const [zoom, setZoom] = useState(false);\n  const [dead, setDead] = useState<Set<string>>(new Set());\n  const track = useRef<HTMLDivElement>(null);\n\n  // images qui ne chargent pas : retirées de la galerie à la volée (jamais\n  // d'icône d'image cassée) ; légendes gardées alignées\n  const alive = images\n    .map((u, i) => ({ u, cap: captions && captions.length === images.length ? captions[i] : \"\" }))\n    .filter(({ u }) => !dead.has(u));\n  const markDead = (u: string) => setDead((d) => new Set(d).add(u));\n\n  const onScroll = () => {\n    const el = track.current;\n    if (el) setIdx(Math.round(el.scrollLeft / el.clientWidth));\n  };\n  const goto = (i: number) =>\n    track.current?.scrollTo({ left: i * track.current.clientWidth, behavior: \"smooth\" });\n\n  useEffect(() => {\n    if (!zoom) return;\n    const onKey = (e: KeyboardEvent) => {\n      if (e.key === \"Escape\") setZoom(false);\n      if (e.key === \"ArrowLeft\") setIdx((i) => Math.max(0, i - 1));\n      if (e.key === \"ArrowRight\") setIdx((i) => Math.min(alive.length - 1, i + 1));\n    };\n    window.addEventListener(\"keydown\", onKey);\n    // figer l'arrière-plan pendant le plein écran (mobile)\n    document.body.style.overflow = \"hidden\";\n    return () => {\n      window.removeEventListener(\"keydown\", onKey);\n      document.body.style.overflow = \"\";\n    };\n  }, [zoom, alive.length]);\n\n  if (alive.length === 0)\n    return <div className=\"carousel\"><div className=\"carousel-empty\"><TypeFallback type={type} /></div></div>;\n\n  const cur = Math.min(idx, alive.length - 1);\n  const swipe = (dir: 1 | -1) =>\n    setIdx((i) => Math.min(alive.length - 1, Math.max(0, i + dir)));\n\n  return (\n    <>\n      <div className=\"carousel\">\n        <div className=\"carousel-track\" ref={track} onScroll={onScroll}>\n          {alive.map(({ u }, i) => (\n            <img key={u} src={u} loading={i <= 1 ? \"eager\" : \"lazy\"} decoding=\"async\"\n              alt={`${titre} — photo ${i + 1} de ${alive.length}`}\n              onError={() => markDead(u)} onClick={() => setZoom(true)} />\n          ))}\n        </div>\n        {alive[cur]?.cap && <span className=\"carousel-caption\">{alive[cur].cap}</span>}\n        <span className=\"carousel-count\" aria-live=\"polite\">{cur + 1}/{alive.length}</span>\n        {cur > 0 && <button className=\"carousel-nav prev\" aria-label=\"Photo précédente\" onClick={() => goto(cur - 1)}>‹</button>}\n        {cur < alive.length - 1 && <button className=\"carousel-nav next\" aria-label=\"Photo suivante\" onClick={() => goto(cur + 1)}>›</button>}\n      </div>\n      {alive.length > 1 && (\n        <div className=\"thumbs\">\n          {alive.map(({ u }, i) => (\n            <button key={u} className={i === cur ? \"on\" : \"\"} onClick={() => goto(i)} aria-label={`Photo ${i + 1}`}>\n              <img src={u} alt=\"\" loading=\"lazy\" decoding=\"async\" onError={() => markDead(u)} />\n            </button>\n          ))}\n        </div>\n      )}\n      {zoom && (\n        <div className=\"lightbox\" onClick={() => setZoom(false)} role=\"dialog\" aria-label=\"Photo agrandie\">\n          <button className=\"lb-close\" aria-label=\"Fermer\" onClick={() => setZoom(false)}>✕</button>\n          {cur > 0 && <button className=\"lb-nav prev\" aria-label=\"Précédente\" onClick={(e) => { e.stopPropagation(); setIdx(cur - 1); }}>‹</button>}\n          <ZoomImg src={alive[cur].u} onSwipe={swipe} />\n          {cur < alive.length - 1 && <button className=\"lb-nav next\" aria-label=\"Suivante\" onClick={(e) => { e.stopPropagation(); setIdx(cur + 1); }}>›</button>}\n          <span className=\"lb-count\">\n            {alive[cur]?.cap ? `${alive[cur].cap} · ` : \"\"}{cur + 1} / {alive.length}\n          </span>\n        </div>\n      )}\n    </>\n  );\n}\n\n// clés techniques de `details` jamais montrées dans « Caractéristiques »\nconst DETAIL_HIDDEN = new Set([\n  \"pieces\", \"price_from\", \"cover_thumb\", \"photo_captions\", \"img_audited\",\n  \"needs_image_review\", \"postal_code\", \"region\", \"transaction\",\n  \"prix_pi2\", \"prix_m2\", \"listing_origin_url\",\n]);\n\n// icône de chaque tuile « spec » (Icons.tsx)\nconst SPEC_ICONS: Record<string, string> = {\n  \"Type\": \"home\", \"Chambres\": \"bed\", \"Salles de bain\": \"bath\",\n  \"Salles d'eau\": \"drop\", \"Superficie\": \"area\", \"Terrain\": \"land\",\n  \"Année\": \"calendar\", \"MLS / Centris\": \"tag\",\n};\n\nexport default function ListingPage() {\n  const { uid } = useParams<{ uid: string }>();\n  const [l, setL] = useState<Listing | null>(null);\n  const [error, setError] = useState<string | null>(null);\n\n  useEffect(() => {\n    fetchSources().then((r) => registerSourceNames(r.sources)).catch(() => {});\n    if (!uid) return;\n    setL(null); setError(null);\n    fetchListing(uid).then(setL).catch((e) => setError(String(e)));\n    window.scrollTo(0, 0);\n  }, [uid]);\n\n  if (error)\n    return (\n      <div className=\"notice container\">\n        <div className=\"big\"><Ico name=\"alert\" size={44} /></div>\n        <h2>Propriété introuvable</h2>\n        <p>{error}</p>\n        <Link className=\"btn btn-primary\" to=\"/\">Retour aux propriétés</Link>\n      </div>\n    );\n\n  if (!l)\n    return (\n      <div className=\"container detail\">\n        <div className=\"fiche\" aria-busy=\"true\">\n          <div className=\"skel\"><div className=\"sk-img\" /></div>\n          <div className=\"skel\"><div className=\"sk-line\" /><div className=\"sk-line\" /><div className=\"sk-line short\" /></div>\n        </div>\n      </div>\n    );\n\n  const specs: { k: string; v: string }[] = [];\n  if (l.property_type) specs.push({ k: \"Type\", v: l.property_type });\n  if (l.bedrooms != null) specs.push({ k: \"Chambres\", v: String(l.bedrooms) });\n  if (l.bathrooms != null) specs.push({ k: \"Salles de bain\", v: String(l.bathrooms) });\n  if (l.powder_rooms != null) specs.push({ k: \"Salles d'eau\", v: String(l.powder_rooms) });\n  if (l.area_sqft != null) specs.push({ k: \"Superficie\", v: fmtArea(l.area_sqft)! });\n  if (l.lot_sqft != null) specs.push({ k: \"Terrain\", v: fmtArea(l.lot_sqft)! });\n  if (l.year_built != null) specs.push({ k: \"Année\", v: String(l.year_built) });\n  if (l.mls) specs.push({ k: \"MLS / Centris\", v: l.mls });\n\n  const rooms: Room[] = Array.isArray(l.details?.pieces) ? (l.details!.pieces as Room[]) : [];\n  const detEntries = Object.entries(l.details ?? {})\n    .filter(([k, v]) => !DETAIL_HIDDEN.has(k) && (typeof v === \"string\" || typeof v === \"number\") && String(v).trim());\n\n  const hist = (l.price_history ?? []).filter((h) => h.price != null);\n  const baisse = hist.length >= 2 && hist[0].price !== hist[1].price\n    ? { de: hist[1].price!, a: hist[0].price! } : null;\n  const updated = l.updated_at ? fmtDate(l.updated_at) : null;\n\n  return (\n    <div className=\"container detail\">\n      <nav className=\"crumbs\" aria-label=\"Fil d'Ariane\">\n        <Link to=\"/\">Propriétés</Link> ›\n        {l.city && <span>{l.city}</span>} ›\n        <span>{l.address || l.title}</span>\n      </nav>\n\n      <div className=\"fiche\">\n        {/* -------- colonne gauche : galerie, prix/synthèse, description, ------\n             -------- caractéristiques, pièces — ordre du DOM = ordre visuel ---- */}\n        <div className=\"f-col\">\n          <section className=\"f-bloc f-galerie\" aria-label=\"Photos\">\n            <Galerie\n              images={l.images ?? []}\n              captions={Array.isArray(l.details?.photo_captions)\n                ? (l.details!.photo_captions as string[]) : undefined}\n              titre={l.address || l.title}\n              type={l.property_type}\n            />\n          </section>\n\n          <section className=\"f-bloc f-hero\">\n            <div className=\"price-kicker\">\n              {l.details?.transaction === \"location\" ? \"Loyer mensuel\" : \"Prix demandé\"}\n            </div>\n            <div className=\"price-row\">\n              <div className=\"price\">\n                {fmtPrice(l.price, l.price_label)}\n                {l.details?.transaction === \"location\" && <span className=\"per-month\"> /mois</span>}\n              </div>\n              {l.property_type && (\n                <span className=\"type-chip\">\n                  <Ico name={SPEC_ICONS[\"Type\"]} size={13} /> {l.property_type}\n                  {l.details?.transaction === \"location\" ? \" · location\" : \"\"}\n                  {l.details?.price_from ? \" · à partir de\" : \"\"}\n                </span>\n              )}\n            </div>\n            {l.price != null && l.area_sqft != null && l.area_sqft > 200 && (\n              <div className=\"price-sub\">{Math.round(l.price / l.area_sqft).toLocaleString(\"fr-CA\")} $ / pi² habitable</div>\n            )}\n            <h1>{l.address || l.title}</h1>\n            <div className=\"loc\"><Ico name=\"pin\" size={13} /> {[l.sector, l.city, l.region].filter(Boolean).join(\" · \")}</div>\n\n            <div className=\"spec-list\">\n              {specs.map((s) => (\n                <div className=\"spec-row\" key={s.k}>\n                  <span className=\"spec-badge\"><Ico name={SPEC_ICONS[s.k] ?? \"tag\"} size={15} /></span>\n                  <span className=\"spec-k\">{s.k}</span>\n                  <b className=\"spec-v\">{s.v}</b>\n                </div>\n              ))}\n            </div>\n\n            {baisse && (\n              <div className={`prix-histo ${baisse.a < baisse.de ? \"down\" : \"\"}`}>\n                <Ico name={baisse.a < baisse.de ? \"trenddown\" : \"trendup\"} size={16} /> Prix passé de {fmtPrice(baisse.de)} à <b>{fmtPrice(baisse.a)}</b>\n              </div>\n            )}\n\n            {l.vraiprix && l.vraiprix.value != null && (\n              <div className=\"vraiprix\">\n                <div className=\"vp-head\">\n                  <span className=\"vp-logo\">Vrai‑Prix</span>\n                  {l.vraiprix.confidence && (\n                    <span className={`vp-conf vp-conf-${l.vraiprix.confidence}`}>\n                      confiance {l.vraiprix.confidence}\n                    </span>\n                  )}\n                </div>\n                <div className=\"vp-k\">Valeur marchande estimée</div>\n                <div className=\"vp-value\">{fmtPrice(l.vraiprix.value)}</div>\n                {l.vraiprix.low != null && l.vraiprix.high != null && (() => {\n                  const lo = l.vraiprix!.low as number, hi = l.vraiprix!.high as number;\n                  const span = Math.max(1, hi - lo);\n                  const pos = (v: number) => `${Math.max(2, Math.min(98, ((v - lo) / span) * 100))}%`;\n                  return (\n                    <div className=\"vp-gauge\">\n                      <div className=\"vp-gauge-track\">\n                        <span className=\"vp-gauge-band\" />\n                        <span className=\"vp-gauge-est\" style={{ left: pos(l.vraiprix!.value as number) }} />\n                        {l.price != null && l.price >= lo * 0.7 && l.price <= hi * 1.3 && (\n                          <span className=\"vp-gauge-ask\" style={{ left: pos(l.price) }} />\n                        )}\n                      </div>\n                      <div className=\"vp-gauge-ends\">\n                        <span>{fmtPrice(lo)}</span>\n                        <span>{fmtPrice(hi)}</span>\n                      </div>\n                      {l.price != null && l.price >= lo * 0.7 && l.price <= hi * 1.3 && (\n                        <div className=\"vp-gauge-legend\">\n                          <span><i className=\"vp-dot-est\" /> estimation</span>\n                          <span><i className=\"vp-dot-ask\" /> prix demandé</span>\n                        </div>\n                      )}\n                    </div>\n                  );\n                })()}\n                {l.price != null && (\n                  (() => {\n                    const diff = l.price - (l.vraiprix!.value as number);\n                    const pct = Math.round((diff / (l.vraiprix!.value as number)) * 100);\n                    const cls = diff > 0 ? \"over\" : \"under\";\n                    const txt = diff > 0\n                      ? `Prix demandé ${pct}% au-dessus de l'estimation`\n                      : `Prix demandé ${Math.abs(pct)}% sous l'estimation`;\n                    if (Math.abs(pct) > 60) return null;   // prix non comparable (loyer, terrain…)\n                    return Math.abs(pct) >= 1\n                      ? <div className={`vp-delta vp-${cls}`}>{diff > 0 ? \"▲\" : \"▼\"} {txt}</div>\n                      : <div className=\"vp-delta vp-fair\">≈ Prix aligné sur l'estimation</div>;\n                  })()\n                )}\n                <a className=\"vp-link\" href={l.vraiprix.url} target=\"_blank\" rel=\"noopener noreferrer\">\n                  Voir l'analyse détaillée ↗\n                </a>\n              </div>\n            )}\n\n            {(l.broker_name || l.broker_phone) && (\n              <div className=\"broker\">\n                <div className=\"broker-k\">Courtier</div>\n                {l.broker_name && <div className=\"broker-name\">{l.broker_name}</div>}\n                {l.broker_phone && <a className=\"broker-tel\" href={`tel:${l.broker_phone.replace(/\\s/g, \"\")}`}><Ico name=\"phone\" size={14} /> {l.broker_phone}</a>}\n              </div>\n            )}\n\n            <a className=\"cta\" href={l.url} target=\"_blank\" rel=\"noopener noreferrer\">\n              Voir l'annonce chez {sourceName(l.source)} <Ico name=\"external\" size={15} />\n            </a>\n            <div className=\"fine\">\n              Agrégé par Immo-Ka — {sourceName(l.source)}{updated ? ` · synchronisé le ${updated}` : \"\"}.\n            </div>\n          </section>\n\n\n          {l.description && (\n            <section className=\"f-bloc f-desc\" id=\"description\">\n              <h2>Description</h2>\n              <p className=\"desc-text\">{l.description}</p>\n            </section>\n          )}\n\n          {detEntries.length > 0 && (\n            <section className=\"f-bloc\" id=\"caracteristiques\">\n              <h2>Caractéristiques</h2>\n              <div className=\"dtable\">\n                {detEntries.map(([k, v]) => (\n                  <div className=\"drow\" key={k}>\n                    <span>{k}</span>\n                    {/^https?:\\/\\//.test(String(v))\n                      ? <b><a href={String(v)} target=\"_blank\" rel=\"noopener noreferrer\">Ouvrir ↗</a></b>\n                      : <b>{String(v)}</b>}\n                  </div>\n                ))}\n              </div>\n            </section>\n          )}\n\n          {rooms.length > 0 && (\n            <section className=\"f-bloc\" id=\"pieces\">\n              <h2>Pièces</h2>\n              <div className=\"rooms-wrap\">\n                <table className=\"rooms\">\n                  <thead><tr><th>Pièce</th><th>Niveau</th><th>Dimensions</th><th>Revêtement</th></tr></thead>\n                  <tbody>\n                    {rooms.map((r, i) => (\n                      <tr key={i}>\n                        <td>{r.nom || \"—\"}</td><td>{r.niveau || \"—\"}</td>\n                        <td>{r.dimensions || \"—\"}</td><td>{r.revetement || \"—\"}</td>\n                      </tr>\n                    ))}\n                  </tbody>\n                </table>\n              </div>\n            </section>\n          )}\n\n        </div>\n\n        {/* -------- colonne droite (desktop) : inclusions, carte ------------- */}\n        <div className=\"f-col\">\n          {l.features && l.features.length > 0 && (\n            <section className=\"f-bloc\" id=\"inclusions\">\n              <h2>Inclusions</h2>\n              <div className=\"amenity-row\">\n                {l.features.map((f, i) => <span className=\"amenity\" key={i}><Ico name=\"check\" size={13} /> {f}</span>)}\n              </div>\n            </section>\n          )}\n\n          {l.lat != null && l.lng != null && (\n            <section className=\"f-bloc\" id=\"carte\">\n              <h2>Emplacement</h2>\n              <Suspense fallback={<div className=\"lmap3d lmap3d-skel map-loading\">Chargement de la carte…</div>}>\n                <PropertyMap\n                  uid={l.uid} lat={l.lat} lng={l.lng} price={l.price}\n                  propertyType={l.property_type} address={l.address || l.title}\n                  city={l.city} image={l.images?.[0]}\n                  deal={l.price != null && l.vraiprix?.value != null\n                        && l.price <= l.vraiprix.value * 0.95}\n                />\n              </Suspense>\n            </section>\n          )}\n        </div>\n      </div>\n\n      {/* quartier : pleine largeur, APRÈS les infos de propriété (ordre mobile correct) */}\n      <RisqueInondation lat={l.lat} lng={l.lng} />\n\n      <QualiteAir lat={l.lat} lng={l.lng} />\n\n      <CommercesProches lat={l.lat} lng={l.lng} />\n\n      <EssenceProche lat={l.lat} lng={l.lng} />\n\n      <HydroEstimation adresse={l.address || l.title} uid={l.uid} lat={l.lat} lng={l.lng} />\n\n      {l.quartier && <QuartierBlock q={l.quartier} />}\n\n      <div className=\"fine f-foot\">\n        Les prix et disponibilités sont ceux affichés par la source — chaque fiche renvoie à\n        l'annonce originale de l'agence.\n      </div>\n\n      <div className=\"cta-sticky\">\n        <span className=\"cta-sticky-prix\">{fmtPrice(l.price, l.price_label)}</span>\n        <a className=\"cta\" href={l.url} target=\"_blank\" rel=\"noopener noreferrer\">\n          Voir chez {sourceName(l.source)} ↗\n        </a>\n      </div>\n    </div>\n  );\n}\n","structuredPatch":[{"oldStart":377,"oldLines":6,"newStart":377,"newLines":9,"lines":["             <a className=\"cta\" href={l.url} target=\"_blank\" rel=\"noopener noreferrer\">","               Voir l'annonce chez {sourceName(l.source)} <Ico name=\"external\" size={15} />","             </a>","+            <a className=\"cta cta-pdf\" href={`/api/listings/${l.uid}/pdf`} download>","+              Télécharger la fiche (PDF) <Ico name=\"download\" size={15} />","+            </a>","             <div className=\"fine\">","               Agrégé par Immo-Ka — {sourceName(l.source)}{updated ? 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align-items: center; gap: 20px; height: 64px; }\n.brand { font-family: var(--font-display); font-weight: 700; font-size: 26px; letter-spacing: -0.04em; display: flex; align-items: center; line-height: 1; }\n.brand .ka { background: var(--ink); color: var(--lime); padding: 2px 7px 4px; border-radius: 6px; margin-left: 3px; transform: rotate(-2deg); transition: transform 0.2s ease; }\n.brand:hover .ka { transform: rotate(0deg); }\n.brand-tag { font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-3); letter-spacing: 0.08em; text-transform: uppercase; margin-left: 12px; }\n@media (max-width: 860px) { .brand-tag { display: none; } }\n.nav { margin-left: auto; display: flex; gap: 4px; }\n.nav a { padding: 9px 16px; border-radius: 999px; font-weight: 600; font-size: 14px; color: var(--ink-2); border: 1.5px solid transparent; transition: all 0.15s ease; min-height: 40px; display: inline-flex; align-items: center; }\n.nav a:hover { border-color: var(--ink); color: var(--ink); }\n.nav a.active { background: var(--ink); color: var(--lime); }\n\n/* ================= Ticker ================= */\n.ticker { background: var(--ink); color: var(--lime); overflow: hidden; font-family: var(--font-mono); font-size: 11.5px; letter-spacing: 0.1em; text-transform: uppercase; padding: 7px 0; white-space: nowrap; border-bottom: 1px solid rgba(226, 55, 68, 0.25); }\n.ticker-track { display: inline-flex; gap: 0; animation: ticker 48s linear infinite; will-change: transform; }\n.ticker span { padding: 0 26px; position: relative; }\n.ticker span::after { content: \"◆\"; position: absolute; right: -6px; opacity: 0.5; font-size: 8px; top: 3px; }\n@keyframes ticker { from { transform: translateX(0); } to { transform: translateX(-50%); } }\n@media (prefers-reduced-motion: reduce) { .ticker-track { animation: none; } * { transition-duration: 0.01ms !important; } }\n\n/* ================= Hero ================= */\n.hero { padding: 58px 0 22px; }\n@media (max-width: 640px) { .hero { padding: 34px 0 14px; } }\n.hero h1 { font-size: clamp(38px, 6.4vw, 78px); font-weight: 700; line-height: 0.98; text-transform: uppercase; letter-spacing: -0.035em; max-width: 940px; margin-top: 14px; }\n.hero h1 .outline { color: transparent; -webkit-text-stroke: 2px var(--ink); }\n.hero h1 .hl { background: var(--lime); color: #fff; padding: 0 10px; border-radius: 8px; display: inline-block; transform: rotate(-1deg); }\n.hero p.lede { color: var(--ink-2); font-size: 16.5px; max-width: 680px; margin: 20px 0 0; }\n.stat-row { display: flex; flex-wrap: wrap; gap: 10px; margin-top: 26px; }\n.stat-chip { background: var(--surface); border: 1.5px solid var(--ink); border-radius: 999px; padding: 8px 16px; font-family: var(--font-mono); font-size: 12px; color: var(--ink-2); display: flex; gap: 8px; align-items: center; box-shadow: 3px 3px 0 rgba(26, 18, 20, 0.12); }\n.stat-chip b { color: var(--ink); font-weight: 700; }\n.stat-chip .pulse { width: 8px; height: 8px; border-radius: 50%; background: var(--green); box-shadow: 0 0 0 4px var(--lime-soft); animation: pulse 2.4s ease infinite; }\n@keyframes pulse { 50% { box-shadow: 0 0 0 7px rgba(226, 55, 68, 0.4); } }\n\n/* ================= Filter bar ================= */\n.filterbar { background: var(--surface); border: 2px solid var(--ink); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); padding: 14px; margin: 30px 0 6px; display: flex; flex-direction: column; gap: 0; min-width: 0; }\n.f-primary { display: flex; gap: 10px; align-items: stretch; flex-wrap: wrap; min-width: 0; }\n.f-search { flex: 1 1 240px; min-width: 0; display: flex; align-items: center; gap: 9px; border: 1.5px solid var(--ink); border-radius: 9px; background: var(--surface-2); padding: 0 13px; min-height: 52px; color: var(--ink-2); transition: box-shadow 0.15s ease; }\n.f-search:focus-within { box-shadow: 3px 3px 0 var(--lime); background: var(--surface); }\n.f-search input { border: none; background: none; outline: none; flex: 1; min-width: 0; font-size: 15px; color: var(--ink); font-family: inherit; }\n.f-clear { border: none; background: var(--line); color: var(--ink-2); border-radius: 50%; width: 20px; height: 20px; font-size: 10px; cursor: pointer; flex: none; display: grid; place-items: center; }\n.f-ctl { flex: 0 1 auto; min-width: 0; display: flex; flex-direction: column; justify-content: center; gap: 2px; border: 1.5px solid var(--ink); border-radius: 9px; background: var(--surface); padding: 7px 12px 6px; min-height: 52px; cursor: pointer; transition: box-shadow 0.15s ease; }\n.f-ctl:focus-within { box-shadow: 3px 3px 0 var(--lime); }\n.f-ctl > span { font-family: var(--font-mono); font-size: 9px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); }\n.f-ctl select { border: none; background: transparent; outline: none; font-family: var(--font-display); font-weight: 700; font-size: 14.5px; color: var(--ink); cursor: pointer; appearance: none; -webkit-appearance: none; padding-right: 16px; min-width: 0; max-width: 180px; text-overflow: ellipsis; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='9' height='5'%3E%3Cpath d='M0 0l4.5 5L9 0z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right center; }\n.range-pair { display: flex; align-items: center; gap: 4px; }\n.range-pair select { max-width: 92px; }\n.range-sep { color: var(--ink-3); font-family: var(--font-mono); font-size: 11px; }\n.f-more { flex: none; align-self: stretch; border: 1.5px solid var(--ink); border-radius: 9px; background: var(--surface); color: var(--ink); padding: 0 18px; cursor: pointer; font-family: var(--font-display); font-weight: 700; font-size: 14px; min-height: 52px; transition: all 0.13s ease; }\n.f-more:hover { background: var(--lime-soft); }\n.f-more.on { background: var(--ink); color: var(--lime); box-shadow: 3px 3px 0 rgba(26, 18, 20,0.22); }\n.f-adv { display: grid; grid-template-columns: repeat(auto-fit, minmax(230px, 1fr)); gap: 16px 22px; border-top: 1.5px dashed var(--line); margin-top: 14px; padding-top: 14px; min-width: 0; animation: adv-in 0.18s ease; }\n@keyframes adv-in { from { opacity: 0; transform: translateY(-4px); } to { opacity: 1; transform: none; } }\n.f-group { display: flex; flex-direction: column; gap: 7px; min-width: 0; }\n.f-group > label { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); }\n.f-group-end { justify-content: flex-end; }\n.f-group .btn:disabled { opacity: 0.4; cursor: default; }\n.f-native { border: 1.5px solid var(--line); background: var(--surface-2); border-radius: var(--r-ctl); padding: 10px 30px 10px 12px; font-size: 14px; color: var(--ink); outline: none; font-family: inherit; min-height: 42px; width: 100%; min-width: 0; appearance: none; -webkit-appearance: none; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='10' height='6'%3E%3Cpath d='M0 0l5 6 5-6z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right 12px center; }\n.f-native:focus { border-color: var(--ink); box-shadow: 3px 3px 0 var(--lime); }\n.seg { display: inline-flex; flex-wrap: wrap; row-gap: 6px; }\n.seg button { border: 1.5px solid var(--ink); background: var(--surface); color: var(--ink-2); padding: 8px 13px; font-family: var(--font-display); font-weight: 600; font-size: 13px; cursor: pointer; margin-left: -1.5px; white-space: nowrap; min-height: 38px; transition: all 0.12s ease; }\n.seg button:first-child { border-radius: 8px 0 0 8px; margin-left: 0; }\n.seg button:last-child { border-radius: 0 8px 8px 0; }\n.seg button:hover { background: var(--lime-soft); color: var(--ink); }\n.seg button.on { background: var(--ink); color: var(--lime); position: relative; z-index: 1; }\n\n.pills { display: flex; flex-wrap: wrap; gap: 8px; margin: 10px 0 2px; }\n.pill { display: inline-flex; align-items: center; gap: 7px; border: 1.5px solid var(--ink); background: var(--lime-soft); color: var(--ink); border-radius: 999px; padding: 6px 13px; font-size: 12.5px; font-weight: 600; font-family: var(--font-mono); cursor: pointer; transition: all 0.13s ease; }\n.pill:hover { background: var(--lime); color: #fff; }\n.pill-x { font-size: 10px; opacity: 0.65; }\n.pill-clear { background: var(--surface); border-color: var(--danger); color: var(--danger); }\n.pill-clear:hover { background: var(--danger); color: #fff; }\n.link-btn { background: none; border: none; padding: 0; color: var(--green); font: inherit; text-decoration: underline; cursor: pointer; }\n\n.btn { border: 1.5px solid var(--ink); border-radius: var(--r-ctl); padding: 11px 20px; font-weight: 700; font-size: 14px; cursor: pointer; min-height: 44px; transition: transform 0.12s ease, box-shadow 0.12s ease, background 0.15s ease; font-family: var(--font-display); letter-spacing: 0.01em; }\n.btn:active { transform: translate(2px, 2px); box-shadow: none !important; }\n.btn-primary { background: var(--ink); color: var(--lime); box-shadow: 4px 4px 0 rgba(26, 18, 20,0.25); }\n.btn-primary:hover { background: var(--green-deep); }\n.btn-ghost { background: transparent; color: var(--ink); }\n.btn-ghost:hover { background: var(--lime); color: #fff; box-shadow: 4px 4px 0 rgba(26, 18, 20,0.2); }\n\n/* ================= Chips ================= */\n.chips { display: flex; gap: 8px; margin: 16px 0 4px; overflow-x: auto; padding-bottom: 6px; scrollbar-width: none; }\n.chips::-webkit-scrollbar { display: none; }\n.chip { border: 1.5px solid var(--ink); background: var(--surface); color: var(--ink); border-radius: 999px; padding: 8px 18px; font-size: 13.5px; font-weight: 600; cursor: pointer; font-family: var(--font-display); white-space: nowrap; min-height: 40px; transition: all 0.13s ease; }\n.chip:hover { background: var(--lime-soft); transform: translateY(-1px); }\n.chip.on { background: var(--ink); color: var(--lime); box-shadow: 3px 3px 0 rgba(26, 18, 20,0.2); }\n\n/* ================= Results ================= */\n.results-head { display: flex; align-items: baseline; gap: 14px; margin: 28px 0 18px; flex-wrap: wrap; }\n.results-head h2 { font-size: 22px; text-transform: uppercase; letter-spacing: -0.02em; }\n.results-head > span { font-family: var(--font-mono); color: var(--ink-3); font-size: 12px; letter-spacing: 0.06em; }\n.results-tools { display: flex; align-items: center; gap: 12px; margin-left: auto; }\n.sort-ctl select { border: 1.5px solid var(--line-strong); background: var(--surface); border-radius: var(--r-ctl); padding: 7px 28px 7px 12px; font-family: var(--font-mono); font-size: 12px; color: var(--ink); cursor: pointer; appearance: none; -webkit-appearance: none; background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='10' height='6'%3E%3Cpath d='M0 0l5 6 5-6z' fill='%23141814'/%3E%3C/svg%3E\"); background-repeat: no-repeat; background-position: right 10px center; }\n.view-toggle { display: inline-flex; border: 1.5px solid var(--line-strong); border-radius: var(--r-ctl); overflow: hidden; background: var(--surface); box-shadow: var(--shadow-flat); }\n.view-toggle button { border: 0; background: transparent; padding: 7px 14px; cursor: pointer; font-family: var(--font-mono); font-size: 12px; color: var(--ink-2); }\n.view-toggle button + button { border-left: 1.5px solid var(--line-strong); }\n.view-toggle button.on { background: var(--ink); color: var(--lime); }\n\n.grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(290px, 1fr)); gap: 22px; padding-bottom: 20px; }\n@media (max-width: 640px) { .grid { grid-template-columns: 1fr; gap: 16px; } }\n.more-wrap { display: flex; justify-content: center; padding: 8px 0 70px; }\n\n.card { background: var(--surface); border: 1.5px solid var(--line-strong); border-radius: var(--r-card); overflow: hidden; display: flex; flex-direction: column; box-shadow: var(--shadow-flat); transition: transform 0.16s ease, box-shadow 0.16s ease; }\n.card:hover { transform: translate(-3px, -3px); box-shadow: var(--shadow-off); }\n.card:focus-visible { outline: 3px solid var(--lime); outline-offset: 2px; }\n.card-img { position: relative; aspect-ratio: 16/10.5; background: repeating-linear-gradient(45deg, #eceae3 0 12px, #f3f1ea 12px 24px); overflow: hidden; }\n.card-img img { width: 100%; height: 100%; object-fit: cover; transition: transform 0.4s ease; }\n.card:hover .card-img img { transform: scale(1.05); }\n.card-img .noimg { display: flex; align-items: center; justify-content: center; height: 100%; color: var(--ink-3); font-size: 34px; }\n.badge { position: absolute; top: 12px; left: 12px; border-radius: 6px; padding: 4px 10px; font-family: var(--font-mono); font-size: 11.5px; font-weight: 700; letter-spacing: 0.04em; background: rgba(255, 255, 255, 0.95); color: var(--ink); border: 1px solid var(--ink); }\n.badge.type { background: var(--ink); color: var(--lime); border-color: var(--ink); }\n.badge.right { left: auto; right: 12px; background: rgba(255,255,255,0.92); border-color: transparent; }\n.card-body { padding: 16px 18px; display: flex; flex-direction: column; gap: 6px; flex: 1; }\n.card-price { font-family: var(--font-display); font-weight: 700; font-size: 21px; letter-spacing: -0.02em; }\n.card-title { font-weight: 600; font-size: 14.5px; color: var(--ink); overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }\n.card-meta { color: var(--ink-3); font-size: 12.5px; display: flex; gap: 7px; flex-wrap: wrap; align-items: center; font-family: var(--font-mono); }\n.card-meta .sep { width: 4px; height: 4px; background: var(--lime); border: 1px solid var(--ink); border-radius: 1px; transform: rotate(45deg); }\n.card-specs { display: flex; gap: 12px; font-size: 12.5px; color: var(--ink-2); font-family: var(--font-mono); }\n.card-foot { margin-top: auto; padding-top: 11px; border-top: 1.5px dashed var(--line); display: flex; justify-content: space-between; align-items: center; gap: 8px; }\n.source-tag { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.08em; color: var(--green-deep); background: var(--lime-soft); border: 1px solid var(--green); border-radius: 4px; padding: 3px 8px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; max-width: 62%; }\n.avail { font-size: 11px; color: var(--ink-3); font-weight: 500; text-align: right; font-family: var(--font-mono); }\n\n/* ================= Skeletons ================= */\n@keyframes shimmer { 0% { background-position: -400px 0; } 100% { background-position: 400px 0; } }\n.skel { border-radius: var(--r-card); border: 1.5px solid var(--line); overflow: hidden; background: var(--surface); }\n.skel .sk-img, .skel .sk-line { background: linear-gradient(90deg, #eeece5 25%, #f7f5ef 50%, #eeece5 75%); background-size: 800px 100%; animation: shimmer 1.4s infinite linear; }\n.skel .sk-img { aspect-ratio: 16/10.5; }\n.skel .sk-line { height: 14px; border-radius: 4px; margin: 12px 16px; }\n.skel .sk-line.short { width: 45%; }\n\n/* ================= Empty / error ================= */\n.notice { text-align: center; padding: 72px 24px; color: var(--ink-2); }\n.notice .big { font-size: 44px; margin-bottom: 10px; }\n.notice h2 { text-transform: uppercase; }\n\n/* ================= Detail page ================= */\n.detail { padding: 30px 0 90px; }\n.crumbs { font-family: var(--font-mono); font-size: 11.5px; letter-spacing: 0.06em; text-transform: uppercase; color: var(--ink-3); margin-bottom: 20px; display: flex; gap: 8px; align-items: center; flex-wrap: wrap; }\n.crumbs a { border-bottom: 1.5px solid transparent; }\n.crumbs a:hover { color: var(--green); border-color: var(--green); }\n\n/* Ordre DOM = ordre visuel sur TOUS les breakpoints (standard Groupe Ka\n   « Ordre des sections — pages détail »). Interdit : `order` / `column-reverse`\n   / `display:contents` pour réordonner — un bloc sans `order` retombe à\n   order:0 et passe devant la galerie sur mobile (bug corrigé sur Lou-Ka).\n   Mobile : colonnes empilées ; desktop : grid 2 colonnes (fiche | annexes). */\n.fiche { display: flex; flex-direction: column; gap: 22px; }\n.f-col { display: flex; flex-direction: column; gap: 22px; min-width: 0; }\n@media (min-width: 900px) {\n  .fiche { display: grid; grid-template-columns: 1.6fr 1fr; gap: 30px; align-items: start; }\n  .f-col { gap: 26px; }\n  .f-col:last-child { position: sticky; top: 88px; }\n}\n.f-bloc { min-width: 0; }\n.f-bloc h2 { font-size: 21px; letter-spacing: -0.02em; margin-bottom: 12px; border-left: 4px solid var(--lime); padding-left: 10px; }\n.f-bloc:empty { display: none; }\n\n/* galerie */\n.carousel { position: relative; border-radius: var(--r-card); overflow: hidden; border: 1.5px solid var(--line-strong); background: var(--surface-2); box-shadow: var(--shadow-off-soft); }\n.carousel-track { display: flex; overflow-x: auto; scroll-snap-type: x mandatory; -webkit-overflow-scrolling: touch; scrollbar-width: none; aspect-ratio: 16/11; }\n.carousel-track::-webkit-scrollbar { display: none; }\n.carousel-track img { flex: 0 0 100%; width: 100%; object-fit: cover; scroll-snap-align: center; cursor: zoom-in; }\n.carousel-empty { display: flex; align-items: center; justify-content: center; aspect-ratio: 16/11; font-size: 48px; }\n.carousel-count { position: absolute; right: 12px; bottom: 12px; z-index: 2; background: rgba(26, 18, 20, 0.82); color: var(--lime); font-family: var(--font-mono); font-size: 12px; font-weight: 700; padding: 4px 10px; border-radius: 999px; }\n.carousel-nav { position: absolute; top: 50%; transform: translateY(-50%); z-index: 2; width: 44px; height: 44px; border-radius: 50%; border: 1.5px solid var(--ink); background: rgba(255, 255, 255, 0.92); font-size: 22px; cursor: pointer; display: flex; align-items: center; justify-content: center; line-height: 1; }\n.carousel-nav.prev { left: 10px; } .carousel-nav.next { right: 10px; }\n@media (max-width: 640px) { .carousel-nav { display: none; } }\n.thumbs { display: grid; grid-template-columns: repeat(auto-fill, minmax(84px, 1fr)); gap: 8px; margin-top: 10px; }\n.thumbs button { border: 2px solid var(--line); border-radius: 8px; overflow: hidden; padding: 0; cursor: pointer; aspect-ratio: 4/3; background: #eceae3; transition: border-color 0.12s ease, transform 0.12s ease; }\n.thumbs button:hover { transform: translateY(-2px); }\n.thumbs button.on { border-color: var(--ink); box-shadow: 3px 3px 0 var(--lime); }\n.thumbs img { width: 100%; height: 100%; object-fit: cover; }\n@media (max-width: 640px) { .thumbs { display: flex; overflow-x: auto; scrollbar-width: none; } .thumbs::-webkit-scrollbar { display: none; } .thumbs button { flex: 0 0 84px; } }\n\n/* lightbox */\n.lightbox { position: fixed; inset: 0; background: rgba(16, 18, 16, 0.94); z-index: 100; display: flex; align-items: center; justify-content: center; cursor: zoom-out; padding: max(16px, env(safe-area-inset-top)) 16px; }\n.lightbox img { max-width: 94vw; max-height: 86vh; border-radius: 6px; border: 2px solid var(--lime); object-fit: contain; }\n.lb-close { position: absolute; top: 18px; right: 22px; background: none; border: none; color: #fff; font-size: 30px; cursor: pointer; }\n.lb-nav { position: absolute; top: 50%; transform: translateY(-50%); background: none; border: none; color: var(--lime); font-size: 52px; cursor: pointer; padding: 0 22px; user-select: none; }\n.lb-nav.prev { left: 6px; } .lb-nav.next { right: 6px; }\n.lb-count { position: absolute; bottom: 24px; color: #c9ccc2; font-family: var(--font-mono); font-size: 13px; }\n\n/* hero panneau (colonne droite) */\n.f-hero { background: var(--surface); border: 1.5px solid var(--ink); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); padding: 26px; }\n.price-kicker { font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.14em; color: var(--green); }\n.price-kicker::before { content: \"\"; display: inline-block; width: 16px; height: 2px; background: var(--lime); vertical-align: 3px; margin-right: 7px; }\n.price-row { display: flex; align-items: baseline; gap: 12px; flex-wrap: wrap; }\n.f-hero .price { font-family: var(--font-display); font-size: clamp(30px, 6vw, 40px); font-weight: 700; letter-spacing: -0.03em; }\n.type-chip { display: inline-flex; align-items: center; gap: 5px; padding: 4px 11px; background: var(--lime-soft); color: var(--green-deep); border: 1px solid rgba(179, 32, 43, 0.25); border-radius: 999px; font-size: 12px; font-weight: 600; }\n.price-sub { font-family: var(--font-mono); font-size: 11.5px; color: var(--ink-3); margin-top: 2px; }\n.f-hero h1 { font-size: clamp(20px, 4.5vw, 24px); margin-top: 10px; }\n.f-hero .loc { color: var(--ink-2); font-size: 13.5px; margin-top: 3px; display: flex; align-items: center; gap: 5px; }\n.f-hero .loc .ico { color: var(--lime); }\n\n/* liste de specs — fiche technique premium */\n.spec-list { margin: 18px 0 4px; border-top: 1.5px solid var(--line-strong); }\n.spec-row { display: grid; grid-template-columns: 30px 1fr auto; align-items: center; gap: 11px; padding: 8.5px 0; border-bottom: 1px dashed var(--line); }\n.spec-row:last-child { border-bottom: 1.5px solid var(--line-strong); }\n.spec-badge { display: inline-flex; align-items: center; justify-content: center; width: 30px; height: 30px; border-radius: 9px; background: var(--lime-soft); color: var(--green-deep); }\n.spec-k { font-family: var(--font-mono); font-size: 10.5px; text-transform: uppercase; letter-spacing: 0.08em; color: var(--ink-2); }\n.spec-v { font-family: var(--font-display); font-size: 15.5px; font-weight: 700; text-align: right; }\n.deal-badge { display: inline-flex; align-items: center; gap: 6px; margin: 8px 0 4px; border-radius: 999px; padding: 6px 13px; font-size: 12.5px; font-weight: 600; border: 1.5px solid var(--line-strong); }\n.deal-ok { background: var(--lime-soft); border-color: var(--green); color: var(--green-deep); }\n\n.specs-grid { display: grid; grid-template-columns: repeat(auto-fill, minmax(96px, 1fr)); gap: 8px; margin: 18px 0; }\n.spec { border: 1.5px solid var(--line-strong); border-radius: var(--r-ctl); padding: 9px 11px; background: var(--surface-2); }\n.spec b { display: block; font-family: var(--font-display); font-size: 16px; }\n.spec span { font-family: var(--font-mono); font-size: 9.5px; text-transform: uppercase; letter-spacing: 0.08em; color: var(--ink-3); }\n\n.broker { margin: 16px 0; padding: 14px; border: 1.5px dashed var(--line-strong); border-radius: var(--r-ctl); background: var(--surface-2); }\n.broker-k { font-family: var(--font-mono); font-size: 9.5px; text-transform: uppercase; letter-spacing: 0.1em; color: var(--ink-3); }\n.broker-name { font-family: var(--font-display); font-weight: 700; font-size: 15px; margin-top: 3px; }\n.broker-tel { display: inline-block; margin-top: 6px; color: var(--green-deep); font-weight: 600; border-bottom: 1.5px solid var(--lime); }\n\n.cta { display: block; text-align: center; background: var(--ink); color: var(--lime); font-weight: 700; font-family: var(--font-display); border-radius: var(--r-ctl); padding: 15px; border: 1.5px solid var(--ink); min-height: 48px; box-shadow: 4px 4px 0 rgba(26, 18, 20,0.25); transition: all 0.14s ease; margin-top: 6px; }\n.cta:hover { background: var(--lime); color: #fff; }\n.cta:active { transform: translate(2px, 2px); box-shadow: none; }\n.f-hero .fine { font-size: 11.5px; color: var(--ink-3); margin-top: 14px; text-align: center; font-family: var(--font-mono); letter-spacing: 0.02em; }\n\n.desc-text { color: var(--ink-2); font-size: 14.5px; white-space: pre-line; line-height: 1.6; margin: 0; }\n\n/* tableau caractéristiques */\n.dtable { display: grid; grid-template-columns: repeat(auto-fill, minmax(240px, 1fr)); gap: 0 22px; }\n.drow { display: flex; justify-content: space-between; gap: 12px; padding: 8px 0; border-bottom: 1px solid var(--line); font-size: 13.5px; }\n.drow span { color: var(--ink-3); } .drow b { text-align: right; font-family: var(--font-display); }\n\n/* tableau pièces */\n.rooms-wrap { overflow-x: auto; border: 1.5px solid var(--line-strong); border-radius: var(--r-card); }\ntable.rooms { width: 100%; border-collapse: collapse; font-size: 13.5px; min-width: 460px; }\ntable.rooms th { text-align: left; background: var(--ink); color: var(--paper); padding: 9px 12px; font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.06em; }\ntable.rooms td { padding: 8px 12px; border-bottom: 1px solid var(--line); }\ntable.rooms tr:last-child td { border-bottom: none; }\ntable.rooms tr:nth-child(even) td { background: var(--surface-2); }\n\n.amenity-row { display: flex; flex-wrap: wrap; gap: 7px; }\n.amenity { background: var(--lime-soft); color: var(--green-deep); font-size: 12px; font-weight: 600; border: 1px solid var(--green); border-radius: 999px; padding: 5px 12px; }\n\n.prix-histo { margin-top: 14px; padding: 10px 14px; border-radius: var(--r-card); border: 1.5px solid var(--line-strong); font-size: 13.5px; background: var(--surface); }\n.prix-histo.down { background: var(--lime-soft); border-color: var(--green); color: var(--green-deep); }\n\n.mini-map { height: 320px; border: 1.5px solid var(--line-strong); border-radius: var(--r-card); overflow: hidden; }\n.mini-map .mapview { border: none; border-radius: 0; box-shadow: none; height: 100%; }\n\n.cta-sticky { position: fixed; left: 0; right: 0; bottom: 0; z-index: 45; display: flex; align-items: center; gap: 12px; background: rgba(247, 241, 239, 0.94); backdrop-filter: blur(12px); border-top: 2px solid var(--ink); padding: 10px 16px calc(10px + env(safe-area-inset-bottom)); }\n.cta-sticky .cta { flex: 1; margin: 0; }\n.cta-sticky-prix { font-family: var(--font-display); font-weight: 700; font-size: 19px; white-space: nowrap; }\n@media (min-width: 900px) { .cta-sticky { display: none; } }\n@media (max-width: 899px) { .detail { padding-bottom: 84px; } }\n.f-foot { margin-top: 26px; }\n.fine { font-size: 12px; color: var(--ink-3); }\n\n/* ================= Sources / Agences ================= */\n.sources { padding: 44px 0 90px; }\n.sources h1 { font-size: clamp(28px, 4vw, 40px); text-transform: uppercase; margin: 10px 0 6px; }\n.sources .sub { color: var(--ink-2); margin-bottom: 30px; max-width: 720px; }\n.src-wrap { overflow-x: auto; border: 1.5px solid var(--ink); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); background: var(--surface); }\n.src-table { width: 100%; border-collapse: separate; border-spacing: 0; min-width: 760px; }\n.src-table th { text-align: left; font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); padding: 14px 18px; border-bottom: 1.5px solid var(--ink); background: var(--surface-2); }\n.src-table td { padding: 13px 18px; border-bottom: 1px solid var(--line); font-size: 14px; vertical-align: top; }\n.src-table tr:last-child td { border-bottom: none; }\n.src-table tr:hover td { background: var(--surface-2); }\n.src-table a { color: var(--green-deep); font-weight: 600; border-bottom: 1.5px solid var(--lime); }\n.pill.ok { background: var(--lime); color: #fff; border: 1px solid var(--ink); border-radius: 4px; padding: 3px 10px; font-family: var(--font-mono); font-size: 10.5px; font-weight: 700; text-transform: uppercase; }\n.pill.todo { background: var(--amber-soft); color: #8a5a12; border: 1px solid var(--amber); border-radius: 4px; padding: 3px 10px; font-family: var(--font-mono); font-size: 10.5px; font-weight: 700; text-transform: uppercase; }\n.count-pill { font-weight: 700; font-family: var(--font-display); font-size: 16px; border-bottom: 1.5px solid var(--lime); }\n\n/* ================= Stats ================= */\n.stats-page { padding: 44px 0 90px; }\n.stats-title { font-size: clamp(30px, 4.6vw, 46px); text-transform: uppercase; margin: 10px 0 6px; }\n.stats-page .sub { color: var(--ink-2); max-width: 660px; margin-bottom: 30px; }\n.tiles { display: grid; grid-template-columns: repeat(auto-fit, minmax(160px, 1fr)); gap: 14px; margin-bottom: 30px; }\n.tile { background: var(--surface); border: 1.5px solid var(--ink); border-radius: var(--r-card); padding: 18px 20px; box-shadow: 3px 3px 0 rgba(26, 18, 20, 0.1); }\n.tile-v { font-family: var(--font-display); font-weight: 700; font-size: clamp(22px, 3vw, 32px); letter-spacing: -0.03em; line-height: 1.05; }\n.tile-k { font-family: var(--font-mono); font-size: 10.5px; text-transform: uppercase; letter-spacing: 0.1em; color: var(--ink-3); margin-top: 6px; }\n.hero-tile { background: var(--ink); color: var(--lime); border-color: var(--ink); }\n.hero-tile .tile-k { color: rgba(226, 55, 68, 0.7); }\n.viz-card { background: var(--surface); border: 1.5px solid var(--ink); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); padding: 26px 28px 20px; margin-bottom: 22px; min-width: 0; max-width: 100%; overflow-x: auto; }\n/* mobile : une table large ne doit JAMAIS déborder la page (grid blowout) —\n   chaque bloc de la page Stats défile horizontalement en interne au besoin */\n.stats-page section, .stats-page > * { min-width: 0; max-width: 100%; }\n@media (max-width: 640px) { .stats-page .viz-card { padding: 18px 14px 14px; } }\n.viz-card h2 { font-size: 19px; text-transform: uppercase; letter-spacing: -0.01em; }\n.viz-sub { font-family: var(--font-mono); font-size: 11px; letter-spacing: 0.06em; text-transform: uppercase; color: var(--ink-3); margin: 4px 0 20px; }\n.viz-grid { display: grid; grid-template-columns: 1fr 1fr; gap: 22px; }\n@media (max-width: 900px) { .viz-grid { grid-template-columns: 1fr; } }\n.hbars { display: flex; flex-direction: column; gap: 7px; }\n.hbar-row { display: grid; grid-template-columns: minmax(96px, 180px) 1fr auto; gap: 12px; align-items: center; min-height: 26px; }\n.hbar-label { font-size: 13px; font-weight: 600; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }\n.hbar-label a { border-bottom: 1.5px solid var(--lime); }\n.hbar-label a:hover { color: var(--green-deep); }\n.hbar-track { background: var(--surface-2); border-radius: 0 4px 4px 0; height: 18px; overflow: hidden; }\n.hbar-fill { display: block; height: 100%; background: var(--green); border-radius: 0 4px 4px 0; min-width: 2px; transition: background 0.12s ease; }\n.hbar-row:hover .hbar-fill { background: var(--ink); box-shadow: inset 0 0 0 2px var(--lime); }\n.hbar-value { font-family: var(--font-mono); font-size: 11.5px; font-weight: 700; white-space: nowrap; }\n.hbar-value em { font-style: normal; font-weight: 500; color: var(--ink-3); }\n.viz-table { margin-top: 4px; }\n.viz-table table { width: 100%; border-collapse: collapse; font-size: 13px; }\n.viz-table th { text-align: left; font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.08em; color: var(--ink-3); padding: 6px 10px; border-bottom: 1.5px solid var(--ink); }\n.viz-table td { padding: 6px 10px; border-bottom: 1px solid var(--line); }\n.stats-foot { font-family: var(--font-mono); font-size: 11px; color: var(--ink-3); letter-spacing: 0.04em; margin-top: 6px; }\n\n/* ---- Iconographie maison (Icons.tsx) ---- */\n.ico { vertical-align: -3px; flex-shrink: 0; }\n.card-specs span { display: inline-flex; align-items: center; gap: 5px; }\n.card-specs .ico { color: var(--green); }\n.badge.right .ico { vertical-align: -2px; }\n.noimg .ico, .carousel-empty .ico { color: var(--ink-3); }\n.notice .big .ico { color: var(--lime); }\n.amenity .ico { color: var(--green); margin-right: 2px; }\n.q-badge .ico { vertical-align: -3px; margin-right: 2px; }\n.q-bar-label { display: inline-flex; align-items: center; gap: 7px; }\n.q-bar-label .ico { color: var(--green); }\n.mm-ico .ico { vertical-align: -4px; }\n.mv-noimg svg { color: var(--ink-3); }\n.broker-tel .ico { vertical-align: -2.5px; }\n.cta .ico { vertical-align: -2.5px; margin-left: 3px; }\n.prix-histo .ico { vertical-align: -3.5px; }\n.spec { position: relative; }\n.spec-ico { position: absolute; top: 10px; right: 10px; color: var(--lime); opacity: 0.85; }\n\n/* ---- Survalorisation vs Vrai-Prix (jauges divergentes) ---- */\n.vpg-axis { display: grid; grid-template-columns: 220px 1fr 74px 90px; gap: 10px; margin-bottom: 2px; }\n.vpg-axis > div { grid-column: 2; display: flex; justify-content: space-between; font-family: var(--font-mono); font-size: 10px; color: var(--ink-3); }\n.vpg-row { display: grid; grid-template-columns: 220px 1fr 74px 90px; align-items: center; gap: 10px; padding: 7px 0; border-bottom: 1px dashed var(--line); }\n.vpg-row:last-of-type { border-bottom: none; }\n.vpg-name { font-size: 13.5px; font-weight: 600; }\n.vpg-name em { display: block; font-style: normal; font-family: var(--font-mono); font-size: 10px; color: var(--ink-3); }\n.vpg-track { position: relative; height: 18px; background: var(--surface-2); border: 1px solid var(--line); border-radius: 999px; overflow: hidden; }\n.vpg-zero { position: absolute; left: 50%; top: 0; bottom: 0; width: 1.5px; background: var(--line-strong); opacity: 0.5; }\n.vpg-band { position: absolute; top: 3px; bottom: 3px; background: rgba(26, 18, 20, 0.14); border-radius: 999px; }\n.vpg-median { position: absolute; top: 1px; bottom: 1px; width: 4px; margin-left: -2px; border-radius: 2px; }\n.vpg-median.vp-sur { background: var(--lime); }\n.vpg-median.vp-juste { background: #d9a942; }\n.vpg-median.vp-sous { background: #4c8b4f; }\n.vpg-value { font-family: var(--font-display); font-weight: 700; font-size: 15px; text-align: right; }\n.vpg-value.vp-sur { color: var(--green); }\n.vpg-value.vp-juste { color: #a97b1e; }\n.vpg-value.vp-sous { color: #3c7440; }\n.vpg-split { display: flex; height: 10px; border-radius: 999px; overflow: hidden; border: 1px solid var(--line); }\n.vps-sous { background: #7fb283; }\n.vps-juste { background: #d9cfc7; }\n.vps-sur { background: var(--lime); }\n.vpg-sep { border-top: 1.5px solid var(--line-strong); margin: 4px 0; }\n.vpg-legend { display: flex; gap: 16px; margin-top: 10px; font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-2); flex-wrap: wrap; }\n.vpg-legend i { display: inline-block; width: 14px; height: 9px; border-radius: 999px; margin-right: 5px; vertical-align: -1px; }\n@media (max-width: 780px) { .vpg-row { grid-template-columns: 1fr 64px; } .vpg-track { grid-column: 1 / -1; } .vpg-split { grid-column: 1 / -1; } .vpg-axis { display: none; } }\n\n/* ================= Carte (Ka Maps — framework partagé Groupe Ka) =========\n   Jetons du design system appliqués au chrome de la carte, mêmes réglages\n   que Lou-Ka Maps. */\n.mapview .ka-map, .lmap3d .ka-map {\n  --ka-accent: var(--accent);\n  --ka-on-accent: var(--on-accent);\n  --ka-surface: var(--surface);\n  --ka-ink: var(--ink);\n  --ka-line: var(--line-strong);\n  --ka-radius: var(--r-ctl);\n  --ka-shadow: 0 8px 24px rgba(26, 18, 20, 0.14);\n  --ka-font: var(--font-body);\n}\n.mapview { position: relative; overflow: hidden; }\n.mapview .ka-map { position: absolute; inset: 0; }\n\n/* mini-carte 3D de la fiche (Emplacement) — bâtiment de l'annonce en cerise */\n.lmap3d {\n  position: relative; height: 340px; border: 1.5px solid var(--ink);\n  border-radius: var(--r-card); box-shadow: var(--shadow-off-soft);\n  overflow: hidden; background: var(--surface-2);\n}\n.lmap3d-skel {\n  background: linear-gradient(90deg, #eeece5 25%, #f7f5ef 50%, #eeece5 75%);\n  background-size: 800px 100%; animation: shimmer 1.4s infinite linear;\n}\n.lmap3d-legende {\n  position: absolute; left: 10px; bottom: 10px; z-index: 5;\n  display: inline-flex; align-items: center; gap: 6px;\n  padding: 4px 10px; border: 1.5px solid var(--ink);\n  border-radius: 999px; background: var(--surface);\n  font-size: 10.5px; font-weight: 500; color: var(--ink-2);\n  pointer-events: none;\n}\n.lmap3d-legende i {\n  width: 10px; height: 10px; border-radius: 3px;\n  background: var(--accent); border: 1px solid rgba(26, 18, 20, 0.25);\n}\n@media (max-width: 780px) { .lmap3d { height: 280px; } }\n\n/* ================= Carte ================= */\n.map-split { display: grid; grid-template-columns: minmax(300px, 400px) 1fr; gap: 18px; height: calc(100dvh - 200px); min-height: 420px; }\n.map-list { overflow-y: auto; display: flex; flex-direction: column; gap: 14px; padding-right: 4px; scrollbar-width: thin; }\n.map-list .card { margin: 0; flex: 0 0 auto; }\n/* sync liste ↔ carte : fiche sélectionnée depuis une pastille de la carte */\n.map-card { border-radius: var(--r-card); }\n.map-card-sel .card { border-color: var(--accent); box-shadow: 4px 4px 0 var(--accent-soft), 0 0 0 2px var(--accent); }\n.mapwrap { position: relative; width: 100%; height: 100%; }\n.mapview { width: 100%; height: 100%; border: 1.5px solid var(--line-strong); border-radius: var(--r-card); box-shadow: var(--shadow-off-soft); overflow: hidden; background: var(--surface-2); }\n.mv-brand { position: absolute; top: 10px; left: 10px; z-index: 3; display: flex; align-items: center; gap: 6px; padding: 5px 11px; background: var(--surface); border: 1.5px solid var(--line-strong); border-radius: 999px; box-shadow: var(--shadow-off-soft); font-family: var(--font-mono); font-size: 11px; letter-spacing: 0.04em; color: var(--ink-2); pointer-events: none; }\n.mv-brand b { color: var(--ink); }\n.mv-brand-dot { width: 8px; height: 8px; border-radius: 50%; background: var(--lime); box-shadow: 0 0 0 3px var(--lime-soft); }\n.mv-legend { position: absolute; bottom: 26px; left: 10px; z-index: 3; display: flex; flex-direction: column; gap: 4px; padding: 8px 11px; background: rgba(255, 255, 255, 0.92); border: 1px solid var(--line); border-radius: var(--r-ctl); font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-2); pointer-events: none; }\n.mv-legend span { display: flex; align-items: center; gap: 7px; }\n.mv-lg-pill { display: inline-block; width: 22px; height: 12px; border-radius: 999px; background: #fff; border: 1.5px solid rgba(26, 18, 20, 0.35); }\n.mv-lg-deal { background: var(--lime); border-color: var(--green); }\n.mv-credit { position: absolute; bottom: 8px; right: 8px; z-index: 3; padding: 4px 10px; background: rgba(255, 255, 255, 0.9); border-radius: 999px; font-family: var(--font-mono); font-size: 10px; letter-spacing: 0.04em; color: var(--ink-2); pointer-events: none; }\n.mv-credit b { color: var(--ink); }\n.mini-map .mv-brand, .mini-map .mv-legend { display: none; }\n.mini-map .mapwrap { height: 100%; }\n\n/* ---- carte propriété (fiche) : marqueur pulsant + anneaux piéton ---- */\n.pm-wrap { position: relative; width: 100%; height: 100%; }\n.pm-marker { display: flex; flex-direction: column; align-items: center; }\n.pm-pill { padding: 5px 13px; background: #fff; border: 1.5px solid var(--line-strong); border-radius: 999px; font-family: var(--font-display); font-weight: 700; font-size: 14px; color: var(--ink); box-shadow: var(--shadow-off-soft); white-space: nowrap; margin-bottom: 4px; }\n.pm-pill-deal { background: var(--lime); border-color: var(--green); color: #fff; }\n.pm-dot { width: 14px; height: 14px; border-radius: 50%; background: var(--lime); border: 2.5px solid #fff; box-shadow: 0 1px 4px rgba(26, 18, 20, 0.4); }\n.pm-pulse { position: absolute; bottom: -8px; width: 30px; height: 30px; border-radius: 50%; background: var(--lime); opacity: 0.35; animation: pm-pulse 2s ease-out infinite; }\n@keyframes pm-pulse { 0% { transform: scale(0.5); opacity: 0.45; } 80% { transform: scale(1.9); opacity: 0; } 100% { opacity: 0; } }\n.pm-rings-legend { position: absolute; bottom: 8px; left: 8px; z-index: 3; display: flex; gap: 12px; padding: 4px 10px; background: rgba(255, 255, 255, 0.9); border-radius: 999px; font-family: var(--font-mono); font-size: 10px; color: var(--green); pointer-events: none; }\n.mini-map .pm-wrap .mv-credit { display: block; }\n.map-loading { display: flex; align-items: center; justify-content: center; color: var(--ink-3); font-family: var(--font-mono); font-size: 13px; }\n@media (max-width: 780px) { .map-split { grid-template-columns: 1fr; height: calc(100dvh - 230px); } .map-list { display: none; } .results-tools { width: 100%; justify-content: space-between; } }\n.maplibregl-popup-content { padding: 0; border-radius: var(--r-card); overflow: hidden; border: 1.5px solid var(--line-strong); box-shadow: var(--shadow-off); font-family: var(--font-body); }\n.maplibregl-popup-close-button { font-size: 18px; padding: 2px 8px; color: var(--paper); z-index: 2; text-shadow: 0 0 4px rgba(26, 18, 20, 0.8); }\n.mv-pop img, .mv-noimg { width: 100%; height: 130px; object-fit: cover; }\n.mv-noimg { display: flex; align-items: center; justify-content: center; font-size: 34px; background: var(--surface-2); }\n.mv-pop-body { padding: 10px 12px 12px; }\n.mv-pop-price { font-family: var(--font-display); font-weight: 700; font-size: 18px; }\n.mv-pop-title { font-size: 13px; color: var(--ink-2); margin-top: 2px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }\n.mv-pop-meta { font-family: var(--font-mono); font-size: 11px; color: var(--ink-2); margin-top: 6px; }\n.mv-pop-src { font-family: var(--font-mono); font-size: 10px; color: var(--ink-3); text-transform: uppercase; letter-spacing: 0.06em; margin-top: 2px; }\n.mv-pop-cta { display: block; margin-top: 10px; padding: 8px 10px; text-align: center; background: var(--ink); color: #fff; border-radius: var(--r-ctl); font-weight: 600; font-size: 13px; }\n.mv-pop-cta:hover { background: var(--green-deep); }\n.mv-pop-deal { display: inline-block; margin-left: 6px; padding: 2px 8px; vertical-align: 3px; background: var(--lime); color: #fff; border-radius: 999px; font-family: var(--font-mono); font-size: 9.5px; text-transform: uppercase; letter-spacing: 0.05em; }\n.maplibregl-ctrl-attrib { font-size: 10px; }\n\n/* ================= Menu mobile ================= */\n.menu-btn { display: none; position: relative; z-index: 60; width: 44px; height: 44px; margin-left: auto; border: 1.5px solid var(--ink); border-radius: var(--r-ctl); background: var(--surface); cursor: pointer; padding: 0; flex-direction: column; align-items: center; justify-content: center; gap: 5px; box-shadow: 3px 3px 0 rgba(26, 18, 20, 0.15); transition: box-shadow 0.15s ease, transform 0.15s ease; }\n.menu-btn:active { transform: translate(2px, 2px); box-shadow: 1px 1px 0 rgba(26, 18, 20,0.15); }\n.menu-btn span { display: block; width: 18px; height: 2px; background: var(--ink); border-radius: 2px; transition: transform 0.25s ease, opacity 0.2s ease; }\n.menu-btn.open span:nth-child(1) { transform: translateY(7px) rotate(45deg); }\n.menu-btn.open span:nth-child(2) { opacity: 0; }\n.menu-btn.open span:nth-child(3) { transform: translateY(-7px) rotate(-45deg); }\n.mobile-menu { display: none; position: absolute; left: 0; right: 0; top: 100%; background: var(--paper); border-bottom: 2px solid var(--ink); box-shadow: 0 18px 30px rgba(26, 18, 20, 0.18); padding: 8px 16px calc(16px + env(safe-area-inset-bottom)); max-height: 0; overflow: hidden; opacity: 0; transition: max-height 0.3s ease, opacity 0.22s ease; }\n.mobile-menu.open { max-height: 480px; opacity: 1; }\n.mm-link { display: flex; align-items: center; gap: 12px; padding: 15px 10px; min-height: 52px; font-family: var(--font-display); font-weight: 700; font-size: 19px; letter-spacing: -0.02em; border-bottom: 1.5px dashed var(--line); opacity: 0; transform: translateY(-8px); transition: opacity 0.25s ease, transform 0.25s ease; }\n.mobile-menu.open .mm-link { opacity: 1; transform: translateY(0); }\n.mm-link.active { color: var(--green); }\n.mm-ico { width: 26px; text-align: center; font-size: 17px; }\n.mm-arrow { margin-left: auto; opacity: 0.25; }\n.mm-link:active { background: var(--lime-soft); border-radius: var(--r-ctl); }\n.mm-foot { padding: 12px 10px 4px; font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-3); letter-spacing: 0.04em; }\n.mm-backdrop { position: fixed; inset: 0; z-index: var(--z-overlay, 800); background: rgba(26, 18, 20, 0.35); backdrop-filter: blur(2px); }\n@media (max-width: 760px) { .menu-btn { display: flex; } .nav { display: none; } .mobile-menu { display: block; } }\n\n/* ================= Footer =================\n   Remplacé par le footer commun Groupe KA (ka/KaFooter.tsx — styles .ka-footer\n   dans ka/tokens.css). */\n\n/* ================= Mobile : feuille de filtres + FAB ================= */\n.sheet-head { display: none; }\n.sheet-apply { display: none; }\n.sheet-backdrop { display: none; }\n.fab { display: none; }\n@media (max-width: 640px) {\n  .header-inner { height: 56px; }\n  .brand { font-size: 21px; }\n  .hero h1 { font-size: clamp(30px, 9.4vw, 44px); }\n  .hero p.lede { font-size: 15px; }\n  .stat-row { flex-wrap: nowrap; overflow-x: auto; scrollbar-width: none; padding-bottom: 6px; margin-right: -16px; padding-right: 16px; }\n  .stat-row::-webkit-scrollbar { display: none; }\n  .stat-chip { flex: 0 0 auto; white-space: nowrap; }\n  .filterbar { display: none; }\n  .filterbar.open .f-primary { flex-direction: column; }\n  .filterbar.open .f-ctl select { max-width: none; width: 100%; }\n  .filterbar.open .f-more { display: none; }\n  .filterbar.open { display: flex; flex-direction: column; gap: 12px; position: fixed; left: 0; right: 0; bottom: 0; z-index: 95; margin: 0; border-radius: 20px 20px 0 0; border-width: 2px 0 0 0; max-height: 82dvh; overflow-y: auto; -webkit-overflow-scrolling: touch; padding: 16px 18px calc(18px + env(safe-area-inset-bottom)); box-shadow: 0 -16px 48px rgba(16, 18, 16, 0.35); animation: sheet-up 0.22s ease; }\n  @keyframes sheet-up { from { transform: translateY(30%); opacity: 0.4; } to { transform: none; opacity: 1; } }\n  .filterbar.open .sheet-head { display: flex; justify-content: space-between; align-items: center; font-family: var(--font-display); font-weight: 700; font-size: 17px; text-transform: uppercase; position: sticky; top: -16px; background: var(--surface); padding: 6px 0 8px; border-bottom: 1.5px solid var(--line); margin-bottom: 2px; z-index: 1; }\n  .sheet-close { border: 1.5px solid var(--ink); background: var(--surface); border-radius: 50%; width: 36px; height: 36px; font-size: 15px; cursor: pointer; line-height: 1; }\n  .filterbar.open .sheet-apply { display: block; width: 100%; }\n  .sheet-backdrop { display: block; position: fixed; inset: 0; z-index: 90; background: rgba(16, 18, 16, 0.45); backdrop-filter: blur(2px); }\n  .fab { display: flex; align-items: center; gap: 6px; position: fixed; left: 50%; transform: translateX(-50%); bottom: calc(18px + env(safe-area-inset-bottom)); z-index: 80; background: var(--ink); color: var(--lime); border: 1.5px solid var(--ink); border-radius: 999px; padding: 13px 24px; font-family: var(--font-display); font-weight: 700; font-size: 15px; cursor: pointer; box-shadow: 0 8px 24px rgba(16, 18, 16, 0.35), 4px 4px 0 rgba(26, 18, 20, 0.25); }\n  .fab:active { transform: translateX(-50%) scale(0.97); }\n  .results-head { margin-top: 20px; }\n  .chips { margin-right: -16px; padding-right: 16px; }\n  .notice { padding: 48px 16px; }\n}\n@media (max-width: 900px) { .f-search input, .f-native, .f-ctl select { font-size: 16px; } }\n\n/* --- Sources : bannières → sous-agences -------------------------------- */\n.franchise-list { display: flex; flex-direction: column; gap: 10px; margin: 18px 0; }\n.franchise { border: 2px solid var(--ink); background: var(--surface); box-shadow: var(--shadow-off-soft); }\n.franchise-head {\n  width: 100%; display: flex; align-items: center; gap: 12px; padding: 14px 16px;\n  background: none; border: 0; cursor: pointer; font-family: var(--font-display);\n  font-size: 16px; font-weight: 600; color: var(--ink); text-align: left;\n}\n.franchise-head:hover { background: var(--surface-2); }\n.fh-caret { color: var(--green); width: 14px; }\n.fh-name { flex: 1; }\n.fh-sub { font-size: 12px; font-weight: 500; color: var(--ink-3); font-family: var(--font-mono); }\n.fh-count {\n  font-family: var(--font-mono); font-weight: 700; background: var(--ink);\n  color: var(--lime); padding: 3px 10px; min-width: 64px; text-align: right;\n}\n.subagency-list { list-style: none; margin: 0; padding: 0 16px 10px 40px; border-top: 1px solid var(--line); }\n.subagency-list li {\n  display: flex; align-items: center; justify-content: space-between; gap: 12px;\n  padding: 8px 0; border-bottom: 1px dotted var(--line); font-size: 14px;\n}\n.subagency-list li:last-child { border-bottom: 0; }\n.sa-name { color: var(--ink-2); }\n\n/* --- Pagination (accueil) ---------------------------------------------- */\n.pager { display: flex; flex-wrap: wrap; align-items: center; justify-content: center;\n  gap: 6px; margin: 32px 0 8px; }\n.pager-btn {\n  font-family: var(--font-mono); font-size: 14px; min-width: 40px; padding: 8px 12px;\n  border: 2px solid var(--ink); background: var(--surface); color: var(--ink);\n  cursor: pointer; box-shadow: var(--shadow-off-soft);\n}\n.pager-btn:hover:not(:disabled) { background: var(--lime-soft); }\n.pager-btn.on { background: var(--ink); color: var(--lime); font-weight: 700; }\n.pager-btn:disabled { opacity: .4; cursor: not-allowed; box-shadow: none; }\n.pager-gap { padding: 0 4px; color: var(--ink-3); }\n.pager-info { width: 100%; text-align: center; margin-top: 8px; font-size: 12px;\n  color: var(--ink-3); font-family: var(--font-mono); }\n\n\n\n/* --- Le quartier (census / proximité / chaleur / crime) — porté de Lou-Ka --- */\n/* --- Section « Le quartier » (fiche) --------------------------------------- */\n.quartier { margin-top: 34px; }\n.quartier h2 { font-size: 24px; letter-spacing: -0.02em; }\n.q-sub { color: var(--ink-3); font-size: 13px; margin: 4px 0 16px; }\n.q-grid {\n  display: grid; grid-template-columns: repeat(3, 1fr); gap: 10px;\n}\n.q-grid { grid-template-columns: repeat(2, 1fr); }\n.q-cell {\n  background: var(--surface); border: 1.5px solid var(--line-strong);\n  border-radius: var(--r-card); padding: 12px 14px; box-shadow: var(--shadow-flat);\n}\n.q-val { font-family: var(--font-display); font-weight: 700; font-size: 19px; letter-spacing: -0.02em; }\n.q-label { font-family: var(--font-mono); font-size: 10.5px; color: var(--ink-3);\n  text-transform: uppercase; letter-spacing: 0.06em; margin-top: 3px; }\n.q-prox { margin-top: 18px; display: flex; flex-direction: column; gap: 8px; }\n.q-bar { display: flex; align-items: center; gap: 10px; font-size: 13px; }\n.q-bar-label { flex: 0 0 150px; color: var(--ink-2); }\n.q-bar-label { flex-basis: 120px; font-size: 12px; }\n.q-bar-track {\n  flex: 1; height: 10px; background: var(--surface-2);\n  border: 1px solid var(--line-strong); border-radius: 999px; overflow: hidden;\n}\n.q-bar-fill { display: block; height: 100%; background: var(--green); border-radius: 999px; }\n.q-bar-num { flex: 0 0 30px; text-align: right; font-family: var(--font-mono);\n  font-size: 11.5px; font-weight: 700; }\n.q-badges { display: flex; flex-wrap: wrap; gap: 8px; margin-top: 16px; }\n.q-badge {\n  display: inline-flex; align-items: center; gap: 6px;\n  border: 1.5px solid var(--line-strong); border-radius: 999px;\n  padding: 7px 13px; font-size: 12.5px; background: var(--surface);\n}\n.q-badge.cool { background: var(--lime-soft); border-color: var(--green); color: var(--green-deep); }\n.q-badge.hot { background: var(--amber-soft); border-color: var(--amber); color: #8a5a12; }\n.quartier .fine { margin-top: 10px; }\n.q-cell { padding: 9px 11px; }\n.q-val { font-size: 16px; }\n.q-bar { gap: 8px; font-size: 12px; }\n.q-bar-track { height: 8px; }\n.q-badge-sub { display: block; font-size: 10.5px; color: var(--ink-3); font-weight: 400; margin-left: 4px; }\n\n/* --- Vrai-Prix : estimation de valeur ---------------------------------- */\n.vraiprix { border: 2px solid var(--ink); background: var(--surface-2);\n  box-shadow: var(--shadow-off-soft); padding: 14px; margin: 14px 0; }\n.vp-head { display: flex; align-items: center; justify-content: space-between; gap: 8px; }\n.vp-logo { font-family: var(--font-display); font-weight: 700; color: var(--green-deep);\n  letter-spacing: -.3px; }\n.vp-conf { font-family: var(--font-mono); font-size: 11px; padding: 2px 7px; border: 1px solid var(--ink); }\n.vp-conf-A { background: var(--lime); color: #fff; } .vp-conf-B { background: var(--lime-soft); }\n.vp-conf-C { background: var(--amber-soft); } .vp-conf-D { background: #f0d9d9; }\n.vp-k { font-size: 11px; color: var(--ink-3); text-transform: uppercase;\n  letter-spacing: .5px; margin-top: 8px; }\n.vp-value { font-family: var(--font-display); font-size: 26px; font-weight: 700;\n  letter-spacing: -1px; color: var(--ink); }\n.vp-range { font-size: 12px; color: var(--ink-2); font-family: var(--font-mono); }\n/* jauge Vrai-Prix : fourchette P10–P90 + marqueurs estimation / prix demandé */\n.vp-gauge { margin-top: 10px; }\n.vp-gauge-track { position: relative; height: 14px; border-radius: 999px; background: var(--surface); border: 1px solid var(--line-strong); overflow: hidden; }\n.vp-gauge-band { position: absolute; inset: 3px 6%; background: linear-gradient(90deg, var(--lime-soft), rgba(226, 55, 68, 0.35), var(--lime-soft)); border-radius: 999px; }\n.vp-gauge-est { position: absolute; top: 0; bottom: 0; width: 4px; margin-left: -2px; background: var(--lime); border-radius: 2px; }\n.vp-gauge-ask { position: absolute; top: 2px; bottom: 2px; width: 4px; margin-left: -2px; background: var(--ink); border-radius: 2px; }\n.vp-gauge-ends { display: flex; justify-content: space-between; font-family: var(--font-mono); font-size: 10px; color: var(--ink-3); margin-top: 4px; }\n.vp-gauge-legend { display: flex; gap: 14px; font-family: var(--font-mono); font-size: 10px; color: var(--ink-2); margin-top: 5px; }\n.vp-dot-est, .vp-dot-ask { display: inline-block; width: 9px; height: 9px; border-radius: 3px; margin-right: 4px; vertical-align: -1px; }\n.vp-dot-est { background: var(--lime); }\n.vp-dot-ask { background: var(--ink); }\n.vp-delta { font-size: 13px; margin-top: 8px; padding: 6px 8px; border-radius: var(--r-ctl); }\n.vp-over { background: #f6e2e2; color: #8a2b2b; }\n.vp-under { background: #e2f0e6; color: #1c5c41; }\n.vp-fair { background: var(--surface); color: var(--ink-2); }\n.vp-link { display: block; text-align: center; margin-top: 10px; font-weight: 700;\n  font-size: 13px; padding: 8px; background: var(--green-deep); color: #fff;\n  text-decoration: none; }\n.vp-link:hover { background: var(--green); }\n\n/* =============================================================================\n   Harmonisation Groupe KA — header (badge + compte KA ID), pages Profil,\n   Contact et légales. Le socle vient de ka/tokens.css.\n============================================================================= */\n\n/* ---- Header : badge « Un service Groupe KA » + zone compte ---- */\n.header-inner .gk-badge { flex: none; }\n@media (max-width: 900px) { .header-inner .gk-badge { display: none; } }\n.header-acct { display: flex; align-items: center; gap: 10px; flex: none; }\n.ka-auth { display: flex; align-items: center; gap: 10px; }\n.ka-login {\n  display: inline-flex; align-items: center; gap: 6px; min-height: 40px;\n  padding: 8px 15px; border: 1.5px solid var(--ink); border-radius: 999px;\n  background: var(--surface); color: var(--ink); cursor: pointer;\n  font-family: var(--font-display); font-weight: 600; font-size: 13.5px;\n  box-shadow: 3px 3px 0 rgba(20, 24, 20, 0.15);\n  transition: transform 0.13s ease, box-shadow 0.13s ease, background 0.15s ease;\n}\n.ka-login b { background: var(--ink); color: var(--accent); border-radius: 5px; padding: 0 6px 1px; transform: rotate(-2deg); transition: transform 0.15s ease; }\n.ka-login:hover { transform: translate(-1px, -1px); box-shadow: 4px 4px 0 rgba(20, 24, 20, 0.2); }\n.ka-login:hover b { transform: rotate(0); }\n.ka-login:active { transform: translate(2px, 2px); box-shadow: none; }\n.ka-signup { font-family: var(--font-mono); font-size: 11px; color: var(--ink-2); text-decoration: underline; text-underline-offset: 3px; white-space: nowrap; }\n.ka-signup:hover { color: var(--accent-deep); }\n@media (max-width: 1100px) { .header-acct .ka-signup { display: none; } }\n.ka-acct {\n  display: inline-flex; align-items: center; gap: 8px; min-height: 40px;\n  padding: 6px 14px; border: 1.5px solid var(--ink); border-radius: 999px;\n  background: var(--surface); font-family: var(--font-display);\n  font-weight: 600; font-size: 13.5px; color: var(--ink);\n  max-width: 160px; overflow: hidden; white-space: nowrap; text-overflow: ellipsis;\n}\n.ka-acct:hover, .ka-acct.active { background: var(--ink); color: var(--accent); }\n.ka-acct-pic { width: 24px; height: 24px; border-radius: 50%; flex: none; border: 1px solid var(--line); }\n@media (max-width: 760px) {\n  .header-acct { margin-left: auto; }\n  .menu-btn { margin-left: 0; }\n  .ka-login { min-height: 44px; }\n}\n@media (max-width: 380px) { .header-acct { display: none; } } /* → menu mobile */\n\n/* ---- Menu mobile : compte + badge groupe ---- */\n.mm-auth { display: flex; align-items: center; gap: 14px; flex-wrap: wrap; padding: 15px 10px; border-bottom: 1.5px dashed var(--line); }\n.mm-auth .ka-login { min-height: 44px; }\n.mm-foot p { margin: 10px 0 0; }\n\n/* ---- Générique : lede (Profil / Contact) ---- */\n.lede { color: var(--ink-2); font-size: 15.5px; max-width: 640px; }\n\n/* ================= Page Profil (KA ID) ================= */\n.profil { padding: 44px 0 90px; }\n.profil h1 { font-size: clamp(28px, 4.6vw, 44px); margin: 10px 0 24px; letter-spacing: -0.03em; }\n.profil .lede { margin-bottom: 22px; }\n.profil .notice { padding: 60px 0; }\n\n/* Carte de membre — encre + cerise */\n.pc {\n  position: relative; overflow: hidden; max-width: 460px;\n  background: var(--ink); color: var(--paper);\n  border: 1.5px solid var(--ink); border-radius: var(--r-card);\n  box-shadow: var(--shadow-off-soft); padding: 22px 26px 0;\n}\n.pc-watermark {\n  position: absolute; right: -18px; bottom: 14px; font-family: var(--font-display);\n  font-weight: 700; font-size: 150px; line-height: 1; letter-spacing: -0.06em;\n  color: rgba(245, 243, 238, 0.05); pointer-events: none; user-select: none;\n}\n.pc-head { display: flex; align-items: baseline; justify-content: space-between; gap: 12px; flex-wrap: wrap; }\n.pc-brand { font-family: var(--font-display); font-weight: 700; font-size: 24px; letter-spacing: -0.03em; }\n.pc-ka { display: inline-block; background: var(--accent); color: var(--on-accent); border-radius: 6px; padding: 0 7px 2px; margin-left: 3px; transform: rotate(-2deg); }\n.pc-label { font-family: var(--font-mono); font-size: 9.5px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.16em; color: rgba(245, 243, 238, 0.5); }\n.pc-id-block { margin: 26px 0 22px; display: flex; flex-direction: column; gap: 4px; }\n.pc-id-label { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.2em; color: var(--accent); }\n.pc-id { font-family: var(--font-mono); font-weight: 700; font-size: clamp(17px, 4.4vw, 22px); letter-spacing: 0.04em; word-break: break-all; }\n.pc-foot { display: flex; align-items: flex-end; justify-content: space-between; gap: 14px; padding-bottom: 18px; }\n.pc-holder { display: flex; flex-direction: column; gap: 3px; min-width: 0; }\n.pc-holder-name { font-family: var(--font-display); font-weight: 600; font-size: 15px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }\n.pc-role-badge { align-self: flex-start; font-family: var(--font-mono); font-size: 9.5px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.1em; background: var(--accent); color: var(--on-accent); border-radius: 999px; padding: 2px 9px; }\n.pc-holder-since { font-family: var(--font-mono); font-size: 10.5px; color: rgba(245, 243, 238, 0.5); letter-spacing: 0.06em; }\n.pc-avatar { width: 52px; height: 52px; border-radius: 50%; border: 2px solid var(--accent); flex: none; object-fit: cover; }\n.pc-strip { display: flex; align-items: flex-end; gap: 5px; margin: 0 -26px; padding: 9px 18px; background: rgba(226, 55, 68, 0.14); border-top: 1px solid rgba(226, 55, 68, 0.4); overflow: hidden; }\n.pc-strip i { display: block; width: 3px; border-radius: 1px; background: var(--accent); opacity: 0.75; }\n.pc-strip i:nth-child(3n) { height: 14px; opacity: 0.4; }\n.pc-strip i:nth-child(3n+1) { height: 9px; }\n.pc-strip i:nth-child(3n+2) { height: 17px; opacity: 0.95; }\n.pc-copy { margin-top: 16px; }\n.pc-copy.ok { background: var(--ink); color: var(--accent); }\n\n.profil-grid {\n  display: grid; grid-template-columns: repeat(auto-fill, minmax(220px, 1fr));\n  gap: 12px; margin-top: 28px; max-width: 720px;\n}\n.pg-item { background: var(--surface); border: 1.5px solid var(--line-strong); border-radius: var(--r-ctl); padding: 12px 14px; display: flex; flex-direction: column; gap: 4px; min-width: 0; }\n.pg-label { font-family: var(--font-mono); font-size: 10px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3); }\n.pg-value { font-weight: 600; font-size: 14.5px; word-break: break-word; }\n.pg-value.mono { font-family: var(--font-mono); color: var(--accent-deep); }\n.profil-note { max-width: 640px; color: var(--ink-2); font-size: 13.5px; margin-top: 22px; }\n.profil-note a { text-decoration: underline; text-underline-offset: 3px; }\n.profil-actions { display: flex; gap: 10px; flex-wrap: wrap; margin-top: 26px; }\n\n/* Profil hub Groupe KA (lecture seule) */\n.hub-profile { margin-top: 34px; padding-top: 24px; border-top: 1.5px solid var(--line-strong); max-width: 720px; display: flex; flex-direction: column; gap: 14px; }\n.hub-profile h3 { font-size: 18px; }\n.hub-profile .hub-bio { color: var(--ink-2); font-size: 14px; margin: 0; max-width: 560px; }\n.hub-profile .pub-meta { display: flex; gap: 8px; flex-wrap: wrap; }\n.hub-profile .stat-chip { display: inline-flex; align-items: center; gap: 6px; }\n.hub-profile a.stat-chip:hover { background: var(--accent-soft); }\n.hub-hint { color: var(--ink-3); font-size: 12.5px; margin: 0; }\n\n/* ================= Pages légales ================= */\n.legal { padding: 48px 0 80px; max-width: 780px; }\n.legal h1 { font-size: clamp(28px, 4.6vw, 44px); margin: 8px 0 6px; }\n.legal .legal-meta { color: var(--ink-3); font-family: var(--font-mono); font-size: 12px; margin: 0 0 26px; }\n.legal h2 { font-size: 20px; margin: 30px 0 8px; border-left: 4px solid var(--accent); padding-left: 10px; }\n.legal p, .legal li { color: var(--ink-2); font-size: 14.5px; }\n.legal ul { padding-left: 20px; display: flex; flex-direction: column; gap: 6px; }\n.legal a { text-decoration: underline; text-underline-offset: 3px; }\n.legal b { color: var(--ink); }\n\n/* ================= Page Contact (écosystème Groupe KA) ================= */\n.contact { padding: 48px 0 90px; }\n.contact h1 { font-size: clamp(30px, 5vw, 52px); margin: 12px 0 14px; text-transform: uppercase; letter-spacing: -0.03em; }\n.contact-cards { display: grid; grid-template-columns: repeat(auto-fit, minmax(230px, 1fr)); gap: 14px; margin: 28px 0 6px; max-width: 900px; }\n.contact-card {\n  display: flex; flex-direction: column; gap: 8px; min-height: var(--touch, 44px);\n  background: var(--surface); border: 1.5px solid var(--ink);\n  border-radius: var(--r-card); padding: 18px 20px;\n  box-shadow: var(--shadow-off-soft);\n  transition: transform 0.15s ease, box-shadow 0.15s ease;\n}\n.contact-card:hover { transform: translate(-2px, -2px); box-shadow: var(--shadow-off); }\n.contact-role { font-family: var(--font-mono); font-size: 10.5px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.1em; color: var(--green); }\n.contact-mail { font-family: var(--font-display); font-weight: 700; font-size: clamp(14px, 2vw, 17px); word-break: break-all; border-bottom: 2px solid var(--accent); align-self: flex-start; }\n.contact-disclaimer { max-width: 720px; margin: 24px 0 0; padding: 14px 16px; border-left: 3px solid var(--accent); background: var(--accent-soft); color: var(--ink-2); font-size: 13.5px; }\n.contact-disclaimer b { color: var(--ink); }\n.contact-hub { display: flex; gap: 12px; flex-wrap: wrap; margin-top: 26px; }\n.contact-sites { margin-top: 46px; }\n.contact-sites h2 { font-size: 21px; text-transform: uppercase; margin-bottom: 14px; }\n.contact-sites ul { list-style: none; margin: 0; padding: 0; display: grid; grid-template-columns: repeat(auto-fill, minmax(230px, 1fr)); gap: 10px; }\n.contact-sites a { display: flex; flex-direction: column; gap: 3px; min-height: var(--touch, 44px); background: var(--surface); border: 1.5px solid var(--line-strong); border-radius: var(--r-ctl); padding: 12px 14px; transition: transform 0.14s ease, box-shadow 0.14s ease; }\n.contact-sites a:hover { transform: translate(-2px, -2px); box-shadow: 4px 4px 0 var(--ink); }\n.contact-sites b { font-family: var(--font-display); font-size: 15px; }\n.contact-sites span { font-size: 12px; color: var(--ink-3); }\n@media (max-width: 480px) {\n  .header-inner { gap: 10px; }\n  .ka-login { font-size: 12px; padding: 6px 10px; gap: 4px; min-height: 44px; }\n}\n\n/* Bulle KA Agent : sur mobile, remontée au-dessus de la barre CTA collante des\n   fiches (sinon elle chevauche le bouton « Voir chez … »). */\n@media (max-width: 899px) {\n  .kaa-btn {\n    bottom: calc(88px + env(safe-area-inset-bottom, 0px)) !important;\n  }\n}\n\n/* Footer commun KA : zones tactiles ≥ 44 px pour les petits liens (Ka2, Ka4…)\n   — padding compensé par une marge négative : aucun changement visuel. */\n.ka-footer .sites a, .ka-footer .legal a, .ka-footer .contacts a {\n  display: inline-block; padding: 12px 8px; margin: -12px -8px;\n}\n\n/* ================= Qualité des annonces / images ================= */\n/* Image de secours par TYPE DE BIEN (jamais d'icône d'image cassée) */\n.type-fallback {\n  display: flex; flex-direction: column; align-items: center; justify-content: center;\n  gap: 9px; width: 100%; height: 100%; min-height: 120px;\n  background:\n    radial-gradient(circle at 78% 18%, rgba(226, 55, 68, 0.14), transparent 52%),\n    repeating-linear-gradient(45deg, #f3ece9 0 14px, #f7f1ef 14px 28px);\n  color: var(--accent-deep);\n}\n.type-fallback .ico { opacity: 0.75; }\n.type-fallback span {\n  font-family: var(--font-mono); font-size: 10.5px; font-weight: 700;\n  text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3);\n}\n.carousel-empty .type-fallback { aspect-ratio: 16/11; }\n\n/* légende de photo (étiquette de pièce de la source) */\n.carousel-caption {\n  position: absolute; left: 12px; bottom: 12px; z-index: 2; max-width: 60%;\n  background: rgba(26, 18, 20, 0.82); color: #f5f3ee;\n  font-family: var(--font-mono); font-size: 11px; font-weight: 600;\n  padding: 4px 10px; border-radius: 999px;\n  overflow: hidden; text-overflow: ellipsis; white-space: nowrap;\n}\n\n/* mention loyer mensuel (transaction = location) */\n.per-month { font-size: 0.55em; font-weight: 600; color: var(--ink-3); letter-spacing: 0; }\n\n/* lightbox : l'image zoomable ne doit pas déclencher le scroll de la page */\n.lightbox img { user-select: none; -webkit-user-drag: none; }\n\n/* ---- Stats : qualité des données ---- */\n.q-table-wrap { overflow-x: auto; }\n.q-table { width: 100%; border-collapse: collapse; font-size: 13px; min-width: 520px; }\n.q-table th { text-align: left; font-family: var(--font-mono); font-size: 10px; text-transform: uppercase; letter-spacing: 0.08em; color: var(--ink-3); padding: 6px 10px; border-bottom: 1.5px solid var(--ink); }\n.q-table td { padding: 6px 10px; border-bottom: 1px solid var(--line); }\n.q-table .num { text-align: right; font-family: var(--font-mono); }\n.q-meter { display: inline-block; width: 74px; height: 9px; background: var(--surface-2); border: 1px solid var(--line-strong); border-radius: 999px; overflow: hidden; vertical-align: -1px; margin-right: 7px; }\n.q-meter i { display: block; height: 100%; background: var(--green); }\n\n\n/* ---- Criminalité détaillée SPVM (bloc quartier) ---- */\n.q-crime { width: 100%; }\n.q-crime-cats { list-style: none; margin: 8px 0 0; padding: 0; }\n.q-crime-cats li { display: flex; align-items: center; gap: 8px;\n                   padding: 3px 0; font-size: 12.5px; }\n.q-crime-nom { flex: 0 0 46%; color: var(--ink-2, #555); overflow: hidden;\n               text-overflow: ellipsis; white-space: nowrap; }\n.q-crime-barre { flex: 1 1 auto; height: 10px; background: var(--surface-2, #f0ede8);\n                 border-radius: 5px; overflow: hidden; }\n.q-crime-barre i { display: block; height: 100%; border-radius: 5px;\n                   background: var(--accent, #e23744); opacity: 0.55; }\n.q-crime-n { flex: 0 0 52px; text-align: right; font-weight: 600;\n             color: var(--ink, #222); white-space: nowrap; }\n.q-crime-n small { font-weight: 400; color: var(--ink-3, #888); }\n.q-crime-src { margin-top: 8px; }\n\n\n/* ---- Risque d'inondation (fiche) — BDZI gouv. du Québec ---- */\n.f-zi .zi-head { display: flex; align-items: center; gap: 10px; }\n.zi-badge { display: inline-block; padding: 3px 10px; border-radius: 999px;\n            font-size: 12.5px; font-weight: 700; }\n.zi-eleve { background: #fde8e8; color: #a12622; }\n.zi-modere { background: #fdf3e0; color: #8a5a00; }\n.zi-present { background: #fdf3e0; color: #8a5a00; }\n.zi-ok { background: #e7f4ea; color: #1e6b34; }\n.zi-nc { background: var(--surface-2, #f0ede8); color: var(--ink-3, #888); }\n.zi-liste { margin: 8px 0 0; padding: 0; list-style: none; font-size: 13px; }\n.zi-liste li { padding: 4px 0; border-top: 1px solid var(--line, #e6e4df); }\n\n\n/* ---- tuiles KPI + tableau compacts (partagés air/essence) ---- */\n.rdl-kpis { display: flex; flex-wrap: wrap; gap: 10px; margin: 2px 0 12px; }\n.rdl-kpi { flex: 1 1 90px; min-width: 90px; background: var(--surface-2, #f7f5f1);\n           border-radius: 10px; padding: 10px 12px; }\n.rdl-kpi-v { display: block; font-size: 20px; font-weight: 700;\n             letter-spacing: -0.02em; color: var(--ink, #222); }\n.rdl-kpi-l { display: block; font-size: 11px; line-height: 1.35;\n             color: var(--ink-3, #888); margin-top: 2px; }\n.rdl-table { width: 100%; border-collapse: collapse; font-size: 13px; }\n.rdl-table td { padding: 5px 8px 5px 0; border-top: 1px solid var(--line, #e6e4df);\n                vertical-align: top; }\n.rdl-table .rdl-addr { max-width: 46%; }\n.rdl-table .rdl-prix { font-weight: 600; white-space: nowrap; }\n.rdl-table .rdl-date { white-space: nowrap; color: var(--ink-2, #555); }\n.rdl-table .rdl-dist { color: var(--ink-3, #888); white-space: nowrap; text-align: right; }\n.rdl-cap { caption-side: top; text-align: left; font-size: 13px; font-weight: 700;\n           color: var(--ink, #222); padding: 6px 0; }\n\n/* ---- Qualité de l'air (RSQAQ) + Essence à proximité ---- */\n.air-liste { list-style: none; margin: 10px 0 0; padding: 0; }\n.air-liste li { display: flex; align-items: center; gap: 8px;\n                padding: 4px 0; font-size: 12.5px; }\n.air-nom { flex: 0 0 44%; color: var(--ink-2); overflow: hidden;\n           text-overflow: ellipsis; white-space: nowrap; }\n.air-barre { flex: 1 1 auto; height: 10px; background: var(--surface-2, #f0ede8);\n             border-radius: 5px; overflow: hidden; }\n.air-barre i { display: block; height: 100%; border-radius: 5px;\n               background: #4d9e64; }\n.air-barre i.air-sur { background: #c96a1f; }\n.air-val { flex: 0 0 34%; text-align: right; font-weight: 600;\n           color: var(--ink); white-space: nowrap; overflow: hidden;\n           text-overflow: ellipsis; }\n.air-val small { font-weight: 400; color: var(--ink-3); }\n.air-val .air-ref { display: block; font-size: 10px; }\n.gaz-head th { text-align: left; font-size: 11px; color: var(--ink-3);\n               font-weight: 600; padding: 2px 8px 2px 0; }\n.gaz-best { display: inline-block; margin-left: 6px; padding: 1px 7px;\n            border-radius: 999px; background: #e7f4ea; color: #1e6b34;\n            font-size: 10.5px; font-weight: 700; }\n.gaz-adr { display: block; font-size: 11px; color: var(--ink-3); }\n\n\n/* ---- Commerces et transport (fiche) ---- */\n.cm-grille { list-style: none; margin: 6px 0 0; padding: 0;\n             display: grid; grid-template-columns: 1fr 1fr; gap: 4px 18px; }\n@media (max-width: 560px) { .cm-grille { grid-template-columns: 1fr; } }\n.cm-item { display: flex; align-items: center; gap: 9px; padding: 5px 0;\n           border-bottom: 1px solid var(--line, #eeece7); min-width: 0; }\n.cm-ico { flex: 0 0 28px; }\n.cm-txt { flex: 1 1 auto; min-width: 0; display: flex; flex-direction: column; }\n.cm-nom { font-size: 13px; font-weight: 600; color: var(--ink, #222);\n          white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }\n.cm-poi { font-size: 11px; color: var(--ink-3, #8a877f);\n          white-space: nowrap; overflow: hidden; text-overflow: ellipsis; }\n.cm-dist { flex: 0 0 auto; font-size: 13px; font-weight: 700;\n           color: var(--ink-2, #4c4a45); white-space: nowrap; }\n\n\n/* ---- Coût d'électricité (Hydro-Québec) ---- */\n.hydro-intro { margin: 2px 0 12px; color: var(--ink-2, #4c4a45); }\n.hydro-btn { appearance: none; border: 0; border-radius: 10px; cursor: pointer;\n             background: var(--accent, #2b7a3b); color: #fff; font-weight: 700;\n             font-size: 14px; padding: 11px 18px; }\n.hydro-btn:disabled { opacity: 0.6; cursor: progress; }\n.hydro-montant { font-size: 26px; font-weight: 800; letter-spacing: -0.02em;\n                 color: var(--navy, #1c1b18); display: flex; align-items: baseline;\n                 gap: 10px; flex-wrap: wrap; }\n.hydro-montant small { font-size: 14px; font-weight: 500; color: var(--ink-3); }\n.hydro-an { font-size: 14px; font-weight: 600; color: var(--ink-2, #4c4a45); }\n.hydro-vide { color: var(--ink-3, #8a877f); }\n\n\n/* ============================================================================\n   PASSE DE DESIGN — « éditorial sharp » appliqué à toute la fiche\n   Blocs = cartes encre (bordure 1,5 px + ombre décalée douce), titres à\n   marqueur accent + filet, tuiles KPI blanches à ombre dure, tableaux zébrés,\n   barres en dégradé, pastilles unifiées (mono, point d'état, liseré).\n   Aucune propriété `order` (règle Groupe Ka : ordre DOM = ordre visuel).\n   ========================================================================== */\n\n/* --- Blocs de la fiche : cartes posées sur le papier --- */\n.f-bloc:not(.f-galerie):not(.f-hero),\n.quartier {\n  background: var(--surface);\n  border: 1.5px solid var(--ink);\n  border-radius: var(--r-card);\n  padding: 18px 20px 16px;\n  box-shadow: var(--shadow-off-soft);\n}\n@media (max-width: 640px) {\n  .f-bloc:not(.f-galerie):not(.f-hero),\n  .quartier { padding: 15px 15px 13px; }\n}\n/* blocs pleine largeur sous la grille : respiration entre les cartes */\n.detail > .f-bloc { margin-top: 22px; }\n\n/* --- Titres de section : carré accent + filet éditorial --- */\n.f-bloc h2, .quartier h2 {\n  display: flex; align-items: center; gap: 10px;\n  font-size: 19px; letter-spacing: -0.015em; margin-bottom: 14px;\n  border-left: 0; padding-left: 0;\n}\n.f-bloc h2::before, .quartier h2::before {\n  content: \"\"; flex: 0 0 9px; width: 9px; height: 9px;\n  background: var(--accent); border: 1.5px solid var(--ink);\n  box-shadow: 2px 2px 0 var(--ink);\n}\n.f-bloc h2::after, .quartier h2::after {\n  content: \"\"; flex: 1 1 auto; height: 1.5px; min-width: 20px;\n  background: var(--line);\n}\n\n/* --- Tuiles KPI : blanches, bord encre, ombre dure accent --- */\n.rdl-kpi {\n  background: var(--surface);\n  border: 1.5px solid var(--ink);\n  border-radius: var(--r-ctl);\n  box-shadow: 3px 3px 0 var(--accent-soft);\n}\n.rdl-kpi-v { font-family: var(--font-display); }\n.q-cell { box-shadow: 3px 3px 0 rgba(20, 24, 20, 0.1); border-radius: var(--r-ctl); }\n.q-val { font-size: 20px; }\n\n/* --- Tableaux : en-têtes mono, zébrure, survol accent --- */\n.rdl-table td { padding: 7px 8px 7px 0; }\n.rdl-table tbody tr:nth-child(even) td,\n.rdl-table > tr:nth-child(even) td { background: var(--surface-2); }\n.rdl-table tr:hover td { background: var(--accent-soft); }\n.gaz-head th {\n  font-family: var(--font-mono); font-size: 10px; font-weight: 700;\n  text-transform: uppercase; letter-spacing: 0.08em;\n  border-bottom: 1.5px solid var(--ink);\n}\n.rdl-cap { font-family: var(--font-display); }\ntable.rooms tbody tr:hover td { background: var(--accent-soft); }\n\n/* --- Barres : pistes bordées + remplissage en dégradé --- */\n.q-bar-track, .q-crime-barre, .air-barre {\n  background: var(--surface-2);\n  border: 1px solid var(--line);\n  height: 12px;\n}\n.q-bar-fill {\n  background: linear-gradient(90deg, var(--accent), var(--accent-deep));\n}\n.q-crime-barre i {\n  background: linear-gradient(90deg, #f0808a, var(--accent));\n  opacity: 0.9;\n}\n.air-barre i { background: linear-gradient(90deg, #6fbc85, #2e7d4a); }\n.air-barre i.air-sur { background: linear-gradient(90deg, #e08a48, #c96a1f); }\n\n/* --- Pastilles d'état unifiées (zi-badge & cie) : mono + point d'état --- */\n.zi-badge {\n  display: inline-flex; align-items: center; gap: 6px;\n  font-family: var(--font-mono); font-size: 10.5px; font-weight: 700;\n  text-transform: uppercase; letter-spacing: 0.06em;\n  padding: 4px 10px; border: 1.5px solid currentColor;\n}\n.zi-badge::before {\n  content: \"\"; width: 6px; height: 6px; border-radius: 50%;\n  background: currentColor;\n}\n.gaz-best { border: 1px solid currentColor; }\n\n/* --- Listes riches (zones d'inondation) : mini-cartes à liseré accent --- */\n.zi-liste { display: flex; flex-direction: column; gap: 8px; }\n.zi-liste li {\n  border: 1px solid var(--line); border-left: 3px solid var(--accent);\n  border-top: 1px solid var(--line);\n  border-radius: var(--r-ctl); padding: 9px 12px;\n  background: var(--surface-2);\n}\n\n/* --- Inclusions : icône et libellé alignés sur une seule ligne --- */\n.amenity { display: inline-flex; align-items: center; gap: 6px; }\n\n/* --- Notes de bas de bloc : filet pointillé --- */\n.f-bloc .fine, .quartier .fine {\n  margin-top: 12px; padding-top: 9px;\n  border-top: 1px dashed var(--line);\n}\n","structuredPatch":[{"oldStart":1064,"oldLines":3,"newStart":1064,"newLines":11,"lines":["   margin-top: 12px; padding-top: 9px;","   border-top: 1px dashed var(--line);"," }","+","+/* --- CTA secondaire : téléchargement de la fiche PDF --- */","+.cta-pdf {","+  background: var(--surface); color: var(--ink);","+  box-shadow: 4px 4px 0 rgba(26, 18, 20, 0.12);","+  margin-top: 10px;","+}","+.cta-pdf:hover { background: var(--ink); color: var(--paper); }"]}],"userModified":false,"replaceAll":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"e90e2d98-2830-4bd9-85a2-50ec6693777f","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"b5ade1f1-54e9-42cd-912a-2b8002290ff8","timestamp":"2026-08-24T21:58:28.300Z","tool_use_result":{"type":"image","file":{"base64":"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","type":"image/png","originalSize":138189,"dimensions":{"originalWidth":390,"originalHeight":844,"displayWidth":390,"displayHeight":844}}}}}
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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"d58fb4f9-2d7b-49ae-b283-040dec941f3f","timestamp":"2026-08-24T21:58:28.389Z","tool_use_result":{"type":"image","file":{"base64":"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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNKgn7JPmscjcFrP7cE1","type":"message","role":"assistant","content":[{"type":"text","text":"Le bouton s'affiche correctement sur desktop et mobile (icône alignée à gauche comme sur le CTA existant — comportement identique au bouton source). Healthcheck public puis commit/push."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1095,"cache_read_input_tokens":50563,"cache_creation":{"ephemeral_5m_input_tokens":1095,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"3f029109-312b-48da-aeed-7076517edcad"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNKgn7JPmscjcFrP7cE1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012ZbRpSzR3PjHsvnvLQEHj1","name":"Bash","input":{"command":"rm scripts/shot-pdf-btn.mjs && curl -s -o /dev/null -w \"site public: %{http_code}\\n\" -L \"https://www.immo-ka.com/propriete/domicilia:17516934\" && curl -s -o /dev/null -w \"pdf public: %{http_code} %{content_type}\\n\" \"https://www.immo-ka.com/api/listings/remax_quebec:17719804/pdf\"","description":"Remove temp script and check public site","timeout":60000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1095,"cache_read_input_tokens":50563,"cache_creation":{"ephemeral_5m_input_tokens":1095,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"03dae02b-7d96-4d79-a13c-93e30fe2af24"}}
{"type":"permission_request","requestId":"6a67a5c1554bce7b","tool":"Bash","input":{"command":"rm scripts/shot-pdf-btn.mjs && curl -s -o /dev/null -w \"site public: %{http_code}\\n\" -L \"https://www.immo-ka.com/propriete/domicilia:17516934\" && curl -s -o /dev/null -w \"pdf public: %{http_code} %{content_type}\\n\" \"https://www.immo-ka.com/api/listings/remax_quebec:17719804/pdf\"","timeout":60000,"description":"Remove temp script and check public site"}}
{"type":"permission_resolved","requestId":"6a67a5c1554bce7b","allow":true,"always":false}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012ZbRpSzR3PjHsvnvLQEHj1","type":"tool_result","content":"site public: 200\npdf public: 200 application/pdf","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"02e2f5fc-e8b4-4d76-a25f-e8e267e4a6df","timestamp":"2026-08-24T21:58:52.886Z","tool_use_result":{"stdout":"site public: 200\npdf public: 200 application/pdf","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"0e99713d-a92f-4119-b923-bfe5f3dd77ad","session_id":"1b217d06-32f6-4569-9b15-be2772e84b82"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNKiadnj7ZtJBi32E2Vp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01V7MauuTSmUafctpUptHNYp","name":"Bash","input":{"command":"git status --short && git log --oneline -3","description":"Show working tree status"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":893,"cache_read_input_tokens":51658,"cache_creation":{"ephemeral_5m_input_tokens":893,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"d849b81e-3d7b-4bbe-b967-5600e82a5fbe"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01V7MauuTSmUafctpUptHNYp","type":"tool_result","content":" M src/components/Icons.tsx\n M src/pages/Listing.tsx\n M src/styles.css\n5fea46d feat(pdf): fiche PDF complète — prix + verdict Vrai-Prix, photos, description, inclusions, caractéristiques, pièces, jauge Vrai-Prix + rôle foncier, historique de prix, inondation, air, hydro, commerces/transport, essence, quartier, références + QR\nfc02d70 feat(design): passe « éditorial sharp » sur la fiche — cartes, KPI, tableaux, pastilles\nc7fe6f5 Revert \"[ka2] fix connecteur imt_ag_tremblant: id du registre aligné sur le source_id BD (imt_tremblant → imt_ag_tremblant) pour que la supervision classe la source retirée en endormi et non stale ; fiches docs régénérées\"","is_error":false}]},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"e5943b78-220c-44b2-ab52-417014696aeb","timestamp":"2026-08-24T21:58:56.387Z","tool_use_result":{"stdout":" M src/components/Icons.tsx\n M src/pages/Listing.tsx\n M src/styles.css\n5fea46d feat(pdf): fiche PDF complète — prix + verdict Vrai-Prix, photos, description, inclusions, caractéristiques, pièces, jauge Vrai-Prix + rôle foncier, historique de prix, inondation, air, hydro, commerces/transport, essence, quartier, références + QR\nfc02d70 feat(design): passe « éditorial sharp » sur la fiche — cartes, KPI, tableaux, pastilles\nc7fe6f5 Revert \"[ka2] fix connecteur imt_ag_tremblant: id du registre aligné sur le source_id BD (imt_tremblant → imt_ag_tremblant) pour que la supervision classe la source retirée en endormi et non stale ; fiches docs régénérées\"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeNKir3F7R8kPfYj7D4xC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Kwz37Te2zAAR6RMigcupte","name":"Bash","input":{"command":"git add src/components/Icons.tsx src/pages/Listing.tsx src/styles.css && git commit -m \"$(cat <<'EOF'\nfeat(fiche): bouton « Télécharger la fiche (PDF) » dans le héro — CTA secondaire + icône download\n\nCo-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>\nEOF\n)\" && git push origin main && git status --short","description":"Commit and push PDF button to origin main","timeout":60000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":389,"cache_read_input_tokens":52551,"cache_creation":{"ephemeral_5m_input_tokens":389,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"context_management":null},"parent_tool_use_id":null,"session_id":"1b217d06-32f6-4569-9b15-be2772e84b82","uuid":"b30e4691-ccf1-4833-a0e4-e666d552841a"}}
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{"type":"permission_resolved","requestId":"f6d26acb534e3a2d","allow":true,"always":false}
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