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align=\"center\">\n\n# Vrai-Prix\n\n**La vraie valeur de votre propriété, sans boîte noire.**\n\nMoteur d'évaluation immobilière **transparent** pour le Québec — **3 747 008 propriétés**,\n**745 119 ventes réelles (2021-2026)**, chaque calcul montré en entier.\n\n![Propriétés](https://img.shields.io/badge/propri%C3%A9t%C3%A9s-3%C2%A0747%C2%A0008-ff5148?style=flat-square)\n![Ventes](https://img.shields.io/badge/ventes_r%C3%A9elles-745%C2%A0119-ff5148?style=flat-square)\n![Municipalités](https://img.shields.io/badge/municipalit%C3%A9s-1%C2%A0097-ff5148?style=flat-square)\n![MdAPE](https://img.shields.io/badge/MdAPE-11,0%25-9e2a25?style=flat-square)\n![Ratio médian](https://img.shields.io/badge/ratio_m%C3%A9dian-0,995-9e2a25?style=flat-square)\n![Appariement](https://img.shields.io/badge/fusion_spatiale-99,88%25-9e2a25?style=flat-square)\n![URLs indexables](https://img.shields.io/badge/URLs_indexables-3,76_M-9e2a25?style=flat-square)\n![Tests](https://img.shields.io/badge/tests-15%2F15-141814?style=flat-square)\n![Next.js](https://img.shields.io/badge/Next.js-16-141814?style=flat-square&logo=nextdotjs)\n![TypeScript](https://img.shields.io/badge/TypeScript-strict-141814?style=flat-square&logo=typescript)\n![Tailwind](https://img.shields.io/badge/Tailwind-4-141814?style=flat-square&logo=tailwindcss)\n![SQLite](https://img.shields.io/badge/SQLite-FTS5-141814?style=flat-square&logo=sqlite)\n![iOS](https://img.shields.io/badge/iOS-SwiftUI-141814?style=flat-square&logo=apple)\n\n**En production : [www.vrai-prix.com](https://www.vrai-prix.com)**\n\n</div>\n\n![Aperçu de Vrai-Prix](docs/screenshot.png)\n\n---\n\n## Sommaire\n\n- [Ce que fait Vrai-Prix](#ce-que-fait-vrai-prix)\n- [Fonctionnalités](#fonctionnalités)\n- [Visite guidée](#visite-guidée)\n- [Les chiffres qui comptent](#les-chiffres-qui-comptent)\n- [Architecture](#architecture)\n- [Le moteur d'estimation](#le-moteur-destimation-srclibenginets)\n- [Ka — l'agent IA](#ka--lagent-ia)\n- [SEO programmatique](#seo-programmatique--376-m-durls-indexables)\n- [Structure du projet](#structure-du-projet)\n- [Endpoints](#endpoints)\n- [Démarrage local](#démarrage-local)\n- [Déploiement](#déploiement)\n- [Développement remote-first](#développement-remote-first)\n- [Design](#design)\n- [Auteur](#auteur)\n\n## Ce que fait Vrai-Prix\n\nVrai-Prix estime la valeur marchande de **n'importe quelle propriété du Québec** (unifamiliale, plex, condo, chalet, terrain) et montre *pourquoi* — **chaque comparable, chaque ajustement en dollars**, la pondération du modèle, la fourchette et l'indice de confiance. Le différenciateur est la **transparence totale** : pas de score magique, **pas de boîte noire**. La méthodologie complète (normes **IAAO**) est publiée intégralement sur le site.\n\nSous le capot : les **6 millésimes (2021-2026)** des rôles d'évaluation foncière géoréférencés du Québec (**MAMH**, données ouvertes) — soit **22 150 285 observations** — fusionnés spatialement avec **745 119 transactions réelles** (**99,88 %** d'appariement, distance médiane **0,6 m**), alimentant un **modèle hédonique LightGBM** et un **moteur de comparables ajustés**.\n\n## Fonctionnalités\n\n| Fonctionnalité | Description |\n|---|---|\n| 🔎 **Estimation par adresse** | Recherche plein-texte (**FTS5**) parmi **3 747 008 unités d'évaluation**, tolérante aux fautes 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align=\"center\">\n\n# Vrai-Prix\n\n**La vraie valeur de votre propriété, sans boîte noire.**\n\nMoteur d'évaluation immobilière **transparent** pour le Québec — **3 747 008 propriétés**,\n**745 119 ventes réelles (2021-2026)**, chaque calcul montré en entier.\n\n![Propriétés](https://img.shields.io/badge/propri%C3%A9t%C3%A9s-3%C2%A0747%C2%A0008-ff5148?style=flat-square)\n![Ventes](https://img.shields.io/badge/ventes_r%C3%A9elles-745%C2%A0119-ff5148?style=flat-square)\n![Municipalités](https://img.shields.io/badge/municipalit%C3%A9s-1%C2%A0097-ff5148?style=flat-square)\n![MdAPE](https://img.shields.io/badge/MdAPE-11,0%25-9e2a25?style=flat-square)\n![Ratio médian](https://img.shields.io/badge/ratio_m%C3%A9dian-0,995-9e2a25?style=flat-square)\n![Appariement](https://img.shields.io/badge/fusion_spatiale-99,88%25-9e2a25?style=flat-square)\n![URLs indexables](https://img.shields.io/badge/URLs_indexables-3,76_M-9e2a25?style=flat-square)\n![Tests](https://img.shields.io/badge/tests-15%2F15-141814?style=flat-square)\n![Next.js](https://img.shields.io/badge/Next.js-16-141814?style=flat-square&logo=nextdotjs)\n![TypeScript](https://img.shields.io/badge/TypeScript-strict-141814?style=flat-square&logo=typescript)\n![Tailwind](https://img.shields.io/badge/Tailwind-4-141814?style=flat-square&logo=tailwindcss)\n![SQLite](https://img.shields.io/badge/SQLite-FTS5-141814?style=flat-square&logo=sqlite)\n![iOS](https://img.shields.io/badge/iOS-SwiftUI-141814?style=flat-square&logo=apple)\n\n**En production : [www.vrai-prix.com](https://www.vrai-prix.com)**\n\n</div>\n\n![Aperçu de Vrai-Prix](docs/screenshot.png)\n\n---\n\n## Sommaire\n\n- [Ce que fait Vrai-Prix](#ce-que-fait-vrai-prix)\n- [Fonctionnalités](#fonctionnalités)\n- [Visite guidée](#visite-guidée)\n- [Les chiffres qui comptent](#les-chiffres-qui-comptent)\n- [Architecture](#architecture)\n- [Le moteur d'estimation](#le-moteur-destimation-srclibenginets)\n- [Ka — l'agent IA](#ka--lagent-ia)\n- [SEO programmatique](#seo-programmatique--376-m-durls-indexables)\n- [Structure du projet](#structure-du-projet)\n- [Endpoints](#endpoints)\n- [Démarrage local](#démarrage-local)\n- [Déploiement](#déploiement)\n- [Développement remote-first](#développement-remote-first)\n- [Design](#design)\n- [Auteur](#auteur)\n\n## Ce que fait Vrai-Prix\n\nVrai-Prix estime la valeur marchande de **n'importe quelle propriété du Québec** (unifamiliale, plex, condo, chalet, terrain) et montre *pourquoi* — **chaque comparable, chaque ajustement en dollars**, la pondération du modèle, la fourchette et l'indice de confiance. Le différenciateur est la **transparence totale** : pas de score magique, **pas de boîte noire**. La méthodologie complète (normes **IAAO**) est publiée intégralement sur le site.\n\nSous le capot : les **6 millésimes (2021-2026)** des rôles d'évaluation foncière géoréférencés du Québec (**MAMH**, données ouvertes) — soit **22 150 285 observations** — fusionnés spatialement avec **745 119 transactions réelles** (**99,88 %** d'appariement, distance médiane **0,6 m**), alimentant un **modèle hédonique LightGBM** et un **moteur de comparables ajustés**.\n\n## Fonctionnalités\n\n| Fonctionnalité | Description |\n|---|---|\n| 🔎 **Estimation par adresse** | Recherche plein-texte (**FTS5**) parmi **3 747 008 unités d'évaluation**, tolérante aux fautes |","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Wt5bF14oLmNTs6nydJBufQ","type":"tool_result","content":"1\t/* -----------------------------------------------------------------------------\n2\t  Vrai-Prix — estimation immobilière transparente (Québec)\n3\t  Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n4\t  Système de design « éditorial sharp » — identité ROUGE distincte de Lou-Ka :\n5\t    · Typo display Space Grotesk / texte Inter / micro-étiquettes JetBrains Mono\n6\t    · Signature : bordures encre + ombres décalées (néo-brutalisme raffiné)\n7\t    · Accent rouge vif #ff5148 + bordeaux #9e2a25 sur encre\n8\t----------------------------------------------------------------------------- */\n9\t@import \"tailwindcss\";\n10\t/* ka-ui (Groupe KA) — tokens + composants communs (.gk-badge, .ka-footer, .card…).\n11\t   Importé dans layer(base) AVANT le corps de ce fichier : il bat le preflight\n12\t   Tailwind mais reste sous les utilitaires (layered) et sous nos règles locales\n13\t   (non-layered). Un import brut dans layout.tsx écraserait les text-[…]/leading-[…]\n14\t   des titres (règles éléments non-layered > utilitaires layered). */\n15\t@import \"../ka/tokens.css\" layer(base);\n16\t\n17\t:root {\n18\t  /* ---- Accent Vrai-Prix (tokens canoniques ka-ui) — identité rouge ---- */\n19\t  --accent: #ff5148;\n20\t  --accent-soft: #ffe3e0;\n21\t  --accent-deep: #9e2a25;\n22\t  --on-accent: #ffffff;\n23\t\n24\t  --paper: #f5f3ee;\n25\t  --surface: #ffffff;\n26\t  --surface-2: #faf9f5;\n27\t  --ink: #141814;\n28\t  --ink-2: #4d5551;\n29\t  --ink-3: #8b928c;\n30\t  --line: rgba(20, 24, 20, 0.14);\n31\t  --line-strong: rgba(20, 24, 20, 0.85);\n32\t  --green: #9e2a25;\n33\t  --green-deep: #771f1b;\n34\t  --lime: var(--accent); /* alias historiques → l'accent est le token canonique */\n35\t  --lime-soft: var(--accent-soft);\n36\t  --amber: #e8a33d;\n37\t  --amber-soft: #fdf3e2;\n38\t  --danger: #b3423a;\n39\t  --danger-soft: #fbe9e7;\n40\t  --r-card: 10px;\n41\t  --r-ctl: 6px;\n42\t  --shadow-flat: 0 1px 2px rgba(20, 24, 20, 0.05);\n43\t  --shadow-off: 6px 6px 0 var(--ink);\n44\t  --shadow-off-soft: 8px 8px 0 rgba(20, 24, 20, 0.08);\n45\t}\n46\t\n47\t@theme inline {\n48\t  --color-paper: var(--paper);\n49\t  --color-surface: var(--surface);\n50\t  --color-surface-2: var(--surface-2);\n51\t  --color-ink: var(--ink);\n52\t  --color-ink-2: var(--ink-2);\n53\t  --color-ink-3: var(--ink-3);\n54\t  --color-green: var(--green);\n55\t  --color-green-deep: var(--green-deep);\n56\t  --color-lime: var(--lime);\n57\t  --color-lime-soft: var(--lime-soft);\n58\t  --color-amber: var(--amber);\n59\t  --color-amber-soft: var(--amber-soft);\n60\t  --color-danger: var(--danger);\n61\t  --font-display: var(--font-space-grotesk), system-ui, sans-serif;\n62\t  --font-body: var(--font-inter), system-ui, sans-serif;\n63\t  --font-mono: var(--font-jetbrains), ui-monospace, monospace;\n64\t}\n65\t\n66\t/* compat ka-ui : tokens.css impose marges + tailles fluides aux titres ; le\n67\t   design local dimensionne les siens via utilitaires → on rétablit le\n68\t   comportement preflight (même layer, source postérieure = prioritaire). */\n69\t@layer base {\n70\t  h1, h2, h3, h4 { margin: 0; font-size: inherit; line-height: inherit; }\n71\t}\n72\t\n73\thtml { scroll-behavior: smooth; }\n74\tbody {\n75\t  background: var(--paper);\n76\t  color: var(--ink);\n77\t  font-family: var(--font-inter), system-ui, sans-serif;\n78\t  font-size: 15px;\n79\t  line-height: 1.55;\n80\t  -webkit-font-smoothing: antialiased;\n81\t  padding-bottom: env(safe-area-inset-bottom);\n82\t}\n83\t/* grain subtil — texture signature */\n84\tbody::before {\n85\t  content: \"\";\n86\t  position: fixed; inset: 0; z-index: 0; pointer-events: none; opacity: 0.35;\n87\t  background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='120' height='120'%3E%3Cfilter id='n'%3E%3CfeTurbulence type='fractalNoise' baseFrequency='0.9' numOctaves='2'/%3E%3CfeColorMatrix values='0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.02 0'/%3E%3C/filter%3E%3Crect width='120' height='120' filter='url(%23n)'/%3E%3C/svg%3E\");\n88\t}\n89\t/* au-dessus du grain (::before positionné, premier dans l'ordre de peinture) ;\n90\t   surtout PAS de z-index ici : une règle hors @layer écraserait les utilitaires\n91\t   Tailwind (échelle --z-* de tokens.css : header 500, menu 900) */\n92\tbody > * { position: relative; }\n93\t\n94\th1, h2, h3, h4 {\n95\t  font-family: var(--font-space-grotesk), system-ui, sans-serif;\n96\t  letter-spacing: -0.03em;\n97\t}\n98\t::selection { background: var(--lime); color: var(--ink); }\n99\t\n100\t/* ---------- primitives signature ---------- */\n101\t.vp-display { font-family: var(--font-space-grotesk), system-ui, sans-serif; }\n102\t.vp-mono { font-family: var(--font-jetbrains), ui-monospace, monospace; }\n103\t\n104\t.kicker {\n105\t  font-family: var(--font-jetbrains), ui-monospace, monospace;\n106\t  font-size: 11.5px; font-weight: 500; text-transform: uppercase;\n107\t  letter-spacing: 0.14em; color: var(--green);\n108\t  display: inline-flex; align-items: center; gap: 8px;\n109\t}\n110\t.kicker::before { content: \"\"; width: 22px; height: 2px; background: var(--green); }\n111\t\n112\t.klabel {\n113\t  font-family: var(--font-jetbrains), ui-monospace, monospace;\n114\t  font-size: 10px; text-transform: uppercase; letter-spacing: 0.1em;\n115\t  color: var(--ink-3); font-weight: 700;\n116\t}\n117\t\n118\t.vp-card {\n119\t  background: var(--surface);\n120\t  border: 1.5px solid var(--ink);\n121\t  border-radius: var(--r-card);\n122\t  box-shadow: var(--shadow-off-soft);\n123\t}\n124\t.vp-card-hover { transition: transform 0.16s ease, box-shadow 0.16s ease; }\n125\t.vp-card-hover:hover { transform: translate(-3px, -3px); box-shadow: var(--shadow-off); }\n126\t\n127\t.vp-input {\n128\t  border: 1.5px solid var(--line); background: var(--surface-2);\n129\t  border-radius: var(--r-ctl); padding: 11px 12px; font-size: 15px;\n130\t  color: var(--ink); outline: none; font-family: inherit; width: 100%;\n131\t  transition: border-color 0.15s ease, box-shadow 0.15s ease; min-height: 44px;\n132\t  appearance: none; -webkit-appearance: none;\n133\t}\n134\t.vp-input:focus { border-color: var(--ink); box-shadow: 3px 3px 0 var(--lime); }\n135\tselect.vp-input {\n136\t  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\");\n137\t  background-repeat: no-repeat; background-position: right 12px center; padding-right: 30px;\n138\t}\n139\t@media (max-width: 900px) { .vp-input { font-size: 16px; } } /* anti-zoom iOS */\n140\t\n141\t.btn {\n142\t  border: 1.5px solid var(--ink); border-radius: var(--r-ctl); padding: 11px 20px;\n143\t  font-weight: 700; font-size: 14px; cursor: pointer; min-height: 44px;\n144\t  transition: transform 0.12s ease, box-shadow 0.12s ease, background 0.15s ease;\n145\t  font-family: var(--font-space-grotesk), system-ui, sans-serif; letter-spacing: 0.01em;\n146\t  display: inline-flex; align-items: center; justify-content: center; gap: 8px;\n147\t}\n148\t.btn:active { transform: translate(2px, 2px); box-shadow: none !important; }\n149\t.btn-primary { background: var(--ink); color: var(--lime); box-shadow: 4px 4px 0 rgba(20,24,20,0.25); }\n150\t.btn-primary:hover { background: var(--green-deep); }\n151\t.btn-ghost { background: transparent; color: var(--ink); border-color: var(--ink); }\n152\t.btn-ghost:hover { background: var(--lime); box-shadow: 4px 4px 0 rgba(20,24,20,0.2); }\n153\t\n154\t.stat-chip {\n155\t  background: var(--surface); border: 1.5px solid var(--ink); border-radius: 999px;\n156\t  padding: 8px 16px; font-family: var(--font-jetbrains), ui-monospace, monospace;\n157\t  font-size: 12px; color: var(--ink-2); display: inline-flex; gap: 8px; align-items: center;\n158\t  box-shadow: 3px 3px 0 rgba(20, 24, 20, 0.12); white-space: nowrap;\n159\t}\n160\t.stat-chip b { color: var(--ink); font-weight: 700; }\n161\t.stat-chip .pulse {\n162\t  width: 8px; height: 8px; border-radius: 50%; background: var(--green);\n163\t  box-shadow: 0 0 0 4px var(--lime-soft); animation: pulse 2.4s ease infinite;\n164\t}\n165\t@keyframes pulse { 50% { box-shadow: 0 0 0 7px rgba(255, 81, 72, 0.4); } }\n166\t\n167\t.hl {\n168\t  background: var(--lime); padding: 0 10px; border-radius: 8px; display: inline-block;\n169\t  transform: rotate(-1deg);\n170\t}\n171\t.outline-txt { color: transparent; -webkit-text-stroke: 2px var(--ink); }\n172\t\n173\t/* ticker */\n174\t.ticker {\n175\t  background: var(--ink); color: var(--lime); overflow: hidden;\n176\t  font-family: var(--font-jetbrains), ui-monospace, monospace;\n177\t  font-size: 11.5px; letter-spacing: 0.1em; text-transform: uppercase;\n178\t  padding: 7px 0; white-space: nowrap;\n179\t  border-bottom: 1px solid rgba(255, 81, 72, 0.25);\n180\t}\n181\t.ticker-track { display: inline-flex; animation: ticker 44s linear infinite; will-change: transform; }\n182\t.ticker span { padding: 0 26px; position: relative; }\n183\t.ticker span::after { content: \"◆\"; position: absolute; right: -6px; opacity: 0.5; font-size: 8px; top: 3px; }\n184\t@keyframes ticker { from { transform: translateX(0); } to { transform: translateX(-50%); } }\n185\t@media (prefers-reduced-motion: reduce) {\n186\t  .ticker-track { animation: none; }\n187\t  * { transition-duration: 0.01ms !important; }\n188\t}\n189\t\n190\t/* kv cells (fiche) */\n191\t.kv-cell {\n192\t  background: var(--surface-2); border: 1.5px solid var(--line);\n193\t  border-radius: var(--r-ctl); padding: 10px 13px;\n194\t}\n195\t.kv-cell .k {\n196\t  font-family: var(--font-jetbrains), ui-monospace, monospace;\n197\t  font-size: 9.5px; text-transform: uppercase; letter-spacing: 0.1em;\n198\t  color: var(--ink-3); font-weight: 700;\n199\t}\n200\t.kv-cell .v {\n201\t  font-weight: 700; font-size: 14.5px; margin-top: 2px;\n202\t  font-family: var(--font-space-grotesk), system-ui, sans-serif;\n203\t}\n204\t\n205\t/* pills */\n206\t.pill {\n207\t  display: inline-block; border-radius: 4px; padding: 3px 10px;\n208\t  font-family: var(--font-jetbrains), ui-monospace, monospace;\n209\t  font-size: 10.5px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.05em;\n210\t}\n211\t.pill-conf-A { background: var(--lime); color: var(--ink); border: 1px solid var(--ink); }\n212\t.pill-conf-B { background: var(--lime-soft); color: var(--green-deep); border: 1px solid var(--green); }\n213\t.pill-conf-C { background: var(--amber-soft); color: #8a5a12; border: 1px solid var(--amber); }\n214\t.pill-conf-D { background: var(--danger-soft); color: var(--danger); border: 1px solid var(--danger); }\n215\t\n216\t/* tableau éditorial */\n217\t.src-wrap {\n218\t  overflow-x: auto; border: 1.5px solid var(--ink); border-radius: var(--r-card);\n219\t  box-shadow: var(--shadow-off-soft); background: var(--surface);\n220\t}\n221\t.src-table { width: 100%; border-collapse: separate; border-spacing: 0; }\n222\t.src-table th {\n223\t  text-align: left; font-family: var(--font-jetbrains), ui-monospace, monospace;\n224\t  font-size: 10px; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3);\n225\t  padding: 12px 14px; border-bottom: 1.5px solid var(--ink); background: var(--surface-2);\n226\t  white-space: nowrap;\n227\t}\n228\t.src-table td { padding: 11px 14px; border-bottom: 1px solid var(--line); font-size: 13.5px; vertical-align: top; }\n229\t.src-table tr:last-child td { border-bottom: none; }\n230\t.src-table tr:hover td { background: var(--surface-2); }\n231\t\n232\t/* animations de révélation */\n233\t@keyframes rise {\n234\t  from { opacity: 0; transform: translateY(12px); }\n235\t  to { opacity: 1; transform: none; }\n236\t}\n237\t.rise { animation: rise 0.55s cubic-bezier(0.2, 0.8, 0.2, 1) both; }\n238\t@keyframes growX { from { transform: scaleX(0); } }\n239\t.grow-x { transform-origin: left; animation: growX 0.9s cubic-bezier(0.2, 0.8, 0.2, 1) both; }\n240\t@keyframes winPop { from { opacity: 0; } }\n241\t.vv-win { animation: winPop 0.4s ease both; }\n242\t@media (prefers-reduced-motion: reduce) {\n243\t  .rise, .grow-x, .vv-win { animation: none; }\n244\t}\n245\t\n246\t/* barres horizontales (historique) */\n247\t.hbar-track { background: var(--surface-2); border-radius: 0 4px 4px 0; height: 18px; overflow: hidden; }\n248\t.hbar-fill {\n249\t  display: block; height: 100%; background: var(--green); border-radius: 0 4px 4px 0;\n250\t  min-width: 2px; transition: background 0.12s ease;\n251\t}\n252\t.hbar-row:hover .hbar-fill { background: var(--ink); box-shadow: inset 0 0 0 2px var(--lime); }\n253\t\n254\t\n255\t/* Vrai-Prix Maps (framework Ka Maps par Groupe Ka) — recherche par carte */\n256\t.vpm-wrap {\n257\t  position: relative;\n258\t  height: 62vh;\n259\t  min-height: 420px;\n260\t  width: 100%;\n261\t  border: 1.5px solid var(--ink);\n262\t  border-radius: 16px;\n263\t  overflow: hidden;\n264\t  box-shadow: 6px 6px 0 rgba(20, 24, 20, 0.22);\n265\t  z-index: 0;\n266\t}\n267\t.vpm-wrap .ka-map {\n268\t  --ka-accent: var(--lime);\n269\t  --ka-on-accent: #ffffff;\n270\t  --ka-surface: var(--surface);\n271\t  --ka-ink: var(--ink);\n272\t  --ka-line: var(--ink);\n273\t  --ka-radius: 10px;\n274\t  --ka-shadow: 4px 4px 0 rgba(20, 24, 20, 0.2);\n275\t  --ka-font: var(--font-inter), system-ui, sans-serif;\n276\t  position: absolute;\n277\t  inset: 0;\n278\t}\n279\t.vpm-chip {\n280\t  position: absolute;\n281\t  bottom: 12px;\n282\t  left: 12px;\n283\t  z-index: 9;\n284\t  background: var(--surface);\n285\t  color: var(--ink);\n286\t  border: 1.5px solid var(--ink);\n287\t  border-radius: 999px;\n288\t  padding: 6px 12px;\n289\t  font-family: var(--font-jetbrains), monospace;\n290\t  font-size: 10.5px;\n291\t  text-transform: uppercase;\n292\t  letter-spacing: 0.06em;\n293\t}\n294\t.vpm-pop { padding: 16px 16px 14px; min-width: 230px; }\n295\t.vpm-pop-value {\n296\t  font-family: var(--font-space-grotesk), sans-serif;\n297\t  font-weight: 700;\n298\t  font-size: 22px;\n299\t  letter-spacing: -0.02em;\n300\t  margin-top: 2px;\n301\t}\n302\t.vpm-pop-addr { font-size: 13.5px; color: var(--ink); margin-top: 6px; }\n303\t.vpm-pop-meta { font-size: 12px; color: var(--ink-2); margin-top: 2px; }\n304\t.vpm-pop-cta {\n305\t  display: inline-block;\n306\t  margin-top: 12px;\n307\t  padding: 9px 14px;\n308\t  background: var(--ink);\n309\t  color: var(--lime);\n310\t  border: 1.5px solid var(--ink);\n311\t  border-radius: 999px;\n312\t  font-size: 12.5px;\n313\t  font-weight: 600;\n314\t  cursor: pointer;\n315\t}\n316\t.vpm-pop-cta:active { transform: translate(2px, 2px); }\n317\t"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"0f40a2c9-451c-4acf-ba5e-c33e32d9b40a","timestamp":"2026-08-23T03:00:26.753Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/app/globals.css","content":"/* -----------------------------------------------------------------------------\n  Vrai-Prix — estimation immobilière transparente (Québec)\n  Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n  Système de design « éditorial sharp » — identité ROUGE distincte de Lou-Ka :\n    · Typo display Space Grotesk / texte Inter / micro-étiquettes JetBrains Mono\n    · Signature : bordures encre + ombres décalées (néo-brutalisme raffiné)\n    · Accent rouge vif #ff5148 + bordeaux #9e2a25 sur encre\n----------------------------------------------------------------------------- */\n@import \"tailwindcss\";\n/* ka-ui (Groupe KA) — tokens + composants communs (.gk-badge, .ka-footer, .card…).\n   Importé dans layer(base) AVANT le corps de ce fichier : il bat le preflight\n   Tailwind mais reste sous les utilitaires (layered) et sous nos règles locales\n   (non-layered). Un import brut dans layout.tsx écraserait les text-[…]/leading-[…]\n   des titres (règles éléments non-layered > utilitaires layered). */\n@import \"../ka/tokens.css\" layer(base);\n\n:root {\n  /* ---- Accent Vrai-Prix (tokens canoniques ka-ui) — identité rouge ---- */\n  --accent: #ff5148;\n  --accent-soft: #ffe3e0;\n  --accent-deep: #9e2a25;\n  --on-accent: #ffffff;\n\n  --paper: #f5f3ee;\n  --surface: #ffffff;\n  --surface-2: #faf9f5;\n  --ink: #141814;\n  --ink-2: #4d5551;\n  --ink-3: #8b928c;\n  --line: rgba(20, 24, 20, 0.14);\n  --line-strong: rgba(20, 24, 20, 0.85);\n  --green: #9e2a25;\n  --green-deep: #771f1b;\n  --lime: var(--accent); /* alias historiques → l'accent est le token canonique */\n  --lime-soft: var(--accent-soft);\n  --amber: #e8a33d;\n  --amber-soft: #fdf3e2;\n  --danger: #b3423a;\n  --danger-soft: #fbe9e7;\n  --r-card: 10px;\n  --r-ctl: 6px;\n  --shadow-flat: 0 1px 2px rgba(20, 24, 20, 0.05);\n  --shadow-off: 6px 6px 0 var(--ink);\n  --shadow-off-soft: 8px 8px 0 rgba(20, 24, 20, 0.08);\n}\n\n@theme inline {\n  --color-paper: var(--paper);\n  --color-surface: var(--surface);\n  --color-surface-2: var(--surface-2);\n  --color-ink: var(--ink);\n  --color-ink-2: var(--ink-2);\n  --color-ink-3: var(--ink-3);\n  --color-green: var(--green);\n  --color-green-deep: var(--green-deep);\n  --color-lime: var(--lime);\n  --color-lime-soft: var(--lime-soft);\n  --color-amber: var(--amber);\n  --color-amber-soft: var(--amber-soft);\n  --color-danger: var(--danger);\n  --font-display: var(--font-space-grotesk), system-ui, sans-serif;\n  --font-body: var(--font-inter), system-ui, sans-serif;\n  --font-mono: var(--font-jetbrains), ui-monospace, monospace;\n}\n\n/* compat ka-ui : tokens.css impose marges + tailles fluides aux titres ; le\n   design local dimensionne les siens via utilitaires → on rétablit le\n   comportement preflight (même layer, source postérieure = prioritaire). */\n@layer base {\n  h1, h2, h3, h4 { margin: 0; font-size: inherit; line-height: inherit; }\n}\n\nhtml { scroll-behavior: smooth; }\nbody {\n  background: var(--paper);\n  color: var(--ink);\n  font-family: var(--font-inter), system-ui, sans-serif;\n  font-size: 15px;\n  line-height: 1.55;\n  -webkit-font-smoothing: antialiased;\n  padding-bottom: env(safe-area-inset-bottom);\n}\n/* grain subtil — texture signature */\nbody::before {\n  content: \"\";\n  position: fixed; inset: 0; z-index: 0; pointer-events: none; opacity: 0.35;\n  background-image: url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='120' height='120'%3E%3Cfilter id='n'%3E%3CfeTurbulence type='fractalNoise' baseFrequency='0.9' numOctaves='2'/%3E%3CfeColorMatrix values='0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.02 0'/%3E%3C/filter%3E%3Crect width='120' height='120' filter='url(%23n)'/%3E%3C/svg%3E\");\n}\n/* au-dessus du grain (::before positionné, premier dans l'ordre de peinture) ;\n   surtout PAS de z-index ici : une règle hors @layer écraserait les utilitaires\n   Tailwind (échelle --z-* de tokens.css : header 500, menu 900) */\nbody > * { position: relative; }\n\nh1, h2, h3, h4 {\n  font-family: var(--font-space-grotesk), system-ui, sans-serif;\n  letter-spacing: -0.03em;\n}\n::selection { background: var(--lime); color: var(--ink); }\n\n/* ---------- primitives signature ---------- */\n.vp-display { font-family: var(--font-space-grotesk), system-ui, sans-serif; }\n.vp-mono { font-family: var(--font-jetbrains), ui-monospace, monospace; }\n\n.kicker {\n  font-family: var(--font-jetbrains), ui-monospace, monospace;\n  font-size: 11.5px; font-weight: 500; text-transform: uppercase;\n  letter-spacing: 0.14em; color: var(--green);\n  display: inline-flex; align-items: center; gap: 8px;\n}\n.kicker::before { content: \"\"; width: 22px; height: 2px; background: var(--green); }\n\n.klabel {\n  font-family: var(--font-jetbrains), ui-monospace, monospace;\n  font-size: 10px; text-transform: uppercase; letter-spacing: 0.1em;\n  color: var(--ink-3); font-weight: 700;\n}\n\n.vp-card {\n  background: var(--surface);\n  border: 1.5px solid var(--ink);\n  border-radius: var(--r-card);\n  box-shadow: var(--shadow-off-soft);\n}\n.vp-card-hover { transition: transform 0.16s ease, box-shadow 0.16s ease; }\n.vp-card-hover:hover { transform: translate(-3px, -3px); box-shadow: var(--shadow-off); }\n\n.vp-input {\n  border: 1.5px solid var(--line); background: var(--surface-2);\n  border-radius: var(--r-ctl); padding: 11px 12px; font-size: 15px;\n  color: var(--ink); outline: none; font-family: inherit; width: 100%;\n  transition: border-color 0.15s ease, box-shadow 0.15s ease; min-height: 44px;\n  appearance: none; -webkit-appearance: none;\n}\n.vp-input:focus { border-color: var(--ink); box-shadow: 3px 3px 0 var(--lime); }\nselect.vp-input {\n  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\");\n  background-repeat: no-repeat; background-position: right 12px center; padding-right: 30px;\n}\n@media (max-width: 900px) { .vp-input { font-size: 16px; } } /* anti-zoom iOS */\n\n.btn {\n  border: 1.5px solid var(--ink); border-radius: var(--r-ctl); padding: 11px 20px;\n  font-weight: 700; font-size: 14px; cursor: pointer; min-height: 44px;\n  transition: transform 0.12s ease, box-shadow 0.12s ease, background 0.15s ease;\n  font-family: var(--font-space-grotesk), system-ui, sans-serif; letter-spacing: 0.01em;\n  display: inline-flex; align-items: center; justify-content: center; gap: 8px;\n}\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(20,24,20,0.25); }\n.btn-primary:hover { background: var(--green-deep); }\n.btn-ghost { background: transparent; color: var(--ink); border-color: var(--ink); }\n.btn-ghost:hover { background: var(--lime); box-shadow: 4px 4px 0 rgba(20,24,20,0.2); }\n\n.stat-chip {\n  background: var(--surface); border: 1.5px solid var(--ink); border-radius: 999px;\n  padding: 8px 16px; font-family: var(--font-jetbrains), ui-monospace, monospace;\n  font-size: 12px; color: var(--ink-2); display: inline-flex; gap: 8px; align-items: center;\n  box-shadow: 3px 3px 0 rgba(20, 24, 20, 0.12); white-space: nowrap;\n}\n.stat-chip b { color: var(--ink); font-weight: 700; }\n.stat-chip .pulse {\n  width: 8px; height: 8px; border-radius: 50%; background: var(--green);\n  box-shadow: 0 0 0 4px var(--lime-soft); animation: pulse 2.4s ease infinite;\n}\n@keyframes pulse { 50% { box-shadow: 0 0 0 7px rgba(255, 81, 72, 0.4); } }\n\n.hl {\n  background: var(--lime); padding: 0 10px; border-radius: 8px; display: inline-block;\n  transform: rotate(-1deg);\n}\n.outline-txt { color: transparent; -webkit-text-stroke: 2px var(--ink); }\n\n/* ticker */\n.ticker {\n  background: var(--ink); color: var(--lime); overflow: hidden;\n  font-family: var(--font-jetbrains), ui-monospace, monospace;\n  font-size: 11.5px; letter-spacing: 0.1em; text-transform: uppercase;\n  padding: 7px 0; white-space: nowrap;\n  border-bottom: 1px solid rgba(255, 81, 72, 0.25);\n}\n.ticker-track { display: inline-flex; animation: ticker 44s 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) {\n  .ticker-track { animation: none; }\n  * { transition-duration: 0.01ms !important; }\n}\n\n/* kv cells (fiche) */\n.kv-cell {\n  background: var(--surface-2); border: 1.5px solid var(--line);\n  border-radius: var(--r-ctl); padding: 10px 13px;\n}\n.kv-cell .k {\n  font-family: var(--font-jetbrains), ui-monospace, monospace;\n  font-size: 9.5px; text-transform: uppercase; letter-spacing: 0.1em;\n  color: var(--ink-3); font-weight: 700;\n}\n.kv-cell .v {\n  font-weight: 700; font-size: 14.5px; margin-top: 2px;\n  font-family: var(--font-space-grotesk), system-ui, sans-serif;\n}\n\n/* pills */\n.pill {\n  display: inline-block; border-radius: 4px; padding: 3px 10px;\n  font-family: var(--font-jetbrains), ui-monospace, monospace;\n  font-size: 10.5px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.05em;\n}\n.pill-conf-A { background: var(--lime); color: var(--ink); border: 1px solid var(--ink); }\n.pill-conf-B { background: var(--lime-soft); color: var(--green-deep); border: 1px solid var(--green); }\n.pill-conf-C { background: var(--amber-soft); color: #8a5a12; border: 1px solid var(--amber); }\n.pill-conf-D { background: var(--danger-soft); color: var(--danger); border: 1px solid var(--danger); }\n\n/* tableau éditorial */\n.src-wrap {\n  overflow-x: auto; border: 1.5px solid var(--ink); border-radius: var(--r-card);\n  box-shadow: var(--shadow-off-soft); background: var(--surface);\n}\n.src-table { width: 100%; border-collapse: separate; border-spacing: 0; }\n.src-table th {\n  text-align: left; font-family: var(--font-jetbrains), ui-monospace, monospace;\n  font-size: 10px; text-transform: uppercase; letter-spacing: 0.12em; color: var(--ink-3);\n  padding: 12px 14px; border-bottom: 1.5px solid var(--ink); background: var(--surface-2);\n  white-space: nowrap;\n}\n.src-table td { padding: 11px 14px; border-bottom: 1px solid var(--line); font-size: 13.5px; vertical-align: top; }\n.src-table tr:last-child td { border-bottom: none; }\n.src-table tr:hover td { background: var(--surface-2); }\n\n/* animations de révélation */\n@keyframes rise {\n  from { opacity: 0; transform: translateY(12px); }\n  to { opacity: 1; transform: none; }\n}\n.rise { animation: rise 0.55s cubic-bezier(0.2, 0.8, 0.2, 1) both; }\n@keyframes growX { from { transform: scaleX(0); } }\n.grow-x { transform-origin: left; animation: growX 0.9s cubic-bezier(0.2, 0.8, 0.2, 1) both; }\n@keyframes winPop { from { opacity: 0; } }\n.vv-win { animation: winPop 0.4s ease both; }\n@media (prefers-reduced-motion: reduce) {\n  .rise, .grow-x, .vv-win { animation: none; }\n}\n\n/* barres horizontales (historique) */\n.hbar-track { background: var(--surface-2); border-radius: 0 4px 4px 0; height: 18px; overflow: hidden; }\n.hbar-fill {\n  display: block; height: 100%; background: var(--green); border-radius: 0 4px 4px 0;\n  min-width: 2px; transition: background 0.12s ease;\n}\n.hbar-row:hover .hbar-fill { background: var(--ink); box-shadow: inset 0 0 0 2px var(--lime); }\n\n\n/* Vrai-Prix Maps (framework Ka Maps par Groupe Ka) — recherche par carte */\n.vpm-wrap {\n  position: relative;\n  height: 62vh;\n  min-height: 420px;\n  width: 100%;\n  border: 1.5px solid var(--ink);\n  border-radius: 16px;\n  overflow: hidden;\n  box-shadow: 6px 6px 0 rgba(20, 24, 20, 0.22);\n  z-index: 0;\n}\n.vpm-wrap .ka-map {\n  --ka-accent: var(--lime);\n  --ka-on-accent: #ffffff;\n  --ka-surface: var(--surface);\n  --ka-ink: var(--ink);\n  --ka-line: var(--ink);\n  --ka-radius: 10px;\n  --ka-shadow: 4px 4px 0 rgba(20, 24, 20, 0.2);\n  --ka-font: var(--font-inter), system-ui, sans-serif;\n  position: absolute;\n  inset: 0;\n}\n.vpm-chip {\n  position: absolute;\n  bottom: 12px;\n  left: 12px;\n  z-index: 9;\n  background: var(--surface);\n  color: var(--ink);\n  border: 1.5px solid var(--ink);\n  border-radius: 999px;\n  padding: 6px 12px;\n  font-family: var(--font-jetbrains), monospace;\n  font-size: 10.5px;\n  text-transform: uppercase;\n  letter-spacing: 0.06em;\n}\n.vpm-pop { padding: 16px 16px 14px; min-width: 230px; }\n.vpm-pop-value {\n  font-family: var(--font-space-grotesk), sans-serif;\n  font-weight: 700;\n  font-size: 22px;\n  letter-spacing: -0.02em;\n  margin-top: 2px;\n}\n.vpm-pop-addr { font-size: 13.5px; color: var(--ink); margin-top: 6px; }\n.vpm-pop-meta { font-size: 12px; color: var(--ink-2); margin-top: 2px; }\n.vpm-pop-cta {\n  display: inline-block;\n  margin-top: 12px;\n  padding: 9px 14px;\n  background: var(--ink);\n  color: var(--lime);\n  border: 1.5px solid var(--ink);\n  border-radius: 999px;\n  font-size: 12.5px;\n  font-weight: 600;\n  cursor: pointer;\n}\n.vpm-pop-cta:active { transform: translate(2px, 2px); }\n","numLines":317,"startLine":1,"totalLines":317}}}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJw66MMaqtHTYk2a9hXE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Q8SRpv6YZfQ9FK9X2C22b4","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/app/HomeClient.tsx"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":484,"cache_read_input_tokens":40878,"cache_creation":{"ephemeral_5m_input_tokens":484,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"9a03e9b1-9b8f-4ed2-8be3-770de504062c","request_id":"req_011CeJw64x2Q5MLE5zvrkLWb"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Q8SRpv6YZfQ9FK9X2C22b4","type":"tool_result","content":"1\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n2\t\"use client\";\n3\timport SearchBox from \"@/components/SearchBox\";\n4\timport EstimateForm from \"@/components/EstimateForm\";\n5\timport { useLang } from \"@/components/LangContext\";\n6\t\n7\texport default function HomeClient() {\n8\t  const { lang, t } = useLang();\n9\t  return (\n10\t    <div className=\"pb-6 pt-10 sm:pt-14\">\n11\t      {/* ---- Héros ---- */}\n12\t      <section>\n13\t        <span className=\"kicker\">\n14\t          {lang === \"fr\" ? \"Québec · 2021-2026\" : \"Québec · 2021-2026\"}\n15\t        </span>\n16\t        <h1 className=\"vp-display mt-3 max-w-4xl text-[clamp(34px,6.4vw,72px)] font-bold uppercase leading-[0.98] tracking-[-0.035em]\">\n17\t          {lang === \"fr\" ? (\n18\t            <>\n19\t              La <span className=\"outline-txt\">vraie</span> valeur.\n20\t              <br />\n21\t              <span className=\"hl\">Sans boîte noire.</span>\n22\t            </>\n23\t          ) : (\n24\t            <>\n25\t              The <span className=\"outline-txt\">true</span> value.\n26\t              <br />\n27\t              <span className=\"hl\">No black box.</span>\n28\t            </>\n29\t          )}\n30\t        </h1>\n31\t        <p className=\"mt-5 max-w-xl text-[16.5px] text-ink-2\">{t(\"sub\")}</p>\n32\t\n33\t        <div className=\"mt-7 flex gap-2.5 overflow-x-auto pb-1.5 [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden\">\n34\t          <span className=\"stat-chip\">\n35\t            <span className=\"pulse\" />\n36\t            <b>3 747 008</b> {lang === \"fr\" ? \"propriétés\" : \"properties\"}\n37\t          </span>\n38\t          <span className=\"stat-chip\">\n39\t            <b>745 119</b> {lang === \"fr\" ? \"ventes réelles\" : \"real sales\"}\n40\t          </span>\n41\t          <span className=\"stat-chip\">\n42\t            <b>1 100</b> {lang === \"fr\" ? \"municipalités\" : \"municipalities\"}\n43\t          </span>\n44\t          <span className=\"stat-chip\">\n45\t            MdAPE <b>11 %</b>\n46\t          </span>\n47\t        </div>\n48\t      </section>\n49\t\n50\t      {/* ---- Recherche ---- */}\n51\t      <section className=\"mt-9\">\n52\t        <div className=\"vp-card p-4 sm:p-5\">\n53\t          <p className=\"klabel mb-2 pl-0.5\">\n54\t            {lang === \"fr\" ? \"Adresse de la propriété\" : \"Property address\"}\n55\t          </p>\n56\t          <SearchBox />\n57\t        </div>\n58\t        <p className=\"vp-mono mt-2.5 pl-1 text-[11px] uppercase tracking-[0.06em] text-ink-3\">\n59\t          {t(\"dataNote\")}\n60\t        </p>\n61\t      </section>\n62\t\n63\t      {/* ---- Formulaire manuel ---- */}\n64\t      <section className=\"mt-14\">\n65\t        <span className=\"kicker\">{lang === \"fr\" ? \"Plan B\" : \"Plan B\"}</span>\n66\t        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n67\t          {t(\"manualTitle\")}\n68\t        </h2>\n69\t        <div className=\"vp-card mt-4 p-4 sm:p-6\">\n70\t          <EstimateForm />\n71\t        </div>\n72\t      </section>\n73\t\n74\t      {/* ---- Manifeste transparence ---- */}\n75\t      <section className=\"mt-14 grid gap-4 sm:grid-cols-3\">\n76\t        {(lang === \"fr\"\n77\t          ? [\n78\t              [\"01\", \"Le calcul, montré\", \"Chaque ajustement de chaque comparable est affiché en dollars. Pas de score magique.\"],\n79\t              [\"02\", \"Fourchette honnête\", \"Un prix unique est un mensonge statistique. On donne P10-P90 et un indice de confiance A-D.\"],\n80\t              [\"03\", \"Données réelles\", \"Rôles fonciers officiels (MAMH) et ventes publiées — locales et récentes en priorité.\"],\n81\t            ]\n82\t          : [\n83\t              [\"01\", \"The math, shown\", \"Every adjustment on every comparable is displayed in dollars. No magic score.\"],\n84\t              [\"02\", \"Honest range\", \"A single price is a statistical lie. We give P10-P90 and an A-D confidence index.\"],\n85\t              [\"03\", \"Real data\", \"Official assessment rolls (MAMH) and published sales — local and recent first.\"],\n86\t            ]\n87\t        ).map(([num, title, body]) => (\n88\t          <div key={num} className=\"vp-card vp-card-hover p-5\">\n89\t            <p className=\"vp-mono text-[11px] font-bold text-green\">{num}</p>\n90\t            <h3 className=\"vp-display mt-1.5 text-[17px] font-bold uppercase tracking-[-0.01em]\">\n91\t              {title}\n92\t            </h3>\n93\t            <p className=\"mt-2 text-[13.5px] text-ink-2\">{body}</p>\n94\t          </div>\n95\t        ))}\n96\t      </section>\n97\t    </div>\n98\t  );\n99\t}\n100\t"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"654ebb63-dea5-4769-ae19-d4fd3758bb59","timestamp":"2026-08-23T03:00:27.341Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/app/HomeClient.tsx","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport SearchBox from \"@/components/SearchBox\";\nimport EstimateForm from \"@/components/EstimateForm\";\nimport { useLang } from \"@/components/LangContext\";\n\nexport default function HomeClient() {\n  const { lang, t } = useLang();\n  return (\n    <div className=\"pb-6 pt-10 sm:pt-14\">\n      {/* ---- Héros ---- */}\n      <section>\n        <span className=\"kicker\">\n          {lang === \"fr\" ? \"Québec · 2021-2026\" : \"Québec · 2021-2026\"}\n        </span>\n        <h1 className=\"vp-display mt-3 max-w-4xl text-[clamp(34px,6.4vw,72px)] font-bold uppercase leading-[0.98] tracking-[-0.035em]\">\n          {lang === \"fr\" ? (\n            <>\n              La <span className=\"outline-txt\">vraie</span> valeur.\n              <br />\n              <span className=\"hl\">Sans boîte noire.</span>\n            </>\n          ) : (\n            <>\n              The <span className=\"outline-txt\">true</span> value.\n              <br />\n              <span className=\"hl\">No black box.</span>\n            </>\n          )}\n        </h1>\n        <p className=\"mt-5 max-w-xl text-[16.5px] text-ink-2\">{t(\"sub\")}</p>\n\n        <div className=\"mt-7 flex gap-2.5 overflow-x-auto pb-1.5 [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden\">\n          <span className=\"stat-chip\">\n            <span className=\"pulse\" />\n            <b>3 747 008</b> {lang === \"fr\" ? \"propriétés\" : \"properties\"}\n          </span>\n          <span className=\"stat-chip\">\n            <b>745 119</b> {lang === \"fr\" ? \"ventes réelles\" : \"real sales\"}\n          </span>\n          <span className=\"stat-chip\">\n            <b>1 100</b> {lang === \"fr\" ? \"municipalités\" : \"municipalities\"}\n          </span>\n          <span className=\"stat-chip\">\n            MdAPE <b>11 %</b>\n          </span>\n        </div>\n      </section>\n\n      {/* ---- Recherche ---- */}\n      <section className=\"mt-9\">\n        <div className=\"vp-card p-4 sm:p-5\">\n          <p className=\"klabel mb-2 pl-0.5\">\n            {lang === \"fr\" ? \"Adresse de la propriété\" : \"Property address\"}\n          </p>\n          <SearchBox />\n        </div>\n        <p className=\"vp-mono mt-2.5 pl-1 text-[11px] uppercase tracking-[0.06em] text-ink-3\">\n          {t(\"dataNote\")}\n        </p>\n      </section>\n\n      {/* ---- Formulaire manuel ---- */}\n      <section className=\"mt-14\">\n        <span className=\"kicker\">{lang === \"fr\" ? \"Plan B\" : \"Plan B\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"manualTitle\")}\n        </h2>\n        <div className=\"vp-card mt-4 p-4 sm:p-6\">\n          <EstimateForm />\n        </div>\n      </section>\n\n      {/* ---- Manifeste transparence ---- */}\n      <section className=\"mt-14 grid gap-4 sm:grid-cols-3\">\n        {(lang === \"fr\"\n          ? [\n              [\"01\", \"Le calcul, montré\", \"Chaque ajustement de chaque comparable est affiché en dollars. Pas de score magique.\"],\n              [\"02\", \"Fourchette honnête\", \"Un prix unique est un mensonge statistique. On donne P10-P90 et un indice de confiance A-D.\"],\n              [\"03\", \"Données réelles\", \"Rôles fonciers officiels (MAMH) et ventes publiées — locales et récentes en priorité.\"],\n            ]\n          : [\n              [\"01\", \"The math, shown\", \"Every adjustment on every comparable is displayed in dollars. No magic score.\"],\n              [\"02\", \"Honest range\", \"A single price is a statistical lie. We give P10-P90 and an A-D confidence index.\"],\n              [\"03\", \"Real data\", \"Official assessment rolls (MAMH) and published sales — local and recent first.\"],\n            ]\n        ).map(([num, title, body]) => (\n          <div key={num} className=\"vp-card vp-card-hover p-5\">\n            <p className=\"vp-mono text-[11px] font-bold text-green\">{num}</p>\n            <h3 className=\"vp-display mt-1.5 text-[17px] font-bold uppercase tracking-[-0.01em]\">\n              {title}\n            </h3>\n            <p className=\"mt-2 text-[13.5px] text-ink-2\">{body}</p>\n          </div>\n        ))}\n      </section>\n    </div>\n  );\n}\n","numLines":100,"startLine":1,"totalLines":100}}}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJw66MMaqtHTYk2a9hXE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KA5nkwosS1CcNz6BZEHjT2","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":484,"cache_read_input_tokens":40878,"cache_creation":{"ephemeral_5m_input_tokens":484,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"87a228ec-afc6-484b-9477-9257169a79e4","request_id":"req_011CeJw64x2Q5MLE5zvrkLWb"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KA5nkwosS1CcNz6BZEHjT2","type":"tool_result","content":"1\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n2\t\"use client\";\n3\timport { useState } from \"react\";\n4\timport Link from \"next/link\";\n5\timport { useLang } from \"./LangContext\";\n6\timport LeadForm from \"./LeadForm\";\n7\timport RadarMap from \"./RadarMap\";\n8\timport {\n9\t  BuildingGlyph,\n10\t  ConfidenceDial,\n11\t  CountUp,\n12\t  EraLine,\n13\t  locNum,\n14\t  LotDiagram,\n15\t  ValueSplit,\n16\t} from \"./MetricViz\";\n17\timport type { EstimateResult } from \"@/lib/engine\";\n18\timport type { UnitEstimate } from \"@/lib/estimator\";\n19\timport type { TKey } from \"@/lib/i18n\";\n20\t\n21\tconst fmt = (v: number | null | undefined, lang: string) =>\n22\t  v == null\n23\t    ? \"—\"\n24\t    : new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n25\t        style: \"currency\",\n26\t        currency: \"CAD\",\n27\t        maximumFractionDigits: 0,\n28\t      }).format(v);\n29\t\n30\tconst signed = (v: number, lang: string) =>\n31\t  `${v >= 0 ? \"+\" : \"−\"}${fmt(Math.abs(Math.round(v)), lang)}`;\n32\t\n33\tfunction Chip({ k, v }: { k: string; v: string | number }) {\n34\t  return (\n35\t    <div className=\"kv-cell\">\n36\t      <p className=\"k\">{k}</p>\n37\t      <p className=\"v break-words\">{v}</p>\n38\t    </div>\n39\t  );\n40\t}\n41\t\n42\texport default function ResultView({ data }: { data: UnitEstimate }) {\n43\t  const { lang, t } = useLang();\n44\t  const [activeComp, setActiveComp] = useState<string | null>(null);\n45\t  const r: EstimateResult = data.result;\n46\t  const u = data.unit;\n47\t  const s = u?.specs;\n48\t  const fr = lang === \"fr\";\n49\t  const histMax = Math.max(...(u?.history.map((h) => h.value ?? 0) ?? [0]), 1);\n50\t  const moneyFmt = (v: number) =>\n51\t    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n52\t      style: \"currency\",\n53\t      currency: \"CAD\",\n54\t      maximumFractionDigits: 0,\n55\t    }).format(Math.round(v / 100) * 100);\n56\t\n57\t  return (\n58\t    <div className=\"space-y-10 py-8\">\n59\t      {/* fil d'ariane */}\n60\t      <nav className=\"vp-mono flex flex-wrap items-center gap-2 text-[11.5px] uppercase tracking-[0.06em] text-ink-3\">\n61\t        <Link href=\"/\" className=\"border-b-[1.5px] border-transparent hover:border-green hover:text-green\">\n62\t          Vrai-Prix\n63\t        </Link>\n64\t        <span>/</span>\n65\t        <span>{u ? `${u.adresse ?? \"\"} · ${u.municipalite ?? \"\"}` : t(\"manualTitle\")}</span>\n66\t      </nav>\n67\t\n68\t      {/* ---- Panneau prix ---- */}\n69\t      <section className=\"vp-card rise p-6 sm:p-8\">\n70\t        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n71\t          <div className=\"min-w-0 flex-1\">\n72\t            <span className=\"kicker\">{t(\"estimatedValue\")}</span>\n73\t            {u && (\n74\t              <h1 className=\"vp-display mt-2 text-[clamp(20px,3vw,28px)] font-bold uppercase leading-tight tracking-[-0.02em]\">\n75\t                {u.adresse}\n76\t                {s?.apt ? ` app. ${s.apt}` : \"\"}\n77\t                {u.municipalite ? <span className=\"text-ink-3\"> · {u.municipalite}</span> : null}\n78\t              </h1>\n79\t            )}\n80\t            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n81\t              <CountUp value={r.estimate} format={moneyFmt} />\n82\t            </p>\n83\t            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n84\t              <div>\n85\t                <p className=\"klabel\">{t(\"range\")}</p>\n86\t                <p className=\"vp-display text-[19px] font-bold\">\n87\t                  {fmt(r.low, lang)} <span className=\"text-ink-3\">→</span> {fmt(r.high, lang)}\n88\t                </p>\n89\t              </div>\n90\t              {u?.valeurRole != null && (\n91\t                <div>\n92\t                  <p className=\"klabel\">{t(\"assessed\")}</p>\n93\t                  <p className=\"vp-display text-[19px] font-bold text-ink-2\">{fmt(u.valeurRole, lang)}</p>\n94\t                </div>\n95\t              )}\n96\t              {u?.valeurRole != null && u.valeurRole > 0 && r.estimate > 0 && (\n97\t                <div>\n98\t                  <p className=\"klabel\">{fr ? \"Écart vs rôle\" : \"vs assessment\"}</p>\n99\t                  <p className=\"vp-display text-[19px] font-bold text-green-deep\">\n100\t                    {r.estimate >= u.valeurRole ? \"+\" : \"\"}\n101\t                    {((r.estimate / u.valeurRole - 1) * 100).toFixed(0)} %\n102\t                  </p>\n103\t                </div>\n104\t              )}\n105\t            </div>\n106\t            {u && (\n107\t              <div className=\"mt-6 flex flex-wrap gap-2.5\">\n108\t                <a\n109\t                  href={`/api/report?id=${encodeURIComponent(u.id)}`}\n110\t                  className=\"btn btn-primary\"\n111\t                  download\n112\t                >\n113\t                  {fr ? \"Rapport PDF\" : \"PDF report\"} ↓\n114\t                </a>\n115\t                <a\n116\t                  href={`/api/report/pro?id=${encodeURIComponent(u.id)}`}\n117\t                  className=\"btn btn-ghost\"\n118\t                  download\n119\t                  title={fr ? \"Structure de rapport d'évaluation — présentation bancaire\" : \"Appraisal-style structure — bank presentation\"}\n120\t                >\n121\t                  {fr ? \"Rapport détaillé format professionnel (banque)\" : \"Detailed professional report (bank)\"} ↓\n122\t                </a>\n123\t              </div>\n124\t            )}\n125\t          </div>\n126\t          <div className=\"shrink-0\">\n127\t            <ConfidenceDial\n128\t              pct={r.confidencePct}\n129\t              level={r.confidenceLevel}\n130\t              label={`${t(\"confidence\")} — ${t(`conf${r.confidenceLevel}` as TKey)}`}\n131\t            />\n132\t          </div>\n133\t        </div>\n134\t        <p className=\"vp-mono mt-5 border-t-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n135\t          {t(\"disclaimer\")}\n136\t        </p>\n137\t      </section>\n138\t\n139\t      {/* ---- Portrait de la propriété (bento du registre) ---- */}\n140\t      {u && s && (\n141\t        <section>\n142\t          <span className=\"kicker\">{fr ? \"Registre officiel — dessiné par les données\" : \"Official record — drawn by the data\"}</span>\n143\t          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n144\t            {fr ? \"Portrait de la propriété\" : \"Property portrait\"}\n145\t          </h2>\n146\t          <div className=\"mt-4 grid gap-4 lg:grid-cols-3\">\n147\t            {/* Bâtiment */}\n148\t            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"60ms\" }}>\n149\t              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n150\t                {fr ? \"Bâtiment\" : \"Building\"}\n151\t              </p>\n152\t              <div className=\"flex justify-center py-2\">\n153\t                <BuildingGlyph\n154\t                  floors={s.nbEtages}\n155\t                  dwellings={u.nbLogements}\n156\t                  genre={s.genreConstruction}\n157\t                  yearBuilt={u.anneeConstruction}\n158\t                  floorAreaM2={u.aireEtagesM2}\n159\t                  lang={lang}\n160\t                />\n161\t              </div>\n162\t              <EraLine year={u.anneeConstruction} lang={lang} />\n163\t              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n164\t                <Chip k={fr ? \"Aire d'étages\" : \"Floor area\"} v={u.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : \"—\"} />\n165\t                <Chip k={fr ? \"Étages × logements\" : \"Storeys × dwellings\"} v={`${s.nbEtages ?? \"—\"} × ${u.nbLogements ?? \"—\"}`} />\n166\t                <Chip k={fr ? \"Genre\" : \"Style\"} v={s.genreConstruction ?? \"—\"} />\n167\t                <Chip k={fr ? \"Lien physique\" : \"Physical link\"} v={s.lienPhysique ?? \"—\"} />\n168\t              </div>\n169\t            </div>\n170\t            {/* Terrain */}\n171\t            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"140ms\" }}>\n172\t              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n173\t                {fr ? \"Terrain — à l'échelle\" : \"Lot — to scale\"}\n174\t              </p>\n175\t              <div className=\"flex justify-center py-2\">\n176\t                <LotDiagram\n177\t                  areaM2={u.superficieTerrainM2}\n178\t                  frontageM={s.frontTerrainM}\n179\t                  footprintM2={\n180\t                    u.aireEtagesM2 && s.nbEtages ? u.aireEtagesM2 / Math.max(1, s.nbEtages) : u.aireEtagesM2\n181\t                  }\n182\t                  lang={lang}\n183\t                  isCondo={!!s.apt || (u.nbLogements != null && u.nbLogements >= 1 && !u.superficieTerrainM2)}\n184\t                />\n185\t              </div>\n186\t              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n187\t                <Chip k={fr ? \"Superficie\" : \"Area\"} v={u.superficieTerrainM2 ? locNum(u.superficieTerrainM2, lang, { unit: \"m²\" }) : \"—\"} />\n188\t                <Chip k={fr ? \"Frontage\" : \"Frontage\"} v={s.frontTerrainM ? locNum(s.frontTerrainM, lang, { unit: \"m\" }) : \"—\"} />\n189\t                <Chip k=\"CUBF\" v={s.cubf ?? \"—\"} />\n190\t                <Chip k={fr ? \"Usage\" : \"Use\"} v={s.cubfLibelle ?? \"—\"} />\n191\t              </div>\n192\t            </div>\n193\t            {/* Valeur au rôle */}\n194\t            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"220ms\" }}>\n195\t              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n196\t                {fr ? \"Valeur au rôle 2026 — composition\" : \"2026 assessed value — split\"}\n197\t              </p>\n198\t              <p className=\"vp-display py-2 text-[26px] font-bold tracking-[-0.02em]\">\n199\t                {fmt(u.valeurRole, lang)}\n200\t              </p>\n201\t              <ValueSplit\n202\t                land={s.valeurTerrain}\n203\t                building={s.valeurBatiment}\n204\t                previous={s.valeurAnterieure}\n205\t                total={u.valeurRole}\n206\t                lang={lang}\n207\t                labels={{\n208\t                  land: fr ? \"Terrain\" : \"Land\",\n209\t                  building: fr ? \"Bâtiment\" : \"Building\",\n210\t                  previous: fr ? \"Rôle antérieur\" : \"Previous roll\",\n211\t                }}\n212\t              />\n213\t              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n214\t                <Chip k={fr ? \"Cond. marché au\" : \"Market cond.\"} v={s.datCondMarche ?? \"—\"} />\n215\t                <Chip k={fr ? \"Matricule\" : \"Roll no.\"} v={s.matricule ?? \"—\"} />\n216\t              </div>\n217\t            </div>\n218\t          </div>\n219\t          {/* registre brut complet */}\n220\t          <details className=\"vp-card mt-4 p-5\">\n221\t            <summary className=\"vp-mono cursor-pointer text-[11px] font-bold uppercase tracking-[0.08em] text-green-deep\">\n222\t              {fr ? \"▸ Toutes les données du registre (brutes)\" : \"▸ All registry data (raw)\"}\n223\t            </summary>\n224\t            <div className=\"mt-4 grid grid-cols-2 gap-2 sm:grid-cols-4\">\n225\t              <Chip k={fr ? \"Année construction\" : \"Year built\"} v={u.anneeConstruction ? `${u.anneeConstruction}${s.anneeEstimee === \"E\" ? (fr ? \" (estimée)\" : \" (est.)\") : \"\"}` : \"—\"} />\n226\t              <Chip k={fr ? \"Locaux non résid.\" : \"Non-res. units\"} v={s.nbLocauxNonResid ?? \"—\"} />\n227\t              <Chip k={fr ? \"Chambres locatives\" : \"Rental rooms\"} v={s.nbChambresLocatives ?? \"—\"} />\n228\t              <Chip k={fr ? \"Unité de voisinage\" : \"Neighbourhood\"} v={s.uniteVoisinage || \"—\"} />\n229\t              <Chip k={fr ? \"Arrondissement\" : \"Borough\"} v={s.arrond || \"—\"} />\n230\t              <Chip k={fr ? \"Adresses sur l'unité\" : \"Addresses\"} v={s.nAdresses ?? \"—\"} />\n231\t              <Chip k={fr ? \"Valeur terrain\" : \"Land value\"} v={fmt(s.valeurTerrain, lang)} />\n232\t              <Chip k={fr ? \"Valeur bâtiment\" : \"Building value\"} v={fmt(s.valeurBatiment, lang)} />\n233\t              <Chip k={fr ? \"Rôle antérieur\" : \"Previous roll\"} v={fmt(s.valeurAnterieure, lang)} />\n234\t              <Chip k=\"ID provincial\" v={u.id} />\n235\t            </div>\n236\t          </details>\n237\t        </section>\n238\t      )}\n239\t\n240\t      {/* ---- Transparence du calcul ---- */}\n241\t      <section>\n242\t        <span className=\"kicker\">{fr ? \"Transparence\" : \"Transparency\"}</span>\n243\t        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n244\t          {t(\"whyTitle\")}\n245\t        </h2>\n246\t        <div className=\"mt-4 grid gap-4 sm:grid-cols-2\">\n247\t          <div className=\"vp-card vp-card-hover rise p-5\">\n248\t            <p className=\"klabel\">{t(\"modelPart\")}</p>\n249\t            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n250\t              {r.modelEstimate != null ? (\n251\t                <CountUp value={r.modelEstimate} format={moneyFmt} />\n252\t              ) : (\n253\t                \"—\"\n254\t              )}\n255\t            </p>\n256\t            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n257\t              {t(\"weightModel\")} : {(r.modelWeight * 100).toFixed(0)} %\n258\t            </p>\n259\t          </div>\n260\t          <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"100ms\" }}>\n261\t            <p className=\"klabel\">{t(\"compsPart\")}</p>\n262\t            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n263\t              {r.compsEstimate != null ? (\n264\t                <CountUp value={r.compsEstimate} format={moneyFmt} />\n265\t              ) : (\n266\t                \"—\"\n267\t              )}\n268\t            </p>\n269\t            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n270\t              {t(\"weightComps\")} : {((1 - r.modelWeight) * 100).toFixed(0)} % · {r.nCompsUsed} comps\n271\t              {r.compsDispersionPct != null ? ` · ±${r.compsDispersionPct} %` : \"\"}\n272\t            </p>\n273\t          </div>\n274\t        </div>\n275\t      </section>\n276\t\n277\t      {/* ---- Historique ---- */}\n278\t      {u && u.history.some((h) => h.value != null) && (\n279\t        <section>\n280\t          <span className=\"kicker\">2021 → 2026</span>\n281\t          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n282\t            {t(\"history\")}\n283\t          </h2>\n284\t          <div className=\"vp-card rise mt-4 p-5 sm:p-6\">\n285\t            <div className=\"flex flex-col gap-2\">\n286\t              {u.history.map((h, i) => {\n287\t                const prev = i > 0 ? u.history[i - 1].value : null;\n288\t                const yoy =\n289\t                  h.value != null && prev != null && prev > 0\n290\t                    ? ((h.value / prev - 1) * 100)\n291\t                    : null;\n292\t                return (\n293\t                  <div key={h.year} className=\"hbar-row grid grid-cols-[44px_1fr_auto] items-center gap-2 sm:grid-cols-[52px_1fr_auto] sm:gap-3\">\n294\t                    <span className=\"vp-mono text-[12px] font-bold\">{h.year}</span>\n295\t                    <span className=\"hbar-track\">\n296\t                      <span\n297\t                        className=\"hbar-fill grow-x\"\n298\t                        style={{\n299\t                          width: `${((h.value ?? 0) / histMax) * 100}%`,\n300\t                          animationDelay: `${i * 90}ms`,\n301\t                          background: h.year === 2026 ? \"var(--ink)\" : undefined,\n302\t                        }}\n303\t                      />\n304\t                    </span>\n305\t                    <span className=\"vp-mono flex items-center justify-end gap-2 text-[12px] font-bold\">\n306\t                      {fmt(h.value, lang)}\n307\t                      {yoy != null && (\n308\t                        <span className={`rounded-[4px] border border-ink px-1 py-px text-[9px] ${yoy >= 0 ? \"bg-lime-soft text-green-deep\" : \"bg-[var(--danger-soft)] text-[var(--danger)]\"}`}>\n309\t                          {yoy >= 0 ? \"+\" : \"\"}\n310\t                          {yoy.toFixed(0)} %\n311\t                        </span>\n312\t                      )}\n313\t                    </span>\n314\t                  </div>\n315\t                );\n316\t              })}\n317\t            </div>\n318\t          </div>\n319\t        </section>\n320\t      )}\n321\t\n322\t      {/* ---- Plan des comparables (carte maison) ---- */}\n323\t      <section>\n324\t        <span className=\"kicker\">\n325\t          {r.nCompsUsed} {fr ? \"ventes réelles\" : \"real sales\"}\n326\t        </span>\n327\t        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n328\t          {t(\"compsTitle\")}\n329\t        </h2>\n330\t        <div className=\"mt-4 grid items-start gap-4 lg:grid-cols-[1.05fr_1fr]\">\n331\t          <div className=\"overflow-hidden rounded-[10px] border-[1.5px] border-ink shadow-[8px_8px_0_rgba(20,24,20,0.08)]\">\n332\t            <RadarMap\n333\t              subject={{\n334\t                lat: u?.lat ?? r.comps[0]?.lat ?? 46.81,\n335\t                lng: u?.lng ?? r.comps[0]?.lng ?? -71.21,\n336\t                label: u?.adresse ?? (fr ? \"Propriété estimée\" : \"Subject property\"),\n337\t              }}\n338\t              comps={r.comps.map((c) => ({\n339\t                id: c.id,\n340\t                lat: c.lat,\n341\t                lng: c.lng,\n342\t                label: `${c.street ?? \"\"}, ${c.city ?? \"\"}`,\n343\t                price: fmt(c.amount, lang),\n344\t                adjusted: fmt(Math.round(c.adjustedPrice), lang),\n345\t                date: c.date,\n346\t                distanceM: c.distanceM,\n347\t                weight: c.weight,\n348\t              }))}\n349\t              highlight={activeComp}\n350\t              onHover={setActiveComp}\n351\t            />\n352\t          </div>\n353\t          <div className=\"src-wrap max-h-[560px] overflow-y-auto\">\n354\t            <table className=\"src-table\">\n355\t              <thead className=\"sticky top-0 z-10\">\n356\t                <tr>\n357\t                  <th>#</th>\n358\t                  <th>{fr ? \"Vente\" : \"Sale\"}</th>\n359\t                  <th className=\"text-right\">{t(\"adjusted\")}</th>\n360\t                </tr>\n361\t              </thead>\n362\t              <tbody>\n363\t                {r.comps.map((c, i) => (\n364\t                  <tr\n365\t                    key={c.id}\n366\t                    onMouseEnter={() => setActiveComp(c.id)}\n367\t                    onMouseLeave={() => setActiveComp(null)}\n368\t                    className={activeComp === c.id ? \"!bg-lime-soft\" : \"\"}\n369\t                  >\n370\t                    <td>\n371\t                      <span\n372\t                        className={`vp-mono inline-flex h-6 w-6 items-center justify-center rounded-full border-[1.5px] border-ink text-[11px] font-bold ${\n373\t                          activeComp === c.id ? \"bg-lime text-ink\" : \"bg-green text-paper\"\n374\t                        }`}\n375\t                      >\n376\t                        {i + 1}\n377\t                      </span>\n378\t                    </td>\n379\t                    <td>\n380\t                      <p className=\"font-semibold leading-tight\">\n381\t                        {c.street}, {c.city}\n382\t                      </p>\n383\t                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n384\t                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n385\t                        {t(\"sold\")} {fmt(c.amount, lang)}\n386\t                      </p>\n387\t                      <p className=\"vp-mono mt-0.5 text-[10.5px] text-ink-2\">\n388\t                        {t(\"adjTime\")} {signed(c.adjTime, lang)} · {t(\"adjArea\")} {signed(c.adjArea, lang)} ·{\" \"}\n389\t                        {t(\"adjAge\")} {signed(c.adjAge, lang)}\n390\t                      </p>\n391\t                    </td>\n392\t                    <td className=\"vp-display text-right font-bold\">\n393\t                      {fmt(Math.round(c.adjustedPrice), lang)}\n394\t                      <p className=\"vp-mono text-[10px] font-normal text-ink-3\">\n395\t                        {t(\"weight\")} {(c.weight * 100).toFixed(0)} %\n396\t                      </p>\n397\t                    </td>\n398\t                  </tr>\n399\t                ))}\n400\t              </tbody>\n401\t            </table>\n402\t          </div>\n403\t        </div>\n404\t      </section>\n405\t\n406\t      {/* ---- Lead ---- */}\n407\t      <section className=\"rounded-[10px] border-[1.5px] border-ink bg-ink p-6 text-paper shadow-[8px_8px_0_rgba(20,24,20,0.2)] sm:p-8\">\n408\t        <h2 className=\"vp-display text-[22px] font-bold uppercase tracking-[-0.02em] text-lime\">\n409\t          {t(\"leadTitle\")}\n410\t        </h2>\n411\t        <div className=\"mt-4 max-w-xl\">\n412\t          <LeadForm unitId={u?.id ?? null} estimate={r.estimate} />\n413\t        </div>\n414\t      </section>\n415\t    </div>\n416\t  );\n417\t}\n418\t"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"e81a537a-c1e9-46f8-a8ea-86c87477ead4","timestamp":"2026-08-23T03:00:27.360Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport { useState } from \"react\";\nimport Link from \"next/link\";\nimport { useLang } from \"./LangContext\";\nimport LeadForm from \"./LeadForm\";\nimport RadarMap from \"./RadarMap\";\nimport {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  ValueSplit,\n} from \"./MetricViz\";\nimport type { EstimateResult } from \"@/lib/engine\";\nimport type { UnitEstimate } from \"@/lib/estimator\";\nimport type { TKey } from \"@/lib/i18n\";\n\nconst fmt = (v: number | null | undefined, lang: string) =>\n  v == null\n    ? \"—\"\n    : new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n        style: \"currency\",\n        currency: \"CAD\",\n        maximumFractionDigits: 0,\n      }).format(v);\n\nconst signed = (v: number, lang: string) =>\n  `${v >= 0 ? \"+\" : \"−\"}${fmt(Math.abs(Math.round(v)), lang)}`;\n\nfunction Chip({ k, v }: { k: string; v: string | number }) {\n  return (\n    <div className=\"kv-cell\">\n      <p className=\"k\">{k}</p>\n      <p className=\"v break-words\">{v}</p>\n    </div>\n  );\n}\n\nexport default function ResultView({ data }: { data: UnitEstimate }) {\n  const { lang, t } = useLang();\n  const [activeComp, setActiveComp] = useState<string | null>(null);\n  const r: EstimateResult = data.result;\n  const u = data.unit;\n  const s = u?.specs;\n  const fr = lang === \"fr\";\n  const histMax = Math.max(...(u?.history.map((h) => h.value ?? 0) ?? [0]), 1);\n  const moneyFmt = (v: number) =>\n    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n    }).format(Math.round(v / 100) * 100);\n\n  return (\n    <div className=\"space-y-10 py-8\">\n      {/* fil d'ariane */}\n      <nav className=\"vp-mono flex flex-wrap items-center gap-2 text-[11.5px] uppercase tracking-[0.06em] text-ink-3\">\n        <Link href=\"/\" className=\"border-b-[1.5px] border-transparent hover:border-green hover:text-green\">\n          Vrai-Prix\n        </Link>\n        <span>/</span>\n        <span>{u ? `${u.adresse ?? \"\"} · ${u.municipalite ?? \"\"}` : t(\"manualTitle\")}</span>\n      </nav>\n\n      {/* ---- Panneau prix ---- */}\n      <section className=\"vp-card rise p-6 sm:p-8\">\n        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          <div className=\"min-w-0 flex-1\">\n            <span className=\"kicker\">{t(\"estimatedValue\")}</span>\n            {u && (\n              <h1 className=\"vp-display mt-2 text-[clamp(20px,3vw,28px)] font-bold uppercase leading-tight tracking-[-0.02em]\">\n                {u.adresse}\n                {s?.apt ? ` app. ${s.apt}` : \"\"}\n                {u.municipalite ? <span className=\"text-ink-3\"> · {u.municipalite}</span> : null}\n              </h1>\n            )}\n            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              <div>\n                <p className=\"klabel\">{t(\"range\")}</p>\n                <p className=\"vp-display text-[19px] font-bold\">\n                  {fmt(r.low, lang)} <span className=\"text-ink-3\">→</span> {fmt(r.high, lang)}\n                </p>\n              </div>\n              {u?.valeurRole != null && (\n                <div>\n                  <p className=\"klabel\">{t(\"assessed\")}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-ink-2\">{fmt(u.valeurRole, lang)}</p>\n                </div>\n              )}\n              {u?.valeurRole != null && u.valeurRole > 0 && r.estimate > 0 && (\n                <div>\n                  <p className=\"klabel\">{fr ? \"Écart vs rôle\" : \"vs assessment\"}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-green-deep\">\n                    {r.estimate >= u.valeurRole ? \"+\" : \"\"}\n                    {((r.estimate / u.valeurRole - 1) * 100).toFixed(0)} %\n                  </p>\n                </div>\n              )}\n            </div>\n            {u && (\n              <div className=\"mt-6 flex flex-wrap gap-2.5\">\n                <a\n                  href={`/api/report?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-primary\"\n                  download\n                >\n                  {fr ? \"Rapport PDF\" : \"PDF report\"} ↓\n                </a>\n                <a\n                  href={`/api/report/pro?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-ghost\"\n                  download\n                  title={fr ? \"Structure de rapport d'évaluation — présentation bancaire\" : \"Appraisal-style structure — bank presentation\"}\n                >\n                  {fr ? \"Rapport détaillé format professionnel (banque)\" : \"Detailed professional report (bank)\"} ↓\n                </a>\n              </div>\n            )}\n          </div>\n          <div className=\"shrink-0\">\n            <ConfidenceDial\n              pct={r.confidencePct}\n              level={r.confidenceLevel}\n              label={`${t(\"confidence\")} — ${t(`conf${r.confidenceLevel}` as TKey)}`}\n            />\n          </div>\n        </div>\n        <p className=\"vp-mono mt-5 border-t-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {t(\"disclaimer\")}\n        </p>\n      </section>\n\n      {/* ---- Portrait de la propriété (bento du registre) ---- */}\n      {u && s && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Registre officiel — dessiné par les données\" : \"Official record — drawn by the data\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Portrait de la propriété\" : \"Property portrait\"}\n          </h2>\n          <div className=\"mt-4 grid gap-4 lg:grid-cols-3\">\n            {/* Bâtiment */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"60ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Bâtiment\" : \"Building\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <BuildingGlyph\n                  floors={s.nbEtages}\n                  dwellings={u.nbLogements}\n                  genre={s.genreConstruction}\n                  yearBuilt={u.anneeConstruction}\n                  floorAreaM2={u.aireEtagesM2}\n                  lang={lang}\n                />\n              </div>\n              <EraLine year={u.anneeConstruction} lang={lang} />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Aire d'étages\" : \"Floor area\"} v={u.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Étages × logements\" : \"Storeys × dwellings\"} v={`${s.nbEtages ?? \"—\"} × ${u.nbLogements ?? \"—\"}`} />\n                <Chip k={fr ? \"Genre\" : \"Style\"} v={s.genreConstruction ?? \"—\"} />\n                <Chip k={fr ? \"Lien physique\" : \"Physical link\"} v={s.lienPhysique ?? \"—\"} />\n              </div>\n            </div>\n            {/* Terrain */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"140ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Terrain — à l'échelle\" : \"Lot — to scale\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <LotDiagram\n                  areaM2={u.superficieTerrainM2}\n                  frontageM={s.frontTerrainM}\n                  footprintM2={\n                    u.aireEtagesM2 && s.nbEtages ? u.aireEtagesM2 / Math.max(1, s.nbEtages) : u.aireEtagesM2\n                  }\n                  lang={lang}\n                  isCondo={!!s.apt || (u.nbLogements != null && u.nbLogements >= 1 && !u.superficieTerrainM2)}\n                />\n              </div>\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Superficie\" : \"Area\"} v={u.superficieTerrainM2 ? locNum(u.superficieTerrainM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Frontage\" : \"Frontage\"} v={s.frontTerrainM ? locNum(s.frontTerrainM, lang, { unit: \"m\" }) : \"—\"} />\n                <Chip k=\"CUBF\" v={s.cubf ?? \"—\"} />\n                <Chip k={fr ? \"Usage\" : \"Use\"} v={s.cubfLibelle ?? \"—\"} />\n              </div>\n            </div>\n            {/* Valeur au rôle */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"220ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Valeur au rôle 2026 — composition\" : \"2026 assessed value — split\"}\n              </p>\n              <p className=\"vp-display py-2 text-[26px] font-bold tracking-[-0.02em]\">\n                {fmt(u.valeurRole, lang)}\n              </p>\n              <ValueSplit\n                land={s.valeurTerrain}\n                building={s.valeurBatiment}\n                previous={s.valeurAnterieure}\n                total={u.valeurRole}\n                lang={lang}\n                labels={{\n                  land: fr ? \"Terrain\" : \"Land\",\n                  building: fr ? \"Bâtiment\" : \"Building\",\n                  previous: fr ? \"Rôle antérieur\" : \"Previous roll\",\n                }}\n              />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Cond. marché au\" : \"Market cond.\"} v={s.datCondMarche ?? \"—\"} />\n                <Chip k={fr ? \"Matricule\" : \"Roll no.\"} v={s.matricule ?? \"—\"} />\n              </div>\n            </div>\n          </div>\n          {/* registre brut complet */}\n          <details className=\"vp-card mt-4 p-5\">\n            <summary className=\"vp-mono cursor-pointer text-[11px] font-bold uppercase tracking-[0.08em] text-green-deep\">\n              {fr ? \"▸ Toutes les données du registre (brutes)\" : \"▸ All registry data (raw)\"}\n            </summary>\n            <div className=\"mt-4 grid grid-cols-2 gap-2 sm:grid-cols-4\">\n              <Chip k={fr ? \"Année construction\" : \"Year built\"} v={u.anneeConstruction ? `${u.anneeConstruction}${s.anneeEstimee === \"E\" ? (fr ? \" (estimée)\" : \" (est.)\") : \"\"}` : \"—\"} />\n              <Chip k={fr ? \"Locaux non résid.\" : \"Non-res. units\"} v={s.nbLocauxNonResid ?? \"—\"} />\n              <Chip k={fr ? \"Chambres locatives\" : \"Rental rooms\"} v={s.nbChambresLocatives ?? \"—\"} />\n              <Chip k={fr ? \"Unité de voisinage\" : \"Neighbourhood\"} v={s.uniteVoisinage || \"—\"} />\n              <Chip k={fr ? \"Arrondissement\" : \"Borough\"} v={s.arrond || \"—\"} />\n              <Chip k={fr ? \"Adresses sur l'unité\" : \"Addresses\"} v={s.nAdresses ?? \"—\"} />\n              <Chip k={fr ? \"Valeur terrain\" : \"Land value\"} v={fmt(s.valeurTerrain, lang)} />\n              <Chip k={fr ? \"Valeur bâtiment\" : \"Building value\"} v={fmt(s.valeurBatiment, lang)} />\n              <Chip k={fr ? \"Rôle antérieur\" : \"Previous roll\"} v={fmt(s.valeurAnterieure, lang)} />\n              <Chip k=\"ID provincial\" v={u.id} />\n            </div>\n          </details>\n        </section>\n      )}\n\n      {/* ---- Transparence du calcul ---- */}\n      <section>\n        <span className=\"kicker\">{fr ? \"Transparence\" : \"Transparency\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"whyTitle\")}\n        </h2>\n        <div className=\"mt-4 grid gap-4 sm:grid-cols-2\">\n          <div className=\"vp-card vp-card-hover rise p-5\">\n            <p className=\"klabel\">{t(\"modelPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.modelEstimate != null ? (\n                <CountUp value={r.modelEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightModel\")} : {(r.modelWeight * 100).toFixed(0)} %\n            </p>\n          </div>\n          <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"100ms\" }}>\n            <p className=\"klabel\">{t(\"compsPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.compsEstimate != null ? (\n                <CountUp value={r.compsEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightComps\")} : {((1 - r.modelWeight) * 100).toFixed(0)} % · {r.nCompsUsed} comps\n              {r.compsDispersionPct != null ? ` · ±${r.compsDispersionPct} %` : \"\"}\n            </p>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Historique ---- */}\n      {u && u.history.some((h) => h.value != null) && (\n        <section>\n          <span className=\"kicker\">2021 → 2026</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {t(\"history\")}\n          </h2>\n          <div className=\"vp-card rise mt-4 p-5 sm:p-6\">\n            <div className=\"flex flex-col gap-2\">\n              {u.history.map((h, i) => {\n                const prev = i > 0 ? u.history[i - 1].value : null;\n                const yoy =\n                  h.value != null && prev != null && prev > 0\n                    ? ((h.value / prev - 1) * 100)\n                    : null;\n                return (\n                  <div key={h.year} className=\"hbar-row grid grid-cols-[44px_1fr_auto] items-center gap-2 sm:grid-cols-[52px_1fr_auto] sm:gap-3\">\n                    <span className=\"vp-mono text-[12px] font-bold\">{h.year}</span>\n                    <span className=\"hbar-track\">\n                      <span\n                        className=\"hbar-fill grow-x\"\n                        style={{\n                          width: `${((h.value ?? 0) / histMax) * 100}%`,\n                          animationDelay: `${i * 90}ms`,\n                          background: h.year === 2026 ? \"var(--ink)\" : undefined,\n                        }}\n                      />\n                    </span>\n                    <span className=\"vp-mono flex items-center justify-end gap-2 text-[12px] font-bold\">\n                      {fmt(h.value, lang)}\n                      {yoy != null && (\n                        <span className={`rounded-[4px] border border-ink px-1 py-px text-[9px] ${yoy >= 0 ? \"bg-lime-soft text-green-deep\" : \"bg-[var(--danger-soft)] text-[var(--danger)]\"}`}>\n                          {yoy >= 0 ? \"+\" : \"\"}\n                          {yoy.toFixed(0)} %\n                        </span>\n                      )}\n                    </span>\n                  </div>\n                );\n              })}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Plan des comparables (carte maison) ---- */}\n      <section>\n        <span className=\"kicker\">\n          {r.nCompsUsed} {fr ? \"ventes réelles\" : \"real sales\"}\n        </span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"compsTitle\")}\n        </h2>\n        <div className=\"mt-4 grid items-start gap-4 lg:grid-cols-[1.05fr_1fr]\">\n          <div className=\"overflow-hidden rounded-[10px] border-[1.5px] border-ink shadow-[8px_8px_0_rgba(20,24,20,0.08)]\">\n            <RadarMap\n              subject={{\n                lat: u?.lat ?? r.comps[0]?.lat ?? 46.81,\n                lng: u?.lng ?? r.comps[0]?.lng ?? -71.21,\n                label: u?.adresse ?? (fr ? \"Propriété estimée\" : \"Subject property\"),\n              }}\n              comps={r.comps.map((c) => ({\n                id: c.id,\n                lat: c.lat,\n                lng: c.lng,\n                label: `${c.street ?? \"\"}, ${c.city ?? \"\"}`,\n                price: fmt(c.amount, lang),\n                adjusted: fmt(Math.round(c.adjustedPrice), lang),\n                date: c.date,\n                distanceM: c.distanceM,\n                weight: c.weight,\n              }))}\n              highlight={activeComp}\n              onHover={setActiveComp}\n            />\n          </div>\n          <div className=\"src-wrap max-h-[560px] overflow-y-auto\">\n            <table className=\"src-table\">\n              <thead className=\"sticky top-0 z-10\">\n                <tr>\n                  <th>#</th>\n                  <th>{fr ? \"Vente\" : \"Sale\"}</th>\n                  <th className=\"text-right\">{t(\"adjusted\")}</th>\n                </tr>\n              </thead>\n              <tbody>\n                {r.comps.map((c, i) => (\n                  <tr\n                    key={c.id}\n                    onMouseEnter={() => setActiveComp(c.id)}\n                    onMouseLeave={() => setActiveComp(null)}\n                    className={activeComp === c.id ? \"!bg-lime-soft\" : \"\"}\n                  >\n                    <td>\n                      <span\n                        className={`vp-mono inline-flex h-6 w-6 items-center justify-center rounded-full border-[1.5px] border-ink text-[11px] font-bold ${\n                          activeComp === c.id ? \"bg-lime text-ink\" : \"bg-green text-paper\"\n                        }`}\n                      >\n                        {i + 1}\n                      </span>\n                    </td>\n                    <td>\n                      <p className=\"font-semibold leading-tight\">\n                        {c.street}, {c.city}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] text-ink-2\">\n                        {t(\"adjTime\")} {signed(c.adjTime, lang)} · {t(\"adjArea\")} {signed(c.adjArea, lang)} ·{\" \"}\n                        {t(\"adjAge\")} {signed(c.adjAge, lang)}\n                      </p>\n                    </td>\n                    <td className=\"vp-display text-right font-bold\">\n                      {fmt(Math.round(c.adjustedPrice), lang)}\n                      <p className=\"vp-mono text-[10px] font-normal text-ink-3\">\n                        {t(\"weight\")} {(c.weight * 100).toFixed(0)} %\n                      </p>\n                    </td>\n                  </tr>\n                ))}\n              </tbody>\n            </table>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Lead ---- */}\n      <section className=\"rounded-[10px] border-[1.5px] border-ink bg-ink p-6 text-paper shadow-[8px_8px_0_rgba(20,24,20,0.2)] sm:p-8\">\n        <h2 className=\"vp-display text-[22px] font-bold uppercase tracking-[-0.02em] text-lime\">\n          {t(\"leadTitle\")}\n        </h2>\n        <div className=\"mt-4 max-w-xl\">\n          <LeadForm unitId={u?.id ?? null} estimate={r.estimate} />\n        </div>\n      </section>\n    </div>\n  );\n}\n","numLines":418,"startLine":1,"totalLines":418}}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XtkAQ4oyjHwLeBajMxPt1j","type":"tool_result","content":"1\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n2\t/**\n3\t * Moteur d'estimation Vrai-Prix — transparent par conception.\n4\t *\n5\t * Deux sources combinées :\n6\t *  1. Modèle hédonique (LightGBM, entraîné sur ~690 k ventes QC 2021-2026,\n7\t *     pré-calculé pour chaque unité d'évaluation) — colonne est_2026 + P10/P90.\n8\t *  2. Comparables : ventes réelles proches, ajustées (marché, superficie, âge),\n9\t *     médiane pondérée (distance, récence, similarité).\n10\t *\n11\t * Chaque ajustement est retourné en dollars pour affichage — aucune boîte noire.\n12\t */\n13\t\n14\texport interface Subject {\n15\t  lat: number;\n16\t  lng: number;\n17\t  typeProp: string; // unifamilial | plex | condo_ou_multi | chalet | maison_mobile | terrain | autre\n18\t  floorArea?: number | null;\n19\t  yearBuilt?: number | null;\n20\t  landArea?: number | null;\n21\t  modelEstimate?: number | null;\n22\t  modelP10?: number | null;\n23\t  modelP90?: number | null;\n24\t}\n25\t\n26\texport interface CompInput {\n27\t  id: string;\n28\t  date: string; // YYYY-MM-DD\n29\t  amount: number;\n30\t  lat: number;\n31\t  lng: number;\n32\t  propertyType: string | null;\n33\t  yearBuilt: number | null;\n34\t  floorArea: number | null;\n35\t  street: string | null;\n36\t  city: string | null;\n37\t}\n38\t\n39\texport interface AdjustedComp extends CompInput {\n40\t  distanceM: number;\n41\t  monthsAgo: number;\n42\t  adjTime: number;\n43\t  adjArea: number;\n44\t  adjAge: number;\n45\t  adjustedPrice: number;\n46\t  weight: number;\n47\t}\n48\t\n49\texport interface MarketIndexPoint {\n50\t  month: string; // YYYY-MM\n51\t  idx: number; // 1.0 = niveau actuel\n52\t}\n53\t\n54\texport interface EstimateResult {\n55\t  estimate: number;\n56\t  low: number;\n57\t  high: number;\n58\t  confidencePct: number;\n59\t  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n60\t  modelEstimate: number | null;\n61\t  compsEstimate: number | null;\n62\t  modelWeight: number;\n63\t  comps: AdjustedComp[];\n64\t  nCompsUsed: number;\n65\t  compsDispersionPct: number | null;\n66\t}\n67\t\n68\t/** Correspondance type d'unité (rôle) -> types de transactions comparables. */\n69\texport const TYPE_MATCH: Record<string, string[]> = {\n70\t  unifamilial: [\"unifamilial\"],\n71\t  plex: [\"plex\"],\n72\t  condo_ou_multi: [\"condo\", \"plex\"],\n73\t  chalet: [\"unifamilial\", \"indéterminé\"],\n74\t  maison_mobile: [\"unifamilial\", \"indéterminé\"],\n75\t  terrain: [\"indéterminé\"],\n76\t  autre: [\"unifamilial\", \"condo\", \"plex\", \"indéterminé\"],\n77\t};\n78\t\n79\texport function haversineM(\n80\t  lat1: number,\n81\t  lng1: number,\n82\t  lat2: number,\n83\t  lng2: number\n84\t): number {\n85\t  const R = 6371000;\n86\t  const toRad = (d: number) => (d * Math.PI) / 180;\n87\t  const dLat = toRad(lat2 - lat1);\n88\t  const dLng = toRad(lng2 - lng1);\n89\t  const a =\n90\t    Math.sin(dLat / 2) ** 2 +\n91\t    Math.cos(toRad(lat1)) * Math.cos(toRad(lat2)) * Math.sin(dLng / 2) ** 2;\n92\t  return 2 * R * Math.asin(Math.sqrt(a));\n93\t}\n94\t\n95\texport function monthsBetween(fromISO: string, toISO: string): number {\n96\t  const a = new Date(fromISO);\n97\t  const b = new Date(toISO);\n98\t  return (b.getTime() - a.getTime()) / (1000 * 3600 * 24 * 30.44);\n99\t}\n100\t\n101\t/** Facteur marché entre le mois de vente et aujourd'hui (idx courant = dernier). */\n102\texport function timeFactor(\n103\t  saleMonth: string,\n104\t  index: MarketIndexPoint[]\n105\t): number {\n106\t  if (index.length === 0) return 1;\n107\t  const last = index[index.length - 1].idx;\n108\t  let saleIdx: number | null = null;\n109\t  for (const p of index) {\n110\t    if (p.month <= saleMonth) saleIdx = p.idx;\n111\t  }\n112\t  if (saleIdx === null) saleIdx = index[0].idx;\n113\t  if (saleIdx <= 0) return 1;\n114\t  return last / saleIdx;\n115\t}\n116\t\n117\t/** Ajustement superficie : 50 % du $/m² marginal du comparable. */\n118\texport function areaAdjustment(\n119\t  subjectArea: number | null | undefined,\n120\t  comp: { floorArea: number | null; amount: number }\n121\t): number {\n122\t  if (!subjectArea || !comp.floorArea || comp.floorArea <= 0) return 0;\n123\t  const ppm2 = comp.amount / comp.floorArea;\n124\t  const adj = (subjectArea - comp.floorArea) * 0.5 * ppm2;\n125\t  // borné à ±25 % du prix du comparable\n126\t  const cap = 0.25 * comp.amount;\n127\t  return Math.max(-cap, Math.min(cap, adj));\n128\t}\n129\t\n130\t/** Ajustement âge : 0,5 %/an d'écart, borné à ±10 %. */\n131\texport function ageAdjustment(\n132\t  subjectYear: number | null | undefined,\n133\t  comp: { yearBuilt: number | null; amount: number }\n134\t): number {\n135\t  if (!subjectYear || !comp.yearBuilt) return 0;\n136\t  const pct = Math.max(-0.1, Math.min(0.1, (subjectYear - comp.yearBuilt) * 0.005));\n137\t  return pct * comp.amount;\n138\t}\n139\t\n140\texport function weightedMedian(values: number[], weights: number[]): number {\n141\t  const order = values\n142\t    .map((v, i) => ({ v, w: weights[i] }))\n143\t    .sort((a, b) => a.v - b.v);\n144\t  const total = order.reduce((s, o) => s + o.w, 0);\n145\t  if (total <= 0) return NaN;\n146\t  let acc = 0;\n147\t  for (const o of order) {\n148\t    acc += o.w;\n149\t    if (acc >= total / 2) return o.v;\n150\t  }\n151\t  return order[order.length - 1].v;\n152\t}\n153\t\n154\texport function adjustComps(\n155\t  subject: Subject,\n156\t  candidates: CompInput[],\n157\t  index: MarketIndexPoint[],\n158\t  nowISO: string\n159\t): AdjustedComp[] {\n160\t  const types = TYPE_MATCH[subject.typeProp] ?? TYPE_MATCH.autre;\n161\t  let pool = candidates.filter(\n162\t    (c) => c.propertyType !== null && types.includes(c.propertyType)\n163\t  );\n164\t  if (pool.length < 6) pool = candidates; // relâche le filtre de type si marché mince\n165\t\n166\t  // filtre superficie ±20 %, relâché à ±40 % puis abandonné si trop peu\n167\t  if (subject.floorArea) {\n168\t    for (const tol of [0.2, 0.4]) {\n169\t      const filtered = pool.filter(\n170\t        (c) =>\n171\t          c.floorArea != null &&\n172\t          Math.abs(c.floorArea - subject.floorArea!) <=\n173\t            tol * subject.floorArea!\n174\t      );\n175\t      if (filtered.length >= 6) {\n176\t        pool = filtered;\n177\t        break;\n178\t      }\n179\t    }\n180\t  }\n181\t\n182\t  const comps = pool.map((c) => {\n183\t    const distanceM = haversineM(subject.lat, subject.lng, c.lat, c.lng);\n184\t    const monthsAgo = monthsBetween(c.date, nowISO);\n185\t    const tf = timeFactor(c.date.slice(0, 7), index);\n186\t    const adjTime = c.amount * (tf - 1);\n187\t    const adjArea = areaAdjustment(subject.floorArea, c);\n188\t    const adjAge = ageAdjustment(subject.yearBuilt, c);\n189\t    const adjustedPrice = c.amount + adjTime + adjArea + adjAge;\n190\t    const areaDiffPct =\n191\t      subject.floorArea && c.floorArea\n192\t        ? Math.abs(c.floorArea - subject.floorArea) / subject.floorArea\n193\t        : 0.15;\n194\t    const weight =\n195\t      Math.exp(-((distanceM / 1500) ** 2)) *\n196\t      Math.exp(-((monthsAgo / 24) ** 2)) *\n197\t      Math.exp(-((areaDiffPct / 0.25) ** 2));\n198\t    return { ...c, distanceM, monthsAgo, adjTime, adjArea, adjAge, adjustedPrice, weight };\n199\t  });\n200\t\n201\t  return comps\n202\t    .filter((c) => c.adjustedPrice > 0 && c.weight > 1e-4)\n203\t    .sort((a, b) => b.weight - a.weight)\n204\t    .slice(0, 12);\n205\t}\n206\t\n207\tfunction pctDispersion(comps: AdjustedComp[], center: number): number | null {\n208\t  if (comps.length < 3 || center <= 0) return null;\n209\t  const dev = comps.map((c) => Math.abs(c.adjustedPrice - center) / center);\n210\t  dev.sort((a, b) => a - b);\n211\t  return dev[Math.floor(dev.length / 2)] * 100;\n212\t}\n213\t\n214\texport function estimate(\n215\t  subject: Subject,\n216\t  candidates: CompInput[],\n217\t  index: MarketIndexPoint[],\n218\t  nowISO: string\n219\t): EstimateResult {\n220\t  const comps = adjustComps(subject, candidates, index, nowISO);\n221\t  const compsEstimate =\n222\t    comps.length >= 3\n223\t      ? weightedMedian(\n224\t          comps.map((c) => c.adjustedPrice),\n225\t          comps.map((c) => c.weight)\n226\t        )\n227\t      : null;\n228\t\n229\t  const model = subject.modelEstimate ?? null;\n230\t  let modelWeight = 0;\n231\t  let estimateValue: number;\n232\t  if (model !== null && compsEstimate !== null) {\n233\t    modelWeight = 0.65;\n234\t    estimateValue = modelWeight * model + (1 - modelWeight) * compsEstimate;\n235\t  } else if (model !== null) {\n236\t    modelWeight = 1;\n237\t    estimateValue = model;\n238\t  } else if (compsEstimate !== null) {\n239\t    estimateValue = compsEstimate;\n240\t  } else {\n241\t    estimateValue = NaN;\n242\t  }\n243\t\n244\t  // fourchette : intervalle du modèle recentré, sinon dispersion des comparables\n245\t  let low: number, high: number;\n246\t  if (model !== null && subject.modelP10 != null && subject.modelP90 != null && model > 0) {\n247\t    low = estimateValue * (subject.modelP10 / model);\n248\t    high = estimateValue * (subject.modelP90 / model);\n249\t  } else {\n250\t    const disp = pctDispersion(comps, compsEstimate ?? estimateValue) ?? 25;\n251\t    low = estimateValue * (1 - disp / 100);\n252\t    high = estimateValue * (1 + disp / 100);\n253\t  }\n254\t\n255\t  const dispersion = pctDispersion(comps, compsEstimate ?? estimateValue);\n256\t  // confiance : nb de comparables, dispersion, largeur de fourchette, présence du modèle\n257\t  let score = 35;\n258\t  score += Math.min(25, comps.length * 2.5);\n259\t  if (dispersion !== null) score += Math.max(0, 20 - dispersion);\n260\t  if (model !== null) score += 15;\n261\t  const widthPct = estimateValue > 0 ? ((high - low) / estimateValue) * 100 : 100;\n262\t  score -= Math.max(0, (widthPct - 30) / 3);\n263\t  score = Math.max(5, Math.min(97, score));\n264\t  const level = score >= 75 ? \"A\" : score >= 60 ? \"B\" : score >= 45 ? \"C\" : \"D\";\n265\t\n266\t  return {\n267\t    estimate: Math.round(estimateValue / 100) * 100,\n268\t    low: Math.round(low / 100) * 100,\n269\t    high: Math.round(high / 100) * 100,\n270\t    confidencePct: Math.round(score),\n271\t    confidenceLevel: level,\n272\t    modelEstimate: model,\n273\t    compsEstimate: compsEstimate !== null ? Math.round(compsEstimate / 100) * 100 : null,\n274\t    modelWeight,\n275\t    comps,\n276\t    nCompsUsed: comps.length,\n277\t    compsDispersionPct: dispersion !== null ? Math.round(dispersion * 10) / 10 : null,\n278\t  };\n279\t}\n280\t"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"8140db00-e908-4728-a800-1926fc414be5","timestamp":"2026-08-23T03:00:37.232Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/engine.ts","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n/**\n * Moteur d'estimation Vrai-Prix — transparent par conception.\n *\n * Deux sources combinées :\n *  1. Modèle hédonique (LightGBM, entraîné sur ~690 k ventes QC 2021-2026,\n *     pré-calculé pour chaque unité d'évaluation) — colonne est_2026 + P10/P90.\n *  2. Comparables : ventes réelles proches, ajustées (marché, superficie, âge),\n *     médiane pondérée (distance, récence, similarité).\n *\n * Chaque ajustement est retourné en dollars pour affichage — aucune boîte noire.\n */\n\nexport interface Subject {\n  lat: number;\n  lng: number;\n  typeProp: string; // unifamilial | plex | condo_ou_multi | chalet | maison_mobile | terrain | autre\n  floorArea?: number | null;\n  yearBuilt?: number | null;\n  landArea?: number | null;\n  modelEstimate?: number | null;\n  modelP10?: number | null;\n  modelP90?: number | null;\n}\n\nexport interface CompInput {\n  id: string;\n  date: string; // YYYY-MM-DD\n  amount: number;\n  lat: number;\n  lng: number;\n  propertyType: string | null;\n  yearBuilt: number | null;\n  floorArea: number | null;\n  street: string | null;\n  city: string | null;\n}\n\nexport interface AdjustedComp extends CompInput {\n  distanceM: number;\n  monthsAgo: number;\n  adjTime: number;\n  adjArea: number;\n  adjAge: number;\n  adjustedPrice: number;\n  weight: number;\n}\n\nexport interface MarketIndexPoint {\n  month: string; // YYYY-MM\n  idx: number; // 1.0 = niveau actuel\n}\n\nexport interface EstimateResult {\n  estimate: number;\n  low: number;\n  high: number;\n  confidencePct: number;\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  modelEstimate: number | null;\n  compsEstimate: number | null;\n  modelWeight: number;\n  comps: AdjustedComp[];\n  nCompsUsed: number;\n  compsDispersionPct: number | null;\n}\n\n/** Correspondance type d'unité (rôle) -> types de transactions comparables. */\nexport const TYPE_MATCH: Record<string, string[]> = {\n  unifamilial: [\"unifamilial\"],\n  plex: [\"plex\"],\n  condo_ou_multi: [\"condo\", \"plex\"],\n  chalet: [\"unifamilial\", \"indéterminé\"],\n  maison_mobile: [\"unifamilial\", \"indéterminé\"],\n  terrain: [\"indéterminé\"],\n  autre: [\"unifamilial\", \"condo\", \"plex\", \"indéterminé\"],\n};\n\nexport function haversineM(\n  lat1: number,\n  lng1: number,\n  lat2: number,\n  lng2: number\n): number {\n  const R = 6371000;\n  const toRad = (d: number) => (d * Math.PI) / 180;\n  const dLat = toRad(lat2 - lat1);\n  const dLng = toRad(lng2 - lng1);\n  const a =\n    Math.sin(dLat / 2) ** 2 +\n    Math.cos(toRad(lat1)) * Math.cos(toRad(lat2)) * Math.sin(dLng / 2) ** 2;\n  return 2 * R * Math.asin(Math.sqrt(a));\n}\n\nexport function monthsBetween(fromISO: string, toISO: string): number {\n  const a = new Date(fromISO);\n  const b = new Date(toISO);\n  return (b.getTime() - a.getTime()) / (1000 * 3600 * 24 * 30.44);\n}\n\n/** Facteur marché entre le mois de vente et aujourd'hui (idx courant = dernier). */\nexport function timeFactor(\n  saleMonth: string,\n  index: MarketIndexPoint[]\n): number {\n  if (index.length === 0) return 1;\n  const last = index[index.length - 1].idx;\n  let saleIdx: number | null = null;\n  for (const p of index) {\n    if (p.month <= saleMonth) saleIdx = p.idx;\n  }\n  if (saleIdx === null) saleIdx = index[0].idx;\n  if (saleIdx <= 0) return 1;\n  return last / saleIdx;\n}\n\n/** Ajustement superficie : 50 % du $/m² marginal du comparable. */\nexport function areaAdjustment(\n  subjectArea: number | null | undefined,\n  comp: { floorArea: number | null; amount: number }\n): number {\n  if (!subjectArea || !comp.floorArea || comp.floorArea <= 0) return 0;\n  const ppm2 = comp.amount / comp.floorArea;\n  const adj = (subjectArea - comp.floorArea) * 0.5 * ppm2;\n  // borné à ±25 % du prix du comparable\n  const cap = 0.25 * comp.amount;\n  return Math.max(-cap, Math.min(cap, adj));\n}\n\n/** Ajustement âge : 0,5 %/an d'écart, borné à ±10 %. */\nexport function ageAdjustment(\n  subjectYear: number | null | undefined,\n  comp: { yearBuilt: number | null; amount: number }\n): number {\n  if (!subjectYear || !comp.yearBuilt) return 0;\n  const pct = Math.max(-0.1, Math.min(0.1, (subjectYear - comp.yearBuilt) * 0.005));\n  return pct * comp.amount;\n}\n\nexport function weightedMedian(values: number[], weights: number[]): number {\n  const order = values\n    .map((v, i) => ({ v, w: weights[i] }))\n    .sort((a, b) => a.v - b.v);\n  const total = order.reduce((s, o) => s + o.w, 0);\n  if (total <= 0) return NaN;\n  let acc = 0;\n  for (const o of order) {\n    acc += o.w;\n    if (acc >= total / 2) return o.v;\n  }\n  return order[order.length - 1].v;\n}\n\nexport function adjustComps(\n  subject: Subject,\n  candidates: CompInput[],\n  index: MarketIndexPoint[],\n  nowISO: string\n): AdjustedComp[] {\n  const types = TYPE_MATCH[subject.typeProp] ?? TYPE_MATCH.autre;\n  let pool = candidates.filter(\n    (c) => c.propertyType !== null && types.includes(c.propertyType)\n  );\n  if (pool.length < 6) pool = candidates; // relâche le filtre de type si marché mince\n\n  // filtre superficie ±20 %, relâché à ±40 % puis abandonné si trop peu\n  if (subject.floorArea) {\n    for (const tol of [0.2, 0.4]) {\n      const filtered = pool.filter(\n        (c) =>\n          c.floorArea != null &&\n          Math.abs(c.floorArea - subject.floorArea!) <=\n            tol * subject.floorArea!\n      );\n      if (filtered.length >= 6) {\n        pool = filtered;\n        break;\n      }\n    }\n  }\n\n  const comps = pool.map((c) => {\n    const distanceM = haversineM(subject.lat, subject.lng, c.lat, c.lng);\n    const monthsAgo = monthsBetween(c.date, nowISO);\n    const tf = timeFactor(c.date.slice(0, 7), index);\n    const adjTime = c.amount * (tf - 1);\n    const adjArea = areaAdjustment(subject.floorArea, c);\n    const adjAge = ageAdjustment(subject.yearBuilt, c);\n    const adjustedPrice = c.amount + adjTime + adjArea + adjAge;\n    const areaDiffPct =\n      subject.floorArea && c.floorArea\n        ? Math.abs(c.floorArea - subject.floorArea) / subject.floorArea\n        : 0.15;\n    const weight =\n      Math.exp(-((distanceM / 1500) ** 2)) *\n      Math.exp(-((monthsAgo / 24) ** 2)) *\n      Math.exp(-((areaDiffPct / 0.25) ** 2));\n    return { ...c, distanceM, monthsAgo, adjTime, adjArea, adjAge, adjustedPrice, weight };\n  });\n\n  return comps\n    .filter((c) => c.adjustedPrice > 0 && c.weight > 1e-4)\n    .sort((a, b) => b.weight - a.weight)\n    .slice(0, 12);\n}\n\nfunction pctDispersion(comps: AdjustedComp[], center: number): number | null {\n  if (comps.length < 3 || center <= 0) return null;\n  const dev = comps.map((c) => Math.abs(c.adjustedPrice - center) / center);\n  dev.sort((a, b) => a - b);\n  return dev[Math.floor(dev.length / 2)] * 100;\n}\n\nexport function estimate(\n  subject: Subject,\n  candidates: CompInput[],\n  index: MarketIndexPoint[],\n  nowISO: string\n): EstimateResult {\n  const comps = adjustComps(subject, candidates, index, nowISO);\n  const compsEstimate =\n    comps.length >= 3\n      ? weightedMedian(\n          comps.map((c) => c.adjustedPrice),\n          comps.map((c) => c.weight)\n        )\n      : null;\n\n  const model = subject.modelEstimate ?? null;\n  let modelWeight = 0;\n  let estimateValue: number;\n  if (model !== null && compsEstimate !== null) {\n    modelWeight = 0.65;\n    estimateValue = modelWeight * model + (1 - modelWeight) * compsEstimate;\n  } else if (model !== null) {\n    modelWeight = 1;\n    estimateValue = model;\n  } else if (compsEstimate !== null) {\n    estimateValue = compsEstimate;\n  } else {\n    estimateValue = NaN;\n  }\n\n  // fourchette : intervalle du modèle recentré, sinon dispersion des comparables\n  let low: number, high: number;\n  if (model !== null && subject.modelP10 != null && subject.modelP90 != null && model > 0) {\n    low = estimateValue * (subject.modelP10 / model);\n    high = estimateValue * (subject.modelP90 / model);\n  } else {\n    const disp = pctDispersion(comps, compsEstimate ?? estimateValue) ?? 25;\n    low = estimateValue * (1 - disp / 100);\n    high = estimateValue * (1 + disp / 100);\n  }\n\n  const dispersion = pctDispersion(comps, compsEstimate ?? estimateValue);\n  // confiance : nb de comparables, dispersion, largeur de fourchette, présence du modèle\n  let score = 35;\n  score += Math.min(25, comps.length * 2.5);\n  if (dispersion !== null) score += Math.max(0, 20 - dispersion);\n  if (model !== null) score += 15;\n  const widthPct = estimateValue > 0 ? ((high - low) / estimateValue) * 100 : 100;\n  score -= Math.max(0, (widthPct - 30) / 3);\n  score = Math.max(5, Math.min(97, score));\n  const level = score >= 75 ? \"A\" : score >= 60 ? \"B\" : score >= 45 ? \"C\" : \"D\";\n\n  return {\n    estimate: Math.round(estimateValue / 100) * 100,\n    low: Math.round(low / 100) * 100,\n    high: Math.round(high / 100) * 100,\n    confidencePct: Math.round(score),\n    confidenceLevel: level,\n    modelEstimate: model,\n    compsEstimate: compsEstimate !== null ? Math.round(compsEstimate / 100) * 100 : null,\n    modelWeight,\n    comps,\n    nCompsUsed: comps.length,\n    compsDispersionPct: dispersion !== null ? Math.round(dispersion * 10) / 10 : null,\n  };\n}\n","numLines":280,"startLine":1,"totalLines":280}}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014aDXR8vb3XmjhT6qqrxmrh","type":"tool_result","content":"1\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n2\timport Database from \"better-sqlite3\";\n3\timport path from \"path\";\n4\t\n5\tlet db: Database.Database | null = null;\n6\t\n7\texport function getDb(): Database.Database {\n8\t  if (!db) {\n9\t    const p =\n10\t      process.env.VRAIPRIX_DB ?? path.join(process.cwd(), \"data\", \"vraiprix.db\");\n11\t    db = new Database(p, { fileMustExist: true });\n12\t    db.pragma(\"journal_mode = WAL\");\n13\t  }\n14\t  return db;\n15\t}\n16\t\n17\texport interface UnitRow {\n18\t  id_provinc: string;\n19\t  adresse: string | null;\n20\t  apt: string | null;\n21\t  municipalite: string | null;\n22\t  arrond: string | null;\n23\t  code_mun: string | null;\n24\t  lat: number;\n25\t  lng: number;\n26\t  cubf: number | null;\n27\t  cubf_libelle: string | null;\n28\t  type_prop: string;\n29\t  annee_construction: number | null;\n30\t  annee_estimee: string | null;\n31\t  aire_etages_m2: number | null;\n32\t  superficie_terrain_m2: number | null;\n33\t  front_terrain_m: number | null;\n34\t  nb_etages: number | null;\n35\t  nb_logements: number | null;\n36\t  nb_locaux_non_resid: number | null;\n37\t  nb_chambres_locatives: number | null;\n38\t  lien_physique: string | null;\n39\t  genre_construction: string | null;\n40\t  matricule: string | null;\n41\t  unite_voisinage: string | null;\n42\t  n_adresses: number | null;\n43\t  dat_cond_marche: string | null;\n44\t  valeur_terrain: number | null;\n45\t  valeur_batiment: number | null;\n46\t  valeur_role: number | null;\n47\t  valeur_anterieure: number | null;\n48\t  est_2021: number | null;\n49\t  est_2022: number | null;\n50\t  est_2023: number | null;\n51\t  est_2024: number | null;\n52\t  est_2025: number | null;\n53\t  est_2026: number | null;\n54\t  p10: number | null;\n55\t  p90: number | null;\n56\t  est_hedo: number | null;\n57\t}\n58\t\n59\texport interface TxRow {\n60\t  id: string;\n61\t  date: string;\n62\t  amount: number;\n63\t  street: string | null;\n64\t  city: string | null;\n65\t  lat: number;\n66\t  lng: number;\n67\t  property_type: string | null;\n68\t  year_built: number | null;\n69\t  floor_area: number | null;\n70\t  building_type: string | null;\n71\t  id_provinc: string | null;\n72\t  valeur_role: number | null;\n73\t  land_area: number | null;\n74\t}\n75\t\n76\texport function getUnit(id: string): UnitRow | undefined {\n77\t  return getDb()\n78\t    .prepare(\"SELECT * FROM units WHERE id_provinc = ?\")\n79\t    .get(id) as UnitRow | undefined;\n80\t}\n81\t\n82\t/** Candidats comparables dans une boîte englobante autour du point. */\n83\texport function getCandidates(\n84\t  lat: number,\n85\t  lng: number,\n86\t  halfDeg: number,\n87\t  sinceDate: string,\n88\t  limit = 500\n89\t): TxRow[] {\n90\t  return getDb()\n91\t    .prepare(\n92\t      `SELECT * FROM transactions\n93\t       WHERE lat BETWEEN ? AND ? AND lng BETWEEN ? AND ?\n94\t         AND date >= ? AND amount >= 50000\n95\t       LIMIT ?`\n96\t    )\n97\t    .all(lat - halfDeg, lat + halfDeg, lng - halfDeg, lng + halfDeg, sinceDate, limit) as TxRow[];\n98\t}\n99\t\n100\texport function getMarketIndex(typeProp: string): { month: string; idx: number }[] {\n101\t  return getDb()\n102\t    .prepare(\n103\t      \"SELECT month, idx FROM market_index WHERE type_prop = ? ORDER BY month\"\n104\t    )\n105\t    .all(typeProp) as { month: string; idx: number }[];\n106\t}\n107\t\n108\texport function searchUnits(q: string, limit = 8): (UnitRow & { score: number })[] {\n109\t  const cleaned = q\n110\t    .replace(/[^\\p{L}\\p{N}\\s'-]/gu, \" \")\n111\t    .trim()\n112\t    .split(/\\s+/)\n113\t    .filter((t) => t.length > 0)\n114\t    .map((t) => `\"${t}\"*`)\n115\t    .join(\" \");\n116\t  if (!cleaned) return [];\n117\t  return getDb()\n118\t    .prepare(\n119\t      `SELECT u.*, bm25(units_fts) AS score\n120\t       FROM units_fts JOIN units u ON u.rowid = units_fts.rowid\n121\t       WHERE units_fts MATCH ?\n122\t       ORDER BY score LIMIT ?`\n123\t    )\n124\t    .all(cleaned, limit) as (UnitRow & { score: number })[];\n125\t}\n126\t\n127\texport interface NearbyUnitRow {\n128\t  id_provinc: string;\n129\t  adresse: string | null;\n130\t  apt: string | null;\n131\t  municipalite: string | null;\n132\t  type_prop: string;\n133\t  lat: number;\n134\t  lng: number;\n135\t  est_2026: number | null;\n136\t}\n137\t\n138\t/** Unités d'évaluation dans une boîte englobante (recherche par carte). */\n139\texport function unitsNear(\n140\t  lat: number,\n141\t  lng: number,\n142\t  halfLat: number,\n143\t  halfLng: number,\n144\t  limit = 300\n145\t): NearbyUnitRow[] {\n146\t  // ORDER BY distance au centre : sans tri, l'index (lat, lng) renverrait\n147\t  // les N unités les plus au sud de la boîte (bande horizontale).\n148\t  return getDb()\n149\t    .prepare(\n150\t      `SELECT id_provinc, adresse, apt, municipalite, type_prop, lat, lng, est_2026\n151\t       FROM units\n152\t       WHERE lat BETWEEN ? AND ? AND lng BETWEEN ? AND ?\n153\t       ORDER BY (lat - ?) * (lat - ?) + 0.49 * (lng - ?) * (lng - ?)\n154\t       LIMIT ?`\n155\t    )\n156\t    .all(\n157\t      lat - halfLat,\n158\t      lat + halfLat,\n159\t      lng - halfLng,\n160\t      lng + halfLng,\n161\t      lat,\n162\t      lat,\n163\t      lng,\n164\t      lng,\n165\t      limit\n166\t    ) as NearbyUnitRow[];\n167\t}\n168\t\n169\texport function municipalityCenter(\n170\t  name: string\n171\t): { lat: number; lng: number; n: number } | undefined {\n172\t  return getDb()\n173\t    .prepare(\n174\t      `SELECT AVG(lat) AS lat, AVG(lng) AS lng, COUNT(*) AS n\n175\t       FROM units WHERE municipalite = ? COLLATE NOCASE`\n176\t    )\n177\t    .get(name) as { lat: number; lng: number; n: number } | undefined;\n178\t}\n179\t\n180\texport function saveLead(email: string, unitId: string | null, estimate: number | null): void {\n181\t  getDb()\n182\t    .prepare(\"INSERT INTO leads (email, unit_id, estimate) VALUES (?, ?, ?)\")\n183\t    .run(email, unitId, estimate);\n184\t}\n185\t"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"70f28009-5599-4bdd-be5f-52d2264ab934","timestamp":"2026-08-23T03:00:37.924Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/db.ts","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\nimport Database from \"better-sqlite3\";\nimport path from \"path\";\n\nlet db: Database.Database | null = null;\n\nexport function getDb(): Database.Database {\n  if (!db) {\n    const p =\n      process.env.VRAIPRIX_DB ?? path.join(process.cwd(), \"data\", \"vraiprix.db\");\n    db = new Database(p, { fileMustExist: true });\n    db.pragma(\"journal_mode = WAL\");\n  }\n  return db;\n}\n\nexport interface UnitRow {\n  id_provinc: string;\n  adresse: string | null;\n  apt: string | null;\n  municipalite: string | null;\n  arrond: string | null;\n  code_mun: string | null;\n  lat: number;\n  lng: number;\n  cubf: number | null;\n  cubf_libelle: string | null;\n  type_prop: string;\n  annee_construction: number | null;\n  annee_estimee: string | null;\n  aire_etages_m2: number | null;\n  superficie_terrain_m2: number | null;\n  front_terrain_m: number | null;\n  nb_etages: number | null;\n  nb_logements: number | null;\n  nb_locaux_non_resid: number | null;\n  nb_chambres_locatives: number | null;\n  lien_physique: string | null;\n  genre_construction: string | null;\n  matricule: string | null;\n  unite_voisinage: string | null;\n  n_adresses: number | null;\n  dat_cond_marche: string | null;\n  valeur_terrain: number | null;\n  valeur_batiment: number | null;\n  valeur_role: number | null;\n  valeur_anterieure: number | null;\n  est_2021: number | null;\n  est_2022: number | null;\n  est_2023: number | null;\n  est_2024: number | null;\n  est_2025: number | null;\n  est_2026: number | null;\n  p10: number | null;\n  p90: number | null;\n  est_hedo: number | null;\n}\n\nexport interface TxRow {\n  id: string;\n  date: string;\n  amount: number;\n  street: string | null;\n  city: string | null;\n  lat: number;\n  lng: number;\n  property_type: string | null;\n  year_built: number | null;\n  floor_area: number | null;\n  building_type: string | null;\n  id_provinc: string | null;\n  valeur_role: number | null;\n  land_area: number | null;\n}\n\nexport function getUnit(id: string): UnitRow | undefined {\n  return getDb()\n    .prepare(\"SELECT * FROM units WHERE id_provinc = ?\")\n    .get(id) as UnitRow | undefined;\n}\n\n/** Candidats comparables dans une boîte englobante autour du point. */\nexport function getCandidates(\n  lat: number,\n  lng: number,\n  halfDeg: number,\n  sinceDate: string,\n  limit = 500\n): TxRow[] {\n  return getDb()\n    .prepare(\n      `SELECT * FROM transactions\n       WHERE lat BETWEEN ? AND ? AND lng BETWEEN ? AND ?\n         AND date >= ? AND amount >= 50000\n       LIMIT ?`\n    )\n    .all(lat - halfDeg, lat + halfDeg, lng - halfDeg, lng + halfDeg, sinceDate, limit) as TxRow[];\n}\n\nexport function getMarketIndex(typeProp: string): { month: string; idx: number }[] {\n  return getDb()\n    .prepare(\n      \"SELECT month, idx FROM market_index WHERE type_prop = ? ORDER BY month\"\n    )\n    .all(typeProp) as { month: string; idx: number }[];\n}\n\nexport function searchUnits(q: string, limit = 8): (UnitRow & { score: number })[] {\n  const cleaned = q\n    .replace(/[^\\p{L}\\p{N}\\s'-]/gu, \" \")\n    .trim()\n    .split(/\\s+/)\n    .filter((t) => t.length > 0)\n    .map((t) => `\"${t}\"*`)\n    .join(\" \");\n  if (!cleaned) return [];\n  return getDb()\n    .prepare(\n      `SELECT u.*, bm25(units_fts) AS score\n       FROM units_fts JOIN units u ON u.rowid = units_fts.rowid\n       WHERE units_fts MATCH ?\n       ORDER BY score LIMIT ?`\n    )\n    .all(cleaned, limit) as (UnitRow & { score: number })[];\n}\n\nexport interface NearbyUnitRow {\n  id_provinc: string;\n  adresse: string | null;\n  apt: string | null;\n  municipalite: string | null;\n  type_prop: string;\n  lat: number;\n  lng: number;\n  est_2026: number | null;\n}\n\n/** Unités d'évaluation dans une boîte englobante (recherche par carte). */\nexport function unitsNear(\n  lat: number,\n  lng: number,\n  halfLat: number,\n  halfLng: number,\n  limit = 300\n): NearbyUnitRow[] {\n  // ORDER BY distance au centre : sans tri, l'index (lat, lng) renverrait\n  // les N unités les plus au sud de la boîte (bande horizontale).\n  return getDb()\n    .prepare(\n      `SELECT id_provinc, adresse, apt, municipalite, type_prop, lat, lng, est_2026\n       FROM units\n       WHERE lat BETWEEN ? AND ? AND lng BETWEEN ? AND ?\n       ORDER BY (lat - ?) * (lat - ?) + 0.49 * (lng - ?) * (lng - ?)\n       LIMIT ?`\n    )\n    .all(\n      lat - halfLat,\n      lat + halfLat,\n      lng - halfLng,\n      lng + halfLng,\n      lat,\n      lat,\n      lng,\n      lng,\n      limit\n    ) as NearbyUnitRow[];\n}\n\nexport function municipalityCenter(\n  name: string\n): { lat: number; lng: number; n: number } | undefined {\n  return getDb()\n    .prepare(\n      `SELECT AVG(lat) AS lat, AVG(lng) AS lng, COUNT(*) AS n\n       FROM units WHERE municipalite = ? COLLATE NOCASE`\n    )\n    .get(name) as { lat: number; lng: number; n: number } | undefined;\n}\n\nexport function saveLead(email: string, unitId: string | null, estimate: number | null): void {\n  getDb()\n    .prepare(\"INSERT INTO leads (email, unit_id, estimate) VALUES (?, ?, ?)\")\n    .run(email, unitId, estimate);\n}\n","numLines":185,"startLine":1,"totalLines":185}}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YWVtyuqzJvXs6wxaz4FHC7","type":"tool_result","content":"1\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n2\t// Service d'estimation : relie la base SQLite au moteur (engine.ts).\n3\timport {\n4\t  getCandidates,\n5\t  getMarketIndex,\n6\t  getUnit,\n7\t  municipalityCenter,\n8\t  type TxRow,\n9\t  type UnitRow,\n10\t} from \"./db\";\n11\timport { estimate, type CompInput, type EstimateResult, type Subject } from \"./engine\";\n12\t\n13\texport interface UnitSpecs {\n14\t  cubf: number | null;\n15\t  cubfLibelle: string | null;\n16\t  anneeEstimee: string | null;\n17\t  frontTerrainM: number | null;\n18\t  nbEtages: number | null;\n19\t  nbLocauxNonResid: number | null;\n20\t  nbChambresLocatives: number | null;\n21\t  lienPhysique: string | null;\n22\t  genreConstruction: string | null;\n23\t  matricule: string | null;\n24\t  uniteVoisinage: string | null;\n25\t  nAdresses: number | null;\n26\t  datCondMarche: string | null;\n27\t  valeurTerrain: number | null;\n28\t  valeurBatiment: number | null;\n29\t  valeurAnterieure: number | null;\n30\t  arrond: string | null;\n31\t  apt: string | null;\n32\t}\n33\t\n34\texport interface UnitEstimate {\n35\t  unit: {\n36\t    id: string;\n37\t    adresse: string | null;\n38\t    municipalite: string | null;\n39\t    typeProp: string;\n40\t    cubfLibelle: string | null;\n41\t    anneeConstruction: number | null;\n42\t    aireEtagesM2: number | null;\n43\t    superficieTerrainM2: number | null;\n44\t    nbLogements: number | null;\n45\t    valeurRole: number | null;\n46\t    lat: number;\n47\t    lng: number;\n48\t    history: { year: number; value: number | null }[];\n49\t    specs: UnitSpecs;\n50\t  } | null;\n51\t  result: EstimateResult;\n52\t}\n53\t\n54\t/** Type de comparable attendu pour le type de tx du marché de l'indice. */\n55\texport function indexType(typeProp: string): string {\n56\t  if (typeProp === \"condo_ou_multi\") return \"condo\";\n57\t  if (typeProp === \"plex\") return \"plex\";\n58\t  return \"unifamilial\";\n59\t}\n60\t\n61\tfunction toComps(rows: TxRow[]): CompInput[] {\n62\t  return rows.map((r) => ({\n63\t    id: r.id,\n64\t    date: r.date,\n65\t    amount: r.amount,\n66\t    lat: r.lat,\n67\t    lng: r.lng,\n68\t    propertyType: r.property_type,\n69\t    yearBuilt: r.year_built,\n70\t    floorArea: r.floor_area,\n71\t    street: r.street,\n72\t    city: r.city,\n73\t  }));\n74\t}\n75\t\n76\tfunction nowISO(): string {\n77\t  return new Date().toISOString().slice(0, 10);\n78\t}\n79\t\n80\tfunction since(months: number): string {\n81\t  const d = new Date();\n82\t  d.setMonth(d.getMonth() - months);\n83\t  return d.toISOString().slice(0, 10);\n84\t}\n85\t\n86\tfunction candidatesAround(lat: number, lng: number): TxRow[] {\n87\t  // ~2,2 km puis élargit à ~11 km si marché mince\n88\t  for (const halfDeg of [0.02, 0.1]) {\n89\t    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n90\t    if (rows.length >= 30) return rows;\n91\t  }\n92\t  return getCandidates(lat, lng, 0.25, since(36), 600);\n93\t}\n94\t\n95\texport function estimateByUnitId(id: string): UnitEstimate | null {\n96\t  const u = getUnit(id);\n97\t  if (!u) return null;\n98\t  const subject: Subject = {\n99\t    lat: u.lat,\n100\t    lng: u.lng,\n101\t    typeProp: u.type_prop,\n102\t    floorArea: u.aire_etages_m2,\n103\t    yearBuilt: u.annee_construction,\n104\t    landArea: u.superficie_terrain_m2,\n105\t    modelEstimate: u.est_2026,\n106\t    modelP10: u.p10,\n107\t    modelP90: u.p90,\n108\t  };\n109\t  const result = estimate(\n110\t    subject,\n111\t    toComps(candidatesAround(u.lat, u.lng)),\n112\t    getMarketIndex(indexType(u.type_prop)),\n113\t    nowISO()\n114\t  );\n115\t  return {\n116\t    unit: {\n117\t      id: u.id_provinc,\n118\t      adresse: u.adresse,\n119\t      municipalite: u.municipalite,\n120\t      typeProp: u.type_prop,\n121\t      cubfLibelle: u.cubf_libelle,\n122\t      anneeConstruction: u.annee_construction,\n123\t      aireEtagesM2: u.aire_etages_m2,\n124\t      superficieTerrainM2: u.superficie_terrain_m2,\n125\t      nbLogements: u.nb_logements,\n126\t      valeurRole: u.valeur_role,\n127\t      lat: u.lat,\n128\t      lng: u.lng,\n129\t      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n130\t        year: y,\n131\t        value: u[`est_${y}` as keyof UnitRow] as number | null,\n132\t      })),\n133\t      specs: {\n134\t        cubf: u.cubf,\n135\t        cubfLibelle: u.cubf_libelle,\n136\t        anneeEstimee: u.annee_estimee,\n137\t        frontTerrainM: u.front_terrain_m,\n138\t        nbEtages: u.nb_etages,\n139\t        nbLocauxNonResid: u.nb_locaux_non_resid,\n140\t        nbChambresLocatives: u.nb_chambres_locatives,\n141\t        lienPhysique: u.lien_physique,\n142\t        genreConstruction: u.genre_construction,\n143\t        matricule: u.matricule,\n144\t        uniteVoisinage: u.unite_voisinage,\n145\t        nAdresses: u.n_adresses,\n146\t        datCondMarche: u.dat_cond_marche,\n147\t        valeurTerrain: u.valeur_terrain,\n148\t        valeurBatiment: u.valeur_batiment,\n149\t        valeurAnterieure: u.valeur_anterieure,\n150\t        arrond: u.arrond,\n151\t        apt: u.apt,\n152\t      },\n153\t    },\n154\t    result,\n155\t  };\n156\t}\n157\t\n158\texport interface PortfolioAggregates {\n159\t  count: number;\n160\t  totalEstimate: number;\n161\t  totalLow: number;\n162\t  totalHigh: number;\n163\t  totalRole: number;\n164\t  ecartRolePct: number | null;\n165\t  confidencePct: number; // moyenne pondérée par valeur\n166\t  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n167\t  totalFloorArea: number;\n168\t  totalLandArea: number;\n169\t  totalDwellings: number;\n170\t  avgYearBuilt: number | null;\n171\t  municipalities: { name: string; count: number; total: number }[];\n172\t  types: { type: string; count: number; total: number }[];\n173\t  history: { year: number; total: number; nCovered: number }[];\n174\t  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n175\t}\n176\t\n177\texport interface PortfolioResult {\n178\t  items: UnitEstimate[];\n179\t  aggregates: PortfolioAggregates;\n180\t}\n181\t\n182\texport function estimatePortfolio(ids: string[]): PortfolioResult {\n183\t  const items = ids\n184\t    .slice(0, 40)\n185\t    .map((id) => estimateByUnitId(id))\n186\t    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n187\t\n188\t  const val = (n: number | null | undefined) => n ?? 0;\n189\t  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n190\t  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n191\t  const wConf =\n192\t    totalEstimate > 0\n193\t      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n194\t        totalEstimate\n195\t      : 0;\n196\t  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n197\t\n198\t  const byKey = (key: (i: UnitEstimate) => string) => {\n199\t    const m = new Map<string, { count: number; total: number }>();\n200\t    for (const i of items) {\n201\t      const k = key(i);\n202\t      const e = m.get(k) ?? { count: 0, total: 0 };\n203\t      e.count += 1;\n204\t      e.total += val(i.result.estimate);\n205\t      m.set(k, e);\n206\t    }\n207\t    return [...m.entries()]\n208\t      .map(([name, v]) => ({ name, ...v }))\n209\t      .sort((a, b) => b.total - a.total);\n210\t  };\n211\t\n212\t  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n213\t  const history = years.map((y) => {\n214\t    let total = 0;\n215\t    let n = 0;\n216\t    for (const i of items) {\n217\t      const h = i.unit!.history.find((hh) => hh.year === y);\n218\t      if (h?.value != null) {\n219\t        total += h.value;\n220\t        n += 1;\n221\t      }\n222\t    }\n223\t    return { year: y, total, nCovered: n };\n224\t  });\n225\t  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n226\t  let g21 = 0;\n227\t  let g26 = 0;\n228\t  for (const i of items) {\n229\t    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n230\t    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n231\t    if (a != null && b != null) {\n232\t      g21 += a;\n233\t      g26 += b;\n234\t    }\n235\t  }\n236\t\n237\t  const yb = items\n238\t    .map((i) => i.unit!.anneeConstruction)\n239\t    .filter((v): v is number => v != null && v > 1600);\n240\t\n241\t  return {\n242\t    items,\n243\t    aggregates: {\n244\t      count: items.length,\n245\t      totalEstimate,\n246\t      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n247\t      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n248\t      totalRole,\n249\t      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n250\t      confidencePct: Math.round(wConf),\n251\t      confidenceLevel: level,\n252\t      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n253\t      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n254\t      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n255\t      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n256\t      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n257\t      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n258\t      history,\n259\t      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n260\t    },\n261\t  };\n262\t}\n263\t\n264\texport interface ManualParams {\n265\t  municipality: string;\n266\t  typeProp: string;\n267\t  floorArea?: number;\n268\t  yearBuilt?: number;\n269\t  landArea?: number;\n270\t}\n271\t\n272\texport function estimateManual(p: ManualParams): UnitEstimate | null {\n273\t  const center = municipalityCenter(p.municipality);\n274\t  if (!center || !center.n) return null;\n275\t  const subject: Subject = {\n276\t    lat: center.lat,\n277\t    lng: center.lng,\n278\t    typeProp: p.typeProp,\n279\t    floorArea: p.floorArea ?? null,\n280\t    yearBuilt: p.yearBuilt ?? null,\n281\t    landArea: p.landArea ?? null,\n282\t    modelEstimate: null,\n283\t    modelP10: null,\n284\t    modelP90: null,\n285\t  };\n286\t  const result = estimate(\n287\t    subject,\n288\t    toComps(candidatesAround(center.lat, center.lng)),\n289\t    getMarketIndex(indexType(p.typeProp)),\n290\t    nowISO()\n291\t  );\n292\t  if (!Number.isFinite(result.estimate)) return null;\n293\t  return { unit: null, result };\n294\t}\n295\t"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"9792dd8d-e46e-43c9-8c68-20ffa8eb5d03","timestamp":"2026-08-23T03:00:38.016Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport { estimate, type CompInput, type EstimateResult, type Subject } from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(u.lat, u.lng)),\n    getMarketIndex(indexType(u.type_prop)),\n    nowISO()\n  );\n  return {\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(center.lat, center.lng)),\n    getMarketIndex(indexType(p.typeProp)),\n    nowISO()\n  );\n  if (!Number.isFinite(result.estimate)) return null;\n  return { unit: null, result };\n}\n","numLines":295,"startLine":1,"totalLines":295}}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YSU7onaCaUvudmoB3Cs8Cr","type":"tool_result","content":"1\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n2\timport type { Metadata } from \"next\";\n3\timport { cache } from \"react\";\n4\timport { notFound } from \"next/navigation\";\n5\timport { estimateByUnitId } from \"@/lib/estimator\";\n6\timport ResultView from \"@/components/ResultView\";\n7\timport {\n8\t  EST_YEAR,\n9\t  SITE_URL,\n10\t  TYPE_LABELS,\n11\t  moneyFr,\n12\t  slugify,\n13\t} from \"@/lib/seo\";\n14\t\n15\texport const dynamic = \"force-dynamic\";\n16\t\n17\t// Une seule estimation par requête, partagée entre generateMetadata et la page.\n18\tconst getEstimate = cache((id: string) => estimateByUnitId(id));\n19\t\n20\tfunction displayAddress(unit: {\n21\t  adresse: string | null;\n22\t  specs: { apt: string | null };\n23\t  municipalite: string | null;\n24\t}): string {\n25\t  const apt = unit.specs.apt ? `, app. ${unit.specs.apt}` : \"\";\n26\t  const addr = unit.adresse ?? \"Propriété\";\n27\t  return unit.municipalite ? `${addr}${apt}, ${unit.municipalite}` : `${addr}${apt}`;\n28\t}\n29\t\n30\texport async function generateMetadata({\n31\t  params,\n32\t}: {\n33\t  params: Promise<{ id: string }>;\n34\t}): Promise<Metadata> {\n35\t  const { id } = await params;\n36\t  const decoded = decodeURIComponent(id);\n37\t  const data = getEstimate(decoded);\n38\t  if (!data?.unit) return { title: \"Propriété introuvable\" };\n39\t  const u = data.unit;\n40\t  const r = data.result;\n41\t  const typeLabel = TYPE_LABELS[u.typeProp]?.singulier ?? \"propriété\";\n42\t  const annee = u.anneeConstruction ? `, construite en ${u.anneeConstruction}` : \"\";\n43\t  const title = `${displayAddress(u)} — estimation ${EST_YEAR} : ${moneyFr(r.estimate)}`;\n44\t  const description = `Valeur estimée de cette ${typeLabel}${annee} : ${moneyFr(\n45\t    r.estimate\n46\t  )} (fourchette ${moneyFr(r.low)} – ${moneyFr(r.high)}, confiance ${\n47\t    r.confidenceLevel\n48\t  }). Valeur au rôle, historique 2021-${EST_YEAR} et comparables réels — calcul montré en entier.`;\n49\t  return {\n50\t    title,\n51\t    description,\n52\t    alternates: { canonical: `/estimation/${encodeURIComponent(decoded)}` },\n53\t    openGraph: { title, description, type: \"website\" },\n54\t  };\n55\t}\n56\t\n57\t/** JSON-LD schema.org : la propriété comme lieu résidentiel + fil d'Ariane. */\n58\tfunction unitJsonLd(data: NonNullable<ReturnType<typeof estimateByUnitId>>) {\n59\t  const u = data.unit!;\n60\t  const r = data.result;\n61\t  const schemaType =\n62\t    u.typeProp === \"unifamilial\" || u.typeProp === \"chalet\" || u.typeProp === \"maison_mobile\"\n63\t      ? \"SingleFamilyResidence\"\n64\t      : u.typeProp === \"plex\" || u.typeProp === \"condo_ou_multi\"\n65\t        ? \"Residence\"\n66\t        : \"Place\";\n67\t  const url = `${SITE_URL}/estimation/${encodeURIComponent(u.id)}`;\n68\t  const place: Record<string, unknown> = {\n69\t    \"@type\": schemaType,\n70\t    \"@id\": `${url}#place`,\n71\t    name: displayAddress(u),\n72\t    url,\n73\t    address: {\n74\t      \"@type\": \"PostalAddress\",\n75\t      streetAddress: u.adresse ?? undefined,\n76\t      addressLocality: u.municipalite ?? undefined,\n77\t      addressRegion: \"QC\",\n78\t      addressCountry: \"CA\",\n79\t    },\n80\t    geo: { \"@type\": \"GeoCoordinates\", latitude: u.lat, longitude: u.lng },\n81\t    additionalProperty: [\n82\t      {\n83\t        \"@type\": \"PropertyValue\",\n84\t        name: `Estimation Vrai-Prix ${EST_YEAR}`,\n85\t        value: r.estimate,\n86\t        unitText: \"CAD\",\n87\t      },\n88\t      {\n89\t        \"@type\": \"PropertyValue\",\n90\t        name: \"Valeur au rôle d'évaluation\",\n91\t        value: u.valeurRole ?? undefined,\n92\t        unitText: \"CAD\",\n93\t      },\n94\t    ],\n95\t  };\n96\t  if (u.aireEtagesM2) {\n97\t    place.floorSize = {\n98\t      \"@type\": \"QuantitativeValue\",\n99\t      value: u.aireEtagesM2,\n100\t      unitCode: \"MTK\",\n101\t    };\n102\t  }\n103\t  if (u.anneeConstruction) place.yearBuilt = u.anneeConstruction;\n104\t  const breadcrumb = {\n105\t    \"@type\": \"BreadcrumbList\",\n106\t    itemListElement: [\n107\t      { \"@type\": \"ListItem\", position: 1, name: \"Accueil\", item: SITE_URL },\n108\t      u.municipalite\n109\t        ? {\n110\t            \"@type\": \"ListItem\",\n111\t            position: 2,\n112\t            name: u.municipalite,\n113\t            item: `${SITE_URL}/municipalite/${slugify(u.municipalite)}`,\n114\t          }\n115\t        : null,\n116\t      {\n117\t        \"@type\": \"ListItem\",\n118\t        position: u.municipalite ? 3 : 2,\n119\t        name: u.adresse ?? u.id,\n120\t        item: url,\n121\t      },\n122\t    ].filter(Boolean),\n123\t  };\n124\t  return { \"@context\": \"https://schema.org\", \"@graph\": [place, breadcrumb] };\n125\t}\n126\t\n127\texport default async function EstimationPage({\n128\t  params,\n129\t}: {\n130\t  params: Promise<{ id: string }>;\n131\t}) {\n132\t  const { id } = await params;\n133\t  const data = getEstimate(decodeURIComponent(id));\n134\t  if (!data) notFound();\n135\t  return (\n136\t    <>\n137\t      {data.unit && (\n138\t        <script\n139\t          type=\"application/ld+json\"\n140\t          dangerouslySetInnerHTML={{ __html: JSON.stringify(unitJsonLd(data)) }}\n141\t        />\n142\t      )}\n143\t      <ResultView data={data} />\n144\t    </>\n145\t  );\n146\t}\n147\t"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"0db8e253-740a-450a-97b0-5c565001a21a","timestamp":"2026-08-23T03:01:10.955Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/app/estimation/[id]/page.tsx","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\nimport type { Metadata } from \"next\";\nimport { cache } from \"react\";\nimport { notFound } from \"next/navigation\";\nimport { estimateByUnitId } from \"@/lib/estimator\";\nimport ResultView from \"@/components/ResultView\";\nimport {\n  EST_YEAR,\n  SITE_URL,\n  TYPE_LABELS,\n  moneyFr,\n  slugify,\n} from \"@/lib/seo\";\n\nexport const dynamic = \"force-dynamic\";\n\n// Une seule estimation par requête, partagée entre generateMetadata et la page.\nconst getEstimate = cache((id: string) => estimateByUnitId(id));\n\nfunction displayAddress(unit: {\n  adresse: string | null;\n  specs: { apt: string | null };\n  municipalite: string | null;\n}): string {\n  const apt = unit.specs.apt ? `, app. ${unit.specs.apt}` : \"\";\n  const addr = unit.adresse ?? \"Propriété\";\n  return unit.municipalite ? `${addr}${apt}, ${unit.municipalite}` : `${addr}${apt}`;\n}\n\nexport async function generateMetadata({\n  params,\n}: {\n  params: Promise<{ id: string }>;\n}): Promise<Metadata> {\n  const { id } = await params;\n  const decoded = decodeURIComponent(id);\n  const data = getEstimate(decoded);\n  if (!data?.unit) return { title: \"Propriété introuvable\" };\n  const u = data.unit;\n  const r = data.result;\n  const typeLabel = TYPE_LABELS[u.typeProp]?.singulier ?? \"propriété\";\n  const annee = u.anneeConstruction ? `, construite en ${u.anneeConstruction}` : \"\";\n  const title = `${displayAddress(u)} — estimation ${EST_YEAR} : ${moneyFr(r.estimate)}`;\n  const description = `Valeur estimée de cette ${typeLabel}${annee} : ${moneyFr(\n    r.estimate\n  )} (fourchette ${moneyFr(r.low)} – ${moneyFr(r.high)}, confiance ${\n    r.confidenceLevel\n  }). Valeur au rôle, historique 2021-${EST_YEAR} et comparables réels — calcul montré en entier.`;\n  return {\n    title,\n    description,\n    alternates: { canonical: `/estimation/${encodeURIComponent(decoded)}` },\n    openGraph: { title, description, type: \"website\" },\n  };\n}\n\n/** JSON-LD schema.org : la propriété comme lieu résidentiel + fil d'Ariane. */\nfunction unitJsonLd(data: NonNullable<ReturnType<typeof estimateByUnitId>>) {\n  const u = data.unit!;\n  const r = data.result;\n  const schemaType =\n    u.typeProp === \"unifamilial\" || u.typeProp === \"chalet\" || u.typeProp === \"maison_mobile\"\n      ? \"SingleFamilyResidence\"\n      : u.typeProp === \"plex\" || u.typeProp === \"condo_ou_multi\"\n        ? \"Residence\"\n        : \"Place\";\n  const url = `${SITE_URL}/estimation/${encodeURIComponent(u.id)}`;\n  const place: Record<string, unknown> = {\n    \"@type\": schemaType,\n    \"@id\": `${url}#place`,\n    name: displayAddress(u),\n    url,\n    address: {\n      \"@type\": \"PostalAddress\",\n      streetAddress: u.adresse ?? undefined,\n      addressLocality: u.municipalite ?? undefined,\n      addressRegion: \"QC\",\n      addressCountry: \"CA\",\n    },\n    geo: { \"@type\": \"GeoCoordinates\", latitude: u.lat, longitude: u.lng },\n    additionalProperty: [\n      {\n        \"@type\": \"PropertyValue\",\n        name: `Estimation Vrai-Prix ${EST_YEAR}`,\n        value: r.estimate,\n        unitText: \"CAD\",\n      },\n      {\n        \"@type\": \"PropertyValue\",\n        name: \"Valeur au rôle d'évaluation\",\n        value: u.valeurRole ?? undefined,\n        unitText: \"CAD\",\n      },\n    ],\n  };\n  if (u.aireEtagesM2) {\n    place.floorSize = {\n      \"@type\": \"QuantitativeValue\",\n      value: u.aireEtagesM2,\n      unitCode: \"MTK\",\n    };\n  }\n  if (u.anneeConstruction) place.yearBuilt = u.anneeConstruction;\n  const breadcrumb = {\n    \"@type\": \"BreadcrumbList\",\n    itemListElement: [\n      { \"@type\": \"ListItem\", position: 1, name: \"Accueil\", item: SITE_URL },\n      u.municipalite\n        ? {\n            \"@type\": \"ListItem\",\n            position: 2,\n            name: u.municipalite,\n            item: `${SITE_URL}/municipalite/${slugify(u.municipalite)}`,\n          }\n        : null,\n      {\n        \"@type\": \"ListItem\",\n        position: u.municipalite ? 3 : 2,\n        name: u.adresse ?? u.id,\n        item: url,\n      },\n    ].filter(Boolean),\n  };\n  return { \"@context\": \"https://schema.org\", \"@graph\": [place, breadcrumb] };\n}\n\nexport default async function EstimationPage({\n  params,\n}: {\n  params: Promise<{ id: string }>;\n}) {\n  const { id } = await params;\n  const data = getEstimate(decodeURIComponent(id));\n  if (!data) notFound();\n  return (\n    <>\n      {data.unit && (\n        <script\n          type=\"application/ld+json\"\n          dangerouslySetInnerHTML={{ __html: JSON.stringify(unitJsonLd(data)) }}\n        />\n      )}\n      <ResultView data={data} />\n    </>\n  );\n}\n","numLines":147,"startLine":1,"totalLines":147}}}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJw7P2jm7TFinvn2KDfi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018sp5aL2mLQQn3pqRwonCay","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/i18n.ts"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":12616,"cache_read_input_tokens":59192,"cache_creation":{"ephemeral_5m_input_tokens":12616,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"881fa696-7dc7-446d-90b3-54e129fce33a","request_id":"req_011CeJw7MDbFQHXnta5fGkCj"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018sp5aL2mLQQn3pqRwonCay","type":"tool_result","content":"1\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n2\texport type Lang = \"fr\" | \"en\";\n3\t\n4\tconst dict = {\n5\t  fr: {\n6\t    tagline: \"La vraie valeur de votre propriété, en toute transparence\",\n7\t    sub: \"Estimation basée sur 3,7 millions d'unités d'évaluation et 745 000 ventes réelles au Québec (2021-2026). Pas de boîte noire : on vous montre le calcul.\",\n8\t    searchPlaceholder: \"Entrez une adresse (ex. 861 route Elgin, Saint-Pamphile)\",\n9\t    estimateBtn: \"Estimer\",\n10\t    manualTitle: \"Pas d'adresse ? Décrivez la propriété\",\n11\t    municipality: \"Municipalité\",\n12\t    propertyType: \"Type de propriété\",\n13\t    floorArea: \"Superficie habitable (m²)\",\n14\t    yearBuilt: \"Année de construction\",\n15\t    landArea: \"Superficie du terrain (m²)\",\n16\t    estimatedValue: \"Valeur estimée\",\n17\t    range: \"Fourchette honnête\",\n18\t    confidence: \"Indice de confiance\",\n19\t    whyTitle: \"Pourquoi ce prix ? Le calcul, sans boîte noire\",\n20\t    modelPart: \"Modèle hédonique (LightGBM, 690 000 ventes QC)\",\n21\t    compsPart: \"Comparables ajustés (médiane pondérée)\",\n22\t    weightModel: \"poids du modèle\",\n23\t    weightComps: \"poids des comparables\",\n24\t    compsTitle: \"Comparables utilisés\",\n25\t    adjTime: \"Ajust. marché\",\n26\t    adjArea: \"Ajust. superficie\",\n27\t    adjAge: \"Ajust. âge\",\n28\t    adjusted: \"Prix ajusté\",\n29\t    sold: \"Vendu\",\n30\t    distance: \"Distance\",\n31\t    weight: \"Poids\",\n32\t    history: \"Valeur estimée par année\",\n33\t    assessed: \"Évaluation municipale (rôle 2026)\",\n34\t    leadTitle: \"Recevoir le rapport détaillé (PDF)\",\n35\t    leadBtn: \"M'envoyer le rapport\",\n36\t    leadOk: \"Merci ! Votre rapport sera envoyé sous peu.\",\n37\t    emailPlaceholder: \"votre@courriel.com\",\n38\t    disclaimer: \"Estimation statistique à titre indicatif — ne remplace pas une évaluation professionnelle.\",\n39\t    unifamilial: \"Unifamiliale\",\n40\t    plex: \"Plex (2-5 logements)\",\n41\t    condo_ou_multi: \"Condo / multirésidentiel\",\n42\t    chalet: \"Chalet\",\n43\t    maison_mobile: \"Maison mobile\",\n44\t    terrain: \"Terrain\",\n45\t    autre: \"Autre\",\n46\t    notFound: \"Adresse introuvable\",\n47\t    dataNote: \"Données : rôles d'évaluation foncière du Québec (MAMH) et transactions publiques.\",\n48\t    confA: \"Très fiable\",\n49\t    confB: \"Fiable\",\n50\t    confC: \"Indicative\",\n51\t    confD: \"Peu fiable — marché mince\",\n52\t    searchNoResult: \"Aucun résultat — essayez « numéro rue municipalité »\",\n53\t    loading: \"Calcul en cours…\",\n54\t  },\n55\t  en: {\n56\t    tagline: \"Your property's true value, fully transparent\",\n57\t    sub: \"Estimates built on 3.7M assessment units and 745k real Québec sales (2021-2026). No black box: we show the math.\",\n58\t    searchPlaceholder: \"Enter an address (e.g. 861 route Elgin, Saint-Pamphile)\",\n59\t    estimateBtn: \"Estimate\",\n60\t    manualTitle: \"No address? Describe the property\",\n61\t    municipality: \"Municipality\",\n62\t    propertyType: \"Property type\",\n63\t    floorArea: \"Living area (m²)\",\n64\t    yearBuilt: \"Year built\",\n65\t    landArea: \"Lot area (m²)\",\n66\t    estimatedValue: \"Estimated value\",\n67\t    range: \"Honest range\",\n68\t    confidence: \"Confidence index\",\n69\t    whyTitle: \"Why this price? The math, no black box\",\n70\t    modelPart: \"Hedonic model (LightGBM, 690k QC sales)\",\n71\t    compsPart: \"Adjusted comparables (weighted median)\",\n72\t    weightModel: \"model weight\",\n73\t    weightComps: \"comparables weight\",\n74\t    compsTitle: \"Comparables used\",\n75\t    adjTime: \"Market adj.\",\n76\t    adjArea: \"Area adj.\",\n77\t    adjAge: \"Age adj.\",\n78\t    adjusted: \"Adjusted price\",\n79\t    sold: \"Sold\",\n80\t    distance: \"Distance\",\n81\t    weight: \"Weight\",\n82\t    history: \"Estimated value by year\",\n83\t    assessed: \"Municipal assessment (2026 roll)\",\n84\t    leadTitle: \"Get the detailed report (PDF)\",\n85\t    leadBtn: \"Send me the report\",\n86\t    leadOk: \"Thanks! Your report is on its way.\",\n87\t    emailPlaceholder: \"you@email.com\",\n88\t    disclaimer: \"Statistical estimate for information only — not a substitute for a professional appraisal.\",\n89\t    unifamilial: \"Single-family\",\n90\t    plex: \"Plex (2-5 units)\",\n91\t    condo_ou_multi: \"Condo / multi-residential\",\n92\t    chalet: \"Cottage\",\n93\t    maison_mobile: \"Mobile home\",\n94\t    terrain: \"Land\",\n95\t    autre: \"Other\",\n96\t    notFound: \"Address not found\",\n97\t    dataNote: \"Data: Québec property assessment rolls (MAMH) and public transactions.\",\n98\t    confA: \"Very reliable\",\n99\t    confB: \"Reliable\",\n100\t    confC: \"Indicative\",\n101\t    confD: \"Low reliability — thin market\",\n102\t    searchNoResult: \"No result — try “number street municipality”\",\n103\t    loading: \"Computing…\",\n104\t  },\n105\t} as const;\n106\t\n107\texport type TKey = keyof (typeof dict)[\"fr\"];\n108\t\n109\texport function t(lang: Lang, key: TKey): string {\n110\t  return dict[lang][key] ?? dict.fr[key];\n111\t}\n112\t"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"dcac0607-1ca8-42c5-a1fb-6317f9d444c2","timestamp":"2026-08-23T03:01:11.661Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/i18n.ts","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\nexport type Lang = \"fr\" | \"en\";\n\nconst dict = {\n  fr: {\n    tagline: \"La vraie valeur de votre propriété, en toute transparence\",\n    sub: \"Estimation basée sur 3,7 millions d'unités d'évaluation et 745 000 ventes réelles au Québec (2021-2026). Pas de boîte noire : on vous montre le calcul.\",\n    searchPlaceholder: \"Entrez une adresse (ex. 861 route Elgin, Saint-Pamphile)\",\n    estimateBtn: \"Estimer\",\n    manualTitle: \"Pas d'adresse ? Décrivez la propriété\",\n    municipality: \"Municipalité\",\n    propertyType: \"Type de propriété\",\n    floorArea: \"Superficie habitable (m²)\",\n    yearBuilt: \"Année de construction\",\n    landArea: \"Superficie du terrain (m²)\",\n    estimatedValue: \"Valeur estimée\",\n    range: \"Fourchette honnête\",\n    confidence: \"Indice de confiance\",\n    whyTitle: \"Pourquoi ce prix ? Le calcul, sans boîte noire\",\n    modelPart: \"Modèle hédonique (LightGBM, 690 000 ventes QC)\",\n    compsPart: \"Comparables ajustés (médiane pondérée)\",\n    weightModel: \"poids du modèle\",\n    weightComps: \"poids des comparables\",\n    compsTitle: \"Comparables utilisés\",\n    adjTime: \"Ajust. marché\",\n    adjArea: \"Ajust. superficie\",\n    adjAge: \"Ajust. âge\",\n    adjusted: \"Prix ajusté\",\n    sold: \"Vendu\",\n    distance: \"Distance\",\n    weight: \"Poids\",\n    history: \"Valeur estimée par année\",\n    assessed: \"Évaluation municipale (rôle 2026)\",\n    leadTitle: \"Recevoir le rapport détaillé (PDF)\",\n    leadBtn: \"M'envoyer le rapport\",\n    leadOk: \"Merci ! Votre rapport sera envoyé sous peu.\",\n    emailPlaceholder: \"votre@courriel.com\",\n    disclaimer: \"Estimation statistique à titre indicatif — ne remplace pas une évaluation professionnelle.\",\n    unifamilial: \"Unifamiliale\",\n    plex: \"Plex (2-5 logements)\",\n    condo_ou_multi: \"Condo / multirésidentiel\",\n    chalet: \"Chalet\",\n    maison_mobile: \"Maison mobile\",\n    terrain: \"Terrain\",\n    autre: \"Autre\",\n    notFound: \"Adresse introuvable\",\n    dataNote: \"Données : rôles d'évaluation foncière du Québec (MAMH) et transactions publiques.\",\n    confA: \"Très fiable\",\n    confB: \"Fiable\",\n    confC: \"Indicative\",\n    confD: \"Peu fiable — marché mince\",\n    searchNoResult: \"Aucun résultat — essayez « numéro rue municipalité »\",\n    loading: \"Calcul en cours…\",\n  },\n  en: {\n    tagline: \"Your property's true value, fully transparent\",\n    sub: \"Estimates built on 3.7M assessment units and 745k real Québec sales (2021-2026). No black box: we show the math.\",\n    searchPlaceholder: \"Enter an address (e.g. 861 route Elgin, Saint-Pamphile)\",\n    estimateBtn: \"Estimate\",\n    manualTitle: \"No address? Describe the property\",\n    municipality: \"Municipality\",\n    propertyType: \"Property type\",\n    floorArea: \"Living area (m²)\",\n    yearBuilt: \"Year built\",\n    landArea: \"Lot area (m²)\",\n    estimatedValue: \"Estimated value\",\n    range: \"Honest range\",\n    confidence: \"Confidence index\",\n    whyTitle: \"Why this price? The math, no black box\",\n    modelPart: \"Hedonic model (LightGBM, 690k QC sales)\",\n    compsPart: \"Adjusted comparables (weighted median)\",\n    weightModel: \"model weight\",\n    weightComps: \"comparables weight\",\n    compsTitle: \"Comparables used\",\n    adjTime: \"Market adj.\",\n    adjArea: \"Area adj.\",\n    adjAge: \"Age adj.\",\n    adjusted: \"Adjusted price\",\n    sold: \"Sold\",\n    distance: \"Distance\",\n    weight: \"Weight\",\n    history: \"Estimated value by year\",\n    assessed: \"Municipal assessment (2026 roll)\",\n    leadTitle: \"Get the detailed report (PDF)\",\n    leadBtn: \"Send me the report\",\n    leadOk: \"Thanks! Your report is on its way.\",\n    emailPlaceholder: \"you@email.com\",\n    disclaimer: \"Statistical estimate for information only — not a substitute for a professional appraisal.\",\n    unifamilial: \"Single-family\",\n    plex: \"Plex (2-5 units)\",\n    condo_ou_multi: \"Condo / multi-residential\",\n    chalet: \"Cottage\",\n    maison_mobile: \"Mobile home\",\n    terrain: \"Land\",\n    autre: \"Other\",\n    notFound: \"Address not found\",\n    dataNote: \"Data: Québec property assessment rolls (MAMH) and public transactions.\",\n    confA: \"Very reliable\",\n    confB: \"Reliable\",\n    confC: \"Indicative\",\n    confD: \"Low reliability — thin market\",\n    searchNoResult: \"No result — try “number street municipality”\",\n    loading: \"Computing…\",\n  },\n} as const;\n\nexport type TKey = keyof (typeof dict)[\"fr\"];\n\nexport function t(lang: Lang, key: TKey): string {\n  return dict[lang][key] ?? dict.fr[key];\n}\n","numLines":112,"startLine":1,"totalLines":112}}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NSJ1HdaK3wh6eZKX1sj6sh","type":"tool_result","content":"1\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n2\timport type { Metadata, Viewport } from \"next\";\n3\timport { Inter, JetBrains_Mono, Space_Grotesk } from \"next/font/google\";\n4\timport \"./globals.css\";\n5\timport { LangProvider, LangToggle } from \"@/components/LangContext\";\n6\timport AccountButton from \"@/components/AccountButton\";\n7\timport CookieConsent from \"@/components/CookieConsent\";\n8\timport FooterNav from \"@/components/FooterNav\";\n9\timport HeaderNav from \"@/components/HeaderNav\";\n10\timport GroupeKaBadge from \"@/ka/GroupeKaBadge\";\n11\timport KaFooter from \"@/ka/KaFooter\";\n12\timport Link from \"next/link\";\n13\timport { SITE_URL } from \"@/lib/seo\";\n14\t\n15\tconst inter = Inter({ subsets: [\"latin\"], variable: \"--font-inter\" });\n16\tconst spaceGrotesk = Space_Grotesk({\n17\t  subsets: [\"latin\"],\n18\t  variable: \"--font-space-grotesk\",\n19\t});\n20\tconst jetbrains = JetBrains_Mono({\n21\t  subsets: [\"latin\"],\n22\t  variable: \"--font-jetbrains\",\n23\t});\n24\t\n25\texport const metadata: Metadata = {\n26\t  metadataBase: new URL(SITE_URL),\n27\t  title: {\n28\t    default: \"Vrai-Prix — Un service Groupe KA\",\n29\t    template: \"%s | Vrai-Prix\",\n30\t  },\n31\t  description:\n32\t    \"Estimation immobilière transparente pour le Québec : modèle hédonique + comparables réels, fourchette honnête et indice de confiance. 3,7 millions de propriétés, 1 100 municipalités.\",\n33\t  applicationName: \"Vrai-Prix\",\n34\t  icons: { icon: \"/favicon.svg\", apple: \"/apple-touch-icon.png\" },\n35\t  openGraph: {\n36\t    type: \"website\",\n37\t    siteName: \"Vrai-Prix\",\n38\t    locale: \"fr_CA\",\n39\t    url: SITE_URL,\n40\t    title: \"Vrai-Prix — Un service Groupe KA\",\n41\t    description:\n42\t      \"Estimation immobilière transparente pour le Québec : modèle hédonique + comparables réels, fourchette honnête et indice de confiance.\",\n43\t    images: [\n44\t      { url: \"https://www.vrai-prix.com/og.png\", width: 1200, height: 630 },\n45\t    ],\n46\t  },\n47\t  twitter: {\n48\t    card: \"summary_large_image\",\n49\t    images: [\n50\t      { url: \"https://www.vrai-prix.com/og.png\", width: 1200, height: 630 },\n51\t    ],\n52\t    title: \"Vrai-Prix — Un service Groupe KA\",\n53\t    description:\n54\t      \"Estimation immobilière transparente pour le Québec : 3,7 millions de propriétés estimées, calcul montré en entier.\",\n55\t  },\n56\t  formatDetection: { telephone: false },\n57\t};\n58\t\n59\t/** Viewport mobile : viewport-fit=cover pour les encoches iOS (safe-area). */\n60\texport const viewport: Viewport = {\n61\t  width: \"device-width\",\n62\t  initialScale: 1,\n63\t  viewportFit: \"cover\",\n64\t};\n65\t\n66\t/** JSON-LD global : l'organisation et le site (entités schema.org). */\n67\tconst ORG_JSONLD = {\n68\t  \"@context\": \"https://schema.org\",\n69\t  \"@graph\": [\n70\t    {\n71\t      \"@type\": \"Organization\",\n72\t      \"@id\": `${SITE_URL}/#org`,\n73\t      name: \"Vrai-Prix\",\n74\t      url: SITE_URL,\n75\t      email: \"contact@spboucher.ai\",\n76\t      founder: { \"@type\": \"Person\", name: \"Simon-Pierre Boucher\" },\n77\t      areaServed: { \"@type\": \"AdministrativeArea\", name: \"Québec, Canada\" },\n78\t    },\n79\t    {\n80\t      \"@type\": \"WebSite\",\n81\t      \"@id\": `${SITE_URL}/#site`,\n82\t      name: \"Vrai-Prix\",\n83\t      url: SITE_URL,\n84\t      inLanguage: \"fr-CA\",\n85\t      publisher: { \"@id\": `${SITE_URL}/#org` },\n86\t      description:\n87\t        \"Estimation immobilière transparente pour le Québec : modèle hédonique + comparables réels, fourchette honnête et indice de confiance.\",\n88\t    },\n89\t  ],\n90\t};\n91\t\n92\tconst TICKER = [\n93\t  \"3 747 008 unités d'évaluation\",\n94\t  \"745 119 ventes réelles 2021-2026\",\n95\t  \"22 M d'estimations calculées\",\n96\t  \"Fourchette honnête P10-P90\",\n97\t  \"Indice de confiance A-D\",\n98\t  \"Zéro boîte noire — le calcul est montré\",\n99\t  \"1 100 municipalités couvertes\",\n100\t  \"Erreur médiane 11 %\",\n101\t];\n102\t\n103\texport default function RootLayout({ children }: LayoutProps<\"/\">) {\n104\t  return (\n105\t    <html lang=\"fr-CA\">\n106\t      <body\n107\t        className={`${inter.variable} ${spaceGrotesk.variable} ${jetbrains.variable}`}\n108\t      >\n109\t        <script\n110\t          type=\"application/ld+json\"\n111\t          dangerouslySetInnerHTML={{ __html: JSON.stringify(ORG_JSONLD) }}\n112\t        />\n113\t        <LangProvider>\n114\t          {/* header opaque (standard header/menu KA 2026-08-22) */}\n115\t          <header className=\"sticky top-0 z-[var(--z-header,500)] border-b-2 border-ink bg-paper\">\n116\t            <div className=\"relative mx-auto flex h-16 max-w-6xl items-center gap-5 px-4 sm:px-6\">\n117\t              <Link\n118\t                href=\"/\"\n119\t                className=\"vp-display group relative flex items-center text-[22px] font-bold leading-none tracking-[-0.04em] after:absolute after:-inset-2 after:content-[''] sm:text-[26px]\"\n120\t              >\n121\t                Vrai\n122\t                <span className=\"ml-1 rotate-[-2deg] rounded-md bg-ink px-[7px] pb-1 pt-[2px] text-lime transition-transform duration-200 group-hover:rotate-0\">\n123\t                  Prix\n124\t                </span>\n125\t              </Link>\n126\t              {/* badge Groupe KA — desktop seulement (l'espace manque sous xl ;\n127\t                  le menu mobile porte sa propre entrée) */}\n128\t              <span className=\"hidden xl:inline-flex\">\n129\t                <GroupeKaBadge />\n130\t              </span>\n131\t              <div className=\"ml-auto flex items-center gap-2\">\n132\t                <HeaderNav />\n133\t                <AccountButton />\n134\t                <LangToggle />\n135\t              </div>\n136\t            </div>\n137\t          </header>\n138\t          <div className=\"ticker\" aria-hidden=\"true\">\n139\t            <div className=\"ticker-track\">\n140\t              {[...TICKER, ...TICKER].map((s, i) => (\n141\t                <span key={i}>{s}</span>\n142\t              ))}\n143\t            </div>\n144\t          </div>\n145\t          <main className=\"mx-auto max-w-6xl px-4 pb-16 sm:px-6\">{children}</main>\n146\t          {/* rangée locale (papier) : marque + liens de navigation du site,\n147\t              puis le bloc encre final = KaFooter (commun Groupe KA) */}\n148\t          <div className=\"mx-auto max-w-6xl px-4 sm:px-6\">\n149\t            <div className=\"border-t-2 border-ink pt-7 text-[13px] text-ink-2\">\n150\t              <p className=\"vp-display mb-3 text-[26px] font-bold tracking-[-0.04em] text-ink\">\n151\t                Vrai\n152\t                <span className=\"ml-1 inline-block rotate-[-2deg] rounded-md bg-ink px-[6px] pb-[3px] pt-[1px] text-lime\">\n153\t                  Prix\n154\t                </span>\n155\t              </p>\n156\t              <p className=\"max-w-2xl\">\n157\t                Estimations bâties sur les rôles d&apos;évaluation foncière du\n158\t                Québec (MAMH, données ouvertes) et 745 119 transactions réelles.\n159\t                Modèle hédonique + comparables ajustés, calcul montré en entier.\n160\t                Estimation statistique à titre indicatif.\n161\t              </p>\n162\t              <FooterNav />\n163\t            </div>\n164\t          </div>\n165\t          <KaFooter\n166\t            siteId=\"vrai-prix\"\n167\t            localLegal={[\n168\t              { label: \"Conditions\", href: \"/conditions\" },\n169\t              { label: \"Confidentialité\", href: \"/confidentialite\" },\n170\t              { label: \"Méthodologie\", href: \"/methodologie\" },\n171\t            ]}\n172\t          />\n173\t          <CookieConsent />\n174\t          <script\n175\t            dangerouslySetInnerHTML={{\n176\t              __html: 'window.KA_AGENT={site:\"vrai-prix\"}',\n177\t            }}\n178\t          />\n179\t          <script src=\"/ka-agent.js\" defer />\n180\t        </LangProvider>\n181\t      </body>\n182\t    </html>\n183\t  );\n184\t}\n185\t"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"8a5bb6dc-ba79-4e0b-972d-b42ae9160794","timestamp":"2026-08-23T03:01:12.254Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/app/layout.tsx","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\nimport type { Metadata, Viewport } from \"next\";\nimport { Inter, JetBrains_Mono, Space_Grotesk } from \"next/font/google\";\nimport \"./globals.css\";\nimport { LangProvider, LangToggle } from \"@/components/LangContext\";\nimport AccountButton from \"@/components/AccountButton\";\nimport CookieConsent from \"@/components/CookieConsent\";\nimport FooterNav from \"@/components/FooterNav\";\nimport HeaderNav from \"@/components/HeaderNav\";\nimport GroupeKaBadge from \"@/ka/GroupeKaBadge\";\nimport KaFooter from \"@/ka/KaFooter\";\nimport Link from \"next/link\";\nimport { SITE_URL } from \"@/lib/seo\";\n\nconst inter = Inter({ subsets: [\"latin\"], variable: \"--font-inter\" });\nconst spaceGrotesk = Space_Grotesk({\n  subsets: [\"latin\"],\n  variable: \"--font-space-grotesk\",\n});\nconst jetbrains = JetBrains_Mono({\n  subsets: [\"latin\"],\n  variable: \"--font-jetbrains\",\n});\n\nexport const metadata: Metadata = {\n  metadataBase: new URL(SITE_URL),\n  title: {\n    default: \"Vrai-Prix — Un service Groupe KA\",\n    template: \"%s | Vrai-Prix\",\n  },\n  description:\n    \"Estimation immobilière transparente pour le Québec : modèle hédonique + comparables réels, fourchette honnête et indice de confiance. 3,7 millions de propriétés, 1 100 municipalités.\",\n  applicationName: \"Vrai-Prix\",\n  icons: { icon: \"/favicon.svg\", apple: \"/apple-touch-icon.png\" },\n  openGraph: {\n    type: \"website\",\n    siteName: \"Vrai-Prix\",\n    locale: \"fr_CA\",\n    url: SITE_URL,\n    title: \"Vrai-Prix — Un service Groupe KA\",\n    description:\n      \"Estimation immobilière transparente pour le Québec : modèle hédonique + comparables réels, fourchette honnête et indice de confiance.\",\n    images: [\n      { url: \"https://www.vrai-prix.com/og.png\", width: 1200, height: 630 },\n    ],\n  },\n  twitter: {\n    card: \"summary_large_image\",\n    images: [\n      { url: \"https://www.vrai-prix.com/og.png\", width: 1200, height: 630 },\n    ],\n    title: \"Vrai-Prix — Un service Groupe KA\",\n    description:\n      \"Estimation immobilière transparente pour le Québec : 3,7 millions de propriétés estimées, calcul montré en entier.\",\n  },\n  formatDetection: { telephone: false },\n};\n\n/** Viewport mobile : viewport-fit=cover pour les encoches iOS (safe-area). */\nexport const viewport: Viewport = {\n  width: \"device-width\",\n  initialScale: 1,\n  viewportFit: \"cover\",\n};\n\n/** JSON-LD global : l'organisation et le site (entités schema.org). */\nconst ORG_JSONLD = {\n  \"@context\": \"https://schema.org\",\n  \"@graph\": [\n    {\n      \"@type\": \"Organization\",\n      \"@id\": `${SITE_URL}/#org`,\n      name: \"Vrai-Prix\",\n      url: SITE_URL,\n      email: \"contact@spboucher.ai\",\n      founder: { \"@type\": \"Person\", name: \"Simon-Pierre Boucher\" },\n      areaServed: { \"@type\": \"AdministrativeArea\", name: \"Québec, Canada\" },\n    },\n    {\n      \"@type\": \"WebSite\",\n      \"@id\": `${SITE_URL}/#site`,\n      name: \"Vrai-Prix\",\n      url: SITE_URL,\n      inLanguage: \"fr-CA\",\n      publisher: { \"@id\": `${SITE_URL}/#org` },\n      description:\n        \"Estimation immobilière transparente pour le Québec : modèle hédonique + comparables réels, fourchette honnête et indice de confiance.\",\n    },\n  ],\n};\n\nconst TICKER = [\n  \"3 747 008 unités d'évaluation\",\n  \"745 119 ventes réelles 2021-2026\",\n  \"22 M d'estimations calculées\",\n  \"Fourchette honnête P10-P90\",\n  \"Indice de confiance A-D\",\n  \"Zéro boîte noire — le calcul est montré\",\n  \"1 100 municipalités couvertes\",\n  \"Erreur médiane 11 %\",\n];\n\nexport default function RootLayout({ children }: LayoutProps<\"/\">) {\n  return (\n    <html lang=\"fr-CA\">\n      <body\n        className={`${inter.variable} ${spaceGrotesk.variable} ${jetbrains.variable}`}\n      >\n        <script\n          type=\"application/ld+json\"\n          dangerouslySetInnerHTML={{ __html: JSON.stringify(ORG_JSONLD) }}\n        />\n        <LangProvider>\n          {/* header opaque (standard header/menu KA 2026-08-22) */}\n          <header className=\"sticky top-0 z-[var(--z-header,500)] border-b-2 border-ink bg-paper\">\n            <div className=\"relative mx-auto flex h-16 max-w-6xl items-center gap-5 px-4 sm:px-6\">\n              <Link\n                href=\"/\"\n                className=\"vp-display group relative flex items-center text-[22px] font-bold leading-none tracking-[-0.04em] after:absolute after:-inset-2 after:content-[''] sm:text-[26px]\"\n              >\n                Vrai\n                <span className=\"ml-1 rotate-[-2deg] rounded-md bg-ink px-[7px] pb-1 pt-[2px] text-lime transition-transform duration-200 group-hover:rotate-0\">\n                  Prix\n                </span>\n              </Link>\n              {/* badge Groupe KA — desktop seulement (l'espace manque sous xl ;\n                  le menu mobile porte sa propre entrée) */}\n              <span className=\"hidden xl:inline-flex\">\n                <GroupeKaBadge />\n              </span>\n              <div className=\"ml-auto flex items-center gap-2\">\n                <HeaderNav />\n                <AccountButton />\n                <LangToggle />\n              </div>\n            </div>\n          </header>\n          <div className=\"ticker\" aria-hidden=\"true\">\n            <div className=\"ticker-track\">\n              {[...TICKER, ...TICKER].map((s, i) => (\n                <span key={i}>{s}</span>\n              ))}\n            </div>\n          </div>\n          <main className=\"mx-auto max-w-6xl px-4 pb-16 sm:px-6\">{children}</main>\n          {/* rangée locale (papier) : marque + liens de navigation du site,\n              puis le bloc encre final = KaFooter (commun Groupe KA) */}\n          <div className=\"mx-auto max-w-6xl px-4 sm:px-6\">\n            <div className=\"border-t-2 border-ink pt-7 text-[13px] text-ink-2\">\n              <p className=\"vp-display mb-3 text-[26px] font-bold tracking-[-0.04em] text-ink\">\n                Vrai\n                <span className=\"ml-1 inline-block rotate-[-2deg] rounded-md bg-ink px-[6px] pb-[3px] pt-[1px] text-lime\">\n                  Prix\n                </span>\n              </p>\n              <p className=\"max-w-2xl\">\n                Estimations bâties sur les rôles d&apos;évaluation foncière du\n                Québec (MAMH, données ouvertes) et 745 119 transactions réelles.\n                Modèle hédonique + comparables ajustés, calcul montré en entier.\n                Estimation statistique à titre indicatif.\n              </p>\n              <FooterNav />\n            </div>\n          </div>\n          <KaFooter\n            siteId=\"vrai-prix\"\n            localLegal={[\n              { label: \"Conditions\", href: \"/conditions\" },\n              { label: \"Confidentialité\", href: \"/confidentialite\" },\n              { label: \"Méthodologie\", href: \"/methodologie\" },\n            ]}\n          />\n          <CookieConsent />\n          <script\n            dangerouslySetInnerHTML={{\n              __html: 'window.KA_AGENT={site:\"vrai-prix\"}',\n            }}\n          />\n          <script src=\"/ka-agent.js\" defer />\n        </LangProvider>\n      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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UQFzQCbpaVbMQYfkm3DEhA","type":"tool_result","content":"1\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n2\t/**\n3\t * MetricViz — visualisations signature générées par les données du registre.\n4\t * Chaque propriété produit ses propres dessins : façade SVG (étages, logements,\n5\t * genre de construction), schéma de terrain à l'échelle (frontage × profondeur),\n6\t * composition de valeur terrain/bâtiment, jauge de confiance, sparkline 2021-2026,\n7\t * compteurs animés. SVG pur, palette encre/lime, aucun aléatoire : mêmes données,\n8\t * même dessin.\n9\t */\n10\t\"use client\";\n11\timport { useEffect, useState } from \"react\";\n12\t\n13\t/** Nombre localisé (« 1 206,6 » en FR, « 1,206.6 » en EN). */\n14\texport function locNum(\n15\t  v: number | null | undefined,\n16\t  lang: string,\n17\t  opts?: { unit?: string; maxFrac?: number }\n18\t): string {\n19\t  if (v == null) return \"—\";\n20\t  const s = v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n21\t    maximumFractionDigits: opts?.maxFrac ?? 1,\n22\t  });\n23\t  return opts?.unit ? `${s} ${opts.unit}` : s;\n24\t}\n25\t\n26\t/* ---------------------------- compteur animé ---------------------------- */\n27\t/** Rendu serveur = valeur finale (jamais « 0 $ » à l'écran) ; anime 0 → valeur au montage. */\n28\texport function CountUp({\n29\t  value,\n30\t  format,\n31\t  duration = 900,\n32\t}: {\n33\t  value: number;\n34\t  format: (v: number) => string;\n35\t  duration?: number;\n36\t}) {\n37\t  const [display, setDisplay] = useState(value);\n38\t\n39\t  useEffect(() => {\n40\t    if (window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches) return;\n41\t    let raf = 0;\n42\t    const t0 = performance.now();\n43\t    const tick = (t: number) => {\n44\t      const p = Math.min(1, (t - t0) / duration);\n45\t      const eased = 1 - Math.pow(1 - p, 3);\n46\t      setDisplay(value * eased);\n47\t      if (p < 1) raf = requestAnimationFrame(tick);\n48\t    };\n49\t    raf = requestAnimationFrame(tick);\n50\t    return () => cancelAnimationFrame(raf);\n51\t  }, [value, duration]);\n52\t\n53\t  return <span>{format(display)}</span>;\n54\t}\n55\t\n56\t/* --------------------------- jauge de confiance --------------------------- */\n57\texport function ConfidenceDial({\n58\t  pct,\n59\t  level,\n60\t  label,\n61\t}: {\n62\t  pct: number;\n63\t  level: \"A\" | \"B\" | \"C\" | \"D\";\n64\t  label: string;\n65\t}) {\n66\t  const [mounted, setMounted] = useState(false);\n67\t  useEffect(() => {\n68\t    const id = setTimeout(() => setMounted(true), 60);\n69\t    return () => clearTimeout(id);\n70\t  }, []);\n71\t  const R = 52;\n72\t  const CIRC = Math.PI * R; // demi-cercle\n73\t  const frac = mounted ? Math.max(0.02, Math.min(1, pct / 100)) : 0.02;\n74\t  const color =\n75\t    level === \"A\" ? \"var(--lime)\" : level === \"B\" ? \"var(--green)\" : level === \"C\" ? \"var(--amber)\" : \"var(--danger)\";\n76\t  return (\n77\t    <div className=\"flex flex-col items-center\" aria-label={`${label} : ${level} ${pct} %`}>\n78\t      <svg viewBox=\"0 0 128 78\" className=\"w-[128px]\">\n79\t        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"var(--surface-2)\" strokeWidth=\"12\" strokeLinecap=\"round\" />\n80\t        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"rgba(20,24,20,0.85)\" strokeWidth=\"13.5\" strokeLinecap=\"round\" opacity=\"0.12\" />\n81\t        <path\n82\t          d=\"M 12 66 A 52 52 0 0 1 116 66\"\n83\t          fill=\"none\"\n84\t          stroke={color}\n85\t          strokeWidth=\"12\"\n86\t          strokeLinecap=\"round\"\n87\t          strokeDasharray={`${CIRC * frac} ${CIRC}`}\n88\t          style={{ transition: \"stroke-dasharray 1s cubic-bezier(.2,.8,.2,1)\" }}\n89\t        />\n90\t        {/* graduations */}\n91\t        {[0, 0.25, 0.5, 0.75, 1].map((g) => {\n92\t          const a = Math.PI * (1 - g);\n93\t          const x1 = 64 + Math.cos(a) * 40;\n94\t          const y1 = 66 - Math.sin(a) * 40;\n95\t          const x2 = 64 + Math.cos(a) * 45;\n96\t          const y2 = 66 - Math.sin(a) * 45;\n97\t          return <line key={g} x1={x1} y1={y1} x2={x2} y2={y2} stroke=\"var(--ink)\" strokeWidth=\"1.4\" opacity=\"0.5\" />;\n98\t        })}\n99\t        <text x=\"64\" y=\"52\" textAnchor=\"middle\" fontSize=\"26\" fontWeight=\"700\" fill=\"var(--ink)\" fontFamily=\"var(--font-space-grotesk)\">\n100\t          {level}\n101\t        </text>\n102\t        <text x=\"64\" y=\"70\" textAnchor=\"middle\" fontSize=\"10.5\" fontWeight=\"700\" fill=\"var(--ink-2)\" fontFamily=\"var(--font-jetbrains)\">\n103\t          {pct} %\n104\t        </text>\n105\t      </svg>\n106\t      <p className=\"klabel mt-1 text-center\">{label}</p>\n107\t    </div>\n108\t  );\n109\t}\n110\t\n111\t/* --------------------------- façade de bâtiment --------------------------- */\n112\t/** Terrain sans bâtiment : borne d'arpenteur et conifères, pas de fausse maison. */\n113\tfunction VacantGlyph({ lang }: { lang: string }) {\n114\t  const tree = (x: number, sc: number) => (\n115\t    <g transform={`translate(${x}, 128) scale(${sc})`}>\n116\t      <path d=\"M0,-46 L13,-14 L5,-14 L16,8 L-16,8 L-5,-14 L-13,-14 Z\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n117\t      <rect x=\"-2.5\" y=\"8\" width=\"5\" height=\"9\" fill=\"var(--ink)\" />\n118\t    </g>\n119\t  );\n120\t  return (\n121\t    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain sans bâtiment\" : \"Vacant land\"}>\n122\t      {tree(52, 1)}\n123\t      {tree(96, 0.72)}\n124\t      {tree(134, 0.9)}\n125\t      {/* borne d'arpenteur */}\n126\t      <g transform=\"translate(24, 128)\">\n127\t        <rect x=\"-2.5\" y=\"-26\" width=\"5\" height=\"26\" fill=\"var(--ink)\" />\n128\t        <rect x=\"-8\" y=\"-34\" width=\"16\" height=\"10\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n129\t      </g>\n130\t      <line x1=\"8\" y1=\"145\" x2=\"182\" y2=\"145\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n131\t      <text x=\"95\" y=\"120\" textAnchor=\"middle\" fontSize=\"9\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n132\t        {lang === \"fr\" ? \"SANS BÂTIMENT\" : \"NO BUILDING\"}\n133\t      </text>\n134\t    </svg>\n135\t  );\n136\t}\n137\t\n138\t/** Façade dessinée à partir du registre : étages, logements, genre de construction.\n139\t *  Sans bâtiment au registre (ni aire, ni année, ni étages) → borne d'arpenteur. */\n140\texport function BuildingGlyph({\n141\t  floors,\n142\t  dwellings,\n143\t  genre,\n144\t  yearBuilt,\n145\t  floorAreaM2,\n146\t  lang = \"fr\",\n147\t}: {\n148\t  floors: number | null;\n149\t  dwellings: number | null;\n150\t  genre: string | null;\n151\t  yearBuilt: number | null;\n152\t  floorAreaM2?: number | null;\n153\t  lang?: string;\n154\t}) {\n155\t  if (!floorAreaM2 && !yearBuilt && !floors && !dwellings) {\n156\t    return <VacantGlyph lang={lang} />;\n157\t  }\n158\t  const nF = Math.max(1, Math.min(6, Math.round(floors ?? 1)));\n159\t  const nD = Math.max(1, Math.min(24, Math.round(dwellings ?? 1)));\n160\t  const winPerFloor = Math.max(1, Math.min(4, Math.ceil(nD / nF)));\n161\t  const W = 190;\n162\t  const bw = 64 + winPerFloor * 18;\n163\t  const bx = (W - bw) / 2;\n164\t  const fh = 24;\n165\t  const groundY = 128;\n166\t  const bodyTop = groundY - nF * fh;\n167\t  const mansard = (genre ?? \"\").toLowerCase().includes(\"mansard\");\n168\t  const flat = (genre ?? \"\").toLowerCase().includes(\"plain-pied\") && nF === 1;\n169\t\n170\t  const windows: React.ReactNode[] = [];\n171\t  for (let f = 0; f < nF; f++) {\n172\t    for (let wi = 0; wi < winPerFloor; wi++) {\n173\t      const idx = f * winPerFloor + wi;\n174\t      windows.push(\n175\t        <rect\n176\t          key={`${f}-${wi}`}\n177\t          x={bx + 14 + wi * ((bw - 28) / winPerFloor) + ((bw - 28) / winPerFloor - 12) / 2}\n178\t          y={bodyTop + f * fh + 6}\n179\t          width=\"12\"\n180\t          height=\"11\"\n181\t          fill={idx < nD ? \"var(--lime)\" : \"var(--surface-2)\"}\n182\t          stroke=\"var(--ink)\"\n183\t          strokeWidth=\"1.2\"\n184\t          className=\"vv-win\"\n185\t          style={{ animationDelay: `${idx * 60}ms` }}\n186\t        />\n187\t      );\n188\t    }\n189\t  }\n190\t\n191\t  return (\n192\t    <svg viewBox={`0 0 ${W} 150`} className=\"w-full max-w-[210px]\" aria-label=\"Silhouette du bâtiment selon le registre\">\n193\t      {/* soleil-repère */}\n194\t      <circle cx={W - 22} cy=\"22\" r=\"9\" fill=\"none\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n195\t      <circle cx={W - 22} cy=\"22\" r=\"3.5\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1\" />\n196\t      {/* ombre décalée signature */}\n197\t      <rect x={bx + 5} y={bodyTop + 5} width={bw} height={nF * fh} fill=\"rgba(20,24,20,0.12)\" />\n198\t      {/* corps */}\n199\t      <rect x={bx} y={bodyTop} width={bw} height={nF * fh} fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n200\t      {/* séparations d'étages */}\n201\t      {Array.from({ length: nF - 1 }, (_, i) => (\n202\t        <line key={i} x1={bx} y1={bodyTop + (i + 1) * fh} x2={bx + bw} y2={bodyTop + (i + 1) * fh} stroke=\"var(--ink)\" strokeWidth=\"1\" strokeDasharray=\"3 3\" opacity=\"0.4\" />\n203\t      ))}\n204\t      {/* toit */}\n205\t      {flat ? (\n206\t        <rect x={bx - 6} y={bodyTop - 8} width={bw + 12} height=\"8\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n207\t      ) : mansard ? (\n208\t        <path d={`M ${bx - 6} ${bodyTop} L ${bx + 10} ${bodyTop - 20} L ${bx + bw - 10} ${bodyTop - 20} L ${bx + bw + 6} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n209\t      ) : (\n210\t        <path d={`M ${bx - 8} ${bodyTop} L ${bx + bw / 2} ${bodyTop - 24} L ${bx + bw + 8} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n211\t      )}\n212\t      {/* cheminée */}\n213\t      <rect x={bx + bw - 22} y={bodyTop - (flat ? 20 : 30)} width=\"9\" height={flat ? 14 : 18} fill=\"var(--ink)\" />\n214\t      {windows}\n215\t      {/* porte */}\n216\t      <rect x={bx + bw / 2 - 7} y={groundY - 17} width=\"14\" height=\"17\" fill=\"var(--ink)\" />\n217\t      <circle cx={bx + bw / 2 + 3.5} cy={groundY - 8} r=\"1.4\" fill=\"var(--lime)\" />\n218\t      {/* sol */}\n219\t      <line x1=\"8\" y1={groundY} x2={W - 8} y2={groundY} stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n220\t      <line x1=\"8\" y1={groundY + 4} x2={W - 8} y2={groundY + 4} stroke=\"var(--ink)\" strokeWidth=\"1\" opacity=\"0.3\" />\n221\t      {/* millésime gravé */}\n222\t      {yearBuilt && (\n223\t        <text x={W / 2} y=\"146\" textAnchor=\"middle\" fontSize=\"10\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n224\t          ANNO {yearBuilt}\n225\t        </text>\n226\t      )}\n227\t    </svg>\n228\t  );\n229\t}\n230\t\n231\t/* ----------------------------- schéma du terrain ----------------------------- */\n232\t/** Terrain à l'échelle réelle : frontage × profondeur déduite de la superficie. */\n233\texport function LotDiagram({\n234\t  areaM2,\n235\t  frontageM,\n236\t  footprintM2,\n237\t  lang,\n238\t  isCondo,\n239\t}: {\n240\t  areaM2: number | null;\n241\t  frontageM: number | null;\n242\t  footprintM2: number | null;\n243\t  lang: string;\n244\t  isCondo?: boolean;\n245\t}) {\n246\t  if (!areaM2 || areaM2 <= 0) {\n247\t    return (\n248\t      <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain non applicable\" : \"Lot not applicable\"}>\n249\t        <rect x=\"30\" y=\"30\" width=\"130\" height=\"86\" fill=\"var(--surface-2)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" strokeDasharray=\"5 5\" rx=\"6\" />\n250\t        <text x=\"95\" y=\"66\" textAnchor=\"middle\" fontSize=\"20\" fill=\"var(--ink-3)\">⌂</text>\n251\t        <text x=\"95\" y=\"90\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"1\">\n252\t          {isCondo\n253\t            ? lang === \"fr\" ? \"QUOTE-PART DE COPROPRIÉTÉ\" : \"CONDOMINIUM SHARE\"\n254\t            : lang === \"fr\" ? \"TERRAIN NON INSCRIT AU RÔLE\" : \"LOT NOT ON ROLL\"}\n255\t        </text>\n256\t        <text x=\"95\" y=\"104\" textAnchor=\"middle\" fontSize=\"7\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n257\t          {isCondo\n258\t            ? lang === \"fr\" ? \"terrain commun indivis\" : \"undivided common land\"\n259\t            : \"—\"}\n260\t        </text>\n261\t      </svg>\n262\t    );\n263\t  }\n264\t  const front = frontageM && frontageM > 0 ? frontageM : Math.sqrt(areaM2);\n265\t  const depth = areaM2 / front;\n266\t  const ratio = Math.max(0.25, Math.min(4, depth / front));\n267\t  const maxW = 150;\n268\t  const maxH = 108;\n269\t  let w = maxW;\n270\t  let h = w * ratio;\n271\t  if (h > maxH) {\n272\t    h = maxH;\n273\t    w = h / ratio;\n274\t  }\n275\t  const x = (190 - w) / 2;\n276\t  const y = 118 - h;\n277\t  // empreinte du bâtiment, à l'échelle de la superficie\n278\t  const fpFrac = footprintM2 ? Math.min(0.8, footprintM2 / areaM2) : 0;\n279\t  const fw = w * Math.sqrt(fpFrac) * 0.9;\n280\t  const fh = h * Math.sqrt(fpFrac) * 0.9;\n281\t  const fmtM = (v: number) => `${v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { maximumFractionDigits: 1 })} m`;\n282\t\n283\t  return (\n284\t    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label=\"Schéma du terrain à l'échelle\">\n285\t      <defs>\n286\t        <pattern id=\"hatch\" width=\"7\" height=\"7\" patternTransform=\"rotate(45)\" patternUnits=\"userSpaceOnUse\">\n287\t          <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"7\" stroke=\"var(--green)\" strokeWidth=\"1\" opacity=\"0.35\" />\n288\t        </pattern>\n289\t      </defs>\n290\t      {/* ombre + parcelle */}\n291\t      <rect x={x + 4} y={y + 4} width={w} height={h} fill=\"rgba(20,24,20,0.12)\" />\n292\t      <rect x={x} y={y} width={w} height={h} fill=\"var(--lime-soft)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n293\t      <rect x={x} y={y} width={w} height={h} fill=\"url(#hatch)\" />\n294\t      {/* empreinte bâtiment */}\n295\t      {fpFrac > 0.005 && (\n296\t        <rect x={x + (w - fw) / 2} y={y + h - fh - h * 0.08} width={fw} height={fh} fill=\"var(--ink)\" rx=\"2\">\n297\t          <title>{lang === \"fr\" ? \"Empreinte approximative du bâtiment\" : \"Approximate building footprint\"}</title>\n298\t        </rect>\n299\t      )}\n300\t      {/* cotes : frontage */}\n301\t      <line x1={x} y1={y + h + 12} x2={x + w} y2={y + h + 12} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n302\t      <line x1={x} y1={y + h + 8} x2={x} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n303\t      <line x1={x + w} y1={y + h + 8} x2={x + w} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n304\t      <text x={x + w / 2} y={y + h + 24} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n305\t        {frontageM ? fmtM(front) : `≈ ${fmtM(front)}`}\n306\t      </text>\n307\t      {/* cotes : profondeur */}\n308\t      <line x1={x - 10} y1={y} x2={x - 10} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n309\t      <line x1={x - 14} y1={y} x2={x - 6} y2={y} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n310\t      <line x1={x - 14} y1={y + h} x2={x - 6} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n311\t      <text x={x - 16} y={y + h / 2} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\" transform={`rotate(-90 ${x - 16} ${y + h / 2})`}>\n312\t        ≈ {fmtM(depth)}\n313\t      </text>\n314\t      {/* rue */}\n315\t      <line x1=\"8\" y1=\"136\" x2=\"182\" y2=\"136\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n316\t      <text x=\"95\" y=\"147\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\" letterSpacing=\"3\">\n317\t        {lang === \"fr\" ? \"RUE\" : \"STREET\"}\n318\t      </text>\n319\t    </svg>\n320\t  );\n321\t}\n322\t\n323\t/* ------------------------- composition de la valeur ------------------------- */\n324\texport function ValueSplit({\n325\t  land,\n326\t  building,\n327\t  previous,\n328\t  total,\n329\t  lang,\n330\t  labels,\n331\t}: {\n332\t  land: number | null;\n333\t  building: number | null;\n334\t  previous: number | null;\n335\t  total: number | null;\n336\t  lang: string;\n337\t  labels: { land: string; building: string; previous: string };\n338\t}) {\n339\t  const [on, setOn] = useState(false);\n340\t  useEffect(() => {\n341\t    const id = setTimeout(() => setOn(true), 80);\n342\t    return () => clearTimeout(id);\n343\t  }, []);\n344\t  const l = land ?? 0;\n345\t  const b = building ?? 0;\n346\t  const sum = l + b;\n347\t  if (sum <= 0) return null;\n348\t  const lPct = (l / sum) * 100;\n349\t  const fmt = (v: number) =>\n350\t    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { style: \"currency\", currency: \"CAD\", maximumFractionDigits: 0 }).format(v);\n351\t  const delta = previous && previous > 0 && total ? ((total / previous - 1) * 100) : null;\n352\t  return (\n353\t    <div>\n354\t      <div className=\"flex h-9 w-full overflow-hidden rounded-md border-[1.5px] border-ink\">\n355\t        {lPct >= 1 && (\n356\t          <div\n357\t            className=\"flex items-center justify-center overflow-hidden bg-green transition-[width] duration-1000 ease-out\"\n358\t            style={{ width: on ? `${lPct}%` : \"2%\" }}\n359\t          >\n360\t            {lPct >= 14 && (\n361\t              <span className=\"vp-mono px-1 text-[10px] font-bold text-paper\">{Math.round(lPct)} %</span>\n362\t            )}\n363\t          </div>\n364\t        )}\n365\t        {lPct <= 99 && (\n366\t          <div className=\"flex flex-1 items-center justify-center overflow-hidden bg-ink\">\n367\t            {100 - lPct >= 14 && (\n368\t              <span className=\"vp-mono px-1 text-[10px] font-bold text-lime\">{Math.round(100 - lPct)} %</span>\n369\t            )}\n370\t          </div>\n371\t        )}\n372\t      </div>\n373\t      <div className=\"vp-mono mt-2 flex flex-wrap items-center gap-x-4 gap-y-1 text-[10.5px] uppercase tracking-[0.05em]\">\n374\t        <span className=\"flex items-center gap-1.5 text-ink-2\">\n375\t          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-green\" />\n376\t          {labels.land} {fmt(l)}\n377\t        </span>\n378\t        <span className=\"flex items-center gap-1.5 text-ink-2\">\n379\t          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-ink\" />\n380\t          {labels.building} {fmt(b)}\n381\t        </span>\n382\t      </div>\n383\t      {delta != null && (\n384\t        <p className=\"vp-mono mt-2.5 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n385\t          {labels.previous} {fmt(previous!)}{\" \"}\n386\t          <span className={`ml-1 rounded-[4px] border border-ink px-1.5 py-0.5 font-bold ${delta >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n387\t            {delta >= 0 ? \"+\" : \"\"}\n388\t            {delta.toFixed(1)} %\n389\t          </span>\n390\t        </p>\n391\t      )}\n392\t    </div>\n393\t  );\n394\t}\n395\t\n396\t/* -------------------------------- sparkline -------------------------------- */\n397\texport function Sparkline({\n398\t  history,\n399\t  height = 44,\n400\t}: {\n401\t  history: { year: number; value: number | null }[];\n402\t  height?: number;\n403\t}) {\n404\t  const pts = history.filter((h) => h.value != null) as { year: number; value: number }[];\n405\t  if (pts.length < 2) return null;\n406\t  const W = 132;\n407\t  const min = Math.min(...pts.map((p) => p.value));\n408\t  const max = Math.max(...pts.map((p) => p.value));\n409\t  const span = max - min || 1;\n410\t  const xy = pts.map((p, i) => [\n411\t    6 + (i / (pts.length - 1)) * (W - 12),\n412\t    height - 8 - ((p.value - min) / span) * (height - 18),\n413\t  ]);\n414\t  const line = xy.map(([x, y]) => `${x},${y}`).join(\" \");\n415\t  const area = `${xy[0][0]},${height - 4} ${line} ${xy[xy.length - 1][0]},${height - 4}`;\n416\t  const growth = ((pts[pts.length - 1].value / pts[0].value - 1) * 100);\n417\t  const [x2, y2] = xy[xy.length - 1];\n418\t  return (\n419\t    <div className=\"flex items-end gap-2\">\n420\t      <svg viewBox={`0 0 ${W} ${height}`} width={W} height={height} aria-label={`Évolution ${pts[0].year}-${pts[pts.length - 1].year}`}>\n421\t        <polygon points={area} fill=\"var(--lime-soft)\" />\n422\t        <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n423\t        {xy.slice(0, -1).map(([x, y], i) => (\n424\t          <circle key={i} cx={x} cy={y} r=\"2\" fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"1.1\" />\n425\t        ))}\n426\t        <circle cx={x2} cy={y2} r=\"3.6\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n427\t      </svg>\n428\t      <span className={`vp-mono mb-0.5 rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${growth >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n429\t        {growth >= 0 ? \"+\" : \"\"}\n430\t        {growth.toFixed(0)} %\n431\t      </span>\n432\t    </div>\n433\t  );\n434\t}\n435\t\n436\t/* ------------------------------ frise temporelle ------------------------------ */\n437\texport function EraLine({ year, lang }: { year: number | null; lang: string }) {\n438\t  if (!year || year < 1750) return null;\n439\t  const min = Math.min(1900, Math.floor((year - 10) / 50) * 50);\n440\t  const max = 2026;\n441\t  const frac = Math.max(0, Math.min(1, (year - min) / (max - min)));\n442\t  const W = 190;\n443\t  const x = 12 + frac * (W - 24);\n444\t  const age = 2026 - year;\n445\t  return (\n446\t    <svg viewBox={`0 0 ${W} 40`} className=\"w-full max-w-[210px]\" aria-label={`Construction ${year}`}>\n447\t      <line x1=\"12\" y1=\"24\" x2={W - 12} y2=\"24\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n448\t      {[...new Set([min, Math.round((min + max) / 2 / 50) * 50, 2000])].map((yr) => {\n449\t        const gx = 12 + ((yr - min) / (max - min)) * (W - 24);\n450\t        return (\n451\t          <g key={yr}>\n452\t            <line x1={gx} y1=\"20\" x2={gx} y2=\"28\" stroke=\"var(--ink)\" strokeWidth=\"1.2\" opacity=\"0.5\" />\n453\t            <text x={gx} y=\"38\" textAnchor=\"middle\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n454\t              {yr}\n455\t            </text>\n456\t          </g>\n457\t        );\n458\t      })}\n459\t      <g transform={`translate(${x}, 24)`}>\n460\t        <rect x=\"-7\" y=\"-7\" width=\"14\" height=\"14\" transform=\"rotate(45)\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n461\t        <text y=\"-13\" textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n462\t          {year}\n463\t        </text>\n464\t      </g>\n465\t      <text x={W - 12} y=\"12\" textAnchor=\"end\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n466\t        {age} {lang === \"fr\" ? \"ANS\" : \"YRS\"}\n467\t      </text>\n468\t    </svg>\n469\t  );\n470\t}\n471\t"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"4decde66-87bf-4c0c-903d-f204ead50cae","timestamp":"2026-08-23T03:01:12.258Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n/**\n * MetricViz — visualisations signature générées par les données du registre.\n * Chaque propriété produit ses propres dessins : façade SVG (étages, logements,\n * genre de construction), schéma de terrain à l'échelle (frontage × profondeur),\n * composition de valeur terrain/bâtiment, jauge de confiance, sparkline 2021-2026,\n * compteurs animés. SVG pur, palette encre/lime, aucun aléatoire : mêmes données,\n * même dessin.\n */\n\"use client\";\nimport { useEffect, useState } from \"react\";\n\n/** Nombre localisé (« 1 206,6 » en FR, « 1,206.6 » en EN). */\nexport function locNum(\n  v: number | null | undefined,\n  lang: string,\n  opts?: { unit?: string; maxFrac?: number }\n): string {\n  if (v == null) return \"—\";\n  const s = v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n    maximumFractionDigits: opts?.maxFrac ?? 1,\n  });\n  return opts?.unit ? `${s} ${opts.unit}` : s;\n}\n\n/* ---------------------------- compteur animé ---------------------------- */\n/** Rendu serveur = valeur finale (jamais « 0 $ » à l'écran) ; anime 0 → valeur au montage. */\nexport function CountUp({\n  value,\n  format,\n  duration = 900,\n}: {\n  value: number;\n  format: (v: number) => string;\n  duration?: number;\n}) {\n  const [display, setDisplay] = useState(value);\n\n  useEffect(() => {\n    if (window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches) return;\n    let raf = 0;\n    const t0 = performance.now();\n    const tick = (t: number) => {\n      const p = Math.min(1, (t - t0) / duration);\n      const eased = 1 - Math.pow(1 - p, 3);\n      setDisplay(value * eased);\n      if (p < 1) raf = requestAnimationFrame(tick);\n    };\n    raf = requestAnimationFrame(tick);\n    return () => cancelAnimationFrame(raf);\n  }, [value, duration]);\n\n  return <span>{format(display)}</span>;\n}\n\n/* --------------------------- jauge de confiance --------------------------- */\nexport function ConfidenceDial({\n  pct,\n  level,\n  label,\n}: {\n  pct: number;\n  level: \"A\" | \"B\" | \"C\" | \"D\";\n  label: string;\n}) {\n  const [mounted, setMounted] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setMounted(true), 60);\n    return () => clearTimeout(id);\n  }, []);\n  const R = 52;\n  const CIRC = Math.PI * R; // demi-cercle\n  const frac = mounted ? Math.max(0.02, Math.min(1, pct / 100)) : 0.02;\n  const color =\n    level === \"A\" ? \"var(--lime)\" : level === \"B\" ? \"var(--green)\" : level === \"C\" ? \"var(--amber)\" : \"var(--danger)\";\n  return (\n    <div className=\"flex flex-col items-center\" aria-label={`${label} : ${level} ${pct} %`}>\n      <svg viewBox=\"0 0 128 78\" className=\"w-[128px]\">\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"var(--surface-2)\" strokeWidth=\"12\" strokeLinecap=\"round\" />\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"rgba(20,24,20,0.85)\" strokeWidth=\"13.5\" strokeLinecap=\"round\" opacity=\"0.12\" />\n        <path\n          d=\"M 12 66 A 52 52 0 0 1 116 66\"\n          fill=\"none\"\n          stroke={color}\n          strokeWidth=\"12\"\n          strokeLinecap=\"round\"\n          strokeDasharray={`${CIRC * frac} ${CIRC}`}\n          style={{ transition: \"stroke-dasharray 1s cubic-bezier(.2,.8,.2,1)\" }}\n        />\n        {/* graduations */}\n        {[0, 0.25, 0.5, 0.75, 1].map((g) => {\n          const a = Math.PI * (1 - g);\n          const x1 = 64 + Math.cos(a) * 40;\n          const y1 = 66 - Math.sin(a) * 40;\n          const x2 = 64 + Math.cos(a) * 45;\n          const y2 = 66 - Math.sin(a) * 45;\n          return <line key={g} x1={x1} y1={y1} x2={x2} y2={y2} stroke=\"var(--ink)\" strokeWidth=\"1.4\" opacity=\"0.5\" />;\n        })}\n        <text x=\"64\" y=\"52\" textAnchor=\"middle\" fontSize=\"26\" fontWeight=\"700\" fill=\"var(--ink)\" fontFamily=\"var(--font-space-grotesk)\">\n          {level}\n        </text>\n        <text x=\"64\" y=\"70\" textAnchor=\"middle\" fontSize=\"10.5\" fontWeight=\"700\" fill=\"var(--ink-2)\" fontFamily=\"var(--font-jetbrains)\">\n          {pct} %\n        </text>\n      </svg>\n      <p className=\"klabel mt-1 text-center\">{label}</p>\n    </div>\n  );\n}\n\n/* --------------------------- façade de bâtiment --------------------------- */\n/** Terrain sans bâtiment : borne d'arpenteur et conifères, pas de fausse maison. */\nfunction VacantGlyph({ lang }: { lang: string }) {\n  const tree = (x: number, sc: number) => (\n    <g transform={`translate(${x}, 128) scale(${sc})`}>\n      <path d=\"M0,-46 L13,-14 L5,-14 L16,8 L-16,8 L-5,-14 L-13,-14 Z\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      <rect x=\"-2.5\" y=\"8\" width=\"5\" height=\"9\" fill=\"var(--ink)\" />\n    </g>\n  );\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain sans bâtiment\" : \"Vacant land\"}>\n      {tree(52, 1)}\n      {tree(96, 0.72)}\n      {tree(134, 0.9)}\n      {/* borne d'arpenteur */}\n      <g transform=\"translate(24, 128)\">\n        <rect x=\"-2.5\" y=\"-26\" width=\"5\" height=\"26\" fill=\"var(--ink)\" />\n        <rect x=\"-8\" y=\"-34\" width=\"16\" height=\"10\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n      </g>\n      <line x1=\"8\" y1=\"145\" x2=\"182\" y2=\"145\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"120\" textAnchor=\"middle\" fontSize=\"9\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n        {lang === \"fr\" ? \"SANS BÂTIMENT\" : \"NO BUILDING\"}\n      </text>\n    </svg>\n  );\n}\n\n/** Façade dessinée à partir du registre : étages, logements, genre de construction.\n *  Sans bâtiment au registre (ni aire, ni année, ni étages) → borne d'arpenteur. */\nexport function BuildingGlyph({\n  floors,\n  dwellings,\n  genre,\n  yearBuilt,\n  floorAreaM2,\n  lang = \"fr\",\n}: {\n  floors: number | null;\n  dwellings: number | null;\n  genre: string | null;\n  yearBuilt: number | null;\n  floorAreaM2?: number | null;\n  lang?: string;\n}) {\n  if (!floorAreaM2 && !yearBuilt && !floors && !dwellings) {\n    return <VacantGlyph lang={lang} />;\n  }\n  const nF = Math.max(1, Math.min(6, Math.round(floors ?? 1)));\n  const nD = Math.max(1, Math.min(24, Math.round(dwellings ?? 1)));\n  const winPerFloor = Math.max(1, Math.min(4, Math.ceil(nD / nF)));\n  const W = 190;\n  const bw = 64 + winPerFloor * 18;\n  const bx = (W - bw) / 2;\n  const fh = 24;\n  const groundY = 128;\n  const bodyTop = groundY - nF * fh;\n  const mansard = (genre ?? \"\").toLowerCase().includes(\"mansard\");\n  const flat = (genre ?? \"\").toLowerCase().includes(\"plain-pied\") && nF === 1;\n\n  const windows: React.ReactNode[] = [];\n  for (let f = 0; f < nF; f++) {\n    for (let wi = 0; wi < winPerFloor; wi++) {\n      const idx = f * winPerFloor + wi;\n      windows.push(\n        <rect\n          key={`${f}-${wi}`}\n          x={bx + 14 + wi * ((bw - 28) / winPerFloor) + ((bw - 28) / winPerFloor - 12) / 2}\n          y={bodyTop + f * fh + 6}\n          width=\"12\"\n          height=\"11\"\n          fill={idx < nD ? \"var(--lime)\" : \"var(--surface-2)\"}\n          stroke=\"var(--ink)\"\n          strokeWidth=\"1.2\"\n          className=\"vv-win\"\n          style={{ animationDelay: `${idx * 60}ms` }}\n        />\n      );\n    }\n  }\n\n  return (\n    <svg viewBox={`0 0 ${W} 150`} className=\"w-full max-w-[210px]\" aria-label=\"Silhouette du bâtiment selon le registre\">\n      {/* soleil-repère */}\n      <circle cx={W - 22} cy=\"22\" r=\"9\" fill=\"none\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      <circle cx={W - 22} cy=\"22\" r=\"3.5\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1\" />\n      {/* ombre décalée signature */}\n      <rect x={bx + 5} y={bodyTop + 5} width={bw} height={nF * fh} fill=\"rgba(20,24,20,0.12)\" />\n      {/* corps */}\n      <rect x={bx} y={bodyTop} width={bw} height={nF * fh} fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      {/* séparations d'étages */}\n      {Array.from({ length: nF - 1 }, (_, i) => (\n        <line key={i} x1={bx} y1={bodyTop + (i + 1) * fh} x2={bx + bw} y2={bodyTop + (i + 1) * fh} stroke=\"var(--ink)\" strokeWidth=\"1\" strokeDasharray=\"3 3\" opacity=\"0.4\" />\n      ))}\n      {/* toit */}\n      {flat ? (\n        <rect x={bx - 6} y={bodyTop - 8} width={bw + 12} height=\"8\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : mansard ? (\n        <path d={`M ${bx - 6} ${bodyTop} L ${bx + 10} ${bodyTop - 20} L ${bx + bw - 10} ${bodyTop - 20} L ${bx + bw + 6} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : (\n        <path d={`M ${bx - 8} ${bodyTop} L ${bx + bw / 2} ${bodyTop - 24} L ${bx + bw + 8} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      )}\n      {/* cheminée */}\n      <rect x={bx + bw - 22} y={bodyTop - (flat ? 20 : 30)} width=\"9\" height={flat ? 14 : 18} fill=\"var(--ink)\" />\n      {windows}\n      {/* porte */}\n      <rect x={bx + bw / 2 - 7} y={groundY - 17} width=\"14\" height=\"17\" fill=\"var(--ink)\" />\n      <circle cx={bx + bw / 2 + 3.5} cy={groundY - 8} r=\"1.4\" fill=\"var(--lime)\" />\n      {/* sol */}\n      <line x1=\"8\" y1={groundY} x2={W - 8} y2={groundY} stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <line x1=\"8\" y1={groundY + 4} x2={W - 8} y2={groundY + 4} stroke=\"var(--ink)\" strokeWidth=\"1\" opacity=\"0.3\" />\n      {/* millésime gravé */}\n      {yearBuilt && (\n        <text x={W / 2} y=\"146\" textAnchor=\"middle\" fontSize=\"10\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n          ANNO {yearBuilt}\n        </text>\n      )}\n    </svg>\n  );\n}\n\n/* ----------------------------- schéma du terrain ----------------------------- */\n/** Terrain à l'échelle réelle : frontage × profondeur déduite de la superficie. */\nexport function LotDiagram({\n  areaM2,\n  frontageM,\n  footprintM2,\n  lang,\n  isCondo,\n}: {\n  areaM2: number | null;\n  frontageM: number | null;\n  footprintM2: number | null;\n  lang: string;\n  isCondo?: boolean;\n}) {\n  if (!areaM2 || areaM2 <= 0) {\n    return (\n      <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain non applicable\" : \"Lot not applicable\"}>\n        <rect x=\"30\" y=\"30\" width=\"130\" height=\"86\" fill=\"var(--surface-2)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" strokeDasharray=\"5 5\" rx=\"6\" />\n        <text x=\"95\" y=\"66\" textAnchor=\"middle\" fontSize=\"20\" fill=\"var(--ink-3)\">⌂</text>\n        <text x=\"95\" y=\"90\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"1\">\n          {isCondo\n            ? lang === \"fr\" ? \"QUOTE-PART DE COPROPRIÉTÉ\" : \"CONDOMINIUM SHARE\"\n            : lang === \"fr\" ? \"TERRAIN NON INSCRIT AU RÔLE\" : \"LOT NOT ON ROLL\"}\n        </text>\n        <text x=\"95\" y=\"104\" textAnchor=\"middle\" fontSize=\"7\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n          {isCondo\n            ? lang === \"fr\" ? \"terrain commun indivis\" : \"undivided common land\"\n            : \"—\"}\n        </text>\n      </svg>\n    );\n  }\n  const front = frontageM && frontageM > 0 ? frontageM : Math.sqrt(areaM2);\n  const depth = areaM2 / front;\n  const ratio = Math.max(0.25, Math.min(4, depth / front));\n  const maxW = 150;\n  const maxH = 108;\n  let w = maxW;\n  let h = w * ratio;\n  if (h > maxH) {\n    h = maxH;\n    w = h / ratio;\n  }\n  const x = (190 - w) / 2;\n  const y = 118 - h;\n  // empreinte du bâtiment, à l'échelle de la superficie\n  const fpFrac = footprintM2 ? Math.min(0.8, footprintM2 / areaM2) : 0;\n  const fw = w * Math.sqrt(fpFrac) * 0.9;\n  const fh = h * Math.sqrt(fpFrac) * 0.9;\n  const fmtM = (v: number) => `${v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { maximumFractionDigits: 1 })} m`;\n\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label=\"Schéma du terrain à l'échelle\">\n      <defs>\n        <pattern id=\"hatch\" width=\"7\" height=\"7\" patternTransform=\"rotate(45)\" patternUnits=\"userSpaceOnUse\">\n          <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"7\" stroke=\"var(--green)\" strokeWidth=\"1\" opacity=\"0.35\" />\n        </pattern>\n      </defs>\n      {/* ombre + parcelle */}\n      <rect x={x + 4} y={y + 4} width={w} height={h} fill=\"rgba(20,24,20,0.12)\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"var(--lime-soft)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"url(#hatch)\" />\n      {/* empreinte bâtiment */}\n      {fpFrac > 0.005 && (\n        <rect x={x + (w - fw) / 2} y={y + h - fh - h * 0.08} width={fw} height={fh} fill=\"var(--ink)\" rx=\"2\">\n          <title>{lang === \"fr\" ? \"Empreinte approximative du bâtiment\" : \"Approximate building footprint\"}</title>\n        </rect>\n      )}\n      {/* cotes : frontage */}\n      <line x1={x} y1={y + h + 12} x2={x + w} y2={y + h + 12} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x} y1={y + h + 8} x2={x} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x + w} y1={y + h + 8} x2={x + w} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x + w / 2} y={y + h + 24} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n        {frontageM ? fmtM(front) : `≈ ${fmtM(front)}`}\n      </text>\n      {/* cotes : profondeur */}\n      <line x1={x - 10} y1={y} x2={x - 10} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y} x2={x - 6} y2={y} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y + h} x2={x - 6} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x - 16} y={y + h / 2} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\" transform={`rotate(-90 ${x - 16} ${y + h / 2})`}>\n        ≈ {fmtM(depth)}\n      </text>\n      {/* rue */}\n      <line x1=\"8\" y1=\"136\" x2=\"182\" y2=\"136\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"147\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\" letterSpacing=\"3\">\n        {lang === \"fr\" ? \"RUE\" : \"STREET\"}\n      </text>\n    </svg>\n  );\n}\n\n/* ------------------------- composition de la valeur ------------------------- */\nexport function ValueSplit({\n  land,\n  building,\n  previous,\n  total,\n  lang,\n  labels,\n}: {\n  land: number | null;\n  building: number | null;\n  previous: number | null;\n  total: number | null;\n  lang: string;\n  labels: { land: string; building: string; previous: string };\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  const l = land ?? 0;\n  const b = building ?? 0;\n  const sum = l + b;\n  if (sum <= 0) return null;\n  const lPct = (l / sum) * 100;\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { style: \"currency\", currency: \"CAD\", maximumFractionDigits: 0 }).format(v);\n  const delta = previous && previous > 0 && total ? ((total / previous - 1) * 100) : null;\n  return (\n    <div>\n      <div className=\"flex h-9 w-full overflow-hidden rounded-md border-[1.5px] border-ink\">\n        {lPct >= 1 && (\n          <div\n            className=\"flex items-center justify-center overflow-hidden bg-green transition-[width] duration-1000 ease-out\"\n            style={{ width: on ? `${lPct}%` : \"2%\" }}\n          >\n            {lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-paper\">{Math.round(lPct)} %</span>\n            )}\n          </div>\n        )}\n        {lPct <= 99 && (\n          <div className=\"flex flex-1 items-center justify-center overflow-hidden bg-ink\">\n            {100 - lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-lime\">{Math.round(100 - lPct)} %</span>\n            )}\n          </div>\n        )}\n      </div>\n      <div className=\"vp-mono mt-2 flex flex-wrap items-center gap-x-4 gap-y-1 text-[10.5px] uppercase tracking-[0.05em]\">\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-green\" />\n          {labels.land} {fmt(l)}\n        </span>\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-ink\" />\n          {labels.building} {fmt(b)}\n        </span>\n      </div>\n      {delta != null && (\n        <p className=\"vp-mono mt-2.5 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {labels.previous} {fmt(previous!)}{\" \"}\n          <span className={`ml-1 rounded-[4px] border border-ink px-1.5 py-0.5 font-bold ${delta >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n            {delta >= 0 ? \"+\" : \"\"}\n            {delta.toFixed(1)} %\n          </span>\n        </p>\n      )}\n    </div>\n  );\n}\n\n/* -------------------------------- sparkline -------------------------------- */\nexport function Sparkline({\n  history,\n  height = 44,\n}: {\n  history: { year: number; value: number | null }[];\n  height?: number;\n}) {\n  const pts = history.filter((h) => h.value != null) as { year: number; value: number }[];\n  if (pts.length < 2) return null;\n  const W = 132;\n  const min = Math.min(...pts.map((p) => p.value));\n  const max = Math.max(...pts.map((p) => p.value));\n  const span = max - min || 1;\n  const xy = pts.map((p, i) => [\n    6 + (i / (pts.length - 1)) * (W - 12),\n    height - 8 - ((p.value - min) / span) * (height - 18),\n  ]);\n  const line = xy.map(([x, y]) => `${x},${y}`).join(\" \");\n  const area = `${xy[0][0]},${height - 4} ${line} ${xy[xy.length - 1][0]},${height - 4}`;\n  const growth = ((pts[pts.length - 1].value / pts[0].value - 1) * 100);\n  const [x2, y2] = xy[xy.length - 1];\n  return (\n    <div className=\"flex items-end gap-2\">\n      <svg viewBox={`0 0 ${W} ${height}`} width={W} height={height} aria-label={`Évolution ${pts[0].year}-${pts[pts.length - 1].year}`}>\n        <polygon points={area} fill=\"var(--lime-soft)\" />\n        <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n        {xy.slice(0, -1).map(([x, y], i) => (\n          <circle key={i} cx={x} cy={y} r=\"2\" fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"1.1\" />\n        ))}\n        <circle cx={x2} cy={y2} r=\"3.6\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      </svg>\n      <span className={`vp-mono mb-0.5 rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${growth >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n        {growth >= 0 ? \"+\" : \"\"}\n        {growth.toFixed(0)} %\n      </span>\n    </div>\n  );\n}\n\n/* ------------------------------ frise temporelle ------------------------------ */\nexport function EraLine({ year, lang }: { year: number | null; lang: string }) {\n  if (!year || year < 1750) return null;\n  const min = Math.min(1900, Math.floor((year - 10) / 50) * 50);\n  const max = 2026;\n  const frac = Math.max(0, Math.min(1, (year - min) / (max - min)));\n  const W = 190;\n  const x = 12 + frac * (W - 24);\n  const age = 2026 - year;\n  return (\n    <svg viewBox={`0 0 ${W} 40`} className=\"w-full max-w-[210px]\" aria-label={`Construction ${year}`}>\n      <line x1=\"12\" y1=\"24\" x2={W - 12} y2=\"24\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      {[...new Set([min, Math.round((min + max) / 2 / 50) * 50, 2000])].map((yr) => {\n        const gx = 12 + ((yr - min) / (max - min)) * (W - 24);\n        return (\n          <g key={yr}>\n            <line x1={gx} y1=\"20\" x2={gx} y2=\"28\" stroke=\"var(--ink)\" strokeWidth=\"1.2\" opacity=\"0.5\" />\n            <text x={gx} y=\"38\" textAnchor=\"middle\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n              {yr}\n            </text>\n          </g>\n        );\n      })}\n      <g transform={`translate(${x}, 24)`}>\n        <rect x=\"-7\" y=\"-7\" width=\"14\" height=\"14\" transform=\"rotate(45)\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n        <text y=\"-13\" textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n          {year}\n        </text>\n      </g>\n      <text x={W - 12} y=\"12\" textAnchor=\"end\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n        {age} {lang === \"fr\" ? \"ANS\" : \"YRS\"}\n      </text>\n    </svg>\n  );\n}\n","numLines":471,"startLine":1,"totalLines":471}}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwD9xbZRAWo5gM5osyi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SFtUQ3wLaYdxQhsLcAUACG","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/app/page.tsx"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":695,"cache_read_input_tokens":95172,"cache_creation":{"ephemeral_5m_input_tokens":695,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"9e1d8be0-2627-416e-a72d-d67ec9a781bf","request_id":"req_011CeJwD85EoeM92FcMeiT3a"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SFtUQ3wLaYdxQhsLcAUACG","type":"tool_result","content":"1\t// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n2\timport type { Metadata } from \"next\";\n3\timport Link from \"next/link\";\n4\timport HomeClient from \"./HomeClient\";\n5\timport stats from \"@/data/stats.json\";\n6\timport { moneyFr, slugify } from \"@/lib/seo\";\n7\t\n8\texport const metadata: Metadata = {\n9\t  title: {\n10\t    absolute: \"Vrai-Prix — estimation immobilière gratuite au Québec · Un service Groupe KA\",\n11\t  },\n12\t  description:\n13\t    \"Estimez gratuitement la valeur de n'importe quelle propriété du Québec : 3,7 millions d'unités d'évaluation, 745 119 ventes réelles, fourchette honnête P10-P90 et calcul montré en entier.\",\n14\t  alternates: { canonical: \"/\" },\n15\t  openGraph: {\n16\t    type: \"website\",\n17\t    siteName: \"Vrai-Prix\",\n18\t    locale: \"fr_CA\",\n19\t    url: \"/\",\n20\t    title:\n21\t      \"Vrai-Prix — estimation immobilière gratuite au Québec · Un service Groupe KA\",\n22\t    description:\n23\t      \"Estimez gratuitement la valeur de n'importe quelle propriété du Québec : 3,7 millions d'unités d'évaluation, 745 119 ventes réelles, fourchette honnête P10-P90 et calcul montré en entier.\",\n24\t    images: [\n25\t      { url: \"https://www.vrai-prix.com/og.png\", width: 1200, height: 630 },\n26\t    ],\n27\t  },\n28\t};\n29\t\n30\tconst TOP_VILLES = (\n31\t  stats as { par_ville: { ville: string; n: number; mediane: number | null }[] }\n32\t).par_ville.slice(0, 24);\n33\t\n34\texport default function Home() {\n35\t  return (\n36\t    <>\n37\t      <HomeClient />\n38\t      {/* ---- Explorer par municipalité (maillage interne indexable) ---- */}\n39\t      <section className=\"mt-14\">\n40\t        <span className=\"kicker\">Explorer par municipalité</span>\n41\t        <h2 className=\"vp-display mt-3 text-[clamp(22px,3.6vw,34px)] font-bold uppercase leading-[1.05] tracking-[-0.03em]\">\n42\t          La valeur des propriétés, ville par ville\n43\t        </h2>\n44\t        <p className=\"mt-3 max-w-2xl text-[15px] text-ink-2\">\n45\t          Statistiques d&apos;évaluation, valeur médiane et propriétés estimées\n46\t          pour chacune des 1 097 municipalités du Québec.\n47\t        </p>\n48\t        <ul className=\"mt-6 grid grid-cols-2 gap-x-6 gap-y-2 sm:grid-cols-3 lg:grid-cols-4\">\n49\t          {TOP_VILLES.map((v) => (\n50\t            <li key={v.ville}>\n51\t              <Link\n52\t                href={`/municipalite/${slugify(v.ville)}`}\n53\t                className=\"group flex items-baseline justify-between gap-2 border-b border-[rgba(20,24,20,0.12)] py-1.5 text-[14px]\"\n54\t              >\n55\t                <span className=\"font-medium group-hover:underline\">\n56\t                  {v.ville}\n57\t                </span>\n58\t                <span className=\"vp-mono text-[11px] text-ink-2\">\n59\t                  {v.mediane != null ? moneyFr(v.mediane) : \"\"}\n60\t                </span>\n61\t              </Link>\n62\t            </li>\n63\t          ))}\n64\t        </ul>\n65\t        <p className=\"mt-5\">\n66\t          <Link href=\"/municipalites\" className=\"btn btn-ghost\">\n67\t            Toutes les municipalités →\n68\t          </Link>\n69\t        </p>\n70\t      </section>\n71\t    </>\n72\t  );\n73\t}\n74\t"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"659c76ec-8ae9-4eaf-a4ee-e095d8b60956","timestamp":"2026-08-23T03:02:25.323Z","tool_use_result":{"type":"text","file":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/app/page.tsx","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\nimport type { Metadata } from \"next\";\nimport Link from \"next/link\";\nimport HomeClient from \"./HomeClient\";\nimport stats from \"@/data/stats.json\";\nimport { moneyFr, slugify } from \"@/lib/seo\";\n\nexport const metadata: Metadata = {\n  title: {\n    absolute: \"Vrai-Prix — estimation immobilière gratuite au Québec · Un service Groupe KA\",\n  },\n  description:\n    \"Estimez gratuitement la valeur de n'importe quelle propriété du Québec : 3,7 millions d'unités d'évaluation, 745 119 ventes réelles, fourchette honnête P10-P90 et calcul montré en entier.\",\n  alternates: { canonical: \"/\" },\n  openGraph: {\n    type: \"website\",\n    siteName: \"Vrai-Prix\",\n    locale: \"fr_CA\",\n    url: \"/\",\n    title:\n      \"Vrai-Prix — estimation immobilière gratuite au Québec · Un service Groupe KA\",\n    description:\n      \"Estimez gratuitement la valeur de n'importe quelle propriété du Québec : 3,7 millions d'unités d'évaluation, 745 119 ventes réelles, fourchette honnête P10-P90 et calcul montré en entier.\",\n    images: [\n      { url: \"https://www.vrai-prix.com/og.png\", width: 1200, height: 630 },\n    ],\n  },\n};\n\nconst TOP_VILLES = (\n  stats as { par_ville: { ville: string; n: number; mediane: number | null }[] }\n).par_ville.slice(0, 24);\n\nexport default function Home() {\n  return (\n    <>\n      <HomeClient />\n      {/* ---- Explorer par municipalité (maillage interne indexable) ---- */}\n      <section className=\"mt-14\">\n        <span className=\"kicker\">Explorer par municipalité</span>\n        <h2 className=\"vp-display mt-3 text-[clamp(22px,3.6vw,34px)] font-bold uppercase leading-[1.05] tracking-[-0.03em]\">\n          La valeur des propriétés, ville par ville\n        </h2>\n        <p className=\"mt-3 max-w-2xl text-[15px] text-ink-2\">\n          Statistiques d&apos;évaluation, valeur médiane et propriétés estimées\n          pour chacune des 1 097 municipalités du Québec.\n        </p>\n        <ul className=\"mt-6 grid grid-cols-2 gap-x-6 gap-y-2 sm:grid-cols-3 lg:grid-cols-4\">\n          {TOP_VILLES.map((v) => (\n            <li key={v.ville}>\n              <Link\n                href={`/municipalite/${slugify(v.ville)}`}\n                className=\"group flex items-baseline justify-between gap-2 border-b border-[rgba(20,24,20,0.12)] py-1.5 text-[14px]\"\n              >\n                <span className=\"font-medium group-hover:underline\">\n                  {v.ville}\n                </span>\n                <span className=\"vp-mono text-[11px] text-ink-2\">\n                  {v.mediane != null ? moneyFr(v.mediane) : \"\"}\n                </span>\n              </Link>\n            </li>\n          ))}\n        </ul>\n        <p className=\"mt-5\">\n          <Link href=\"/municipalites\" className=\"btn btn-ghost\">\n            Toutes les municipalités →\n          </Link>\n        </p>\n      </section>\n    </>\n  );\n}\n","numLines":74,"startLine":1,"totalLines":74}}}}
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t(\"searchPlaceholder\")}\n56\t        className=\"vp-input text-base\"\n57\t        aria-label={placeholder ?? t(\"searchPlaceholder\")}\n58\t      />\n59\t      {open && sugg.length > 0 && (\n60\t        <ul className=\"absolute z-[var(--z-dropdown,700)] mt-2 w-full overflow-hidden rounded-[10px] border-[1.5px] border-ink bg-surface shadow-[6px_6px_0_rgba(20,24,20,0.85)]\">\n61\t          {sugg.map((s) => (\n62\t            <li key={s.id} className=\"border-b border-[rgba(20,24,20,0.1)] last:border-0\">\n63\t              <button\n64\t                className=\"flex w-full items-baseline justify-between gap-2 px-4 py-3 text-left transition-colors hover:bg-lime-soft\"\n65\t                onMouseDown={() => {\n66\t                  if (onPick) {\n67\t                    onPick(s);\n68\t                    setQ(\"\");\n69\t                    setSugg([]);\n70\t                    setSearched(false);\n71\t                  } else {\n72\t                    router.push(`/estimation/${s.id}`);\n73\t                  }\n74\t                }}\n75\t              >\n76\t                <span className=\"vp-display font-bold\">\n77\t                  {s.adresse}\n78\t                  {s.apt ? 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t(\"searchPlaceholder\")}\n        className=\"vp-input text-base\"\n        aria-label={placeholder ?? t(\"searchPlaceholder\")}\n      />\n      {open && sugg.length > 0 && (\n        <ul className=\"absolute z-[var(--z-dropdown,700)] mt-2 w-full overflow-hidden rounded-[10px] border-[1.5px] border-ink bg-surface shadow-[6px_6px_0_rgba(20,24,20,0.85)]\">\n          {sugg.map((s) => (\n            <li key={s.id} className=\"border-b border-[rgba(20,24,20,0.1)] last:border-0\">\n              <button\n                className=\"flex w-full items-baseline justify-between gap-2 px-4 py-3 text-left transition-colors hover:bg-lime-soft\"\n                onMouseDown={() => {\n                  if (onPick) {\n                    onPick(s);\n                    setQ(\"\");\n                    setSugg([]);\n                    setSearched(false);\n                  } else {\n                    router.push(`/estimation/${s.id}`);\n                  }\n                }}\n              >\n                <span className=\"vp-display font-bold\">\n                  {s.adresse}\n                  {s.apt ? 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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwFKXry5ZhYjQLVtdpq","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwFKXry5ZhYjQLVtdpq","type":"message","role":"assistant","content":[{"type":"text","text":"Maintenant j'implémente. D'abord les nouvelles fonctions de données (`db.ts`), puis le contexte marché dans `estimator.ts`."}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":4650,"cache_read_input_tokens":95867,"cache_creation":{"ephemeral_5m_input_tokens":4650,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"fbe9b6bb-b693-4d8a-a55c-43497b48f80a","request_id":"req_011CeJwFHDiCTLeMopncvdnC"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwFKXry5ZhYjQLVtdpq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GqXE9fukHvTuCNNhLnHL6J","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/db.ts","old_string":"export function saveLead(email: string, unitId: string | null, estimate: number | null): void {","new_string":"/** Rang de la valeur dans la municipalité (percentile sur est_2026). */\nexport function municipalityPercentile(\n  municipalite: string,\n  value: number\n): { below: number; total: number } | null {\n  const row = getDb()\n    .prepare(\n      `SELECT COUNT(*) AS total,\n              SUM(CASE WHEN est_2026 < ? THEN 1 ELSE 0 END) AS below\n       FROM units\n       WHERE municipalite = ? COLLATE NOCASE\n         AND est_2026 IS NOT NULL AND est_2026 > 0`\n    )\n    .get(value, municipalite) as { total: number; below: number | null } | undefined;\n  if (!row || row.total < 30) return null;\n  return { below: row.below ?? 0, total: row.total };\n}\n\nexport interface RecentSaleRow {\n  id: string;\n  date: string;\n  amount: number;\n  street: string | null;\n  city: string | null;\n  property_type: string | null;\n  id_provinc: string | null;\n  valeur_role: number | null;\n}\n\n/** Dernières ventes publiées appariées à une unité (fil « ventes récentes »). */\nexport function recentSales(limit = 6): RecentSaleRow[] {\n  return getDb()\n    .prepare(\n      `SELECT id, date, amount, street, city, property_type, id_provinc, valeur_role\n       FROM transactions\n       WHERE id_provinc IS NOT NULL AND street IS NOT NULL AND amount >= 50000\n       ORDER BY date DESC, amount DESC\n       LIMIT ?`\n    )\n    .all(limit) as RecentSaleRow[];\n}\n\nexport function salesCountSince(sinceDate: string): number {\n  const row = getDb()\n    .prepare(\"SELECT COUNT(*) AS n FROM transactions WHERE date >= ?\")\n    .get(sinceDate) as { n: number };\n  return row.n;\n}\n\nexport function saveLead(email: string, unitId: string | null, estimate: number | null): void {"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":4650,"cache_read_input_tokens":95867,"cache_creation":{"ephemeral_5m_input_tokens":4650,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"fb0ce023-218a-4227-a210-52afa7b6e0e4","request_id":"req_011CeJwFHDiCTLeMopncvdnC"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GqXE9fukHvTuCNNhLnHL6J","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/db.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"4f8bf3aa-e9dd-43ee-b30c-2ba56b7a9f99","timestamp":"2026-08-23T03:03:37.550Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/db.ts","oldString":"export function saveLead(email: string, unitId: string | null, estimate: number | null): void {","newString":"/** Rang de la valeur dans la municipalité (percentile sur est_2026). */\nexport function municipalityPercentile(\n  municipalite: string,\n  value: number\n): { below: number; total: number } | null {\n  const row = getDb()\n    .prepare(\n      `SELECT COUNT(*) AS total,\n              SUM(CASE WHEN est_2026 < ? THEN 1 ELSE 0 END) AS below\n       FROM units\n       WHERE municipalite = ? COLLATE NOCASE\n         AND est_2026 IS NOT NULL AND est_2026 > 0`\n    )\n    .get(value, municipalite) as { total: number; below: number | null } | undefined;\n  if (!row || row.total < 30) return null;\n  return { below: row.below ?? 0, total: row.total };\n}\n\nexport interface RecentSaleRow {\n  id: string;\n  date: string;\n  amount: number;\n  street: string | null;\n  city: string | null;\n  property_type: string | null;\n  id_provinc: string | null;\n  valeur_role: number | null;\n}\n\n/** Dernières ventes publiées appariées à une unité (fil « ventes récentes »). */\nexport function recentSales(limit = 6): RecentSaleRow[] {\n  return getDb()\n    .prepare(\n      `SELECT id, date, amount, street, city, property_type, id_provinc, valeur_role\n       FROM transactions\n       WHERE id_provinc IS NOT NULL AND street IS NOT NULL AND amount >= 50000\n       ORDER BY date DESC, amount DESC\n       LIMIT ?`\n    )\n    .all(limit) as RecentSaleRow[];\n}\n\nexport function salesCountSince(sinceDate: string): number {\n  const row = getDb()\n    .prepare(\"SELECT COUNT(*) AS n FROM transactions WHERE date >= ?\")\n    .get(sinceDate) as { n: number };\n  return row.n;\n}\n\nexport function saveLead(email: string, unitId: string | null, estimate: number | null): void {","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\nimport Database from \"better-sqlite3\";\nimport path from \"path\";\n\nlet db: Database.Database | null = null;\n\nexport function getDb(): Database.Database {\n  if (!db) {\n    const p =\n      process.env.VRAIPRIX_DB ?? path.join(process.cwd(), \"data\", \"vraiprix.db\");\n    db = new Database(p, { fileMustExist: true });\n    db.pragma(\"journal_mode = WAL\");\n  }\n  return db;\n}\n\nexport interface UnitRow {\n  id_provinc: string;\n  adresse: string | null;\n  apt: string | null;\n  municipalite: string | null;\n  arrond: string | null;\n  code_mun: string | null;\n  lat: number;\n  lng: number;\n  cubf: number | null;\n  cubf_libelle: string | null;\n  type_prop: string;\n  annee_construction: number | null;\n  annee_estimee: string | null;\n  aire_etages_m2: number | null;\n  superficie_terrain_m2: number | null;\n  front_terrain_m: number | null;\n  nb_etages: number | null;\n  nb_logements: number | null;\n  nb_locaux_non_resid: number | null;\n  nb_chambres_locatives: number | null;\n  lien_physique: string | null;\n  genre_construction: string | null;\n  matricule: string | null;\n  unite_voisinage: string | null;\n  n_adresses: number | null;\n  dat_cond_marche: string | null;\n  valeur_terrain: number | null;\n  valeur_batiment: number | null;\n  valeur_role: number | null;\n  valeur_anterieure: number | null;\n  est_2021: number | null;\n  est_2022: number | null;\n  est_2023: number | null;\n  est_2024: number | null;\n  est_2025: number | null;\n  est_2026: number | null;\n  p10: number | null;\n  p90: number | null;\n  est_hedo: number | null;\n}\n\nexport interface TxRow {\n  id: string;\n  date: string;\n  amount: number;\n  street: string | null;\n  city: string | null;\n  lat: number;\n  lng: number;\n  property_type: string | null;\n  year_built: number | null;\n  floor_area: number | null;\n  building_type: string | null;\n  id_provinc: string | null;\n  valeur_role: number | null;\n  land_area: number | null;\n}\n\nexport function getUnit(id: string): UnitRow | undefined {\n  return getDb()\n    .prepare(\"SELECT * FROM units WHERE id_provinc = ?\")\n    .get(id) as UnitRow | undefined;\n}\n\n/** Candidats comparables dans une boîte englobante autour du point. */\nexport function getCandidates(\n  lat: number,\n  lng: number,\n  halfDeg: number,\n  sinceDate: string,\n  limit = 500\n): TxRow[] {\n  return getDb()\n    .prepare(\n      `SELECT * FROM transactions\n       WHERE lat BETWEEN ? AND ? AND lng BETWEEN ? AND ?\n         AND date >= ? AND amount >= 50000\n       LIMIT ?`\n    )\n    .all(lat - halfDeg, lat + halfDeg, lng - halfDeg, lng + halfDeg, sinceDate, limit) as TxRow[];\n}\n\nexport function getMarketIndex(typeProp: string): { month: string; idx: number }[] {\n  return getDb()\n    .prepare(\n      \"SELECT month, idx FROM market_index WHERE type_prop = ? ORDER BY month\"\n    )\n    .all(typeProp) as { month: string; idx: number }[];\n}\n\nexport function searchUnits(q: string, limit = 8): (UnitRow & { score: number })[] {\n  const cleaned = q\n    .replace(/[^\\p{L}\\p{N}\\s'-]/gu, \" \")\n    .trim()\n    .split(/\\s+/)\n    .filter((t) => t.length > 0)\n    .map((t) => `\"${t}\"*`)\n    .join(\" \");\n  if (!cleaned) return [];\n  return getDb()\n    .prepare(\n      `SELECT u.*, bm25(units_fts) AS score\n       FROM units_fts JOIN units u ON u.rowid = units_fts.rowid\n       WHERE units_fts MATCH ?\n       ORDER BY score LIMIT ?`\n    )\n    .all(cleaned, limit) as (UnitRow & { score: number })[];\n}\n\nexport interface NearbyUnitRow {\n  id_provinc: string;\n  adresse: string | null;\n  apt: string | null;\n  municipalite: string | null;\n  type_prop: string;\n  lat: number;\n  lng: number;\n  est_2026: number | null;\n}\n\n/** Unités d'évaluation dans une boîte englobante (recherche par carte). */\nexport function unitsNear(\n  lat: number,\n  lng: number,\n  halfLat: number,\n  halfLng: number,\n  limit = 300\n): NearbyUnitRow[] {\n  // ORDER BY distance au centre : sans tri, l'index (lat, lng) renverrait\n  // les N unités les plus au sud de la boîte (bande horizontale).\n  return getDb()\n    .prepare(\n      `SELECT id_provinc, adresse, apt, municipalite, type_prop, lat, lng, est_2026\n       FROM units\n       WHERE lat BETWEEN ? AND ? AND lng BETWEEN ? AND ?\n       ORDER BY (lat - ?) * (lat - ?) + 0.49 * (lng - ?) * (lng - ?)\n       LIMIT ?`\n    )\n    .all(\n      lat - halfLat,\n      lat + halfLat,\n      lng - halfLng,\n      lng + halfLng,\n      lat,\n      lat,\n      lng,\n      lng,\n      limit\n    ) as NearbyUnitRow[];\n}\n\nexport function municipalityCenter(\n  name: string\n): { lat: number; lng: number; n: number } | undefined {\n  return getDb()\n    .prepare(\n      `SELECT AVG(lat) AS lat, AVG(lng) AS lng, COUNT(*) AS n\n       FROM units WHERE municipalite = ? COLLATE NOCASE`\n    )\n    .get(name) as { lat: number; lng: number; n: number } | undefined;\n}\n\nexport function saveLead(email: string, unitId: string | null, estimate: number | null): void {\n  getDb()\n    .prepare(\"INSERT INTO leads (email, unit_id, estimate) VALUES (?, ?, ?)\")\n    .run(email, unitId, estimate);\n}\n","structuredPatch":[{"oldStart":177,"oldLines":6,"newStart":177,"newLines":55,"lines":["     .get(name) as { lat: number; lng: number; n: number } | undefined;"," }"," ","+/** Rang de la valeur dans la municipalité (percentile sur est_2026). */","+export function municipalityPercentile(","+  municipalite: string,","+  value: number","+): { below: number; total: number } | null {","+  const row = getDb()","+    .prepare(","+      `SELECT COUNT(*) AS total,","+              SUM(CASE WHEN est_2026 < ? THEN 1 ELSE 0 END) AS below","+       FROM units","+       WHERE municipalite = ? COLLATE NOCASE","+         AND est_2026 IS NOT NULL AND est_2026 > 0`","+    )","+    .get(value, municipalite) as { total: number; below: number | null } | undefined;","+  if (!row || row.total < 30) return null;","+  return { below: row.below ?? 0, total: row.total };","+}","+","+export interface RecentSaleRow {","+  id: string;","+  date: string;","+  amount: number;","+  street: string | null;","+  city: string | null;","+  property_type: string | null;","+  id_provinc: string | null;","+  valeur_role: number | null;","+}","+","+/** Dernières ventes publiées appariées à une unité (fil « ventes récentes »). */","+export function recentSales(limit = 6): RecentSaleRow[] {","+  return getDb()","+    .prepare(","+      `SELECT id, date, amount, street, city, property_type, id_provinc, valeur_role","+       FROM transactions","+       WHERE id_provinc IS NOT NULL AND street IS NOT NULL AND amount >= 50000","+       ORDER BY date DESC, amount DESC","+       LIMIT ?`","+    )","+    .all(limit) as RecentSaleRow[];","+}","+","+export function salesCountSince(sinceDate: string): number {","+  const row = getDb()","+    .prepare(\"SELECT COUNT(*) AS n FROM transactions WHERE date >= ?\")","+    .get(sinceDate) as { n: number };","+  return row.n;","+}","+"," export function saveLead(email: string, unitId: string | null, estimate: number | null): void {","   getDb()","     .prepare(\"INSERT INTO leads (email, unit_id, estimate) VALUES (?, ?, ?)\")"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwLcDrBgz6BbySc2eip","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LCm8kPmYpXiQRU5MVxMvQq","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","old_string":"import {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport { estimate, type CompInput, type EstimateResult, type Subject } from \"./engine\";","new_string":"import {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5718,"cache_read_input_tokens":100517,"cache_creation":{"ephemeral_5m_input_tokens":5718,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"cab925c0-b2c3-47a4-9dfc-cce68108b6e6","request_id":"req_011CeJwLa95kb4uGTXrm1WjQ"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LCm8kPmYpXiQRU5MVxMvQq","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"c1aee0fd-c5b1-4e85-9f73-ae80a585f7df","timestamp":"2026-08-23T03:03:43.342Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","oldString":"import {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport { estimate, type CompInput, type EstimateResult, type Subject } from \"./engine\";","newString":"import {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport { estimate, type CompInput, type EstimateResult, type Subject } from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(u.lat, u.lng)),\n    getMarketIndex(indexType(u.type_prop)),\n    nowISO()\n  );\n  return {\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(center.lat, center.lng)),\n    getMarketIndex(indexType(p.typeProp)),\n    nowISO()\n  );\n  if (!Number.isFinite(result.estimate)) return null;\n  return { unit: null, result };\n}\n","structuredPatch":[{"oldStart":5,"oldLines":10,"newStart":5,"newLines":17,"lines":["   getMarketIndex,","   getUnit,","   municipalityCenter,","+  municipalityPercentile,","   type TxRow,","   type UnitRow,"," } from \"./db\";","-import { estimate, type CompInput, type EstimateResult, type Subject } from \"./engine\";","+import {","+  estimate,","+  haversineM,","+  type CompInput,","+  type EstimateResult,","+  type Subject,","+} from \"./engine\";"," "," export interface UnitSpecs {","   cubf: number | null;"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwM3AY6Wx4MEUHbetTf","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DgiAjHFrgBSpcuRu3BnACj","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","old_string":"export interface UnitEstimate {\n  unit: {","new_string":"/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */\nexport interface SectorStats {\n  nSales12m: number;\n  medianPrice: number | null;\n  medianPpm2: number | null;\n  radiusKm: number;\n}\n\n/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];\n  index12mPct: number | null;\n  indexSince2021Pct: number | null;\n  sector: SectorStats | null;\n  /** % des propriétés de la municipalité valant moins que le sujet. */\n  muniPercentile: number | null;\n  muniTotal: number | null;\n}\n\nexport interface UnitEstimate {\n  unit: {"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":455,"cache_read_input_tokens":106235,"cache_creation":{"ephemeral_5m_input_tokens":455,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"d65e87a2-ab9e-4741-bda6-670fec7f764e","request_id":"req_011CeJwLzq8voNp8LAGYmN7q"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DgiAjHFrgBSpcuRu3BnACj","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"c813e580-d7cf-4369-9a01-b25aadcb41d3","timestamp":"2026-08-23T03:03:50.925Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","oldString":"export interface UnitEstimate {\n  unit: {","newString":"/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */\nexport interface SectorStats {\n  nSales12m: number;\n  medianPrice: number | null;\n  medianPpm2: number | null;\n  radiusKm: number;\n}\n\n/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];\n  index12mPct: number | null;\n  indexSince2021Pct: number | null;\n  sector: SectorStats | null;\n  /** % des propriétés de la municipalité valant moins que le sujet. */\n  muniPercentile: number | null;\n  muniTotal: number | null;\n}\n\nexport interface UnitEstimate {\n  unit: {","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(u.lat, u.lng)),\n    getMarketIndex(indexType(u.type_prop)),\n    nowISO()\n  );\n  return {\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(center.lat, center.lng)),\n    getMarketIndex(indexType(p.typeProp)),\n    nowISO()\n  );\n  if (!Number.isFinite(result.estimate)) return null;\n  return { unit: null, result };\n}\n","structuredPatch":[{"oldStart":38,"oldLines":6,"newStart":38,"newLines":26,"lines":["   apt: string | null;"," }"," ","+/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */","+export interface SectorStats {","+  nSales12m: number;","+  medianPrice: number | null;","+  medianPpm2: number | null;","+  radiusKm: number;","+}","+","+/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */","+export interface MarketContext {","+  /** Indice de marché mensuel du type (1.0 = niveau courant). */","+  index: { month: string; idx: number }[];","+  index12mPct: number | null;","+  indexSince2021Pct: number | null;","+  sector: SectorStats | null;","+  /** % des propriétés de la municipalité valant moins que le sujet. */","+  muniPercentile: number | null;","+  muniTotal: number | null;","+}","+"," export interface UnitEstimate {","   unit: {","     id: string;"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NW4xwUwhVzWwbWtnkdwiHb","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"684d7c65-9d1d-427d-a824-bfbf5006cab0","timestamp":"2026-08-23T03:03:55.691Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","oldString":"    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n}","newString":"    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n  context?: MarketContext;\n}","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\n/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */\nexport interface SectorStats {\n  nSales12m: number;\n  medianPrice: number | null;\n  medianPpm2: number | null;\n  radiusKm: number;\n}\n\n/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];\n  index12mPct: number | null;\n  indexSince2021Pct: number | null;\n  sector: SectorStats | null;\n  /** % des propriétés de la municipalité valant moins que le sujet. */\n  muniPercentile: number | null;\n  muniTotal: number | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(u.lat, u.lng)),\n    getMarketIndex(indexType(u.type_prop)),\n    nowISO()\n  );\n  return {\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(center.lat, center.lng)),\n    getMarketIndex(indexType(p.typeProp)),\n    nowISO()\n  );\n  if (!Number.isFinite(result.estimate)) return null;\n  return { unit: null, result };\n}\n","structuredPatch":[{"oldStart":76,"oldLines":6,"newStart":76,"newLines":7,"lines":["     specs: UnitSpecs;","   } | null;","   result: EstimateResult;","+  context?: MarketContext;"," }"," "," /** Type de comparable attendu pour le type de tx du marché de l'indice. */"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwMvyFDRxyMrevhJ9mT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016Lo9jDryTsXkeZyybyyo4V","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","old_string":"export function estimateByUnitId(id: string): UnitEstimate | null {","new_string":"function median(xs: number[]): number | null {\n  if (!xs.length) return null;\n  const s = [...xs].sort((a, b) => a - b);\n  const m = Math.floor(s.length / 2);\n  return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;\n}\n\n/** Stats du secteur à partir des candidats déjà chargés (aucune requête en plus). */\nfunction sectorStats(lat: number, lng: number, rows: TxRow[]): SectorStats | null {\n  const cutoff = since(12);\n  const recent = rows.filter((r) => r.date >= cutoff);\n  for (const radiusKm of [2, 5, 10]) {\n    const within = recent.filter(\n      (r) => haversineM(lat, lng, r.lat, r.lng) <= radiusKm * 1000\n    );\n    if (within.length >= 8 || radiusKm === 10) {\n      if (within.length === 0) return null;\n      return {\n        nSales12m: within.length,\n        medianPrice: median(within.map((r) => r.amount)),\n        medianPpm2: median(\n          within\n            .filter((r) => r.floor_area != null && r.floor_area > 20)\n            .map((r) => r.amount / r.floor_area!)\n        ),\n        radiusKm,\n      };\n    }\n  }\n  return null;\n}\n\nfunction buildContext(\n  lat: number,\n  lng: number,\n  candidates: TxRow[],\n  index: { month: string; idx: number }[],\n  municipalite: string | null,\n  estimateValue: number\n): MarketContext {\n  const last = index.length ? index[index.length - 1].idx : null;\n  const yearAgo = index.length >= 13 ? index[index.length - 13].idx : null;\n  const first = index.length >= 2 ? index[0].idx : null;\n  const pr =\n    municipalite && Number.isFinite(estimateValue) && estimateValue > 0\n      ? municipalityPercentile(municipalite, estimateValue)\n      : null;\n  return {\n    index,\n    index12mPct:\n      last != null && yearAgo != null && yearAgo > 0\n        ? (last / yearAgo - 1) * 100\n        : null,\n    indexSince2021Pct:\n      last != null && first != null && first > 0 ? (last / first - 1) * 100 : null,\n    sector: sectorStats(lat, lng, candidates),\n    muniPercentile: pr ? (pr.below / pr.total) * 100 : null,\n    muniTotal: pr ? pr.total : null,\n  };\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":291,"cache_read_input_tokens":107216,"cache_creation":{"ephemeral_5m_input_tokens":291,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"c3865383-9bcc-4744-a84c-b01d9309387f","request_id":"req_011CeJwMuctR6pccvLcQhZN1"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016Lo9jDryTsXkeZyybyyo4V","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"c46f4085-7c01-4085-b059-ab76bf278993","timestamp":"2026-08-23T03:04:14.232Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","oldString":"export function estimateByUnitId(id: string): UnitEstimate | null {","newString":"function median(xs: number[]): number | null {\n  if (!xs.length) return null;\n  const s = [...xs].sort((a, b) => a - b);\n  const m = Math.floor(s.length / 2);\n  return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;\n}\n\n/** Stats du secteur à partir des candidats déjà chargés (aucune requête en plus). */\nfunction sectorStats(lat: number, lng: number, rows: TxRow[]): SectorStats | null {\n  const cutoff = since(12);\n  const recent = rows.filter((r) => r.date >= cutoff);\n  for (const radiusKm of [2, 5, 10]) {\n    const within = recent.filter(\n      (r) => haversineM(lat, lng, r.lat, r.lng) <= radiusKm * 1000\n    );\n    if (within.length >= 8 || radiusKm === 10) {\n      if (within.length === 0) return null;\n      return {\n        nSales12m: within.length,\n        medianPrice: median(within.map((r) => r.amount)),\n        medianPpm2: median(\n          within\n            .filter((r) => r.floor_area != null && r.floor_area > 20)\n            .map((r) => r.amount / r.floor_area!)\n        ),\n        radiusKm,\n      };\n    }\n  }\n  return null;\n}\n\nfunction buildContext(\n  lat: number,\n  lng: number,\n  candidates: TxRow[],\n  index: { month: string; idx: number }[],\n  municipalite: string | null,\n  estimateValue: number\n): MarketContext {\n  const last = index.length ? index[index.length - 1].idx : null;\n  const yearAgo = index.length >= 13 ? index[index.length - 13].idx : null;\n  const first = index.length >= 2 ? index[0].idx : null;\n  const pr =\n    municipalite && Number.isFinite(estimateValue) && estimateValue > 0\n      ? municipalityPercentile(municipalite, estimateValue)\n      : null;\n  return {\n    index,\n    index12mPct:\n      last != null && yearAgo != null && yearAgo > 0\n        ? (last / yearAgo - 1) * 100\n        : null,\n    indexSince2021Pct:\n      last != null && first != null && first > 0 ? (last / first - 1) * 100 : null,\n    sector: sectorStats(lat, lng, candidates),\n    muniPercentile: pr ? (pr.below / pr.total) * 100 : null,\n    muniTotal: pr ? pr.total : null,\n  };\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\n/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */\nexport interface SectorStats {\n  nSales12m: number;\n  medianPrice: number | null;\n  medianPpm2: number | null;\n  radiusKm: number;\n}\n\n/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];\n  index12mPct: number | null;\n  indexSince2021Pct: number | null;\n  sector: SectorStats | null;\n  /** % des propriétés de la municipalité valant moins que le sujet. */\n  muniPercentile: number | null;\n  muniTotal: number | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n  context?: MarketContext;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(u.lat, u.lng)),\n    getMarketIndex(indexType(u.type_prop)),\n    nowISO()\n  );\n  return {\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(center.lat, center.lng)),\n    getMarketIndex(indexType(p.typeProp)),\n    nowISO()\n  );\n  if (!Number.isFinite(result.estimate)) return null;\n  return { unit: null, result };\n}\n","structuredPatch":[{"oldStart":120,"oldLines":6,"newStart":120,"newLines":67,"lines":["   return getCandidates(lat, lng, 0.25, since(36), 600);"," }"," ","+function median(xs: number[]): number | null {","+  if (!xs.length) return null;","+  const s = [...xs].sort((a, b) => a - b);","+  const m = Math.floor(s.length / 2);","+  return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;","+}","+","+/** Stats du secteur à partir des candidats déjà chargés (aucune requête en plus). */","+function sectorStats(lat: number, lng: number, rows: TxRow[]): SectorStats | null {","+  const cutoff = since(12);","+  const recent = rows.filter((r) => r.date >= cutoff);","+  for (const radiusKm of [2, 5, 10]) {","+    const within = recent.filter(","+      (r) => haversineM(lat, lng, r.lat, r.lng) <= radiusKm * 1000","+    );","+    if (within.length >= 8 || radiusKm === 10) {","+      if (within.length === 0) return null;","+      return {","+        nSales12m: within.length,","+        medianPrice: median(within.map((r) => r.amount)),","+        medianPpm2: median(","+          within","+            .filter((r) => r.floor_area != null && r.floor_area > 20)","+            .map((r) => r.amount / r.floor_area!)","+        ),","+        radiusKm,","+      };","+    }","+  }","+  return null;","+}","+","+function buildContext(","+  lat: number,","+  lng: number,","+  candidates: TxRow[],","+  index: { month: string; idx: number }[],","+  municipalite: string | null,","+  estimateValue: number","+): MarketContext {","+  const last = index.length ? index[index.length - 1].idx : null;","+  const yearAgo = index.length >= 13 ? index[index.length - 13].idx : null;","+  const first = index.length >= 2 ? index[0].idx : null;","+  const pr =","+    municipalite && Number.isFinite(estimateValue) && estimateValue > 0","+      ? municipalityPercentile(municipalite, estimateValue)","+      : null;","+  return {","+    index,","+    index12mPct:","+      last != null && yearAgo != null && yearAgo > 0","+        ? (last / yearAgo - 1) * 100","+        : null,","+    indexSince2021Pct:","+      last != null && first != null && first > 0 ? (last / first - 1) * 100 : null,","+    sector: sectorStats(lat, lng, candidates),","+    muniPercentile: pr ? (pr.below / pr.total) * 100 : null,","+    muniTotal: pr ? pr.total : null,","+  };","+}","+"," export function estimateByUnitId(id: string): UnitEstimate | null {","   const u = getUnit(id);","   if (!u) return null;"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KKDLbYson86v5srBGS5w87","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"07563c68-95de-4d5a-85f3-f7383660dca4","timestamp":"2026-08-23T03:04:21.919Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","oldString":"  const result = estimate(\n    subject,\n    toComps(candidatesAround(u.lat, u.lng)),\n    getMarketIndex(indexType(u.type_prop)),\n    nowISO()\n  );\n  return {","newString":"  const candidates = candidatesAround(u.lat, u.lng);\n  const index = getMarketIndex(indexType(u.type_prop));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  const context = buildContext(\n    u.lat,\n    u.lng,\n    candidates,\n    index,\n    u.municipalite,\n    result.estimate\n  );\n  return {\n    context,","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\n/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */\nexport interface SectorStats {\n  nSales12m: number;\n  medianPrice: number | null;\n  medianPpm2: number | null;\n  radiusKm: number;\n}\n\n/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];\n  index12mPct: number | null;\n  indexSince2021Pct: number | null;\n  sector: SectorStats | null;\n  /** % des propriétés de la municipalité valant moins que le sujet. */\n  muniPercentile: number | null;\n  muniTotal: number | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n  context?: MarketContext;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nfunction median(xs: number[]): number | null {\n  if (!xs.length) return null;\n  const s = [...xs].sort((a, b) => a - b);\n  const m = Math.floor(s.length / 2);\n  return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;\n}\n\n/** Stats du secteur à partir des candidats déjà chargés (aucune requête en plus). */\nfunction sectorStats(lat: number, lng: number, rows: TxRow[]): SectorStats | null {\n  const cutoff = since(12);\n  const recent = rows.filter((r) => r.date >= cutoff);\n  for (const radiusKm of [2, 5, 10]) {\n    const within = recent.filter(\n      (r) => haversineM(lat, lng, r.lat, r.lng) <= radiusKm * 1000\n    );\n    if (within.length >= 8 || radiusKm === 10) {\n      if (within.length === 0) return null;\n      return {\n        nSales12m: within.length,\n        medianPrice: median(within.map((r) => r.amount)),\n        medianPpm2: median(\n          within\n            .filter((r) => r.floor_area != null && r.floor_area > 20)\n            .map((r) => r.amount / r.floor_area!)\n        ),\n        radiusKm,\n      };\n    }\n  }\n  return null;\n}\n\nfunction buildContext(\n  lat: number,\n  lng: number,\n  candidates: TxRow[],\n  index: { month: string; idx: number }[],\n  municipalite: string | null,\n  estimateValue: number\n): MarketContext {\n  const last = index.length ? index[index.length - 1].idx : null;\n  const yearAgo = index.length >= 13 ? index[index.length - 13].idx : null;\n  const first = index.length >= 2 ? index[0].idx : null;\n  const pr =\n    municipalite && Number.isFinite(estimateValue) && estimateValue > 0\n      ? municipalityPercentile(municipalite, estimateValue)\n      : null;\n  return {\n    index,\n    index12mPct:\n      last != null && yearAgo != null && yearAgo > 0\n        ? (last / yearAgo - 1) * 100\n        : null,\n    indexSince2021Pct:\n      last != null && first != null && first > 0 ? (last / first - 1) * 100 : null,\n    sector: sectorStats(lat, lng, candidates),\n    muniPercentile: pr ? (pr.below / pr.total) * 100 : null,\n    muniTotal: pr ? pr.total : null,\n  };\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(u.lat, u.lng)),\n    getMarketIndex(indexType(u.type_prop)),\n    nowISO()\n  );\n  return {\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(center.lat, center.lng)),\n    getMarketIndex(indexType(p.typeProp)),\n    nowISO()\n  );\n  if (!Number.isFinite(result.estimate)) return null;\n  return { unit: null, result };\n}\n","structuredPatch":[{"oldStart":195,"oldLines":13,"newStart":195,"newLines":19,"lines":["     modelP10: u.p10,","     modelP90: u.p90,","   };","-  const result = estimate(","-    subject,","-    toComps(candidatesAround(u.lat, u.lng)),","-    getMarketIndex(indexType(u.type_prop)),","-    nowISO()","+  const candidates = candidatesAround(u.lat, u.lng);","+  const index = getMarketIndex(indexType(u.type_prop));","+  const result = estimate(subject, toComps(candidates), index, nowISO());","+  const context = buildContext(","+    u.lat,","+    u.lng,","+    candidates,","+    index,","+    u.municipalite,","+    result.estimate","   );","   return {","+    context,","     unit: {","       id: u.id_provinc,","       adresse: u.adresse,"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwPsbedwDqFo6cE8GCR","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":426,"cache_read_input_tokens":108662,"cache_creation":{"ephemeral_5m_input_tokens":426,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"1d426e06-9e55-4c47-8ff8-c79dba77b878","request_id":"req_011CeJwPqmX72yffAbFeAEi7"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwPsbedwDqFo6cE8GCR","type":"message","role":"assistant","content":[{"type":"text","text":"Maintenant `estimateManual` reçoit aussi le contexte :"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":426,"cache_read_input_tokens":108662,"cache_creation":{"ephemeral_5m_input_tokens":426,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"cec3d442-24c5-44f3-ad64-2e0d4ffe39e0","request_id":"req_011CeJwPqmX72yffAbFeAEi7"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwPsbedwDqFo6cE8GCR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KRv2qacjczoU27F38ENM7i","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","old_string":"  const result = estimate(\n    subject,\n    toComps(candidatesAround(center.lat, center.lng)),\n    getMarketIndex(indexType(p.typeProp)),\n    nowISO()\n  );\n  if (!Number.isFinite(result.estimate)) return null;\n  return { unit: null, result };\n}","new_string":"  const candidates = candidatesAround(center.lat, center.lng);\n  const index = getMarketIndex(indexType(p.typeProp));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  if (!Number.isFinite(result.estimate)) return null;\n  const context = buildContext(\n    center.lat,\n    center.lng,\n    candidates,\n    index,\n    p.municipality,\n    result.estimate\n  );\n  return { unit: null, result, context };\n}"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":426,"cache_read_input_tokens":108662,"cache_creation":{"ephemeral_5m_input_tokens":426,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"73eb756c-d5d4-4025-9c2a-0f4e116c6e51","request_id":"req_011CeJwPqmX72yffAbFeAEi7"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KRv2qacjczoU27F38ENM7i","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"cb345fa8-1d0b-4b07-ac3e-fa5404a9d8bd","timestamp":"2026-08-23T03:04:31.808Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","oldString":"  const result = estimate(\n    subject,\n    toComps(candidatesAround(center.lat, center.lng)),\n    getMarketIndex(indexType(p.typeProp)),\n    nowISO()\n  );\n  if (!Number.isFinite(result.estimate)) return null;\n  return { unit: null, result };\n}","newString":"  const candidates = candidatesAround(center.lat, center.lng);\n  const index = getMarketIndex(indexType(p.typeProp));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  if (!Number.isFinite(result.estimate)) return null;\n  const context = buildContext(\n    center.lat,\n    center.lng,\n    candidates,\n    index,\n    p.municipality,\n    result.estimate\n  );\n  return { unit: null, result, context };\n}","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\n/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */\nexport interface SectorStats {\n  nSales12m: number;\n  medianPrice: number | null;\n  medianPpm2: number | null;\n  radiusKm: number;\n}\n\n/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];\n  index12mPct: number | null;\n  indexSince2021Pct: number | null;\n  sector: SectorStats | null;\n  /** % des propriétés de la municipalité valant moins que le sujet. */\n  muniPercentile: number | null;\n  muniTotal: number | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n  context?: MarketContext;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nfunction median(xs: number[]): number | null {\n  if (!xs.length) return null;\n  const s = [...xs].sort((a, b) => a - b);\n  const m = Math.floor(s.length / 2);\n  return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;\n}\n\n/** Stats du secteur à partir des candidats déjà chargés (aucune requête en plus). */\nfunction sectorStats(lat: number, lng: number, rows: TxRow[]): SectorStats | null {\n  const cutoff = since(12);\n  const recent = rows.filter((r) => r.date >= cutoff);\n  for (const radiusKm of [2, 5, 10]) {\n    const within = recent.filter(\n      (r) => haversineM(lat, lng, r.lat, r.lng) <= radiusKm * 1000\n    );\n    if (within.length >= 8 || radiusKm === 10) {\n      if (within.length === 0) return null;\n      return {\n        nSales12m: within.length,\n        medianPrice: median(within.map((r) => r.amount)),\n        medianPpm2: median(\n          within\n            .filter((r) => r.floor_area != null && r.floor_area > 20)\n            .map((r) => r.amount / r.floor_area!)\n        ),\n        radiusKm,\n      };\n    }\n  }\n  return null;\n}\n\nfunction buildContext(\n  lat: number,\n  lng: number,\n  candidates: TxRow[],\n  index: { month: string; idx: number }[],\n  municipalite: string | null,\n  estimateValue: number\n): MarketContext {\n  const last = index.length ? index[index.length - 1].idx : null;\n  const yearAgo = index.length >= 13 ? index[index.length - 13].idx : null;\n  const first = index.length >= 2 ? index[0].idx : null;\n  const pr =\n    municipalite && Number.isFinite(estimateValue) && estimateValue > 0\n      ? municipalityPercentile(municipalite, estimateValue)\n      : null;\n  return {\n    index,\n    index12mPct:\n      last != null && yearAgo != null && yearAgo > 0\n        ? (last / yearAgo - 1) * 100\n        : null,\n    indexSince2021Pct:\n      last != null && first != null && first > 0 ? (last / first - 1) * 100 : null,\n    sector: sectorStats(lat, lng, candidates),\n    muniPercentile: pr ? (pr.below / pr.total) * 100 : null,\n    muniTotal: pr ? pr.total : null,\n  };\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const candidates = candidatesAround(u.lat, u.lng);\n  const index = getMarketIndex(indexType(u.type_prop));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  const context = buildContext(\n    u.lat,\n    u.lng,\n    candidates,\n    index,\n    u.municipalite,\n    result.estimate\n  );\n  return {\n    context,\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const result = estimate(\n    subject,\n    toComps(candidatesAround(center.lat, center.lng)),\n    getMarketIndex(indexType(p.typeProp)),\n    nowISO()\n  );\n  if (!Number.isFinite(result.estimate)) return null;\n  return { unit: null, result };\n}\n","structuredPatch":[{"oldStart":378,"oldLines":12,"newStart":378,"newLines":17,"lines":["     modelP10: null,","     modelP90: null,","   };","-  const result = estimate(","-    subject,","-    toComps(candidatesAround(center.lat, center.lng)),","-    getMarketIndex(indexType(p.typeProp)),","-    nowISO()","-  );","+  const candidates = candidatesAround(center.lat, center.lng);","+  const index = getMarketIndex(indexType(p.typeProp));","+  const result = estimate(subject, toComps(candidates), index, nowISO());","   if (!Number.isFinite(result.estimate)) return null;","-  return { unit: null, result };","+  const context = buildContext(","+    center.lat,","+    center.lng,","+    candidates,","+    index,","+    p.municipality,","+    result.estimate","+  );","+  return { unit: null, result, context };"," }"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwQc3ns8CGFZkVS5EyD","type":"message","role":"assistant","content":[{"type":"text","text":"Maintenant les nouvelles visualisations dans `MetricViz.tsx` (barre de fourchette, courbe d'indice, percentile, mini-spark) :"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":726,"cache_read_input_tokens":109088,"cache_creation":{"ephemeral_5m_input_tokens":726,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"401c7aae-b2ac-4382-a594-a2567dae039a","request_id":"req_011CeJwQa4Es6yspzxWLkBuE"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwQc3ns8CGFZkVS5EyD","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PLhN5hDoJa7rVc852RB3Q8","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","old_string":"/* ------------------------------ frise temporelle ------------------------------ */","new_string":"/* ------------------------- fourchette visuelle P10-P90 ------------------------- */\n/** Barre de fourchette : segment P10→P90, losange = estimation, tiret = rôle. */\nexport function RangeBar({\n  low,\n  estimate,\n  high,\n  role,\n  lang,\n}: {\n  low: number;\n  estimate: number;\n  high: number;\n  role: number | null;\n  lang: string;\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  if (!(high > low) || !Number.isFinite(estimate)) return null;\n  const lo = Math.min(low, role ?? low);\n  const hi = Math.max(high, role ?? high);\n  const pad = (hi - lo) * 0.09 || hi * 0.05;\n  const min = lo - pad;\n  const max = hi + pad;\n  const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n      notation: v >= 1_000_000 ? \"compact\" : \"standard\",\n    }).format(v);\n  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;\n  return (\n    <div aria-label={`${fmt(low)} – ${fmt(high)}`}>\n      <div className=\"relative h-[64px]\">\n        {/* étiquette estimation */}\n        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <span className=\"vp-mono rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\">\n            {lang === \"fr\" ? \"ESTIMATION\" : \"ESTIMATE\"} {fmt(estimate)}\n          </span>\n        </div>\n        {/* piste */}\n        <div className=\"absolute left-0 right-0 top-[30px] h-[14px] rounded-[7px] border-[1.5px] border-ink bg-surface-2\" />\n        {/* segment P10-P90 */}\n        <div\n          className=\"absolute top-[30px] h-[14px] origin-left rounded-[7px] border-[1.5px] border-ink transition-transform duration-1000 ease-out\"\n          style={{\n            left: `${X(low)}%`,\n            width: `${Math.max(2, X(high) - X(low))}%`,\n            transform: on ? \"scaleX(1)\" : \"scaleX(0.05)\",\n            background:\n              \"repeating-linear-gradient(-45deg, var(--lime-soft), var(--lime-soft) 5px, rgba(255,81,72,0.35) 5px, rgba(255,81,72,0.35) 7px)\",\n          }}\n        />\n        {/* marqueur estimation (losange) */}\n        <div\n          className=\"absolute top-[29px] -translate-x-1/2 transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <div className=\"h-[16px] w-[16px] rotate-45 border-[1.8px] border-ink bg-lime\" />\n        </div>\n        {/* marqueur rôle */}\n        {role != null && role > 0 && (\n          <div\n            className=\"absolute top-[22px] -translate-x-1/2 text-center\"\n            style={{ left: `${X(role)}%` }}\n          >\n            <div className=\"mx-auto h-[30px] w-0 border-l-[2px] border-dashed border-ink opacity-70\" />\n            {!roleClose && (\n              <span className=\"vp-mono whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\">\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </div>\n        )}\n        {/* bornes */}\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(low)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(high)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>\n      </div>\n    </div>\n  );\n}\n\n/* --------------------------- courbe d'indice de marché --------------------------- */\n/** Indice mensuel rebasé à 100 au premier mois — la trajectoire du marché local. */\nexport function MarketIndexChart({\n  series,\n  lang,\n}: {\n  series: { month: string; idx: number }[];\n  lang: string;\n}) {\n  if (series.length < 6) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const W = 640;\n  const H = 200;\n  const padL = 40;\n  const padR = 52;\n  const padT = 18;\n  const padB = 28;\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const x = (i: number) => padL + (i / (pts.length - 1)) * (W - padL - padR);\n  const y = (v: number) => padT + (1 - (v - min) / span) * (H - padT - padB);\n  const line = pts.map((v, i) => `${x(i).toFixed(1)},${y(v).toFixed(1)}`).join(\" \");\n  const area = `${x(0)},${H - padB} ${line} ${x(pts.length - 1)},${H - padB}`;\n  // graduations : janvier de chaque année\n  const years = series\n    .map((p, i) => ({ i, month: p.month }))\n    .filter((p) => p.month.endsWith(\"-01\"));\n  // lignes horizontales : min / 100 / max arrondis\n  const gridVals = [...new Set([Math.round(min), 100, Math.round(max)])].filter(\n    (v) => v >= min - 1 && v <= max + 1\n  );\n  const lastV = pts[pts.length - 1];\n  return (\n    <svg\n      viewBox={`0 0 ${W} ${H}`}\n      className=\"h-auto w-full\"\n      role=\"img\"\n      aria-label={\n        lang === \"fr\"\n          ? `Indice de marché ${series[0].month} à ${series[series.length - 1].month}`\n          : `Market index ${series[0].month} to ${series[series.length - 1].month}`\n      }\n    >\n      {gridVals.map((v) => (\n        <g key={v}>\n          <line\n            x1={padL}\n            y1={y(v)}\n            x2={W - padR}\n            y2={y(v)}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            strokeDasharray=\"3 5\"\n            opacity={v === 100 ? 0.45 : 0.18}\n          />\n          <text\n            x={padL - 6}\n            y={y(v) + 3.5}\n            textAnchor=\"end\"\n            fontSize=\"10\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-3)\"\n          >\n            {v}\n          </text>\n        </g>\n      ))}\n      {years.map((p) => (\n        <g key={p.month}>\n          <line\n            x1={x(p.i)}\n            y1={padT}\n            x2={x(p.i)}\n            y2={H - padB}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            opacity=\"0.1\"\n          />\n          <text\n            x={x(p.i)}\n            y={H - 10}\n            textAnchor=\"middle\"\n            fontSize=\"10\"\n            fontWeight=\"700\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-2)\"\n          >\n            {p.month.slice(0, 4)}\n          </text>\n        </g>\n      ))}\n      <polygon points={area} fill=\"var(--lime-soft)\" opacity=\"0.8\" />\n      <polyline\n        points={line}\n        fill=\"none\"\n        stroke=\"var(--green)\"\n        strokeWidth=\"2.6\"\n        strokeLinejoin=\"round\"\n        strokeLinecap=\"round\"\n      />\n      <circle\n        cx={x(pts.length - 1)}\n        cy={y(lastV)}\n        r=\"5\"\n        fill=\"var(--lime)\"\n        stroke=\"var(--ink)\"\n        strokeWidth=\"1.8\"\n      />\n      <text\n        x={x(pts.length - 1) + 9}\n        y={y(lastV) + 4}\n        fontSize=\"11.5\"\n        fontWeight=\"700\"\n        fontFamily=\"var(--font-jetbrains)\"\n        fill=\"var(--ink)\"\n      >\n        {Math.round(lastV)}\n      </text>\n    </svg>\n  );\n}\n\n/* ----------------------- position dans la municipalité ----------------------- */\n/** Distribution stylisée (cloche) : les barres sous le percentile sont pleines. */\nexport function PercentileBar({\n  pct,\n  lang,\n}: {\n  pct: number;\n  lang: string;\n}) {\n  const N = 36;\n  const bars = Array.from({ length: N }, (_, i) => {\n    const z = (i / (N - 1) - 0.42) / 0.24; // cloche légèrement asymétrique (droite étirée)\n    return Math.exp(-0.5 * z * z) * (i / N > 0.5 ? 1 + (i / N - 0.5) * 0.35 : 1);\n  });\n  const maxB = Math.max(...bars);\n  const p = Math.max(0, Math.min(100, pct));\n  return (\n    <div aria-label={`${Math.round(p)} %`}>\n      <div className=\"relative flex h-[72px] items-end gap-[2.2%] px-0.5\" style={{ gap: \"2px\" }}>\n        {bars.map((b, i) => {\n          const filled = (i + 0.5) / N <= p / 100;\n          return (\n            <div\n              key={i}\n              className=\"min-w-0 flex-1 rounded-t-[2px] border border-ink transition-colors\"\n              style={{\n                height: `${8 + (b / maxB) * 88}%`,\n                background: filled ? \"var(--lime)\" : \"var(--surface-2)\",\n                borderWidth: \"1px\",\n                opacity: filled ? 1 : 0.55,\n              }}\n            />\n          );\n        })}\n        <div\n          className=\"absolute bottom-0 top-[-6px] w-0 border-l-[2px] border-ink\"\n          style={{ left: `${p}%` }}\n        >\n          <span className=\"vp-mono absolute -top-1 left-1.5 whitespace-nowrap rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\" style={p > 55 ? { left: \"auto\", right: \"6px\" } : undefined}>\n            {lang === \"fr\" ? \"P\" : \"P\"}\n            {Math.round(p)}\n          </span>\n        </div>\n      </div>\n    </div>\n  );\n}\n\n/* ------------------------ mini-courbe d'indice (accueil) ------------------------ */\nexport function IndexSpark({\n  series,\n  width = 150,\n  height = 46,\n}: {\n  series: { month: string; idx: number }[];\n  width?: number;\n  height?: number;\n}) {\n  if (series.length < 2) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const xy = pts.map((v, i) => [\n    4 + (i / (pts.length - 1)) * (width - 8),\n    height - 6 - ((v - min) / span) * (height - 14),\n  ]);\n  const line = xy.map(([x, y]) => `${x.toFixed(1)},${y.toFixed(1)}`).join(\" \");\n  const area = `${xy[0][0]},${height - 3} ${line} ${xy[xy.length - 1][0]},${height - 3}`;\n  const [lx, ly] = xy[xy.length - 1];\n  return (\n    <svg viewBox={`0 0 ${width} ${height}`} className=\"h-auto w-full\" aria-hidden=\"true\">\n      <polygon points={area} fill=\"var(--lime-soft)\" />\n      <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n      <circle cx={lx} cy={ly} r=\"3.4\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n    </svg>\n  );\n}\n\n/* ------------------------------ frise temporelle ------------------------------ */"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":726,"cache_read_input_tokens":109088,"cache_creation":{"ephemeral_5m_input_tokens":726,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"18844d48-fbdb-4734-ba10-dd4357b06181","request_id":"req_011CeJwQa4Es6yspzxWLkBuE"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PLhN5hDoJa7rVc852RB3Q8","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"abf70550-b7a7-472f-b419-1c7e74c55f4c","timestamp":"2026-08-23T03:05:18.646Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","oldString":"/* ------------------------------ frise temporelle ------------------------------ */","newString":"/* ------------------------- fourchette visuelle P10-P90 ------------------------- */\n/** Barre de fourchette : segment P10→P90, losange = estimation, tiret = rôle. */\nexport function RangeBar({\n  low,\n  estimate,\n  high,\n  role,\n  lang,\n}: {\n  low: number;\n  estimate: number;\n  high: number;\n  role: number | null;\n  lang: string;\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  if (!(high > low) || !Number.isFinite(estimate)) return null;\n  const lo = Math.min(low, role ?? low);\n  const hi = Math.max(high, role ?? high);\n  const pad = (hi - lo) * 0.09 || hi * 0.05;\n  const min = lo - pad;\n  const max = hi + pad;\n  const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n      notation: v >= 1_000_000 ? \"compact\" : \"standard\",\n    }).format(v);\n  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;\n  return (\n    <div aria-label={`${fmt(low)} – ${fmt(high)}`}>\n      <div className=\"relative h-[64px]\">\n        {/* étiquette estimation */}\n        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <span className=\"vp-mono rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\">\n            {lang === \"fr\" ? \"ESTIMATION\" : \"ESTIMATE\"} {fmt(estimate)}\n          </span>\n        </div>\n        {/* piste */}\n        <div className=\"absolute left-0 right-0 top-[30px] h-[14px] rounded-[7px] border-[1.5px] border-ink bg-surface-2\" />\n        {/* segment P10-P90 */}\n        <div\n          className=\"absolute top-[30px] h-[14px] origin-left rounded-[7px] border-[1.5px] border-ink transition-transform duration-1000 ease-out\"\n          style={{\n            left: `${X(low)}%`,\n            width: `${Math.max(2, X(high) - X(low))}%`,\n            transform: on ? \"scaleX(1)\" : \"scaleX(0.05)\",\n            background:\n              \"repeating-linear-gradient(-45deg, var(--lime-soft), var(--lime-soft) 5px, rgba(255,81,72,0.35) 5px, rgba(255,81,72,0.35) 7px)\",\n          }}\n        />\n        {/* marqueur estimation (losange) */}\n        <div\n          className=\"absolute top-[29px] -translate-x-1/2 transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <div className=\"h-[16px] w-[16px] rotate-45 border-[1.8px] border-ink bg-lime\" />\n        </div>\n        {/* marqueur rôle */}\n        {role != null && role > 0 && (\n          <div\n            className=\"absolute top-[22px] -translate-x-1/2 text-center\"\n            style={{ left: `${X(role)}%` }}\n          >\n            <div className=\"mx-auto h-[30px] w-0 border-l-[2px] border-dashed border-ink opacity-70\" />\n            {!roleClose && (\n              <span className=\"vp-mono whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\">\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </div>\n        )}\n        {/* bornes */}\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(low)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(high)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>\n      </div>\n    </div>\n  );\n}\n\n/* --------------------------- courbe d'indice de marché --------------------------- */\n/** Indice mensuel rebasé à 100 au premier mois — la trajectoire du marché local. */\nexport function MarketIndexChart({\n  series,\n  lang,\n}: {\n  series: { month: string; idx: number }[];\n  lang: string;\n}) {\n  if (series.length < 6) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const W = 640;\n  const H = 200;\n  const padL = 40;\n  const padR = 52;\n  const padT = 18;\n  const padB = 28;\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const x = (i: number) => padL + (i / (pts.length - 1)) * (W - padL - padR);\n  const y = (v: number) => padT + (1 - (v - min) / span) * (H - padT - padB);\n  const line = pts.map((v, i) => `${x(i).toFixed(1)},${y(v).toFixed(1)}`).join(\" \");\n  const area = `${x(0)},${H - padB} ${line} ${x(pts.length - 1)},${H - padB}`;\n  // graduations : janvier de chaque année\n  const years = series\n    .map((p, i) => ({ i, month: p.month }))\n    .filter((p) => p.month.endsWith(\"-01\"));\n  // lignes horizontales : min / 100 / max arrondis\n  const gridVals = [...new Set([Math.round(min), 100, Math.round(max)])].filter(\n    (v) => v >= min - 1 && v <= max + 1\n  );\n  const lastV = pts[pts.length - 1];\n  return (\n    <svg\n      viewBox={`0 0 ${W} ${H}`}\n      className=\"h-auto w-full\"\n      role=\"img\"\n      aria-label={\n        lang === \"fr\"\n          ? `Indice de marché ${series[0].month} à ${series[series.length - 1].month}`\n          : `Market index ${series[0].month} to ${series[series.length - 1].month}`\n      }\n    >\n      {gridVals.map((v) => (\n        <g key={v}>\n          <line\n            x1={padL}\n            y1={y(v)}\n            x2={W - padR}\n            y2={y(v)}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            strokeDasharray=\"3 5\"\n            opacity={v === 100 ? 0.45 : 0.18}\n          />\n          <text\n            x={padL - 6}\n            y={y(v) + 3.5}\n            textAnchor=\"end\"\n            fontSize=\"10\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-3)\"\n          >\n            {v}\n          </text>\n        </g>\n      ))}\n      {years.map((p) => (\n        <g key={p.month}>\n          <line\n            x1={x(p.i)}\n            y1={padT}\n            x2={x(p.i)}\n            y2={H - padB}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            opacity=\"0.1\"\n          />\n          <text\n            x={x(p.i)}\n            y={H - 10}\n            textAnchor=\"middle\"\n            fontSize=\"10\"\n            fontWeight=\"700\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-2)\"\n          >\n            {p.month.slice(0, 4)}\n          </text>\n        </g>\n      ))}\n      <polygon points={area} fill=\"var(--lime-soft)\" opacity=\"0.8\" />\n      <polyline\n        points={line}\n        fill=\"none\"\n        stroke=\"var(--green)\"\n        strokeWidth=\"2.6\"\n        strokeLinejoin=\"round\"\n        strokeLinecap=\"round\"\n      />\n      <circle\n        cx={x(pts.length - 1)}\n        cy={y(lastV)}\n        r=\"5\"\n        fill=\"var(--lime)\"\n        stroke=\"var(--ink)\"\n        strokeWidth=\"1.8\"\n      />\n      <text\n        x={x(pts.length - 1) + 9}\n        y={y(lastV) + 4}\n        fontSize=\"11.5\"\n        fontWeight=\"700\"\n        fontFamily=\"var(--font-jetbrains)\"\n        fill=\"var(--ink)\"\n      >\n        {Math.round(lastV)}\n      </text>\n    </svg>\n  );\n}\n\n/* ----------------------- position dans la municipalité ----------------------- */\n/** Distribution stylisée (cloche) : les barres sous le percentile sont pleines. */\nexport function PercentileBar({\n  pct,\n  lang,\n}: {\n  pct: number;\n  lang: string;\n}) {\n  const N = 36;\n  const bars = Array.from({ length: N }, (_, i) => {\n    const z = (i / (N - 1) - 0.42) / 0.24; // cloche légèrement asymétrique (droite étirée)\n    return Math.exp(-0.5 * z * z) * (i / N > 0.5 ? 1 + (i / N - 0.5) * 0.35 : 1);\n  });\n  const maxB = Math.max(...bars);\n  const p = Math.max(0, Math.min(100, pct));\n  return (\n    <div aria-label={`${Math.round(p)} %`}>\n      <div className=\"relative flex h-[72px] items-end gap-[2.2%] px-0.5\" style={{ gap: \"2px\" }}>\n        {bars.map((b, i) => {\n          const filled = (i + 0.5) / N <= p / 100;\n          return (\n            <div\n              key={i}\n              className=\"min-w-0 flex-1 rounded-t-[2px] border border-ink transition-colors\"\n              style={{\n                height: `${8 + (b / maxB) * 88}%`,\n                background: filled ? \"var(--lime)\" : \"var(--surface-2)\",\n                borderWidth: \"1px\",\n                opacity: filled ? 1 : 0.55,\n              }}\n            />\n          );\n        })}\n        <div\n          className=\"absolute bottom-0 top-[-6px] w-0 border-l-[2px] border-ink\"\n          style={{ left: `${p}%` }}\n        >\n          <span className=\"vp-mono absolute -top-1 left-1.5 whitespace-nowrap rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\" style={p > 55 ? { left: \"auto\", right: \"6px\" } : undefined}>\n            {lang === \"fr\" ? \"P\" : \"P\"}\n            {Math.round(p)}\n          </span>\n        </div>\n      </div>\n    </div>\n  );\n}\n\n/* ------------------------ mini-courbe d'indice (accueil) ------------------------ */\nexport function IndexSpark({\n  series,\n  width = 150,\n  height = 46,\n}: {\n  series: { month: string; idx: number }[];\n  width?: number;\n  height?: number;\n}) {\n  if (series.length < 2) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const xy = pts.map((v, i) => [\n    4 + (i / (pts.length - 1)) * (width - 8),\n    height - 6 - ((v - min) / span) * (height - 14),\n  ]);\n  const line = xy.map(([x, y]) => `${x.toFixed(1)},${y.toFixed(1)}`).join(\" \");\n  const area = `${xy[0][0]},${height - 3} ${line} ${xy[xy.length - 1][0]},${height - 3}`;\n  const [lx, ly] = xy[xy.length - 1];\n  return (\n    <svg viewBox={`0 0 ${width} ${height}`} className=\"h-auto w-full\" aria-hidden=\"true\">\n      <polygon points={area} fill=\"var(--lime-soft)\" />\n      <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n      <circle cx={lx} cy={ly} r=\"3.4\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n    </svg>\n  );\n}\n\n/* ------------------------------ frise temporelle ------------------------------ */","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n/**\n * MetricViz — visualisations signature générées par les données du registre.\n * Chaque propriété produit ses propres dessins : façade SVG (étages, logements,\n * genre de construction), schéma de terrain à l'échelle (frontage × profondeur),\n * composition de valeur terrain/bâtiment, jauge de confiance, sparkline 2021-2026,\n * compteurs animés. SVG pur, palette encre/lime, aucun aléatoire : mêmes données,\n * même dessin.\n */\n\"use client\";\nimport { useEffect, useState } from \"react\";\n\n/** Nombre localisé (« 1 206,6 » en FR, « 1,206.6 » en EN). */\nexport function locNum(\n  v: number | null | undefined,\n  lang: string,\n  opts?: { unit?: string; maxFrac?: number }\n): string {\n  if (v == null) return \"—\";\n  const s = v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n    maximumFractionDigits: opts?.maxFrac ?? 1,\n  });\n  return opts?.unit ? `${s} ${opts.unit}` : s;\n}\n\n/* ---------------------------- compteur animé ---------------------------- */\n/** Rendu serveur = valeur finale (jamais « 0 $ » à l'écran) ; anime 0 → valeur au montage. */\nexport function CountUp({\n  value,\n  format,\n  duration = 900,\n}: {\n  value: number;\n  format: (v: number) => string;\n  duration?: number;\n}) {\n  const [display, setDisplay] = useState(value);\n\n  useEffect(() => {\n    if (window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches) return;\n    let raf = 0;\n    const t0 = performance.now();\n    const tick = (t: number) => {\n      const p = Math.min(1, (t - t0) / duration);\n      const eased = 1 - Math.pow(1 - p, 3);\n      setDisplay(value * eased);\n      if (p < 1) raf = requestAnimationFrame(tick);\n    };\n    raf = requestAnimationFrame(tick);\n    return () => cancelAnimationFrame(raf);\n  }, [value, duration]);\n\n  return <span>{format(display)}</span>;\n}\n\n/* --------------------------- jauge de confiance --------------------------- */\nexport function ConfidenceDial({\n  pct,\n  level,\n  label,\n}: {\n  pct: number;\n  level: \"A\" | \"B\" | \"C\" | \"D\";\n  label: string;\n}) {\n  const [mounted, setMounted] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setMounted(true), 60);\n    return () => clearTimeout(id);\n  }, []);\n  const R = 52;\n  const CIRC = Math.PI * R; // demi-cercle\n  const frac = mounted ? Math.max(0.02, Math.min(1, pct / 100)) : 0.02;\n  const color =\n    level === \"A\" ? \"var(--lime)\" : level === \"B\" ? \"var(--green)\" : level === \"C\" ? \"var(--amber)\" : \"var(--danger)\";\n  return (\n    <div className=\"flex flex-col items-center\" aria-label={`${label} : ${level} ${pct} %`}>\n      <svg viewBox=\"0 0 128 78\" className=\"w-[128px]\">\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"var(--surface-2)\" strokeWidth=\"12\" strokeLinecap=\"round\" />\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"rgba(20,24,20,0.85)\" strokeWidth=\"13.5\" strokeLinecap=\"round\" opacity=\"0.12\" />\n        <path\n          d=\"M 12 66 A 52 52 0 0 1 116 66\"\n          fill=\"none\"\n          stroke={color}\n          strokeWidth=\"12\"\n          strokeLinecap=\"round\"\n          strokeDasharray={`${CIRC * frac} ${CIRC}`}\n          style={{ transition: \"stroke-dasharray 1s cubic-bezier(.2,.8,.2,1)\" }}\n        />\n        {/* graduations */}\n        {[0, 0.25, 0.5, 0.75, 1].map((g) => {\n          const a = Math.PI * (1 - g);\n          const x1 = 64 + Math.cos(a) * 40;\n          const y1 = 66 - Math.sin(a) * 40;\n          const x2 = 64 + Math.cos(a) * 45;\n          const y2 = 66 - Math.sin(a) * 45;\n          return <line key={g} x1={x1} y1={y1} x2={x2} y2={y2} stroke=\"var(--ink)\" strokeWidth=\"1.4\" opacity=\"0.5\" />;\n        })}\n        <text x=\"64\" y=\"52\" textAnchor=\"middle\" fontSize=\"26\" fontWeight=\"700\" fill=\"var(--ink)\" fontFamily=\"var(--font-space-grotesk)\">\n          {level}\n        </text>\n        <text x=\"64\" y=\"70\" textAnchor=\"middle\" fontSize=\"10.5\" fontWeight=\"700\" fill=\"var(--ink-2)\" fontFamily=\"var(--font-jetbrains)\">\n          {pct} %\n        </text>\n      </svg>\n      <p className=\"klabel mt-1 text-center\">{label}</p>\n    </div>\n  );\n}\n\n/* --------------------------- façade de bâtiment --------------------------- */\n/** Terrain sans bâtiment : borne d'arpenteur et conifères, pas de fausse maison. */\nfunction VacantGlyph({ lang }: { lang: string }) {\n  const tree = (x: number, sc: number) => (\n    <g transform={`translate(${x}, 128) scale(${sc})`}>\n      <path d=\"M0,-46 L13,-14 L5,-14 L16,8 L-16,8 L-5,-14 L-13,-14 Z\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      <rect x=\"-2.5\" y=\"8\" width=\"5\" height=\"9\" fill=\"var(--ink)\" />\n    </g>\n  );\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain sans bâtiment\" : \"Vacant land\"}>\n      {tree(52, 1)}\n      {tree(96, 0.72)}\n      {tree(134, 0.9)}\n      {/* borne d'arpenteur */}\n      <g transform=\"translate(24, 128)\">\n        <rect x=\"-2.5\" y=\"-26\" width=\"5\" height=\"26\" fill=\"var(--ink)\" />\n        <rect x=\"-8\" y=\"-34\" width=\"16\" height=\"10\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n      </g>\n      <line x1=\"8\" y1=\"145\" x2=\"182\" y2=\"145\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"120\" textAnchor=\"middle\" fontSize=\"9\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n        {lang === \"fr\" ? \"SANS BÂTIMENT\" : \"NO BUILDING\"}\n      </text>\n    </svg>\n  );\n}\n\n/** Façade dessinée à partir du registre : étages, logements, genre de construction.\n *  Sans bâtiment au registre (ni aire, ni année, ni étages) → borne d'arpenteur. */\nexport function BuildingGlyph({\n  floors,\n  dwellings,\n  genre,\n  yearBuilt,\n  floorAreaM2,\n  lang = \"fr\",\n}: {\n  floors: number | null;\n  dwellings: number | null;\n  genre: string | null;\n  yearBuilt: number | null;\n  floorAreaM2?: number | null;\n  lang?: string;\n}) {\n  if (!floorAreaM2 && !yearBuilt && !floors && !dwellings) {\n    return <VacantGlyph lang={lang} />;\n  }\n  const nF = Math.max(1, Math.min(6, Math.round(floors ?? 1)));\n  const nD = Math.max(1, Math.min(24, Math.round(dwellings ?? 1)));\n  const winPerFloor = Math.max(1, Math.min(4, Math.ceil(nD / nF)));\n  const W = 190;\n  const bw = 64 + winPerFloor * 18;\n  const bx = (W - bw) / 2;\n  const fh = 24;\n  const groundY = 128;\n  const bodyTop = groundY - nF * fh;\n  const mansard = (genre ?? \"\").toLowerCase().includes(\"mansard\");\n  const flat = (genre ?? \"\").toLowerCase().includes(\"plain-pied\") && nF === 1;\n\n  const windows: React.ReactNode[] = [];\n  for (let f = 0; f < nF; f++) {\n    for (let wi = 0; wi < winPerFloor; wi++) {\n      const idx = f * winPerFloor + wi;\n      windows.push(\n        <rect\n          key={`${f}-${wi}`}\n          x={bx + 14 + wi * ((bw - 28) / winPerFloor) + ((bw - 28) / winPerFloor - 12) / 2}\n          y={bodyTop + f * fh + 6}\n          width=\"12\"\n          height=\"11\"\n          fill={idx < nD ? \"var(--lime)\" : \"var(--surface-2)\"}\n          stroke=\"var(--ink)\"\n          strokeWidth=\"1.2\"\n          className=\"vv-win\"\n          style={{ animationDelay: `${idx * 60}ms` }}\n        />\n      );\n    }\n  }\n\n  return (\n    <svg viewBox={`0 0 ${W} 150`} className=\"w-full max-w-[210px]\" aria-label=\"Silhouette du bâtiment selon le registre\">\n      {/* soleil-repère */}\n      <circle cx={W - 22} cy=\"22\" r=\"9\" fill=\"none\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      <circle cx={W - 22} cy=\"22\" r=\"3.5\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1\" />\n      {/* ombre décalée signature */}\n      <rect x={bx + 5} y={bodyTop + 5} width={bw} height={nF * fh} fill=\"rgba(20,24,20,0.12)\" />\n      {/* corps */}\n      <rect x={bx} y={bodyTop} width={bw} height={nF * fh} fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      {/* séparations d'étages */}\n      {Array.from({ length: nF - 1 }, (_, i) => (\n        <line key={i} x1={bx} y1={bodyTop + (i + 1) * fh} x2={bx + bw} y2={bodyTop + (i + 1) * fh} stroke=\"var(--ink)\" strokeWidth=\"1\" strokeDasharray=\"3 3\" opacity=\"0.4\" />\n      ))}\n      {/* toit */}\n      {flat ? (\n        <rect x={bx - 6} y={bodyTop - 8} width={bw + 12} height=\"8\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : mansard ? (\n        <path d={`M ${bx - 6} ${bodyTop} L ${bx + 10} ${bodyTop - 20} L ${bx + bw - 10} ${bodyTop - 20} L ${bx + bw + 6} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : (\n        <path d={`M ${bx - 8} ${bodyTop} L ${bx + bw / 2} ${bodyTop - 24} L ${bx + bw + 8} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      )}\n      {/* cheminée */}\n      <rect x={bx + bw - 22} y={bodyTop - (flat ? 20 : 30)} width=\"9\" height={flat ? 14 : 18} fill=\"var(--ink)\" />\n      {windows}\n      {/* porte */}\n      <rect x={bx + bw / 2 - 7} y={groundY - 17} width=\"14\" height=\"17\" fill=\"var(--ink)\" />\n      <circle cx={bx + bw / 2 + 3.5} cy={groundY - 8} r=\"1.4\" fill=\"var(--lime)\" />\n      {/* sol */}\n      <line x1=\"8\" y1={groundY} x2={W - 8} y2={groundY} stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <line x1=\"8\" y1={groundY + 4} x2={W - 8} y2={groundY + 4} stroke=\"var(--ink)\" strokeWidth=\"1\" opacity=\"0.3\" />\n      {/* millésime gravé */}\n      {yearBuilt && (\n        <text x={W / 2} y=\"146\" textAnchor=\"middle\" fontSize=\"10\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n          ANNO {yearBuilt}\n        </text>\n      )}\n    </svg>\n  );\n}\n\n/* ----------------------------- schéma du terrain ----------------------------- */\n/** Terrain à l'échelle réelle : frontage × profondeur déduite de la superficie. */\nexport function LotDiagram({\n  areaM2,\n  frontageM,\n  footprintM2,\n  lang,\n  isCondo,\n}: {\n  areaM2: number | null;\n  frontageM: number | null;\n  footprintM2: number | null;\n  lang: string;\n  isCondo?: boolean;\n}) {\n  if (!areaM2 || areaM2 <= 0) {\n    return (\n      <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain non applicable\" : \"Lot not applicable\"}>\n        <rect x=\"30\" y=\"30\" width=\"130\" height=\"86\" fill=\"var(--surface-2)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" strokeDasharray=\"5 5\" rx=\"6\" />\n        <text x=\"95\" y=\"66\" textAnchor=\"middle\" fontSize=\"20\" fill=\"var(--ink-3)\">⌂</text>\n        <text x=\"95\" y=\"90\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"1\">\n          {isCondo\n            ? lang === \"fr\" ? \"QUOTE-PART DE COPROPRIÉTÉ\" : \"CONDOMINIUM SHARE\"\n            : lang === \"fr\" ? \"TERRAIN NON INSCRIT AU RÔLE\" : \"LOT NOT ON ROLL\"}\n        </text>\n        <text x=\"95\" y=\"104\" textAnchor=\"middle\" fontSize=\"7\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n          {isCondo\n            ? lang === \"fr\" ? \"terrain commun indivis\" : \"undivided common land\"\n            : \"—\"}\n        </text>\n      </svg>\n    );\n  }\n  const front = frontageM && frontageM > 0 ? frontageM : Math.sqrt(areaM2);\n  const depth = areaM2 / front;\n  const ratio = Math.max(0.25, Math.min(4, depth / front));\n  const maxW = 150;\n  const maxH = 108;\n  let w = maxW;\n  let h = w * ratio;\n  if (h > maxH) {\n    h = maxH;\n    w = h / ratio;\n  }\n  const x = (190 - w) / 2;\n  const y = 118 - h;\n  // empreinte du bâtiment, à l'échelle de la superficie\n  const fpFrac = footprintM2 ? Math.min(0.8, footprintM2 / areaM2) : 0;\n  const fw = w * Math.sqrt(fpFrac) * 0.9;\n  const fh = h * Math.sqrt(fpFrac) * 0.9;\n  const fmtM = (v: number) => `${v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { maximumFractionDigits: 1 })} m`;\n\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label=\"Schéma du terrain à l'échelle\">\n      <defs>\n        <pattern id=\"hatch\" width=\"7\" height=\"7\" patternTransform=\"rotate(45)\" patternUnits=\"userSpaceOnUse\">\n          <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"7\" stroke=\"var(--green)\" strokeWidth=\"1\" opacity=\"0.35\" />\n        </pattern>\n      </defs>\n      {/* ombre + parcelle */}\n      <rect x={x + 4} y={y + 4} width={w} height={h} fill=\"rgba(20,24,20,0.12)\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"var(--lime-soft)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"url(#hatch)\" />\n      {/* empreinte bâtiment */}\n      {fpFrac > 0.005 && (\n        <rect x={x + (w - fw) / 2} y={y + h - fh - h * 0.08} width={fw} height={fh} fill=\"var(--ink)\" rx=\"2\">\n          <title>{lang === \"fr\" ? \"Empreinte approximative du bâtiment\" : \"Approximate building footprint\"}</title>\n        </rect>\n      )}\n      {/* cotes : frontage */}\n      <line x1={x} y1={y + h + 12} x2={x + w} y2={y + h + 12} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x} y1={y + h + 8} x2={x} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x + w} y1={y + h + 8} x2={x + w} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x + w / 2} y={y + h + 24} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n        {frontageM ? fmtM(front) : `≈ ${fmtM(front)}`}\n      </text>\n      {/* cotes : profondeur */}\n      <line x1={x - 10} y1={y} x2={x - 10} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y} x2={x - 6} y2={y} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y + h} x2={x - 6} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x - 16} y={y + h / 2} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\" transform={`rotate(-90 ${x - 16} ${y + h / 2})`}>\n        ≈ {fmtM(depth)}\n      </text>\n      {/* rue */}\n      <line x1=\"8\" y1=\"136\" x2=\"182\" y2=\"136\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"147\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\" letterSpacing=\"3\">\n        {lang === \"fr\" ? \"RUE\" : \"STREET\"}\n      </text>\n    </svg>\n  );\n}\n\n/* ------------------------- composition de la valeur ------------------------- */\nexport function ValueSplit({\n  land,\n  building,\n  previous,\n  total,\n  lang,\n  labels,\n}: {\n  land: number | null;\n  building: number | null;\n  previous: number | null;\n  total: number | null;\n  lang: string;\n  labels: { land: string; building: string; previous: string };\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  const l = land ?? 0;\n  const b = building ?? 0;\n  const sum = l + b;\n  if (sum <= 0) return null;\n  const lPct = (l / sum) * 100;\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { style: \"currency\", currency: \"CAD\", maximumFractionDigits: 0 }).format(v);\n  const delta = previous && previous > 0 && total ? ((total / previous - 1) * 100) : null;\n  return (\n    <div>\n      <div className=\"flex h-9 w-full overflow-hidden rounded-md border-[1.5px] border-ink\">\n        {lPct >= 1 && (\n          <div\n            className=\"flex items-center justify-center overflow-hidden bg-green transition-[width] duration-1000 ease-out\"\n            style={{ width: on ? `${lPct}%` : \"2%\" }}\n          >\n            {lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-paper\">{Math.round(lPct)} %</span>\n            )}\n          </div>\n        )}\n        {lPct <= 99 && (\n          <div className=\"flex flex-1 items-center justify-center overflow-hidden bg-ink\">\n            {100 - lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-lime\">{Math.round(100 - lPct)} %</span>\n            )}\n          </div>\n        )}\n      </div>\n      <div className=\"vp-mono mt-2 flex flex-wrap items-center gap-x-4 gap-y-1 text-[10.5px] uppercase tracking-[0.05em]\">\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-green\" />\n          {labels.land} {fmt(l)}\n        </span>\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-ink\" />\n          {labels.building} {fmt(b)}\n        </span>\n      </div>\n      {delta != null && (\n        <p className=\"vp-mono mt-2.5 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {labels.previous} {fmt(previous!)}{\" \"}\n          <span className={`ml-1 rounded-[4px] border border-ink px-1.5 py-0.5 font-bold ${delta >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n            {delta >= 0 ? \"+\" : \"\"}\n            {delta.toFixed(1)} %\n          </span>\n        </p>\n      )}\n    </div>\n  );\n}\n\n/* -------------------------------- sparkline -------------------------------- */\nexport function Sparkline({\n  history,\n  height = 44,\n}: {\n  history: { year: number; value: number | null }[];\n  height?: number;\n}) {\n  const pts = history.filter((h) => h.value != null) as { year: number; value: number }[];\n  if (pts.length < 2) return null;\n  const W = 132;\n  const min = Math.min(...pts.map((p) => p.value));\n  const max = Math.max(...pts.map((p) => p.value));\n  const span = max - min || 1;\n  const xy = pts.map((p, i) => [\n    6 + (i / (pts.length - 1)) * (W - 12),\n    height - 8 - ((p.value - min) / span) * (height - 18),\n  ]);\n  const line = xy.map(([x, y]) => `${x},${y}`).join(\" \");\n  const area = `${xy[0][0]},${height - 4} ${line} ${xy[xy.length - 1][0]},${height - 4}`;\n  const growth = ((pts[pts.length - 1].value / pts[0].value - 1) * 100);\n  const [x2, y2] = xy[xy.length - 1];\n  return (\n    <div className=\"flex items-end gap-2\">\n      <svg viewBox={`0 0 ${W} ${height}`} width={W} height={height} aria-label={`Évolution ${pts[0].year}-${pts[pts.length - 1].year}`}>\n        <polygon points={area} fill=\"var(--lime-soft)\" />\n        <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n        {xy.slice(0, -1).map(([x, y], i) => (\n          <circle key={i} cx={x} cy={y} r=\"2\" fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"1.1\" />\n        ))}\n        <circle cx={x2} cy={y2} r=\"3.6\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      </svg>\n      <span className={`vp-mono mb-0.5 rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${growth >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n        {growth >= 0 ? \"+\" : \"\"}\n        {growth.toFixed(0)} %\n      </span>\n    </div>\n  );\n}\n\n/* ------------------------------ frise temporelle ------------------------------ */\nexport function EraLine({ year, lang }: { year: number | null; lang: string }) {\n  if (!year || year < 1750) return null;\n  const min = Math.min(1900, Math.floor((year - 10) / 50) * 50);\n  const max = 2026;\n  const frac = Math.max(0, Math.min(1, (year - min) / (max - min)));\n  const W = 190;\n  const x = 12 + frac * (W - 24);\n  const age = 2026 - year;\n  return (\n    <svg viewBox={`0 0 ${W} 40`} className=\"w-full max-w-[210px]\" aria-label={`Construction ${year}`}>\n      <line x1=\"12\" y1=\"24\" x2={W - 12} y2=\"24\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      {[...new Set([min, Math.round((min + max) / 2 / 50) * 50, 2000])].map((yr) => {\n        const gx = 12 + ((yr - min) / (max - min)) * (W - 24);\n        return (\n          <g key={yr}>\n            <line x1={gx} y1=\"20\" x2={gx} y2=\"28\" stroke=\"var(--ink)\" strokeWidth=\"1.2\" opacity=\"0.5\" />\n            <text x={gx} y=\"38\" textAnchor=\"middle\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n              {yr}\n            </text>\n          </g>\n        );\n      })}\n      <g transform={`translate(${x}, 24)`}>\n        <rect x=\"-7\" y=\"-7\" width=\"14\" height=\"14\" transform=\"rotate(45)\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n        <text y=\"-13\" textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n          {year}\n        </text>\n      </g>\n      <text x={W - 12} y=\"12\" textAnchor=\"end\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n        {age} {lang === \"fr\" ? \"ANS\" : \"YRS\"}\n      </text>\n    </svg>\n  );\n}\n","structuredPatch":[{"oldStart":433,"oldLines":6,"newStart":433,"newLines":309,"lines":["   );"," }"," ","+/* ------------------------- fourchette visuelle P10-P90 ------------------------- */","+/** Barre de fourchette : segment P10→P90, losange = estimation, tiret = rôle. */","+export function RangeBar({","+  low,","+  estimate,","+  high,","+  role,","+  lang,","+}: {","+  low: number;","+  estimate: number;","+  high: number;","+  role: number | null;","+  lang: string;","+}) {","+  const [on, setOn] = useState(false);","+  useEffect(() => {","+    const id = setTimeout(() => setOn(true), 80);","+    return () => clearTimeout(id);","+  }, []);","+  if (!(high > low) || !Number.isFinite(estimate)) return null;","+  const lo = Math.min(low, role ?? low);","+  const hi = Math.max(high, role ?? high);","+  const pad = (hi - lo) * 0.09 || hi * 0.05;","+  const min = lo - pad;","+  const max = hi + pad;","+  const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));","+  const fmt = (v: number) =>","+    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {","+      style: \"currency\",","+      currency: \"CAD\",","+      maximumFractionDigits: 0,","+      notation: v >= 1_000_000 ? \"compact\" : \"standard\",","+    }).format(v);","+  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;","+  return (","+    <div aria-label={`${fmt(low)} – ${fmt(high)}`}>","+      <div className=\"relative h-[64px]\">","+        {/* étiquette estimation */}","+        <div","+          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"","+          style={{ left: `${on ? X(estimate) : 50}%` }}","+        >","+          <span className=\"vp-mono rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\">","+            {lang === \"fr\" ? \"ESTIMATION\" : \"ESTIMATE\"} {fmt(estimate)}","+          </span>","+        </div>","+        {/* piste */}","+        <div className=\"absolute left-0 right-0 top-[30px] h-[14px] rounded-[7px] border-[1.5px] border-ink bg-surface-2\" />","+        {/* segment P10-P90 */}","+        <div","+          className=\"absolute top-[30px] h-[14px] origin-left rounded-[7px] border-[1.5px] border-ink transition-transform duration-1000 ease-out\"","+          style={{","+            left: `${X(low)}%`,","+            width: `${Math.max(2, X(high) - X(low))}%`,","+            transform: on ? \"scaleX(1)\" : \"scaleX(0.05)\",","+            background:","+              \"repeating-linear-gradient(-45deg, var(--lime-soft), var(--lime-soft) 5px, rgba(255,81,72,0.35) 5px, rgba(255,81,72,0.35) 7px)\",","+          }}","+        />","+        {/* marqueur estimation (losange) */}","+        <div","+          className=\"absolute top-[29px] -translate-x-1/2 transition-[left] duration-700 ease-out\"","+          style={{ left: `${on ? X(estimate) : 50}%` }}","+        >","+          <div className=\"h-[16px] w-[16px] rotate-45 border-[1.8px] border-ink bg-lime\" />","+        </div>","+        {/* marqueur rôle */}","+        {role != null && role > 0 && (","+          <div","+            className=\"absolute top-[22px] -translate-x-1/2 text-center\"","+            style={{ left: `${X(role)}%` }}","+          >","+            <div className=\"mx-auto h-[30px] w-0 border-l-[2px] border-dashed border-ink opacity-70\" />","+            {!roleClose && (","+              <span className=\"vp-mono whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\">","+                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}","+              </span>","+            )}","+          </div>","+        )}","+        {/* bornes */}","+        <span","+          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"","+          style={{ left: `${X(low)}%` }}","+        >","+          P10 {fmt(low)}","+        </span>","+        <span","+          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"","+          style={{ left: `${X(high)}%` }}","+        >","+          P90 {fmt(high)}","+        </span>","+      </div>","+    </div>","+  );","+}","+","+/* --------------------------- courbe d'indice de marché --------------------------- */","+/** Indice mensuel rebasé à 100 au premier mois — la trajectoire du marché local. */","+export function MarketIndexChart({","+  series,","+  lang,","+}: {","+  series: { month: string; idx: number }[];","+  lang: string;","+}) {","+  if (series.length < 6) return null;","+  const base = series[0].idx || 1;","+  const pts = series.map((p) => (p.idx / base) * 100);","+  const W = 640;","+  const H = 200;","+  const padL = 40;","+  const padR = 52;","+  const padT = 18;","+  const padB = 28;","+  const min = Math.min(...pts);","+  const max = Math.max(...pts);","+  const span = max - min || 1;","+  const x = (i: number) => padL + (i / (pts.length - 1)) * (W - padL - padR);","+  const y = (v: number) => padT + (1 - (v - min) / span) * (H - padT - padB);","+  const line = pts.map((v, i) => `${x(i).toFixed(1)},${y(v).toFixed(1)}`).join(\" \");","+  const area = `${x(0)},${H - padB} ${line} ${x(pts.length - 1)},${H - padB}`;","+  // graduations : janvier de chaque année","+  const years = series","+    .map((p, i) => ({ i, month: p.month }))","+    .filter((p) => p.month.endsWith(\"-01\"));","+  // lignes horizontales : min / 100 / max arrondis","+  const gridVals = [...new Set([Math.round(min), 100, Math.round(max)])].filter(","+    (v) => v >= min - 1 && v <= max + 1","+  );","+  const lastV = pts[pts.length - 1];","+  return (","+    <svg","+      viewBox={`0 0 ${W} ${H}`}","+      className=\"h-auto w-full\"","+      role=\"img\"","+      aria-label={","+        lang === \"fr\"","+          ? `Indice de marché ${series[0].month} à ${series[series.length - 1].month}`","+          : `Market index ${series[0].month} to ${series[series.length - 1].month}`","+      }","+    >","+      {gridVals.map((v) => (","+        <g key={v}>","+          <line","+            x1={padL}","+            y1={y(v)}","+            x2={W - padR}","+            y2={y(v)}","+            stroke=\"var(--ink)\"","+            strokeWidth=\"1\"","+            strokeDasharray=\"3 5\"","+            opacity={v === 100 ? 0.45 : 0.18}","+          />","+          <text","+            x={padL - 6}","+            y={y(v) + 3.5}","+            textAnchor=\"end\"","+            fontSize=\"10\"","+            fontFamily=\"var(--font-jetbrains)\"","+            fill=\"var(--ink-3)\"","+          >","+            {v}","+          </text>","+        </g>","+      ))}","+      {years.map((p) => (","+        <g key={p.month}>","+          <line","+            x1={x(p.i)}","+            y1={padT}","+            x2={x(p.i)}","+            y2={H - padB}","+            stroke=\"var(--ink)\"","+            strokeWidth=\"1\"","+            opacity=\"0.1\"","+          />","+          <text","+            x={x(p.i)}","+            y={H - 10}","+            textAnchor=\"middle\"","+            fontSize=\"10\"","+            fontWeight=\"700\"","+            fontFamily=\"var(--font-jetbrains)\"","+            fill=\"var(--ink-2)\"","+          >","+            {p.month.slice(0, 4)}","+          </text>","+        </g>","+      ))}","+      <polygon points={area} fill=\"var(--lime-soft)\" opacity=\"0.8\" />","+      <polyline","+        points={line}","+        fill=\"none\"","+        stroke=\"var(--green)\"","+        strokeWidth=\"2.6\"","+        strokeLinejoin=\"round\"","+        strokeLinecap=\"round\"","+      />","+      <circle","+        cx={x(pts.length - 1)}","+        cy={y(lastV)}","+        r=\"5\"","+        fill=\"var(--lime)\"","+        stroke=\"var(--ink)\"","+        strokeWidth=\"1.8\"","+      />","+      <text","+        x={x(pts.length - 1) + 9}","+        y={y(lastV) + 4}","+        fontSize=\"11.5\"","+        fontWeight=\"700\"","+        fontFamily=\"var(--font-jetbrains)\"","+        fill=\"var(--ink)\"","+      >","+        {Math.round(lastV)}","+      </text>","+    </svg>","+  );","+}","+","+/* ----------------------- position dans la municipalité ----------------------- */","+/** Distribution stylisée (cloche) : les barres sous le percentile sont pleines. */","+export function PercentileBar({","+  pct,","+  lang,","+}: {","+  pct: number;","+  lang: string;","+}) {","+  const N = 36;","+  const bars = Array.from({ length: N }, (_, i) => {","+    const z = (i / (N - 1) - 0.42) / 0.24; // cloche légèrement asymétrique (droite étirée)","+    return Math.exp(-0.5 * z * z) * (i / N > 0.5 ? 1 + (i / N - 0.5) * 0.35 : 1);","+  });","+  const maxB = Math.max(...bars);","+  const p = Math.max(0, Math.min(100, pct));","+  return (","+    <div aria-label={`${Math.round(p)} %`}>","+      <div className=\"relative flex h-[72px] items-end gap-[2.2%] px-0.5\" style={{ gap: \"2px\" }}>","+        {bars.map((b, i) => {","+          const filled = (i + 0.5) / N <= p / 100;","+          return (","+            <div","+              key={i}","+              className=\"min-w-0 flex-1 rounded-t-[2px] border border-ink transition-colors\"","+              style={{","+                height: `${8 + (b / maxB) * 88}%`,","+                background: filled ? \"var(--lime)\" : \"var(--surface-2)\",","+                borderWidth: \"1px\",","+                opacity: filled ? 1 : 0.55,","+              }}","+            />","+          );","+        })}","+        <div","+          className=\"absolute bottom-0 top-[-6px] w-0 border-l-[2px] border-ink\"","+          style={{ left: `${p}%` }}","+        >","+          <span className=\"vp-mono absolute -top-1 left-1.5 whitespace-nowrap rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\" style={p > 55 ? { left: \"auto\", right: \"6px\" } : undefined}>","+            {lang === \"fr\" ? \"P\" : \"P\"}","+            {Math.round(p)}","+          </span>","+        </div>","+      </div>","+    </div>","+  );","+}","+","+/* ------------------------ mini-courbe d'indice (accueil) ------------------------ */","+export function IndexSpark({","+  series,","+  width = 150,","+  height = 46,","+}: {","+  series: { month: string; idx: number }[];","+  width?: number;","+  height?: number;","+}) {","+  if (series.length < 2) return null;","+  const base = series[0].idx || 1;","+  const pts = series.map((p) => (p.idx / base) * 100);","+  const min = Math.min(...pts);","+  const max = Math.max(...pts);","+  const span = max - min || 1;","+  const xy = pts.map((v, i) => [","+    4 + (i / (pts.length - 1)) * (width - 8),","+    height - 6 - ((v - min) / span) * (height - 14),","+  ]);","+  const line = xy.map(([x, y]) => `${x.toFixed(1)},${y.toFixed(1)}`).join(\" \");","+  const area = `${xy[0][0]},${height - 3} ${line} ${xy[xy.length - 1][0]},${height - 3}`;","+  const [lx, ly] = xy[xy.length - 1];","+  return (","+    <svg viewBox={`0 0 ${width} ${height}`} className=\"h-auto w-full\" aria-hidden=\"true\">","+      <polygon points={area} fill=\"var(--lime-soft)\" />","+      <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />","+      <circle cx={lx} cy={ly} r=\"3.4\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />","+    </svg>","+  );","+}","+"," /* ------------------------------ frise temporelle ------------------------------ */"," export function EraLine({ year, lang }: { year: number | null; lang: string }) {","   if (!year || year < 1750) return null;"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwU4gGUKVkMr3NB8vAz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NWqXv2W5V8uTiTvWy3oDuZ","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","old_string":"      <div className=\"relative flex h-[72px] items-end gap-[2.2%] px-0.5\" style={{ gap: \"2px\" }}>","new_string":"      <div className=\"relative flex h-[72px] items-end px-0.5\" style={{ gap: \"2px\" }}>"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5005,"cache_read_input_tokens":109814,"cache_creation":{"ephemeral_5m_input_tokens":5005,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"748646ff-5d03-42f8-adb1-3b402fd46700","request_id":"req_011CeJwU2LNoCL65jinGC3an"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NWqXv2W5V8uTiTvWy3oDuZ","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"e0906798-3dc4-4c81-a0be-307758a30e16","timestamp":"2026-08-23T03:05:25.701Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","oldString":"      <div className=\"relative flex h-[72px] items-end gap-[2.2%] px-0.5\" style={{ gap: \"2px\" }}>","newString":"      <div className=\"relative flex h-[72px] items-end px-0.5\" style={{ gap: \"2px\" }}>","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n/**\n * MetricViz — visualisations signature générées par les données du registre.\n * Chaque propriété produit ses propres dessins : façade SVG (étages, logements,\n * genre de construction), schéma de terrain à l'échelle (frontage × profondeur),\n * composition de valeur terrain/bâtiment, jauge de confiance, sparkline 2021-2026,\n * compteurs animés. SVG pur, palette encre/lime, aucun aléatoire : mêmes données,\n * même dessin.\n */\n\"use client\";\nimport { useEffect, useState } from \"react\";\n\n/** Nombre localisé (« 1 206,6 » en FR, « 1,206.6 » en EN). */\nexport function locNum(\n  v: number | null | undefined,\n  lang: string,\n  opts?: { unit?: string; maxFrac?: number }\n): string {\n  if (v == null) return \"—\";\n  const s = v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n    maximumFractionDigits: opts?.maxFrac ?? 1,\n  });\n  return opts?.unit ? `${s} ${opts.unit}` : s;\n}\n\n/* ---------------------------- compteur animé ---------------------------- */\n/** Rendu serveur = valeur finale (jamais « 0 $ » à l'écran) ; anime 0 → valeur au montage. */\nexport function CountUp({\n  value,\n  format,\n  duration = 900,\n}: {\n  value: number;\n  format: (v: number) => string;\n  duration?: number;\n}) {\n  const [display, setDisplay] = useState(value);\n\n  useEffect(() => {\n    if (window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches) return;\n    let raf = 0;\n    const t0 = performance.now();\n    const tick = (t: number) => {\n      const p = Math.min(1, (t - t0) / duration);\n      const eased = 1 - Math.pow(1 - p, 3);\n      setDisplay(value * eased);\n      if (p < 1) raf = requestAnimationFrame(tick);\n    };\n    raf = requestAnimationFrame(tick);\n    return () => cancelAnimationFrame(raf);\n  }, [value, duration]);\n\n  return <span>{format(display)}</span>;\n}\n\n/* --------------------------- jauge de confiance --------------------------- */\nexport function ConfidenceDial({\n  pct,\n  level,\n  label,\n}: {\n  pct: number;\n  level: \"A\" | \"B\" | \"C\" | \"D\";\n  label: string;\n}) {\n  const [mounted, setMounted] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setMounted(true), 60);\n    return () => clearTimeout(id);\n  }, []);\n  const R = 52;\n  const CIRC = Math.PI * R; // demi-cercle\n  const frac = mounted ? Math.max(0.02, Math.min(1, pct / 100)) : 0.02;\n  const color =\n    level === \"A\" ? \"var(--lime)\" : level === \"B\" ? \"var(--green)\" : level === \"C\" ? \"var(--amber)\" : \"var(--danger)\";\n  return (\n    <div className=\"flex flex-col items-center\" aria-label={`${label} : ${level} ${pct} %`}>\n      <svg viewBox=\"0 0 128 78\" className=\"w-[128px]\">\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"var(--surface-2)\" strokeWidth=\"12\" strokeLinecap=\"round\" />\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"rgba(20,24,20,0.85)\" strokeWidth=\"13.5\" strokeLinecap=\"round\" opacity=\"0.12\" />\n        <path\n          d=\"M 12 66 A 52 52 0 0 1 116 66\"\n          fill=\"none\"\n          stroke={color}\n          strokeWidth=\"12\"\n          strokeLinecap=\"round\"\n          strokeDasharray={`${CIRC * frac} ${CIRC}`}\n          style={{ transition: \"stroke-dasharray 1s cubic-bezier(.2,.8,.2,1)\" }}\n        />\n        {/* graduations */}\n        {[0, 0.25, 0.5, 0.75, 1].map((g) => {\n          const a = Math.PI * (1 - g);\n          const x1 = 64 + Math.cos(a) * 40;\n          const y1 = 66 - Math.sin(a) * 40;\n          const x2 = 64 + Math.cos(a) * 45;\n          const y2 = 66 - Math.sin(a) * 45;\n          return <line key={g} x1={x1} y1={y1} x2={x2} y2={y2} stroke=\"var(--ink)\" strokeWidth=\"1.4\" opacity=\"0.5\" />;\n        })}\n        <text x=\"64\" y=\"52\" textAnchor=\"middle\" fontSize=\"26\" fontWeight=\"700\" fill=\"var(--ink)\" fontFamily=\"var(--font-space-grotesk)\">\n          {level}\n        </text>\n        <text x=\"64\" y=\"70\" textAnchor=\"middle\" fontSize=\"10.5\" fontWeight=\"700\" fill=\"var(--ink-2)\" fontFamily=\"var(--font-jetbrains)\">\n          {pct} %\n        </text>\n      </svg>\n      <p className=\"klabel mt-1 text-center\">{label}</p>\n    </div>\n  );\n}\n\n/* --------------------------- façade de bâtiment --------------------------- */\n/** Terrain sans bâtiment : borne d'arpenteur et conifères, pas de fausse maison. */\nfunction VacantGlyph({ lang }: { lang: string }) {\n  const tree = (x: number, sc: number) => (\n    <g transform={`translate(${x}, 128) scale(${sc})`}>\n      <path d=\"M0,-46 L13,-14 L5,-14 L16,8 L-16,8 L-5,-14 L-13,-14 Z\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      <rect x=\"-2.5\" y=\"8\" width=\"5\" height=\"9\" fill=\"var(--ink)\" />\n    </g>\n  );\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain sans bâtiment\" : \"Vacant land\"}>\n      {tree(52, 1)}\n      {tree(96, 0.72)}\n      {tree(134, 0.9)}\n      {/* borne d'arpenteur */}\n      <g transform=\"translate(24, 128)\">\n        <rect x=\"-2.5\" y=\"-26\" width=\"5\" height=\"26\" fill=\"var(--ink)\" />\n        <rect x=\"-8\" y=\"-34\" width=\"16\" height=\"10\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n      </g>\n      <line x1=\"8\" y1=\"145\" x2=\"182\" y2=\"145\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"120\" textAnchor=\"middle\" fontSize=\"9\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n        {lang === \"fr\" ? \"SANS BÂTIMENT\" : \"NO BUILDING\"}\n      </text>\n    </svg>\n  );\n}\n\n/** Façade dessinée à partir du registre : étages, logements, genre de construction.\n *  Sans bâtiment au registre (ni aire, ni année, ni étages) → borne d'arpenteur. */\nexport function BuildingGlyph({\n  floors,\n  dwellings,\n  genre,\n  yearBuilt,\n  floorAreaM2,\n  lang = \"fr\",\n}: {\n  floors: number | null;\n  dwellings: number | null;\n  genre: string | null;\n  yearBuilt: number | null;\n  floorAreaM2?: number | null;\n  lang?: string;\n}) {\n  if (!floorAreaM2 && !yearBuilt && !floors && !dwellings) {\n    return <VacantGlyph lang={lang} />;\n  }\n  const nF = Math.max(1, Math.min(6, Math.round(floors ?? 1)));\n  const nD = Math.max(1, Math.min(24, Math.round(dwellings ?? 1)));\n  const winPerFloor = Math.max(1, Math.min(4, Math.ceil(nD / nF)));\n  const W = 190;\n  const bw = 64 + winPerFloor * 18;\n  const bx = (W - bw) / 2;\n  const fh = 24;\n  const groundY = 128;\n  const bodyTop = groundY - nF * fh;\n  const mansard = (genre ?? \"\").toLowerCase().includes(\"mansard\");\n  const flat = (genre ?? \"\").toLowerCase().includes(\"plain-pied\") && nF === 1;\n\n  const windows: React.ReactNode[] = [];\n  for (let f = 0; f < nF; f++) {\n    for (let wi = 0; wi < winPerFloor; wi++) {\n      const idx = f * winPerFloor + wi;\n      windows.push(\n        <rect\n          key={`${f}-${wi}`}\n          x={bx + 14 + wi * ((bw - 28) / winPerFloor) + ((bw - 28) / winPerFloor - 12) / 2}\n          y={bodyTop + f * fh + 6}\n          width=\"12\"\n          height=\"11\"\n          fill={idx < nD ? \"var(--lime)\" : \"var(--surface-2)\"}\n          stroke=\"var(--ink)\"\n          strokeWidth=\"1.2\"\n          className=\"vv-win\"\n          style={{ animationDelay: `${idx * 60}ms` }}\n        />\n      );\n    }\n  }\n\n  return (\n    <svg viewBox={`0 0 ${W} 150`} className=\"w-full max-w-[210px]\" aria-label=\"Silhouette du bâtiment selon le registre\">\n      {/* soleil-repère */}\n      <circle cx={W - 22} cy=\"22\" r=\"9\" fill=\"none\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      <circle cx={W - 22} cy=\"22\" r=\"3.5\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1\" />\n      {/* ombre décalée signature */}\n      <rect x={bx + 5} y={bodyTop + 5} width={bw} height={nF * fh} fill=\"rgba(20,24,20,0.12)\" />\n      {/* corps */}\n      <rect x={bx} y={bodyTop} width={bw} height={nF * fh} fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      {/* séparations d'étages */}\n      {Array.from({ length: nF - 1 }, (_, i) => (\n        <line key={i} x1={bx} y1={bodyTop + (i + 1) * fh} x2={bx + bw} y2={bodyTop + (i + 1) * fh} stroke=\"var(--ink)\" strokeWidth=\"1\" strokeDasharray=\"3 3\" opacity=\"0.4\" />\n      ))}\n      {/* toit */}\n      {flat ? (\n        <rect x={bx - 6} y={bodyTop - 8} width={bw + 12} height=\"8\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : mansard ? (\n        <path d={`M ${bx - 6} ${bodyTop} L ${bx + 10} ${bodyTop - 20} L ${bx + bw - 10} ${bodyTop - 20} L ${bx + bw + 6} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : (\n        <path d={`M ${bx - 8} ${bodyTop} L ${bx + bw / 2} ${bodyTop - 24} L ${bx + bw + 8} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      )}\n      {/* cheminée */}\n      <rect x={bx + bw - 22} y={bodyTop - (flat ? 20 : 30)} width=\"9\" height={flat ? 14 : 18} fill=\"var(--ink)\" />\n      {windows}\n      {/* porte */}\n      <rect x={bx + bw / 2 - 7} y={groundY - 17} width=\"14\" height=\"17\" fill=\"var(--ink)\" />\n      <circle cx={bx + bw / 2 + 3.5} cy={groundY - 8} r=\"1.4\" fill=\"var(--lime)\" />\n      {/* sol */}\n      <line x1=\"8\" y1={groundY} x2={W - 8} y2={groundY} stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <line x1=\"8\" y1={groundY + 4} x2={W - 8} y2={groundY + 4} stroke=\"var(--ink)\" strokeWidth=\"1\" opacity=\"0.3\" />\n      {/* millésime gravé */}\n      {yearBuilt && (\n        <text x={W / 2} y=\"146\" textAnchor=\"middle\" fontSize=\"10\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n          ANNO {yearBuilt}\n        </text>\n      )}\n    </svg>\n  );\n}\n\n/* ----------------------------- schéma du terrain ----------------------------- */\n/** Terrain à l'échelle réelle : frontage × profondeur déduite de la superficie. */\nexport function LotDiagram({\n  areaM2,\n  frontageM,\n  footprintM2,\n  lang,\n  isCondo,\n}: {\n  areaM2: number | null;\n  frontageM: number | null;\n  footprintM2: number | null;\n  lang: string;\n  isCondo?: boolean;\n}) {\n  if (!areaM2 || areaM2 <= 0) {\n    return (\n      <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain non applicable\" : \"Lot not applicable\"}>\n        <rect x=\"30\" y=\"30\" width=\"130\" height=\"86\" fill=\"var(--surface-2)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" strokeDasharray=\"5 5\" rx=\"6\" />\n        <text x=\"95\" y=\"66\" textAnchor=\"middle\" fontSize=\"20\" fill=\"var(--ink-3)\">⌂</text>\n        <text x=\"95\" y=\"90\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"1\">\n          {isCondo\n            ? lang === \"fr\" ? \"QUOTE-PART DE COPROPRIÉTÉ\" : \"CONDOMINIUM SHARE\"\n            : lang === \"fr\" ? \"TERRAIN NON INSCRIT AU RÔLE\" : \"LOT NOT ON ROLL\"}\n        </text>\n        <text x=\"95\" y=\"104\" textAnchor=\"middle\" fontSize=\"7\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n          {isCondo\n            ? lang === \"fr\" ? \"terrain commun indivis\" : \"undivided common land\"\n            : \"—\"}\n        </text>\n      </svg>\n    );\n  }\n  const front = frontageM && frontageM > 0 ? frontageM : Math.sqrt(areaM2);\n  const depth = areaM2 / front;\n  const ratio = Math.max(0.25, Math.min(4, depth / front));\n  const maxW = 150;\n  const maxH = 108;\n  let w = maxW;\n  let h = w * ratio;\n  if (h > maxH) {\n    h = maxH;\n    w = h / ratio;\n  }\n  const x = (190 - w) / 2;\n  const y = 118 - h;\n  // empreinte du bâtiment, à l'échelle de la superficie\n  const fpFrac = footprintM2 ? Math.min(0.8, footprintM2 / areaM2) : 0;\n  const fw = w * Math.sqrt(fpFrac) * 0.9;\n  const fh = h * Math.sqrt(fpFrac) * 0.9;\n  const fmtM = (v: number) => `${v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { maximumFractionDigits: 1 })} m`;\n\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label=\"Schéma du terrain à l'échelle\">\n      <defs>\n        <pattern id=\"hatch\" width=\"7\" height=\"7\" patternTransform=\"rotate(45)\" patternUnits=\"userSpaceOnUse\">\n          <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"7\" stroke=\"var(--green)\" strokeWidth=\"1\" opacity=\"0.35\" />\n        </pattern>\n      </defs>\n      {/* ombre + parcelle */}\n      <rect x={x + 4} y={y + 4} width={w} height={h} fill=\"rgba(20,24,20,0.12)\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"var(--lime-soft)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"url(#hatch)\" />\n      {/* empreinte bâtiment */}\n      {fpFrac > 0.005 && (\n        <rect x={x + (w - fw) / 2} y={y + h - fh - h * 0.08} width={fw} height={fh} fill=\"var(--ink)\" rx=\"2\">\n          <title>{lang === \"fr\" ? \"Empreinte approximative du bâtiment\" : \"Approximate building footprint\"}</title>\n        </rect>\n      )}\n      {/* cotes : frontage */}\n      <line x1={x} y1={y + h + 12} x2={x + w} y2={y + h + 12} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x} y1={y + h + 8} x2={x} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x + w} y1={y + h + 8} x2={x + w} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x + w / 2} y={y + h + 24} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n        {frontageM ? fmtM(front) : `≈ ${fmtM(front)}`}\n      </text>\n      {/* cotes : profondeur */}\n      <line x1={x - 10} y1={y} x2={x - 10} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y} x2={x - 6} y2={y} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y + h} x2={x - 6} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x - 16} y={y + h / 2} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\" transform={`rotate(-90 ${x - 16} ${y + h / 2})`}>\n        ≈ {fmtM(depth)}\n      </text>\n      {/* rue */}\n      <line x1=\"8\" y1=\"136\" x2=\"182\" y2=\"136\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"147\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\" letterSpacing=\"3\">\n        {lang === \"fr\" ? \"RUE\" : \"STREET\"}\n      </text>\n    </svg>\n  );\n}\n\n/* ------------------------- composition de la valeur ------------------------- */\nexport function ValueSplit({\n  land,\n  building,\n  previous,\n  total,\n  lang,\n  labels,\n}: {\n  land: number | null;\n  building: number | null;\n  previous: number | null;\n  total: number | null;\n  lang: string;\n  labels: { land: string; building: string; previous: string };\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  const l = land ?? 0;\n  const b = building ?? 0;\n  const sum = l + b;\n  if (sum <= 0) return null;\n  const lPct = (l / sum) * 100;\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { style: \"currency\", currency: \"CAD\", maximumFractionDigits: 0 }).format(v);\n  const delta = previous && previous > 0 && total ? ((total / previous - 1) * 100) : null;\n  return (\n    <div>\n      <div className=\"flex h-9 w-full overflow-hidden rounded-md border-[1.5px] border-ink\">\n        {lPct >= 1 && (\n          <div\n            className=\"flex items-center justify-center overflow-hidden bg-green transition-[width] duration-1000 ease-out\"\n            style={{ width: on ? `${lPct}%` : \"2%\" }}\n          >\n            {lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-paper\">{Math.round(lPct)} %</span>\n            )}\n          </div>\n        )}\n        {lPct <= 99 && (\n          <div className=\"flex flex-1 items-center justify-center overflow-hidden bg-ink\">\n            {100 - lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-lime\">{Math.round(100 - lPct)} %</span>\n            )}\n          </div>\n        )}\n      </div>\n      <div className=\"vp-mono mt-2 flex flex-wrap items-center gap-x-4 gap-y-1 text-[10.5px] uppercase tracking-[0.05em]\">\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-green\" />\n          {labels.land} {fmt(l)}\n        </span>\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-ink\" />\n          {labels.building} {fmt(b)}\n        </span>\n      </div>\n      {delta != null && (\n        <p className=\"vp-mono mt-2.5 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {labels.previous} {fmt(previous!)}{\" \"}\n          <span className={`ml-1 rounded-[4px] border border-ink px-1.5 py-0.5 font-bold ${delta >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n            {delta >= 0 ? \"+\" : \"\"}\n            {delta.toFixed(1)} %\n          </span>\n        </p>\n      )}\n    </div>\n  );\n}\n\n/* -------------------------------- sparkline -------------------------------- */\nexport function Sparkline({\n  history,\n  height = 44,\n}: {\n  history: { year: number; value: number | null }[];\n  height?: number;\n}) {\n  const pts = history.filter((h) => h.value != null) as { year: number; value: number }[];\n  if (pts.length < 2) return null;\n  const W = 132;\n  const min = Math.min(...pts.map((p) => p.value));\n  const max = Math.max(...pts.map((p) => p.value));\n  const span = max - min || 1;\n  const xy = pts.map((p, i) => [\n    6 + (i / (pts.length - 1)) * (W - 12),\n    height - 8 - ((p.value - min) / span) * (height - 18),\n  ]);\n  const line = xy.map(([x, y]) => `${x},${y}`).join(\" \");\n  const area = `${xy[0][0]},${height - 4} ${line} ${xy[xy.length - 1][0]},${height - 4}`;\n  const growth = ((pts[pts.length - 1].value / pts[0].value - 1) * 100);\n  const [x2, y2] = xy[xy.length - 1];\n  return (\n    <div className=\"flex items-end gap-2\">\n      <svg viewBox={`0 0 ${W} ${height}`} width={W} height={height} aria-label={`Évolution ${pts[0].year}-${pts[pts.length - 1].year}`}>\n        <polygon points={area} fill=\"var(--lime-soft)\" />\n        <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n        {xy.slice(0, -1).map(([x, y], i) => (\n          <circle key={i} cx={x} cy={y} r=\"2\" fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"1.1\" />\n        ))}\n        <circle cx={x2} cy={y2} r=\"3.6\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      </svg>\n      <span className={`vp-mono mb-0.5 rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${growth >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n        {growth >= 0 ? \"+\" : \"\"}\n        {growth.toFixed(0)} %\n      </span>\n    </div>\n  );\n}\n\n/* ------------------------- fourchette visuelle P10-P90 ------------------------- */\n/** Barre de fourchette : segment P10→P90, losange = estimation, tiret = rôle. */\nexport function RangeBar({\n  low,\n  estimate,\n  high,\n  role,\n  lang,\n}: {\n  low: number;\n  estimate: number;\n  high: number;\n  role: number | null;\n  lang: string;\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  if (!(high > low) || !Number.isFinite(estimate)) return null;\n  const lo = Math.min(low, role ?? low);\n  const hi = Math.max(high, role ?? high);\n  const pad = (hi - lo) * 0.09 || hi * 0.05;\n  const min = lo - pad;\n  const max = hi + pad;\n  const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n      notation: v >= 1_000_000 ? \"compact\" : \"standard\",\n    }).format(v);\n  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;\n  return (\n    <div aria-label={`${fmt(low)} – ${fmt(high)}`}>\n      <div className=\"relative h-[64px]\">\n        {/* étiquette estimation */}\n        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <span className=\"vp-mono rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\">\n            {lang === \"fr\" ? \"ESTIMATION\" : \"ESTIMATE\"} {fmt(estimate)}\n          </span>\n        </div>\n        {/* piste */}\n        <div className=\"absolute left-0 right-0 top-[30px] h-[14px] rounded-[7px] border-[1.5px] border-ink bg-surface-2\" />\n        {/* segment P10-P90 */}\n        <div\n          className=\"absolute top-[30px] h-[14px] origin-left rounded-[7px] border-[1.5px] border-ink transition-transform duration-1000 ease-out\"\n          style={{\n            left: `${X(low)}%`,\n            width: `${Math.max(2, X(high) - X(low))}%`,\n            transform: on ? \"scaleX(1)\" : \"scaleX(0.05)\",\n            background:\n              \"repeating-linear-gradient(-45deg, var(--lime-soft), var(--lime-soft) 5px, rgba(255,81,72,0.35) 5px, rgba(255,81,72,0.35) 7px)\",\n          }}\n        />\n        {/* marqueur estimation (losange) */}\n        <div\n          className=\"absolute top-[29px] -translate-x-1/2 transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <div className=\"h-[16px] w-[16px] rotate-45 border-[1.8px] border-ink bg-lime\" />\n        </div>\n        {/* marqueur rôle */}\n        {role != null && role > 0 && (\n          <div\n            className=\"absolute top-[22px] -translate-x-1/2 text-center\"\n            style={{ left: `${X(role)}%` }}\n          >\n            <div className=\"mx-auto h-[30px] w-0 border-l-[2px] border-dashed border-ink opacity-70\" />\n            {!roleClose && (\n              <span className=\"vp-mono whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\">\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </div>\n        )}\n        {/* bornes */}\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(low)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(high)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>\n      </div>\n    </div>\n  );\n}\n\n/* --------------------------- courbe d'indice de marché --------------------------- */\n/** Indice mensuel rebasé à 100 au premier mois — la trajectoire du marché local. */\nexport function MarketIndexChart({\n  series,\n  lang,\n}: {\n  series: { month: string; idx: number }[];\n  lang: string;\n}) {\n  if (series.length < 6) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const W = 640;\n  const H = 200;\n  const padL = 40;\n  const padR = 52;\n  const padT = 18;\n  const padB = 28;\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const x = (i: number) => padL + (i / (pts.length - 1)) * (W - padL - padR);\n  const y = (v: number) => padT + (1 - (v - min) / span) * (H - padT - padB);\n  const line = pts.map((v, i) => `${x(i).toFixed(1)},${y(v).toFixed(1)}`).join(\" \");\n  const area = `${x(0)},${H - padB} ${line} ${x(pts.length - 1)},${H - padB}`;\n  // graduations : janvier de chaque année\n  const years = series\n    .map((p, i) => ({ i, month: p.month }))\n    .filter((p) => p.month.endsWith(\"-01\"));\n  // lignes horizontales : min / 100 / max arrondis\n  const gridVals = [...new Set([Math.round(min), 100, Math.round(max)])].filter(\n    (v) => v >= min - 1 && v <= max + 1\n  );\n  const lastV = pts[pts.length - 1];\n  return (\n    <svg\n      viewBox={`0 0 ${W} ${H}`}\n      className=\"h-auto w-full\"\n      role=\"img\"\n      aria-label={\n        lang === \"fr\"\n          ? `Indice de marché ${series[0].month} à ${series[series.length - 1].month}`\n          : `Market index ${series[0].month} to ${series[series.length - 1].month}`\n      }\n    >\n      {gridVals.map((v) => (\n        <g key={v}>\n          <line\n            x1={padL}\n            y1={y(v)}\n            x2={W - padR}\n            y2={y(v)}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            strokeDasharray=\"3 5\"\n            opacity={v === 100 ? 0.45 : 0.18}\n          />\n          <text\n            x={padL - 6}\n            y={y(v) + 3.5}\n            textAnchor=\"end\"\n            fontSize=\"10\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-3)\"\n          >\n            {v}\n          </text>\n        </g>\n      ))}\n      {years.map((p) => (\n        <g key={p.month}>\n          <line\n            x1={x(p.i)}\n            y1={padT}\n            x2={x(p.i)}\n            y2={H - padB}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            opacity=\"0.1\"\n          />\n          <text\n            x={x(p.i)}\n            y={H - 10}\n            textAnchor=\"middle\"\n            fontSize=\"10\"\n            fontWeight=\"700\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-2)\"\n          >\n            {p.month.slice(0, 4)}\n          </text>\n        </g>\n      ))}\n      <polygon points={area} fill=\"var(--lime-soft)\" opacity=\"0.8\" />\n      <polyline\n        points={line}\n        fill=\"none\"\n        stroke=\"var(--green)\"\n        strokeWidth=\"2.6\"\n        strokeLinejoin=\"round\"\n        strokeLinecap=\"round\"\n      />\n      <circle\n        cx={x(pts.length - 1)}\n        cy={y(lastV)}\n        r=\"5\"\n        fill=\"var(--lime)\"\n        stroke=\"var(--ink)\"\n        strokeWidth=\"1.8\"\n      />\n      <text\n        x={x(pts.length - 1) + 9}\n        y={y(lastV) + 4}\n        fontSize=\"11.5\"\n        fontWeight=\"700\"\n        fontFamily=\"var(--font-jetbrains)\"\n        fill=\"var(--ink)\"\n      >\n        {Math.round(lastV)}\n      </text>\n    </svg>\n  );\n}\n\n/* ----------------------- position dans la municipalité ----------------------- */\n/** Distribution stylisée (cloche) : les barres sous le percentile sont pleines. */\nexport function PercentileBar({\n  pct,\n  lang,\n}: {\n  pct: number;\n  lang: string;\n}) {\n  const N = 36;\n  const bars = Array.from({ length: N }, (_, i) => {\n    const z = (i / (N - 1) - 0.42) / 0.24; // cloche légèrement asymétrique (droite étirée)\n    return Math.exp(-0.5 * z * z) * (i / N > 0.5 ? 1 + (i / N - 0.5) * 0.35 : 1);\n  });\n  const maxB = Math.max(...bars);\n  const p = Math.max(0, Math.min(100, pct));\n  return (\n    <div aria-label={`${Math.round(p)} %`}>\n      <div className=\"relative flex h-[72px] items-end gap-[2.2%] px-0.5\" style={{ gap: \"2px\" }}>\n        {bars.map((b, i) => {\n          const filled = (i + 0.5) / N <= p / 100;\n          return (\n            <div\n              key={i}\n              className=\"min-w-0 flex-1 rounded-t-[2px] border border-ink transition-colors\"\n              style={{\n                height: `${8 + (b / maxB) * 88}%`,\n                background: filled ? \"var(--lime)\" : \"var(--surface-2)\",\n                borderWidth: \"1px\",\n                opacity: filled ? 1 : 0.55,\n              }}\n            />\n          );\n        })}\n        <div\n          className=\"absolute bottom-0 top-[-6px] w-0 border-l-[2px] border-ink\"\n          style={{ left: `${p}%` }}\n        >\n          <span className=\"vp-mono absolute -top-1 left-1.5 whitespace-nowrap rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\" style={p > 55 ? { left: \"auto\", right: \"6px\" } : undefined}>\n            {lang === \"fr\" ? \"P\" : \"P\"}\n            {Math.round(p)}\n          </span>\n        </div>\n      </div>\n    </div>\n  );\n}\n\n/* ------------------------ mini-courbe d'indice (accueil) ------------------------ */\nexport function IndexSpark({\n  series,\n  width = 150,\n  height = 46,\n}: {\n  series: { month: string; idx: number }[];\n  width?: number;\n  height?: number;\n}) {\n  if (series.length < 2) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const xy = pts.map((v, i) => [\n    4 + (i / (pts.length - 1)) * (width - 8),\n    height - 6 - ((v - min) / span) * (height - 14),\n  ]);\n  const line = xy.map(([x, y]) => `${x.toFixed(1)},${y.toFixed(1)}`).join(\" \");\n  const area = `${xy[0][0]},${height - 3} ${line} ${xy[xy.length - 1][0]},${height - 3}`;\n  const [lx, ly] = xy[xy.length - 1];\n  return (\n    <svg viewBox={`0 0 ${width} ${height}`} className=\"h-auto w-full\" aria-hidden=\"true\">\n      <polygon points={area} fill=\"var(--lime-soft)\" />\n      <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n      <circle cx={lx} cy={ly} r=\"3.4\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n    </svg>\n  );\n}\n\n/* ------------------------------ frise temporelle ------------------------------ */\nexport function EraLine({ year, lang }: { year: number | null; lang: string }) {\n  if (!year || year < 1750) return null;\n  const min = Math.min(1900, Math.floor((year - 10) / 50) * 50);\n  const max = 2026;\n  const frac = Math.max(0, Math.min(1, (year - min) / (max - min)));\n  const W = 190;\n  const x = 12 + frac * (W - 24);\n  const age = 2026 - year;\n  return (\n    <svg viewBox={`0 0 ${W} 40`} className=\"w-full max-w-[210px]\" aria-label={`Construction ${year}`}>\n      <line x1=\"12\" y1=\"24\" x2={W - 12} y2=\"24\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      {[...new Set([min, Math.round((min + max) / 2 / 50) * 50, 2000])].map((yr) => {\n        const gx = 12 + ((yr - min) / (max - min)) * (W - 24);\n        return (\n          <g key={yr}>\n            <line x1={gx} y1=\"20\" x2={gx} y2=\"28\" stroke=\"var(--ink)\" strokeWidth=\"1.2\" opacity=\"0.5\" />\n            <text x={gx} y=\"38\" textAnchor=\"middle\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n              {yr}\n            </text>\n          </g>\n        );\n      })}\n      <g transform={`translate(${x}, 24)`}>\n        <rect x=\"-7\" y=\"-7\" width=\"14\" height=\"14\" transform=\"rotate(45)\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n        <text y=\"-13\" textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n          {year}\n        </text>\n      </g>\n      <text x={W - 12} y=\"12\" textAnchor=\"end\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n        {age} {lang === \"fr\" ? \"ANS\" : \"YRS\"}\n      </text>\n    </svg>\n  );\n}\n","structuredPatch":[{"oldStart":674,"oldLines":7,"newStart":674,"newLines":7,"lines":["   const p = Math.max(0, Math.min(100, pct));","   return (","     <div aria-label={`${Math.round(p)} %`}>","-      <div className=\"relative flex h-[72px] items-end gap-[2.2%] px-0.5\" style={{ gap: \"2px\" }}>","+      <div className=\"relative flex h-[72px] items-end px-0.5\" style={{ gap: \"2px\" }}>","         {bars.map((b, i) => {","           const filled = (i + 0.5) / N <= p / 100;","           return ("]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwUc6dQbUaoR6DB8jUo","type":"message","role":"assistant","content":[{"type":"text","text":"Maintenant l'intégration dans `ResultView.tsx` — panneau prix enrichi (faits rapides + barre de fourchette) :"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":415,"cache_read_input_tokens":114819,"cache_creation":{"ephemeral_5m_input_tokens":415,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"3511f671-8bc1-4865-a405-a2a587d04b1a","request_id":"req_011CeJwUYRcnzLKxw61vzHg8"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwUc6dQbUaoR6DB8jUo","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01S1eNHL9QxAfJ9iPt9unDbH","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","old_string":"import {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  ValueSplit,\n} from \"./MetricViz\";","new_string":"import {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  MarketIndexChart,\n  PercentileBar,\n  RangeBar,\n  ValueSplit,\n} from \"./MetricViz\";"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":415,"cache_read_input_tokens":114819,"cache_creation":{"ephemeral_5m_input_tokens":415,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"28dca9c0-83ef-4793-bef3-93ffc2168a68","request_id":"req_011CeJwUYRcnzLKxw61vzHg8"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01S1eNHL9QxAfJ9iPt9unDbH","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"c64e1309-57b7-4cdc-8e1e-cd3c3048f6ad","timestamp":"2026-08-23T03:05:37.046Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","oldString":"import {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  ValueSplit,\n} from \"./MetricViz\";","newString":"import {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  MarketIndexChart,\n  PercentileBar,\n  RangeBar,\n  ValueSplit,\n} from \"./MetricViz\";","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport { useState } from \"react\";\nimport Link from \"next/link\";\nimport { useLang } from \"./LangContext\";\nimport LeadForm from \"./LeadForm\";\nimport RadarMap from \"./RadarMap\";\nimport {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  ValueSplit,\n} from \"./MetricViz\";\nimport type { EstimateResult } from \"@/lib/engine\";\nimport type { UnitEstimate } from \"@/lib/estimator\";\nimport type { TKey } from \"@/lib/i18n\";\n\nconst fmt = (v: number | null | undefined, lang: string) =>\n  v == null\n    ? \"—\"\n    : new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n        style: \"currency\",\n        currency: \"CAD\",\n        maximumFractionDigits: 0,\n      }).format(v);\n\nconst signed = (v: number, lang: string) =>\n  `${v >= 0 ? \"+\" : \"−\"}${fmt(Math.abs(Math.round(v)), lang)}`;\n\nfunction Chip({ k, v }: { k: string; v: string | number }) {\n  return (\n    <div className=\"kv-cell\">\n      <p className=\"k\">{k}</p>\n      <p className=\"v break-words\">{v}</p>\n    </div>\n  );\n}\n\nexport default function ResultView({ data }: { data: UnitEstimate }) {\n  const { lang, t } = useLang();\n  const [activeComp, setActiveComp] = useState<string | null>(null);\n  const r: EstimateResult = data.result;\n  const u = data.unit;\n  const s = u?.specs;\n  const fr = lang === \"fr\";\n  const histMax = Math.max(...(u?.history.map((h) => h.value ?? 0) ?? [0]), 1);\n  const moneyFmt = (v: number) =>\n    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n    }).format(Math.round(v / 100) * 100);\n\n  return (\n    <div className=\"space-y-10 py-8\">\n      {/* fil d'ariane */}\n      <nav className=\"vp-mono flex flex-wrap items-center gap-2 text-[11.5px] uppercase tracking-[0.06em] text-ink-3\">\n        <Link href=\"/\" className=\"border-b-[1.5px] border-transparent hover:border-green hover:text-green\">\n          Vrai-Prix\n        </Link>\n        <span>/</span>\n        <span>{u ? `${u.adresse ?? \"\"} · ${u.municipalite ?? \"\"}` : t(\"manualTitle\")}</span>\n      </nav>\n\n      {/* ---- Panneau prix ---- */}\n      <section className=\"vp-card rise p-6 sm:p-8\">\n        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          <div className=\"min-w-0 flex-1\">\n            <span className=\"kicker\">{t(\"estimatedValue\")}</span>\n            {u && (\n              <h1 className=\"vp-display mt-2 text-[clamp(20px,3vw,28px)] font-bold uppercase leading-tight tracking-[-0.02em]\">\n                {u.adresse}\n                {s?.apt ? ` app. ${s.apt}` : \"\"}\n                {u.municipalite ? <span className=\"text-ink-3\"> · {u.municipalite}</span> : null}\n              </h1>\n            )}\n            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              <div>\n                <p className=\"klabel\">{t(\"range\")}</p>\n                <p className=\"vp-display text-[19px] font-bold\">\n                  {fmt(r.low, lang)} <span className=\"text-ink-3\">→</span> {fmt(r.high, lang)}\n                </p>\n              </div>\n              {u?.valeurRole != null && (\n                <div>\n                  <p className=\"klabel\">{t(\"assessed\")}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-ink-2\">{fmt(u.valeurRole, lang)}</p>\n                </div>\n              )}\n              {u?.valeurRole != null && u.valeurRole > 0 && r.estimate > 0 && (\n                <div>\n                  <p className=\"klabel\">{fr ? \"Écart vs rôle\" : \"vs assessment\"}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-green-deep\">\n                    {r.estimate >= u.valeurRole ? \"+\" : \"\"}\n                    {((r.estimate / u.valeurRole - 1) * 100).toFixed(0)} %\n                  </p>\n                </div>\n              )}\n            </div>\n            {u && (\n              <div className=\"mt-6 flex flex-wrap gap-2.5\">\n                <a\n                  href={`/api/report?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-primary\"\n                  download\n                >\n                  {fr ? \"Rapport PDF\" : \"PDF report\"} ↓\n                </a>\n                <a\n                  href={`/api/report/pro?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-ghost\"\n                  download\n                  title={fr ? \"Structure de rapport d'évaluation — présentation bancaire\" : \"Appraisal-style structure — bank presentation\"}\n                >\n                  {fr ? \"Rapport détaillé format professionnel (banque)\" : \"Detailed professional report (bank)\"} ↓\n                </a>\n              </div>\n            )}\n          </div>\n          <div className=\"shrink-0\">\n            <ConfidenceDial\n              pct={r.confidencePct}\n              level={r.confidenceLevel}\n              label={`${t(\"confidence\")} — ${t(`conf${r.confidenceLevel}` as TKey)}`}\n            />\n          </div>\n        </div>\n        <p className=\"vp-mono mt-5 border-t-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {t(\"disclaimer\")}\n        </p>\n      </section>\n\n      {/* ---- Portrait de la propriété (bento du registre) ---- */}\n      {u && s && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Registre officiel — dessiné par les données\" : \"Official record — drawn by the data\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Portrait de la propriété\" : \"Property portrait\"}\n          </h2>\n          <div className=\"mt-4 grid gap-4 lg:grid-cols-3\">\n            {/* Bâtiment */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"60ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Bâtiment\" : \"Building\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <BuildingGlyph\n                  floors={s.nbEtages}\n                  dwellings={u.nbLogements}\n                  genre={s.genreConstruction}\n                  yearBuilt={u.anneeConstruction}\n                  floorAreaM2={u.aireEtagesM2}\n                  lang={lang}\n                />\n              </div>\n              <EraLine year={u.anneeConstruction} lang={lang} />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Aire d'étages\" : \"Floor area\"} v={u.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Étages × logements\" : \"Storeys × dwellings\"} v={`${s.nbEtages ?? \"—\"} × ${u.nbLogements ?? \"—\"}`} />\n                <Chip k={fr ? \"Genre\" : \"Style\"} v={s.genreConstruction ?? \"—\"} />\n                <Chip k={fr ? \"Lien physique\" : \"Physical link\"} v={s.lienPhysique ?? \"—\"} />\n              </div>\n            </div>\n            {/* Terrain */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"140ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Terrain — à l'échelle\" : \"Lot — to scale\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <LotDiagram\n                  areaM2={u.superficieTerrainM2}\n                  frontageM={s.frontTerrainM}\n                  footprintM2={\n                    u.aireEtagesM2 && s.nbEtages ? u.aireEtagesM2 / Math.max(1, s.nbEtages) : u.aireEtagesM2\n                  }\n                  lang={lang}\n                  isCondo={!!s.apt || (u.nbLogements != null && u.nbLogements >= 1 && !u.superficieTerrainM2)}\n                />\n              </div>\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Superficie\" : \"Area\"} v={u.superficieTerrainM2 ? locNum(u.superficieTerrainM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Frontage\" : \"Frontage\"} v={s.frontTerrainM ? locNum(s.frontTerrainM, lang, { unit: \"m\" }) : \"—\"} />\n                <Chip k=\"CUBF\" v={s.cubf ?? \"—\"} />\n                <Chip k={fr ? \"Usage\" : \"Use\"} v={s.cubfLibelle ?? \"—\"} />\n              </div>\n            </div>\n            {/* Valeur au rôle */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"220ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Valeur au rôle 2026 — composition\" : \"2026 assessed value — split\"}\n              </p>\n              <p className=\"vp-display py-2 text-[26px] font-bold tracking-[-0.02em]\">\n                {fmt(u.valeurRole, lang)}\n              </p>\n              <ValueSplit\n                land={s.valeurTerrain}\n                building={s.valeurBatiment}\n                previous={s.valeurAnterieure}\n                total={u.valeurRole}\n                lang={lang}\n                labels={{\n                  land: fr ? \"Terrain\" : \"Land\",\n                  building: fr ? \"Bâtiment\" : \"Building\",\n                  previous: fr ? \"Rôle antérieur\" : \"Previous roll\",\n                }}\n              />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Cond. marché au\" : \"Market cond.\"} v={s.datCondMarche ?? \"—\"} />\n                <Chip k={fr ? \"Matricule\" : \"Roll no.\"} v={s.matricule ?? \"—\"} />\n              </div>\n            </div>\n          </div>\n          {/* registre brut complet */}\n          <details className=\"vp-card mt-4 p-5\">\n            <summary className=\"vp-mono cursor-pointer text-[11px] font-bold uppercase tracking-[0.08em] text-green-deep\">\n              {fr ? \"▸ Toutes les données du registre (brutes)\" : \"▸ All registry data (raw)\"}\n            </summary>\n            <div className=\"mt-4 grid grid-cols-2 gap-2 sm:grid-cols-4\">\n              <Chip k={fr ? \"Année construction\" : \"Year built\"} v={u.anneeConstruction ? `${u.anneeConstruction}${s.anneeEstimee === \"E\" ? (fr ? \" (estimée)\" : \" (est.)\") : \"\"}` : \"—\"} />\n              <Chip k={fr ? \"Locaux non résid.\" : \"Non-res. units\"} v={s.nbLocauxNonResid ?? \"—\"} />\n              <Chip k={fr ? \"Chambres locatives\" : \"Rental rooms\"} v={s.nbChambresLocatives ?? \"—\"} />\n              <Chip k={fr ? \"Unité de voisinage\" : \"Neighbourhood\"} v={s.uniteVoisinage || \"—\"} />\n              <Chip k={fr ? \"Arrondissement\" : \"Borough\"} v={s.arrond || \"—\"} />\n              <Chip k={fr ? \"Adresses sur l'unité\" : \"Addresses\"} v={s.nAdresses ?? \"—\"} />\n              <Chip k={fr ? \"Valeur terrain\" : \"Land value\"} v={fmt(s.valeurTerrain, lang)} />\n              <Chip k={fr ? \"Valeur bâtiment\" : \"Building value\"} v={fmt(s.valeurBatiment, lang)} />\n              <Chip k={fr ? \"Rôle antérieur\" : \"Previous roll\"} v={fmt(s.valeurAnterieure, lang)} />\n              <Chip k=\"ID provincial\" v={u.id} />\n            </div>\n          </details>\n        </section>\n      )}\n\n      {/* ---- Transparence du calcul ---- */}\n      <section>\n        <span className=\"kicker\">{fr ? \"Transparence\" : \"Transparency\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"whyTitle\")}\n        </h2>\n        <div className=\"mt-4 grid gap-4 sm:grid-cols-2\">\n          <div className=\"vp-card vp-card-hover rise p-5\">\n            <p className=\"klabel\">{t(\"modelPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.modelEstimate != null ? (\n                <CountUp value={r.modelEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightModel\")} : {(r.modelWeight * 100).toFixed(0)} %\n            </p>\n          </div>\n          <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"100ms\" }}>\n            <p className=\"klabel\">{t(\"compsPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.compsEstimate != null ? (\n                <CountUp value={r.compsEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightComps\")} : {((1 - r.modelWeight) * 100).toFixed(0)} % · {r.nCompsUsed} comps\n              {r.compsDispersionPct != null ? ` · ±${r.compsDispersionPct} %` : \"\"}\n            </p>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Historique ---- */}\n      {u && u.history.some((h) => h.value != null) && (\n        <section>\n          <span className=\"kicker\">2021 → 2026</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {t(\"history\")}\n          </h2>\n          <div className=\"vp-card rise mt-4 p-5 sm:p-6\">\n            <div className=\"flex flex-col gap-2\">\n              {u.history.map((h, i) => {\n                const prev = i > 0 ? u.history[i - 1].value : null;\n                const yoy =\n                  h.value != null && prev != null && prev > 0\n                    ? ((h.value / prev - 1) * 100)\n                    : null;\n                return (\n                  <div key={h.year} className=\"hbar-row grid grid-cols-[44px_1fr_auto] items-center gap-2 sm:grid-cols-[52px_1fr_auto] sm:gap-3\">\n                    <span className=\"vp-mono text-[12px] font-bold\">{h.year}</span>\n                    <span className=\"hbar-track\">\n                      <span\n                        className=\"hbar-fill grow-x\"\n                        style={{\n                          width: `${((h.value ?? 0) / histMax) * 100}%`,\n                          animationDelay: `${i * 90}ms`,\n                          background: h.year === 2026 ? \"var(--ink)\" : undefined,\n                        }}\n                      />\n                    </span>\n                    <span className=\"vp-mono flex items-center justify-end gap-2 text-[12px] font-bold\">\n                      {fmt(h.value, lang)}\n                      {yoy != null && (\n                        <span className={`rounded-[4px] border border-ink px-1 py-px text-[9px] ${yoy >= 0 ? \"bg-lime-soft text-green-deep\" : \"bg-[var(--danger-soft)] text-[var(--danger)]\"}`}>\n                          {yoy >= 0 ? \"+\" : \"\"}\n                          {yoy.toFixed(0)} %\n                        </span>\n                      )}\n                    </span>\n                  </div>\n                );\n              })}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Plan des comparables (carte maison) ---- */}\n      <section>\n        <span className=\"kicker\">\n          {r.nCompsUsed} {fr ? \"ventes réelles\" : \"real sales\"}\n        </span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"compsTitle\")}\n        </h2>\n        <div className=\"mt-4 grid items-start gap-4 lg:grid-cols-[1.05fr_1fr]\">\n          <div className=\"overflow-hidden rounded-[10px] border-[1.5px] border-ink shadow-[8px_8px_0_rgba(20,24,20,0.08)]\">\n            <RadarMap\n              subject={{\n                lat: u?.lat ?? r.comps[0]?.lat ?? 46.81,\n                lng: u?.lng ?? r.comps[0]?.lng ?? -71.21,\n                label: u?.adresse ?? (fr ? \"Propriété estimée\" : \"Subject property\"),\n              }}\n              comps={r.comps.map((c) => ({\n                id: c.id,\n                lat: c.lat,\n                lng: c.lng,\n                label: `${c.street ?? \"\"}, ${c.city ?? \"\"}`,\n                price: fmt(c.amount, lang),\n                adjusted: fmt(Math.round(c.adjustedPrice), lang),\n                date: c.date,\n                distanceM: c.distanceM,\n                weight: c.weight,\n              }))}\n              highlight={activeComp}\n              onHover={setActiveComp}\n            />\n          </div>\n          <div className=\"src-wrap max-h-[560px] overflow-y-auto\">\n            <table className=\"src-table\">\n              <thead className=\"sticky top-0 z-10\">\n                <tr>\n                  <th>#</th>\n                  <th>{fr ? \"Vente\" : \"Sale\"}</th>\n                  <th className=\"text-right\">{t(\"adjusted\")}</th>\n                </tr>\n              </thead>\n              <tbody>\n                {r.comps.map((c, i) => (\n                  <tr\n                    key={c.id}\n                    onMouseEnter={() => setActiveComp(c.id)}\n                    onMouseLeave={() => setActiveComp(null)}\n                    className={activeComp === c.id ? \"!bg-lime-soft\" : \"\"}\n                  >\n                    <td>\n                      <span\n                        className={`vp-mono inline-flex h-6 w-6 items-center justify-center rounded-full border-[1.5px] border-ink text-[11px] font-bold ${\n                          activeComp === c.id ? \"bg-lime text-ink\" : \"bg-green text-paper\"\n                        }`}\n                      >\n                        {i + 1}\n                      </span>\n                    </td>\n                    <td>\n                      <p className=\"font-semibold leading-tight\">\n                        {c.street}, {c.city}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] text-ink-2\">\n                        {t(\"adjTime\")} {signed(c.adjTime, lang)} · {t(\"adjArea\")} {signed(c.adjArea, lang)} ·{\" \"}\n                        {t(\"adjAge\")} {signed(c.adjAge, lang)}\n                      </p>\n                    </td>\n                    <td className=\"vp-display text-right font-bold\">\n                      {fmt(Math.round(c.adjustedPrice), lang)}\n                      <p className=\"vp-mono text-[10px] font-normal text-ink-3\">\n                        {t(\"weight\")} {(c.weight * 100).toFixed(0)} %\n                      </p>\n                    </td>\n                  </tr>\n                ))}\n              </tbody>\n            </table>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Lead ---- */}\n      <section className=\"rounded-[10px] border-[1.5px] border-ink bg-ink p-6 text-paper shadow-[8px_8px_0_rgba(20,24,20,0.2)] sm:p-8\">\n        <h2 className=\"vp-display text-[22px] font-bold uppercase tracking-[-0.02em] text-lime\">\n          {t(\"leadTitle\")}\n        </h2>\n        <div className=\"mt-4 max-w-xl\">\n          <LeadForm unitId={u?.id ?? null} estimate={r.estimate} />\n        </div>\n      </section>\n    </div>\n  );\n}\n","structuredPatch":[{"oldStart":12,"oldLines":6,"newStart":12,"newLines":9,"lines":["   EraLine,","   locNum,","   LotDiagram,","+  MarketIndexChart,","+  PercentileBar,","+  RangeBar,","   ValueSplit,"," } from \"./MetricViz\";"," import type { EstimateResult } from \"@/lib/engine\";"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwVRBaZ8chU7gtrZjN5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VwqgUMnZrBGTVfVjxnween","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","old_string":"            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              <div>\n                <p className=\"klabel\">{t(\"range\")}</p>\n                <p className=\"vp-display text-[19px] font-bold\">\n                  {fmt(r.low, lang)} <span className=\"text-ink-3\">→</span> {fmt(r.high, lang)}\n                </p>\n              </div>\n              {u?.valeurRole != null && (","new_string":"            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            {/* faits rapides */}\n            <div className=\"mt-4 flex flex-wrap gap-2\">\n              {[\n                u ? t(u.typeProp as TKey) : null,\n                u?.aireEtagesM2 && r.estimate > 0\n                  ? `${locNum(Math.round(r.estimate / u.aireEtagesM2), lang, { maxFrac: 0 })} $/m²`\n                  : null,\n                u?.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : null,\n                u?.anneeConstruction ? `${fr ? \"constr.\" : \"built\"} ${u.anneeConstruction}` : null,\n                u?.superficieTerrainM2\n                  ? `${fr ? \"terrain\" : \"lot\"} ${locNum(u.superficieTerrainM2, lang, { unit: \"m²\" })}`\n                  : null,\n              ]\n                .filter((x): x is string => x != null)\n                .map((x) => (\n                  <span\n                    key={x}\n                    className=\"vp-mono rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"\n                  >\n                    {x}\n                  </span>\n                ))}\n            </div>\n            {/* fourchette visuelle P10-P90 avec position du rôle */}\n            <div className=\"mt-6 max-w-2xl\">\n              <p className=\"klabel mb-1\">{t(\"range\")}</p>\n              <RangeBar\n                low={r.low}\n                estimate={r.estimate}\n                high={r.high}\n                role={u?.valeurRole ?? null}\n                lang={lang}\n              />\n            </div>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              {u?.valeurRole != null && ("},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":774,"cache_read_input_tokens":115234,"cache_creation":{"ephemeral_5m_input_tokens":774,"ephemeral_1h_input_tokens":0},"output_tokens":15,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"db203c15-c7b1-4ef7-b7df-f03f22701172","request_id":"req_011CeJwVNwtW5Huu2whKS1Us"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VwqgUMnZrBGTVfVjxnween","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"1c8f1c44-4997-4981-aa2d-5e473597e785","timestamp":"2026-08-23T03:05:49.355Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","oldString":"            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              <div>\n                <p className=\"klabel\">{t(\"range\")}</p>\n                <p className=\"vp-display text-[19px] font-bold\">\n                  {fmt(r.low, lang)} <span className=\"text-ink-3\">→</span> {fmt(r.high, lang)}\n                </p>\n              </div>\n              {u?.valeurRole != null && (","newString":"            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            {/* faits rapides */}\n            <div className=\"mt-4 flex flex-wrap gap-2\">\n              {[\n                u ? t(u.typeProp as TKey) : null,\n                u?.aireEtagesM2 && r.estimate > 0\n                  ? `${locNum(Math.round(r.estimate / u.aireEtagesM2), lang, { maxFrac: 0 })} $/m²`\n                  : null,\n                u?.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : null,\n                u?.anneeConstruction ? `${fr ? \"constr.\" : \"built\"} ${u.anneeConstruction}` : null,\n                u?.superficieTerrainM2\n                  ? `${fr ? \"terrain\" : \"lot\"} ${locNum(u.superficieTerrainM2, lang, { unit: \"m²\" })}`\n                  : null,\n              ]\n                .filter((x): x is string => x != null)\n                .map((x) => (\n                  <span\n                    key={x}\n                    className=\"vp-mono rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"\n                  >\n                    {x}\n                  </span>\n                ))}\n            </div>\n            {/* fourchette visuelle P10-P90 avec position du rôle */}\n            <div className=\"mt-6 max-w-2xl\">\n              <p className=\"klabel mb-1\">{t(\"range\")}</p>\n              <RangeBar\n                low={r.low}\n                estimate={r.estimate}\n                high={r.high}\n                role={u?.valeurRole ?? null}\n                lang={lang}\n              />\n            </div>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              {u?.valeurRole != null && (","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport { useState } from \"react\";\nimport Link from \"next/link\";\nimport { useLang } from \"./LangContext\";\nimport LeadForm from \"./LeadForm\";\nimport RadarMap from \"./RadarMap\";\nimport {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  MarketIndexChart,\n  PercentileBar,\n  RangeBar,\n  ValueSplit,\n} from \"./MetricViz\";\nimport type { EstimateResult } from \"@/lib/engine\";\nimport type { UnitEstimate } from \"@/lib/estimator\";\nimport type { TKey } from \"@/lib/i18n\";\n\nconst fmt = (v: number | null | undefined, lang: string) =>\n  v == null\n    ? \"—\"\n    : new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n        style: \"currency\",\n        currency: \"CAD\",\n        maximumFractionDigits: 0,\n      }).format(v);\n\nconst signed = (v: number, lang: string) =>\n  `${v >= 0 ? \"+\" : \"−\"}${fmt(Math.abs(Math.round(v)), lang)}`;\n\nfunction Chip({ k, v }: { k: string; v: string | number }) {\n  return (\n    <div className=\"kv-cell\">\n      <p className=\"k\">{k}</p>\n      <p className=\"v break-words\">{v}</p>\n    </div>\n  );\n}\n\nexport default function ResultView({ data }: { data: UnitEstimate }) {\n  const { lang, t } = useLang();\n  const [activeComp, setActiveComp] = useState<string | null>(null);\n  const r: EstimateResult = data.result;\n  const u = data.unit;\n  const s = u?.specs;\n  const fr = lang === \"fr\";\n  const histMax = Math.max(...(u?.history.map((h) => h.value ?? 0) ?? [0]), 1);\n  const moneyFmt = (v: number) =>\n    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n    }).format(Math.round(v / 100) * 100);\n\n  return (\n    <div className=\"space-y-10 py-8\">\n      {/* fil d'ariane */}\n      <nav className=\"vp-mono flex flex-wrap items-center gap-2 text-[11.5px] uppercase tracking-[0.06em] text-ink-3\">\n        <Link href=\"/\" className=\"border-b-[1.5px] border-transparent hover:border-green hover:text-green\">\n          Vrai-Prix\n        </Link>\n        <span>/</span>\n        <span>{u ? `${u.adresse ?? \"\"} · ${u.municipalite ?? \"\"}` : t(\"manualTitle\")}</span>\n      </nav>\n\n      {/* ---- Panneau prix ---- */}\n      <section className=\"vp-card rise p-6 sm:p-8\">\n        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          <div className=\"min-w-0 flex-1\">\n            <span className=\"kicker\">{t(\"estimatedValue\")}</span>\n            {u && (\n              <h1 className=\"vp-display mt-2 text-[clamp(20px,3vw,28px)] font-bold uppercase leading-tight tracking-[-0.02em]\">\n                {u.adresse}\n                {s?.apt ? ` app. ${s.apt}` : \"\"}\n                {u.municipalite ? <span className=\"text-ink-3\"> · {u.municipalite}</span> : null}\n              </h1>\n            )}\n            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              <div>\n                <p className=\"klabel\">{t(\"range\")}</p>\n                <p className=\"vp-display text-[19px] font-bold\">\n                  {fmt(r.low, lang)} <span className=\"text-ink-3\">→</span> {fmt(r.high, lang)}\n                </p>\n              </div>\n              {u?.valeurRole != null && (\n                <div>\n                  <p className=\"klabel\">{t(\"assessed\")}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-ink-2\">{fmt(u.valeurRole, lang)}</p>\n                </div>\n              )}\n              {u?.valeurRole != null && u.valeurRole > 0 && r.estimate > 0 && (\n                <div>\n                  <p className=\"klabel\">{fr ? \"Écart vs rôle\" : \"vs assessment\"}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-green-deep\">\n                    {r.estimate >= u.valeurRole ? \"+\" : \"\"}\n                    {((r.estimate / u.valeurRole - 1) * 100).toFixed(0)} %\n                  </p>\n                </div>\n              )}\n            </div>\n            {u && (\n              <div className=\"mt-6 flex flex-wrap gap-2.5\">\n                <a\n                  href={`/api/report?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-primary\"\n                  download\n                >\n                  {fr ? \"Rapport PDF\" : \"PDF report\"} ↓\n                </a>\n                <a\n                  href={`/api/report/pro?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-ghost\"\n                  download\n                  title={fr ? \"Structure de rapport d'évaluation — présentation bancaire\" : \"Appraisal-style structure — bank presentation\"}\n                >\n                  {fr ? \"Rapport détaillé format professionnel (banque)\" : \"Detailed professional report (bank)\"} ↓\n                </a>\n              </div>\n            )}\n          </div>\n          <div className=\"shrink-0\">\n            <ConfidenceDial\n              pct={r.confidencePct}\n              level={r.confidenceLevel}\n              label={`${t(\"confidence\")} — ${t(`conf${r.confidenceLevel}` as TKey)}`}\n            />\n          </div>\n        </div>\n        <p className=\"vp-mono mt-5 border-t-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {t(\"disclaimer\")}\n        </p>\n      </section>\n\n      {/* ---- Portrait de la propriété (bento du registre) ---- */}\n      {u && s && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Registre officiel — dessiné par les données\" : \"Official record — drawn by the data\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Portrait de la propriété\" : \"Property portrait\"}\n          </h2>\n          <div className=\"mt-4 grid gap-4 lg:grid-cols-3\">\n            {/* Bâtiment */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"60ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Bâtiment\" : \"Building\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <BuildingGlyph\n                  floors={s.nbEtages}\n                  dwellings={u.nbLogements}\n                  genre={s.genreConstruction}\n                  yearBuilt={u.anneeConstruction}\n                  floorAreaM2={u.aireEtagesM2}\n                  lang={lang}\n                />\n              </div>\n              <EraLine year={u.anneeConstruction} lang={lang} />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Aire d'étages\" : \"Floor area\"} v={u.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Étages × logements\" : \"Storeys × dwellings\"} v={`${s.nbEtages ?? \"—\"} × ${u.nbLogements ?? \"—\"}`} />\n                <Chip k={fr ? \"Genre\" : \"Style\"} v={s.genreConstruction ?? \"—\"} />\n                <Chip k={fr ? \"Lien physique\" : \"Physical link\"} v={s.lienPhysique ?? \"—\"} />\n              </div>\n            </div>\n            {/* Terrain */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"140ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Terrain — à l'échelle\" : \"Lot — to scale\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <LotDiagram\n                  areaM2={u.superficieTerrainM2}\n                  frontageM={s.frontTerrainM}\n                  footprintM2={\n                    u.aireEtagesM2 && s.nbEtages ? u.aireEtagesM2 / Math.max(1, s.nbEtages) : u.aireEtagesM2\n                  }\n                  lang={lang}\n                  isCondo={!!s.apt || (u.nbLogements != null && u.nbLogements >= 1 && !u.superficieTerrainM2)}\n                />\n              </div>\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Superficie\" : \"Area\"} v={u.superficieTerrainM2 ? locNum(u.superficieTerrainM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Frontage\" : \"Frontage\"} v={s.frontTerrainM ? locNum(s.frontTerrainM, lang, { unit: \"m\" }) : \"—\"} />\n                <Chip k=\"CUBF\" v={s.cubf ?? \"—\"} />\n                <Chip k={fr ? \"Usage\" : \"Use\"} v={s.cubfLibelle ?? \"—\"} />\n              </div>\n            </div>\n            {/* Valeur au rôle */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"220ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Valeur au rôle 2026 — composition\" : \"2026 assessed value — split\"}\n              </p>\n              <p className=\"vp-display py-2 text-[26px] font-bold tracking-[-0.02em]\">\n                {fmt(u.valeurRole, lang)}\n              </p>\n              <ValueSplit\n                land={s.valeurTerrain}\n                building={s.valeurBatiment}\n                previous={s.valeurAnterieure}\n                total={u.valeurRole}\n                lang={lang}\n                labels={{\n                  land: fr ? \"Terrain\" : \"Land\",\n                  building: fr ? \"Bâtiment\" : \"Building\",\n                  previous: fr ? \"Rôle antérieur\" : \"Previous roll\",\n                }}\n              />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Cond. marché au\" : \"Market cond.\"} v={s.datCondMarche ?? \"—\"} />\n                <Chip k={fr ? \"Matricule\" : \"Roll no.\"} v={s.matricule ?? \"—\"} />\n              </div>\n            </div>\n          </div>\n          {/* registre brut complet */}\n          <details className=\"vp-card mt-4 p-5\">\n            <summary className=\"vp-mono cursor-pointer text-[11px] font-bold uppercase tracking-[0.08em] text-green-deep\">\n              {fr ? \"▸ Toutes les données du registre (brutes)\" : \"▸ All registry data (raw)\"}\n            </summary>\n            <div className=\"mt-4 grid grid-cols-2 gap-2 sm:grid-cols-4\">\n              <Chip k={fr ? \"Année construction\" : \"Year built\"} v={u.anneeConstruction ? `${u.anneeConstruction}${s.anneeEstimee === \"E\" ? (fr ? \" (estimée)\" : \" (est.)\") : \"\"}` : \"—\"} />\n              <Chip k={fr ? \"Locaux non résid.\" : \"Non-res. units\"} v={s.nbLocauxNonResid ?? \"—\"} />\n              <Chip k={fr ? \"Chambres locatives\" : \"Rental rooms\"} v={s.nbChambresLocatives ?? \"—\"} />\n              <Chip k={fr ? \"Unité de voisinage\" : \"Neighbourhood\"} v={s.uniteVoisinage || \"—\"} />\n              <Chip k={fr ? \"Arrondissement\" : \"Borough\"} v={s.arrond || \"—\"} />\n              <Chip k={fr ? \"Adresses sur l'unité\" : \"Addresses\"} v={s.nAdresses ?? \"—\"} />\n              <Chip k={fr ? \"Valeur terrain\" : \"Land value\"} v={fmt(s.valeurTerrain, lang)} />\n              <Chip k={fr ? \"Valeur bâtiment\" : \"Building value\"} v={fmt(s.valeurBatiment, lang)} />\n              <Chip k={fr ? \"Rôle antérieur\" : \"Previous roll\"} v={fmt(s.valeurAnterieure, lang)} />\n              <Chip k=\"ID provincial\" v={u.id} />\n            </div>\n          </details>\n        </section>\n      )}\n\n      {/* ---- Transparence du calcul ---- */}\n      <section>\n        <span className=\"kicker\">{fr ? \"Transparence\" : \"Transparency\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"whyTitle\")}\n        </h2>\n        <div className=\"mt-4 grid gap-4 sm:grid-cols-2\">\n          <div className=\"vp-card vp-card-hover rise p-5\">\n            <p className=\"klabel\">{t(\"modelPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.modelEstimate != null ? (\n                <CountUp value={r.modelEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightModel\")} : {(r.modelWeight * 100).toFixed(0)} %\n            </p>\n          </div>\n          <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"100ms\" }}>\n            <p className=\"klabel\">{t(\"compsPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.compsEstimate != null ? (\n                <CountUp value={r.compsEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightComps\")} : {((1 - r.modelWeight) * 100).toFixed(0)} % · {r.nCompsUsed} comps\n              {r.compsDispersionPct != null ? ` · ±${r.compsDispersionPct} %` : \"\"}\n            </p>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Historique ---- */}\n      {u && u.history.some((h) => h.value != null) && (\n        <section>\n          <span className=\"kicker\">2021 → 2026</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {t(\"history\")}\n          </h2>\n          <div className=\"vp-card rise mt-4 p-5 sm:p-6\">\n            <div className=\"flex flex-col gap-2\">\n              {u.history.map((h, i) => {\n                const prev = i > 0 ? u.history[i - 1].value : null;\n                const yoy =\n                  h.value != null && prev != null && prev > 0\n                    ? ((h.value / prev - 1) * 100)\n                    : null;\n                return (\n                  <div key={h.year} className=\"hbar-row grid grid-cols-[44px_1fr_auto] items-center gap-2 sm:grid-cols-[52px_1fr_auto] sm:gap-3\">\n                    <span className=\"vp-mono text-[12px] font-bold\">{h.year}</span>\n                    <span className=\"hbar-track\">\n                      <span\n                        className=\"hbar-fill grow-x\"\n                        style={{\n                          width: `${((h.value ?? 0) / histMax) * 100}%`,\n                          animationDelay: `${i * 90}ms`,\n                          background: h.year === 2026 ? \"var(--ink)\" : undefined,\n                        }}\n                      />\n                    </span>\n                    <span className=\"vp-mono flex items-center justify-end gap-2 text-[12px] font-bold\">\n                      {fmt(h.value, lang)}\n                      {yoy != null && (\n                        <span className={`rounded-[4px] border border-ink px-1 py-px text-[9px] ${yoy >= 0 ? \"bg-lime-soft text-green-deep\" : \"bg-[var(--danger-soft)] text-[var(--danger)]\"}`}>\n                          {yoy >= 0 ? \"+\" : \"\"}\n                          {yoy.toFixed(0)} %\n                        </span>\n                      )}\n                    </span>\n                  </div>\n                );\n              })}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Plan des comparables (carte maison) ---- */}\n      <section>\n        <span className=\"kicker\">\n          {r.nCompsUsed} {fr ? \"ventes réelles\" : \"real sales\"}\n        </span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"compsTitle\")}\n        </h2>\n        <div className=\"mt-4 grid items-start gap-4 lg:grid-cols-[1.05fr_1fr]\">\n          <div className=\"overflow-hidden rounded-[10px] border-[1.5px] border-ink shadow-[8px_8px_0_rgba(20,24,20,0.08)]\">\n            <RadarMap\n              subject={{\n                lat: u?.lat ?? r.comps[0]?.lat ?? 46.81,\n                lng: u?.lng ?? r.comps[0]?.lng ?? -71.21,\n                label: u?.adresse ?? (fr ? \"Propriété estimée\" : \"Subject property\"),\n              }}\n              comps={r.comps.map((c) => ({\n                id: c.id,\n                lat: c.lat,\n                lng: c.lng,\n                label: `${c.street ?? \"\"}, ${c.city ?? \"\"}`,\n                price: fmt(c.amount, lang),\n                adjusted: fmt(Math.round(c.adjustedPrice), lang),\n                date: c.date,\n                distanceM: c.distanceM,\n                weight: c.weight,\n              }))}\n              highlight={activeComp}\n              onHover={setActiveComp}\n            />\n          </div>\n          <div className=\"src-wrap max-h-[560px] overflow-y-auto\">\n            <table className=\"src-table\">\n              <thead className=\"sticky top-0 z-10\">\n                <tr>\n                  <th>#</th>\n                  <th>{fr ? \"Vente\" : \"Sale\"}</th>\n                  <th className=\"text-right\">{t(\"adjusted\")}</th>\n                </tr>\n              </thead>\n              <tbody>\n                {r.comps.map((c, i) => (\n                  <tr\n                    key={c.id}\n                    onMouseEnter={() => setActiveComp(c.id)}\n                    onMouseLeave={() => setActiveComp(null)}\n                    className={activeComp === c.id ? \"!bg-lime-soft\" : \"\"}\n                  >\n                    <td>\n                      <span\n                        className={`vp-mono inline-flex h-6 w-6 items-center justify-center rounded-full border-[1.5px] border-ink text-[11px] font-bold ${\n                          activeComp === c.id ? \"bg-lime text-ink\" : \"bg-green text-paper\"\n                        }`}\n                      >\n                        {i + 1}\n                      </span>\n                    </td>\n                    <td>\n                      <p className=\"font-semibold leading-tight\">\n                        {c.street}, {c.city}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] text-ink-2\">\n                        {t(\"adjTime\")} {signed(c.adjTime, lang)} · {t(\"adjArea\")} {signed(c.adjArea, lang)} ·{\" \"}\n                        {t(\"adjAge\")} {signed(c.adjAge, lang)}\n                      </p>\n                    </td>\n                    <td className=\"vp-display text-right font-bold\">\n                      {fmt(Math.round(c.adjustedPrice), lang)}\n                      <p className=\"vp-mono text-[10px] font-normal text-ink-3\">\n                        {t(\"weight\")} {(c.weight * 100).toFixed(0)} %\n                      </p>\n                    </td>\n                  </tr>\n                ))}\n              </tbody>\n            </table>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Lead ---- */}\n      <section className=\"rounded-[10px] border-[1.5px] border-ink bg-ink p-6 text-paper shadow-[8px_8px_0_rgba(20,24,20,0.2)] sm:p-8\">\n        <h2 className=\"vp-display text-[22px] font-bold uppercase tracking-[-0.02em] text-lime\">\n          {t(\"leadTitle\")}\n        </h2>\n        <div className=\"mt-4 max-w-xl\">\n          <LeadForm unitId={u?.id ?? null} estimate={r.estimate} />\n        </div>\n      </section>\n    </div>\n  );\n}\n","structuredPatch":[{"oldStart":83,"oldLines":13,"newStart":83,"newLines":41,"lines":["             <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">","               <CountUp value={r.estimate} format={moneyFmt} />","             </p>","+            {/* faits rapides */}","+            <div className=\"mt-4 flex flex-wrap gap-2\">","+              {[","+                u ? t(u.typeProp as TKey) : null,","+                u?.aireEtagesM2 && r.estimate > 0","+                  ? `${locNum(Math.round(r.estimate / u.aireEtagesM2), lang, { maxFrac: 0 })} $/m²`","+                  : null,","+                u?.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : null,","+                u?.anneeConstruction ? `${fr ? \"constr.\" : \"built\"} ${u.anneeConstruction}` : null,","+                u?.superficieTerrainM2","+                  ? `${fr ? \"terrain\" : \"lot\"} ${locNum(u.superficieTerrainM2, lang, { unit: \"m²\" })}`","+                  : null,","+              ]","+                .filter((x): x is string => x != null)","+                .map((x) => (","+                  <span","+                    key={x}","+                    className=\"vp-mono rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"","+                  >","+                    {x}","+                  </span>","+                ))}","+            </div>","+            {/* fourchette visuelle P10-P90 avec position du rôle */}","+            <div className=\"mt-6 max-w-2xl\">","+              <p className=\"klabel mb-1\">{t(\"range\")}</p>","+              <RangeBar","+                low={r.low}","+                estimate={r.estimate}","+                high={r.high}","+                role={u?.valeurRole ?? null}","+                lang={lang}","+              />","+            </div>","             <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">","-              <div>","-                <p className=\"klabel\">{t(\"range\")}</p>","-                <p className=\"vp-display text-[19px] font-bold\">","-                  {fmt(r.low, lang)} <span className=\"text-ink-3\">→</span> {fmt(r.high, lang)}","-                </p>","-              </div>","               {u?.valeurRole != null && (","                 <div>","                   <p className=\"klabel\">{t(\"assessed\")}</p>"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"system","subtype":"thinking_tokens","estimated_tokens":50,"estimated_tokens_delta":50,"uuid":"5f873d93-6d9c-4821-8863-4f0bbafef882","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"system","subtype":"thinking_tokens","estimated_tokens":132,"estimated_tokens_delta":82,"uuid":"25d44a44-cb79-4ef6-8d9c-3f0d537ac5c3","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwWKouEL6A1hzB7ykpC","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":1173,"cache_read_input_tokens":116008,"cache_creation":{"ephemeral_5m_input_tokens":1173,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"fad455d2-eec7-4c26-8d36-13cf993da91d","request_id":"req_011CeJwWHfR844s9sUwKTB2J"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwWKouEL6A1hzB7ykpC","type":"message","role":"assistant","content":[{"type":"text","text":"Maintenant la nouvelle section « Marché local » (courbe d'indice, stats du secteur, percentile municipal) :"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1173,"cache_read_input_tokens":116008,"cache_creation":{"ephemeral_5m_input_tokens":1173,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"11dc01d9-1d60-422c-b540-465c18dc8a1b","request_id":"req_011CeJwWHfR844s9sUwKTB2J"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwWKouEL6A1hzB7ykpC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018c4vqSQKVpzRjirkPusF4c","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","old_string":"  const r: EstimateResult = data.result;\n  const u = data.unit;","new_string":"  const r: EstimateResult = data.result;\n  const u = data.unit;\n  const ctx = data.context;"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1173,"cache_read_input_tokens":116008,"cache_creation":{"ephemeral_5m_input_tokens":1173,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"6ad95296-6e99-4f30-a5dd-afdac53eb75e","request_id":"req_011CeJwWHfR844s9sUwKTB2J"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018c4vqSQKVpzRjirkPusF4c","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"b489dbb8-1ec1-4bab-a81a-1dff35b296a7","timestamp":"2026-08-23T03:05:55.859Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","oldString":"  const r: EstimateResult = data.result;\n  const u = data.unit;","newString":"  const r: EstimateResult = data.result;\n  const u = data.unit;\n  const ctx = data.context;","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport { useState } from \"react\";\nimport Link from \"next/link\";\nimport { useLang } from \"./LangContext\";\nimport LeadForm from \"./LeadForm\";\nimport RadarMap from \"./RadarMap\";\nimport {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  MarketIndexChart,\n  PercentileBar,\n  RangeBar,\n  ValueSplit,\n} from \"./MetricViz\";\nimport type { EstimateResult } from \"@/lib/engine\";\nimport type { UnitEstimate } from \"@/lib/estimator\";\nimport type { TKey } from \"@/lib/i18n\";\n\nconst fmt = (v: number | null | undefined, lang: string) =>\n  v == null\n    ? \"—\"\n    : new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n        style: \"currency\",\n        currency: \"CAD\",\n        maximumFractionDigits: 0,\n      }).format(v);\n\nconst signed = (v: number, lang: string) =>\n  `${v >= 0 ? \"+\" : \"−\"}${fmt(Math.abs(Math.round(v)), lang)}`;\n\nfunction Chip({ k, v }: { k: string; v: string | number }) {\n  return (\n    <div className=\"kv-cell\">\n      <p className=\"k\">{k}</p>\n      <p className=\"v break-words\">{v}</p>\n    </div>\n  );\n}\n\nexport default function ResultView({ data }: { data: UnitEstimate }) {\n  const { lang, t } = useLang();\n  const [activeComp, setActiveComp] = useState<string | null>(null);\n  const r: EstimateResult = data.result;\n  const u = data.unit;\n  const s = u?.specs;\n  const fr = lang === \"fr\";\n  const histMax = Math.max(...(u?.history.map((h) => h.value ?? 0) ?? [0]), 1);\n  const moneyFmt = (v: number) =>\n    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n    }).format(Math.round(v / 100) * 100);\n\n  return (\n    <div className=\"space-y-10 py-8\">\n      {/* fil d'ariane */}\n      <nav className=\"vp-mono flex flex-wrap items-center gap-2 text-[11.5px] uppercase tracking-[0.06em] text-ink-3\">\n        <Link href=\"/\" className=\"border-b-[1.5px] border-transparent hover:border-green hover:text-green\">\n          Vrai-Prix\n        </Link>\n        <span>/</span>\n        <span>{u ? `${u.adresse ?? \"\"} · ${u.municipalite ?? \"\"}` : t(\"manualTitle\")}</span>\n      </nav>\n\n      {/* ---- Panneau prix ---- */}\n      <section className=\"vp-card rise p-6 sm:p-8\">\n        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          <div className=\"min-w-0 flex-1\">\n            <span className=\"kicker\">{t(\"estimatedValue\")}</span>\n            {u && (\n              <h1 className=\"vp-display mt-2 text-[clamp(20px,3vw,28px)] font-bold uppercase leading-tight tracking-[-0.02em]\">\n                {u.adresse}\n                {s?.apt ? ` app. ${s.apt}` : \"\"}\n                {u.municipalite ? <span className=\"text-ink-3\"> · {u.municipalite}</span> : null}\n              </h1>\n            )}\n            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            {/* faits rapides */}\n            <div className=\"mt-4 flex flex-wrap gap-2\">\n              {[\n                u ? t(u.typeProp as TKey) : null,\n                u?.aireEtagesM2 && r.estimate > 0\n                  ? `${locNum(Math.round(r.estimate / u.aireEtagesM2), lang, { maxFrac: 0 })} $/m²`\n                  : null,\n                u?.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : null,\n                u?.anneeConstruction ? `${fr ? \"constr.\" : \"built\"} ${u.anneeConstruction}` : null,\n                u?.superficieTerrainM2\n                  ? `${fr ? \"terrain\" : \"lot\"} ${locNum(u.superficieTerrainM2, lang, { unit: \"m²\" })}`\n                  : null,\n              ]\n                .filter((x): x is string => x != null)\n                .map((x) => (\n                  <span\n                    key={x}\n                    className=\"vp-mono rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"\n                  >\n                    {x}\n                  </span>\n                ))}\n            </div>\n            {/* fourchette visuelle P10-P90 avec position du rôle */}\n            <div className=\"mt-6 max-w-2xl\">\n              <p className=\"klabel mb-1\">{t(\"range\")}</p>\n              <RangeBar\n                low={r.low}\n                estimate={r.estimate}\n                high={r.high}\n                role={u?.valeurRole ?? null}\n                lang={lang}\n              />\n            </div>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              {u?.valeurRole != null && (\n                <div>\n                  <p className=\"klabel\">{t(\"assessed\")}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-ink-2\">{fmt(u.valeurRole, lang)}</p>\n                </div>\n              )}\n              {u?.valeurRole != null && u.valeurRole > 0 && r.estimate > 0 && (\n                <div>\n                  <p className=\"klabel\">{fr ? \"Écart vs rôle\" : \"vs assessment\"}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-green-deep\">\n                    {r.estimate >= u.valeurRole ? \"+\" : \"\"}\n                    {((r.estimate / u.valeurRole - 1) * 100).toFixed(0)} %\n                  </p>\n                </div>\n              )}\n            </div>\n            {u && (\n              <div className=\"mt-6 flex flex-wrap gap-2.5\">\n                <a\n                  href={`/api/report?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-primary\"\n                  download\n                >\n                  {fr ? \"Rapport PDF\" : \"PDF report\"} ↓\n                </a>\n                <a\n                  href={`/api/report/pro?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-ghost\"\n                  download\n                  title={fr ? \"Structure de rapport d'évaluation — présentation bancaire\" : \"Appraisal-style structure — bank presentation\"}\n                >\n                  {fr ? \"Rapport détaillé format professionnel (banque)\" : \"Detailed professional report (bank)\"} ↓\n                </a>\n              </div>\n            )}\n          </div>\n          <div className=\"shrink-0\">\n            <ConfidenceDial\n              pct={r.confidencePct}\n              level={r.confidenceLevel}\n              label={`${t(\"confidence\")} — ${t(`conf${r.confidenceLevel}` as TKey)}`}\n            />\n          </div>\n        </div>\n        <p className=\"vp-mono mt-5 border-t-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {t(\"disclaimer\")}\n        </p>\n      </section>\n\n      {/* ---- Portrait de la propriété (bento du registre) ---- */}\n      {u && s && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Registre officiel — dessiné par les données\" : \"Official record — drawn by the data\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Portrait de la propriété\" : \"Property portrait\"}\n          </h2>\n          <div className=\"mt-4 grid gap-4 lg:grid-cols-3\">\n            {/* Bâtiment */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"60ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Bâtiment\" : \"Building\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <BuildingGlyph\n                  floors={s.nbEtages}\n                  dwellings={u.nbLogements}\n                  genre={s.genreConstruction}\n                  yearBuilt={u.anneeConstruction}\n                  floorAreaM2={u.aireEtagesM2}\n                  lang={lang}\n                />\n              </div>\n              <EraLine year={u.anneeConstruction} lang={lang} />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Aire d'étages\" : \"Floor area\"} v={u.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Étages × logements\" : \"Storeys × dwellings\"} v={`${s.nbEtages ?? \"—\"} × ${u.nbLogements ?? \"—\"}`} />\n                <Chip k={fr ? \"Genre\" : \"Style\"} v={s.genreConstruction ?? \"—\"} />\n                <Chip k={fr ? \"Lien physique\" : \"Physical link\"} v={s.lienPhysique ?? \"—\"} />\n              </div>\n            </div>\n            {/* Terrain */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"140ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Terrain — à l'échelle\" : \"Lot — to scale\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <LotDiagram\n                  areaM2={u.superficieTerrainM2}\n                  frontageM={s.frontTerrainM}\n                  footprintM2={\n                    u.aireEtagesM2 && s.nbEtages ? u.aireEtagesM2 / Math.max(1, s.nbEtages) : u.aireEtagesM2\n                  }\n                  lang={lang}\n                  isCondo={!!s.apt || (u.nbLogements != null && u.nbLogements >= 1 && !u.superficieTerrainM2)}\n                />\n              </div>\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Superficie\" : \"Area\"} v={u.superficieTerrainM2 ? locNum(u.superficieTerrainM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Frontage\" : \"Frontage\"} v={s.frontTerrainM ? locNum(s.frontTerrainM, lang, { unit: \"m\" }) : \"—\"} />\n                <Chip k=\"CUBF\" v={s.cubf ?? \"—\"} />\n                <Chip k={fr ? \"Usage\" : \"Use\"} v={s.cubfLibelle ?? \"—\"} />\n              </div>\n            </div>\n            {/* Valeur au rôle */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"220ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Valeur au rôle 2026 — composition\" : \"2026 assessed value — split\"}\n              </p>\n              <p className=\"vp-display py-2 text-[26px] font-bold tracking-[-0.02em]\">\n                {fmt(u.valeurRole, lang)}\n              </p>\n              <ValueSplit\n                land={s.valeurTerrain}\n                building={s.valeurBatiment}\n                previous={s.valeurAnterieure}\n                total={u.valeurRole}\n                lang={lang}\n                labels={{\n                  land: fr ? \"Terrain\" : \"Land\",\n                  building: fr ? \"Bâtiment\" : \"Building\",\n                  previous: fr ? \"Rôle antérieur\" : \"Previous roll\",\n                }}\n              />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Cond. marché au\" : \"Market cond.\"} v={s.datCondMarche ?? \"—\"} />\n                <Chip k={fr ? \"Matricule\" : \"Roll no.\"} v={s.matricule ?? \"—\"} />\n              </div>\n            </div>\n          </div>\n          {/* registre brut complet */}\n          <details className=\"vp-card mt-4 p-5\">\n            <summary className=\"vp-mono cursor-pointer text-[11px] font-bold uppercase tracking-[0.08em] text-green-deep\">\n              {fr ? \"▸ Toutes les données du registre (brutes)\" : \"▸ All registry data (raw)\"}\n            </summary>\n            <div className=\"mt-4 grid grid-cols-2 gap-2 sm:grid-cols-4\">\n              <Chip k={fr ? \"Année construction\" : \"Year built\"} v={u.anneeConstruction ? `${u.anneeConstruction}${s.anneeEstimee === \"E\" ? (fr ? \" (estimée)\" : \" (est.)\") : \"\"}` : \"—\"} />\n              <Chip k={fr ? \"Locaux non résid.\" : \"Non-res. units\"} v={s.nbLocauxNonResid ?? \"—\"} />\n              <Chip k={fr ? \"Chambres locatives\" : \"Rental rooms\"} v={s.nbChambresLocatives ?? \"—\"} />\n              <Chip k={fr ? \"Unité de voisinage\" : \"Neighbourhood\"} v={s.uniteVoisinage || \"—\"} />\n              <Chip k={fr ? \"Arrondissement\" : \"Borough\"} v={s.arrond || \"—\"} />\n              <Chip k={fr ? \"Adresses sur l'unité\" : \"Addresses\"} v={s.nAdresses ?? \"—\"} />\n              <Chip k={fr ? \"Valeur terrain\" : \"Land value\"} v={fmt(s.valeurTerrain, lang)} />\n              <Chip k={fr ? \"Valeur bâtiment\" : \"Building value\"} v={fmt(s.valeurBatiment, lang)} />\n              <Chip k={fr ? \"Rôle antérieur\" : \"Previous roll\"} v={fmt(s.valeurAnterieure, lang)} />\n              <Chip k=\"ID provincial\" v={u.id} />\n            </div>\n          </details>\n        </section>\n      )}\n\n      {/* ---- Transparence du calcul ---- */}\n      <section>\n        <span className=\"kicker\">{fr ? \"Transparence\" : \"Transparency\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"whyTitle\")}\n        </h2>\n        <div className=\"mt-4 grid gap-4 sm:grid-cols-2\">\n          <div className=\"vp-card vp-card-hover rise p-5\">\n            <p className=\"klabel\">{t(\"modelPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.modelEstimate != null ? (\n                <CountUp value={r.modelEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightModel\")} : {(r.modelWeight * 100).toFixed(0)} %\n            </p>\n          </div>\n          <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"100ms\" }}>\n            <p className=\"klabel\">{t(\"compsPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.compsEstimate != null ? (\n                <CountUp value={r.compsEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightComps\")} : {((1 - r.modelWeight) * 100).toFixed(0)} % · {r.nCompsUsed} comps\n              {r.compsDispersionPct != null ? ` · ±${r.compsDispersionPct} %` : \"\"}\n            </p>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Historique ---- */}\n      {u && u.history.some((h) => h.value != null) && (\n        <section>\n          <span className=\"kicker\">2021 → 2026</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {t(\"history\")}\n          </h2>\n          <div className=\"vp-card rise mt-4 p-5 sm:p-6\">\n            <div className=\"flex flex-col gap-2\">\n              {u.history.map((h, i) => {\n                const prev = i > 0 ? u.history[i - 1].value : null;\n                const yoy =\n                  h.value != null && prev != null && prev > 0\n                    ? ((h.value / prev - 1) * 100)\n                    : null;\n                return (\n                  <div key={h.year} className=\"hbar-row grid grid-cols-[44px_1fr_auto] items-center gap-2 sm:grid-cols-[52px_1fr_auto] sm:gap-3\">\n                    <span className=\"vp-mono text-[12px] font-bold\">{h.year}</span>\n                    <span className=\"hbar-track\">\n                      <span\n                        className=\"hbar-fill grow-x\"\n                        style={{\n                          width: `${((h.value ?? 0) / histMax) * 100}%`,\n                          animationDelay: `${i * 90}ms`,\n                          background: h.year === 2026 ? \"var(--ink)\" : undefined,\n                        }}\n                      />\n                    </span>\n                    <span className=\"vp-mono flex items-center justify-end gap-2 text-[12px] font-bold\">\n                      {fmt(h.value, lang)}\n                      {yoy != null && (\n                        <span className={`rounded-[4px] border border-ink px-1 py-px text-[9px] ${yoy >= 0 ? \"bg-lime-soft text-green-deep\" : \"bg-[var(--danger-soft)] text-[var(--danger)]\"}`}>\n                          {yoy >= 0 ? \"+\" : \"\"}\n                          {yoy.toFixed(0)} %\n                        </span>\n                      )}\n                    </span>\n                  </div>\n                );\n              })}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Plan des comparables (carte maison) ---- */}\n      <section>\n        <span className=\"kicker\">\n          {r.nCompsUsed} {fr ? \"ventes réelles\" : \"real sales\"}\n        </span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"compsTitle\")}\n        </h2>\n        <div className=\"mt-4 grid items-start gap-4 lg:grid-cols-[1.05fr_1fr]\">\n          <div className=\"overflow-hidden rounded-[10px] border-[1.5px] border-ink shadow-[8px_8px_0_rgba(20,24,20,0.08)]\">\n            <RadarMap\n              subject={{\n                lat: u?.lat ?? r.comps[0]?.lat ?? 46.81,\n                lng: u?.lng ?? r.comps[0]?.lng ?? -71.21,\n                label: u?.adresse ?? (fr ? \"Propriété estimée\" : \"Subject property\"),\n              }}\n              comps={r.comps.map((c) => ({\n                id: c.id,\n                lat: c.lat,\n                lng: c.lng,\n                label: `${c.street ?? \"\"}, ${c.city ?? \"\"}`,\n                price: fmt(c.amount, lang),\n                adjusted: fmt(Math.round(c.adjustedPrice), lang),\n                date: c.date,\n                distanceM: c.distanceM,\n                weight: c.weight,\n              }))}\n              highlight={activeComp}\n              onHover={setActiveComp}\n            />\n          </div>\n          <div className=\"src-wrap max-h-[560px] overflow-y-auto\">\n            <table className=\"src-table\">\n              <thead className=\"sticky top-0 z-10\">\n                <tr>\n                  <th>#</th>\n                  <th>{fr ? \"Vente\" : \"Sale\"}</th>\n                  <th className=\"text-right\">{t(\"adjusted\")}</th>\n                </tr>\n              </thead>\n              <tbody>\n                {r.comps.map((c, i) => (\n                  <tr\n                    key={c.id}\n                    onMouseEnter={() => setActiveComp(c.id)}\n                    onMouseLeave={() => setActiveComp(null)}\n                    className={activeComp === c.id ? \"!bg-lime-soft\" : \"\"}\n                  >\n                    <td>\n                      <span\n                        className={`vp-mono inline-flex h-6 w-6 items-center justify-center rounded-full border-[1.5px] border-ink text-[11px] font-bold ${\n                          activeComp === c.id ? \"bg-lime text-ink\" : \"bg-green text-paper\"\n                        }`}\n                      >\n                        {i + 1}\n                      </span>\n                    </td>\n                    <td>\n                      <p className=\"font-semibold leading-tight\">\n                        {c.street}, {c.city}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] text-ink-2\">\n                        {t(\"adjTime\")} {signed(c.adjTime, lang)} · {t(\"adjArea\")} {signed(c.adjArea, lang)} ·{\" \"}\n                        {t(\"adjAge\")} {signed(c.adjAge, lang)}\n                      </p>\n                    </td>\n                    <td className=\"vp-display text-right font-bold\">\n                      {fmt(Math.round(c.adjustedPrice), lang)}\n                      <p className=\"vp-mono text-[10px] font-normal text-ink-3\">\n                        {t(\"weight\")} {(c.weight * 100).toFixed(0)} %\n                      </p>\n                    </td>\n                  </tr>\n                ))}\n              </tbody>\n            </table>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Lead ---- */}\n      <section className=\"rounded-[10px] border-[1.5px] border-ink bg-ink p-6 text-paper shadow-[8px_8px_0_rgba(20,24,20,0.2)] sm:p-8\">\n        <h2 className=\"vp-display text-[22px] font-bold uppercase tracking-[-0.02em] text-lime\">\n          {t(\"leadTitle\")}\n        </h2>\n        <div className=\"mt-4 max-w-xl\">\n          <LeadForm unitId={u?.id ?? null} estimate={r.estimate} />\n        </div>\n      </section>\n    </div>\n  );\n}\n","structuredPatch":[{"oldStart":47,"oldLines":6,"newStart":47,"newLines":7,"lines":["   const [activeComp, setActiveComp] = useState<string | null>(null);","   const r: EstimateResult = data.result;","   const u = data.unit;","+  const ctx = data.context;","   const s = u?.specs;","   const fr = lang === \"fr\";","   const histMax = Math.max(...(u?.history.map((h) => h.value ?? 0) ?? [0]), 1);"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwWnZEbE47S1f5VnjDx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HKWPQcJ9zp9sqPzPqaoDsx","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","old_string":"      {/* ---- Historique ---- */}","new_string":"      {/* ---- Marché local ---- */}\n      {ctx && (ctx.index.length >= 6 || ctx.sector || ctx.muniPercentile != null) && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Contexte de marché\" : \"Market context\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Le marché autour de cette propriété\" : \"The market around this property\"}\n          </h2>\n          <div className=\"mt-4 grid items-stretch gap-4 lg:grid-cols-[1.35fr_1fr]\">\n            {/* trajectoire du marché */}\n            {ctx.index.length >= 6 && (\n              <div className=\"vp-card rise p-5\">\n                <div className=\"flex flex-wrap items-center justify-between gap-2\">\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Indice de marché — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 en ${ctx.index[0].month.slice(0, 4)})`\n                      : `Market index — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 in ${ctx.index[0].month.slice(0, 4)})`}\n                  </p>\n                  <div className=\"flex gap-2\">\n                    {ctx.index12mPct != null && (\n                      <span className={`vp-mono rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${ctx.index12mPct >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n                        12 {fr ? \"mois\" : \"mo\"} {ctx.index12mPct >= 0 ? \"+\" : \"\"}\n                        {ctx.index12mPct.toFixed(1)} %\n                      </span>\n                    )}\n                    {ctx.indexSince2021Pct != null && (\n                      <span className=\"vp-mono rounded-[4px] border border-ink bg-surface-2 px-1.5 py-0.5 text-[10px] font-bold text-ink-2\">\n                        {ctx.index[0].month.slice(0, 4)}→ {ctx.indexSince2021Pct >= 0 ? \"+\" : \"\"}\n                        {ctx.indexSince2021Pct.toFixed(0)} %\n                      </span>\n                    )}\n                  </div>\n                </div>\n                <div className=\"mt-3\">\n                  <MarketIndexChart series={ctx.index} lang={lang} />\n                </div>\n                <p className=\"vp-mono mt-2 text-[10px] uppercase tracking-[0.05em] text-ink-3\">\n                  {fr\n                    ? \"Indice utilisé pour l'ajustement « marché » des comparables — même donnée, même calcul.\"\n                    : \"Index used for the comparables' market adjustment — same data, same math.\"}\n                </p>\n              </div>\n            )}\n            <div className=\"flex flex-col gap-4\">\n              {/* ventes du secteur */}\n              {ctx.sector && (\n                <div className=\"vp-card rise p-5\" style={{ animationDelay: \"80ms\" }}>\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Ventes réelles — rayon ${ctx.sector.radiusKm} km · 12 mois`\n                      : `Real sales — ${ctx.sector.radiusKm} km radius · 12 months`}\n                  </p>\n                  <div className=\"mt-3 grid grid-cols-3 gap-2\">\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Ventes\" : \"Sales\"}</p>\n                      <p className=\"v\">{ctx.sector.nSales12m}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Prix médian\" : \"Median price\"}</p>\n                      <p className=\"v\">{ctx.sector.medianPrice != null ? fmt(Math.round(ctx.sector.medianPrice), lang) : \"—\"}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">$/m² {fr ? \"médian\" : \"median\"}</p>\n                      <p className=\"v\">\n                        {ctx.sector.medianPpm2 != null\n                          ? locNum(Math.round(ctx.sector.medianPpm2), lang, { maxFrac: 0 })\n                          : \"—\"}\n                      </p>\n                    </div>\n                  </div>\n                  {ctx.sector.medianPrice != null && r.estimate > 0 && (\n                    <p className=\"vp-mono mt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-2\">\n                      {fr ? \"Cette propriété\" : \"This property\"} :{\" \"}\n                      <b className=\"text-ink\">\n                        {r.estimate >= ctx.sector.medianPrice ? \"+\" : \"−\"}\n                        {Math.abs((r.estimate / ctx.sector.medianPrice - 1) * 100).toFixed(0)} %\n                      </b>{\" \"}\n                      {fr ? \"vs médiane du secteur\" : \"vs sector median\"}\n                    </p>\n                  )}\n                </div>\n              )}\n              {/* position dans la municipalité */}\n              {ctx.muniPercentile != null && u?.municipalite && (\n                <div className=\"vp-card rise flex-1 p-5\" style={{ animationDelay: \"160ms\" }}>\n                  <p className=\"klabel\">\n                    {fr ? `Position dans ${u.municipalite}` : `Position in ${u.municipalite}`}\n                  </p>\n                  <div className=\"mt-3\">\n                    <PercentileBar pct={ctx.muniPercentile} lang={lang} />\n                  </div>\n                  <p className=\"mt-3 text-[13px] text-ink-2\">\n                    {fr ? (\n                      <>\n                        Vaut plus que{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> des{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        propriétés estimées de la municipalité.\n                      </>\n                    ) : (\n                      <>\n                        Worth more than{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> of the{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        estimated properties in the municipality.\n                      </>\n                    )}\n                  </p>\n                </div>\n              )}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Historique ---- */}"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":409,"cache_read_input_tokens":117181,"cache_creation":{"ephemeral_5m_input_tokens":409,"ephemeral_1h_input_tokens":0},"output_tokens":15,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"912985ff-af38-4154-b8eb-d2560eeb33dc","request_id":"req_011CeJwWmPnJMN55TGv9jkoV"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HKWPQcJ9zp9sqPzPqaoDsx","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"90a69f7e-6ebe-4c74-a37d-48b68ca6022e","timestamp":"2026-08-23T03:06:20.712Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","oldString":"      {/* ---- Historique ---- */}","newString":"      {/* ---- Marché local ---- */}\n      {ctx && (ctx.index.length >= 6 || ctx.sector || ctx.muniPercentile != null) && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Contexte de marché\" : \"Market context\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Le marché autour de cette propriété\" : \"The market around this property\"}\n          </h2>\n          <div className=\"mt-4 grid items-stretch gap-4 lg:grid-cols-[1.35fr_1fr]\">\n            {/* trajectoire du marché */}\n            {ctx.index.length >= 6 && (\n              <div className=\"vp-card rise p-5\">\n                <div className=\"flex flex-wrap items-center justify-between gap-2\">\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Indice de marché — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 en ${ctx.index[0].month.slice(0, 4)})`\n                      : `Market index — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 in ${ctx.index[0].month.slice(0, 4)})`}\n                  </p>\n                  <div className=\"flex gap-2\">\n                    {ctx.index12mPct != null && (\n                      <span className={`vp-mono rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${ctx.index12mPct >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n                        12 {fr ? \"mois\" : \"mo\"} {ctx.index12mPct >= 0 ? \"+\" : \"\"}\n                        {ctx.index12mPct.toFixed(1)} %\n                      </span>\n                    )}\n                    {ctx.indexSince2021Pct != null && (\n                      <span className=\"vp-mono rounded-[4px] border border-ink bg-surface-2 px-1.5 py-0.5 text-[10px] font-bold text-ink-2\">\n                        {ctx.index[0].month.slice(0, 4)}→ {ctx.indexSince2021Pct >= 0 ? \"+\" : \"\"}\n                        {ctx.indexSince2021Pct.toFixed(0)} %\n                      </span>\n                    )}\n                  </div>\n                </div>\n                <div className=\"mt-3\">\n                  <MarketIndexChart series={ctx.index} lang={lang} />\n                </div>\n                <p className=\"vp-mono mt-2 text-[10px] uppercase tracking-[0.05em] text-ink-3\">\n                  {fr\n                    ? \"Indice utilisé pour l'ajustement « marché » des comparables — même donnée, même calcul.\"\n                    : \"Index used for the comparables' market adjustment — same data, same math.\"}\n                </p>\n              </div>\n            )}\n            <div className=\"flex flex-col gap-4\">\n              {/* ventes du secteur */}\n              {ctx.sector && (\n                <div className=\"vp-card rise p-5\" style={{ animationDelay: \"80ms\" }}>\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Ventes réelles — rayon ${ctx.sector.radiusKm} km · 12 mois`\n                      : `Real sales — ${ctx.sector.radiusKm} km radius · 12 months`}\n                  </p>\n                  <div className=\"mt-3 grid grid-cols-3 gap-2\">\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Ventes\" : \"Sales\"}</p>\n                      <p className=\"v\">{ctx.sector.nSales12m}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Prix médian\" : \"Median price\"}</p>\n                      <p className=\"v\">{ctx.sector.medianPrice != null ? fmt(Math.round(ctx.sector.medianPrice), lang) : \"—\"}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">$/m² {fr ? \"médian\" : \"median\"}</p>\n                      <p className=\"v\">\n                        {ctx.sector.medianPpm2 != null\n                          ? locNum(Math.round(ctx.sector.medianPpm2), lang, { maxFrac: 0 })\n                          : \"—\"}\n                      </p>\n                    </div>\n                  </div>\n                  {ctx.sector.medianPrice != null && r.estimate > 0 && (\n                    <p className=\"vp-mono mt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-2\">\n                      {fr ? \"Cette propriété\" : \"This property\"} :{\" \"}\n                      <b className=\"text-ink\">\n                        {r.estimate >= ctx.sector.medianPrice ? \"+\" : \"−\"}\n                        {Math.abs((r.estimate / ctx.sector.medianPrice - 1) * 100).toFixed(0)} %\n                      </b>{\" \"}\n                      {fr ? \"vs médiane du secteur\" : \"vs sector median\"}\n                    </p>\n                  )}\n                </div>\n              )}\n              {/* position dans la municipalité */}\n              {ctx.muniPercentile != null && u?.municipalite && (\n                <div className=\"vp-card rise flex-1 p-5\" style={{ animationDelay: \"160ms\" }}>\n                  <p className=\"klabel\">\n                    {fr ? `Position dans ${u.municipalite}` : `Position in ${u.municipalite}`}\n                  </p>\n                  <div className=\"mt-3\">\n                    <PercentileBar pct={ctx.muniPercentile} lang={lang} />\n                  </div>\n                  <p className=\"mt-3 text-[13px] text-ink-2\">\n                    {fr ? (\n                      <>\n                        Vaut plus que{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> des{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        propriétés estimées de la municipalité.\n                      </>\n                    ) : (\n                      <>\n                        Worth more than{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> of the{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        estimated properties in the municipality.\n                      </>\n                    )}\n                  </p>\n                </div>\n              )}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Historique ---- */}","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport { useState } from \"react\";\nimport Link from \"next/link\";\nimport { useLang } from \"./LangContext\";\nimport LeadForm from \"./LeadForm\";\nimport RadarMap from \"./RadarMap\";\nimport {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  MarketIndexChart,\n  PercentileBar,\n  RangeBar,\n  ValueSplit,\n} from \"./MetricViz\";\nimport type { EstimateResult } from \"@/lib/engine\";\nimport type { UnitEstimate } from \"@/lib/estimator\";\nimport type { TKey } from \"@/lib/i18n\";\n\nconst fmt = (v: number | null | undefined, lang: string) =>\n  v == null\n    ? \"—\"\n    : new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n        style: \"currency\",\n        currency: \"CAD\",\n        maximumFractionDigits: 0,\n      }).format(v);\n\nconst signed = (v: number, lang: string) =>\n  `${v >= 0 ? \"+\" : \"−\"}${fmt(Math.abs(Math.round(v)), lang)}`;\n\nfunction Chip({ k, v }: { k: string; v: string | number }) {\n  return (\n    <div className=\"kv-cell\">\n      <p className=\"k\">{k}</p>\n      <p className=\"v break-words\">{v}</p>\n    </div>\n  );\n}\n\nexport default function ResultView({ data }: { data: UnitEstimate }) {\n  const { lang, t } = useLang();\n  const [activeComp, setActiveComp] = useState<string | null>(null);\n  const r: EstimateResult = data.result;\n  const u = data.unit;\n  const ctx = data.context;\n  const s = u?.specs;\n  const fr = lang === \"fr\";\n  const histMax = Math.max(...(u?.history.map((h) => h.value ?? 0) ?? [0]), 1);\n  const moneyFmt = (v: number) =>\n    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n    }).format(Math.round(v / 100) * 100);\n\n  return (\n    <div className=\"space-y-10 py-8\">\n      {/* fil d'ariane */}\n      <nav className=\"vp-mono flex flex-wrap items-center gap-2 text-[11.5px] uppercase tracking-[0.06em] text-ink-3\">\n        <Link href=\"/\" className=\"border-b-[1.5px] border-transparent hover:border-green hover:text-green\">\n          Vrai-Prix\n        </Link>\n        <span>/</span>\n        <span>{u ? `${u.adresse ?? \"\"} · ${u.municipalite ?? \"\"}` : t(\"manualTitle\")}</span>\n      </nav>\n\n      {/* ---- Panneau prix ---- */}\n      <section className=\"vp-card rise p-6 sm:p-8\">\n        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          <div className=\"min-w-0 flex-1\">\n            <span className=\"kicker\">{t(\"estimatedValue\")}</span>\n            {u && (\n              <h1 className=\"vp-display mt-2 text-[clamp(20px,3vw,28px)] font-bold uppercase leading-tight tracking-[-0.02em]\">\n                {u.adresse}\n                {s?.apt ? ` app. ${s.apt}` : \"\"}\n                {u.municipalite ? <span className=\"text-ink-3\"> · {u.municipalite}</span> : null}\n              </h1>\n            )}\n            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            {/* faits rapides */}\n            <div className=\"mt-4 flex flex-wrap gap-2\">\n              {[\n                u ? t(u.typeProp as TKey) : null,\n                u?.aireEtagesM2 && r.estimate > 0\n                  ? `${locNum(Math.round(r.estimate / u.aireEtagesM2), lang, { maxFrac: 0 })} $/m²`\n                  : null,\n                u?.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : null,\n                u?.anneeConstruction ? `${fr ? \"constr.\" : \"built\"} ${u.anneeConstruction}` : null,\n                u?.superficieTerrainM2\n                  ? `${fr ? \"terrain\" : \"lot\"} ${locNum(u.superficieTerrainM2, lang, { unit: \"m²\" })}`\n                  : null,\n              ]\n                .filter((x): x is string => x != null)\n                .map((x) => (\n                  <span\n                    key={x}\n                    className=\"vp-mono rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"\n                  >\n                    {x}\n                  </span>\n                ))}\n            </div>\n            {/* fourchette visuelle P10-P90 avec position du rôle */}\n            <div className=\"mt-6 max-w-2xl\">\n              <p className=\"klabel mb-1\">{t(\"range\")}</p>\n              <RangeBar\n                low={r.low}\n                estimate={r.estimate}\n                high={r.high}\n                role={u?.valeurRole ?? null}\n                lang={lang}\n              />\n            </div>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              {u?.valeurRole != null && (\n                <div>\n                  <p className=\"klabel\">{t(\"assessed\")}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-ink-2\">{fmt(u.valeurRole, lang)}</p>\n                </div>\n              )}\n              {u?.valeurRole != null && u.valeurRole > 0 && r.estimate > 0 && (\n                <div>\n                  <p className=\"klabel\">{fr ? \"Écart vs rôle\" : \"vs assessment\"}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-green-deep\">\n                    {r.estimate >= u.valeurRole ? \"+\" : \"\"}\n                    {((r.estimate / u.valeurRole - 1) * 100).toFixed(0)} %\n                  </p>\n                </div>\n              )}\n            </div>\n            {u && (\n              <div className=\"mt-6 flex flex-wrap gap-2.5\">\n                <a\n                  href={`/api/report?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-primary\"\n                  download\n                >\n                  {fr ? \"Rapport PDF\" : \"PDF report\"} ↓\n                </a>\n                <a\n                  href={`/api/report/pro?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-ghost\"\n                  download\n                  title={fr ? \"Structure de rapport d'évaluation — présentation bancaire\" : \"Appraisal-style structure — bank presentation\"}\n                >\n                  {fr ? \"Rapport détaillé format professionnel (banque)\" : \"Detailed professional report (bank)\"} ↓\n                </a>\n              </div>\n            )}\n          </div>\n          <div className=\"shrink-0\">\n            <ConfidenceDial\n              pct={r.confidencePct}\n              level={r.confidenceLevel}\n              label={`${t(\"confidence\")} — ${t(`conf${r.confidenceLevel}` as TKey)}`}\n            />\n          </div>\n        </div>\n        <p className=\"vp-mono mt-5 border-t-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {t(\"disclaimer\")}\n        </p>\n      </section>\n\n      {/* ---- Portrait de la propriété (bento du registre) ---- */}\n      {u && s && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Registre officiel — dessiné par les données\" : \"Official record — drawn by the data\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Portrait de la propriété\" : \"Property portrait\"}\n          </h2>\n          <div className=\"mt-4 grid gap-4 lg:grid-cols-3\">\n            {/* Bâtiment */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"60ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Bâtiment\" : \"Building\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <BuildingGlyph\n                  floors={s.nbEtages}\n                  dwellings={u.nbLogements}\n                  genre={s.genreConstruction}\n                  yearBuilt={u.anneeConstruction}\n                  floorAreaM2={u.aireEtagesM2}\n                  lang={lang}\n                />\n              </div>\n              <EraLine year={u.anneeConstruction} lang={lang} />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Aire d'étages\" : \"Floor area\"} v={u.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Étages × logements\" : \"Storeys × dwellings\"} v={`${s.nbEtages ?? \"—\"} × ${u.nbLogements ?? \"—\"}`} />\n                <Chip k={fr ? \"Genre\" : \"Style\"} v={s.genreConstruction ?? \"—\"} />\n                <Chip k={fr ? \"Lien physique\" : \"Physical link\"} v={s.lienPhysique ?? \"—\"} />\n              </div>\n            </div>\n            {/* Terrain */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"140ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Terrain — à l'échelle\" : \"Lot — to scale\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <LotDiagram\n                  areaM2={u.superficieTerrainM2}\n                  frontageM={s.frontTerrainM}\n                  footprintM2={\n                    u.aireEtagesM2 && s.nbEtages ? u.aireEtagesM2 / Math.max(1, s.nbEtages) : u.aireEtagesM2\n                  }\n                  lang={lang}\n                  isCondo={!!s.apt || (u.nbLogements != null && u.nbLogements >= 1 && !u.superficieTerrainM2)}\n                />\n              </div>\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Superficie\" : \"Area\"} v={u.superficieTerrainM2 ? locNum(u.superficieTerrainM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Frontage\" : \"Frontage\"} v={s.frontTerrainM ? locNum(s.frontTerrainM, lang, { unit: \"m\" }) : \"—\"} />\n                <Chip k=\"CUBF\" v={s.cubf ?? \"—\"} />\n                <Chip k={fr ? \"Usage\" : \"Use\"} v={s.cubfLibelle ?? \"—\"} />\n              </div>\n            </div>\n            {/* Valeur au rôle */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"220ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Valeur au rôle 2026 — composition\" : \"2026 assessed value — split\"}\n              </p>\n              <p className=\"vp-display py-2 text-[26px] font-bold tracking-[-0.02em]\">\n                {fmt(u.valeurRole, lang)}\n              </p>\n              <ValueSplit\n                land={s.valeurTerrain}\n                building={s.valeurBatiment}\n                previous={s.valeurAnterieure}\n                total={u.valeurRole}\n                lang={lang}\n                labels={{\n                  land: fr ? \"Terrain\" : \"Land\",\n                  building: fr ? \"Bâtiment\" : \"Building\",\n                  previous: fr ? \"Rôle antérieur\" : \"Previous roll\",\n                }}\n              />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Cond. marché au\" : \"Market cond.\"} v={s.datCondMarche ?? \"—\"} />\n                <Chip k={fr ? \"Matricule\" : \"Roll no.\"} v={s.matricule ?? \"—\"} />\n              </div>\n            </div>\n          </div>\n          {/* registre brut complet */}\n          <details className=\"vp-card mt-4 p-5\">\n            <summary className=\"vp-mono cursor-pointer text-[11px] font-bold uppercase tracking-[0.08em] text-green-deep\">\n              {fr ? \"▸ Toutes les données du registre (brutes)\" : \"▸ All registry data (raw)\"}\n            </summary>\n            <div className=\"mt-4 grid grid-cols-2 gap-2 sm:grid-cols-4\">\n              <Chip k={fr ? \"Année construction\" : \"Year built\"} v={u.anneeConstruction ? `${u.anneeConstruction}${s.anneeEstimee === \"E\" ? (fr ? \" (estimée)\" : \" (est.)\") : \"\"}` : \"—\"} />\n              <Chip k={fr ? \"Locaux non résid.\" : \"Non-res. units\"} v={s.nbLocauxNonResid ?? \"—\"} />\n              <Chip k={fr ? \"Chambres locatives\" : \"Rental rooms\"} v={s.nbChambresLocatives ?? \"—\"} />\n              <Chip k={fr ? \"Unité de voisinage\" : \"Neighbourhood\"} v={s.uniteVoisinage || \"—\"} />\n              <Chip k={fr ? \"Arrondissement\" : \"Borough\"} v={s.arrond || \"—\"} />\n              <Chip k={fr ? \"Adresses sur l'unité\" : \"Addresses\"} v={s.nAdresses ?? \"—\"} />\n              <Chip k={fr ? \"Valeur terrain\" : \"Land value\"} v={fmt(s.valeurTerrain, lang)} />\n              <Chip k={fr ? \"Valeur bâtiment\" : \"Building value\"} v={fmt(s.valeurBatiment, lang)} />\n              <Chip k={fr ? \"Rôle antérieur\" : \"Previous roll\"} v={fmt(s.valeurAnterieure, lang)} />\n              <Chip k=\"ID provincial\" v={u.id} />\n            </div>\n          </details>\n        </section>\n      )}\n\n      {/* ---- Transparence du calcul ---- */}\n      <section>\n        <span className=\"kicker\">{fr ? \"Transparence\" : \"Transparency\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"whyTitle\")}\n        </h2>\n        <div className=\"mt-4 grid gap-4 sm:grid-cols-2\">\n          <div className=\"vp-card vp-card-hover rise p-5\">\n            <p className=\"klabel\">{t(\"modelPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.modelEstimate != null ? (\n                <CountUp value={r.modelEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightModel\")} : {(r.modelWeight * 100).toFixed(0)} %\n            </p>\n          </div>\n          <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"100ms\" }}>\n            <p className=\"klabel\">{t(\"compsPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.compsEstimate != null ? (\n                <CountUp value={r.compsEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightComps\")} : {((1 - r.modelWeight) * 100).toFixed(0)} % · {r.nCompsUsed} comps\n              {r.compsDispersionPct != null ? ` · ±${r.compsDispersionPct} %` : \"\"}\n            </p>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Historique ---- */}\n      {u && u.history.some((h) => h.value != null) && (\n        <section>\n          <span className=\"kicker\">2021 → 2026</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {t(\"history\")}\n          </h2>\n          <div className=\"vp-card rise mt-4 p-5 sm:p-6\">\n            <div className=\"flex flex-col gap-2\">\n              {u.history.map((h, i) => {\n                const prev = i > 0 ? u.history[i - 1].value : null;\n                const yoy =\n                  h.value != null && prev != null && prev > 0\n                    ? ((h.value / prev - 1) * 100)\n                    : null;\n                return (\n                  <div key={h.year} className=\"hbar-row grid grid-cols-[44px_1fr_auto] items-center gap-2 sm:grid-cols-[52px_1fr_auto] sm:gap-3\">\n                    <span className=\"vp-mono text-[12px] font-bold\">{h.year}</span>\n                    <span className=\"hbar-track\">\n                      <span\n                        className=\"hbar-fill grow-x\"\n                        style={{\n                          width: `${((h.value ?? 0) / histMax) * 100}%`,\n                          animationDelay: `${i * 90}ms`,\n                          background: h.year === 2026 ? \"var(--ink)\" : undefined,\n                        }}\n                      />\n                    </span>\n                    <span className=\"vp-mono flex items-center justify-end gap-2 text-[12px] font-bold\">\n                      {fmt(h.value, lang)}\n                      {yoy != null && (\n                        <span className={`rounded-[4px] border border-ink px-1 py-px text-[9px] ${yoy >= 0 ? \"bg-lime-soft text-green-deep\" : \"bg-[var(--danger-soft)] text-[var(--danger)]\"}`}>\n                          {yoy >= 0 ? \"+\" : \"\"}\n                          {yoy.toFixed(0)} %\n                        </span>\n                      )}\n                    </span>\n                  </div>\n                );\n              })}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Plan des comparables (carte maison) ---- */}\n      <section>\n        <span className=\"kicker\">\n          {r.nCompsUsed} {fr ? \"ventes réelles\" : \"real sales\"}\n        </span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"compsTitle\")}\n        </h2>\n        <div className=\"mt-4 grid items-start gap-4 lg:grid-cols-[1.05fr_1fr]\">\n          <div className=\"overflow-hidden rounded-[10px] border-[1.5px] border-ink shadow-[8px_8px_0_rgba(20,24,20,0.08)]\">\n            <RadarMap\n              subject={{\n                lat: u?.lat ?? r.comps[0]?.lat ?? 46.81,\n                lng: u?.lng ?? r.comps[0]?.lng ?? -71.21,\n                label: u?.adresse ?? (fr ? \"Propriété estimée\" : \"Subject property\"),\n              }}\n              comps={r.comps.map((c) => ({\n                id: c.id,\n                lat: c.lat,\n                lng: c.lng,\n                label: `${c.street ?? \"\"}, ${c.city ?? \"\"}`,\n                price: fmt(c.amount, lang),\n                adjusted: fmt(Math.round(c.adjustedPrice), lang),\n                date: c.date,\n                distanceM: c.distanceM,\n                weight: c.weight,\n              }))}\n              highlight={activeComp}\n              onHover={setActiveComp}\n            />\n          </div>\n          <div className=\"src-wrap max-h-[560px] overflow-y-auto\">\n            <table className=\"src-table\">\n              <thead className=\"sticky top-0 z-10\">\n                <tr>\n                  <th>#</th>\n                  <th>{fr ? \"Vente\" : \"Sale\"}</th>\n                  <th className=\"text-right\">{t(\"adjusted\")}</th>\n                </tr>\n              </thead>\n              <tbody>\n                {r.comps.map((c, i) => (\n                  <tr\n                    key={c.id}\n                    onMouseEnter={() => setActiveComp(c.id)}\n                    onMouseLeave={() => setActiveComp(null)}\n                    className={activeComp === c.id ? \"!bg-lime-soft\" : \"\"}\n                  >\n                    <td>\n                      <span\n                        className={`vp-mono inline-flex h-6 w-6 items-center justify-center rounded-full border-[1.5px] border-ink text-[11px] font-bold ${\n                          activeComp === c.id ? \"bg-lime text-ink\" : \"bg-green text-paper\"\n                        }`}\n                      >\n                        {i + 1}\n                      </span>\n                    </td>\n                    <td>\n                      <p className=\"font-semibold leading-tight\">\n                        {c.street}, {c.city}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] text-ink-2\">\n                        {t(\"adjTime\")} {signed(c.adjTime, lang)} · {t(\"adjArea\")} {signed(c.adjArea, lang)} ·{\" \"}\n                        {t(\"adjAge\")} {signed(c.adjAge, lang)}\n                      </p>\n                    </td>\n                    <td className=\"vp-display text-right font-bold\">\n                      {fmt(Math.round(c.adjustedPrice), lang)}\n                      <p className=\"vp-mono text-[10px] font-normal text-ink-3\">\n                        {t(\"weight\")} {(c.weight * 100).toFixed(0)} %\n                      </p>\n                    </td>\n                  </tr>\n                ))}\n              </tbody>\n            </table>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Lead ---- */}\n      <section className=\"rounded-[10px] border-[1.5px] border-ink bg-ink p-6 text-paper shadow-[8px_8px_0_rgba(20,24,20,0.2)] sm:p-8\">\n        <h2 className=\"vp-display text-[22px] font-bold uppercase tracking-[-0.02em] text-lime\">\n          {t(\"leadTitle\")}\n        </h2>\n        <div className=\"mt-4 max-w-xl\">\n          <LeadForm unitId={u?.id ?? null} estimate={r.estimate} />\n        </div>\n      </section>\n    </div>\n  );\n}\n","structuredPatch":[{"oldStart":306,"oldLines":6,"newStart":306,"newLines":120,"lines":["         </div>","       </section>"," ","+      {/* ---- Marché local ---- */}","+      {ctx && (ctx.index.length >= 6 || ctx.sector || ctx.muniPercentile != null) && (","+        <section>","+          <span className=\"kicker\">{fr ? \"Contexte de marché\" : \"Market context\"}</span>","+          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">","+            {fr ? \"Le marché autour de cette propriété\" : \"The market around this property\"}","+          </h2>","+          <div className=\"mt-4 grid items-stretch gap-4 lg:grid-cols-[1.35fr_1fr]\">","+            {/* trajectoire du marché */}","+            {ctx.index.length >= 6 && (","+              <div className=\"vp-card rise p-5\">","+                <div className=\"flex flex-wrap items-center justify-between gap-2\">","+                  <p className=\"klabel\">","+                    {fr","+                      ? `Indice de marché — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 en ${ctx.index[0].month.slice(0, 4)})`","+                      : `Market index — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 in ${ctx.index[0].month.slice(0, 4)})`}","+                  </p>","+                  <div className=\"flex gap-2\">","+                    {ctx.index12mPct != null && (","+                      <span className={`vp-mono rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${ctx.index12mPct >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>","+                        12 {fr ? \"mois\" : \"mo\"} {ctx.index12mPct >= 0 ? \"+\" : \"\"}","+                        {ctx.index12mPct.toFixed(1)} %","+                      </span>","+                    )}","+                    {ctx.indexSince2021Pct != null && (","+                      <span className=\"vp-mono rounded-[4px] border border-ink bg-surface-2 px-1.5 py-0.5 text-[10px] font-bold text-ink-2\">","+                        {ctx.index[0].month.slice(0, 4)}→ {ctx.indexSince2021Pct >= 0 ? \"+\" : \"\"}","+                        {ctx.indexSince2021Pct.toFixed(0)} %","+                      </span>","+                    )}","+                  </div>","+                </div>","+                <div className=\"mt-3\">","+                  <MarketIndexChart series={ctx.index} lang={lang} />","+                </div>","+                <p className=\"vp-mono mt-2 text-[10px] uppercase tracking-[0.05em] text-ink-3\">","+                  {fr","+                    ? \"Indice utilisé pour l'ajustement « marché » des comparables — même donnée, même calcul.\"","+                    : \"Index used for the comparables' market adjustment — same data, same math.\"}","+                </p>","+              </div>","+            )}","+            <div className=\"flex flex-col gap-4\">","+              {/* ventes du secteur */}","+              {ctx.sector && (","+                <div className=\"vp-card rise p-5\" style={{ animationDelay: \"80ms\" }}>","+                  <p className=\"klabel\">","+                    {fr","+                      ? `Ventes réelles — rayon ${ctx.sector.radiusKm} km · 12 mois`","+                      : `Real sales — ${ctx.sector.radiusKm} km radius · 12 months`}","+                  </p>","+                  <div className=\"mt-3 grid grid-cols-3 gap-2\">","+                    <div className=\"kv-cell\">","+                      <p className=\"k\">{fr ? \"Ventes\" : \"Sales\"}</p>","+                      <p className=\"v\">{ctx.sector.nSales12m}</p>","+                    </div>","+                    <div className=\"kv-cell\">","+                      <p className=\"k\">{fr ? \"Prix médian\" : \"Median price\"}</p>","+                      <p className=\"v\">{ctx.sector.medianPrice != null ? fmt(Math.round(ctx.sector.medianPrice), lang) : \"—\"}</p>","+                    </div>","+                    <div className=\"kv-cell\">","+                      <p className=\"k\">$/m² {fr ? \"médian\" : \"median\"}</p>","+                      <p className=\"v\">","+                        {ctx.sector.medianPpm2 != null","+                          ? locNum(Math.round(ctx.sector.medianPpm2), lang, { maxFrac: 0 })","+                          : \"—\"}","+                      </p>","+                    </div>","+                  </div>","+                  {ctx.sector.medianPrice != null && r.estimate > 0 && (","+                    <p className=\"vp-mono mt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-2\">","+                      {fr ? \"Cette propriété\" : \"This property\"} :{\" \"}","+                      <b className=\"text-ink\">","+                        {r.estimate >= ctx.sector.medianPrice ? \"+\" : \"−\"}","+                        {Math.abs((r.estimate / ctx.sector.medianPrice - 1) * 100).toFixed(0)} %","+                      </b>{\" \"}","+                      {fr ? \"vs médiane du secteur\" : \"vs sector median\"}","+                    </p>","+                  )}","+                </div>","+              )}","+              {/* position dans la municipalité */}","+              {ctx.muniPercentile != null && u?.municipalite && (","+                <div className=\"vp-card rise flex-1 p-5\" style={{ animationDelay: \"160ms\" }}>","+                  <p className=\"klabel\">","+                    {fr ? `Position dans ${u.municipalite}` : `Position in ${u.municipalite}`}","+                  </p>","+                  <div className=\"mt-3\">","+                    <PercentileBar pct={ctx.muniPercentile} lang={lang} />","+                  </div>","+                  <p className=\"mt-3 text-[13px] text-ink-2\">","+                    {fr ? (","+                      <>","+                        Vaut plus que{\" \"}","+                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> des{\" \"}","+                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}","+                        propriétés estimées de la municipalité.","+                      </>","+                    ) : (","+                      <>","+                        Worth more than{\" \"}","+                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> of the{\" \"}","+                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}","+                        estimated properties in the municipality.","+                      </>","+                    )}","+                  </p>","+                </div>","+              )}","+            </div>","+          </div>","+        </section>","+      )}","+","       {/* ---- Historique ---- */}","       {u && u.history.some((h) => h.value != null) && (","         <section>"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwYdETS83ADuWFiZy8i","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KCx1N3kmT6xZPozx84mBHQ","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","old_string":"                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                      </p>","new_string":"                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                        {c.floorArea && c.floorArea > 20\n                          ? ` (${locNum(Math.round(c.amount / c.floorArea), lang, { maxFrac: 0 })} $/m²)`\n                          : \"\"}\n                      </p>\n                      {(c.floorArea || c.yearBuilt) && (\n                        <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                          {[\n                            c.floorArea ? locNum(c.floorArea, lang, { unit: \"m²\" }) : null,\n                            c.yearBuilt ? `${fr ? \"constr.\" : \"built\"} ${c.yearBuilt}` : null,\n                          ]\n                            .filter(Boolean)\n                            .join(\" · \")}\n                        </p>\n                      )}"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":2565,"cache_read_input_tokens":117590,"cache_creation":{"ephemeral_5m_input_tokens":2565,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"38d289e5-c2cd-441f-ba23-061e112bdc5a","request_id":"req_011CeJwYbmeisgn6iPGtMAg3"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KCx1N3kmT6xZPozx84mBHQ","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"6e11874b-888b-4056-9009-b1fcac7eabaa","timestamp":"2026-08-23T03:06:30.465Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","oldString":"                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                      </p>","newString":"                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                        {c.floorArea && c.floorArea > 20\n                          ? ` (${locNum(Math.round(c.amount / c.floorArea), lang, { maxFrac: 0 })} $/m²)`\n                          : \"\"}\n                      </p>\n                      {(c.floorArea || c.yearBuilt) && (\n                        <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                          {[\n                            c.floorArea ? locNum(c.floorArea, lang, { unit: \"m²\" }) : null,\n                            c.yearBuilt ? `${fr ? \"constr.\" : \"built\"} ${c.yearBuilt}` : null,\n                          ]\n                            .filter(Boolean)\n                            .join(\" · \")}\n                        </p>\n                      )}","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport { useState } from \"react\";\nimport Link from \"next/link\";\nimport { useLang } from \"./LangContext\";\nimport LeadForm from \"./LeadForm\";\nimport RadarMap from \"./RadarMap\";\nimport {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  MarketIndexChart,\n  PercentileBar,\n  RangeBar,\n  ValueSplit,\n} from \"./MetricViz\";\nimport type { EstimateResult } from \"@/lib/engine\";\nimport type { UnitEstimate } from \"@/lib/estimator\";\nimport type { TKey } from \"@/lib/i18n\";\n\nconst fmt = (v: number | null | undefined, lang: string) =>\n  v == null\n    ? \"—\"\n    : new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n        style: \"currency\",\n        currency: \"CAD\",\n        maximumFractionDigits: 0,\n      }).format(v);\n\nconst signed = (v: number, lang: string) =>\n  `${v >= 0 ? \"+\" : \"−\"}${fmt(Math.abs(Math.round(v)), lang)}`;\n\nfunction Chip({ k, v }: { k: string; v: string | number }) {\n  return (\n    <div className=\"kv-cell\">\n      <p className=\"k\">{k}</p>\n      <p className=\"v break-words\">{v}</p>\n    </div>\n  );\n}\n\nexport default function ResultView({ data }: { data: UnitEstimate }) {\n  const { lang, t } = useLang();\n  const [activeComp, setActiveComp] = useState<string | null>(null);\n  const r: EstimateResult = data.result;\n  const u = data.unit;\n  const ctx = data.context;\n  const s = u?.specs;\n  const fr = lang === \"fr\";\n  const histMax = Math.max(...(u?.history.map((h) => h.value ?? 0) ?? [0]), 1);\n  const moneyFmt = (v: number) =>\n    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n    }).format(Math.round(v / 100) * 100);\n\n  return (\n    <div className=\"space-y-10 py-8\">\n      {/* fil d'ariane */}\n      <nav className=\"vp-mono flex flex-wrap items-center gap-2 text-[11.5px] uppercase tracking-[0.06em] text-ink-3\">\n        <Link href=\"/\" className=\"border-b-[1.5px] border-transparent hover:border-green hover:text-green\">\n          Vrai-Prix\n        </Link>\n        <span>/</span>\n        <span>{u ? `${u.adresse ?? \"\"} · ${u.municipalite ?? \"\"}` : t(\"manualTitle\")}</span>\n      </nav>\n\n      {/* ---- Panneau prix ---- */}\n      <section className=\"vp-card rise p-6 sm:p-8\">\n        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          <div className=\"min-w-0 flex-1\">\n            <span className=\"kicker\">{t(\"estimatedValue\")}</span>\n            {u && (\n              <h1 className=\"vp-display mt-2 text-[clamp(20px,3vw,28px)] font-bold uppercase leading-tight tracking-[-0.02em]\">\n                {u.adresse}\n                {s?.apt ? ` app. ${s.apt}` : \"\"}\n                {u.municipalite ? <span className=\"text-ink-3\"> · {u.municipalite}</span> : null}\n              </h1>\n            )}\n            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            {/* faits rapides */}\n            <div className=\"mt-4 flex flex-wrap gap-2\">\n              {[\n                u ? t(u.typeProp as TKey) : null,\n                u?.aireEtagesM2 && r.estimate > 0\n                  ? `${locNum(Math.round(r.estimate / u.aireEtagesM2), lang, { maxFrac: 0 })} $/m²`\n                  : null,\n                u?.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : null,\n                u?.anneeConstruction ? `${fr ? \"constr.\" : \"built\"} ${u.anneeConstruction}` : null,\n                u?.superficieTerrainM2\n                  ? `${fr ? \"terrain\" : \"lot\"} ${locNum(u.superficieTerrainM2, lang, { unit: \"m²\" })}`\n                  : null,\n              ]\n                .filter((x): x is string => x != null)\n                .map((x) => (\n                  <span\n                    key={x}\n                    className=\"vp-mono rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"\n                  >\n                    {x}\n                  </span>\n                ))}\n            </div>\n            {/* fourchette visuelle P10-P90 avec position du rôle */}\n            <div className=\"mt-6 max-w-2xl\">\n              <p className=\"klabel mb-1\">{t(\"range\")}</p>\n              <RangeBar\n                low={r.low}\n                estimate={r.estimate}\n                high={r.high}\n                role={u?.valeurRole ?? null}\n                lang={lang}\n              />\n            </div>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              {u?.valeurRole != null && (\n                <div>\n                  <p className=\"klabel\">{t(\"assessed\")}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-ink-2\">{fmt(u.valeurRole, lang)}</p>\n                </div>\n              )}\n              {u?.valeurRole != null && u.valeurRole > 0 && r.estimate > 0 && (\n                <div>\n                  <p className=\"klabel\">{fr ? \"Écart vs rôle\" : \"vs assessment\"}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-green-deep\">\n                    {r.estimate >= u.valeurRole ? \"+\" : \"\"}\n                    {((r.estimate / u.valeurRole - 1) * 100).toFixed(0)} %\n                  </p>\n                </div>\n              )}\n            </div>\n            {u && (\n              <div className=\"mt-6 flex flex-wrap gap-2.5\">\n                <a\n                  href={`/api/report?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-primary\"\n                  download\n                >\n                  {fr ? \"Rapport PDF\" : \"PDF report\"} ↓\n                </a>\n                <a\n                  href={`/api/report/pro?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-ghost\"\n                  download\n                  title={fr ? \"Structure de rapport d'évaluation — présentation bancaire\" : \"Appraisal-style structure — bank presentation\"}\n                >\n                  {fr ? \"Rapport détaillé format professionnel (banque)\" : \"Detailed professional report (bank)\"} ↓\n                </a>\n              </div>\n            )}\n          </div>\n          <div className=\"shrink-0\">\n            <ConfidenceDial\n              pct={r.confidencePct}\n              level={r.confidenceLevel}\n              label={`${t(\"confidence\")} — ${t(`conf${r.confidenceLevel}` as TKey)}`}\n            />\n          </div>\n        </div>\n        <p className=\"vp-mono mt-5 border-t-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {t(\"disclaimer\")}\n        </p>\n      </section>\n\n      {/* ---- Portrait de la propriété (bento du registre) ---- */}\n      {u && s && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Registre officiel — dessiné par les données\" : \"Official record — drawn by the data\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Portrait de la propriété\" : \"Property portrait\"}\n          </h2>\n          <div className=\"mt-4 grid gap-4 lg:grid-cols-3\">\n            {/* Bâtiment */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"60ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Bâtiment\" : \"Building\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <BuildingGlyph\n                  floors={s.nbEtages}\n                  dwellings={u.nbLogements}\n                  genre={s.genreConstruction}\n                  yearBuilt={u.anneeConstruction}\n                  floorAreaM2={u.aireEtagesM2}\n                  lang={lang}\n                />\n              </div>\n              <EraLine year={u.anneeConstruction} lang={lang} />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Aire d'étages\" : \"Floor area\"} v={u.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Étages × logements\" : \"Storeys × dwellings\"} v={`${s.nbEtages ?? \"—\"} × ${u.nbLogements ?? \"—\"}`} />\n                <Chip k={fr ? \"Genre\" : \"Style\"} v={s.genreConstruction ?? \"—\"} />\n                <Chip k={fr ? \"Lien physique\" : \"Physical link\"} v={s.lienPhysique ?? \"—\"} />\n              </div>\n            </div>\n            {/* Terrain */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"140ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Terrain — à l'échelle\" : \"Lot — to scale\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <LotDiagram\n                  areaM2={u.superficieTerrainM2}\n                  frontageM={s.frontTerrainM}\n                  footprintM2={\n                    u.aireEtagesM2 && s.nbEtages ? u.aireEtagesM2 / Math.max(1, s.nbEtages) : u.aireEtagesM2\n                  }\n                  lang={lang}\n                  isCondo={!!s.apt || (u.nbLogements != null && u.nbLogements >= 1 && !u.superficieTerrainM2)}\n                />\n              </div>\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Superficie\" : \"Area\"} v={u.superficieTerrainM2 ? locNum(u.superficieTerrainM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Frontage\" : \"Frontage\"} v={s.frontTerrainM ? locNum(s.frontTerrainM, lang, { unit: \"m\" }) : \"—\"} />\n                <Chip k=\"CUBF\" v={s.cubf ?? \"—\"} />\n                <Chip k={fr ? \"Usage\" : \"Use\"} v={s.cubfLibelle ?? \"—\"} />\n              </div>\n            </div>\n            {/* Valeur au rôle */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"220ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Valeur au rôle 2026 — composition\" : \"2026 assessed value — split\"}\n              </p>\n              <p className=\"vp-display py-2 text-[26px] font-bold tracking-[-0.02em]\">\n                {fmt(u.valeurRole, lang)}\n              </p>\n              <ValueSplit\n                land={s.valeurTerrain}\n                building={s.valeurBatiment}\n                previous={s.valeurAnterieure}\n                total={u.valeurRole}\n                lang={lang}\n                labels={{\n                  land: fr ? \"Terrain\" : \"Land\",\n                  building: fr ? \"Bâtiment\" : \"Building\",\n                  previous: fr ? \"Rôle antérieur\" : \"Previous roll\",\n                }}\n              />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Cond. marché au\" : \"Market cond.\"} v={s.datCondMarche ?? \"—\"} />\n                <Chip k={fr ? \"Matricule\" : \"Roll no.\"} v={s.matricule ?? \"—\"} />\n              </div>\n            </div>\n          </div>\n          {/* registre brut complet */}\n          <details className=\"vp-card mt-4 p-5\">\n            <summary className=\"vp-mono cursor-pointer text-[11px] font-bold uppercase tracking-[0.08em] text-green-deep\">\n              {fr ? \"▸ Toutes les données du registre (brutes)\" : \"▸ All registry data (raw)\"}\n            </summary>\n            <div className=\"mt-4 grid grid-cols-2 gap-2 sm:grid-cols-4\">\n              <Chip k={fr ? \"Année construction\" : \"Year built\"} v={u.anneeConstruction ? `${u.anneeConstruction}${s.anneeEstimee === \"E\" ? (fr ? \" (estimée)\" : \" (est.)\") : \"\"}` : \"—\"} />\n              <Chip k={fr ? \"Locaux non résid.\" : \"Non-res. units\"} v={s.nbLocauxNonResid ?? \"—\"} />\n              <Chip k={fr ? \"Chambres locatives\" : \"Rental rooms\"} v={s.nbChambresLocatives ?? \"—\"} />\n              <Chip k={fr ? \"Unité de voisinage\" : \"Neighbourhood\"} v={s.uniteVoisinage || \"—\"} />\n              <Chip k={fr ? \"Arrondissement\" : \"Borough\"} v={s.arrond || \"—\"} />\n              <Chip k={fr ? \"Adresses sur l'unité\" : \"Addresses\"} v={s.nAdresses ?? \"—\"} />\n              <Chip k={fr ? \"Valeur terrain\" : \"Land value\"} v={fmt(s.valeurTerrain, lang)} />\n              <Chip k={fr ? \"Valeur bâtiment\" : \"Building value\"} v={fmt(s.valeurBatiment, lang)} />\n              <Chip k={fr ? \"Rôle antérieur\" : \"Previous roll\"} v={fmt(s.valeurAnterieure, lang)} />\n              <Chip k=\"ID provincial\" v={u.id} />\n            </div>\n          </details>\n        </section>\n      )}\n\n      {/* ---- Transparence du calcul ---- */}\n      <section>\n        <span className=\"kicker\">{fr ? \"Transparence\" : \"Transparency\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"whyTitle\")}\n        </h2>\n        <div className=\"mt-4 grid gap-4 sm:grid-cols-2\">\n          <div className=\"vp-card vp-card-hover rise p-5\">\n            <p className=\"klabel\">{t(\"modelPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.modelEstimate != null ? (\n                <CountUp value={r.modelEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightModel\")} : {(r.modelWeight * 100).toFixed(0)} %\n            </p>\n          </div>\n          <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"100ms\" }}>\n            <p className=\"klabel\">{t(\"compsPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.compsEstimate != null ? (\n                <CountUp value={r.compsEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightComps\")} : {((1 - r.modelWeight) * 100).toFixed(0)} % · {r.nCompsUsed} comps\n              {r.compsDispersionPct != null ? ` · ±${r.compsDispersionPct} %` : \"\"}\n            </p>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Marché local ---- */}\n      {ctx && (ctx.index.length >= 6 || ctx.sector || ctx.muniPercentile != null) && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Contexte de marché\" : \"Market context\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Le marché autour de cette propriété\" : \"The market around this property\"}\n          </h2>\n          <div className=\"mt-4 grid items-stretch gap-4 lg:grid-cols-[1.35fr_1fr]\">\n            {/* trajectoire du marché */}\n            {ctx.index.length >= 6 && (\n              <div className=\"vp-card rise p-5\">\n                <div className=\"flex flex-wrap items-center justify-between gap-2\">\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Indice de marché — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 en ${ctx.index[0].month.slice(0, 4)})`\n                      : `Market index — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 in ${ctx.index[0].month.slice(0, 4)})`}\n                  </p>\n                  <div className=\"flex gap-2\">\n                    {ctx.index12mPct != null && (\n                      <span className={`vp-mono rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${ctx.index12mPct >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n                        12 {fr ? \"mois\" : \"mo\"} {ctx.index12mPct >= 0 ? \"+\" : \"\"}\n                        {ctx.index12mPct.toFixed(1)} %\n                      </span>\n                    )}\n                    {ctx.indexSince2021Pct != null && (\n                      <span className=\"vp-mono rounded-[4px] border border-ink bg-surface-2 px-1.5 py-0.5 text-[10px] font-bold text-ink-2\">\n                        {ctx.index[0].month.slice(0, 4)}→ {ctx.indexSince2021Pct >= 0 ? \"+\" : \"\"}\n                        {ctx.indexSince2021Pct.toFixed(0)} %\n                      </span>\n                    )}\n                  </div>\n                </div>\n                <div className=\"mt-3\">\n                  <MarketIndexChart series={ctx.index} lang={lang} />\n                </div>\n                <p className=\"vp-mono mt-2 text-[10px] uppercase tracking-[0.05em] text-ink-3\">\n                  {fr\n                    ? \"Indice utilisé pour l'ajustement « marché » des comparables — même donnée, même calcul.\"\n                    : \"Index used for the comparables' market adjustment — same data, same math.\"}\n                </p>\n              </div>\n            )}\n            <div className=\"flex flex-col gap-4\">\n              {/* ventes du secteur */}\n              {ctx.sector && (\n                <div className=\"vp-card rise p-5\" style={{ animationDelay: \"80ms\" }}>\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Ventes réelles — rayon ${ctx.sector.radiusKm} km · 12 mois`\n                      : `Real sales — ${ctx.sector.radiusKm} km radius · 12 months`}\n                  </p>\n                  <div className=\"mt-3 grid grid-cols-3 gap-2\">\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Ventes\" : \"Sales\"}</p>\n                      <p className=\"v\">{ctx.sector.nSales12m}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Prix médian\" : \"Median price\"}</p>\n                      <p className=\"v\">{ctx.sector.medianPrice != null ? fmt(Math.round(ctx.sector.medianPrice), lang) : \"—\"}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">$/m² {fr ? \"médian\" : \"median\"}</p>\n                      <p className=\"v\">\n                        {ctx.sector.medianPpm2 != null\n                          ? locNum(Math.round(ctx.sector.medianPpm2), lang, { maxFrac: 0 })\n                          : \"—\"}\n                      </p>\n                    </div>\n                  </div>\n                  {ctx.sector.medianPrice != null && r.estimate > 0 && (\n                    <p className=\"vp-mono mt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-2\">\n                      {fr ? \"Cette propriété\" : \"This property\"} :{\" \"}\n                      <b className=\"text-ink\">\n                        {r.estimate >= ctx.sector.medianPrice ? \"+\" : \"−\"}\n                        {Math.abs((r.estimate / ctx.sector.medianPrice - 1) * 100).toFixed(0)} %\n                      </b>{\" \"}\n                      {fr ? \"vs médiane du secteur\" : \"vs sector median\"}\n                    </p>\n                  )}\n                </div>\n              )}\n              {/* position dans la municipalité */}\n              {ctx.muniPercentile != null && u?.municipalite && (\n                <div className=\"vp-card rise flex-1 p-5\" style={{ animationDelay: \"160ms\" }}>\n                  <p className=\"klabel\">\n                    {fr ? `Position dans ${u.municipalite}` : `Position in ${u.municipalite}`}\n                  </p>\n                  <div className=\"mt-3\">\n                    <PercentileBar pct={ctx.muniPercentile} lang={lang} />\n                  </div>\n                  <p className=\"mt-3 text-[13px] text-ink-2\">\n                    {fr ? (\n                      <>\n                        Vaut plus que{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> des{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        propriétés estimées de la municipalité.\n                      </>\n                    ) : (\n                      <>\n                        Worth more than{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> of the{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        estimated properties in the municipality.\n                      </>\n                    )}\n                  </p>\n                </div>\n              )}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Historique ---- */}\n      {u && u.history.some((h) => h.value != null) && (\n        <section>\n          <span className=\"kicker\">2021 → 2026</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {t(\"history\")}\n          </h2>\n          <div className=\"vp-card rise mt-4 p-5 sm:p-6\">\n            <div className=\"flex flex-col gap-2\">\n              {u.history.map((h, i) => {\n                const prev = i > 0 ? u.history[i - 1].value : null;\n                const yoy =\n                  h.value != null && prev != null && prev > 0\n                    ? ((h.value / prev - 1) * 100)\n                    : null;\n                return (\n                  <div key={h.year} className=\"hbar-row grid grid-cols-[44px_1fr_auto] items-center gap-2 sm:grid-cols-[52px_1fr_auto] sm:gap-3\">\n                    <span className=\"vp-mono text-[12px] font-bold\">{h.year}</span>\n                    <span className=\"hbar-track\">\n                      <span\n                        className=\"hbar-fill grow-x\"\n                        style={{\n                          width: `${((h.value ?? 0) / histMax) * 100}%`,\n                          animationDelay: `${i * 90}ms`,\n                          background: h.year === 2026 ? \"var(--ink)\" : undefined,\n                        }}\n                      />\n                    </span>\n                    <span className=\"vp-mono flex items-center justify-end gap-2 text-[12px] font-bold\">\n                      {fmt(h.value, lang)}\n                      {yoy != null && (\n                        <span className={`rounded-[4px] border border-ink px-1 py-px text-[9px] ${yoy >= 0 ? \"bg-lime-soft text-green-deep\" : \"bg-[var(--danger-soft)] text-[var(--danger)]\"}`}>\n                          {yoy >= 0 ? \"+\" : \"\"}\n                          {yoy.toFixed(0)} %\n                        </span>\n                      )}\n                    </span>\n                  </div>\n                );\n              })}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Plan des comparables (carte maison) ---- */}\n      <section>\n        <span className=\"kicker\">\n          {r.nCompsUsed} {fr ? \"ventes réelles\" : \"real sales\"}\n        </span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"compsTitle\")}\n        </h2>\n        <div className=\"mt-4 grid items-start gap-4 lg:grid-cols-[1.05fr_1fr]\">\n          <div className=\"overflow-hidden rounded-[10px] border-[1.5px] border-ink shadow-[8px_8px_0_rgba(20,24,20,0.08)]\">\n            <RadarMap\n              subject={{\n                lat: u?.lat ?? r.comps[0]?.lat ?? 46.81,\n                lng: u?.lng ?? r.comps[0]?.lng ?? -71.21,\n                label: u?.adresse ?? (fr ? \"Propriété estimée\" : \"Subject property\"),\n              }}\n              comps={r.comps.map((c) => ({\n                id: c.id,\n                lat: c.lat,\n                lng: c.lng,\n                label: `${c.street ?? \"\"}, ${c.city ?? \"\"}`,\n                price: fmt(c.amount, lang),\n                adjusted: fmt(Math.round(c.adjustedPrice), lang),\n                date: c.date,\n                distanceM: c.distanceM,\n                weight: c.weight,\n              }))}\n              highlight={activeComp}\n              onHover={setActiveComp}\n            />\n          </div>\n          <div className=\"src-wrap max-h-[560px] overflow-y-auto\">\n            <table className=\"src-table\">\n              <thead className=\"sticky top-0 z-10\">\n                <tr>\n                  <th>#</th>\n                  <th>{fr ? \"Vente\" : \"Sale\"}</th>\n                  <th className=\"text-right\">{t(\"adjusted\")}</th>\n                </tr>\n              </thead>\n              <tbody>\n                {r.comps.map((c, i) => (\n                  <tr\n                    key={c.id}\n                    onMouseEnter={() => setActiveComp(c.id)}\n                    onMouseLeave={() => setActiveComp(null)}\n                    className={activeComp === c.id ? \"!bg-lime-soft\" : \"\"}\n                  >\n                    <td>\n                      <span\n                        className={`vp-mono inline-flex h-6 w-6 items-center justify-center rounded-full border-[1.5px] border-ink text-[11px] font-bold ${\n                          activeComp === c.id ? \"bg-lime text-ink\" : \"bg-green text-paper\"\n                        }`}\n                      >\n                        {i + 1}\n                      </span>\n                    </td>\n                    <td>\n                      <p className=\"font-semibold leading-tight\">\n                        {c.street}, {c.city}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] text-ink-2\">\n                        {t(\"adjTime\")} {signed(c.adjTime, lang)} · {t(\"adjArea\")} {signed(c.adjArea, lang)} ·{\" \"}\n                        {t(\"adjAge\")} {signed(c.adjAge, lang)}\n                      </p>\n                    </td>\n                    <td className=\"vp-display text-right font-bold\">\n                      {fmt(Math.round(c.adjustedPrice), lang)}\n                      <p className=\"vp-mono text-[10px] font-normal text-ink-3\">\n                        {t(\"weight\")} {(c.weight * 100).toFixed(0)} %\n                      </p>\n                    </td>\n                  </tr>\n                ))}\n              </tbody>\n            </table>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Lead ---- */}\n      <section className=\"rounded-[10px] border-[1.5px] border-ink bg-ink p-6 text-paper shadow-[8px_8px_0_rgba(20,24,20,0.2)] sm:p-8\">\n        <h2 className=\"vp-display text-[22px] font-bold uppercase tracking-[-0.02em] text-lime\">\n          {t(\"leadTitle\")}\n        </h2>\n        <div className=\"mt-4 max-w-xl\">\n          <LeadForm unitId={u?.id ?? null} estimate={r.estimate} />\n        </div>\n      </section>\n    </div>\n  );\n}\n","structuredPatch":[{"oldStart":529,"oldLines":7,"newStart":529,"newLines":20,"lines":["                       <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">","                         {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}","                         {t(\"sold\")} {fmt(c.amount, lang)}","+                        {c.floorArea && c.floorArea > 20","+                          ? ` (${locNum(Math.round(c.amount / c.floorArea), lang, { maxFrac: 0 })} $/m²)`","+                          : \"\"}","                       </p>","+                      {(c.floorArea || c.yearBuilt) && (","+                        <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">","+                          {[","+                            c.floorArea ? locNum(c.floorArea, lang, { unit: \"m²\" }) : null,","+                            c.yearBuilt ? `${fr ? \"constr.\" : \"built\"} ${c.yearBuilt}` : null,","+                          ]","+                            .filter(Boolean)","+                            .join(\" · \")}","+                        </p>","+                      )}","                       <p className=\"vp-mono mt-0.5 text-[10.5px] text-ink-2\">","                         {t(\"adjTime\")} {signed(c.adjTime, lang)} · {t(\"adjArea\")} {signed(c.adjArea, lang)} ·{\" \"}","                         {t(\"adjAge\")} {signed(c.adjAge, lang)}"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013BwRTZu4y51CrQH5HAebfr","type":"tool_result","content":"---\ntitle: Route Segment Config\ndescription: Learn about how to configure options for Next.js route segments.\n---\n\nThe Route Segment Config options allow you to configure the behavior of a [Page](/docs/app/api-reference/file-conventions/page), [Layout](/docs/app/api-reference/file-conventions/layout), or [Route Handler](/docs/app/api-reference/file-conventions/route) by directly exporting the following variables:\n\n| Option                                                                                             | Type                                                              | Default                    |\n| -------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------- | -------------------------- |\n| [`dynamicParams`](/docs/app/api-reference/file-conventions/route-segment-config/dynamicParams)     | `boolean`                                                         | `true`                     |\n| [`runtime`](/docs/app/api-reference/file-conventions/route-segment-config/runtime)                 | `'nodejs' \\| 'edge' (deprecated)`                                 | `'nodejs'`                 |\n| [`preferredRegion`](/docs/app/api-reference/file-conventions/route-segment-config/preferredRegion) | `'auto' \\| 'global' \\| 'home' \\| string \\| string[] (deprecated)` | `'auto'`                   |\n| [`maxDuration`](/docs/app/api-reference/file-conventions/route-segment-config/maxDuration)         | `number`                                                          | Set by deployment platform |\n\n## Version History\n\n| Version      |                                                                                                                                                                                                                                                                                                |\n| ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| `v16.0.0`    | `dynamic`, `dynamicParams`, `revalidate`, and `fetchCache` removed when [Cache Components](/docs/app/api-reference/config/next-config-js/cacheComponents) is enabled. See [Caching and Revalidating (Previous Model)](/docs/app/guides/caching-without-cache-components#route-segment-config). |\n| `v16.0.0`    | `export const experimental_ppr = true` removed. A [codemod](/docs/app/guides/upgrading/codemods#remove-experimental_ppr-route-segment-config-from-app-router-pages-and-layouts) is available.                                                                                                  |\n| `v15.0.0-RC` | `export const runtime = \"experimental-edge\"` deprecated. A [codemod](/docs/app/guides/upgrading/codemods#transform-app-router-route-segment-config-runtime-value-from-experimental-edge-to-edge) is available.                                                                                 |","is_error":false}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"db06a759-5977-4ec9-8dfa-0625d86a7628","timestamp":"2026-08-23T03:07:12.970Z","tool_use_result":{"stdout":"---\ntitle: Route Segment Config\ndescription: Learn about how to configure options for Next.js route segments.\n---\n\nThe Route Segment Config options allow you to configure the behavior of a [Page](/docs/app/api-reference/file-conventions/page), [Layout](/docs/app/api-reference/file-conventions/layout), or [Route Handler](/docs/app/api-reference/file-conventions/route) by directly exporting the following variables:\n\n| Option                                                                                             | Type                                                              | Default                    |\n| -------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------- | -------------------------- |\n| [`dynamicParams`](/docs/app/api-reference/file-conventions/route-segment-config/dynamicParams)     | `boolean`                                                         | `true`                     |\n| [`runtime`](/docs/app/api-reference/file-conventions/route-segment-config/runtime)                 | `'nodejs' \\| 'edge' (deprecated)`                                 | `'nodejs'`                 |\n| [`preferredRegion`](/docs/app/api-reference/file-conventions/route-segment-config/preferredRegion) | `'auto' \\| 'global' \\| 'home' \\| string \\| string[] (deprecated)` | `'auto'`                   |\n| [`maxDuration`](/docs/app/api-reference/file-conventions/route-segment-config/maxDuration)         | `number`                                                          | Set by deployment platform |\n\n## Version History\n\n| Version      |                                                                                                                                                                                                                                                                                                |\n| ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| `v16.0.0`    | `dynamic`, `dynamicParams`, `revalidate`, and `fetchCache` removed when [Cache Components](/docs/app/api-reference/config/next-config-js/cacheComponents) is enabled. 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moneyFr(v.mediane) : \"\"}\n                </span>\n              </Link>\n            </li>\n          ))}\n        </ul>\n        <p className=\"mt-5\">\n          <Link href=\"/municipalites\" className=\"btn btn-ghost\">\n            Toutes les municipalités →\n          </Link>\n        </p>\n      </section>\n    </>\n  );\n}\n","structuredPatch":[{"oldStart":1,"oldLines":10,"newStart":1,"newLines":15,"lines":[" // Auteur : Simon-Pierre Boucher — contact@spboucher.ai"," import type { Metadata } from \"next\";"," import Link from \"next/link\";","-import HomeClient from \"./HomeClient\";","+import HomeClient, { type PulseItem, type RecentSaleItem } from \"./HomeClient\";"," import stats from \"@/data/stats.json\";","+import { getMarketIndex, recentSales, salesCountSince } from \"@/lib/db\";"," import { moneyFr, slugify } from \"@/lib/seo\";"," ","+// Le pouls du marché et les dernières ventes bougent au rythme du connecteur","+// (quotidien) — régénération ISR horaire, le reste de la page est stable.","+export const revalidate = 3600;","+"," export const metadata: Metadata = {","   title: {","     absolute: \"Vrai-Prix — estimation immobilière gratuite au Québec · Un service Groupe KA\","]}],"userModified":false,"replaceAll":false}}}
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(file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"74bdcf1d-511b-470a-b5df-4ce955a15348","timestamp":"2026-08-23T03:07:40.528Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/app/page.tsx","oldString":"export default function Home() {\n  return (\n    <>\n      <HomeClient />","newString":"/** Séries d'indice par grand type — 25 derniers mois, avec variations. */\nfunction buildPulse(): PulseItem[] {\n  return ([\"unifamilial\", \"condo\", \"plex\"] as const).map((type) => {\n    const full = getMarketIndex(type);\n    const series = full.slice(-25);\n    const last = full.length ? full[full.length - 1].idx : null;\n    const yearAgo = full.length >= 13 ? full[full.length - 13].idx : null;\n    const first = full.length >= 2 ? full[0].idx : null;\n    return {\n      type,\n      series,\n      pct12m:\n        last != null && yearAgo != null && yearAgo > 0\n          ? 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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJweWRbjBuEertbkfDuR","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAIS4gIKpwEIERgCKkDO2PI3wlDQr0edYpAzvLzUP4YFFSNwrzIJM/6Ts1qpSR3z8uFpgO/il/xj3BlEuf9P/e6xqlo9xXYgUnQJaubCMg5jbGF1ZGUtZmFibGUtNTgBQgh0aGlua2luZ1okNmZjYzhmODMtMWY5Yy00ZDMzLWJhMDctNGQ0ZGYzN2Q2N2Y1chAdIVqGJg7PzozfP3tSqTZWiAEBqAGByKnUBrABAhIM+17pi09z5JZ87O/HGgyQ/udXABq3npcdvtAiMIGlD0tN7ElzR/ON8hYTKAfDvzx3TM8WkNgaolqkLQsG/3VIbR01qHC9zJrZ3GGh5ipohqnR5q3QyMFr9QHFm01TtXqct+WfgonNtJ+x42Z5qZiFUvK6fPJGHE6hNRtj7GFAcscfHeUJuJlsE1q18Wx28mGsTHi0tw+0upQIGtPHmJ6/rSgJPo42M4XfWpFw4SrwQqvYxp/ZDXsYAQ=="}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":985,"cache_read_input_tokens":126182,"cache_creation":{"ephemeral_5m_input_tokens":985,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"8dc5d4e5-83f3-46e8-98c3-15a614f87d64","request_id":"req_011CeJweUs5xSW5crzXAnchM"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJweWRbjBuEertbkfDuR","type":"message","role":"assistant","content":[{"type":"text","text":"Maintenant le nouveau `HomeClient.tsx` avec pouls du marché, dernières ventes et « comment ça marche » :"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":985,"cache_read_input_tokens":126182,"cache_creation":{"ephemeral_5m_input_tokens":985,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"a1763154-ce18-44fc-9314-630cda61bbce","request_id":"req_011CeJweUs5xSW5crzXAnchM"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJweWRbjBuEertbkfDuR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WSx8xYzeu6bfuhq4eVGibU","name":"Write","input":{"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/app/HomeClient.tsx","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport Link from \"next/link\";\nimport SearchBox from \"@/components/SearchBox\";\nimport EstimateForm from \"@/components/EstimateForm\";\nimport { IndexSpark, locNum } from \"@/components/MetricViz\";\nimport { useLang } from \"@/components/LangContext\";\n\n/** Indice de marché d'un grand type — série + variations (calculé côté serveur). */\nexport interface PulseItem {\n  type: \"unifamilial\" | \"condo\" | \"plex\";\n  series: { month: string; idx: number }[];\n  pct12m: number | null;\n  since2021: number | null;\n}\n\n/** Vente réelle récente appariée à une unité d'évaluation. */\nexport interface RecentSaleItem {\n  id: string;\n  date: string;\n  amount: number;\n  street: string | null;\n  city: string | null;\n  propertyType: string | null;\n  idProvinc: string | null;\n  vsRolePct: number | null;\n}\n\nconst PULSE_LABELS: Record<PulseItem[\"type\"], { fr: string; en: string }> = {\n  unifamilial: { fr: \"Unifamiliale\", en: \"Single-family\" },\n  condo: { fr: \"Condo\", en: \"Condo\" },\n  plex: { fr: \"Plex\", en: \"Plex\" },\n};\n\nfunction TrendBadge({ pct, label }: { pct: number | null; label: string }) {\n  if (pct == null) return null;\n  return (\n    <span\n      className={`vp-mono rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${\n        pct >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"\n      }`}\n    >\n      {label} {pct >= 0 ? \"+\" : \"\"}\n      {pct.toFixed(1)} %\n    </span>\n  );\n}\n\nexport default function HomeClient({\n  pulse,\n  recent,\n  salesLast30d,\n}: {\n  pulse: PulseItem[];\n  recent: RecentSaleItem[];\n  salesLast30d: number;\n}) {\n  const { lang, t } = useLang();\n  const fr = lang === \"fr\";\n  const money = (v: number) =>\n    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n    }).format(v);\n  const dateFmt = (d: string) =>\n    new Date(`${d}T12:00:00`).toLocaleDateString(fr ? \"fr-CA\" : \"en-CA\", {\n      day: \"numeric\",\n      month: \"short\",\n    });\n\n  return (\n    <div className=\"pb-6 pt-10 sm:pt-14\">\n      {/* ---- Héros ---- */}\n      <section>\n        <span className=\"kicker\">\n          {lang === \"fr\" ? \"Québec · 2021-2026\" : \"Québec · 2021-2026\"}\n        </span>\n        <h1 className=\"vp-display mt-3 max-w-4xl text-[clamp(34px,6.4vw,72px)] font-bold uppercase leading-[0.98] tracking-[-0.035em]\">\n          {lang === \"fr\" ? (\n            <>\n              La <span className=\"outline-txt\">vraie</span> valeur.\n              <br />\n              <span className=\"hl\">Sans boîte noire.</span>\n            </>\n          ) : (\n            <>\n              The <span className=\"outline-txt\">true</span> value.\n              <br />\n              <span className=\"hl\">No black box.</span>\n            </>\n          )}\n        </h1>\n        <p className=\"mt-5 max-w-xl text-[16.5px] text-ink-2\">{t(\"sub\")}</p>\n\n        <div className=\"mt-7 flex gap-2.5 overflow-x-auto pb-1.5 [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden\">\n          <span className=\"stat-chip\">\n            <span className=\"pulse\" />\n            <b>3 747 008</b> {lang === \"fr\" ? \"propriétés\" : \"properties\"}\n          </span>\n          <span className=\"stat-chip\">\n            <b>745 119</b> {lang === \"fr\" ? \"ventes réelles\" : \"real sales\"}\n          </span>\n          <span className=\"stat-chip\">\n            <b>{locNum(salesLast30d, lang, { maxFrac: 0 })}</b>{\" \"}\n            {lang === \"fr\" ? \"ventes captées / 30 j\" : \"sales captured / 30 d\"}\n          </span>\n          <span className=\"stat-chip\">\n            <b>1 100</b> {lang === \"fr\" ? \"municipalités\" : \"municipalities\"}\n          </span>\n          <span className=\"stat-chip\">\n            MdAPE <b>11 %</b>\n          </span>\n        </div>\n      </section>\n\n      {/* ---- Recherche ---- */}\n      <section className=\"mt-9\">\n        <div className=\"vp-card p-4 sm:p-5\">\n          <p className=\"klabel mb-2 pl-0.5\">\n            {lang === \"fr\" ? \"Adresse de la propriété\" : \"Property address\"}\n          </p>\n          <SearchBox />\n        </div>\n        <p className=\"vp-mono mt-2.5 pl-1 text-[11px] uppercase tracking-[0.06em] text-ink-3\">\n          {t(\"dataNote\")}\n        </p>\n      </section>\n\n      {/* ---- Comment ça marche (bande compacte) ---- */}\n      <section className=\"mt-10 grid gap-3 sm:grid-cols-3\">\n        {(fr\n          ? [\n              [\"01\", \"Cherchez l'adresse\", \"3,7 M d'unités d'évaluation, recherche tolérante aux fautes.\"],\n              [\"02\", \"Le moteur croise deux sources\", \"Modèle hédonique (690 k ventes) + comparables réels ajustés en dollars.\"],\n              [\"03\", \"Fourchette + confiance\", \"P10-P90, indice A-D, rapport PDF — le calcul montré en entier.\"],\n            ]\n          : [\n              [\"01\", \"Search the address\", \"3.7M assessment units, typo-tolerant search.\"],\n              [\"02\", \"Two sources, crossed\", \"Hedonic model (690k sales) + real comparables adjusted in dollars.\"],\n              [\"03\", \"Range + confidence\", \"P10-P90, A-D index, PDF report — the full math, shown.\"],\n            ]\n        ).map(([num, title, body], i) => (\n          <div key={num} className=\"flex items-start gap-3 rounded-[10px] border-[1.5px] border-dashed border-[rgba(20,24,20,0.3)] bg-surface-2 p-4\">\n            <span className=\"vp-mono flex h-8 w-8 shrink-0 items-center justify-center rounded-full border-[1.5px] border-ink bg-ink text-[12px] font-bold text-lime\">\n              {num}\n            </span>\n            <div>\n              <p className=\"vp-display text-[14.5px] font-bold uppercase tracking-[-0.01em]\">\n                {title}\n                {i < 2 && <span className=\"ml-2 text-ink-3\">→</span>}\n              </p>\n              <p className=\"mt-1 text-[12.5px] leading-snug text-ink-2\">{body}</p>\n            </div>\n          </div>\n        ))}\n      </section>\n\n      {/* ---- Pouls du marché ---- */}\n      <section className=\"mt-14\">\n        <span className=\"kicker\">{fr ? \"Indice Vrai-Prix\" : \"Vrai-Prix index\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {fr ? \"Le pouls du marché québécois\" : \"The pulse of the Québec market\"}\n        </h2>\n        <p className=\"mt-2 max-w-2xl text-[14px] text-ink-2\">\n          {fr\n            ? \"L'indice mensuel calculé sur les ventes réelles — celui-là même qui ajuste les comparables de chaque estimation. Base 100 il y a 24 mois.\"\n            : \"The monthly index built from real sales — the same one adjusting every estimate's comparables. Base 100 twenty-four months ago.\"}\n        </p>\n        <div className=\"mt-4 grid gap-4 sm:grid-cols-3\">\n          {pulse.map((p) => (\n            <div key={p.type} className=\"vp-card vp-card-hover p-5\">\n              <div className=\"flex items-center justify-between gap-2\">\n                <p className=\"vp-display text-[16px] font-bold uppercase tracking-[-0.01em]\">\n                  {PULSE_LABELS[p.type][lang]}\n                </p>\n                <TrendBadge pct={p.pct12m} label={fr ? \"12 mois\" : \"12 mo\"} />\n              </div>\n              <div className=\"mt-3\">\n                <IndexSpark series={p.series} />\n              </div>\n              {p.since2021 != null && (\n                <p className=\"vp-mono mt-2 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n                  {fr ? \"Depuis 2021\" : \"Since 2021\"} :{\" \"}\n                  <b className=\"text-ink\">\n                    {p.since2021 >= 0 ? \"+\" : \"\"}\n                    {p.since2021.toFixed(0)} %\n                  </b>\n                </p>\n              )}\n            </div>\n          ))}\n        </div>\n      </section>\n\n      {/* ---- Dernières ventes captées ---- */}\n      {recent.length > 0 && (\n        <section className=\"mt-14\">\n          <span className=\"kicker\">\n            <span className=\"pulse\" style={{ width: 8, height: 8 }} />\n            {fr ? \"Flux en continu\" : \"Live feed\"}\n          </span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Dernières ventes réelles captées\" : \"Latest real sales captured\"}\n          </h2>\n          <p className=\"mt-2 max-w-2xl text-[14px] text-ink-2\">\n            {fr\n              ? \"Transactions publiées, appariées automatiquement à leur unité d'évaluation. Cliquez pour voir l'estimation complète de la propriété vendue.\"\n              : \"Published transactions, automatically matched to their assessment unit. Click for the sold property's full estimate.\"}\n          </p>\n          <div className=\"src-wrap mt-4\">\n            <table className=\"src-table\">\n              <thead>\n                <tr>\n                  <th>{fr ? \"Date\" : \"Date\"}</th>\n                  <th>{fr ? \"Propriété\" : \"Property\"}</th>\n                  <th className=\"text-right\">{fr ? \"Prix de vente\" : \"Sale price\"}</th>\n                  <th className=\"text-right\"></th>\n                </tr>\n              </thead>\n              <tbody>\n                {recent.map((s) => (\n                  <tr key={s.id}>\n                    <td className=\"vp-mono whitespace-nowrap text-[12px] font-bold uppercase\">\n                      {dateFmt(s.date)}\n                    </td>\n                    <td>\n                      <p className=\"font-semibold leading-tight\">\n                        {s.street}\n                        {s.city ? `, ${s.city}` : \"\"}\n                      </p>\n                      {s.propertyType && s.propertyType !== \"indéterminé\" && (\n                        <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                          {s.propertyType}\n                        </p>\n                      )}\n                    </td>\n                    <td className=\"text-right\">\n                      <span className=\"vp-display font-bold\">{money(s.amount)}</span>\n                      {s.vsRolePct != null && Math.abs(s.vsRolePct) < 400 && (\n                        <p className=\"vp-mono mt-0.5 text-[10px] text-ink-3\">\n                          {s.vsRolePct >= 0 ? \"+\" : \"−\"}\n                          {Math.abs(s.vsRolePct).toFixed(0)} %{\" \"}\n                          {fr ? \"vs rôle\" : \"vs roll\"}\n                        </p>\n                      )}\n                    </td>\n                    <td className=\"text-right\">\n                      {s.idProvinc && (\n                        <Link\n                          href={`/estimation/${encodeURIComponent(s.idProvinc)}`}\n                          className=\"vp-mono whitespace-nowrap rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1.5 text-[11px] font-bold uppercase tracking-[0.04em] transition-colors hover:bg-lime\"\n                        >\n                          {fr ? \"Estimer\" : \"Estimate\"} →\n                        </Link>\n                      )}\n                    </td>\n                  </tr>\n                ))}\n              </tbody>\n            </table>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Formulaire manuel ---- */}\n      <section className=\"mt-14\">\n        <span className=\"kicker\">{lang === \"fr\" ? \"Plan B\" : \"Plan B\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"manualTitle\")}\n        </h2>\n        <div className=\"vp-card mt-4 p-4 sm:p-6\">\n          <EstimateForm />\n        </div>\n      </section>\n\n      {/* ---- Manifeste transparence ---- */}\n      <section className=\"mt-14 grid gap-4 sm:grid-cols-3\">\n        {(lang === \"fr\"\n          ? [\n              [\"01\", \"Le calcul, montré\", \"Chaque ajustement de chaque comparable est affiché en dollars. Pas de score magique.\"],\n              [\"02\", \"Fourchette honnête\", \"Un prix unique est un mensonge statistique. On donne P10-P90 et un indice de confiance A-D.\"],\n              [\"03\", \"Données réelles\", \"Rôles fonciers officiels (MAMH) et ventes publiées — locales et récentes en priorité.\"],\n            ]\n          : [\n              [\"01\", \"The math, shown\", \"Every adjustment on every comparable is displayed in dollars. No magic score.\"],\n              [\"02\", \"Honest range\", \"A single price is a statistical lie. We give P10-P90 and an A-D confidence index.\"],\n              [\"03\", \"Real data\", \"Official assessment rolls (MAMH) and published sales — local and recent first.\"],\n            ]\n        ).map(([num, title, body]) => (\n          <div key={num} className=\"vp-card vp-card-hover p-5\">\n            <p className=\"vp-mono text-[11px] font-bold text-green\">{num}</p>\n            <h3 className=\"vp-display mt-1.5 text-[17px] font-bold uppercase tracking-[-0.01em]\">\n              {title}\n            </h3>\n            <p className=\"mt-2 text-[13.5px] text-ink-2\">{body}</p>\n          </div>\n        ))}\n      </section>\n    </div>\n  );\n}\n"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":985,"cache_read_input_tokens":126182,"cache_creation":{"ephemeral_5m_input_tokens":985,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"c09583c5-f338-4abb-8459-a1d45999bac7","request_id":"req_011CeJweUs5xSW5crzXAnchM"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WSx8xYzeu6bfuhq4eVGibU","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/app/HomeClient.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"05c1c54a-0979-4c59-a765-62bc51cf0455","timestamp":"2026-08-23T03:08:30.265Z","tool_use_result":{"type":"update","filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/app/HomeClient.tsx","content":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport Link from \"next/link\";\nimport SearchBox from \"@/components/SearchBox\";\nimport EstimateForm from \"@/components/EstimateForm\";\nimport { IndexSpark, locNum } from \"@/components/MetricViz\";\nimport { useLang } from \"@/components/LangContext\";\n\n/** Indice de marché d'un grand type — série + variations (calculé côté serveur). */\nexport interface PulseItem {\n  type: \"unifamilial\" | \"condo\" | \"plex\";\n  series: { month: string; idx: number }[];\n  pct12m: number | null;\n  since2021: number | null;\n}\n\n/** Vente réelle récente appariée à une unité d'évaluation. */\nexport interface RecentSaleItem {\n  id: string;\n  date: string;\n  amount: number;\n  street: string | null;\n  city: string | null;\n  propertyType: string | null;\n  idProvinc: string | null;\n  vsRolePct: number | null;\n}\n\nconst PULSE_LABELS: Record<PulseItem[\"type\"], { fr: string; en: string }> = {\n  unifamilial: { fr: \"Unifamiliale\", en: \"Single-family\" },\n  condo: { fr: \"Condo\", en: \"Condo\" },\n  plex: { fr: \"Plex\", en: \"Plex\" },\n};\n\nfunction TrendBadge({ pct, label }: { pct: number | null; label: string }) {\n  if (pct == null) return null;\n  return (\n    <span\n      className={`vp-mono rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${\n        pct >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"\n      }`}\n    >\n      {label} {pct >= 0 ? \"+\" : \"\"}\n      {pct.toFixed(1)} %\n    </span>\n  );\n}\n\nexport default function HomeClient({\n  pulse,\n  recent,\n  salesLast30d,\n}: {\n  pulse: PulseItem[];\n  recent: RecentSaleItem[];\n  salesLast30d: number;\n}) {\n  const { lang, t } = useLang();\n  const fr = lang === \"fr\";\n  const money = (v: number) =>\n    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n    }).format(v);\n  const dateFmt = (d: string) =>\n    new Date(`${d}T12:00:00`).toLocaleDateString(fr ? \"fr-CA\" : \"en-CA\", {\n      day: \"numeric\",\n      month: \"short\",\n    });\n\n  return (\n    <div className=\"pb-6 pt-10 sm:pt-14\">\n      {/* ---- Héros ---- */}\n      <section>\n        <span className=\"kicker\">\n          {lang === \"fr\" ? \"Québec · 2021-2026\" : \"Québec · 2021-2026\"}\n        </span>\n        <h1 className=\"vp-display mt-3 max-w-4xl text-[clamp(34px,6.4vw,72px)] font-bold uppercase leading-[0.98] tracking-[-0.035em]\">\n          {lang === \"fr\" ? (\n            <>\n              La <span className=\"outline-txt\">vraie</span> valeur.\n              <br />\n              <span className=\"hl\">Sans boîte noire.</span>\n            </>\n          ) : (\n            <>\n              The <span className=\"outline-txt\">true</span> value.\n              <br />\n              <span className=\"hl\">No black box.</span>\n            </>\n          )}\n        </h1>\n        <p className=\"mt-5 max-w-xl text-[16.5px] text-ink-2\">{t(\"sub\")}</p>\n\n        <div className=\"mt-7 flex gap-2.5 overflow-x-auto pb-1.5 [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden\">\n          <span className=\"stat-chip\">\n            <span className=\"pulse\" />\n            <b>3 747 008</b> {lang === \"fr\" ? \"propriétés\" : \"properties\"}\n          </span>\n          <span className=\"stat-chip\">\n            <b>745 119</b> {lang === \"fr\" ? \"ventes réelles\" : \"real sales\"}\n          </span>\n          <span className=\"stat-chip\">\n            <b>{locNum(salesLast30d, lang, { maxFrac: 0 })}</b>{\" \"}\n            {lang === \"fr\" ? \"ventes captées / 30 j\" : \"sales captured / 30 d\"}\n          </span>\n          <span className=\"stat-chip\">\n            <b>1 100</b> {lang === \"fr\" ? \"municipalités\" : \"municipalities\"}\n          </span>\n          <span className=\"stat-chip\">\n            MdAPE <b>11 %</b>\n          </span>\n        </div>\n      </section>\n\n      {/* ---- Recherche ---- */}\n      <section className=\"mt-9\">\n        <div className=\"vp-card p-4 sm:p-5\">\n          <p className=\"klabel mb-2 pl-0.5\">\n            {lang === \"fr\" ? \"Adresse de la propriété\" : \"Property address\"}\n          </p>\n          <SearchBox />\n        </div>\n        <p className=\"vp-mono mt-2.5 pl-1 text-[11px] uppercase tracking-[0.06em] text-ink-3\">\n          {t(\"dataNote\")}\n        </p>\n      </section>\n\n      {/* ---- Comment ça marche (bande compacte) ---- */}\n      <section className=\"mt-10 grid gap-3 sm:grid-cols-3\">\n        {(fr\n          ? [\n              [\"01\", \"Cherchez l'adresse\", \"3,7 M d'unités d'évaluation, recherche tolérante aux fautes.\"],\n              [\"02\", \"Le moteur croise deux sources\", \"Modèle hédonique (690 k ventes) + comparables réels ajustés en dollars.\"],\n              [\"03\", \"Fourchette + confiance\", \"P10-P90, indice A-D, rapport PDF — le calcul montré en entier.\"],\n            ]\n          : [\n              [\"01\", \"Search the address\", \"3.7M assessment units, typo-tolerant search.\"],\n              [\"02\", \"Two sources, crossed\", \"Hedonic model (690k sales) + real comparables adjusted in dollars.\"],\n              [\"03\", \"Range + confidence\", \"P10-P90, A-D index, PDF report — the full math, shown.\"],\n            ]\n        ).map(([num, title, body], i) => (\n          <div key={num} className=\"flex items-start gap-3 rounded-[10px] border-[1.5px] border-dashed border-[rgba(20,24,20,0.3)] bg-surface-2 p-4\">\n            <span className=\"vp-mono flex h-8 w-8 shrink-0 items-center justify-center rounded-full border-[1.5px] border-ink bg-ink text-[12px] font-bold text-lime\">\n              {num}\n            </span>\n            <div>\n              <p className=\"vp-display text-[14.5px] font-bold uppercase tracking-[-0.01em]\">\n                {title}\n                {i < 2 && <span className=\"ml-2 text-ink-3\">→</span>}\n              </p>\n              <p className=\"mt-1 text-[12.5px] leading-snug text-ink-2\">{body}</p>\n            </div>\n          </div>\n        ))}\n      </section>\n\n      {/* ---- Pouls du marché ---- */}\n      <section className=\"mt-14\">\n        <span className=\"kicker\">{fr ? \"Indice Vrai-Prix\" : \"Vrai-Prix index\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {fr ? \"Le pouls du marché québécois\" : \"The pulse of the Québec market\"}\n        </h2>\n        <p className=\"mt-2 max-w-2xl text-[14px] text-ink-2\">\n          {fr\n            ? \"L'indice mensuel calculé sur les ventes réelles — celui-là même qui ajuste les comparables de chaque estimation. Base 100 il y a 24 mois.\"\n            : \"The monthly index built from real sales — the same one adjusting every estimate's comparables. Base 100 twenty-four months ago.\"}\n        </p>\n        <div className=\"mt-4 grid gap-4 sm:grid-cols-3\">\n          {pulse.map((p) => (\n            <div key={p.type} className=\"vp-card vp-card-hover p-5\">\n              <div className=\"flex items-center justify-between gap-2\">\n                <p className=\"vp-display text-[16px] font-bold uppercase tracking-[-0.01em]\">\n                  {PULSE_LABELS[p.type][lang]}\n                </p>\n                <TrendBadge pct={p.pct12m} label={fr ? \"12 mois\" : \"12 mo\"} />\n              </div>\n              <div className=\"mt-3\">\n                <IndexSpark series={p.series} />\n              </div>\n              {p.since2021 != null && (\n                <p className=\"vp-mono mt-2 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n                  {fr ? \"Depuis 2021\" : \"Since 2021\"} :{\" \"}\n                  <b className=\"text-ink\">\n                    {p.since2021 >= 0 ? \"+\" : \"\"}\n                    {p.since2021.toFixed(0)} %\n                  </b>\n                </p>\n              )}\n            </div>\n          ))}\n        </div>\n      </section>\n\n      {/* ---- Dernières ventes captées ---- */}\n      {recent.length > 0 && (\n        <section className=\"mt-14\">\n          <span className=\"kicker\">\n            <span className=\"pulse\" style={{ width: 8, height: 8 }} />\n            {fr ? \"Flux en continu\" : \"Live feed\"}\n          </span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Dernières ventes réelles captées\" : \"Latest real sales captured\"}\n          </h2>\n          <p className=\"mt-2 max-w-2xl text-[14px] text-ink-2\">\n            {fr\n              ? \"Transactions publiées, appariées automatiquement à leur unité d'évaluation. Cliquez pour voir l'estimation complète de la propriété vendue.\"\n              : \"Published transactions, automatically matched to their assessment unit. Click for the sold property's full estimate.\"}\n          </p>\n          <div className=\"src-wrap mt-4\">\n            <table className=\"src-table\">\n              <thead>\n                <tr>\n                  <th>{fr ? \"Date\" : \"Date\"}</th>\n                  <th>{fr ? \"Propriété\" : \"Property\"}</th>\n                  <th className=\"text-right\">{fr ? \"Prix de vente\" : \"Sale price\"}</th>\n                  <th className=\"text-right\"></th>\n                </tr>\n              </thead>\n              <tbody>\n                {recent.map((s) => (\n                  <tr key={s.id}>\n                    <td className=\"vp-mono whitespace-nowrap text-[12px] font-bold uppercase\">\n                      {dateFmt(s.date)}\n                    </td>\n                    <td>\n                      <p className=\"font-semibold leading-tight\">\n                        {s.street}\n                        {s.city ? `, ${s.city}` : \"\"}\n                      </p>\n                      {s.propertyType && s.propertyType !== \"indéterminé\" && (\n                        <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                          {s.propertyType}\n                        </p>\n                      )}\n                    </td>\n                    <td className=\"text-right\">\n                      <span className=\"vp-display font-bold\">{money(s.amount)}</span>\n                      {s.vsRolePct != null && Math.abs(s.vsRolePct) < 400 && (\n                        <p className=\"vp-mono mt-0.5 text-[10px] text-ink-3\">\n                          {s.vsRolePct >= 0 ? \"+\" : \"−\"}\n                          {Math.abs(s.vsRolePct).toFixed(0)} %{\" \"}\n                          {fr ? \"vs rôle\" : \"vs roll\"}\n                        </p>\n                      )}\n                    </td>\n                    <td className=\"text-right\">\n                      {s.idProvinc && (\n                        <Link\n                          href={`/estimation/${encodeURIComponent(s.idProvinc)}`}\n                          className=\"vp-mono whitespace-nowrap rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1.5 text-[11px] font-bold uppercase tracking-[0.04em] transition-colors hover:bg-lime\"\n                        >\n                          {fr ? \"Estimer\" : \"Estimate\"} →\n                        </Link>\n                      )}\n                    </td>\n                  </tr>\n                ))}\n              </tbody>\n            </table>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Formulaire manuel ---- */}\n      <section className=\"mt-14\">\n        <span className=\"kicker\">{lang === \"fr\" ? \"Plan B\" : \"Plan B\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"manualTitle\")}\n        </h2>\n        <div className=\"vp-card mt-4 p-4 sm:p-6\">\n          <EstimateForm />\n        </div>\n      </section>\n\n      {/* ---- Manifeste transparence ---- */}\n      <section className=\"mt-14 grid gap-4 sm:grid-cols-3\">\n        {(lang === \"fr\"\n          ? [\n              [\"01\", \"Le calcul, montré\", \"Chaque ajustement de chaque comparable est affiché en dollars. Pas de score magique.\"],\n              [\"02\", \"Fourchette honnête\", \"Un prix unique est un mensonge statistique. On donne P10-P90 et un indice de confiance A-D.\"],\n              [\"03\", \"Données réelles\", \"Rôles fonciers officiels (MAMH) et ventes publiées — locales et récentes en priorité.\"],\n            ]\n          : [\n              [\"01\", \"The math, shown\", \"Every adjustment on every comparable is displayed in dollars. No magic score.\"],\n              [\"02\", \"Honest range\", \"A single price is a statistical lie. We give P10-P90 and an A-D confidence index.\"],\n              [\"03\", \"Real data\", \"Official assessment rolls (MAMH) and published sales — local and recent first.\"],\n            ]\n        ).map(([num, title, body]) => (\n          <div key={num} className=\"vp-card vp-card-hover p-5\">\n            <p className=\"vp-mono text-[11px] font-bold text-green\">{num}</p>\n            <h3 className=\"vp-display mt-1.5 text-[17px] font-bold uppercase tracking-[-0.01em]\">\n              {title}\n            </h3>\n            <p className=\"mt-2 text-[13.5px] text-ink-2\">{body}</p>\n          </div>\n        ))}\n      </section>\n    </div>\n  );\n}\n","structuredPatch":[{"oldStart":1,"oldLines":11,"newStart":1,"newLines":74,"lines":[" // Auteur : Simon-Pierre Boucher — contact@spboucher.ai"," \"use client\";","+import Link from \"next/link\";"," import SearchBox from \"@/components/SearchBox\";"," import EstimateForm from \"@/components/EstimateForm\";","+import { IndexSpark, locNum } from \"@/components/MetricViz\";"," import { useLang } from \"@/components/LangContext\";"," ","-export default function HomeClient() {","+/** Indice de marché d'un grand type — série + variations (calculé côté serveur). */","+export interface PulseItem {","+  type: \"unifamilial\" | \"condo\" | \"plex\";","+  series: { month: string; idx: number }[];","+  pct12m: number | null;","+  since2021: number | null;","+}","+","+/** Vente réelle récente appariée à une unité d'évaluation. */","+export interface RecentSaleItem {","+  id: string;","+  date: string;","+  amount: number;","+  street: string | null;","+  city: string | null;","+  propertyType: string | null;","+  idProvinc: string | null;","+  vsRolePct: number | null;","+}","+","+const PULSE_LABELS: Record<PulseItem[\"type\"], { fr: string; en: string }> = {","+  unifamilial: { fr: \"Unifamiliale\", en: \"Single-family\" },","+  condo: { fr: \"Condo\", en: \"Condo\" },","+  plex: { fr: \"Plex\", en: \"Plex\" },","+};","+","+function TrendBadge({ pct, label }: { pct: number | null; label: string }) {","+  if (pct == null) return null;","+  return (","+    <span","+      className={`vp-mono rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${","+        pct >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"","+      }`}","+    >","+      {label} {pct >= 0 ? \"+\" : \"\"}","+      {pct.toFixed(1)} %","+    </span>","+  );","+}","+","+export default function HomeClient({","+  pulse,","+  recent,","+  salesLast30d,","+}: {","+  pulse: PulseItem[];","+  recent: RecentSaleItem[];","+  salesLast30d: number;","+}) {","   const { lang, t } = useLang();","+  const fr = lang === \"fr\";","+  const money = (v: number) =>","+    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {","+      style: \"currency\",","+      currency: \"CAD\",","+      maximumFractionDigits: 0,","+    }).format(v);","+  const dateFmt = (d: string) =>","+    new Date(`${d}T12:00:00`).toLocaleDateString(fr ? \"fr-CA\" : \"en-CA\", {","+      day: \"numeric\",","+      month: \"short\",","+    });","+","   return (","     <div className=\"pb-6 pt-10 sm:pt-14\">","       {/* ---- Héros ---- */}"]},{"oldStart":39,"oldLines":6,"newStart":102,"newLines":10,"lines":["             <b>745 119</b> {lang === \"fr\" ? \"ventes réelles\" : \"real sales\"}","           </span>","           <span className=\"stat-chip\">","+            <b>{locNum(salesLast30d, lang, { maxFrac: 0 })}</b>{\" \"}","+            {lang === \"fr\" ? \"ventes captées / 30 j\" : \"sales captured / 30 d\"}","+          </span>","+          <span className=\"stat-chip\">","             <b>1 100</b> {lang === \"fr\" ? \"municipalités\" : \"municipalities\"}","           </span>","           <span className=\"stat-chip\">"]},{"oldStart":60,"oldLines":6,"newStart":127,"newLines":142,"lines":["         </p>","       </section>"," ","+      {/* ---- Comment ça marche (bande compacte) ---- */}","+      <section className=\"mt-10 grid gap-3 sm:grid-cols-3\">","+        {(fr","+          ? [","+              [\"01\", \"Cherchez l'adresse\", \"3,7 M d'unités d'évaluation, recherche tolérante aux fautes.\"],","+              [\"02\", \"Le moteur croise deux sources\", \"Modèle hédonique (690 k ventes) + comparables réels ajustés en dollars.\"],","+              [\"03\", \"Fourchette + confiance\", \"P10-P90, indice A-D, rapport PDF — le calcul montré en entier.\"],","+            ]","+          : [","+              [\"01\", \"Search the address\", \"3.7M assessment units, typo-tolerant search.\"],","+              [\"02\", \"Two sources, crossed\", \"Hedonic model (690k sales) + real comparables adjusted in dollars.\"],","+              [\"03\", \"Range + confidence\", \"P10-P90, A-D index, PDF report — the full math, shown.\"],","+            ]","+        ).map(([num, title, body], i) => (","+          <div key={num} className=\"flex items-start gap-3 rounded-[10px] border-[1.5px] border-dashed border-[rgba(20,24,20,0.3)] bg-surface-2 p-4\">","+            <span className=\"vp-mono flex h-8 w-8 shrink-0 items-center justify-center rounded-full border-[1.5px] border-ink bg-ink text-[12px] font-bold text-lime\">","+              {num}","+            </span>","+            <div>","+              <p className=\"vp-display text-[14.5px] font-bold uppercase tracking-[-0.01em]\">","+                {title}","+                {i < 2 && <span className=\"ml-2 text-ink-3\">→</span>}","+              </p>","+              <p className=\"mt-1 text-[12.5px] leading-snug text-ink-2\">{body}</p>","+            </div>","+          </div>","+        ))}","+      </section>","+","+      {/* ---- Pouls du marché ---- */}","+      <section className=\"mt-14\">","+        <span className=\"kicker\">{fr ? \"Indice Vrai-Prix\" : \"Vrai-Prix index\"}</span>","+        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">","+          {fr ? \"Le pouls du marché québécois\" : \"The pulse of the Québec market\"}","+        </h2>","+        <p className=\"mt-2 max-w-2xl text-[14px] text-ink-2\">","+          {fr","+            ? \"L'indice mensuel calculé sur les ventes réelles — celui-là même qui ajuste les comparables de chaque estimation. Base 100 il y a 24 mois.\"","+            : \"The monthly index built from real sales — the same one adjusting every estimate's comparables. Base 100 twenty-four months ago.\"}","+        </p>","+        <div className=\"mt-4 grid gap-4 sm:grid-cols-3\">","+          {pulse.map((p) => (","+            <div key={p.type} className=\"vp-card vp-card-hover p-5\">","+              <div className=\"flex items-center justify-between gap-2\">","+                <p className=\"vp-display text-[16px] font-bold uppercase tracking-[-0.01em]\">","+                  {PULSE_LABELS[p.type][lang]}","+                </p>","+                <TrendBadge pct={p.pct12m} label={fr ? \"12 mois\" : \"12 mo\"} />","+              </div>","+              <div className=\"mt-3\">","+                <IndexSpark series={p.series} />","+              </div>","+              {p.since2021 != null && (","+                <p className=\"vp-mono mt-2 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">","+                  {fr ? \"Depuis 2021\" : \"Since 2021\"} :{\" \"}","+                  <b className=\"text-ink\">","+                    {p.since2021 >= 0 ? \"+\" : \"\"}","+                    {p.since2021.toFixed(0)} %","+                  </b>","+                </p>","+              )}","+            </div>","+          ))}","+        </div>","+      </section>","+","+      {/* ---- Dernières ventes captées ---- */}","+      {recent.length > 0 && (","+        <section className=\"mt-14\">","+          <span className=\"kicker\">","+            <span className=\"pulse\" style={{ width: 8, height: 8 }} />","+            {fr ? \"Flux en continu\" : \"Live feed\"}","+          </span>","+          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">","+            {fr ? \"Dernières ventes réelles captées\" : \"Latest real sales captured\"}","+          </h2>","+          <p className=\"mt-2 max-w-2xl text-[14px] text-ink-2\">","+            {fr","+              ? \"Transactions publiées, appariées automatiquement à leur unité d'évaluation. Cliquez pour voir l'estimation complète de la propriété vendue.\"","+              : \"Published transactions, automatically matched to their assessment unit. Click for the sold property's full estimate.\"}","+          </p>","+          <div className=\"src-wrap mt-4\">","+            <table className=\"src-table\">","+              <thead>","+                <tr>","+                  <th>{fr ? \"Date\" : \"Date\"}</th>","+                  <th>{fr ? \"Propriété\" : \"Property\"}</th>","+                  <th className=\"text-right\">{fr ? \"Prix de vente\" : \"Sale price\"}</th>","+                  <th className=\"text-right\"></th>","+                </tr>","+              </thead>","+              <tbody>","+                {recent.map((s) => (","+                  <tr key={s.id}>","+                    <td className=\"vp-mono whitespace-nowrap text-[12px] font-bold uppercase\">","+                      {dateFmt(s.date)}","+                    </td>","+                    <td>","+                      <p className=\"font-semibold leading-tight\">","+                        {s.street}","+                        {s.city ? `, ${s.city}` : \"\"}","+                      </p>","+                      {s.propertyType && s.propertyType !== \"indéterminé\" && (","+                        <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">","+                          {s.propertyType}","+                        </p>","+                      )}","+                    </td>","+                    <td className=\"text-right\">","+                      <span className=\"vp-display font-bold\">{money(s.amount)}</span>","+                      {s.vsRolePct != null && Math.abs(s.vsRolePct) < 400 && (","+                        <p className=\"vp-mono mt-0.5 text-[10px] text-ink-3\">","+                          {s.vsRolePct >= 0 ? \"+\" : \"−\"}","+                          {Math.abs(s.vsRolePct).toFixed(0)} %{\" \"}","+                          {fr ? \"vs rôle\" : \"vs roll\"}","+                        </p>","+                      )}","+                    </td>","+                    <td className=\"text-right\">","+                      {s.idProvinc && (","+                        <Link","+                          href={`/estimation/${encodeURIComponent(s.idProvinc)}`}","+                          className=\"vp-mono whitespace-nowrap rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1.5 text-[11px] font-bold uppercase tracking-[0.04em] transition-colors hover:bg-lime\"","+                        >","+                          {fr ? \"Estimer\" : \"Estimate\"} →","+                        </Link>","+                      )}","+                    </td>","+                  </tr>","+                ))}","+              </tbody>","+            </table>","+          </div>","+        </section>","+      )}","+","       {/* ---- Formulaire manuel ---- */}","       <section className=\"mt-14\">","         <span className=\"kicker\">{lang === \"fr\" ? \"Plan B\" : \"Plan B\"}</span>"]}],"originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport SearchBox from \"@/components/SearchBox\";\nimport EstimateForm from \"@/components/EstimateForm\";\nimport { useLang } from \"@/components/LangContext\";\n\nexport default function HomeClient() {\n  const { lang, t } = useLang();\n  return (\n    <div className=\"pb-6 pt-10 sm:pt-14\">\n      {/* ---- Héros ---- */}\n      <section>\n        <span className=\"kicker\">\n          {lang === \"fr\" ? \"Québec · 2021-2026\" : \"Québec · 2021-2026\"}\n        </span>\n        <h1 className=\"vp-display mt-3 max-w-4xl text-[clamp(34px,6.4vw,72px)] font-bold uppercase leading-[0.98] tracking-[-0.035em]\">\n          {lang === \"fr\" ? (\n            <>\n              La <span className=\"outline-txt\">vraie</span> valeur.\n              <br />\n              <span className=\"hl\">Sans boîte noire.</span>\n            </>\n          ) : (\n            <>\n              The <span className=\"outline-txt\">true</span> value.\n              <br />\n              <span className=\"hl\">No black box.</span>\n            </>\n          )}\n        </h1>\n        <p className=\"mt-5 max-w-xl text-[16.5px] text-ink-2\">{t(\"sub\")}</p>\n\n        <div className=\"mt-7 flex gap-2.5 overflow-x-auto pb-1.5 [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden\">\n          <span className=\"stat-chip\">\n            <span className=\"pulse\" />\n            <b>3 747 008</b> {lang === \"fr\" ? \"propriétés\" : \"properties\"}\n          </span>\n          <span className=\"stat-chip\">\n            <b>745 119</b> {lang === \"fr\" ? \"ventes réelles\" : \"real sales\"}\n          </span>\n          <span className=\"stat-chip\">\n            <b>1 100</b> {lang === \"fr\" ? \"municipalités\" : \"municipalities\"}\n          </span>\n          <span className=\"stat-chip\">\n            MdAPE <b>11 %</b>\n          </span>\n        </div>\n      </section>\n\n      {/* ---- Recherche ---- */}\n      <section className=\"mt-9\">\n        <div className=\"vp-card p-4 sm:p-5\">\n          <p className=\"klabel mb-2 pl-0.5\">\n            {lang === \"fr\" ? \"Adresse de la propriété\" : \"Property address\"}\n          </p>\n          <SearchBox />\n        </div>\n        <p className=\"vp-mono mt-2.5 pl-1 text-[11px] uppercase tracking-[0.06em] text-ink-3\">\n          {t(\"dataNote\")}\n        </p>\n      </section>\n\n      {/* ---- Formulaire manuel ---- */}\n      <section className=\"mt-14\">\n        <span className=\"kicker\">{lang === \"fr\" ? \"Plan B\" : \"Plan B\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"manualTitle\")}\n        </h2>\n        <div className=\"vp-card mt-4 p-4 sm:p-6\">\n          <EstimateForm />\n        </div>\n      </section>\n\n      {/* ---- Manifeste transparence ---- */}\n      <section className=\"mt-14 grid gap-4 sm:grid-cols-3\">\n        {(lang === \"fr\"\n          ? [\n              [\"01\", \"Le calcul, montré\", \"Chaque ajustement de chaque comparable est affiché en dollars. Pas de score magique.\"],\n              [\"02\", \"Fourchette honnête\", \"Un prix unique est un mensonge statistique. On donne P10-P90 et un indice de confiance A-D.\"],\n              [\"03\", \"Données réelles\", \"Rôles fonciers officiels (MAMH) et ventes publiées — locales et récentes en priorité.\"],\n            ]\n          : [\n              [\"01\", \"The math, shown\", \"Every adjustment on every comparable is displayed in dollars. No magic score.\"],\n              [\"02\", \"Honest range\", \"A single price is a statistical lie. We give P10-P90 and an A-D confidence index.\"],\n              [\"03\", \"Real data\", \"Official assessment rolls (MAMH) and published sales — local and recent first.\"],\n            ]\n        ).map(([num, title, body]) => (\n          <div key={num} className=\"vp-card vp-card-hover p-5\">\n            <p className=\"vp-mono text-[11px] font-bold text-green\">{num}</p>\n            <h3 className=\"vp-display mt-1.5 text-[17px] font-bold uppercase tracking-[-0.01em]\">\n              {title}\n            </h3>\n            <p className=\"mt-2 text-[13.5px] text-ink-2\">{body}</p>\n          </div>\n        ))}\n      </section>\n    </div>\n  );\n}\n","userModified":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013aJi445fsFVpm84tkaTvPN","type":"tool_result","content":"(eval):1: no matches found: /Users/simon-pierreboucher/apps/vrai-prix/ecosystem.config.*\n\nCOMMAND   PID                USER   FD   TYPE             DEVICE SIZE/OFF NODE NAME\nnode    44679 simon-pierreboucher   13u  IPv4 0x5dca90edc638793c      0t0  TCP *:8090 (LISTEN)","is_error":false}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"747cc52a-c5f1-47e5-99a7-19ada13a4d8d","timestamp":"2026-08-23T03:09:12.255Z","tool_use_result":{"stdout":"(eval):1: no matches found: /Users/simon-pierreboucher/apps/vrai-prix/ecosystem.config.*\n\nCOMMAND   PID                USER   FD   TYPE             DEVICE SIZE/OFF NODE NAME\nnode    44679 simon-pierreboucher   13u  IPv4 0x5dca90edc638793c      0t0  TCP *:8090 (LISTEN)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"629ee953-ed82-4543-8f68-8d73fd7d1b0b","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwmGfKA6gEjdzZBcEEu","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01ACR8j6xrEQaBiFb9WSniCK","name":"Bash","input":{"command":"curl -s -o /dev/null -w \"home: %{http_code} %{time_total}s\\n\" http://localhost:8090/ && curl -s http://localhost:8090/ | grep -o \"pouls du marché\\|Dernières ventes réelles captées\\|ventes captées\" | sort -u && ID=$(sqlite3 data/vraiprix.db \"SELECT id_provinc FROM transactions WHERE id_provinc IS NOT NULL ORDER BY date DESC LIMIT 1;\") && curl -s -o /tmp/est.html -w \"estimation: %{http_code} %{time_total}s\\n\" \"http://localhost:8090/estimation/$ID\" && grep -o \"Le marché autour de cette propriété\\|ESTIMATION\\|Vaut plus que\\|P10\\|P90\" /tmp/est.html | sort | uniq -c","description":"Healthcheck home and estimation pages for new sections"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":388,"cache_read_input_tokens":134088,"cache_creation":{"ephemeral_5m_input_tokens":388,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"eb708081-3d60-4029-b532-82817c1c538c","request_id":"req_011CeJwmEzrJ46iB4cBVQS7y"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ACR8j6xrEQaBiFb9WSniCK","type":"tool_result","content":"home: 200 0.015514s\nDernières ventes réelles captées\npouls du marché\nventes captées\nestimation: 200 0.311971s\n   1 ESTIMATION\n   1 Le marché autour de cette propriété\n   5 P10\n   5 P90\n   1 Vaut plus que","is_error":false}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"1487401c-5732-4960-88c8-434392f0640d","timestamp":"2026-08-23T03:09:20.152Z","tool_use_result":{"stdout":"home: 200 0.015514s\nDernières ventes réelles captées\npouls du marché\nventes captées\nestimation: 200 0.311971s\n   1 ESTIMATION\n   1 Le marché autour de cette propriété\n   5 P10\n   5 P90\n   1 Vaut plus que","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwmrGtkzLQYJa8uCWTt","type":"message","role":"assistant","content":[{"type":"text","text":"Les deux pages rendent les nouvelles sections. Je fais une vérification visuelle rapide avec Playwright :"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":603,"cache_read_input_tokens":134476,"cache_creation":{"ephemeral_5m_input_tokens":603,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"de8dcb9d-d75c-49e3-81a3-dc46b4c5289c","request_id":"req_011CeJwmpo6Ejkc3hNGZWqf3"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwmrGtkzLQYJa8uCWTt","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HtPQwFsuMGv9Kd6wCjNrVr","name":"Bash","input":{"command":"ID=$(sqlite3 data/vraiprix.db \"SELECT id_provinc FROM transactions WHERE id_provinc IS NOT NULL ORDER BY date DESC LIMIT 1;\") && node -e \"\nconst { chromium } = require('playwright');\n(async () => {\n  const b = await chromium.launch();\n  const p = await b.newPage({ viewport: { width: 1280, height: 900 } });\n  await p.goto('http://localhost:8090/', { waitUntil: 'networkidle' });\n  await p.screenshot({ path: '/tmp/vp-home.png', fullPage: true });\n  await p.goto('http://localhost:8090/estimation/$ID', { waitUntil: 'networkidle' });\n  await p.screenshot({ path: '/tmp/vp-est.png', fullPage: true });\n  const m = await b.newPage({ viewport: { width: 390, height: 844 } });\n  await m.goto('http://localhost:8090/estimation/$ID', { waitUntil: 'networkidle' });\n  await m.screenshot({ path: '/tmp/vp-est-mobile.png', fullPage: true });\n  await b.close();\n})().catch(e => { console.error(e); process.exit(1); });\n\" && echo OK","description":"Screenshot home and estimation pages (desktop + mobile)","timeout":180000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":603,"cache_read_input_tokens":134476,"cache_creation":{"ephemeral_5m_input_tokens":603,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"67c69244-b5bf-449f-882b-2c0626ae36ad","request_id":"req_011CeJwmpo6Ejkc3hNGZWqf3"}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HtPQwFsuMGv9Kd6wCjNrVr","type":"tool_result","content":"OK","is_error":false}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"009ab371-9b29-4f71-b7e3-8fed3ae21c7a","timestamp":"2026-08-23T03:09:34.113Z","tool_use_result":{"stdout":"OK","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"f2a4056a-c257-4c2d-a414-e003474fb984","timestamp":"2026-08-23T03:09:37.642Z","tool_use_result":{"type":"image","file":{"base64":"iVBORw0KGgoAAAANSUhEUgAAAkQAAAfPCAIAAABezWexAAdfW0lEQVR4Ae3AA6AkWZbG8f937o3IzKdyS2Oubdu2bdu2bdu2bWmMnpZKr54yMyLu+Xa3anqmhztr1a8e7l/gqquuuuqqq/6XAiC46qqrrrrqqv+9AAiuuuqqq6666n8vAIKrrrrqqquu+t8LgMr/D7Z5TgZsSZK46qqrrvqfzeBM2wZs/g0kgaSI4EVg27YkXgAbcETw3w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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwqmktffzDWzcKP7b1P","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":610,"cache_read_input_tokens":140829,"cache_creation":{"ephemeral_5m_input_tokens":610,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"8f89108c-a881-49d4-9d5b-9313cfcc6ce2","request_id":"req_011CeJwqiXvF6jmLQ2yyxidB"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwqmktffzDWzcKP7b1P","type":"message","role":"assistant","content":[{"type":"text","text":"Le badge de la barre de fourchette semble coupé à gauche sur mobile. Je zoome pour vérifier :"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":610,"cache_read_input_tokens":140829,"cache_creation":{"ephemeral_5m_input_tokens":610,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"4d957c14-9786-4de9-9396-95e23ab4c1e1","request_id":"req_011CeJwqiXvF6jmLQ2yyxidB"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwqmktffzDWzcKP7b1P","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Qqq9BHmP2ZnEB8eQf5abVG","name":"Bash","input":{"command":"cd /tmp && python3 -c \"\nfrom PIL import Image\nim = Image.open('/tmp/vp-est-mobile.png')\n# hero panel range bar region: roughly y 380-560 at full res\nim.crop((0, 300, 390, 650)).resize((780, 700)).save('/tmp/vp-range-mobile.png')\n\"","description":"Crop mobile screenshot around range bar"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":610,"cache_read_input_tokens":140829,"cache_creation":{"ephemeral_5m_input_tokens":610,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"7cbae678-2ba5-48d4-b925-bb56964254b3","request_id":"req_011CeJwqiXvF6jmLQ2yyxidB"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Qqq9BHmP2ZnEB8eQf5abVG","type":"tool_result","content":"Shell cwd was reset to /Users/simon-pierreboucher/apps/vrai-prix","is_error":false}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"34bd862b-7a56-4ebf-ab01-9d4fde2ce2b0","timestamp":"2026-08-23T03:11:00.477Z","tool_use_result":{"stdout":"","stderr":"\nShell cwd was reset to /Users/simon-pierreboucher/apps/vrai-prix","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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","media_type":"image/png"}}]}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"be15cebf-61a4-43ed-91c9-44b8f72b2210","timestamp":"2026-08-23T03:11:03.938Z","tool_use_result":{"type":"image","file":{"base64":"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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwuYVHc7s4XNCuWLmxs","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":910,"cache_read_input_tokens":144206,"cache_creation":{"ephemeral_5m_input_tokens":910,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"0f677e96-9010-43fe-89fd-e21bdec93368","request_id":"req_011CeJwuUatZk728ris18g6T"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwuYVHc7s4XNCuWLmxs","type":"message","role":"assistant","content":[{"type":"text","text":"Deux problèmes mobiles : la colonne texte est écrasée par la jauge de confiance (chips empilés, barre trop étroite), et les chips se cassent sur deux lignes. Je corrige :"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":910,"cache_read_input_tokens":144206,"cache_creation":{"ephemeral_5m_input_tokens":910,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"419c1bed-1b10-40cd-95b2-ad6b92d95489","request_id":"req_011CeJwuUatZk728ris18g6T"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwuYVHc7s4XNCuWLmxs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017UCy9iy1E81iYkBHLijv6S","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","old_string":"        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          <div className=\"min-w-0 flex-1\">","new_string":"        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          {/* basis large : sous ~380px de large la jauge passe dessous au lieu d'écraser la colonne */}\n          <div className=\"min-w-0 flex-1 basis-[300px]\">"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":910,"cache_read_input_tokens":144206,"cache_creation":{"ephemeral_5m_input_tokens":910,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"7df35660-f772-4a80-a555-153880422148","request_id":"req_011CeJwuUatZk728ris18g6T"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017UCy9iy1E81iYkBHLijv6S","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"f441a51f-2adc-422d-aa0d-d3a7f3e42a44","timestamp":"2026-08-23T03:11:29.142Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","oldString":"        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          <div className=\"min-w-0 flex-1\">","newString":"        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          {/* basis large : sous ~380px de large la jauge passe dessous au lieu d'écraser la colonne */}\n          <div className=\"min-w-0 flex-1 basis-[300px]\">","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport { useState } from \"react\";\nimport Link from \"next/link\";\nimport { useLang } from \"./LangContext\";\nimport LeadForm from \"./LeadForm\";\nimport RadarMap from \"./RadarMap\";\nimport {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  MarketIndexChart,\n  PercentileBar,\n  RangeBar,\n  ValueSplit,\n} from \"./MetricViz\";\nimport type { EstimateResult } from \"@/lib/engine\";\nimport type { UnitEstimate } from \"@/lib/estimator\";\nimport type { TKey } from \"@/lib/i18n\";\n\nconst fmt = (v: number | null | undefined, lang: string) =>\n  v == null\n    ? \"—\"\n    : new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n        style: \"currency\",\n        currency: \"CAD\",\n        maximumFractionDigits: 0,\n      }).format(v);\n\nconst signed = (v: number, lang: string) =>\n  `${v >= 0 ? \"+\" : \"−\"}${fmt(Math.abs(Math.round(v)), lang)}`;\n\nfunction Chip({ k, v }: { k: string; v: string | number }) {\n  return (\n    <div className=\"kv-cell\">\n      <p className=\"k\">{k}</p>\n      <p className=\"v break-words\">{v}</p>\n    </div>\n  );\n}\n\nexport default function ResultView({ data }: { data: UnitEstimate }) {\n  const { lang, t } = useLang();\n  const [activeComp, setActiveComp] = useState<string | null>(null);\n  const r: EstimateResult = data.result;\n  const u = data.unit;\n  const ctx = data.context;\n  const s = u?.specs;\n  const fr = lang === \"fr\";\n  const histMax = Math.max(...(u?.history.map((h) => h.value ?? 0) ?? [0]), 1);\n  const moneyFmt = (v: number) =>\n    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n    }).format(Math.round(v / 100) * 100);\n\n  return (\n    <div className=\"space-y-10 py-8\">\n      {/* fil d'ariane */}\n      <nav className=\"vp-mono flex flex-wrap items-center gap-2 text-[11.5px] uppercase tracking-[0.06em] text-ink-3\">\n        <Link href=\"/\" className=\"border-b-[1.5px] border-transparent hover:border-green hover:text-green\">\n          Vrai-Prix\n        </Link>\n        <span>/</span>\n        <span>{u ? `${u.adresse ?? \"\"} · ${u.municipalite ?? \"\"}` : t(\"manualTitle\")}</span>\n      </nav>\n\n      {/* ---- Panneau prix ---- */}\n      <section className=\"vp-card rise p-6 sm:p-8\">\n        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          <div className=\"min-w-0 flex-1\">\n            <span className=\"kicker\">{t(\"estimatedValue\")}</span>\n            {u && (\n              <h1 className=\"vp-display mt-2 text-[clamp(20px,3vw,28px)] font-bold uppercase leading-tight tracking-[-0.02em]\">\n                {u.adresse}\n                {s?.apt ? ` app. ${s.apt}` : \"\"}\n                {u.municipalite ? <span className=\"text-ink-3\"> · {u.municipalite}</span> : null}\n              </h1>\n            )}\n            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            {/* faits rapides */}\n            <div className=\"mt-4 flex flex-wrap gap-2\">\n              {[\n                u ? t(u.typeProp as TKey) : null,\n                u?.aireEtagesM2 && r.estimate > 0\n                  ? `${locNum(Math.round(r.estimate / u.aireEtagesM2), lang, { maxFrac: 0 })} $/m²`\n                  : null,\n                u?.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : null,\n                u?.anneeConstruction ? `${fr ? \"constr.\" : \"built\"} ${u.anneeConstruction}` : null,\n                u?.superficieTerrainM2\n                  ? `${fr ? \"terrain\" : \"lot\"} ${locNum(u.superficieTerrainM2, lang, { unit: \"m²\" })}`\n                  : null,\n              ]\n                .filter((x): x is string => x != null)\n                .map((x) => (\n                  <span\n                    key={x}\n                    className=\"vp-mono rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"\n                  >\n                    {x}\n                  </span>\n                ))}\n            </div>\n            {/* fourchette visuelle P10-P90 avec position du rôle */}\n            <div className=\"mt-6 max-w-2xl\">\n              <p className=\"klabel mb-1\">{t(\"range\")}</p>\n              <RangeBar\n                low={r.low}\n                estimate={r.estimate}\n                high={r.high}\n                role={u?.valeurRole ?? null}\n                lang={lang}\n              />\n            </div>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              {u?.valeurRole != null && (\n                <div>\n                  <p className=\"klabel\">{t(\"assessed\")}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-ink-2\">{fmt(u.valeurRole, lang)}</p>\n                </div>\n              )}\n              {u?.valeurRole != null && u.valeurRole > 0 && r.estimate > 0 && (\n                <div>\n                  <p className=\"klabel\">{fr ? \"Écart vs rôle\" : \"vs assessment\"}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-green-deep\">\n                    {r.estimate >= u.valeurRole ? \"+\" : \"\"}\n                    {((r.estimate / u.valeurRole - 1) * 100).toFixed(0)} %\n                  </p>\n                </div>\n              )}\n            </div>\n            {u && (\n              <div className=\"mt-6 flex flex-wrap gap-2.5\">\n                <a\n                  href={`/api/report?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-primary\"\n                  download\n                >\n                  {fr ? \"Rapport PDF\" : \"PDF report\"} ↓\n                </a>\n                <a\n                  href={`/api/report/pro?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-ghost\"\n                  download\n                  title={fr ? \"Structure de rapport d'évaluation — présentation bancaire\" : \"Appraisal-style structure — bank presentation\"}\n                >\n                  {fr ? \"Rapport détaillé format professionnel (banque)\" : \"Detailed professional report (bank)\"} ↓\n                </a>\n              </div>\n            )}\n          </div>\n          <div className=\"shrink-0\">\n            <ConfidenceDial\n              pct={r.confidencePct}\n              level={r.confidenceLevel}\n              label={`${t(\"confidence\")} — ${t(`conf${r.confidenceLevel}` as TKey)}`}\n            />\n          </div>\n        </div>\n        <p className=\"vp-mono mt-5 border-t-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {t(\"disclaimer\")}\n        </p>\n      </section>\n\n      {/* ---- Portrait de la propriété (bento du registre) ---- */}\n      {u && s && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Registre officiel — dessiné par les données\" : \"Official record — drawn by the data\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Portrait de la propriété\" : \"Property portrait\"}\n          </h2>\n          <div className=\"mt-4 grid gap-4 lg:grid-cols-3\">\n            {/* Bâtiment */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"60ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Bâtiment\" : \"Building\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <BuildingGlyph\n                  floors={s.nbEtages}\n                  dwellings={u.nbLogements}\n                  genre={s.genreConstruction}\n                  yearBuilt={u.anneeConstruction}\n                  floorAreaM2={u.aireEtagesM2}\n                  lang={lang}\n                />\n              </div>\n              <EraLine year={u.anneeConstruction} lang={lang} />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Aire d'étages\" : \"Floor area\"} v={u.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Étages × logements\" : \"Storeys × dwellings\"} v={`${s.nbEtages ?? \"—\"} × ${u.nbLogements ?? \"—\"}`} />\n                <Chip k={fr ? \"Genre\" : \"Style\"} v={s.genreConstruction ?? \"—\"} />\n                <Chip k={fr ? \"Lien physique\" : \"Physical link\"} v={s.lienPhysique ?? \"—\"} />\n              </div>\n            </div>\n            {/* Terrain */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"140ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Terrain — à l'échelle\" : \"Lot — to scale\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <LotDiagram\n                  areaM2={u.superficieTerrainM2}\n                  frontageM={s.frontTerrainM}\n                  footprintM2={\n                    u.aireEtagesM2 && s.nbEtages ? u.aireEtagesM2 / Math.max(1, s.nbEtages) : u.aireEtagesM2\n                  }\n                  lang={lang}\n                  isCondo={!!s.apt || (u.nbLogements != null && u.nbLogements >= 1 && !u.superficieTerrainM2)}\n                />\n              </div>\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Superficie\" : \"Area\"} v={u.superficieTerrainM2 ? locNum(u.superficieTerrainM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Frontage\" : \"Frontage\"} v={s.frontTerrainM ? locNum(s.frontTerrainM, lang, { unit: \"m\" }) : \"—\"} />\n                <Chip k=\"CUBF\" v={s.cubf ?? \"—\"} />\n                <Chip k={fr ? \"Usage\" : \"Use\"} v={s.cubfLibelle ?? \"—\"} />\n              </div>\n            </div>\n            {/* Valeur au rôle */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"220ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Valeur au rôle 2026 — composition\" : \"2026 assessed value — split\"}\n              </p>\n              <p className=\"vp-display py-2 text-[26px] font-bold tracking-[-0.02em]\">\n                {fmt(u.valeurRole, lang)}\n              </p>\n              <ValueSplit\n                land={s.valeurTerrain}\n                building={s.valeurBatiment}\n                previous={s.valeurAnterieure}\n                total={u.valeurRole}\n                lang={lang}\n                labels={{\n                  land: fr ? \"Terrain\" : \"Land\",\n                  building: fr ? \"Bâtiment\" : \"Building\",\n                  previous: fr ? \"Rôle antérieur\" : \"Previous roll\",\n                }}\n              />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Cond. marché au\" : \"Market cond.\"} v={s.datCondMarche ?? \"—\"} />\n                <Chip k={fr ? \"Matricule\" : \"Roll no.\"} v={s.matricule ?? \"—\"} />\n              </div>\n            </div>\n          </div>\n          {/* registre brut complet */}\n          <details className=\"vp-card mt-4 p-5\">\n            <summary className=\"vp-mono cursor-pointer text-[11px] font-bold uppercase tracking-[0.08em] text-green-deep\">\n              {fr ? \"▸ Toutes les données du registre (brutes)\" : \"▸ All registry data (raw)\"}\n            </summary>\n            <div className=\"mt-4 grid grid-cols-2 gap-2 sm:grid-cols-4\">\n              <Chip k={fr ? \"Année construction\" : \"Year built\"} v={u.anneeConstruction ? `${u.anneeConstruction}${s.anneeEstimee === \"E\" ? (fr ? \" (estimée)\" : \" (est.)\") : \"\"}` : \"—\"} />\n              <Chip k={fr ? \"Locaux non résid.\" : \"Non-res. units\"} v={s.nbLocauxNonResid ?? \"—\"} />\n              <Chip k={fr ? \"Chambres locatives\" : \"Rental rooms\"} v={s.nbChambresLocatives ?? \"—\"} />\n              <Chip k={fr ? \"Unité de voisinage\" : \"Neighbourhood\"} v={s.uniteVoisinage || \"—\"} />\n              <Chip k={fr ? \"Arrondissement\" : \"Borough\"} v={s.arrond || \"—\"} />\n              <Chip k={fr ? \"Adresses sur l'unité\" : \"Addresses\"} v={s.nAdresses ?? \"—\"} />\n              <Chip k={fr ? \"Valeur terrain\" : \"Land value\"} v={fmt(s.valeurTerrain, lang)} />\n              <Chip k={fr ? \"Valeur bâtiment\" : \"Building value\"} v={fmt(s.valeurBatiment, lang)} />\n              <Chip k={fr ? \"Rôle antérieur\" : \"Previous roll\"} v={fmt(s.valeurAnterieure, lang)} />\n              <Chip k=\"ID provincial\" v={u.id} />\n            </div>\n          </details>\n        </section>\n      )}\n\n      {/* ---- Transparence du calcul ---- */}\n      <section>\n        <span className=\"kicker\">{fr ? \"Transparence\" : \"Transparency\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"whyTitle\")}\n        </h2>\n        <div className=\"mt-4 grid gap-4 sm:grid-cols-2\">\n          <div className=\"vp-card vp-card-hover rise p-5\">\n            <p className=\"klabel\">{t(\"modelPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.modelEstimate != null ? (\n                <CountUp value={r.modelEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightModel\")} : {(r.modelWeight * 100).toFixed(0)} %\n            </p>\n          </div>\n          <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"100ms\" }}>\n            <p className=\"klabel\">{t(\"compsPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.compsEstimate != null ? (\n                <CountUp value={r.compsEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightComps\")} : {((1 - r.modelWeight) * 100).toFixed(0)} % · {r.nCompsUsed} comps\n              {r.compsDispersionPct != null ? ` · ±${r.compsDispersionPct} %` : \"\"}\n            </p>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Marché local ---- */}\n      {ctx && (ctx.index.length >= 6 || ctx.sector || ctx.muniPercentile != null) && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Contexte de marché\" : \"Market context\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Le marché autour de cette propriété\" : \"The market around this property\"}\n          </h2>\n          <div className=\"mt-4 grid items-stretch gap-4 lg:grid-cols-[1.35fr_1fr]\">\n            {/* trajectoire du marché */}\n            {ctx.index.length >= 6 && (\n              <div className=\"vp-card rise p-5\">\n                <div className=\"flex flex-wrap items-center justify-between gap-2\">\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Indice de marché — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 en ${ctx.index[0].month.slice(0, 4)})`\n                      : `Market index — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 in ${ctx.index[0].month.slice(0, 4)})`}\n                  </p>\n                  <div className=\"flex gap-2\">\n                    {ctx.index12mPct != null && (\n                      <span className={`vp-mono rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${ctx.index12mPct >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n                        12 {fr ? \"mois\" : \"mo\"} {ctx.index12mPct >= 0 ? \"+\" : \"\"}\n                        {ctx.index12mPct.toFixed(1)} %\n                      </span>\n                    )}\n                    {ctx.indexSince2021Pct != null && (\n                      <span className=\"vp-mono rounded-[4px] border border-ink bg-surface-2 px-1.5 py-0.5 text-[10px] font-bold text-ink-2\">\n                        {ctx.index[0].month.slice(0, 4)}→ {ctx.indexSince2021Pct >= 0 ? \"+\" : \"\"}\n                        {ctx.indexSince2021Pct.toFixed(0)} %\n                      </span>\n                    )}\n                  </div>\n                </div>\n                <div className=\"mt-3\">\n                  <MarketIndexChart series={ctx.index} lang={lang} />\n                </div>\n                <p className=\"vp-mono mt-2 text-[10px] uppercase tracking-[0.05em] text-ink-3\">\n                  {fr\n                    ? \"Indice utilisé pour l'ajustement « marché » des comparables — même donnée, même calcul.\"\n                    : \"Index used for the comparables' market adjustment — same data, same math.\"}\n                </p>\n              </div>\n            )}\n            <div className=\"flex flex-col gap-4\">\n              {/* ventes du secteur */}\n              {ctx.sector && (\n                <div className=\"vp-card rise p-5\" style={{ animationDelay: \"80ms\" }}>\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Ventes réelles — rayon ${ctx.sector.radiusKm} km · 12 mois`\n                      : `Real sales — ${ctx.sector.radiusKm} km radius · 12 months`}\n                  </p>\n                  <div className=\"mt-3 grid grid-cols-3 gap-2\">\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Ventes\" : \"Sales\"}</p>\n                      <p className=\"v\">{ctx.sector.nSales12m}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Prix médian\" : \"Median price\"}</p>\n                      <p className=\"v\">{ctx.sector.medianPrice != null ? fmt(Math.round(ctx.sector.medianPrice), lang) : \"—\"}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">$/m² {fr ? \"médian\" : \"median\"}</p>\n                      <p className=\"v\">\n                        {ctx.sector.medianPpm2 != null\n                          ? locNum(Math.round(ctx.sector.medianPpm2), lang, { maxFrac: 0 })\n                          : \"—\"}\n                      </p>\n                    </div>\n                  </div>\n                  {ctx.sector.medianPrice != null && r.estimate > 0 && (\n                    <p className=\"vp-mono mt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-2\">\n                      {fr ? \"Cette propriété\" : \"This property\"} :{\" \"}\n                      <b className=\"text-ink\">\n                        {r.estimate >= ctx.sector.medianPrice ? \"+\" : \"−\"}\n                        {Math.abs((r.estimate / ctx.sector.medianPrice - 1) * 100).toFixed(0)} %\n                      </b>{\" \"}\n                      {fr ? \"vs médiane du secteur\" : \"vs sector median\"}\n                    </p>\n                  )}\n                </div>\n              )}\n              {/* position dans la municipalité */}\n              {ctx.muniPercentile != null && u?.municipalite && (\n                <div className=\"vp-card rise flex-1 p-5\" style={{ animationDelay: \"160ms\" }}>\n                  <p className=\"klabel\">\n                    {fr ? `Position dans ${u.municipalite}` : `Position in ${u.municipalite}`}\n                  </p>\n                  <div className=\"mt-3\">\n                    <PercentileBar pct={ctx.muniPercentile} lang={lang} />\n                  </div>\n                  <p className=\"mt-3 text-[13px] text-ink-2\">\n                    {fr ? (\n                      <>\n                        Vaut plus que{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> des{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        propriétés estimées de la municipalité.\n                      </>\n                    ) : (\n                      <>\n                        Worth more than{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> of the{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        estimated properties in the municipality.\n                      </>\n                    )}\n                  </p>\n                </div>\n              )}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Historique ---- */}\n      {u && u.history.some((h) => h.value != null) && (\n        <section>\n          <span className=\"kicker\">2021 → 2026</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {t(\"history\")}\n          </h2>\n          <div className=\"vp-card rise mt-4 p-5 sm:p-6\">\n            <div className=\"flex flex-col gap-2\">\n              {u.history.map((h, i) => {\n                const prev = i > 0 ? u.history[i - 1].value : null;\n                const yoy =\n                  h.value != null && prev != null && prev > 0\n                    ? ((h.value / prev - 1) * 100)\n                    : null;\n                return (\n                  <div key={h.year} className=\"hbar-row grid grid-cols-[44px_1fr_auto] items-center gap-2 sm:grid-cols-[52px_1fr_auto] sm:gap-3\">\n                    <span className=\"vp-mono text-[12px] font-bold\">{h.year}</span>\n                    <span className=\"hbar-track\">\n                      <span\n                        className=\"hbar-fill grow-x\"\n                        style={{\n                          width: `${((h.value ?? 0) / histMax) * 100}%`,\n                          animationDelay: `${i * 90}ms`,\n                          background: h.year === 2026 ? \"var(--ink)\" : undefined,\n                        }}\n                      />\n                    </span>\n                    <span className=\"vp-mono flex items-center justify-end gap-2 text-[12px] font-bold\">\n                      {fmt(h.value, lang)}\n                      {yoy != null && (\n                        <span className={`rounded-[4px] border border-ink px-1 py-px text-[9px] ${yoy >= 0 ? \"bg-lime-soft text-green-deep\" : \"bg-[var(--danger-soft)] text-[var(--danger)]\"}`}>\n                          {yoy >= 0 ? \"+\" : \"\"}\n                          {yoy.toFixed(0)} %\n                        </span>\n                      )}\n                    </span>\n                  </div>\n                );\n              })}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Plan des comparables (carte maison) ---- */}\n      <section>\n        <span className=\"kicker\">\n          {r.nCompsUsed} {fr ? \"ventes réelles\" : \"real sales\"}\n        </span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"compsTitle\")}\n        </h2>\n        <div className=\"mt-4 grid items-start gap-4 lg:grid-cols-[1.05fr_1fr]\">\n          <div className=\"overflow-hidden rounded-[10px] border-[1.5px] border-ink shadow-[8px_8px_0_rgba(20,24,20,0.08)]\">\n            <RadarMap\n              subject={{\n                lat: u?.lat ?? r.comps[0]?.lat ?? 46.81,\n                lng: u?.lng ?? r.comps[0]?.lng ?? -71.21,\n                label: u?.adresse ?? (fr ? \"Propriété estimée\" : \"Subject property\"),\n              }}\n              comps={r.comps.map((c) => ({\n                id: c.id,\n                lat: c.lat,\n                lng: c.lng,\n                label: `${c.street ?? \"\"}, ${c.city ?? \"\"}`,\n                price: fmt(c.amount, lang),\n                adjusted: fmt(Math.round(c.adjustedPrice), lang),\n                date: c.date,\n                distanceM: c.distanceM,\n                weight: c.weight,\n              }))}\n              highlight={activeComp}\n              onHover={setActiveComp}\n            />\n          </div>\n          <div className=\"src-wrap max-h-[560px] overflow-y-auto\">\n            <table className=\"src-table\">\n              <thead className=\"sticky top-0 z-10\">\n                <tr>\n                  <th>#</th>\n                  <th>{fr ? \"Vente\" : \"Sale\"}</th>\n                  <th className=\"text-right\">{t(\"adjusted\")}</th>\n                </tr>\n              </thead>\n              <tbody>\n                {r.comps.map((c, i) => (\n                  <tr\n                    key={c.id}\n                    onMouseEnter={() => setActiveComp(c.id)}\n                    onMouseLeave={() => setActiveComp(null)}\n                    className={activeComp === c.id ? \"!bg-lime-soft\" : \"\"}\n                  >\n                    <td>\n                      <span\n                        className={`vp-mono inline-flex h-6 w-6 items-center justify-center rounded-full border-[1.5px] border-ink text-[11px] font-bold ${\n                          activeComp === c.id ? \"bg-lime text-ink\" : \"bg-green text-paper\"\n                        }`}\n                      >\n                        {i + 1}\n                      </span>\n                    </td>\n                    <td>\n                      <p className=\"font-semibold leading-tight\">\n                        {c.street}, {c.city}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                        {c.floorArea && c.floorArea > 20\n                          ? ` (${locNum(Math.round(c.amount / c.floorArea), lang, { maxFrac: 0 })} $/m²)`\n                          : \"\"}\n                      </p>\n                      {(c.floorArea || c.yearBuilt) && (\n                        <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                          {[\n                            c.floorArea ? locNum(c.floorArea, lang, { unit: \"m²\" }) : null,\n                            c.yearBuilt ? `${fr ? \"constr.\" : \"built\"} ${c.yearBuilt}` : null,\n                          ]\n                            .filter(Boolean)\n                            .join(\" · \")}\n                        </p>\n                      )}\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] text-ink-2\">\n                        {t(\"adjTime\")} {signed(c.adjTime, lang)} · {t(\"adjArea\")} {signed(c.adjArea, lang)} ·{\" \"}\n                        {t(\"adjAge\")} {signed(c.adjAge, lang)}\n                      </p>\n                    </td>\n                    <td className=\"vp-display text-right font-bold\">\n                      {fmt(Math.round(c.adjustedPrice), lang)}\n                      <p className=\"vp-mono text-[10px] font-normal text-ink-3\">\n                        {t(\"weight\")} {(c.weight * 100).toFixed(0)} %\n                      </p>\n                    </td>\n                  </tr>\n                ))}\n              </tbody>\n            </table>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Lead ---- */}\n      <section className=\"rounded-[10px] border-[1.5px] border-ink bg-ink p-6 text-paper shadow-[8px_8px_0_rgba(20,24,20,0.2)] sm:p-8\">\n        <h2 className=\"vp-display text-[22px] font-bold uppercase tracking-[-0.02em] text-lime\">\n          {t(\"leadTitle\")}\n        </h2>\n        <div className=\"mt-4 max-w-xl\">\n          <LeadForm unitId={u?.id ?? null} estimate={r.estimate} />\n        </div>\n      </section>\n    </div>\n  );\n}\n","structuredPatch":[{"oldStart":72,"oldLines":7,"newStart":72,"newLines":8,"lines":["       {/* ---- Panneau prix ---- */}","       <section className=\"vp-card rise p-6 sm:p-8\">","         <div className=\"flex flex-wrap items-start justify-between gap-6\">","-          <div className=\"min-w-0 flex-1\">","+          {/* basis large : sous ~380px de large la jauge passe dessous au lieu d'écraser la colonne */}","+          <div className=\"min-w-0 flex-1 basis-[300px]\">","             <span className=\"kicker\">{t(\"estimatedValue\")}</span>","             {u && (","               <h1 className=\"vp-display mt-2 text-[clamp(20px,3vw,28px)] font-bold uppercase leading-tight tracking-[-0.02em]\">"]}],"userModified":false,"replaceAll":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"661ddbdc-645c-4544-9d0a-5d7bb42b59a1","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwwNT6WpcXsTTC4uY8b","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CDSg3o6bssPoGRB9LM66xr","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","old_string":"                    className=\"vp-mono rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"","new_string":"                    className=\"vp-mono whitespace-nowrap rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\""},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1436,"cache_read_input_tokens":145116,"cache_creation":{"ephemeral_5m_input_tokens":1436,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"00cadc15-8f1c-437e-96f0-e60d3a49343e","request_id":"req_011CeJwwLLbphsddWVMV1T33"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CDSg3o6bssPoGRB9LM66xr","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"81f4da2b-a1ef-4d98-89d1-ec565e894562","timestamp":"2026-08-23T03:11:34.552Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","oldString":"                    className=\"vp-mono rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"","newString":"                    className=\"vp-mono whitespace-nowrap rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport { useState } from \"react\";\nimport Link from \"next/link\";\nimport { useLang } from \"./LangContext\";\nimport LeadForm from \"./LeadForm\";\nimport RadarMap from \"./RadarMap\";\nimport {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  MarketIndexChart,\n  PercentileBar,\n  RangeBar,\n  ValueSplit,\n} from \"./MetricViz\";\nimport type { EstimateResult } from \"@/lib/engine\";\nimport type { UnitEstimate } from \"@/lib/estimator\";\nimport type { TKey } from \"@/lib/i18n\";\n\nconst fmt = (v: number | null | undefined, lang: string) =>\n  v == null\n    ? \"—\"\n    : new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n        style: \"currency\",\n        currency: \"CAD\",\n        maximumFractionDigits: 0,\n      }).format(v);\n\nconst signed = (v: number, lang: string) =>\n  `${v >= 0 ? \"+\" : \"−\"}${fmt(Math.abs(Math.round(v)), lang)}`;\n\nfunction Chip({ k, v }: { k: string; v: string | number }) {\n  return (\n    <div className=\"kv-cell\">\n      <p className=\"k\">{k}</p>\n      <p className=\"v break-words\">{v}</p>\n    </div>\n  );\n}\n\nexport default function ResultView({ data }: { data: UnitEstimate }) {\n  const { lang, t } = useLang();\n  const [activeComp, setActiveComp] = useState<string | null>(null);\n  const r: EstimateResult = data.result;\n  const u = data.unit;\n  const ctx = data.context;\n  const s = u?.specs;\n  const fr = lang === \"fr\";\n  const histMax = Math.max(...(u?.history.map((h) => h.value ?? 0) ?? [0]), 1);\n  const moneyFmt = (v: number) =>\n    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n    }).format(Math.round(v / 100) * 100);\n\n  return (\n    <div className=\"space-y-10 py-8\">\n      {/* fil d'ariane */}\n      <nav className=\"vp-mono flex flex-wrap items-center gap-2 text-[11.5px] uppercase tracking-[0.06em] text-ink-3\">\n        <Link href=\"/\" className=\"border-b-[1.5px] border-transparent hover:border-green hover:text-green\">\n          Vrai-Prix\n        </Link>\n        <span>/</span>\n        <span>{u ? `${u.adresse ?? \"\"} · ${u.municipalite ?? \"\"}` : t(\"manualTitle\")}</span>\n      </nav>\n\n      {/* ---- Panneau prix ---- */}\n      <section className=\"vp-card rise p-6 sm:p-8\">\n        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          {/* basis large : sous ~380px de large la jauge passe dessous au lieu d'écraser la colonne */}\n          <div className=\"min-w-0 flex-1 basis-[300px]\">\n            <span className=\"kicker\">{t(\"estimatedValue\")}</span>\n            {u && (\n              <h1 className=\"vp-display mt-2 text-[clamp(20px,3vw,28px)] font-bold uppercase leading-tight tracking-[-0.02em]\">\n                {u.adresse}\n                {s?.apt ? ` app. ${s.apt}` : \"\"}\n                {u.municipalite ? <span className=\"text-ink-3\"> · {u.municipalite}</span> : null}\n              </h1>\n            )}\n            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            {/* faits rapides */}\n            <div className=\"mt-4 flex flex-wrap gap-2\">\n              {[\n                u ? t(u.typeProp as TKey) : null,\n                u?.aireEtagesM2 && r.estimate > 0\n                  ? `${locNum(Math.round(r.estimate / u.aireEtagesM2), lang, { maxFrac: 0 })} $/m²`\n                  : null,\n                u?.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : null,\n                u?.anneeConstruction ? `${fr ? \"constr.\" : \"built\"} ${u.anneeConstruction}` : null,\n                u?.superficieTerrainM2\n                  ? `${fr ? \"terrain\" : \"lot\"} ${locNum(u.superficieTerrainM2, lang, { unit: \"m²\" })}`\n                  : null,\n              ]\n                .filter((x): x is string => x != null)\n                .map((x) => (\n                  <span\n                    key={x}\n                    className=\"vp-mono rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"\n                  >\n                    {x}\n                  </span>\n                ))}\n            </div>\n            {/* fourchette visuelle P10-P90 avec position du rôle */}\n            <div className=\"mt-6 max-w-2xl\">\n              <p className=\"klabel mb-1\">{t(\"range\")}</p>\n              <RangeBar\n                low={r.low}\n                estimate={r.estimate}\n                high={r.high}\n                role={u?.valeurRole ?? null}\n                lang={lang}\n              />\n            </div>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              {u?.valeurRole != null && (\n                <div>\n                  <p className=\"klabel\">{t(\"assessed\")}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-ink-2\">{fmt(u.valeurRole, lang)}</p>\n                </div>\n              )}\n              {u?.valeurRole != null && u.valeurRole > 0 && r.estimate > 0 && (\n                <div>\n                  <p className=\"klabel\">{fr ? \"Écart vs rôle\" : \"vs assessment\"}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-green-deep\">\n                    {r.estimate >= u.valeurRole ? \"+\" : \"\"}\n                    {((r.estimate / u.valeurRole - 1) * 100).toFixed(0)} %\n                  </p>\n                </div>\n              )}\n            </div>\n            {u && (\n              <div className=\"mt-6 flex flex-wrap gap-2.5\">\n                <a\n                  href={`/api/report?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-primary\"\n                  download\n                >\n                  {fr ? \"Rapport PDF\" : \"PDF report\"} ↓\n                </a>\n                <a\n                  href={`/api/report/pro?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-ghost\"\n                  download\n                  title={fr ? \"Structure de rapport d'évaluation — présentation bancaire\" : \"Appraisal-style structure — bank presentation\"}\n                >\n                  {fr ? \"Rapport détaillé format professionnel (banque)\" : \"Detailed professional report (bank)\"} ↓\n                </a>\n              </div>\n            )}\n          </div>\n          <div className=\"shrink-0\">\n            <ConfidenceDial\n              pct={r.confidencePct}\n              level={r.confidenceLevel}\n              label={`${t(\"confidence\")} — ${t(`conf${r.confidenceLevel}` as TKey)}`}\n            />\n          </div>\n        </div>\n        <p className=\"vp-mono mt-5 border-t-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {t(\"disclaimer\")}\n        </p>\n      </section>\n\n      {/* ---- Portrait de la propriété (bento du registre) ---- */}\n      {u && s && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Registre officiel — dessiné par les données\" : \"Official record — drawn by the data\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Portrait de la propriété\" : \"Property portrait\"}\n          </h2>\n          <div className=\"mt-4 grid gap-4 lg:grid-cols-3\">\n            {/* Bâtiment */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"60ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Bâtiment\" : \"Building\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <BuildingGlyph\n                  floors={s.nbEtages}\n                  dwellings={u.nbLogements}\n                  genre={s.genreConstruction}\n                  yearBuilt={u.anneeConstruction}\n                  floorAreaM2={u.aireEtagesM2}\n                  lang={lang}\n                />\n              </div>\n              <EraLine year={u.anneeConstruction} lang={lang} />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Aire d'étages\" : \"Floor area\"} v={u.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Étages × logements\" : \"Storeys × dwellings\"} v={`${s.nbEtages ?? \"—\"} × ${u.nbLogements ?? \"—\"}`} />\n                <Chip k={fr ? \"Genre\" : \"Style\"} v={s.genreConstruction ?? \"—\"} />\n                <Chip k={fr ? \"Lien physique\" : \"Physical link\"} v={s.lienPhysique ?? \"—\"} />\n              </div>\n            </div>\n            {/* Terrain */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"140ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Terrain — à l'échelle\" : \"Lot — to scale\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <LotDiagram\n                  areaM2={u.superficieTerrainM2}\n                  frontageM={s.frontTerrainM}\n                  footprintM2={\n                    u.aireEtagesM2 && s.nbEtages ? u.aireEtagesM2 / Math.max(1, s.nbEtages) : u.aireEtagesM2\n                  }\n                  lang={lang}\n                  isCondo={!!s.apt || (u.nbLogements != null && u.nbLogements >= 1 && !u.superficieTerrainM2)}\n                />\n              </div>\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Superficie\" : \"Area\"} v={u.superficieTerrainM2 ? locNum(u.superficieTerrainM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Frontage\" : \"Frontage\"} v={s.frontTerrainM ? locNum(s.frontTerrainM, lang, { unit: \"m\" }) : \"—\"} />\n                <Chip k=\"CUBF\" v={s.cubf ?? \"—\"} />\n                <Chip k={fr ? \"Usage\" : \"Use\"} v={s.cubfLibelle ?? \"—\"} />\n              </div>\n            </div>\n            {/* Valeur au rôle */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"220ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Valeur au rôle 2026 — composition\" : \"2026 assessed value — split\"}\n              </p>\n              <p className=\"vp-display py-2 text-[26px] font-bold tracking-[-0.02em]\">\n                {fmt(u.valeurRole, lang)}\n              </p>\n              <ValueSplit\n                land={s.valeurTerrain}\n                building={s.valeurBatiment}\n                previous={s.valeurAnterieure}\n                total={u.valeurRole}\n                lang={lang}\n                labels={{\n                  land: fr ? \"Terrain\" : \"Land\",\n                  building: fr ? \"Bâtiment\" : \"Building\",\n                  previous: fr ? \"Rôle antérieur\" : \"Previous roll\",\n                }}\n              />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Cond. marché au\" : \"Market cond.\"} v={s.datCondMarche ?? \"—\"} />\n                <Chip k={fr ? \"Matricule\" : \"Roll no.\"} v={s.matricule ?? \"—\"} />\n              </div>\n            </div>\n          </div>\n          {/* registre brut complet */}\n          <details className=\"vp-card mt-4 p-5\">\n            <summary className=\"vp-mono cursor-pointer text-[11px] font-bold uppercase tracking-[0.08em] text-green-deep\">\n              {fr ? \"▸ Toutes les données du registre (brutes)\" : \"▸ All registry data (raw)\"}\n            </summary>\n            <div className=\"mt-4 grid grid-cols-2 gap-2 sm:grid-cols-4\">\n              <Chip k={fr ? \"Année construction\" : \"Year built\"} v={u.anneeConstruction ? `${u.anneeConstruction}${s.anneeEstimee === \"E\" ? (fr ? \" (estimée)\" : \" (est.)\") : \"\"}` : \"—\"} />\n              <Chip k={fr ? \"Locaux non résid.\" : \"Non-res. units\"} v={s.nbLocauxNonResid ?? \"—\"} />\n              <Chip k={fr ? \"Chambres locatives\" : \"Rental rooms\"} v={s.nbChambresLocatives ?? \"—\"} />\n              <Chip k={fr ? \"Unité de voisinage\" : \"Neighbourhood\"} v={s.uniteVoisinage || \"—\"} />\n              <Chip k={fr ? \"Arrondissement\" : \"Borough\"} v={s.arrond || \"—\"} />\n              <Chip k={fr ? \"Adresses sur l'unité\" : \"Addresses\"} v={s.nAdresses ?? \"—\"} />\n              <Chip k={fr ? \"Valeur terrain\" : \"Land value\"} v={fmt(s.valeurTerrain, lang)} />\n              <Chip k={fr ? \"Valeur bâtiment\" : \"Building value\"} v={fmt(s.valeurBatiment, lang)} />\n              <Chip k={fr ? \"Rôle antérieur\" : \"Previous roll\"} v={fmt(s.valeurAnterieure, lang)} />\n              <Chip k=\"ID provincial\" v={u.id} />\n            </div>\n          </details>\n        </section>\n      )}\n\n      {/* ---- Transparence du calcul ---- */}\n      <section>\n        <span className=\"kicker\">{fr ? \"Transparence\" : \"Transparency\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"whyTitle\")}\n        </h2>\n        <div className=\"mt-4 grid gap-4 sm:grid-cols-2\">\n          <div className=\"vp-card vp-card-hover rise p-5\">\n            <p className=\"klabel\">{t(\"modelPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.modelEstimate != null ? (\n                <CountUp value={r.modelEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightModel\")} : {(r.modelWeight * 100).toFixed(0)} %\n            </p>\n          </div>\n          <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"100ms\" }}>\n            <p className=\"klabel\">{t(\"compsPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.compsEstimate != null ? (\n                <CountUp value={r.compsEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightComps\")} : {((1 - r.modelWeight) * 100).toFixed(0)} % · {r.nCompsUsed} comps\n              {r.compsDispersionPct != null ? ` · ±${r.compsDispersionPct} %` : \"\"}\n            </p>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Marché local ---- */}\n      {ctx && (ctx.index.length >= 6 || ctx.sector || ctx.muniPercentile != null) && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Contexte de marché\" : \"Market context\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Le marché autour de cette propriété\" : \"The market around this property\"}\n          </h2>\n          <div className=\"mt-4 grid items-stretch gap-4 lg:grid-cols-[1.35fr_1fr]\">\n            {/* trajectoire du marché */}\n            {ctx.index.length >= 6 && (\n              <div className=\"vp-card rise p-5\">\n                <div className=\"flex flex-wrap items-center justify-between gap-2\">\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Indice de marché — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 en ${ctx.index[0].month.slice(0, 4)})`\n                      : `Market index — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 in ${ctx.index[0].month.slice(0, 4)})`}\n                  </p>\n                  <div className=\"flex gap-2\">\n                    {ctx.index12mPct != null && (\n                      <span className={`vp-mono rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${ctx.index12mPct >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n                        12 {fr ? \"mois\" : \"mo\"} {ctx.index12mPct >= 0 ? \"+\" : \"\"}\n                        {ctx.index12mPct.toFixed(1)} %\n                      </span>\n                    )}\n                    {ctx.indexSince2021Pct != null && (\n                      <span className=\"vp-mono rounded-[4px] border border-ink bg-surface-2 px-1.5 py-0.5 text-[10px] font-bold text-ink-2\">\n                        {ctx.index[0].month.slice(0, 4)}→ {ctx.indexSince2021Pct >= 0 ? \"+\" : \"\"}\n                        {ctx.indexSince2021Pct.toFixed(0)} %\n                      </span>\n                    )}\n                  </div>\n                </div>\n                <div className=\"mt-3\">\n                  <MarketIndexChart series={ctx.index} lang={lang} />\n                </div>\n                <p className=\"vp-mono mt-2 text-[10px] uppercase tracking-[0.05em] text-ink-3\">\n                  {fr\n                    ? \"Indice utilisé pour l'ajustement « marché » des comparables — même donnée, même calcul.\"\n                    : \"Index used for the comparables' market adjustment — same data, same math.\"}\n                </p>\n              </div>\n            )}\n            <div className=\"flex flex-col gap-4\">\n              {/* ventes du secteur */}\n              {ctx.sector && (\n                <div className=\"vp-card rise p-5\" style={{ animationDelay: \"80ms\" }}>\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Ventes réelles — rayon ${ctx.sector.radiusKm} km · 12 mois`\n                      : `Real sales — ${ctx.sector.radiusKm} km radius · 12 months`}\n                  </p>\n                  <div className=\"mt-3 grid grid-cols-3 gap-2\">\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Ventes\" : \"Sales\"}</p>\n                      <p className=\"v\">{ctx.sector.nSales12m}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Prix médian\" : \"Median price\"}</p>\n                      <p className=\"v\">{ctx.sector.medianPrice != null ? fmt(Math.round(ctx.sector.medianPrice), lang) : \"—\"}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">$/m² {fr ? \"médian\" : \"median\"}</p>\n                      <p className=\"v\">\n                        {ctx.sector.medianPpm2 != null\n                          ? locNum(Math.round(ctx.sector.medianPpm2), lang, { maxFrac: 0 })\n                          : \"—\"}\n                      </p>\n                    </div>\n                  </div>\n                  {ctx.sector.medianPrice != null && r.estimate > 0 && (\n                    <p className=\"vp-mono mt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-2\">\n                      {fr ? \"Cette propriété\" : \"This property\"} :{\" \"}\n                      <b className=\"text-ink\">\n                        {r.estimate >= ctx.sector.medianPrice ? \"+\" : \"−\"}\n                        {Math.abs((r.estimate / ctx.sector.medianPrice - 1) * 100).toFixed(0)} %\n                      </b>{\" \"}\n                      {fr ? \"vs médiane du secteur\" : \"vs sector median\"}\n                    </p>\n                  )}\n                </div>\n              )}\n              {/* position dans la municipalité */}\n              {ctx.muniPercentile != null && u?.municipalite && (\n                <div className=\"vp-card rise flex-1 p-5\" style={{ animationDelay: \"160ms\" }}>\n                  <p className=\"klabel\">\n                    {fr ? `Position dans ${u.municipalite}` : `Position in ${u.municipalite}`}\n                  </p>\n                  <div className=\"mt-3\">\n                    <PercentileBar pct={ctx.muniPercentile} lang={lang} />\n                  </div>\n                  <p className=\"mt-3 text-[13px] text-ink-2\">\n                    {fr ? (\n                      <>\n                        Vaut plus que{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> des{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        propriétés estimées de la municipalité.\n                      </>\n                    ) : (\n                      <>\n                        Worth more than{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> of the{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        estimated properties in the municipality.\n                      </>\n                    )}\n                  </p>\n                </div>\n              )}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Historique ---- */}\n      {u && u.history.some((h) => h.value != null) && (\n        <section>\n          <span className=\"kicker\">2021 → 2026</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {t(\"history\")}\n          </h2>\n          <div className=\"vp-card rise mt-4 p-5 sm:p-6\">\n            <div className=\"flex flex-col gap-2\">\n              {u.history.map((h, i) => {\n                const prev = i > 0 ? u.history[i - 1].value : null;\n                const yoy =\n                  h.value != null && prev != null && prev > 0\n                    ? ((h.value / prev - 1) * 100)\n                    : null;\n                return (\n                  <div key={h.year} className=\"hbar-row grid grid-cols-[44px_1fr_auto] items-center gap-2 sm:grid-cols-[52px_1fr_auto] sm:gap-3\">\n                    <span className=\"vp-mono text-[12px] font-bold\">{h.year}</span>\n                    <span className=\"hbar-track\">\n                      <span\n                        className=\"hbar-fill grow-x\"\n                        style={{\n                          width: `${((h.value ?? 0) / histMax) * 100}%`,\n                          animationDelay: `${i * 90}ms`,\n                          background: h.year === 2026 ? \"var(--ink)\" : undefined,\n                        }}\n                      />\n                    </span>\n                    <span className=\"vp-mono flex items-center justify-end gap-2 text-[12px] font-bold\">\n                      {fmt(h.value, lang)}\n                      {yoy != null && (\n                        <span className={`rounded-[4px] border border-ink px-1 py-px text-[9px] ${yoy >= 0 ? \"bg-lime-soft text-green-deep\" : \"bg-[var(--danger-soft)] text-[var(--danger)]\"}`}>\n                          {yoy >= 0 ? \"+\" : \"\"}\n                          {yoy.toFixed(0)} %\n                        </span>\n                      )}\n                    </span>\n                  </div>\n                );\n              })}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Plan des comparables (carte maison) ---- */}\n      <section>\n        <span className=\"kicker\">\n          {r.nCompsUsed} {fr ? \"ventes réelles\" : \"real sales\"}\n        </span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"compsTitle\")}\n        </h2>\n        <div className=\"mt-4 grid items-start gap-4 lg:grid-cols-[1.05fr_1fr]\">\n          <div className=\"overflow-hidden rounded-[10px] border-[1.5px] border-ink shadow-[8px_8px_0_rgba(20,24,20,0.08)]\">\n            <RadarMap\n              subject={{\n                lat: u?.lat ?? r.comps[0]?.lat ?? 46.81,\n                lng: u?.lng ?? r.comps[0]?.lng ?? -71.21,\n                label: u?.adresse ?? (fr ? \"Propriété estimée\" : \"Subject property\"),\n              }}\n              comps={r.comps.map((c) => ({\n                id: c.id,\n                lat: c.lat,\n                lng: c.lng,\n                label: `${c.street ?? \"\"}, ${c.city ?? \"\"}`,\n                price: fmt(c.amount, lang),\n                adjusted: fmt(Math.round(c.adjustedPrice), lang),\n                date: c.date,\n                distanceM: c.distanceM,\n                weight: c.weight,\n              }))}\n              highlight={activeComp}\n              onHover={setActiveComp}\n            />\n          </div>\n          <div className=\"src-wrap max-h-[560px] overflow-y-auto\">\n            <table className=\"src-table\">\n              <thead className=\"sticky top-0 z-10\">\n                <tr>\n                  <th>#</th>\n                  <th>{fr ? \"Vente\" : \"Sale\"}</th>\n                  <th className=\"text-right\">{t(\"adjusted\")}</th>\n                </tr>\n              </thead>\n              <tbody>\n                {r.comps.map((c, i) => (\n                  <tr\n                    key={c.id}\n                    onMouseEnter={() => setActiveComp(c.id)}\n                    onMouseLeave={() => setActiveComp(null)}\n                    className={activeComp === c.id ? \"!bg-lime-soft\" : \"\"}\n                  >\n                    <td>\n                      <span\n                        className={`vp-mono inline-flex h-6 w-6 items-center justify-center rounded-full border-[1.5px] border-ink text-[11px] font-bold ${\n                          activeComp === c.id ? \"bg-lime text-ink\" : \"bg-green text-paper\"\n                        }`}\n                      >\n                        {i + 1}\n                      </span>\n                    </td>\n                    <td>\n                      <p className=\"font-semibold leading-tight\">\n                        {c.street}, {c.city}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                        {c.floorArea && c.floorArea > 20\n                          ? ` (${locNum(Math.round(c.amount / c.floorArea), lang, { maxFrac: 0 })} $/m²)`\n                          : \"\"}\n                      </p>\n                      {(c.floorArea || c.yearBuilt) && (\n                        <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                          {[\n                            c.floorArea ? locNum(c.floorArea, lang, { unit: \"m²\" }) : null,\n                            c.yearBuilt ? `${fr ? \"constr.\" : \"built\"} ${c.yearBuilt}` : null,\n                          ]\n                            .filter(Boolean)\n                            .join(\" · \")}\n                        </p>\n                      )}\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] text-ink-2\">\n                        {t(\"adjTime\")} {signed(c.adjTime, lang)} · {t(\"adjArea\")} {signed(c.adjArea, lang)} ·{\" \"}\n                        {t(\"adjAge\")} {signed(c.adjAge, lang)}\n                      </p>\n                    </td>\n                    <td className=\"vp-display text-right font-bold\">\n                      {fmt(Math.round(c.adjustedPrice), lang)}\n                      <p className=\"vp-mono text-[10px] font-normal text-ink-3\">\n                        {t(\"weight\")} {(c.weight * 100).toFixed(0)} %\n                      </p>\n                    </td>\n                  </tr>\n                ))}\n              </tbody>\n            </table>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Lead ---- */}\n      <section className=\"rounded-[10px] border-[1.5px] border-ink bg-ink p-6 text-paper shadow-[8px_8px_0_rgba(20,24,20,0.2)] sm:p-8\">\n        <h2 className=\"vp-display text-[22px] font-bold uppercase tracking-[-0.02em] text-lime\">\n          {t(\"leadTitle\")}\n        </h2>\n        <div className=\"mt-4 max-w-xl\">\n          <LeadForm unitId={u?.id ?? 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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017ZPFWK4GYDsnwpLYdUzaAq","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"a03edcea-5a31-4e14-83ca-e2a1002f5eb7","timestamp":"2026-08-23T03:11:41.151Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","oldString":"  const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));\n  const fmt = (v: number) =>","newString":"  const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));\n  // les étiquettes (centrées, nowrap) restent dans la carte même aux extrêmes\n  const clampX = (v: number, lo: number, hi: number) => Math.max(lo, Math.min(hi, X(v)));\n  const fmt = (v: number) =>","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n/**\n * MetricViz — visualisations signature générées par les données du registre.\n * Chaque propriété produit ses propres dessins : façade SVG (étages, logements,\n * genre de construction), schéma de terrain à l'échelle (frontage × profondeur),\n * composition de valeur terrain/bâtiment, jauge de confiance, sparkline 2021-2026,\n * compteurs animés. SVG pur, palette encre/lime, aucun aléatoire : mêmes données,\n * même dessin.\n */\n\"use client\";\nimport { useEffect, useState } from \"react\";\n\n/** Nombre localisé (« 1 206,6 » en FR, « 1,206.6 » en EN). */\nexport function locNum(\n  v: number | null | undefined,\n  lang: string,\n  opts?: { unit?: string; maxFrac?: number }\n): string {\n  if (v == null) return \"—\";\n  const s = v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n    maximumFractionDigits: opts?.maxFrac ?? 1,\n  });\n  return opts?.unit ? `${s} ${opts.unit}` : s;\n}\n\n/* ---------------------------- compteur animé ---------------------------- */\n/** Rendu serveur = valeur finale (jamais « 0 $ » à l'écran) ; anime 0 → valeur au montage. */\nexport function CountUp({\n  value,\n  format,\n  duration = 900,\n}: {\n  value: number;\n  format: (v: number) => string;\n  duration?: number;\n}) {\n  const [display, setDisplay] = useState(value);\n\n  useEffect(() => {\n    if (window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches) return;\n    let raf = 0;\n    const t0 = performance.now();\n    const tick = (t: number) => {\n      const p = Math.min(1, (t - t0) / duration);\n      const eased = 1 - Math.pow(1 - p, 3);\n      setDisplay(value * eased);\n      if (p < 1) raf = requestAnimationFrame(tick);\n    };\n    raf = requestAnimationFrame(tick);\n    return () => cancelAnimationFrame(raf);\n  }, [value, duration]);\n\n  return <span>{format(display)}</span>;\n}\n\n/* --------------------------- jauge de confiance --------------------------- */\nexport function ConfidenceDial({\n  pct,\n  level,\n  label,\n}: {\n  pct: number;\n  level: \"A\" | \"B\" | \"C\" | \"D\";\n  label: string;\n}) {\n  const [mounted, setMounted] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setMounted(true), 60);\n    return () => clearTimeout(id);\n  }, []);\n  const R = 52;\n  const CIRC = Math.PI * R; // demi-cercle\n  const frac = mounted ? Math.max(0.02, Math.min(1, pct / 100)) : 0.02;\n  const color =\n    level === \"A\" ? \"var(--lime)\" : level === \"B\" ? \"var(--green)\" : level === \"C\" ? \"var(--amber)\" : \"var(--danger)\";\n  return (\n    <div className=\"flex flex-col items-center\" aria-label={`${label} : ${level} ${pct} %`}>\n      <svg viewBox=\"0 0 128 78\" className=\"w-[128px]\">\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"var(--surface-2)\" strokeWidth=\"12\" strokeLinecap=\"round\" />\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"rgba(20,24,20,0.85)\" strokeWidth=\"13.5\" strokeLinecap=\"round\" opacity=\"0.12\" />\n        <path\n          d=\"M 12 66 A 52 52 0 0 1 116 66\"\n          fill=\"none\"\n          stroke={color}\n          strokeWidth=\"12\"\n          strokeLinecap=\"round\"\n          strokeDasharray={`${CIRC * frac} ${CIRC}`}\n          style={{ transition: \"stroke-dasharray 1s cubic-bezier(.2,.8,.2,1)\" }}\n        />\n        {/* graduations */}\n        {[0, 0.25, 0.5, 0.75, 1].map((g) => {\n          const a = Math.PI * (1 - g);\n          const x1 = 64 + Math.cos(a) * 40;\n          const y1 = 66 - Math.sin(a) * 40;\n          const x2 = 64 + Math.cos(a) * 45;\n          const y2 = 66 - Math.sin(a) * 45;\n          return <line key={g} x1={x1} y1={y1} x2={x2} y2={y2} stroke=\"var(--ink)\" strokeWidth=\"1.4\" opacity=\"0.5\" />;\n        })}\n        <text x=\"64\" y=\"52\" textAnchor=\"middle\" fontSize=\"26\" fontWeight=\"700\" fill=\"var(--ink)\" fontFamily=\"var(--font-space-grotesk)\">\n          {level}\n        </text>\n        <text x=\"64\" y=\"70\" textAnchor=\"middle\" fontSize=\"10.5\" fontWeight=\"700\" fill=\"var(--ink-2)\" fontFamily=\"var(--font-jetbrains)\">\n          {pct} %\n        </text>\n      </svg>\n      <p className=\"klabel mt-1 text-center\">{label}</p>\n    </div>\n  );\n}\n\n/* --------------------------- façade de bâtiment --------------------------- */\n/** Terrain sans bâtiment : borne d'arpenteur et conifères, pas de fausse maison. */\nfunction VacantGlyph({ lang }: { lang: string }) {\n  const tree = (x: number, sc: number) => (\n    <g transform={`translate(${x}, 128) scale(${sc})`}>\n      <path d=\"M0,-46 L13,-14 L5,-14 L16,8 L-16,8 L-5,-14 L-13,-14 Z\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      <rect x=\"-2.5\" y=\"8\" width=\"5\" height=\"9\" fill=\"var(--ink)\" />\n    </g>\n  );\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain sans bâtiment\" : \"Vacant land\"}>\n      {tree(52, 1)}\n      {tree(96, 0.72)}\n      {tree(134, 0.9)}\n      {/* borne d'arpenteur */}\n      <g transform=\"translate(24, 128)\">\n        <rect x=\"-2.5\" y=\"-26\" width=\"5\" height=\"26\" fill=\"var(--ink)\" />\n        <rect x=\"-8\" y=\"-34\" width=\"16\" height=\"10\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n      </g>\n      <line x1=\"8\" y1=\"145\" x2=\"182\" y2=\"145\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"120\" textAnchor=\"middle\" fontSize=\"9\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n        {lang === \"fr\" ? \"SANS BÂTIMENT\" : \"NO BUILDING\"}\n      </text>\n    </svg>\n  );\n}\n\n/** Façade dessinée à partir du registre : étages, logements, genre de construction.\n *  Sans bâtiment au registre (ni aire, ni année, ni étages) → borne d'arpenteur. */\nexport function BuildingGlyph({\n  floors,\n  dwellings,\n  genre,\n  yearBuilt,\n  floorAreaM2,\n  lang = \"fr\",\n}: {\n  floors: number | null;\n  dwellings: number | null;\n  genre: string | null;\n  yearBuilt: number | null;\n  floorAreaM2?: number | null;\n  lang?: string;\n}) {\n  if (!floorAreaM2 && !yearBuilt && !floors && !dwellings) {\n    return <VacantGlyph lang={lang} />;\n  }\n  const nF = Math.max(1, Math.min(6, Math.round(floors ?? 1)));\n  const nD = Math.max(1, Math.min(24, Math.round(dwellings ?? 1)));\n  const winPerFloor = Math.max(1, Math.min(4, Math.ceil(nD / nF)));\n  const W = 190;\n  const bw = 64 + winPerFloor * 18;\n  const bx = (W - bw) / 2;\n  const fh = 24;\n  const groundY = 128;\n  const bodyTop = groundY - nF * fh;\n  const mansard = (genre ?? \"\").toLowerCase().includes(\"mansard\");\n  const flat = (genre ?? \"\").toLowerCase().includes(\"plain-pied\") && nF === 1;\n\n  const windows: React.ReactNode[] = [];\n  for (let f = 0; f < nF; f++) {\n    for (let wi = 0; wi < winPerFloor; wi++) {\n      const idx = f * winPerFloor + wi;\n      windows.push(\n        <rect\n          key={`${f}-${wi}`}\n          x={bx + 14 + wi * ((bw - 28) / winPerFloor) + ((bw - 28) / winPerFloor - 12) / 2}\n          y={bodyTop + f * fh + 6}\n          width=\"12\"\n          height=\"11\"\n          fill={idx < nD ? \"var(--lime)\" : \"var(--surface-2)\"}\n          stroke=\"var(--ink)\"\n          strokeWidth=\"1.2\"\n          className=\"vv-win\"\n          style={{ animationDelay: `${idx * 60}ms` }}\n        />\n      );\n    }\n  }\n\n  return (\n    <svg viewBox={`0 0 ${W} 150`} className=\"w-full max-w-[210px]\" aria-label=\"Silhouette du bâtiment selon le registre\">\n      {/* soleil-repère */}\n      <circle cx={W - 22} cy=\"22\" r=\"9\" fill=\"none\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      <circle cx={W - 22} cy=\"22\" r=\"3.5\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1\" />\n      {/* ombre décalée signature */}\n      <rect x={bx + 5} y={bodyTop + 5} width={bw} height={nF * fh} fill=\"rgba(20,24,20,0.12)\" />\n      {/* corps */}\n      <rect x={bx} y={bodyTop} width={bw} height={nF * fh} fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      {/* séparations d'étages */}\n      {Array.from({ length: nF - 1 }, (_, i) => (\n        <line key={i} x1={bx} y1={bodyTop + (i + 1) * fh} x2={bx + bw} y2={bodyTop + (i + 1) * fh} stroke=\"var(--ink)\" strokeWidth=\"1\" strokeDasharray=\"3 3\" opacity=\"0.4\" />\n      ))}\n      {/* toit */}\n      {flat ? (\n        <rect x={bx - 6} y={bodyTop - 8} width={bw + 12} height=\"8\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : mansard ? (\n        <path d={`M ${bx - 6} ${bodyTop} L ${bx + 10} ${bodyTop - 20} L ${bx + bw - 10} ${bodyTop - 20} L ${bx + bw + 6} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : (\n        <path d={`M ${bx - 8} ${bodyTop} L ${bx + bw / 2} ${bodyTop - 24} L ${bx + bw + 8} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      )}\n      {/* cheminée */}\n      <rect x={bx + bw - 22} y={bodyTop - (flat ? 20 : 30)} width=\"9\" height={flat ? 14 : 18} fill=\"var(--ink)\" />\n      {windows}\n      {/* porte */}\n      <rect x={bx + bw / 2 - 7} y={groundY - 17} width=\"14\" height=\"17\" fill=\"var(--ink)\" />\n      <circle cx={bx + bw / 2 + 3.5} cy={groundY - 8} r=\"1.4\" fill=\"var(--lime)\" />\n      {/* sol */}\n      <line x1=\"8\" y1={groundY} x2={W - 8} y2={groundY} stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <line x1=\"8\" y1={groundY + 4} x2={W - 8} y2={groundY + 4} stroke=\"var(--ink)\" strokeWidth=\"1\" opacity=\"0.3\" />\n      {/* millésime gravé */}\n      {yearBuilt && (\n        <text x={W / 2} y=\"146\" textAnchor=\"middle\" fontSize=\"10\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n          ANNO {yearBuilt}\n        </text>\n      )}\n    </svg>\n  );\n}\n\n/* ----------------------------- schéma du terrain ----------------------------- */\n/** Terrain à l'échelle réelle : frontage × profondeur déduite de la superficie. */\nexport function LotDiagram({\n  areaM2,\n  frontageM,\n  footprintM2,\n  lang,\n  isCondo,\n}: {\n  areaM2: number | null;\n  frontageM: number | null;\n  footprintM2: number | null;\n  lang: string;\n  isCondo?: boolean;\n}) {\n  if (!areaM2 || areaM2 <= 0) {\n    return (\n      <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain non applicable\" : \"Lot not applicable\"}>\n        <rect x=\"30\" y=\"30\" width=\"130\" height=\"86\" fill=\"var(--surface-2)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" strokeDasharray=\"5 5\" rx=\"6\" />\n        <text x=\"95\" y=\"66\" textAnchor=\"middle\" fontSize=\"20\" fill=\"var(--ink-3)\">⌂</text>\n        <text x=\"95\" y=\"90\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"1\">\n          {isCondo\n            ? lang === \"fr\" ? \"QUOTE-PART DE COPROPRIÉTÉ\" : \"CONDOMINIUM SHARE\"\n            : lang === \"fr\" ? \"TERRAIN NON INSCRIT AU RÔLE\" : \"LOT NOT ON ROLL\"}\n        </text>\n        <text x=\"95\" y=\"104\" textAnchor=\"middle\" fontSize=\"7\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n          {isCondo\n            ? lang === \"fr\" ? \"terrain commun indivis\" : \"undivided common land\"\n            : \"—\"}\n        </text>\n      </svg>\n    );\n  }\n  const front = frontageM && frontageM > 0 ? frontageM : Math.sqrt(areaM2);\n  const depth = areaM2 / front;\n  const ratio = Math.max(0.25, Math.min(4, depth / front));\n  const maxW = 150;\n  const maxH = 108;\n  let w = maxW;\n  let h = w * ratio;\n  if (h > maxH) {\n    h = maxH;\n    w = h / ratio;\n  }\n  const x = (190 - w) / 2;\n  const y = 118 - h;\n  // empreinte du bâtiment, à l'échelle de la superficie\n  const fpFrac = footprintM2 ? Math.min(0.8, footprintM2 / areaM2) : 0;\n  const fw = w * Math.sqrt(fpFrac) * 0.9;\n  const fh = h * Math.sqrt(fpFrac) * 0.9;\n  const fmtM = (v: number) => `${v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { maximumFractionDigits: 1 })} m`;\n\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label=\"Schéma du terrain à l'échelle\">\n      <defs>\n        <pattern id=\"hatch\" width=\"7\" height=\"7\" patternTransform=\"rotate(45)\" patternUnits=\"userSpaceOnUse\">\n          <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"7\" stroke=\"var(--green)\" strokeWidth=\"1\" opacity=\"0.35\" />\n        </pattern>\n      </defs>\n      {/* ombre + parcelle */}\n      <rect x={x + 4} y={y + 4} width={w} height={h} fill=\"rgba(20,24,20,0.12)\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"var(--lime-soft)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"url(#hatch)\" />\n      {/* empreinte bâtiment */}\n      {fpFrac > 0.005 && (\n        <rect x={x + (w - fw) / 2} y={y + h - fh - h * 0.08} width={fw} height={fh} fill=\"var(--ink)\" rx=\"2\">\n          <title>{lang === \"fr\" ? \"Empreinte approximative du bâtiment\" : \"Approximate building footprint\"}</title>\n        </rect>\n      )}\n      {/* cotes : frontage */}\n      <line x1={x} y1={y + h + 12} x2={x + w} y2={y + h + 12} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x} y1={y + h + 8} x2={x} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x + w} y1={y + h + 8} x2={x + w} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x + w / 2} y={y + h + 24} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n        {frontageM ? fmtM(front) : `≈ ${fmtM(front)}`}\n      </text>\n      {/* cotes : profondeur */}\n      <line x1={x - 10} y1={y} x2={x - 10} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y} x2={x - 6} y2={y} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y + h} x2={x - 6} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x - 16} y={y + h / 2} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\" transform={`rotate(-90 ${x - 16} ${y + h / 2})`}>\n        ≈ {fmtM(depth)}\n      </text>\n      {/* rue */}\n      <line x1=\"8\" y1=\"136\" x2=\"182\" y2=\"136\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"147\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\" letterSpacing=\"3\">\n        {lang === \"fr\" ? \"RUE\" : \"STREET\"}\n      </text>\n    </svg>\n  );\n}\n\n/* ------------------------- composition de la valeur ------------------------- */\nexport function ValueSplit({\n  land,\n  building,\n  previous,\n  total,\n  lang,\n  labels,\n}: {\n  land: number | null;\n  building: number | null;\n  previous: number | null;\n  total: number | null;\n  lang: string;\n  labels: { land: string; building: string; previous: string };\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  const l = land ?? 0;\n  const b = building ?? 0;\n  const sum = l + b;\n  if (sum <= 0) return null;\n  const lPct = (l / sum) * 100;\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { style: \"currency\", currency: \"CAD\", maximumFractionDigits: 0 }).format(v);\n  const delta = previous && previous > 0 && total ? ((total / previous - 1) * 100) : null;\n  return (\n    <div>\n      <div className=\"flex h-9 w-full overflow-hidden rounded-md border-[1.5px] border-ink\">\n        {lPct >= 1 && (\n          <div\n            className=\"flex items-center justify-center overflow-hidden bg-green transition-[width] duration-1000 ease-out\"\n            style={{ width: on ? `${lPct}%` : \"2%\" }}\n          >\n            {lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-paper\">{Math.round(lPct)} %</span>\n            )}\n          </div>\n        )}\n        {lPct <= 99 && (\n          <div className=\"flex flex-1 items-center justify-center overflow-hidden bg-ink\">\n            {100 - lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-lime\">{Math.round(100 - lPct)} %</span>\n            )}\n          </div>\n        )}\n      </div>\n      <div className=\"vp-mono mt-2 flex flex-wrap items-center gap-x-4 gap-y-1 text-[10.5px] uppercase tracking-[0.05em]\">\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-green\" />\n          {labels.land} {fmt(l)}\n        </span>\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-ink\" />\n          {labels.building} {fmt(b)}\n        </span>\n      </div>\n      {delta != null && (\n        <p className=\"vp-mono mt-2.5 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {labels.previous} {fmt(previous!)}{\" \"}\n          <span className={`ml-1 rounded-[4px] border border-ink px-1.5 py-0.5 font-bold ${delta >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n            {delta >= 0 ? \"+\" : \"\"}\n            {delta.toFixed(1)} %\n          </span>\n        </p>\n      )}\n    </div>\n  );\n}\n\n/* -------------------------------- sparkline -------------------------------- */\nexport function Sparkline({\n  history,\n  height = 44,\n}: {\n  history: { year: number; value: number | null }[];\n  height?: number;\n}) {\n  const pts = history.filter((h) => h.value != null) as { year: number; value: number }[];\n  if (pts.length < 2) return null;\n  const W = 132;\n  const min = Math.min(...pts.map((p) => p.value));\n  const max = Math.max(...pts.map((p) => p.value));\n  const span = max - min || 1;\n  const xy = pts.map((p, i) => [\n    6 + (i / (pts.length - 1)) * (W - 12),\n    height - 8 - ((p.value - min) / span) * (height - 18),\n  ]);\n  const line = xy.map(([x, y]) => `${x},${y}`).join(\" \");\n  const area = `${xy[0][0]},${height - 4} ${line} ${xy[xy.length - 1][0]},${height - 4}`;\n  const growth = ((pts[pts.length - 1].value / pts[0].value - 1) * 100);\n  const [x2, y2] = xy[xy.length - 1];\n  return (\n    <div className=\"flex items-end gap-2\">\n      <svg viewBox={`0 0 ${W} ${height}`} width={W} height={height} aria-label={`Évolution ${pts[0].year}-${pts[pts.length - 1].year}`}>\n        <polygon points={area} fill=\"var(--lime-soft)\" />\n        <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n        {xy.slice(0, -1).map(([x, y], i) => (\n          <circle key={i} cx={x} cy={y} r=\"2\" fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"1.1\" />\n        ))}\n        <circle cx={x2} cy={y2} r=\"3.6\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      </svg>\n      <span className={`vp-mono mb-0.5 rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${growth >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n        {growth >= 0 ? \"+\" : \"\"}\n        {growth.toFixed(0)} %\n      </span>\n    </div>\n  );\n}\n\n/* ------------------------- fourchette visuelle P10-P90 ------------------------- */\n/** Barre de fourchette : segment P10→P90, losange = estimation, tiret = rôle. */\nexport function RangeBar({\n  low,\n  estimate,\n  high,\n  role,\n  lang,\n}: {\n  low: number;\n  estimate: number;\n  high: number;\n  role: number | null;\n  lang: string;\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  if (!(high > low) || !Number.isFinite(estimate)) return null;\n  const lo = Math.min(low, role ?? low);\n  const hi = Math.max(high, role ?? high);\n  const pad = (hi - lo) * 0.09 || hi * 0.05;\n  const min = lo - pad;\n  const max = hi + pad;\n  const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n      notation: v >= 1_000_000 ? \"compact\" : \"standard\",\n    }).format(v);\n  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;\n  return (\n    <div aria-label={`${fmt(low)} – ${fmt(high)}`}>\n      <div className=\"relative h-[64px]\">\n        {/* étiquette estimation */}\n        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <span className=\"vp-mono rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\">\n            {lang === \"fr\" ? \"ESTIMATION\" : \"ESTIMATE\"} {fmt(estimate)}\n          </span>\n        </div>\n        {/* piste */}\n        <div className=\"absolute left-0 right-0 top-[30px] h-[14px] rounded-[7px] border-[1.5px] border-ink bg-surface-2\" />\n        {/* segment P10-P90 */}\n        <div\n          className=\"absolute top-[30px] h-[14px] origin-left rounded-[7px] border-[1.5px] border-ink transition-transform duration-1000 ease-out\"\n          style={{\n            left: `${X(low)}%`,\n            width: `${Math.max(2, X(high) - X(low))}%`,\n            transform: on ? \"scaleX(1)\" : \"scaleX(0.05)\",\n            background:\n              \"repeating-linear-gradient(-45deg, var(--lime-soft), var(--lime-soft) 5px, rgba(255,81,72,0.35) 5px, rgba(255,81,72,0.35) 7px)\",\n          }}\n        />\n        {/* marqueur estimation (losange) */}\n        <div\n          className=\"absolute top-[29px] -translate-x-1/2 transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <div className=\"h-[16px] w-[16px] rotate-45 border-[1.8px] border-ink bg-lime\" />\n        </div>\n        {/* marqueur rôle */}\n        {role != null && role > 0 && (\n          <div\n            className=\"absolute top-[22px] -translate-x-1/2 text-center\"\n            style={{ left: `${X(role)}%` }}\n          >\n            <div className=\"mx-auto h-[30px] w-0 border-l-[2px] border-dashed border-ink opacity-70\" />\n            {!roleClose && (\n              <span className=\"vp-mono whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\">\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </div>\n        )}\n        {/* bornes */}\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(low)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(high)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>\n      </div>\n    </div>\n  );\n}\n\n/* --------------------------- courbe d'indice de marché --------------------------- */\n/** Indice mensuel rebasé à 100 au premier mois — la trajectoire du marché local. */\nexport function MarketIndexChart({\n  series,\n  lang,\n}: {\n  series: { month: string; idx: number }[];\n  lang: string;\n}) {\n  if (series.length < 6) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const W = 640;\n  const H = 200;\n  const padL = 40;\n  const padR = 52;\n  const padT = 18;\n  const padB = 28;\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const x = (i: number) => padL + (i / (pts.length - 1)) * (W - padL - padR);\n  const y = (v: number) => padT + (1 - (v - min) / span) * (H - padT - padB);\n  const line = pts.map((v, i) => `${x(i).toFixed(1)},${y(v).toFixed(1)}`).join(\" \");\n  const area = `${x(0)},${H - padB} ${line} ${x(pts.length - 1)},${H - padB}`;\n  // graduations : janvier de chaque année\n  const years = series\n    .map((p, i) => ({ i, month: p.month }))\n    .filter((p) => p.month.endsWith(\"-01\"));\n  // lignes horizontales : min / 100 / max arrondis\n  const gridVals = [...new Set([Math.round(min), 100, Math.round(max)])].filter(\n    (v) => v >= min - 1 && v <= max + 1\n  );\n  const lastV = pts[pts.length - 1];\n  return (\n    <svg\n      viewBox={`0 0 ${W} ${H}`}\n      className=\"h-auto w-full\"\n      role=\"img\"\n      aria-label={\n        lang === \"fr\"\n          ? `Indice de marché ${series[0].month} à ${series[series.length - 1].month}`\n          : `Market index ${series[0].month} to ${series[series.length - 1].month}`\n      }\n    >\n      {gridVals.map((v) => (\n        <g key={v}>\n          <line\n            x1={padL}\n            y1={y(v)}\n            x2={W - padR}\n            y2={y(v)}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            strokeDasharray=\"3 5\"\n            opacity={v === 100 ? 0.45 : 0.18}\n          />\n          <text\n            x={padL - 6}\n            y={y(v) + 3.5}\n            textAnchor=\"end\"\n            fontSize=\"10\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-3)\"\n          >\n            {v}\n          </text>\n        </g>\n      ))}\n      {years.map((p) => (\n        <g key={p.month}>\n          <line\n            x1={x(p.i)}\n            y1={padT}\n            x2={x(p.i)}\n            y2={H - padB}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            opacity=\"0.1\"\n          />\n          <text\n            x={x(p.i)}\n            y={H - 10}\n            textAnchor=\"middle\"\n            fontSize=\"10\"\n            fontWeight=\"700\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-2)\"\n          >\n            {p.month.slice(0, 4)}\n          </text>\n        </g>\n      ))}\n      <polygon points={area} fill=\"var(--lime-soft)\" opacity=\"0.8\" />\n      <polyline\n        points={line}\n        fill=\"none\"\n        stroke=\"var(--green)\"\n        strokeWidth=\"2.6\"\n        strokeLinejoin=\"round\"\n        strokeLinecap=\"round\"\n      />\n      <circle\n        cx={x(pts.length - 1)}\n        cy={y(lastV)}\n        r=\"5\"\n        fill=\"var(--lime)\"\n        stroke=\"var(--ink)\"\n        strokeWidth=\"1.8\"\n      />\n      <text\n        x={x(pts.length - 1) + 9}\n        y={y(lastV) + 4}\n        fontSize=\"11.5\"\n        fontWeight=\"700\"\n        fontFamily=\"var(--font-jetbrains)\"\n        fill=\"var(--ink)\"\n      >\n        {Math.round(lastV)}\n      </text>\n    </svg>\n  );\n}\n\n/* ----------------------- position dans la municipalité ----------------------- */\n/** Distribution stylisée (cloche) : les barres sous le percentile sont pleines. */\nexport function PercentileBar({\n  pct,\n  lang,\n}: {\n  pct: number;\n  lang: string;\n}) {\n  const N = 36;\n  const bars = Array.from({ length: N }, (_, i) => {\n    const z = (i / (N - 1) - 0.42) / 0.24; // cloche légèrement asymétrique (droite étirée)\n    return Math.exp(-0.5 * z * z) * (i / N > 0.5 ? 1 + (i / N - 0.5) * 0.35 : 1);\n  });\n  const maxB = Math.max(...bars);\n  const p = Math.max(0, Math.min(100, pct));\n  return (\n    <div aria-label={`${Math.round(p)} %`}>\n      <div className=\"relative flex h-[72px] items-end px-0.5\" style={{ gap: \"2px\" }}>\n        {bars.map((b, i) => {\n          const filled = (i + 0.5) / N <= p / 100;\n          return (\n            <div\n              key={i}\n              className=\"min-w-0 flex-1 rounded-t-[2px] border border-ink transition-colors\"\n              style={{\n                height: `${8 + (b / maxB) * 88}%`,\n                background: filled ? \"var(--lime)\" : \"var(--surface-2)\",\n                borderWidth: \"1px\",\n                opacity: filled ? 1 : 0.55,\n              }}\n            />\n          );\n        })}\n        <div\n          className=\"absolute bottom-0 top-[-6px] w-0 border-l-[2px] border-ink\"\n          style={{ left: `${p}%` }}\n        >\n          <span className=\"vp-mono absolute -top-1 left-1.5 whitespace-nowrap rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\" style={p > 55 ? { left: \"auto\", right: \"6px\" } : undefined}>\n            {lang === \"fr\" ? \"P\" : \"P\"}\n            {Math.round(p)}\n          </span>\n        </div>\n      </div>\n    </div>\n  );\n}\n\n/* ------------------------ mini-courbe d'indice (accueil) ------------------------ */\nexport function IndexSpark({\n  series,\n  width = 150,\n  height = 46,\n}: {\n  series: { month: string; idx: number }[];\n  width?: number;\n  height?: number;\n}) {\n  if (series.length < 2) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const xy = pts.map((v, i) => [\n    4 + (i / (pts.length - 1)) * (width - 8),\n    height - 6 - ((v - min) / span) * (height - 14),\n  ]);\n  const line = xy.map(([x, y]) => `${x.toFixed(1)},${y.toFixed(1)}`).join(\" \");\n  const area = `${xy[0][0]},${height - 3} ${line} ${xy[xy.length - 1][0]},${height - 3}`;\n  const [lx, ly] = xy[xy.length - 1];\n  return (\n    <svg viewBox={`0 0 ${width} ${height}`} className=\"h-auto w-full\" aria-hidden=\"true\">\n      <polygon points={area} fill=\"var(--lime-soft)\" />\n      <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n      <circle cx={lx} cy={ly} r=\"3.4\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n    </svg>\n  );\n}\n\n/* ------------------------------ frise temporelle ------------------------------ */\nexport function EraLine({ year, lang }: { year: number | null; lang: string }) {\n  if (!year || year < 1750) return null;\n  const min = Math.min(1900, Math.floor((year - 10) / 50) * 50);\n  const max = 2026;\n  const frac = Math.max(0, Math.min(1, (year - min) / (max - min)));\n  const W = 190;\n  const x = 12 + frac * (W - 24);\n  const age = 2026 - year;\n  return (\n    <svg viewBox={`0 0 ${W} 40`} className=\"w-full max-w-[210px]\" aria-label={`Construction ${year}`}>\n      <line x1=\"12\" y1=\"24\" x2={W - 12} y2=\"24\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      {[...new Set([min, Math.round((min + max) / 2 / 50) * 50, 2000])].map((yr) => {\n        const gx = 12 + ((yr - min) / (max - min)) * (W - 24);\n        return (\n          <g key={yr}>\n            <line x1={gx} y1=\"20\" x2={gx} y2=\"28\" stroke=\"var(--ink)\" strokeWidth=\"1.2\" opacity=\"0.5\" />\n            <text x={gx} y=\"38\" textAnchor=\"middle\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n              {yr}\n            </text>\n          </g>\n        );\n      })}\n      <g transform={`translate(${x}, 24)`}>\n        <rect x=\"-7\" y=\"-7\" width=\"14\" height=\"14\" transform=\"rotate(45)\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n        <text y=\"-13\" textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n          {year}\n        </text>\n      </g>\n      <text x={W - 12} y=\"12\" textAnchor=\"end\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n        {age} {lang === \"fr\" ? \"ANS\" : \"YRS\"}\n      </text>\n    </svg>\n  );\n}\n","structuredPatch":[{"oldStart":460,"oldLines":6,"newStart":460,"newLines":8,"lines":["   const min = lo - pad;","   const max = hi + pad;","   const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));","+  // les étiquettes (centrées, nowrap) restent dans la carte même aux extrêmes","+  const clampX = (v: number, lo: number, hi: number) => Math.max(lo, Math.min(hi, X(v)));","   const fmt = (v: number) =>","     new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {","       style: \"currency\","]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwxHAPC6WZJfPQR4vG5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019FXnXfj2A5iyDrkjY4ef7A","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","old_string":"        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >","new_string":"        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? clampX(estimate, 18, 82) : 50}%` }}\n        >"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":446,"cache_read_input_tokens":146925,"cache_creation":{"ephemeral_5m_input_tokens":446,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"5be11c7e-d3f0-4a5b-85a7-ec09ec73a244","request_id":"req_011CeJwxDeYnV1L7rK2pjamD"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019FXnXfj2A5iyDrkjY4ef7A","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"a5692bc3-f2e2-4cb4-a810-73efa202d527","timestamp":"2026-08-23T03:11:48.159Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","oldString":"        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >","newString":"        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? clampX(estimate, 18, 82) : 50}%` }}\n        >","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n/**\n * MetricViz — visualisations signature générées par les données du registre.\n * Chaque propriété produit ses propres dessins : façade SVG (étages, logements,\n * genre de construction), schéma de terrain à l'échelle (frontage × profondeur),\n * composition de valeur terrain/bâtiment, jauge de confiance, sparkline 2021-2026,\n * compteurs animés. SVG pur, palette encre/lime, aucun aléatoire : mêmes données,\n * même dessin.\n */\n\"use client\";\nimport { useEffect, useState } from \"react\";\n\n/** Nombre localisé (« 1 206,6 » en FR, « 1,206.6 » en EN). */\nexport function locNum(\n  v: number | null | undefined,\n  lang: string,\n  opts?: { unit?: string; maxFrac?: number }\n): string {\n  if (v == null) return \"—\";\n  const s = v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n    maximumFractionDigits: opts?.maxFrac ?? 1,\n  });\n  return opts?.unit ? `${s} ${opts.unit}` : s;\n}\n\n/* ---------------------------- compteur animé ---------------------------- */\n/** Rendu serveur = valeur finale (jamais « 0 $ » à l'écran) ; anime 0 → valeur au montage. */\nexport function CountUp({\n  value,\n  format,\n  duration = 900,\n}: {\n  value: number;\n  format: (v: number) => string;\n  duration?: number;\n}) {\n  const [display, setDisplay] = useState(value);\n\n  useEffect(() => {\n    if (window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches) return;\n    let raf = 0;\n    const t0 = performance.now();\n    const tick = (t: number) => {\n      const p = Math.min(1, (t - t0) / duration);\n      const eased = 1 - Math.pow(1 - p, 3);\n      setDisplay(value * eased);\n      if (p < 1) raf = requestAnimationFrame(tick);\n    };\n    raf = requestAnimationFrame(tick);\n    return () => cancelAnimationFrame(raf);\n  }, [value, duration]);\n\n  return <span>{format(display)}</span>;\n}\n\n/* --------------------------- jauge de confiance --------------------------- */\nexport function ConfidenceDial({\n  pct,\n  level,\n  label,\n}: {\n  pct: number;\n  level: \"A\" | \"B\" | \"C\" | \"D\";\n  label: string;\n}) {\n  const [mounted, setMounted] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setMounted(true), 60);\n    return () => clearTimeout(id);\n  }, []);\n  const R = 52;\n  const CIRC = Math.PI * R; // demi-cercle\n  const frac = mounted ? Math.max(0.02, Math.min(1, pct / 100)) : 0.02;\n  const color =\n    level === \"A\" ? \"var(--lime)\" : level === \"B\" ? \"var(--green)\" : level === \"C\" ? \"var(--amber)\" : \"var(--danger)\";\n  return (\n    <div className=\"flex flex-col items-center\" aria-label={`${label} : ${level} ${pct} %`}>\n      <svg viewBox=\"0 0 128 78\" className=\"w-[128px]\">\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"var(--surface-2)\" strokeWidth=\"12\" strokeLinecap=\"round\" />\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"rgba(20,24,20,0.85)\" strokeWidth=\"13.5\" strokeLinecap=\"round\" opacity=\"0.12\" />\n        <path\n          d=\"M 12 66 A 52 52 0 0 1 116 66\"\n          fill=\"none\"\n          stroke={color}\n          strokeWidth=\"12\"\n          strokeLinecap=\"round\"\n          strokeDasharray={`${CIRC * frac} ${CIRC}`}\n          style={{ transition: \"stroke-dasharray 1s cubic-bezier(.2,.8,.2,1)\" }}\n        />\n        {/* graduations */}\n        {[0, 0.25, 0.5, 0.75, 1].map((g) => {\n          const a = Math.PI * (1 - g);\n          const x1 = 64 + Math.cos(a) * 40;\n          const y1 = 66 - Math.sin(a) * 40;\n          const x2 = 64 + Math.cos(a) * 45;\n          const y2 = 66 - Math.sin(a) * 45;\n          return <line key={g} x1={x1} y1={y1} x2={x2} y2={y2} stroke=\"var(--ink)\" strokeWidth=\"1.4\" opacity=\"0.5\" />;\n        })}\n        <text x=\"64\" y=\"52\" textAnchor=\"middle\" fontSize=\"26\" fontWeight=\"700\" fill=\"var(--ink)\" fontFamily=\"var(--font-space-grotesk)\">\n          {level}\n        </text>\n        <text x=\"64\" y=\"70\" textAnchor=\"middle\" fontSize=\"10.5\" fontWeight=\"700\" fill=\"var(--ink-2)\" fontFamily=\"var(--font-jetbrains)\">\n          {pct} %\n        </text>\n      </svg>\n      <p className=\"klabel mt-1 text-center\">{label}</p>\n    </div>\n  );\n}\n\n/* --------------------------- façade de bâtiment --------------------------- */\n/** Terrain sans bâtiment : borne d'arpenteur et conifères, pas de fausse maison. */\nfunction VacantGlyph({ lang }: { lang: string }) {\n  const tree = (x: number, sc: number) => (\n    <g transform={`translate(${x}, 128) scale(${sc})`}>\n      <path d=\"M0,-46 L13,-14 L5,-14 L16,8 L-16,8 L-5,-14 L-13,-14 Z\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      <rect x=\"-2.5\" y=\"8\" width=\"5\" height=\"9\" fill=\"var(--ink)\" />\n    </g>\n  );\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain sans bâtiment\" : \"Vacant land\"}>\n      {tree(52, 1)}\n      {tree(96, 0.72)}\n      {tree(134, 0.9)}\n      {/* borne d'arpenteur */}\n      <g transform=\"translate(24, 128)\">\n        <rect x=\"-2.5\" y=\"-26\" width=\"5\" height=\"26\" fill=\"var(--ink)\" />\n        <rect x=\"-8\" y=\"-34\" width=\"16\" height=\"10\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n      </g>\n      <line x1=\"8\" y1=\"145\" x2=\"182\" y2=\"145\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"120\" textAnchor=\"middle\" fontSize=\"9\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n        {lang === \"fr\" ? \"SANS BÂTIMENT\" : \"NO BUILDING\"}\n      </text>\n    </svg>\n  );\n}\n\n/** Façade dessinée à partir du registre : étages, logements, genre de construction.\n *  Sans bâtiment au registre (ni aire, ni année, ni étages) → borne d'arpenteur. */\nexport function BuildingGlyph({\n  floors,\n  dwellings,\n  genre,\n  yearBuilt,\n  floorAreaM2,\n  lang = \"fr\",\n}: {\n  floors: number | null;\n  dwellings: number | null;\n  genre: string | null;\n  yearBuilt: number | null;\n  floorAreaM2?: number | null;\n  lang?: string;\n}) {\n  if (!floorAreaM2 && !yearBuilt && !floors && !dwellings) {\n    return <VacantGlyph lang={lang} />;\n  }\n  const nF = Math.max(1, Math.min(6, Math.round(floors ?? 1)));\n  const nD = Math.max(1, Math.min(24, Math.round(dwellings ?? 1)));\n  const winPerFloor = Math.max(1, Math.min(4, Math.ceil(nD / nF)));\n  const W = 190;\n  const bw = 64 + winPerFloor * 18;\n  const bx = (W - bw) / 2;\n  const fh = 24;\n  const groundY = 128;\n  const bodyTop = groundY - nF * fh;\n  const mansard = (genre ?? \"\").toLowerCase().includes(\"mansard\");\n  const flat = (genre ?? \"\").toLowerCase().includes(\"plain-pied\") && nF === 1;\n\n  const windows: React.ReactNode[] = [];\n  for (let f = 0; f < nF; f++) {\n    for (let wi = 0; wi < winPerFloor; wi++) {\n      const idx = f * winPerFloor + wi;\n      windows.push(\n        <rect\n          key={`${f}-${wi}`}\n          x={bx + 14 + wi * ((bw - 28) / winPerFloor) + ((bw - 28) / winPerFloor - 12) / 2}\n          y={bodyTop + f * fh + 6}\n          width=\"12\"\n          height=\"11\"\n          fill={idx < nD ? \"var(--lime)\" : \"var(--surface-2)\"}\n          stroke=\"var(--ink)\"\n          strokeWidth=\"1.2\"\n          className=\"vv-win\"\n          style={{ animationDelay: `${idx * 60}ms` }}\n        />\n      );\n    }\n  }\n\n  return (\n    <svg viewBox={`0 0 ${W} 150`} className=\"w-full max-w-[210px]\" aria-label=\"Silhouette du bâtiment selon le registre\">\n      {/* soleil-repère */}\n      <circle cx={W - 22} cy=\"22\" r=\"9\" fill=\"none\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      <circle cx={W - 22} cy=\"22\" r=\"3.5\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1\" />\n      {/* ombre décalée signature */}\n      <rect x={bx + 5} y={bodyTop + 5} width={bw} height={nF * fh} fill=\"rgba(20,24,20,0.12)\" />\n      {/* corps */}\n      <rect x={bx} y={bodyTop} width={bw} height={nF * fh} fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      {/* séparations d'étages */}\n      {Array.from({ length: nF - 1 }, (_, i) => (\n        <line key={i} x1={bx} y1={bodyTop + (i + 1) * fh} x2={bx + bw} y2={bodyTop + (i + 1) * fh} stroke=\"var(--ink)\" strokeWidth=\"1\" strokeDasharray=\"3 3\" opacity=\"0.4\" />\n      ))}\n      {/* toit */}\n      {flat ? (\n        <rect x={bx - 6} y={bodyTop - 8} width={bw + 12} height=\"8\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : mansard ? (\n        <path d={`M ${bx - 6} ${bodyTop} L ${bx + 10} ${bodyTop - 20} L ${bx + bw - 10} ${bodyTop - 20} L ${bx + bw + 6} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : (\n        <path d={`M ${bx - 8} ${bodyTop} L ${bx + bw / 2} ${bodyTop - 24} L ${bx + bw + 8} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      )}\n      {/* cheminée */}\n      <rect x={bx + bw - 22} y={bodyTop - (flat ? 20 : 30)} width=\"9\" height={flat ? 14 : 18} fill=\"var(--ink)\" />\n      {windows}\n      {/* porte */}\n      <rect x={bx + bw / 2 - 7} y={groundY - 17} width=\"14\" height=\"17\" fill=\"var(--ink)\" />\n      <circle cx={bx + bw / 2 + 3.5} cy={groundY - 8} r=\"1.4\" fill=\"var(--lime)\" />\n      {/* sol */}\n      <line x1=\"8\" y1={groundY} x2={W - 8} y2={groundY} stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <line x1=\"8\" y1={groundY + 4} x2={W - 8} y2={groundY + 4} stroke=\"var(--ink)\" strokeWidth=\"1\" opacity=\"0.3\" />\n      {/* millésime gravé */}\n      {yearBuilt && (\n        <text x={W / 2} y=\"146\" textAnchor=\"middle\" fontSize=\"10\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n          ANNO {yearBuilt}\n        </text>\n      )}\n    </svg>\n  );\n}\n\n/* ----------------------------- schéma du terrain ----------------------------- */\n/** Terrain à l'échelle réelle : frontage × profondeur déduite de la superficie. */\nexport function LotDiagram({\n  areaM2,\n  frontageM,\n  footprintM2,\n  lang,\n  isCondo,\n}: {\n  areaM2: number | null;\n  frontageM: number | null;\n  footprintM2: number | null;\n  lang: string;\n  isCondo?: boolean;\n}) {\n  if (!areaM2 || areaM2 <= 0) {\n    return (\n      <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain non applicable\" : \"Lot not applicable\"}>\n        <rect x=\"30\" y=\"30\" width=\"130\" height=\"86\" fill=\"var(--surface-2)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" strokeDasharray=\"5 5\" rx=\"6\" />\n        <text x=\"95\" y=\"66\" textAnchor=\"middle\" fontSize=\"20\" fill=\"var(--ink-3)\">⌂</text>\n        <text x=\"95\" y=\"90\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"1\">\n          {isCondo\n            ? lang === \"fr\" ? \"QUOTE-PART DE COPROPRIÉTÉ\" : \"CONDOMINIUM SHARE\"\n            : lang === \"fr\" ? \"TERRAIN NON INSCRIT AU RÔLE\" : \"LOT NOT ON ROLL\"}\n        </text>\n        <text x=\"95\" y=\"104\" textAnchor=\"middle\" fontSize=\"7\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n          {isCondo\n            ? lang === \"fr\" ? \"terrain commun indivis\" : \"undivided common land\"\n            : \"—\"}\n        </text>\n      </svg>\n    );\n  }\n  const front = frontageM && frontageM > 0 ? frontageM : Math.sqrt(areaM2);\n  const depth = areaM2 / front;\n  const ratio = Math.max(0.25, Math.min(4, depth / front));\n  const maxW = 150;\n  const maxH = 108;\n  let w = maxW;\n  let h = w * ratio;\n  if (h > maxH) {\n    h = maxH;\n    w = h / ratio;\n  }\n  const x = (190 - w) / 2;\n  const y = 118 - h;\n  // empreinte du bâtiment, à l'échelle de la superficie\n  const fpFrac = footprintM2 ? Math.min(0.8, footprintM2 / areaM2) : 0;\n  const fw = w * Math.sqrt(fpFrac) * 0.9;\n  const fh = h * Math.sqrt(fpFrac) * 0.9;\n  const fmtM = (v: number) => `${v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { maximumFractionDigits: 1 })} m`;\n\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label=\"Schéma du terrain à l'échelle\">\n      <defs>\n        <pattern id=\"hatch\" width=\"7\" height=\"7\" patternTransform=\"rotate(45)\" patternUnits=\"userSpaceOnUse\">\n          <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"7\" stroke=\"var(--green)\" strokeWidth=\"1\" opacity=\"0.35\" />\n        </pattern>\n      </defs>\n      {/* ombre + parcelle */}\n      <rect x={x + 4} y={y + 4} width={w} height={h} fill=\"rgba(20,24,20,0.12)\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"var(--lime-soft)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"url(#hatch)\" />\n      {/* empreinte bâtiment */}\n      {fpFrac > 0.005 && (\n        <rect x={x + (w - fw) / 2} y={y + h - fh - h * 0.08} width={fw} height={fh} fill=\"var(--ink)\" rx=\"2\">\n          <title>{lang === \"fr\" ? \"Empreinte approximative du bâtiment\" : \"Approximate building footprint\"}</title>\n        </rect>\n      )}\n      {/* cotes : frontage */}\n      <line x1={x} y1={y + h + 12} x2={x + w} y2={y + h + 12} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x} y1={y + h + 8} x2={x} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x + w} y1={y + h + 8} x2={x + w} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x + w / 2} y={y + h + 24} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n        {frontageM ? fmtM(front) : `≈ ${fmtM(front)}`}\n      </text>\n      {/* cotes : profondeur */}\n      <line x1={x - 10} y1={y} x2={x - 10} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y} x2={x - 6} y2={y} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y + h} x2={x - 6} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x - 16} y={y + h / 2} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\" transform={`rotate(-90 ${x - 16} ${y + h / 2})`}>\n        ≈ {fmtM(depth)}\n      </text>\n      {/* rue */}\n      <line x1=\"8\" y1=\"136\" x2=\"182\" y2=\"136\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"147\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\" letterSpacing=\"3\">\n        {lang === \"fr\" ? \"RUE\" : \"STREET\"}\n      </text>\n    </svg>\n  );\n}\n\n/* ------------------------- composition de la valeur ------------------------- */\nexport function ValueSplit({\n  land,\n  building,\n  previous,\n  total,\n  lang,\n  labels,\n}: {\n  land: number | null;\n  building: number | null;\n  previous: number | null;\n  total: number | null;\n  lang: string;\n  labels: { land: string; building: string; previous: string };\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  const l = land ?? 0;\n  const b = building ?? 0;\n  const sum = l + b;\n  if (sum <= 0) return null;\n  const lPct = (l / sum) * 100;\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { style: \"currency\", currency: \"CAD\", maximumFractionDigits: 0 }).format(v);\n  const delta = previous && previous > 0 && total ? ((total / previous - 1) * 100) : null;\n  return (\n    <div>\n      <div className=\"flex h-9 w-full overflow-hidden rounded-md border-[1.5px] border-ink\">\n        {lPct >= 1 && (\n          <div\n            className=\"flex items-center justify-center overflow-hidden bg-green transition-[width] duration-1000 ease-out\"\n            style={{ width: on ? `${lPct}%` : \"2%\" }}\n          >\n            {lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-paper\">{Math.round(lPct)} %</span>\n            )}\n          </div>\n        )}\n        {lPct <= 99 && (\n          <div className=\"flex flex-1 items-center justify-center overflow-hidden bg-ink\">\n            {100 - lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-lime\">{Math.round(100 - lPct)} %</span>\n            )}\n          </div>\n        )}\n      </div>\n      <div className=\"vp-mono mt-2 flex flex-wrap items-center gap-x-4 gap-y-1 text-[10.5px] uppercase tracking-[0.05em]\">\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-green\" />\n          {labels.land} {fmt(l)}\n        </span>\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-ink\" />\n          {labels.building} {fmt(b)}\n        </span>\n      </div>\n      {delta != null && (\n        <p className=\"vp-mono mt-2.5 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {labels.previous} {fmt(previous!)}{\" \"}\n          <span className={`ml-1 rounded-[4px] border border-ink px-1.5 py-0.5 font-bold ${delta >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n            {delta >= 0 ? \"+\" : \"\"}\n            {delta.toFixed(1)} %\n          </span>\n        </p>\n      )}\n    </div>\n  );\n}\n\n/* -------------------------------- sparkline -------------------------------- */\nexport function Sparkline({\n  history,\n  height = 44,\n}: {\n  history: { year: number; value: number | null }[];\n  height?: number;\n}) {\n  const pts = history.filter((h) => h.value != null) as { year: number; value: number }[];\n  if (pts.length < 2) return null;\n  const W = 132;\n  const min = Math.min(...pts.map((p) => p.value));\n  const max = Math.max(...pts.map((p) => p.value));\n  const span = max - min || 1;\n  const xy = pts.map((p, i) => [\n    6 + (i / (pts.length - 1)) * (W - 12),\n    height - 8 - ((p.value - min) / span) * (height - 18),\n  ]);\n  const line = xy.map(([x, y]) => `${x},${y}`).join(\" \");\n  const area = `${xy[0][0]},${height - 4} ${line} ${xy[xy.length - 1][0]},${height - 4}`;\n  const growth = ((pts[pts.length - 1].value / pts[0].value - 1) * 100);\n  const [x2, y2] = xy[xy.length - 1];\n  return (\n    <div className=\"flex items-end gap-2\">\n      <svg viewBox={`0 0 ${W} ${height}`} width={W} height={height} aria-label={`Évolution ${pts[0].year}-${pts[pts.length - 1].year}`}>\n        <polygon points={area} fill=\"var(--lime-soft)\" />\n        <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n        {xy.slice(0, -1).map(([x, y], i) => (\n          <circle key={i} cx={x} cy={y} r=\"2\" fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"1.1\" />\n        ))}\n        <circle cx={x2} cy={y2} r=\"3.6\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      </svg>\n      <span className={`vp-mono mb-0.5 rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${growth >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n        {growth >= 0 ? \"+\" : \"\"}\n        {growth.toFixed(0)} %\n      </span>\n    </div>\n  );\n}\n\n/* ------------------------- fourchette visuelle P10-P90 ------------------------- */\n/** Barre de fourchette : segment P10→P90, losange = estimation, tiret = rôle. */\nexport function RangeBar({\n  low,\n  estimate,\n  high,\n  role,\n  lang,\n}: {\n  low: number;\n  estimate: number;\n  high: number;\n  role: number | null;\n  lang: string;\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  if (!(high > low) || !Number.isFinite(estimate)) return null;\n  const lo = Math.min(low, role ?? low);\n  const hi = Math.max(high, role ?? high);\n  const pad = (hi - lo) * 0.09 || hi * 0.05;\n  const min = lo - pad;\n  const max = hi + pad;\n  const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));\n  // les étiquettes (centrées, nowrap) restent dans la carte même aux extrêmes\n  const clampX = (v: number, lo: number, hi: number) => Math.max(lo, Math.min(hi, X(v)));\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n      notation: v >= 1_000_000 ? \"compact\" : \"standard\",\n    }).format(v);\n  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;\n  return (\n    <div aria-label={`${fmt(low)} – ${fmt(high)}`}>\n      <div className=\"relative h-[64px]\">\n        {/* étiquette estimation */}\n        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <span className=\"vp-mono rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\">\n            {lang === \"fr\" ? \"ESTIMATION\" : \"ESTIMATE\"} {fmt(estimate)}\n          </span>\n        </div>\n        {/* piste */}\n        <div className=\"absolute left-0 right-0 top-[30px] h-[14px] rounded-[7px] border-[1.5px] border-ink bg-surface-2\" />\n        {/* segment P10-P90 */}\n        <div\n          className=\"absolute top-[30px] h-[14px] origin-left rounded-[7px] border-[1.5px] border-ink transition-transform duration-1000 ease-out\"\n          style={{\n            left: `${X(low)}%`,\n            width: `${Math.max(2, X(high) - X(low))}%`,\n            transform: on ? \"scaleX(1)\" : \"scaleX(0.05)\",\n            background:\n              \"repeating-linear-gradient(-45deg, var(--lime-soft), var(--lime-soft) 5px, rgba(255,81,72,0.35) 5px, rgba(255,81,72,0.35) 7px)\",\n          }}\n        />\n        {/* marqueur estimation (losange) */}\n        <div\n          className=\"absolute top-[29px] -translate-x-1/2 transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <div className=\"h-[16px] w-[16px] rotate-45 border-[1.8px] border-ink bg-lime\" />\n        </div>\n        {/* marqueur rôle */}\n        {role != null && role > 0 && (\n          <div\n            className=\"absolute top-[22px] -translate-x-1/2 text-center\"\n            style={{ left: `${X(role)}%` }}\n          >\n            <div className=\"mx-auto h-[30px] w-0 border-l-[2px] border-dashed border-ink opacity-70\" />\n            {!roleClose && (\n              <span className=\"vp-mono whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\">\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </div>\n        )}\n        {/* bornes */}\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(low)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(high)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>\n      </div>\n    </div>\n  );\n}\n\n/* --------------------------- courbe d'indice de marché --------------------------- */\n/** Indice mensuel rebasé à 100 au premier mois — la trajectoire du marché local. */\nexport function MarketIndexChart({\n  series,\n  lang,\n}: {\n  series: { month: string; idx: number }[];\n  lang: string;\n}) {\n  if (series.length < 6) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const W = 640;\n  const H = 200;\n  const padL = 40;\n  const padR = 52;\n  const padT = 18;\n  const padB = 28;\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const x = (i: number) => padL + (i / (pts.length - 1)) * (W - padL - padR);\n  const y = (v: number) => padT + (1 - (v - min) / span) * (H - padT - padB);\n  const line = pts.map((v, i) => `${x(i).toFixed(1)},${y(v).toFixed(1)}`).join(\" \");\n  const area = `${x(0)},${H - padB} ${line} ${x(pts.length - 1)},${H - padB}`;\n  // graduations : janvier de chaque année\n  const years = series\n    .map((p, i) => ({ i, month: p.month }))\n    .filter((p) => p.month.endsWith(\"-01\"));\n  // lignes horizontales : min / 100 / max arrondis\n  const gridVals = [...new Set([Math.round(min), 100, Math.round(max)])].filter(\n    (v) => v >= min - 1 && v <= max + 1\n  );\n  const lastV = pts[pts.length - 1];\n  return (\n    <svg\n      viewBox={`0 0 ${W} ${H}`}\n      className=\"h-auto w-full\"\n      role=\"img\"\n      aria-label={\n        lang === \"fr\"\n          ? `Indice de marché ${series[0].month} à ${series[series.length - 1].month}`\n          : `Market index ${series[0].month} to ${series[series.length - 1].month}`\n      }\n    >\n      {gridVals.map((v) => (\n        <g key={v}>\n          <line\n            x1={padL}\n            y1={y(v)}\n            x2={W - padR}\n            y2={y(v)}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            strokeDasharray=\"3 5\"\n            opacity={v === 100 ? 0.45 : 0.18}\n          />\n          <text\n            x={padL - 6}\n            y={y(v) + 3.5}\n            textAnchor=\"end\"\n            fontSize=\"10\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-3)\"\n          >\n            {v}\n          </text>\n        </g>\n      ))}\n      {years.map((p) => (\n        <g key={p.month}>\n          <line\n            x1={x(p.i)}\n            y1={padT}\n            x2={x(p.i)}\n            y2={H - padB}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            opacity=\"0.1\"\n          />\n          <text\n            x={x(p.i)}\n            y={H - 10}\n            textAnchor=\"middle\"\n            fontSize=\"10\"\n            fontWeight=\"700\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-2)\"\n          >\n            {p.month.slice(0, 4)}\n          </text>\n        </g>\n      ))}\n      <polygon points={area} fill=\"var(--lime-soft)\" opacity=\"0.8\" />\n      <polyline\n        points={line}\n        fill=\"none\"\n        stroke=\"var(--green)\"\n        strokeWidth=\"2.6\"\n        strokeLinejoin=\"round\"\n        strokeLinecap=\"round\"\n      />\n      <circle\n        cx={x(pts.length - 1)}\n        cy={y(lastV)}\n        r=\"5\"\n        fill=\"var(--lime)\"\n        stroke=\"var(--ink)\"\n        strokeWidth=\"1.8\"\n      />\n      <text\n        x={x(pts.length - 1) + 9}\n        y={y(lastV) + 4}\n        fontSize=\"11.5\"\n        fontWeight=\"700\"\n        fontFamily=\"var(--font-jetbrains)\"\n        fill=\"var(--ink)\"\n      >\n        {Math.round(lastV)}\n      </text>\n    </svg>\n  );\n}\n\n/* ----------------------- position dans la municipalité ----------------------- */\n/** Distribution stylisée (cloche) : les barres sous le percentile sont pleines. */\nexport function PercentileBar({\n  pct,\n  lang,\n}: {\n  pct: number;\n  lang: string;\n}) {\n  const N = 36;\n  const bars = Array.from({ length: N }, (_, i) => {\n    const z = (i / (N - 1) - 0.42) / 0.24; // cloche légèrement asymétrique (droite étirée)\n    return Math.exp(-0.5 * z * z) * (i / N > 0.5 ? 1 + (i / N - 0.5) * 0.35 : 1);\n  });\n  const maxB = Math.max(...bars);\n  const p = Math.max(0, Math.min(100, pct));\n  return (\n    <div aria-label={`${Math.round(p)} %`}>\n      <div className=\"relative flex h-[72px] items-end px-0.5\" style={{ gap: \"2px\" }}>\n        {bars.map((b, i) => {\n          const filled = (i + 0.5) / N <= p / 100;\n          return (\n            <div\n              key={i}\n              className=\"min-w-0 flex-1 rounded-t-[2px] border border-ink transition-colors\"\n              style={{\n                height: `${8 + (b / maxB) * 88}%`,\n                background: filled ? \"var(--lime)\" : \"var(--surface-2)\",\n                borderWidth: \"1px\",\n                opacity: filled ? 1 : 0.55,\n              }}\n            />\n          );\n        })}\n        <div\n          className=\"absolute bottom-0 top-[-6px] w-0 border-l-[2px] border-ink\"\n          style={{ left: `${p}%` }}\n        >\n          <span className=\"vp-mono absolute -top-1 left-1.5 whitespace-nowrap rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\" style={p > 55 ? { left: \"auto\", right: \"6px\" } : undefined}>\n            {lang === \"fr\" ? \"P\" : \"P\"}\n            {Math.round(p)}\n          </span>\n        </div>\n      </div>\n    </div>\n  );\n}\n\n/* ------------------------ mini-courbe d'indice (accueil) ------------------------ */\nexport function IndexSpark({\n  series,\n  width = 150,\n  height = 46,\n}: {\n  series: { month: string; idx: number }[];\n  width?: number;\n  height?: number;\n}) {\n  if (series.length < 2) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const xy = pts.map((v, i) => [\n    4 + (i / (pts.length - 1)) * (width - 8),\n    height - 6 - ((v - min) / span) * (height - 14),\n  ]);\n  const line = xy.map(([x, y]) => `${x.toFixed(1)},${y.toFixed(1)}`).join(\" \");\n  const area = `${xy[0][0]},${height - 3} ${line} ${xy[xy.length - 1][0]},${height - 3}`;\n  const [lx, ly] = xy[xy.length - 1];\n  return (\n    <svg viewBox={`0 0 ${width} ${height}`} className=\"h-auto w-full\" aria-hidden=\"true\">\n      <polygon points={area} fill=\"var(--lime-soft)\" />\n      <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n      <circle cx={lx} cy={ly} r=\"3.4\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n    </svg>\n  );\n}\n\n/* ------------------------------ frise temporelle ------------------------------ */\nexport function EraLine({ year, lang }: { year: number | null; lang: string }) {\n  if (!year || year < 1750) return null;\n  const min = Math.min(1900, Math.floor((year - 10) / 50) * 50);\n  const max = 2026;\n  const frac = Math.max(0, Math.min(1, (year - min) / (max - min)));\n  const W = 190;\n  const x = 12 + frac * (W - 24);\n  const age = 2026 - year;\n  return (\n    <svg viewBox={`0 0 ${W} 40`} className=\"w-full max-w-[210px]\" aria-label={`Construction ${year}`}>\n      <line x1=\"12\" y1=\"24\" x2={W - 12} y2=\"24\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      {[...new Set([min, Math.round((min + max) / 2 / 50) * 50, 2000])].map((yr) => {\n        const gx = 12 + ((yr - min) / (max - min)) * (W - 24);\n        return (\n          <g key={yr}>\n            <line x1={gx} y1=\"20\" x2={gx} y2=\"28\" stroke=\"var(--ink)\" strokeWidth=\"1.2\" opacity=\"0.5\" />\n            <text x={gx} y=\"38\" textAnchor=\"middle\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n              {yr}\n            </text>\n          </g>\n        );\n      })}\n      <g transform={`translate(${x}, 24)`}>\n        <rect x=\"-7\" y=\"-7\" width=\"14\" height=\"14\" transform=\"rotate(45)\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n        <text y=\"-13\" textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n          {year}\n        </text>\n      </g>\n      <text x={W - 12} y=\"12\" textAnchor=\"end\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n        {age} {lang === \"fr\" ? \"ANS\" : \"YRS\"}\n      </text>\n    </svg>\n  );\n}\n","structuredPatch":[{"oldStart":476,"oldLines":7,"newStart":476,"newLines":7,"lines":["         {/* étiquette estimation */}","         <div","           className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"","-          style={{ left: `${on ? X(estimate) : 50}%` }}","+          style={{ left: `${on ? clampX(estimate, 18, 82) : 50}%` }}","         >","           <span className=\"vp-mono rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\">","             {lang === \"fr\" ? \"ESTIMATION\" : \"ESTIMATE\"} {fmt(estimate)}"]}],"userModified":false,"replaceAll":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"d005c67e-5d5c-4834-a3bb-a611fd9ca2e6","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwxnff53w3fBdu3f6u9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FY13mQ5uookaPnkL8Sjnfj","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","old_string":"        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(low)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(high)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>","new_string":"        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 whitespace-nowrap text-[10px] font-bold text-ink-2\"\n          style={{ left: `${clampX(low, 11, 89)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 whitespace-nowrap text-[10px] font-bold text-ink-2\"\n          style={{ left: `${clampX(high, 11, 89)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":388,"cache_read_input_tokens":147371,"cache_creation":{"ephemeral_5m_input_tokens":388,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"2478c1bf-dbcc-4c4e-b72a-e0e4bd252573","request_id":"req_011CeJwxjrz75dau6Jf2xodq"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FY13mQ5uookaPnkL8Sjnfj","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"7d0fed55-cf82-4b1c-a589-a434ce9c6df2","timestamp":"2026-08-23T03:11:55.870Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","oldString":"        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(low)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(high)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>","newString":"        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 whitespace-nowrap text-[10px] font-bold text-ink-2\"\n          style={{ left: `${clampX(low, 11, 89)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 whitespace-nowrap text-[10px] font-bold text-ink-2\"\n          style={{ left: `${clampX(high, 11, 89)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n/**\n * MetricViz — visualisations signature générées par les données du registre.\n * Chaque propriété produit ses propres dessins : façade SVG (étages, logements,\n * genre de construction), schéma de terrain à l'échelle (frontage × profondeur),\n * composition de valeur terrain/bâtiment, jauge de confiance, sparkline 2021-2026,\n * compteurs animés. SVG pur, palette encre/lime, aucun aléatoire : mêmes données,\n * même dessin.\n */\n\"use client\";\nimport { useEffect, useState } from \"react\";\n\n/** Nombre localisé (« 1 206,6 » en FR, « 1,206.6 » en EN). */\nexport function locNum(\n  v: number | null | undefined,\n  lang: string,\n  opts?: { unit?: string; maxFrac?: number }\n): string {\n  if (v == null) return \"—\";\n  const s = v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n    maximumFractionDigits: opts?.maxFrac ?? 1,\n  });\n  return opts?.unit ? `${s} ${opts.unit}` : s;\n}\n\n/* ---------------------------- compteur animé ---------------------------- */\n/** Rendu serveur = valeur finale (jamais « 0 $ » à l'écran) ; anime 0 → valeur au montage. */\nexport function CountUp({\n  value,\n  format,\n  duration = 900,\n}: {\n  value: number;\n  format: (v: number) => string;\n  duration?: number;\n}) {\n  const [display, setDisplay] = useState(value);\n\n  useEffect(() => {\n    if (window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches) return;\n    let raf = 0;\n    const t0 = performance.now();\n    const tick = (t: number) => {\n      const p = Math.min(1, (t - t0) / duration);\n      const eased = 1 - Math.pow(1 - p, 3);\n      setDisplay(value * eased);\n      if (p < 1) raf = requestAnimationFrame(tick);\n    };\n    raf = requestAnimationFrame(tick);\n    return () => cancelAnimationFrame(raf);\n  }, [value, duration]);\n\n  return <span>{format(display)}</span>;\n}\n\n/* --------------------------- jauge de confiance --------------------------- */\nexport function ConfidenceDial({\n  pct,\n  level,\n  label,\n}: {\n  pct: number;\n  level: \"A\" | \"B\" | \"C\" | \"D\";\n  label: string;\n}) {\n  const [mounted, setMounted] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setMounted(true), 60);\n    return () => clearTimeout(id);\n  }, []);\n  const R = 52;\n  const CIRC = Math.PI * R; // demi-cercle\n  const frac = mounted ? Math.max(0.02, Math.min(1, pct / 100)) : 0.02;\n  const color =\n    level === \"A\" ? \"var(--lime)\" : level === \"B\" ? \"var(--green)\" : level === \"C\" ? \"var(--amber)\" : \"var(--danger)\";\n  return (\n    <div className=\"flex flex-col items-center\" aria-label={`${label} : ${level} ${pct} %`}>\n      <svg viewBox=\"0 0 128 78\" className=\"w-[128px]\">\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"var(--surface-2)\" strokeWidth=\"12\" strokeLinecap=\"round\" />\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"rgba(20,24,20,0.85)\" strokeWidth=\"13.5\" strokeLinecap=\"round\" opacity=\"0.12\" />\n        <path\n          d=\"M 12 66 A 52 52 0 0 1 116 66\"\n          fill=\"none\"\n          stroke={color}\n          strokeWidth=\"12\"\n          strokeLinecap=\"round\"\n          strokeDasharray={`${CIRC * frac} ${CIRC}`}\n          style={{ transition: \"stroke-dasharray 1s cubic-bezier(.2,.8,.2,1)\" }}\n        />\n        {/* graduations */}\n        {[0, 0.25, 0.5, 0.75, 1].map((g) => {\n          const a = Math.PI * (1 - g);\n          const x1 = 64 + Math.cos(a) * 40;\n          const y1 = 66 - Math.sin(a) * 40;\n          const x2 = 64 + Math.cos(a) * 45;\n          const y2 = 66 - Math.sin(a) * 45;\n          return <line key={g} x1={x1} y1={y1} x2={x2} y2={y2} stroke=\"var(--ink)\" strokeWidth=\"1.4\" opacity=\"0.5\" />;\n        })}\n        <text x=\"64\" y=\"52\" textAnchor=\"middle\" fontSize=\"26\" fontWeight=\"700\" fill=\"var(--ink)\" fontFamily=\"var(--font-space-grotesk)\">\n          {level}\n        </text>\n        <text x=\"64\" y=\"70\" textAnchor=\"middle\" fontSize=\"10.5\" fontWeight=\"700\" fill=\"var(--ink-2)\" fontFamily=\"var(--font-jetbrains)\">\n          {pct} %\n        </text>\n      </svg>\n      <p className=\"klabel mt-1 text-center\">{label}</p>\n    </div>\n  );\n}\n\n/* --------------------------- façade de bâtiment --------------------------- */\n/** Terrain sans bâtiment : borne d'arpenteur et conifères, pas de fausse maison. */\nfunction VacantGlyph({ lang }: { lang: string }) {\n  const tree = (x: number, sc: number) => (\n    <g transform={`translate(${x}, 128) scale(${sc})`}>\n      <path d=\"M0,-46 L13,-14 L5,-14 L16,8 L-16,8 L-5,-14 L-13,-14 Z\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      <rect x=\"-2.5\" y=\"8\" width=\"5\" height=\"9\" fill=\"var(--ink)\" />\n    </g>\n  );\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain sans bâtiment\" : \"Vacant land\"}>\n      {tree(52, 1)}\n      {tree(96, 0.72)}\n      {tree(134, 0.9)}\n      {/* borne d'arpenteur */}\n      <g transform=\"translate(24, 128)\">\n        <rect x=\"-2.5\" y=\"-26\" width=\"5\" height=\"26\" fill=\"var(--ink)\" />\n        <rect x=\"-8\" y=\"-34\" width=\"16\" height=\"10\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n      </g>\n      <line x1=\"8\" y1=\"145\" x2=\"182\" y2=\"145\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"120\" textAnchor=\"middle\" fontSize=\"9\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n        {lang === \"fr\" ? \"SANS BÂTIMENT\" : \"NO BUILDING\"}\n      </text>\n    </svg>\n  );\n}\n\n/** Façade dessinée à partir du registre : étages, logements, genre de construction.\n *  Sans bâtiment au registre (ni aire, ni année, ni étages) → borne d'arpenteur. */\nexport function BuildingGlyph({\n  floors,\n  dwellings,\n  genre,\n  yearBuilt,\n  floorAreaM2,\n  lang = \"fr\",\n}: {\n  floors: number | null;\n  dwellings: number | null;\n  genre: string | null;\n  yearBuilt: number | null;\n  floorAreaM2?: number | null;\n  lang?: string;\n}) {\n  if (!floorAreaM2 && !yearBuilt && !floors && !dwellings) {\n    return <VacantGlyph lang={lang} />;\n  }\n  const nF = Math.max(1, Math.min(6, Math.round(floors ?? 1)));\n  const nD = Math.max(1, Math.min(24, Math.round(dwellings ?? 1)));\n  const winPerFloor = Math.max(1, Math.min(4, Math.ceil(nD / nF)));\n  const W = 190;\n  const bw = 64 + winPerFloor * 18;\n  const bx = (W - bw) / 2;\n  const fh = 24;\n  const groundY = 128;\n  const bodyTop = groundY - nF * fh;\n  const mansard = (genre ?? \"\").toLowerCase().includes(\"mansard\");\n  const flat = (genre ?? \"\").toLowerCase().includes(\"plain-pied\") && nF === 1;\n\n  const windows: React.ReactNode[] = [];\n  for (let f = 0; f < nF; f++) {\n    for (let wi = 0; wi < winPerFloor; wi++) {\n      const idx = f * winPerFloor + wi;\n      windows.push(\n        <rect\n          key={`${f}-${wi}`}\n          x={bx + 14 + wi * ((bw - 28) / winPerFloor) + ((bw - 28) / winPerFloor - 12) / 2}\n          y={bodyTop + f * fh + 6}\n          width=\"12\"\n          height=\"11\"\n          fill={idx < nD ? \"var(--lime)\" : \"var(--surface-2)\"}\n          stroke=\"var(--ink)\"\n          strokeWidth=\"1.2\"\n          className=\"vv-win\"\n          style={{ animationDelay: `${idx * 60}ms` }}\n        />\n      );\n    }\n  }\n\n  return (\n    <svg viewBox={`0 0 ${W} 150`} className=\"w-full max-w-[210px]\" aria-label=\"Silhouette du bâtiment selon le registre\">\n      {/* soleil-repère */}\n      <circle cx={W - 22} cy=\"22\" r=\"9\" fill=\"none\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      <circle cx={W - 22} cy=\"22\" r=\"3.5\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1\" />\n      {/* ombre décalée signature */}\n      <rect x={bx + 5} y={bodyTop + 5} width={bw} height={nF * fh} fill=\"rgba(20,24,20,0.12)\" />\n      {/* corps */}\n      <rect x={bx} y={bodyTop} width={bw} height={nF * fh} fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      {/* séparations d'étages */}\n      {Array.from({ length: nF - 1 }, (_, i) => (\n        <line key={i} x1={bx} y1={bodyTop + (i + 1) * fh} x2={bx + bw} y2={bodyTop + (i + 1) * fh} stroke=\"var(--ink)\" strokeWidth=\"1\" strokeDasharray=\"3 3\" opacity=\"0.4\" />\n      ))}\n      {/* toit */}\n      {flat ? (\n        <rect x={bx - 6} y={bodyTop - 8} width={bw + 12} height=\"8\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : mansard ? (\n        <path d={`M ${bx - 6} ${bodyTop} L ${bx + 10} ${bodyTop - 20} L ${bx + bw - 10} ${bodyTop - 20} L ${bx + bw + 6} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : (\n        <path d={`M ${bx - 8} ${bodyTop} L ${bx + bw / 2} ${bodyTop - 24} L ${bx + bw + 8} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      )}\n      {/* cheminée */}\n      <rect x={bx + bw - 22} y={bodyTop - (flat ? 20 : 30)} width=\"9\" height={flat ? 14 : 18} fill=\"var(--ink)\" />\n      {windows}\n      {/* porte */}\n      <rect x={bx + bw / 2 - 7} y={groundY - 17} width=\"14\" height=\"17\" fill=\"var(--ink)\" />\n      <circle cx={bx + bw / 2 + 3.5} cy={groundY - 8} r=\"1.4\" fill=\"var(--lime)\" />\n      {/* sol */}\n      <line x1=\"8\" y1={groundY} x2={W - 8} y2={groundY} stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <line x1=\"8\" y1={groundY + 4} x2={W - 8} y2={groundY + 4} stroke=\"var(--ink)\" strokeWidth=\"1\" opacity=\"0.3\" />\n      {/* millésime gravé */}\n      {yearBuilt && (\n        <text x={W / 2} y=\"146\" textAnchor=\"middle\" fontSize=\"10\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n          ANNO {yearBuilt}\n        </text>\n      )}\n    </svg>\n  );\n}\n\n/* ----------------------------- schéma du terrain ----------------------------- */\n/** Terrain à l'échelle réelle : frontage × profondeur déduite de la superficie. */\nexport function LotDiagram({\n  areaM2,\n  frontageM,\n  footprintM2,\n  lang,\n  isCondo,\n}: {\n  areaM2: number | null;\n  frontageM: number | null;\n  footprintM2: number | null;\n  lang: string;\n  isCondo?: boolean;\n}) {\n  if (!areaM2 || areaM2 <= 0) {\n    return (\n      <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain non applicable\" : \"Lot not applicable\"}>\n        <rect x=\"30\" y=\"30\" width=\"130\" height=\"86\" fill=\"var(--surface-2)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" strokeDasharray=\"5 5\" rx=\"6\" />\n        <text x=\"95\" y=\"66\" textAnchor=\"middle\" fontSize=\"20\" fill=\"var(--ink-3)\">⌂</text>\n        <text x=\"95\" y=\"90\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"1\">\n          {isCondo\n            ? lang === \"fr\" ? \"QUOTE-PART DE COPROPRIÉTÉ\" : \"CONDOMINIUM SHARE\"\n            : lang === \"fr\" ? \"TERRAIN NON INSCRIT AU RÔLE\" : \"LOT NOT ON ROLL\"}\n        </text>\n        <text x=\"95\" y=\"104\" textAnchor=\"middle\" fontSize=\"7\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n          {isCondo\n            ? lang === \"fr\" ? \"terrain commun indivis\" : \"undivided common land\"\n            : \"—\"}\n        </text>\n      </svg>\n    );\n  }\n  const front = frontageM && frontageM > 0 ? frontageM : Math.sqrt(areaM2);\n  const depth = areaM2 / front;\n  const ratio = Math.max(0.25, Math.min(4, depth / front));\n  const maxW = 150;\n  const maxH = 108;\n  let w = maxW;\n  let h = w * ratio;\n  if (h > maxH) {\n    h = maxH;\n    w = h / ratio;\n  }\n  const x = (190 - w) / 2;\n  const y = 118 - h;\n  // empreinte du bâtiment, à l'échelle de la superficie\n  const fpFrac = footprintM2 ? Math.min(0.8, footprintM2 / areaM2) : 0;\n  const fw = w * Math.sqrt(fpFrac) * 0.9;\n  const fh = h * Math.sqrt(fpFrac) * 0.9;\n  const fmtM = (v: number) => `${v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { maximumFractionDigits: 1 })} m`;\n\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label=\"Schéma du terrain à l'échelle\">\n      <defs>\n        <pattern id=\"hatch\" width=\"7\" height=\"7\" patternTransform=\"rotate(45)\" patternUnits=\"userSpaceOnUse\">\n          <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"7\" stroke=\"var(--green)\" strokeWidth=\"1\" opacity=\"0.35\" />\n        </pattern>\n      </defs>\n      {/* ombre + parcelle */}\n      <rect x={x + 4} y={y + 4} width={w} height={h} fill=\"rgba(20,24,20,0.12)\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"var(--lime-soft)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"url(#hatch)\" />\n      {/* empreinte bâtiment */}\n      {fpFrac > 0.005 && (\n        <rect x={x + (w - fw) / 2} y={y + h - fh - h * 0.08} width={fw} height={fh} fill=\"var(--ink)\" rx=\"2\">\n          <title>{lang === \"fr\" ? \"Empreinte approximative du bâtiment\" : \"Approximate building footprint\"}</title>\n        </rect>\n      )}\n      {/* cotes : frontage */}\n      <line x1={x} y1={y + h + 12} x2={x + w} y2={y + h + 12} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x} y1={y + h + 8} x2={x} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x + w} y1={y + h + 8} x2={x + w} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x + w / 2} y={y + h + 24} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n        {frontageM ? fmtM(front) : `≈ ${fmtM(front)}`}\n      </text>\n      {/* cotes : profondeur */}\n      <line x1={x - 10} y1={y} x2={x - 10} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y} x2={x - 6} y2={y} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y + h} x2={x - 6} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x - 16} y={y + h / 2} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\" transform={`rotate(-90 ${x - 16} ${y + h / 2})`}>\n        ≈ {fmtM(depth)}\n      </text>\n      {/* rue */}\n      <line x1=\"8\" y1=\"136\" x2=\"182\" y2=\"136\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"147\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\" letterSpacing=\"3\">\n        {lang === \"fr\" ? \"RUE\" : \"STREET\"}\n      </text>\n    </svg>\n  );\n}\n\n/* ------------------------- composition de la valeur ------------------------- */\nexport function ValueSplit({\n  land,\n  building,\n  previous,\n  total,\n  lang,\n  labels,\n}: {\n  land: number | null;\n  building: number | null;\n  previous: number | null;\n  total: number | null;\n  lang: string;\n  labels: { land: string; building: string; previous: string };\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  const l = land ?? 0;\n  const b = building ?? 0;\n  const sum = l + b;\n  if (sum <= 0) return null;\n  const lPct = (l / sum) * 100;\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { style: \"currency\", currency: \"CAD\", maximumFractionDigits: 0 }).format(v);\n  const delta = previous && previous > 0 && total ? ((total / previous - 1) * 100) : null;\n  return (\n    <div>\n      <div className=\"flex h-9 w-full overflow-hidden rounded-md border-[1.5px] border-ink\">\n        {lPct >= 1 && (\n          <div\n            className=\"flex items-center justify-center overflow-hidden bg-green transition-[width] duration-1000 ease-out\"\n            style={{ width: on ? `${lPct}%` : \"2%\" }}\n          >\n            {lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-paper\">{Math.round(lPct)} %</span>\n            )}\n          </div>\n        )}\n        {lPct <= 99 && (\n          <div className=\"flex flex-1 items-center justify-center overflow-hidden bg-ink\">\n            {100 - lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-lime\">{Math.round(100 - lPct)} %</span>\n            )}\n          </div>\n        )}\n      </div>\n      <div className=\"vp-mono mt-2 flex flex-wrap items-center gap-x-4 gap-y-1 text-[10.5px] uppercase tracking-[0.05em]\">\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-green\" />\n          {labels.land} {fmt(l)}\n        </span>\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-ink\" />\n          {labels.building} {fmt(b)}\n        </span>\n      </div>\n      {delta != null && (\n        <p className=\"vp-mono mt-2.5 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {labels.previous} {fmt(previous!)}{\" \"}\n          <span className={`ml-1 rounded-[4px] border border-ink px-1.5 py-0.5 font-bold ${delta >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n            {delta >= 0 ? \"+\" : \"\"}\n            {delta.toFixed(1)} %\n          </span>\n        </p>\n      )}\n    </div>\n  );\n}\n\n/* -------------------------------- sparkline -------------------------------- */\nexport function Sparkline({\n  history,\n  height = 44,\n}: {\n  history: { year: number; value: number | null }[];\n  height?: number;\n}) {\n  const pts = history.filter((h) => h.value != null) as { year: number; value: number }[];\n  if (pts.length < 2) return null;\n  const W = 132;\n  const min = Math.min(...pts.map((p) => p.value));\n  const max = Math.max(...pts.map((p) => p.value));\n  const span = max - min || 1;\n  const xy = pts.map((p, i) => [\n    6 + (i / (pts.length - 1)) * (W - 12),\n    height - 8 - ((p.value - min) / span) * (height - 18),\n  ]);\n  const line = xy.map(([x, y]) => `${x},${y}`).join(\" \");\n  const area = `${xy[0][0]},${height - 4} ${line} ${xy[xy.length - 1][0]},${height - 4}`;\n  const growth = ((pts[pts.length - 1].value / pts[0].value - 1) * 100);\n  const [x2, y2] = xy[xy.length - 1];\n  return (\n    <div className=\"flex items-end gap-2\">\n      <svg viewBox={`0 0 ${W} ${height}`} width={W} height={height} aria-label={`Évolution ${pts[0].year}-${pts[pts.length - 1].year}`}>\n        <polygon points={area} fill=\"var(--lime-soft)\" />\n        <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n        {xy.slice(0, -1).map(([x, y], i) => (\n          <circle key={i} cx={x} cy={y} r=\"2\" fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"1.1\" />\n        ))}\n        <circle cx={x2} cy={y2} r=\"3.6\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      </svg>\n      <span className={`vp-mono mb-0.5 rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${growth >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n        {growth >= 0 ? \"+\" : \"\"}\n        {growth.toFixed(0)} %\n      </span>\n    </div>\n  );\n}\n\n/* ------------------------- fourchette visuelle P10-P90 ------------------------- */\n/** Barre de fourchette : segment P10→P90, losange = estimation, tiret = rôle. */\nexport function RangeBar({\n  low,\n  estimate,\n  high,\n  role,\n  lang,\n}: {\n  low: number;\n  estimate: number;\n  high: number;\n  role: number | null;\n  lang: string;\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  if (!(high > low) || !Number.isFinite(estimate)) return null;\n  const lo = Math.min(low, role ?? low);\n  const hi = Math.max(high, role ?? high);\n  const pad = (hi - lo) * 0.09 || hi * 0.05;\n  const min = lo - pad;\n  const max = hi + pad;\n  const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));\n  // les étiquettes (centrées, nowrap) restent dans la carte même aux extrêmes\n  const clampX = (v: number, lo: number, hi: number) => Math.max(lo, Math.min(hi, X(v)));\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n      notation: v >= 1_000_000 ? \"compact\" : \"standard\",\n    }).format(v);\n  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;\n  return (\n    <div aria-label={`${fmt(low)} – ${fmt(high)}`}>\n      <div className=\"relative h-[64px]\">\n        {/* étiquette estimation */}\n        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? clampX(estimate, 18, 82) : 50}%` }}\n        >\n          <span className=\"vp-mono rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\">\n            {lang === \"fr\" ? \"ESTIMATION\" : \"ESTIMATE\"} {fmt(estimate)}\n          </span>\n        </div>\n        {/* piste */}\n        <div className=\"absolute left-0 right-0 top-[30px] h-[14px] rounded-[7px] border-[1.5px] border-ink bg-surface-2\" />\n        {/* segment P10-P90 */}\n        <div\n          className=\"absolute top-[30px] h-[14px] origin-left rounded-[7px] border-[1.5px] border-ink transition-transform duration-1000 ease-out\"\n          style={{\n            left: `${X(low)}%`,\n            width: `${Math.max(2, X(high) - X(low))}%`,\n            transform: on ? \"scaleX(1)\" : \"scaleX(0.05)\",\n            background:\n              \"repeating-linear-gradient(-45deg, var(--lime-soft), var(--lime-soft) 5px, rgba(255,81,72,0.35) 5px, rgba(255,81,72,0.35) 7px)\",\n          }}\n        />\n        {/* marqueur estimation (losange) */}\n        <div\n          className=\"absolute top-[29px] -translate-x-1/2 transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <div className=\"h-[16px] w-[16px] rotate-45 border-[1.8px] border-ink bg-lime\" />\n        </div>\n        {/* marqueur rôle */}\n        {role != null && role > 0 && (\n          <div\n            className=\"absolute top-[22px] -translate-x-1/2 text-center\"\n            style={{ left: `${X(role)}%` }}\n          >\n            <div className=\"mx-auto h-[30px] w-0 border-l-[2px] border-dashed border-ink opacity-70\" />\n            {!roleClose && (\n              <span className=\"vp-mono whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\">\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </div>\n        )}\n        {/* bornes */}\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(low)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"\n          style={{ left: `${X(high)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>\n      </div>\n    </div>\n  );\n}\n\n/* --------------------------- courbe d'indice de marché --------------------------- */\n/** Indice mensuel rebasé à 100 au premier mois — la trajectoire du marché local. */\nexport function MarketIndexChart({\n  series,\n  lang,\n}: {\n  series: { month: string; idx: number }[];\n  lang: string;\n}) {\n  if (series.length < 6) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const W = 640;\n  const H = 200;\n  const padL = 40;\n  const padR = 52;\n  const padT = 18;\n  const padB = 28;\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const x = (i: number) => padL + (i / (pts.length - 1)) * (W - padL - padR);\n  const y = (v: number) => padT + (1 - (v - min) / span) * (H - padT - padB);\n  const line = pts.map((v, i) => `${x(i).toFixed(1)},${y(v).toFixed(1)}`).join(\" \");\n  const area = `${x(0)},${H - padB} ${line} ${x(pts.length - 1)},${H - padB}`;\n  // graduations : janvier de chaque année\n  const years = series\n    .map((p, i) => ({ i, month: p.month }))\n    .filter((p) => p.month.endsWith(\"-01\"));\n  // lignes horizontales : min / 100 / max arrondis\n  const gridVals = [...new Set([Math.round(min), 100, Math.round(max)])].filter(\n    (v) => v >= min - 1 && v <= max + 1\n  );\n  const lastV = pts[pts.length - 1];\n  return (\n    <svg\n      viewBox={`0 0 ${W} ${H}`}\n      className=\"h-auto w-full\"\n      role=\"img\"\n      aria-label={\n        lang === \"fr\"\n          ? `Indice de marché ${series[0].month} à ${series[series.length - 1].month}`\n          : `Market index ${series[0].month} to ${series[series.length - 1].month}`\n      }\n    >\n      {gridVals.map((v) => (\n        <g key={v}>\n          <line\n            x1={padL}\n            y1={y(v)}\n            x2={W - padR}\n            y2={y(v)}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            strokeDasharray=\"3 5\"\n            opacity={v === 100 ? 0.45 : 0.18}\n          />\n          <text\n            x={padL - 6}\n            y={y(v) + 3.5}\n            textAnchor=\"end\"\n            fontSize=\"10\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-3)\"\n          >\n            {v}\n          </text>\n        </g>\n      ))}\n      {years.map((p) => (\n        <g key={p.month}>\n          <line\n            x1={x(p.i)}\n            y1={padT}\n            x2={x(p.i)}\n            y2={H - padB}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            opacity=\"0.1\"\n          />\n          <text\n            x={x(p.i)}\n            y={H - 10}\n            textAnchor=\"middle\"\n            fontSize=\"10\"\n            fontWeight=\"700\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-2)\"\n          >\n            {p.month.slice(0, 4)}\n          </text>\n        </g>\n      ))}\n      <polygon points={area} fill=\"var(--lime-soft)\" opacity=\"0.8\" />\n      <polyline\n        points={line}\n        fill=\"none\"\n        stroke=\"var(--green)\"\n        strokeWidth=\"2.6\"\n        strokeLinejoin=\"round\"\n        strokeLinecap=\"round\"\n      />\n      <circle\n        cx={x(pts.length - 1)}\n        cy={y(lastV)}\n        r=\"5\"\n        fill=\"var(--lime)\"\n        stroke=\"var(--ink)\"\n        strokeWidth=\"1.8\"\n      />\n      <text\n        x={x(pts.length - 1) + 9}\n        y={y(lastV) + 4}\n        fontSize=\"11.5\"\n        fontWeight=\"700\"\n        fontFamily=\"var(--font-jetbrains)\"\n        fill=\"var(--ink)\"\n      >\n        {Math.round(lastV)}\n      </text>\n    </svg>\n  );\n}\n\n/* ----------------------- position dans la municipalité ----------------------- */\n/** Distribution stylisée (cloche) : les barres sous le percentile sont pleines. */\nexport function PercentileBar({\n  pct,\n  lang,\n}: {\n  pct: number;\n  lang: string;\n}) {\n  const N = 36;\n  const bars = Array.from({ length: N }, (_, i) => {\n    const z = (i / (N - 1) - 0.42) / 0.24; // cloche légèrement asymétrique (droite étirée)\n    return Math.exp(-0.5 * z * z) * (i / N > 0.5 ? 1 + (i / N - 0.5) * 0.35 : 1);\n  });\n  const maxB = Math.max(...bars);\n  const p = Math.max(0, Math.min(100, pct));\n  return (\n    <div aria-label={`${Math.round(p)} %`}>\n      <div className=\"relative flex h-[72px] items-end px-0.5\" style={{ gap: \"2px\" }}>\n        {bars.map((b, i) => {\n          const filled = (i + 0.5) / N <= p / 100;\n          return (\n            <div\n              key={i}\n              className=\"min-w-0 flex-1 rounded-t-[2px] border border-ink transition-colors\"\n              style={{\n                height: `${8 + (b / maxB) * 88}%`,\n                background: filled ? \"var(--lime)\" : \"var(--surface-2)\",\n                borderWidth: \"1px\",\n                opacity: filled ? 1 : 0.55,\n              }}\n            />\n          );\n        })}\n        <div\n          className=\"absolute bottom-0 top-[-6px] w-0 border-l-[2px] border-ink\"\n          style={{ left: `${p}%` }}\n        >\n          <span className=\"vp-mono absolute -top-1 left-1.5 whitespace-nowrap rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\" style={p > 55 ? { left: \"auto\", right: \"6px\" } : undefined}>\n            {lang === \"fr\" ? \"P\" : \"P\"}\n            {Math.round(p)}\n          </span>\n        </div>\n      </div>\n    </div>\n  );\n}\n\n/* ------------------------ mini-courbe d'indice (accueil) ------------------------ */\nexport function IndexSpark({\n  series,\n  width = 150,\n  height = 46,\n}: {\n  series: { month: string; idx: number }[];\n  width?: number;\n  height?: number;\n}) {\n  if (series.length < 2) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const xy = pts.map((v, i) => [\n    4 + (i / (pts.length - 1)) * (width - 8),\n    height - 6 - ((v - min) / span) * (height - 14),\n  ]);\n  const line = xy.map(([x, y]) => `${x.toFixed(1)},${y.toFixed(1)}`).join(\" \");\n  const area = `${xy[0][0]},${height - 3} ${line} ${xy[xy.length - 1][0]},${height - 3}`;\n  const [lx, ly] = xy[xy.length - 1];\n  return (\n    <svg viewBox={`0 0 ${width} ${height}`} className=\"h-auto w-full\" aria-hidden=\"true\">\n      <polygon points={area} fill=\"var(--lime-soft)\" />\n      <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n      <circle cx={lx} cy={ly} r=\"3.4\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n    </svg>\n  );\n}\n\n/* ------------------------------ frise temporelle ------------------------------ */\nexport function EraLine({ year, lang }: { year: number | null; lang: string }) {\n  if (!year || year < 1750) return null;\n  const min = Math.min(1900, Math.floor((year - 10) / 50) * 50);\n  const max = 2026;\n  const frac = Math.max(0, Math.min(1, (year - min) / (max - min)));\n  const W = 190;\n  const x = 12 + frac * (W - 24);\n  const age = 2026 - year;\n  return (\n    <svg viewBox={`0 0 ${W} 40`} className=\"w-full max-w-[210px]\" aria-label={`Construction ${year}`}>\n      <line x1=\"12\" y1=\"24\" x2={W - 12} y2=\"24\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      {[...new Set([min, Math.round((min + max) / 2 / 50) * 50, 2000])].map((yr) => {\n        const gx = 12 + ((yr - min) / (max - min)) * (W - 24);\n        return (\n          <g key={yr}>\n            <line x1={gx} y1=\"20\" x2={gx} y2=\"28\" stroke=\"var(--ink)\" strokeWidth=\"1.2\" opacity=\"0.5\" />\n            <text x={gx} y=\"38\" textAnchor=\"middle\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n              {yr}\n            </text>\n          </g>\n        );\n      })}\n      <g transform={`translate(${x}, 24)`}>\n        <rect x=\"-7\" y=\"-7\" width=\"14\" height=\"14\" transform=\"rotate(45)\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n        <text y=\"-13\" textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n          {year}\n        </text>\n      </g>\n      <text x={W - 12} y=\"12\" textAnchor=\"end\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n        {age} {lang === \"fr\" ? \"ANS\" : \"YRS\"}\n      </text>\n    </svg>\n  );\n}\n","structuredPatch":[{"oldStart":518,"oldLines":14,"newStart":518,"newLines":14,"lines":["         )}","         {/* bornes */}","         <span","-          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"","-          style={{ left: `${X(low)}%` }}","+          className=\"vp-mono absolute top-[50px] -translate-x-1/2 whitespace-nowrap text-[10px] font-bold text-ink-2\"","+          style={{ left: `${clampX(low, 11, 89)}%` }}","         >","           P10 {fmt(low)}","         </span>","         <span","-          className=\"vp-mono absolute top-[50px] -translate-x-1/2 text-[10px] font-bold text-ink-2\"","-          style={{ left: `${X(high)}%` }}","+          className=\"vp-mono absolute top-[50px] -translate-x-1/2 whitespace-nowrap text-[10px] font-bold text-ink-2\"","+          style={{ left: `${clampX(high, 11, 89)}%` }}","         >","           P90 {fmt(high)}","         </span>"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwyMFiqDPViKr1q7Tfe","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PJTFyssQHdGs7Pjjs81ued","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","old_string":"        {role != null && role > 0 && (\n          <div\n            className=\"absolute top-[22px] -translate-x-1/2 text-center\"\n            style={{ left: `${X(role)}%` }}\n          >\n            <div className=\"mx-auto h-[30px] w-0 border-l-[2px] border-dashed border-ink opacity-70\" />\n            {!roleClose && (\n              <span className=\"vp-mono whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\">\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </div>\n        )}","new_string":"        {role != null && role > 0 && (\n          <>\n            <div\n              className=\"absolute top-[22px] h-[30px] w-0 -translate-x-1/2 border-l-[2px] border-dashed border-ink opacity-70\"\n              style={{ left: `${X(role)}%` }}\n            />\n            {!roleClose && (\n              <span\n                className=\"vp-mono absolute top-[52px] -translate-x-1/2 whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\"\n                style={{ left: `${clampX(role, 11, 89)}%` }}\n              >\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </>\n        )}"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":608,"cache_read_input_tokens":147759,"cache_creation":{"ephemeral_5m_input_tokens":608,"ephemeral_1h_input_tokens":0},"output_tokens":5,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"f775812d-61d0-4cc1-abff-2788052e01c0","request_id":"req_011CeJwyJaVRcxa9vE2ykJEi"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PJTFyssQHdGs7Pjjs81ued","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"0227ef41-897d-4778-bd12-e5f8e4ae79aa","timestamp":"2026-08-23T03:12:10.226Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","oldString":"        {role != null && role > 0 && (\n          <div\n            className=\"absolute top-[22px] -translate-x-1/2 text-center\"\n            style={{ left: `${X(role)}%` }}\n          >\n            <div className=\"mx-auto h-[30px] w-0 border-l-[2px] border-dashed border-ink opacity-70\" />\n            {!roleClose && (\n              <span className=\"vp-mono whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\">\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </div>\n        )}","newString":"        {role != null && role > 0 && (\n          <>\n            <div\n              className=\"absolute top-[22px] h-[30px] w-0 -translate-x-1/2 border-l-[2px] border-dashed border-ink opacity-70\"\n              style={{ left: `${X(role)}%` }}\n            />\n            {!roleClose && (\n              <span\n                className=\"vp-mono absolute top-[52px] -translate-x-1/2 whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\"\n                style={{ left: `${clampX(role, 11, 89)}%` }}\n              >\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </>\n        )}","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n/**\n * MetricViz — visualisations signature générées par les données du registre.\n * Chaque propriété produit ses propres dessins : façade SVG (étages, logements,\n * genre de construction), schéma de terrain à l'échelle (frontage × profondeur),\n * composition de valeur terrain/bâtiment, jauge de confiance, sparkline 2021-2026,\n * compteurs animés. SVG pur, palette encre/lime, aucun aléatoire : mêmes données,\n * même dessin.\n */\n\"use client\";\nimport { useEffect, useState } from \"react\";\n\n/** Nombre localisé (« 1 206,6 » en FR, « 1,206.6 » en EN). */\nexport function locNum(\n  v: number | null | undefined,\n  lang: string,\n  opts?: { unit?: string; maxFrac?: number }\n): string {\n  if (v == null) return \"—\";\n  const s = v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n    maximumFractionDigits: opts?.maxFrac ?? 1,\n  });\n  return opts?.unit ? `${s} ${opts.unit}` : s;\n}\n\n/* ---------------------------- compteur animé ---------------------------- */\n/** Rendu serveur = valeur finale (jamais « 0 $ » à l'écran) ; anime 0 → valeur au montage. */\nexport function CountUp({\n  value,\n  format,\n  duration = 900,\n}: {\n  value: number;\n  format: (v: number) => string;\n  duration?: number;\n}) {\n  const [display, setDisplay] = useState(value);\n\n  useEffect(() => {\n    if (window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches) return;\n    let raf = 0;\n    const t0 = performance.now();\n    const tick = (t: number) => {\n      const p = Math.min(1, (t - t0) / duration);\n      const eased = 1 - Math.pow(1 - p, 3);\n      setDisplay(value * eased);\n      if (p < 1) raf = requestAnimationFrame(tick);\n    };\n    raf = requestAnimationFrame(tick);\n    return () => cancelAnimationFrame(raf);\n  }, [value, duration]);\n\n  return <span>{format(display)}</span>;\n}\n\n/* --------------------------- jauge de confiance --------------------------- */\nexport function ConfidenceDial({\n  pct,\n  level,\n  label,\n}: {\n  pct: number;\n  level: \"A\" | \"B\" | \"C\" | \"D\";\n  label: string;\n}) {\n  const [mounted, setMounted] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setMounted(true), 60);\n    return () => clearTimeout(id);\n  }, []);\n  const R = 52;\n  const CIRC = Math.PI * R; // demi-cercle\n  const frac = mounted ? Math.max(0.02, Math.min(1, pct / 100)) : 0.02;\n  const color =\n    level === \"A\" ? \"var(--lime)\" : level === \"B\" ? \"var(--green)\" : level === \"C\" ? \"var(--amber)\" : \"var(--danger)\";\n  return (\n    <div className=\"flex flex-col items-center\" aria-label={`${label} : ${level} ${pct} %`}>\n      <svg viewBox=\"0 0 128 78\" className=\"w-[128px]\">\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"var(--surface-2)\" strokeWidth=\"12\" strokeLinecap=\"round\" />\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"rgba(20,24,20,0.85)\" strokeWidth=\"13.5\" strokeLinecap=\"round\" opacity=\"0.12\" />\n        <path\n          d=\"M 12 66 A 52 52 0 0 1 116 66\"\n          fill=\"none\"\n          stroke={color}\n          strokeWidth=\"12\"\n          strokeLinecap=\"round\"\n          strokeDasharray={`${CIRC * frac} ${CIRC}`}\n          style={{ transition: \"stroke-dasharray 1s cubic-bezier(.2,.8,.2,1)\" }}\n        />\n        {/* graduations */}\n        {[0, 0.25, 0.5, 0.75, 1].map((g) => {\n          const a = Math.PI * (1 - g);\n          const x1 = 64 + Math.cos(a) * 40;\n          const y1 = 66 - Math.sin(a) * 40;\n          const x2 = 64 + Math.cos(a) * 45;\n          const y2 = 66 - Math.sin(a) * 45;\n          return <line key={g} x1={x1} y1={y1} x2={x2} y2={y2} stroke=\"var(--ink)\" strokeWidth=\"1.4\" opacity=\"0.5\" />;\n        })}\n        <text x=\"64\" y=\"52\" textAnchor=\"middle\" fontSize=\"26\" fontWeight=\"700\" fill=\"var(--ink)\" fontFamily=\"var(--font-space-grotesk)\">\n          {level}\n        </text>\n        <text x=\"64\" y=\"70\" textAnchor=\"middle\" fontSize=\"10.5\" fontWeight=\"700\" fill=\"var(--ink-2)\" fontFamily=\"var(--font-jetbrains)\">\n          {pct} %\n        </text>\n      </svg>\n      <p className=\"klabel mt-1 text-center\">{label}</p>\n    </div>\n  );\n}\n\n/* --------------------------- façade de bâtiment --------------------------- */\n/** Terrain sans bâtiment : borne d'arpenteur et conifères, pas de fausse maison. */\nfunction VacantGlyph({ lang }: { lang: string }) {\n  const tree = (x: number, sc: number) => (\n    <g transform={`translate(${x}, 128) scale(${sc})`}>\n      <path d=\"M0,-46 L13,-14 L5,-14 L16,8 L-16,8 L-5,-14 L-13,-14 Z\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      <rect x=\"-2.5\" y=\"8\" width=\"5\" height=\"9\" fill=\"var(--ink)\" />\n    </g>\n  );\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain sans bâtiment\" : \"Vacant land\"}>\n      {tree(52, 1)}\n      {tree(96, 0.72)}\n      {tree(134, 0.9)}\n      {/* borne d'arpenteur */}\n      <g transform=\"translate(24, 128)\">\n        <rect x=\"-2.5\" y=\"-26\" width=\"5\" height=\"26\" fill=\"var(--ink)\" />\n        <rect x=\"-8\" y=\"-34\" width=\"16\" height=\"10\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n      </g>\n      <line x1=\"8\" y1=\"145\" x2=\"182\" y2=\"145\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"120\" textAnchor=\"middle\" fontSize=\"9\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n        {lang === \"fr\" ? \"SANS BÂTIMENT\" : \"NO BUILDING\"}\n      </text>\n    </svg>\n  );\n}\n\n/** Façade dessinée à partir du registre : étages, logements, genre de construction.\n *  Sans bâtiment au registre (ni aire, ni année, ni étages) → borne d'arpenteur. */\nexport function BuildingGlyph({\n  floors,\n  dwellings,\n  genre,\n  yearBuilt,\n  floorAreaM2,\n  lang = \"fr\",\n}: {\n  floors: number | null;\n  dwellings: number | null;\n  genre: string | null;\n  yearBuilt: number | null;\n  floorAreaM2?: number | null;\n  lang?: string;\n}) {\n  if (!floorAreaM2 && !yearBuilt && !floors && !dwellings) {\n    return <VacantGlyph lang={lang} />;\n  }\n  const nF = Math.max(1, Math.min(6, Math.round(floors ?? 1)));\n  const nD = Math.max(1, Math.min(24, Math.round(dwellings ?? 1)));\n  const winPerFloor = Math.max(1, Math.min(4, Math.ceil(nD / nF)));\n  const W = 190;\n  const bw = 64 + winPerFloor * 18;\n  const bx = (W - bw) / 2;\n  const fh = 24;\n  const groundY = 128;\n  const bodyTop = groundY - nF * fh;\n  const mansard = (genre ?? \"\").toLowerCase().includes(\"mansard\");\n  const flat = (genre ?? \"\").toLowerCase().includes(\"plain-pied\") && nF === 1;\n\n  const windows: React.ReactNode[] = [];\n  for (let f = 0; f < nF; f++) {\n    for (let wi = 0; wi < winPerFloor; wi++) {\n      const idx = f * winPerFloor + wi;\n      windows.push(\n        <rect\n          key={`${f}-${wi}`}\n          x={bx + 14 + wi * ((bw - 28) / winPerFloor) + ((bw - 28) / winPerFloor - 12) / 2}\n          y={bodyTop + f * fh + 6}\n          width=\"12\"\n          height=\"11\"\n          fill={idx < nD ? \"var(--lime)\" : \"var(--surface-2)\"}\n          stroke=\"var(--ink)\"\n          strokeWidth=\"1.2\"\n          className=\"vv-win\"\n          style={{ animationDelay: `${idx * 60}ms` }}\n        />\n      );\n    }\n  }\n\n  return (\n    <svg viewBox={`0 0 ${W} 150`} className=\"w-full max-w-[210px]\" aria-label=\"Silhouette du bâtiment selon le registre\">\n      {/* soleil-repère */}\n      <circle cx={W - 22} cy=\"22\" r=\"9\" fill=\"none\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      <circle cx={W - 22} cy=\"22\" r=\"3.5\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1\" />\n      {/* ombre décalée signature */}\n      <rect x={bx + 5} y={bodyTop + 5} width={bw} height={nF * fh} fill=\"rgba(20,24,20,0.12)\" />\n      {/* corps */}\n      <rect x={bx} y={bodyTop} width={bw} height={nF * fh} fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      {/* séparations d'étages */}\n      {Array.from({ length: nF - 1 }, (_, i) => (\n        <line key={i} x1={bx} y1={bodyTop + (i + 1) * fh} x2={bx + bw} y2={bodyTop + (i + 1) * fh} stroke=\"var(--ink)\" strokeWidth=\"1\" strokeDasharray=\"3 3\" opacity=\"0.4\" />\n      ))}\n      {/* toit */}\n      {flat ? (\n        <rect x={bx - 6} y={bodyTop - 8} width={bw + 12} height=\"8\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : mansard ? (\n        <path d={`M ${bx - 6} ${bodyTop} L ${bx + 10} ${bodyTop - 20} L ${bx + bw - 10} ${bodyTop - 20} L ${bx + bw + 6} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : (\n        <path d={`M ${bx - 8} ${bodyTop} L ${bx + bw / 2} ${bodyTop - 24} L ${bx + bw + 8} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      )}\n      {/* cheminée */}\n      <rect x={bx + bw - 22} y={bodyTop - (flat ? 20 : 30)} width=\"9\" height={flat ? 14 : 18} fill=\"var(--ink)\" />\n      {windows}\n      {/* porte */}\n      <rect x={bx + bw / 2 - 7} y={groundY - 17} width=\"14\" height=\"17\" fill=\"var(--ink)\" />\n      <circle cx={bx + bw / 2 + 3.5} cy={groundY - 8} r=\"1.4\" fill=\"var(--lime)\" />\n      {/* sol */}\n      <line x1=\"8\" y1={groundY} x2={W - 8} y2={groundY} stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <line x1=\"8\" y1={groundY + 4} x2={W - 8} y2={groundY + 4} stroke=\"var(--ink)\" strokeWidth=\"1\" opacity=\"0.3\" />\n      {/* millésime gravé */}\n      {yearBuilt && (\n        <text x={W / 2} y=\"146\" textAnchor=\"middle\" fontSize=\"10\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n          ANNO {yearBuilt}\n        </text>\n      )}\n    </svg>\n  );\n}\n\n/* ----------------------------- schéma du terrain ----------------------------- */\n/** Terrain à l'échelle réelle : frontage × profondeur déduite de la superficie. */\nexport function LotDiagram({\n  areaM2,\n  frontageM,\n  footprintM2,\n  lang,\n  isCondo,\n}: {\n  areaM2: number | null;\n  frontageM: number | null;\n  footprintM2: number | null;\n  lang: string;\n  isCondo?: boolean;\n}) {\n  if (!areaM2 || areaM2 <= 0) {\n    return (\n      <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain non applicable\" : \"Lot not applicable\"}>\n        <rect x=\"30\" y=\"30\" width=\"130\" height=\"86\" fill=\"var(--surface-2)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" strokeDasharray=\"5 5\" rx=\"6\" />\n        <text x=\"95\" y=\"66\" textAnchor=\"middle\" fontSize=\"20\" fill=\"var(--ink-3)\">⌂</text>\n        <text x=\"95\" y=\"90\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"1\">\n          {isCondo\n            ? lang === \"fr\" ? \"QUOTE-PART DE COPROPRIÉTÉ\" : \"CONDOMINIUM SHARE\"\n            : lang === \"fr\" ? \"TERRAIN NON INSCRIT AU RÔLE\" : \"LOT NOT ON ROLL\"}\n        </text>\n        <text x=\"95\" y=\"104\" textAnchor=\"middle\" fontSize=\"7\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n          {isCondo\n            ? lang === \"fr\" ? \"terrain commun indivis\" : \"undivided common land\"\n            : \"—\"}\n        </text>\n      </svg>\n    );\n  }\n  const front = frontageM && frontageM > 0 ? frontageM : Math.sqrt(areaM2);\n  const depth = areaM2 / front;\n  const ratio = Math.max(0.25, Math.min(4, depth / front));\n  const maxW = 150;\n  const maxH = 108;\n  let w = maxW;\n  let h = w * ratio;\n  if (h > maxH) {\n    h = maxH;\n    w = h / ratio;\n  }\n  const x = (190 - w) / 2;\n  const y = 118 - h;\n  // empreinte du bâtiment, à l'échelle de la superficie\n  const fpFrac = footprintM2 ? Math.min(0.8, footprintM2 / areaM2) : 0;\n  const fw = w * Math.sqrt(fpFrac) * 0.9;\n  const fh = h * Math.sqrt(fpFrac) * 0.9;\n  const fmtM = (v: number) => `${v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { maximumFractionDigits: 1 })} m`;\n\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label=\"Schéma du terrain à l'échelle\">\n      <defs>\n        <pattern id=\"hatch\" width=\"7\" height=\"7\" patternTransform=\"rotate(45)\" patternUnits=\"userSpaceOnUse\">\n          <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"7\" stroke=\"var(--green)\" strokeWidth=\"1\" opacity=\"0.35\" />\n        </pattern>\n      </defs>\n      {/* ombre + parcelle */}\n      <rect x={x + 4} y={y + 4} width={w} height={h} fill=\"rgba(20,24,20,0.12)\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"var(--lime-soft)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"url(#hatch)\" />\n      {/* empreinte bâtiment */}\n      {fpFrac > 0.005 && (\n        <rect x={x + (w - fw) / 2} y={y + h - fh - h * 0.08} width={fw} height={fh} fill=\"var(--ink)\" rx=\"2\">\n          <title>{lang === \"fr\" ? \"Empreinte approximative du bâtiment\" : \"Approximate building footprint\"}</title>\n        </rect>\n      )}\n      {/* cotes : frontage */}\n      <line x1={x} y1={y + h + 12} x2={x + w} y2={y + h + 12} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x} y1={y + h + 8} x2={x} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x + w} y1={y + h + 8} x2={x + w} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x + w / 2} y={y + h + 24} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n        {frontageM ? fmtM(front) : `≈ ${fmtM(front)}`}\n      </text>\n      {/* cotes : profondeur */}\n      <line x1={x - 10} y1={y} x2={x - 10} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y} x2={x - 6} y2={y} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y + h} x2={x - 6} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x - 16} y={y + h / 2} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\" transform={`rotate(-90 ${x - 16} ${y + h / 2})`}>\n        ≈ {fmtM(depth)}\n      </text>\n      {/* rue */}\n      <line x1=\"8\" y1=\"136\" x2=\"182\" y2=\"136\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"147\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\" letterSpacing=\"3\">\n        {lang === \"fr\" ? \"RUE\" : \"STREET\"}\n      </text>\n    </svg>\n  );\n}\n\n/* ------------------------- composition de la valeur ------------------------- */\nexport function ValueSplit({\n  land,\n  building,\n  previous,\n  total,\n  lang,\n  labels,\n}: {\n  land: number | null;\n  building: number | null;\n  previous: number | null;\n  total: number | null;\n  lang: string;\n  labels: { land: string; building: string; previous: string };\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  const l = land ?? 0;\n  const b = building ?? 0;\n  const sum = l + b;\n  if (sum <= 0) return null;\n  const lPct = (l / sum) * 100;\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { style: \"currency\", currency: \"CAD\", maximumFractionDigits: 0 }).format(v);\n  const delta = previous && previous > 0 && total ? ((total / previous - 1) * 100) : null;\n  return (\n    <div>\n      <div className=\"flex h-9 w-full overflow-hidden rounded-md border-[1.5px] border-ink\">\n        {lPct >= 1 && (\n          <div\n            className=\"flex items-center justify-center overflow-hidden bg-green transition-[width] duration-1000 ease-out\"\n            style={{ width: on ? `${lPct}%` : \"2%\" }}\n          >\n            {lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-paper\">{Math.round(lPct)} %</span>\n            )}\n          </div>\n        )}\n        {lPct <= 99 && (\n          <div className=\"flex flex-1 items-center justify-center overflow-hidden bg-ink\">\n            {100 - lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-lime\">{Math.round(100 - lPct)} %</span>\n            )}\n          </div>\n        )}\n      </div>\n      <div className=\"vp-mono mt-2 flex flex-wrap items-center gap-x-4 gap-y-1 text-[10.5px] uppercase tracking-[0.05em]\">\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-green\" />\n          {labels.land} {fmt(l)}\n        </span>\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-ink\" />\n          {labels.building} {fmt(b)}\n        </span>\n      </div>\n      {delta != null && (\n        <p className=\"vp-mono mt-2.5 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {labels.previous} {fmt(previous!)}{\" \"}\n          <span className={`ml-1 rounded-[4px] border border-ink px-1.5 py-0.5 font-bold ${delta >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n            {delta >= 0 ? \"+\" : \"\"}\n            {delta.toFixed(1)} %\n          </span>\n        </p>\n      )}\n    </div>\n  );\n}\n\n/* -------------------------------- sparkline -------------------------------- */\nexport function Sparkline({\n  history,\n  height = 44,\n}: {\n  history: { year: number; value: number | null }[];\n  height?: number;\n}) {\n  const pts = history.filter((h) => h.value != null) as { year: number; value: number }[];\n  if (pts.length < 2) return null;\n  const W = 132;\n  const min = Math.min(...pts.map((p) => p.value));\n  const max = Math.max(...pts.map((p) => p.value));\n  const span = max - min || 1;\n  const xy = pts.map((p, i) => [\n    6 + (i / (pts.length - 1)) * (W - 12),\n    height - 8 - ((p.value - min) / span) * (height - 18),\n  ]);\n  const line = xy.map(([x, y]) => `${x},${y}`).join(\" \");\n  const area = `${xy[0][0]},${height - 4} ${line} ${xy[xy.length - 1][0]},${height - 4}`;\n  const growth = ((pts[pts.length - 1].value / pts[0].value - 1) * 100);\n  const [x2, y2] = xy[xy.length - 1];\n  return (\n    <div className=\"flex items-end gap-2\">\n      <svg viewBox={`0 0 ${W} ${height}`} width={W} height={height} aria-label={`Évolution ${pts[0].year}-${pts[pts.length - 1].year}`}>\n        <polygon points={area} fill=\"var(--lime-soft)\" />\n        <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n        {xy.slice(0, -1).map(([x, y], i) => (\n          <circle key={i} cx={x} cy={y} r=\"2\" fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"1.1\" />\n        ))}\n        <circle cx={x2} cy={y2} r=\"3.6\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      </svg>\n      <span className={`vp-mono mb-0.5 rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${growth >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n        {growth >= 0 ? \"+\" : \"\"}\n        {growth.toFixed(0)} %\n      </span>\n    </div>\n  );\n}\n\n/* ------------------------- fourchette visuelle P10-P90 ------------------------- */\n/** Barre de fourchette : segment P10→P90, losange = estimation, tiret = rôle. */\nexport function RangeBar({\n  low,\n  estimate,\n  high,\n  role,\n  lang,\n}: {\n  low: number;\n  estimate: number;\n  high: number;\n  role: number | null;\n  lang: string;\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  if (!(high > low) || !Number.isFinite(estimate)) return null;\n  const lo = Math.min(low, role ?? low);\n  const hi = Math.max(high, role ?? high);\n  const pad = (hi - lo) * 0.09 || hi * 0.05;\n  const min = lo - pad;\n  const max = hi + pad;\n  const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));\n  // les étiquettes (centrées, nowrap) restent dans la carte même aux extrêmes\n  const clampX = (v: number, lo: number, hi: number) => Math.max(lo, Math.min(hi, X(v)));\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n      notation: v >= 1_000_000 ? \"compact\" : \"standard\",\n    }).format(v);\n  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;\n  return (\n    <div aria-label={`${fmt(low)} – ${fmt(high)}`}>\n      <div className=\"relative h-[64px]\">\n        {/* étiquette estimation */}\n        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? clampX(estimate, 18, 82) : 50}%` }}\n        >\n          <span className=\"vp-mono rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\">\n            {lang === \"fr\" ? \"ESTIMATION\" : \"ESTIMATE\"} {fmt(estimate)}\n          </span>\n        </div>\n        {/* piste */}\n        <div className=\"absolute left-0 right-0 top-[30px] h-[14px] rounded-[7px] border-[1.5px] border-ink bg-surface-2\" />\n        {/* segment P10-P90 */}\n        <div\n          className=\"absolute top-[30px] h-[14px] origin-left rounded-[7px] border-[1.5px] border-ink transition-transform duration-1000 ease-out\"\n          style={{\n            left: `${X(low)}%`,\n            width: `${Math.max(2, X(high) - X(low))}%`,\n            transform: on ? \"scaleX(1)\" : \"scaleX(0.05)\",\n            background:\n              \"repeating-linear-gradient(-45deg, var(--lime-soft), var(--lime-soft) 5px, rgba(255,81,72,0.35) 5px, rgba(255,81,72,0.35) 7px)\",\n          }}\n        />\n        {/* marqueur estimation (losange) */}\n        <div\n          className=\"absolute top-[29px] -translate-x-1/2 transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <div className=\"h-[16px] w-[16px] rotate-45 border-[1.8px] border-ink bg-lime\" />\n        </div>\n        {/* marqueur rôle */}\n        {role != null && role > 0 && (\n          <div\n            className=\"absolute top-[22px] -translate-x-1/2 text-center\"\n            style={{ left: `${X(role)}%` }}\n          >\n            <div className=\"mx-auto h-[30px] w-0 border-l-[2px] border-dashed border-ink opacity-70\" />\n            {!roleClose && (\n              <span className=\"vp-mono whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\">\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </div>\n        )}\n        {/* bornes */}\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 whitespace-nowrap text-[10px] font-bold text-ink-2\"\n          style={{ left: `${clampX(low, 11, 89)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 whitespace-nowrap text-[10px] font-bold text-ink-2\"\n          style={{ left: `${clampX(high, 11, 89)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>\n      </div>\n    </div>\n  );\n}\n\n/* --------------------------- courbe d'indice de marché --------------------------- */\n/** Indice mensuel rebasé à 100 au premier mois — la trajectoire du marché local. */\nexport function MarketIndexChart({\n  series,\n  lang,\n}: {\n  series: { month: string; idx: number }[];\n  lang: string;\n}) {\n  if (series.length < 6) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const W = 640;\n  const H = 200;\n  const padL = 40;\n  const padR = 52;\n  const padT = 18;\n  const padB = 28;\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const x = (i: number) => padL + (i / (pts.length - 1)) * (W - padL - padR);\n  const y = (v: number) => padT + (1 - (v - min) / span) * (H - padT - padB);\n  const line = pts.map((v, i) => `${x(i).toFixed(1)},${y(v).toFixed(1)}`).join(\" \");\n  const area = `${x(0)},${H - padB} ${line} ${x(pts.length - 1)},${H - padB}`;\n  // graduations : janvier de chaque année\n  const years = series\n    .map((p, i) => ({ i, month: p.month }))\n    .filter((p) => p.month.endsWith(\"-01\"));\n  // lignes horizontales : min / 100 / max arrondis\n  const gridVals = [...new Set([Math.round(min), 100, Math.round(max)])].filter(\n    (v) => v >= min - 1 && v <= max + 1\n  );\n  const lastV = pts[pts.length - 1];\n  return (\n    <svg\n      viewBox={`0 0 ${W} ${H}`}\n      className=\"h-auto w-full\"\n      role=\"img\"\n      aria-label={\n        lang === \"fr\"\n          ? `Indice de marché ${series[0].month} à ${series[series.length - 1].month}`\n          : `Market index ${series[0].month} to ${series[series.length - 1].month}`\n      }\n    >\n      {gridVals.map((v) => (\n        <g key={v}>\n          <line\n            x1={padL}\n            y1={y(v)}\n            x2={W - padR}\n            y2={y(v)}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            strokeDasharray=\"3 5\"\n            opacity={v === 100 ? 0.45 : 0.18}\n          />\n          <text\n            x={padL - 6}\n            y={y(v) + 3.5}\n            textAnchor=\"end\"\n            fontSize=\"10\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-3)\"\n          >\n            {v}\n          </text>\n        </g>\n      ))}\n      {years.map((p) => (\n        <g key={p.month}>\n          <line\n            x1={x(p.i)}\n            y1={padT}\n            x2={x(p.i)}\n            y2={H - padB}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            opacity=\"0.1\"\n          />\n          <text\n            x={x(p.i)}\n            y={H - 10}\n            textAnchor=\"middle\"\n            fontSize=\"10\"\n            fontWeight=\"700\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-2)\"\n          >\n            {p.month.slice(0, 4)}\n          </text>\n        </g>\n      ))}\n      <polygon points={area} fill=\"var(--lime-soft)\" opacity=\"0.8\" />\n      <polyline\n        points={line}\n        fill=\"none\"\n        stroke=\"var(--green)\"\n        strokeWidth=\"2.6\"\n        strokeLinejoin=\"round\"\n        strokeLinecap=\"round\"\n      />\n      <circle\n        cx={x(pts.length - 1)}\n        cy={y(lastV)}\n        r=\"5\"\n        fill=\"var(--lime)\"\n        stroke=\"var(--ink)\"\n        strokeWidth=\"1.8\"\n      />\n      <text\n        x={x(pts.length - 1) + 9}\n        y={y(lastV) + 4}\n        fontSize=\"11.5\"\n        fontWeight=\"700\"\n        fontFamily=\"var(--font-jetbrains)\"\n        fill=\"var(--ink)\"\n      >\n        {Math.round(lastV)}\n      </text>\n    </svg>\n  );\n}\n\n/* ----------------------- position dans la municipalité ----------------------- */\n/** Distribution stylisée (cloche) : les barres sous le percentile sont pleines. */\nexport function PercentileBar({\n  pct,\n  lang,\n}: {\n  pct: number;\n  lang: string;\n}) {\n  const N = 36;\n  const bars = Array.from({ length: N }, (_, i) => {\n    const z = (i / (N - 1) - 0.42) / 0.24; // cloche légèrement asymétrique (droite étirée)\n    return Math.exp(-0.5 * z * z) * (i / N > 0.5 ? 1 + (i / N - 0.5) * 0.35 : 1);\n  });\n  const maxB = Math.max(...bars);\n  const p = Math.max(0, Math.min(100, pct));\n  return (\n    <div aria-label={`${Math.round(p)} %`}>\n      <div className=\"relative flex h-[72px] items-end px-0.5\" style={{ gap: \"2px\" }}>\n        {bars.map((b, i) => {\n          const filled = (i + 0.5) / N <= p / 100;\n          return (\n            <div\n              key={i}\n              className=\"min-w-0 flex-1 rounded-t-[2px] border border-ink transition-colors\"\n              style={{\n                height: `${8 + (b / maxB) * 88}%`,\n                background: filled ? \"var(--lime)\" : \"var(--surface-2)\",\n                borderWidth: \"1px\",\n                opacity: filled ? 1 : 0.55,\n              }}\n            />\n          );\n        })}\n        <div\n          className=\"absolute bottom-0 top-[-6px] w-0 border-l-[2px] border-ink\"\n          style={{ left: `${p}%` }}\n        >\n          <span className=\"vp-mono absolute -top-1 left-1.5 whitespace-nowrap rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\" style={p > 55 ? { left: \"auto\", right: \"6px\" } : undefined}>\n            {lang === \"fr\" ? \"P\" : \"P\"}\n            {Math.round(p)}\n          </span>\n        </div>\n      </div>\n    </div>\n  );\n}\n\n/* ------------------------ mini-courbe d'indice (accueil) ------------------------ */\nexport function IndexSpark({\n  series,\n  width = 150,\n  height = 46,\n}: {\n  series: { month: string; idx: number }[];\n  width?: number;\n  height?: number;\n}) {\n  if (series.length < 2) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const xy = pts.map((v, i) => [\n    4 + (i / (pts.length - 1)) * (width - 8),\n    height - 6 - ((v - min) / span) * (height - 14),\n  ]);\n  const line = xy.map(([x, y]) => `${x.toFixed(1)},${y.toFixed(1)}`).join(\" \");\n  const area = `${xy[0][0]},${height - 3} ${line} ${xy[xy.length - 1][0]},${height - 3}`;\n  const [lx, ly] = xy[xy.length - 1];\n  return (\n    <svg viewBox={`0 0 ${width} ${height}`} className=\"h-auto w-full\" aria-hidden=\"true\">\n      <polygon points={area} fill=\"var(--lime-soft)\" />\n      <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n      <circle cx={lx} cy={ly} r=\"3.4\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n    </svg>\n  );\n}\n\n/* ------------------------------ frise temporelle ------------------------------ */\nexport function EraLine({ year, lang }: { year: number | null; lang: string }) {\n  if (!year || year < 1750) return null;\n  const min = Math.min(1900, Math.floor((year - 10) / 50) * 50);\n  const max = 2026;\n  const frac = Math.max(0, Math.min(1, (year - min) / (max - min)));\n  const W = 190;\n  const x = 12 + frac * (W - 24);\n  const age = 2026 - year;\n  return (\n    <svg viewBox={`0 0 ${W} 40`} className=\"w-full max-w-[210px]\" aria-label={`Construction ${year}`}>\n      <line x1=\"12\" y1=\"24\" x2={W - 12} y2=\"24\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      {[...new Set([min, Math.round((min + max) / 2 / 50) * 50, 2000])].map((yr) => {\n        const gx = 12 + ((yr - min) / (max - min)) * (W - 24);\n        return (\n          <g key={yr}>\n            <line x1={gx} y1=\"20\" x2={gx} y2=\"28\" stroke=\"var(--ink)\" strokeWidth=\"1.2\" opacity=\"0.5\" />\n            <text x={gx} y=\"38\" textAnchor=\"middle\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n              {yr}\n            </text>\n          </g>\n        );\n      })}\n      <g transform={`translate(${x}, 24)`}>\n        <rect x=\"-7\" y=\"-7\" width=\"14\" height=\"14\" transform=\"rotate(45)\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n        <text y=\"-13\" textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n          {year}\n        </text>\n      </g>\n      <text x={W - 12} y=\"12\" textAnchor=\"end\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n        {age} {lang === \"fr\" ? \"ANS\" : \"YRS\"}\n      </text>\n    </svg>\n  );\n}\n","structuredPatch":[{"oldStart":504,"oldLines":17,"newStart":504,"newLines":20,"lines":["         </div>","         {/* marqueur rôle */}","         {role != null && role > 0 && (","-          <div","-            className=\"absolute top-[22px] -translate-x-1/2 text-center\"","-            style={{ left: `${X(role)}%` }}","-          >","-            <div className=\"mx-auto h-[30px] w-0 border-l-[2px] border-dashed border-ink opacity-70\" />","+          <>","+            <div","+              className=\"absolute top-[22px] h-[30px] w-0 -translate-x-1/2 border-l-[2px] border-dashed border-ink opacity-70\"","+              style={{ left: `${X(role)}%` }}","+            />","             {!roleClose && (","-              <span className=\"vp-mono whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\">","+              <span","+                className=\"vp-mono absolute top-[52px] -translate-x-1/2 whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\"","+                style={{ left: `${clampX(role, 11, 89)}%` }}","+              >","                 {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}","               </span>","             )}","-          </div>","+          </>","         )}","         {/* bornes */}","         <span"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwzQ8tFUnfQUdNkdQDq","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":131,"cache_creation_input_tokens":1094,"cache_read_input_tokens":148367,"cache_creation":{"ephemeral_5m_input_tokens":1094,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"b77eefcf-9a7c-4fdb-ae9b-0b63b971a003","request_id":"req_011CeJwzMzQFakicTY3m8LHq"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwzQ8tFUnfQUdNkdQDq","type":"message","role":"assistant","content":[{"type":"text","text":"J'ajoute une garde : l'étiquette « Rôle » se masque si elle chevaucherait P10/P90 (la valeur du rôle est de toute façon affichée juste dessous) :"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":1094,"cache_read_input_tokens":148367,"cache_creation":{"ephemeral_5m_input_tokens":1094,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"9730696d-fc6a-4251-b8f2-7caaf726f843","request_id":"req_011CeJwzMzQFakicTY3m8LHq"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJwzQ8tFUnfQUdNkdQDq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017t97XifaNSGtbUoYnYZgEt","name":"Edit","input":{"replace_all":false,"file_path":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","old_string":"  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;","new_string":"  // l'étiquette du rôle s'efface si elle chevaucherait l'estimation ou les bornes\n  const roleClose =\n    role != null &&\n    (Math.abs(X(role) - X(estimate)) < 9 ||\n      Math.abs(X(role) - X(low)) < 15 ||\n      Math.abs(X(role) - X(high)) < 15);"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":1094,"cache_read_input_tokens":148367,"cache_creation":{"ephemeral_5m_input_tokens":1094,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"e992d691-0ef2-4c6f-96e7-b24ac65de314","request_id":"req_011CeJwzMzQFakicTY3m8LHq"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017t97XifaNSGtbUoYnYZgEt","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"48a66234-5b16-48d5-bad9-2f9d4e5928c4","timestamp":"2026-08-23T03:12:25.732Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/MetricViz.tsx","oldString":"  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;","newString":"  // l'étiquette du rôle s'efface si elle chevaucherait l'estimation ou les bornes\n  const roleClose =\n    role != null &&\n    (Math.abs(X(role) - X(estimate)) < 9 ||\n      Math.abs(X(role) - X(low)) < 15 ||\n      Math.abs(X(role) - X(high)) < 15);","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n/**\n * MetricViz — visualisations signature générées par les données du registre.\n * Chaque propriété produit ses propres dessins : façade SVG (étages, logements,\n * genre de construction), schéma de terrain à l'échelle (frontage × profondeur),\n * composition de valeur terrain/bâtiment, jauge de confiance, sparkline 2021-2026,\n * compteurs animés. SVG pur, palette encre/lime, aucun aléatoire : mêmes données,\n * même dessin.\n */\n\"use client\";\nimport { useEffect, useState } from \"react\";\n\n/** Nombre localisé (« 1 206,6 » en FR, « 1,206.6 » en EN). */\nexport function locNum(\n  v: number | null | undefined,\n  lang: string,\n  opts?: { unit?: string; maxFrac?: number }\n): string {\n  if (v == null) return \"—\";\n  const s = v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n    maximumFractionDigits: opts?.maxFrac ?? 1,\n  });\n  return opts?.unit ? `${s} ${opts.unit}` : s;\n}\n\n/* ---------------------------- compteur animé ---------------------------- */\n/** Rendu serveur = valeur finale (jamais « 0 $ » à l'écran) ; anime 0 → valeur au montage. */\nexport function CountUp({\n  value,\n  format,\n  duration = 900,\n}: {\n  value: number;\n  format: (v: number) => string;\n  duration?: number;\n}) {\n  const [display, setDisplay] = useState(value);\n\n  useEffect(() => {\n    if (window.matchMedia(\"(prefers-reduced-motion: reduce)\").matches) return;\n    let raf = 0;\n    const t0 = performance.now();\n    const tick = (t: number) => {\n      const p = Math.min(1, (t - t0) / duration);\n      const eased = 1 - Math.pow(1 - p, 3);\n      setDisplay(value * eased);\n      if (p < 1) raf = requestAnimationFrame(tick);\n    };\n    raf = requestAnimationFrame(tick);\n    return () => cancelAnimationFrame(raf);\n  }, [value, duration]);\n\n  return <span>{format(display)}</span>;\n}\n\n/* --------------------------- jauge de confiance --------------------------- */\nexport function ConfidenceDial({\n  pct,\n  level,\n  label,\n}: {\n  pct: number;\n  level: \"A\" | \"B\" | \"C\" | \"D\";\n  label: string;\n}) {\n  const [mounted, setMounted] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setMounted(true), 60);\n    return () => clearTimeout(id);\n  }, []);\n  const R = 52;\n  const CIRC = Math.PI * R; // demi-cercle\n  const frac = mounted ? Math.max(0.02, Math.min(1, pct / 100)) : 0.02;\n  const color =\n    level === \"A\" ? \"var(--lime)\" : level === \"B\" ? \"var(--green)\" : level === \"C\" ? \"var(--amber)\" : \"var(--danger)\";\n  return (\n    <div className=\"flex flex-col items-center\" aria-label={`${label} : ${level} ${pct} %`}>\n      <svg viewBox=\"0 0 128 78\" className=\"w-[128px]\">\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"var(--surface-2)\" strokeWidth=\"12\" strokeLinecap=\"round\" />\n        <path d=\"M 12 66 A 52 52 0 0 1 116 66\" fill=\"none\" stroke=\"rgba(20,24,20,0.85)\" strokeWidth=\"13.5\" strokeLinecap=\"round\" opacity=\"0.12\" />\n        <path\n          d=\"M 12 66 A 52 52 0 0 1 116 66\"\n          fill=\"none\"\n          stroke={color}\n          strokeWidth=\"12\"\n          strokeLinecap=\"round\"\n          strokeDasharray={`${CIRC * frac} ${CIRC}`}\n          style={{ transition: \"stroke-dasharray 1s cubic-bezier(.2,.8,.2,1)\" }}\n        />\n        {/* graduations */}\n        {[0, 0.25, 0.5, 0.75, 1].map((g) => {\n          const a = Math.PI * (1 - g);\n          const x1 = 64 + Math.cos(a) * 40;\n          const y1 = 66 - Math.sin(a) * 40;\n          const x2 = 64 + Math.cos(a) * 45;\n          const y2 = 66 - Math.sin(a) * 45;\n          return <line key={g} x1={x1} y1={y1} x2={x2} y2={y2} stroke=\"var(--ink)\" strokeWidth=\"1.4\" opacity=\"0.5\" />;\n        })}\n        <text x=\"64\" y=\"52\" textAnchor=\"middle\" fontSize=\"26\" fontWeight=\"700\" fill=\"var(--ink)\" fontFamily=\"var(--font-space-grotesk)\">\n          {level}\n        </text>\n        <text x=\"64\" y=\"70\" textAnchor=\"middle\" fontSize=\"10.5\" fontWeight=\"700\" fill=\"var(--ink-2)\" fontFamily=\"var(--font-jetbrains)\">\n          {pct} %\n        </text>\n      </svg>\n      <p className=\"klabel mt-1 text-center\">{label}</p>\n    </div>\n  );\n}\n\n/* --------------------------- façade de bâtiment --------------------------- */\n/** Terrain sans bâtiment : borne d'arpenteur et conifères, pas de fausse maison. */\nfunction VacantGlyph({ lang }: { lang: string }) {\n  const tree = (x: number, sc: number) => (\n    <g transform={`translate(${x}, 128) scale(${sc})`}>\n      <path d=\"M0,-46 L13,-14 L5,-14 L16,8 L-16,8 L-5,-14 L-13,-14 Z\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      <rect x=\"-2.5\" y=\"8\" width=\"5\" height=\"9\" fill=\"var(--ink)\" />\n    </g>\n  );\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain sans bâtiment\" : \"Vacant land\"}>\n      {tree(52, 1)}\n      {tree(96, 0.72)}\n      {tree(134, 0.9)}\n      {/* borne d'arpenteur */}\n      <g transform=\"translate(24, 128)\">\n        <rect x=\"-2.5\" y=\"-26\" width=\"5\" height=\"26\" fill=\"var(--ink)\" />\n        <rect x=\"-8\" y=\"-34\" width=\"16\" height=\"10\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n      </g>\n      <line x1=\"8\" y1=\"145\" x2=\"182\" y2=\"145\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"120\" textAnchor=\"middle\" fontSize=\"9\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n        {lang === \"fr\" ? \"SANS BÂTIMENT\" : \"NO BUILDING\"}\n      </text>\n    </svg>\n  );\n}\n\n/** Façade dessinée à partir du registre : étages, logements, genre de construction.\n *  Sans bâtiment au registre (ni aire, ni année, ni étages) → borne d'arpenteur. */\nexport function BuildingGlyph({\n  floors,\n  dwellings,\n  genre,\n  yearBuilt,\n  floorAreaM2,\n  lang = \"fr\",\n}: {\n  floors: number | null;\n  dwellings: number | null;\n  genre: string | null;\n  yearBuilt: number | null;\n  floorAreaM2?: number | null;\n  lang?: string;\n}) {\n  if (!floorAreaM2 && !yearBuilt && !floors && !dwellings) {\n    return <VacantGlyph lang={lang} />;\n  }\n  const nF = Math.max(1, Math.min(6, Math.round(floors ?? 1)));\n  const nD = Math.max(1, Math.min(24, Math.round(dwellings ?? 1)));\n  const winPerFloor = Math.max(1, Math.min(4, Math.ceil(nD / nF)));\n  const W = 190;\n  const bw = 64 + winPerFloor * 18;\n  const bx = (W - bw) / 2;\n  const fh = 24;\n  const groundY = 128;\n  const bodyTop = groundY - nF * fh;\n  const mansard = (genre ?? \"\").toLowerCase().includes(\"mansard\");\n  const flat = (genre ?? \"\").toLowerCase().includes(\"plain-pied\") && nF === 1;\n\n  const windows: React.ReactNode[] = [];\n  for (let f = 0; f < nF; f++) {\n    for (let wi = 0; wi < winPerFloor; wi++) {\n      const idx = f * winPerFloor + wi;\n      windows.push(\n        <rect\n          key={`${f}-${wi}`}\n          x={bx + 14 + wi * ((bw - 28) / winPerFloor) + ((bw - 28) / winPerFloor - 12) / 2}\n          y={bodyTop + f * fh + 6}\n          width=\"12\"\n          height=\"11\"\n          fill={idx < nD ? \"var(--lime)\" : \"var(--surface-2)\"}\n          stroke=\"var(--ink)\"\n          strokeWidth=\"1.2\"\n          className=\"vv-win\"\n          style={{ animationDelay: `${idx * 60}ms` }}\n        />\n      );\n    }\n  }\n\n  return (\n    <svg viewBox={`0 0 ${W} 150`} className=\"w-full max-w-[210px]\" aria-label=\"Silhouette du bâtiment selon le registre\">\n      {/* soleil-repère */}\n      <circle cx={W - 22} cy=\"22\" r=\"9\" fill=\"none\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      <circle cx={W - 22} cy=\"22\" r=\"3.5\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1\" />\n      {/* ombre décalée signature */}\n      <rect x={bx + 5} y={bodyTop + 5} width={bw} height={nF * fh} fill=\"rgba(20,24,20,0.12)\" />\n      {/* corps */}\n      <rect x={bx} y={bodyTop} width={bw} height={nF * fh} fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      {/* séparations d'étages */}\n      {Array.from({ length: nF - 1 }, (_, i) => (\n        <line key={i} x1={bx} y1={bodyTop + (i + 1) * fh} x2={bx + bw} y2={bodyTop + (i + 1) * fh} stroke=\"var(--ink)\" strokeWidth=\"1\" strokeDasharray=\"3 3\" opacity=\"0.4\" />\n      ))}\n      {/* toit */}\n      {flat ? (\n        <rect x={bx - 6} y={bodyTop - 8} width={bw + 12} height=\"8\" fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : mansard ? (\n        <path d={`M ${bx - 6} ${bodyTop} L ${bx + 10} ${bodyTop - 20} L ${bx + bw - 10} ${bodyTop - 20} L ${bx + bw + 6} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      ) : (\n        <path d={`M ${bx - 8} ${bodyTop} L ${bx + bw / 2} ${bodyTop - 24} L ${bx + bw + 8} ${bodyTop} Z`} fill=\"var(--green)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      )}\n      {/* cheminée */}\n      <rect x={bx + bw - 22} y={bodyTop - (flat ? 20 : 30)} width=\"9\" height={flat ? 14 : 18} fill=\"var(--ink)\" />\n      {windows}\n      {/* porte */}\n      <rect x={bx + bw / 2 - 7} y={groundY - 17} width=\"14\" height=\"17\" fill=\"var(--ink)\" />\n      <circle cx={bx + bw / 2 + 3.5} cy={groundY - 8} r=\"1.4\" fill=\"var(--lime)\" />\n      {/* sol */}\n      <line x1=\"8\" y1={groundY} x2={W - 8} y2={groundY} stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <line x1=\"8\" y1={groundY + 4} x2={W - 8} y2={groundY + 4} stroke=\"var(--ink)\" strokeWidth=\"1\" opacity=\"0.3\" />\n      {/* millésime gravé */}\n      {yearBuilt && (\n        <text x={W / 2} y=\"146\" textAnchor=\"middle\" fontSize=\"10\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"2\">\n          ANNO {yearBuilt}\n        </text>\n      )}\n    </svg>\n  );\n}\n\n/* ----------------------------- schéma du terrain ----------------------------- */\n/** Terrain à l'échelle réelle : frontage × profondeur déduite de la superficie. */\nexport function LotDiagram({\n  areaM2,\n  frontageM,\n  footprintM2,\n  lang,\n  isCondo,\n}: {\n  areaM2: number | null;\n  frontageM: number | null;\n  footprintM2: number | null;\n  lang: string;\n  isCondo?: boolean;\n}) {\n  if (!areaM2 || areaM2 <= 0) {\n    return (\n      <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label={lang === \"fr\" ? \"Terrain non applicable\" : \"Lot not applicable\"}>\n        <rect x=\"30\" y=\"30\" width=\"130\" height=\"86\" fill=\"var(--surface-2)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" strokeDasharray=\"5 5\" rx=\"6\" />\n        <text x=\"95\" y=\"66\" textAnchor=\"middle\" fontSize=\"20\" fill=\"var(--ink-3)\">⌂</text>\n        <text x=\"95\" y=\"90\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-2)\" letterSpacing=\"1\">\n          {isCondo\n            ? lang === \"fr\" ? \"QUOTE-PART DE COPROPRIÉTÉ\" : \"CONDOMINIUM SHARE\"\n            : lang === \"fr\" ? \"TERRAIN NON INSCRIT AU RÔLE\" : \"LOT NOT ON ROLL\"}\n        </text>\n        <text x=\"95\" y=\"104\" textAnchor=\"middle\" fontSize=\"7\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n          {isCondo\n            ? lang === \"fr\" ? \"terrain commun indivis\" : \"undivided common land\"\n            : \"—\"}\n        </text>\n      </svg>\n    );\n  }\n  const front = frontageM && frontageM > 0 ? frontageM : Math.sqrt(areaM2);\n  const depth = areaM2 / front;\n  const ratio = Math.max(0.25, Math.min(4, depth / front));\n  const maxW = 150;\n  const maxH = 108;\n  let w = maxW;\n  let h = w * ratio;\n  if (h > maxH) {\n    h = maxH;\n    w = h / ratio;\n  }\n  const x = (190 - w) / 2;\n  const y = 118 - h;\n  // empreinte du bâtiment, à l'échelle de la superficie\n  const fpFrac = footprintM2 ? Math.min(0.8, footprintM2 / areaM2) : 0;\n  const fw = w * Math.sqrt(fpFrac) * 0.9;\n  const fh = h * Math.sqrt(fpFrac) * 0.9;\n  const fmtM = (v: number) => `${v.toLocaleString(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { maximumFractionDigits: 1 })} m`;\n\n  return (\n    <svg viewBox=\"0 0 190 150\" className=\"w-full max-w-[210px]\" aria-label=\"Schéma du terrain à l'échelle\">\n      <defs>\n        <pattern id=\"hatch\" width=\"7\" height=\"7\" patternTransform=\"rotate(45)\" patternUnits=\"userSpaceOnUse\">\n          <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"7\" stroke=\"var(--green)\" strokeWidth=\"1\" opacity=\"0.35\" />\n        </pattern>\n      </defs>\n      {/* ombre + parcelle */}\n      <rect x={x + 4} y={y + 4} width={w} height={h} fill=\"rgba(20,24,20,0.12)\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"var(--lime-soft)\" stroke=\"var(--ink)\" strokeWidth=\"2\" />\n      <rect x={x} y={y} width={w} height={h} fill=\"url(#hatch)\" />\n      {/* empreinte bâtiment */}\n      {fpFrac > 0.005 && (\n        <rect x={x + (w - fw) / 2} y={y + h - fh - h * 0.08} width={fw} height={fh} fill=\"var(--ink)\" rx=\"2\">\n          <title>{lang === \"fr\" ? \"Empreinte approximative du bâtiment\" : \"Approximate building footprint\"}</title>\n        </rect>\n      )}\n      {/* cotes : frontage */}\n      <line x1={x} y1={y + h + 12} x2={x + w} y2={y + h + 12} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x} y1={y + h + 8} x2={x} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x + w} y1={y + h + 8} x2={x + w} y2={y + h + 16} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x + w / 2} y={y + h + 24} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n        {frontageM ? fmtM(front) : `≈ ${fmtM(front)}`}\n      </text>\n      {/* cotes : profondeur */}\n      <line x1={x - 10} y1={y} x2={x - 10} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y} x2={x - 6} y2={y} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <line x1={x - 14} y1={y + h} x2={x - 6} y2={y + h} stroke=\"var(--ink)\" strokeWidth=\"1.3\" />\n      <text x={x - 16} y={y + h / 2} textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\" transform={`rotate(-90 ${x - 16} ${y + h / 2})`}>\n        ≈ {fmtM(depth)}\n      </text>\n      {/* rue */}\n      <line x1=\"8\" y1=\"136\" x2=\"182\" y2=\"136\" stroke=\"var(--ink)\" strokeWidth=\"2.2\" />\n      <text x=\"95\" y=\"147\" textAnchor=\"middle\" fontSize=\"8.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\" letterSpacing=\"3\">\n        {lang === \"fr\" ? \"RUE\" : \"STREET\"}\n      </text>\n    </svg>\n  );\n}\n\n/* ------------------------- composition de la valeur ------------------------- */\nexport function ValueSplit({\n  land,\n  building,\n  previous,\n  total,\n  lang,\n  labels,\n}: {\n  land: number | null;\n  building: number | null;\n  previous: number | null;\n  total: number | null;\n  lang: string;\n  labels: { land: string; building: string; previous: string };\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  const l = land ?? 0;\n  const b = building ?? 0;\n  const sum = l + b;\n  if (sum <= 0) return null;\n  const lPct = (l / sum) * 100;\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", { style: \"currency\", currency: \"CAD\", maximumFractionDigits: 0 }).format(v);\n  const delta = previous && previous > 0 && total ? ((total / previous - 1) * 100) : null;\n  return (\n    <div>\n      <div className=\"flex h-9 w-full overflow-hidden rounded-md border-[1.5px] border-ink\">\n        {lPct >= 1 && (\n          <div\n            className=\"flex items-center justify-center overflow-hidden bg-green transition-[width] duration-1000 ease-out\"\n            style={{ width: on ? `${lPct}%` : \"2%\" }}\n          >\n            {lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-paper\">{Math.round(lPct)} %</span>\n            )}\n          </div>\n        )}\n        {lPct <= 99 && (\n          <div className=\"flex flex-1 items-center justify-center overflow-hidden bg-ink\">\n            {100 - lPct >= 14 && (\n              <span className=\"vp-mono px-1 text-[10px] font-bold text-lime\">{Math.round(100 - lPct)} %</span>\n            )}\n          </div>\n        )}\n      </div>\n      <div className=\"vp-mono mt-2 flex flex-wrap items-center gap-x-4 gap-y-1 text-[10.5px] uppercase tracking-[0.05em]\">\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-green\" />\n          {labels.land} {fmt(l)}\n        </span>\n        <span className=\"flex items-center gap-1.5 text-ink-2\">\n          <span className=\"inline-block h-2.5 w-2.5 rounded-[2px] border border-ink bg-ink\" />\n          {labels.building} {fmt(b)}\n        </span>\n      </div>\n      {delta != null && (\n        <p className=\"vp-mono mt-2.5 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {labels.previous} {fmt(previous!)}{\" \"}\n          <span className={`ml-1 rounded-[4px] border border-ink px-1.5 py-0.5 font-bold ${delta >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n            {delta >= 0 ? \"+\" : \"\"}\n            {delta.toFixed(1)} %\n          </span>\n        </p>\n      )}\n    </div>\n  );\n}\n\n/* -------------------------------- sparkline -------------------------------- */\nexport function Sparkline({\n  history,\n  height = 44,\n}: {\n  history: { year: number; value: number | null }[];\n  height?: number;\n}) {\n  const pts = history.filter((h) => h.value != null) as { year: number; value: number }[];\n  if (pts.length < 2) return null;\n  const W = 132;\n  const min = Math.min(...pts.map((p) => p.value));\n  const max = Math.max(...pts.map((p) => p.value));\n  const span = max - min || 1;\n  const xy = pts.map((p, i) => [\n    6 + (i / (pts.length - 1)) * (W - 12),\n    height - 8 - ((p.value - min) / span) * (height - 18),\n  ]);\n  const line = xy.map(([x, y]) => `${x},${y}`).join(\" \");\n  const area = `${xy[0][0]},${height - 4} ${line} ${xy[xy.length - 1][0]},${height - 4}`;\n  const growth = ((pts[pts.length - 1].value / pts[0].value - 1) * 100);\n  const [x2, y2] = xy[xy.length - 1];\n  return (\n    <div className=\"flex items-end gap-2\">\n      <svg viewBox={`0 0 ${W} ${height}`} width={W} height={height} aria-label={`Évolution ${pts[0].year}-${pts[pts.length - 1].year}`}>\n        <polygon points={area} fill=\"var(--lime-soft)\" />\n        <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n        {xy.slice(0, -1).map(([x, y], i) => (\n          <circle key={i} cx={x} cy={y} r=\"2\" fill=\"var(--surface)\" stroke=\"var(--ink)\" strokeWidth=\"1.1\" />\n        ))}\n        <circle cx={x2} cy={y2} r=\"3.6\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.5\" />\n      </svg>\n      <span className={`vp-mono mb-0.5 rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${growth >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n        {growth >= 0 ? \"+\" : \"\"}\n        {growth.toFixed(0)} %\n      </span>\n    </div>\n  );\n}\n\n/* ------------------------- fourchette visuelle P10-P90 ------------------------- */\n/** Barre de fourchette : segment P10→P90, losange = estimation, tiret = rôle. */\nexport function RangeBar({\n  low,\n  estimate,\n  high,\n  role,\n  lang,\n}: {\n  low: number;\n  estimate: number;\n  high: number;\n  role: number | null;\n  lang: string;\n}) {\n  const [on, setOn] = useState(false);\n  useEffect(() => {\n    const id = setTimeout(() => setOn(true), 80);\n    return () => clearTimeout(id);\n  }, []);\n  if (!(high > low) || !Number.isFinite(estimate)) return null;\n  const lo = Math.min(low, role ?? low);\n  const hi = Math.max(high, role ?? high);\n  const pad = (hi - lo) * 0.09 || hi * 0.05;\n  const min = lo - pad;\n  const max = hi + pad;\n  const X = (v: number) => Math.max(0, Math.min(100, ((v - min) / (max - min)) * 100));\n  // les étiquettes (centrées, nowrap) restent dans la carte même aux extrêmes\n  const clampX = (v: number, lo: number, hi: number) => Math.max(lo, Math.min(hi, X(v)));\n  const fmt = (v: number) =>\n    new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n      notation: v >= 1_000_000 ? \"compact\" : \"standard\",\n    }).format(v);\n  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;\n  return (\n    <div aria-label={`${fmt(low)} – ${fmt(high)}`}>\n      <div className=\"relative h-[64px]\">\n        {/* étiquette estimation */}\n        <div\n          className=\"absolute top-0 -translate-x-1/2 whitespace-nowrap transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? clampX(estimate, 18, 82) : 50}%` }}\n        >\n          <span className=\"vp-mono rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\">\n            {lang === \"fr\" ? \"ESTIMATION\" : \"ESTIMATE\"} {fmt(estimate)}\n          </span>\n        </div>\n        {/* piste */}\n        <div className=\"absolute left-0 right-0 top-[30px] h-[14px] rounded-[7px] border-[1.5px] border-ink bg-surface-2\" />\n        {/* segment P10-P90 */}\n        <div\n          className=\"absolute top-[30px] h-[14px] origin-left rounded-[7px] border-[1.5px] border-ink transition-transform duration-1000 ease-out\"\n          style={{\n            left: `${X(low)}%`,\n            width: `${Math.max(2, X(high) - X(low))}%`,\n            transform: on ? \"scaleX(1)\" : \"scaleX(0.05)\",\n            background:\n              \"repeating-linear-gradient(-45deg, var(--lime-soft), var(--lime-soft) 5px, rgba(255,81,72,0.35) 5px, rgba(255,81,72,0.35) 7px)\",\n          }}\n        />\n        {/* marqueur estimation (losange) */}\n        <div\n          className=\"absolute top-[29px] -translate-x-1/2 transition-[left] duration-700 ease-out\"\n          style={{ left: `${on ? X(estimate) : 50}%` }}\n        >\n          <div className=\"h-[16px] w-[16px] rotate-45 border-[1.8px] border-ink bg-lime\" />\n        </div>\n        {/* marqueur rôle */}\n        {role != null && role > 0 && (\n          <>\n            <div\n              className=\"absolute top-[22px] h-[30px] w-0 -translate-x-1/2 border-l-[2px] border-dashed border-ink opacity-70\"\n              style={{ left: `${X(role)}%` }}\n            />\n            {!roleClose && (\n              <span\n                className=\"vp-mono absolute top-[52px] -translate-x-1/2 whitespace-nowrap text-[9px] font-bold uppercase tracking-[0.06em] text-ink-2\"\n                style={{ left: `${clampX(role, 11, 89)}%` }}\n              >\n                {lang === \"fr\" ? \"Rôle\" : \"Roll\"} {fmt(role)}\n              </span>\n            )}\n          </>\n        )}\n        {/* bornes */}\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 whitespace-nowrap text-[10px] font-bold text-ink-2\"\n          style={{ left: `${clampX(low, 11, 89)}%` }}\n        >\n          P10 {fmt(low)}\n        </span>\n        <span\n          className=\"vp-mono absolute top-[50px] -translate-x-1/2 whitespace-nowrap text-[10px] font-bold text-ink-2\"\n          style={{ left: `${clampX(high, 11, 89)}%` }}\n        >\n          P90 {fmt(high)}\n        </span>\n      </div>\n    </div>\n  );\n}\n\n/* --------------------------- courbe d'indice de marché --------------------------- */\n/** Indice mensuel rebasé à 100 au premier mois — la trajectoire du marché local. */\nexport function MarketIndexChart({\n  series,\n  lang,\n}: {\n  series: { month: string; idx: number }[];\n  lang: string;\n}) {\n  if (series.length < 6) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const W = 640;\n  const H = 200;\n  const padL = 40;\n  const padR = 52;\n  const padT = 18;\n  const padB = 28;\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const x = (i: number) => padL + (i / (pts.length - 1)) * (W - padL - padR);\n  const y = (v: number) => padT + (1 - (v - min) / span) * (H - padT - padB);\n  const line = pts.map((v, i) => `${x(i).toFixed(1)},${y(v).toFixed(1)}`).join(\" \");\n  const area = `${x(0)},${H - padB} ${line} ${x(pts.length - 1)},${H - padB}`;\n  // graduations : janvier de chaque année\n  const years = series\n    .map((p, i) => ({ i, month: p.month }))\n    .filter((p) => p.month.endsWith(\"-01\"));\n  // lignes horizontales : min / 100 / max arrondis\n  const gridVals = [...new Set([Math.round(min), 100, Math.round(max)])].filter(\n    (v) => v >= min - 1 && v <= max + 1\n  );\n  const lastV = pts[pts.length - 1];\n  return (\n    <svg\n      viewBox={`0 0 ${W} ${H}`}\n      className=\"h-auto w-full\"\n      role=\"img\"\n      aria-label={\n        lang === \"fr\"\n          ? `Indice de marché ${series[0].month} à ${series[series.length - 1].month}`\n          : `Market index ${series[0].month} to ${series[series.length - 1].month}`\n      }\n    >\n      {gridVals.map((v) => (\n        <g key={v}>\n          <line\n            x1={padL}\n            y1={y(v)}\n            x2={W - padR}\n            y2={y(v)}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            strokeDasharray=\"3 5\"\n            opacity={v === 100 ? 0.45 : 0.18}\n          />\n          <text\n            x={padL - 6}\n            y={y(v) + 3.5}\n            textAnchor=\"end\"\n            fontSize=\"10\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-3)\"\n          >\n            {v}\n          </text>\n        </g>\n      ))}\n      {years.map((p) => (\n        <g key={p.month}>\n          <line\n            x1={x(p.i)}\n            y1={padT}\n            x2={x(p.i)}\n            y2={H - padB}\n            stroke=\"var(--ink)\"\n            strokeWidth=\"1\"\n            opacity=\"0.1\"\n          />\n          <text\n            x={x(p.i)}\n            y={H - 10}\n            textAnchor=\"middle\"\n            fontSize=\"10\"\n            fontWeight=\"700\"\n            fontFamily=\"var(--font-jetbrains)\"\n            fill=\"var(--ink-2)\"\n          >\n            {p.month.slice(0, 4)}\n          </text>\n        </g>\n      ))}\n      <polygon points={area} fill=\"var(--lime-soft)\" opacity=\"0.8\" />\n      <polyline\n        points={line}\n        fill=\"none\"\n        stroke=\"var(--green)\"\n        strokeWidth=\"2.6\"\n        strokeLinejoin=\"round\"\n        strokeLinecap=\"round\"\n      />\n      <circle\n        cx={x(pts.length - 1)}\n        cy={y(lastV)}\n        r=\"5\"\n        fill=\"var(--lime)\"\n        stroke=\"var(--ink)\"\n        strokeWidth=\"1.8\"\n      />\n      <text\n        x={x(pts.length - 1) + 9}\n        y={y(lastV) + 4}\n        fontSize=\"11.5\"\n        fontWeight=\"700\"\n        fontFamily=\"var(--font-jetbrains)\"\n        fill=\"var(--ink)\"\n      >\n        {Math.round(lastV)}\n      </text>\n    </svg>\n  );\n}\n\n/* ----------------------- position dans la municipalité ----------------------- */\n/** Distribution stylisée (cloche) : les barres sous le percentile sont pleines. */\nexport function PercentileBar({\n  pct,\n  lang,\n}: {\n  pct: number;\n  lang: string;\n}) {\n  const N = 36;\n  const bars = Array.from({ length: N }, (_, i) => {\n    const z = (i / (N - 1) - 0.42) / 0.24; // cloche légèrement asymétrique (droite étirée)\n    return Math.exp(-0.5 * z * z) * (i / N > 0.5 ? 1 + (i / N - 0.5) * 0.35 : 1);\n  });\n  const maxB = Math.max(...bars);\n  const p = Math.max(0, Math.min(100, pct));\n  return (\n    <div aria-label={`${Math.round(p)} %`}>\n      <div className=\"relative flex h-[72px] items-end px-0.5\" style={{ gap: \"2px\" }}>\n        {bars.map((b, i) => {\n          const filled = (i + 0.5) / N <= p / 100;\n          return (\n            <div\n              key={i}\n              className=\"min-w-0 flex-1 rounded-t-[2px] border border-ink transition-colors\"\n              style={{\n                height: `${8 + (b / maxB) * 88}%`,\n                background: filled ? \"var(--lime)\" : \"var(--surface-2)\",\n                borderWidth: \"1px\",\n                opacity: filled ? 1 : 0.55,\n              }}\n            />\n          );\n        })}\n        <div\n          className=\"absolute bottom-0 top-[-6px] w-0 border-l-[2px] border-ink\"\n          style={{ left: `${p}%` }}\n        >\n          <span className=\"vp-mono absolute -top-1 left-1.5 whitespace-nowrap rounded-[4px] border border-ink bg-ink px-1.5 py-0.5 text-[10px] font-bold text-lime\" style={p > 55 ? { left: \"auto\", right: \"6px\" } : undefined}>\n            {lang === \"fr\" ? \"P\" : \"P\"}\n            {Math.round(p)}\n          </span>\n        </div>\n      </div>\n    </div>\n  );\n}\n\n/* ------------------------ mini-courbe d'indice (accueil) ------------------------ */\nexport function IndexSpark({\n  series,\n  width = 150,\n  height = 46,\n}: {\n  series: { month: string; idx: number }[];\n  width?: number;\n  height?: number;\n}) {\n  if (series.length < 2) return null;\n  const base = series[0].idx || 1;\n  const pts = series.map((p) => (p.idx / base) * 100);\n  const min = Math.min(...pts);\n  const max = Math.max(...pts);\n  const span = max - min || 1;\n  const xy = pts.map((v, i) => [\n    4 + (i / (pts.length - 1)) * (width - 8),\n    height - 6 - ((v - min) / span) * (height - 14),\n  ]);\n  const line = xy.map(([x, y]) => `${x.toFixed(1)},${y.toFixed(1)}`).join(\" \");\n  const area = `${xy[0][0]},${height - 3} ${line} ${xy[xy.length - 1][0]},${height - 3}`;\n  const [lx, ly] = xy[xy.length - 1];\n  return (\n    <svg viewBox={`0 0 ${width} ${height}`} className=\"h-auto w-full\" aria-hidden=\"true\">\n      <polygon points={area} fill=\"var(--lime-soft)\" />\n      <polyline points={line} fill=\"none\" stroke=\"var(--green)\" strokeWidth=\"2.2\" strokeLinejoin=\"round\" strokeLinecap=\"round\" />\n      <circle cx={lx} cy={ly} r=\"3.4\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.4\" />\n    </svg>\n  );\n}\n\n/* ------------------------------ frise temporelle ------------------------------ */\nexport function EraLine({ year, lang }: { year: number | null; lang: string }) {\n  if (!year || year < 1750) return null;\n  const min = Math.min(1900, Math.floor((year - 10) / 50) * 50);\n  const max = 2026;\n  const frac = Math.max(0, Math.min(1, (year - min) / (max - min)));\n  const W = 190;\n  const x = 12 + frac * (W - 24);\n  const age = 2026 - year;\n  return (\n    <svg viewBox={`0 0 ${W} 40`} className=\"w-full max-w-[210px]\" aria-label={`Construction ${year}`}>\n      <line x1=\"12\" y1=\"24\" x2={W - 12} y2=\"24\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n      {[...new Set([min, Math.round((min + max) / 2 / 50) * 50, 2000])].map((yr) => {\n        const gx = 12 + ((yr - min) / (max - min)) * (W - 24);\n        return (\n          <g key={yr}>\n            <line x1={gx} y1=\"20\" x2={gx} y2=\"28\" stroke=\"var(--ink)\" strokeWidth=\"1.2\" opacity=\"0.5\" />\n            <text x={gx} y=\"38\" textAnchor=\"middle\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n              {yr}\n            </text>\n          </g>\n        );\n      })}\n      <g transform={`translate(${x}, 24)`}>\n        <rect x=\"-7\" y=\"-7\" width=\"14\" height=\"14\" transform=\"rotate(45)\" fill=\"var(--lime)\" stroke=\"var(--ink)\" strokeWidth=\"1.6\" />\n        <text y=\"-13\" textAnchor=\"middle\" fontSize=\"9.5\" fontWeight=\"700\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink)\">\n          {year}\n        </text>\n      </g>\n      <text x={W - 12} y=\"12\" textAnchor=\"end\" fontSize=\"7.5\" fontFamily=\"var(--font-jetbrains)\" fill=\"var(--ink-3)\">\n        {age} {lang === \"fr\" ? \"ANS\" : \"YRS\"}\n      </text>\n    </svg>\n  );\n}\n","structuredPatch":[{"oldStart":469,"oldLines":7,"newStart":469,"newLines":12,"lines":["       maximumFractionDigits: 0,","       notation: v >= 1_000_000 ? \"compact\" : \"standard\",","     }).format(v);","-  const roleClose = role != null && Math.abs(X(role) - X(estimate)) < 9;","+  // l'étiquette du rôle s'efface si elle chevaucherait l'estimation ou les bornes","+  const roleClose =","+    role != null &&","+    (Math.abs(X(role) - X(estimate)) < 9 ||","+      Math.abs(X(role) - X(low)) < 15 ||","+      Math.abs(X(role) - X(high)) < 15);","   return (","     <div aria-label={`${fmt(low)} – ${fmt(high)}`}>","       <div className=\"relative h-[64px]\">"]}],"userModified":false,"replaceAll":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"ec2c734d-0f54-4eb1-8228-f0b5414d97eb","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJx1Z6R8e1fFYdgFdqHm","type":"message","role":"assistant","content":[{"type":"text","text":"Rebuild et re-vérification visuelle mobile + desktop :"}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1164,"cache_read_input_tokens":149461,"cache_creation":{"ephemeral_5m_input_tokens":1164,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"fb0f2a61-459f-4efe-a210-362aecbf2fa7","request_id":"req_011CeJx1WLULsiEfXoLL7L8q"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJx1Z6R8e1fFYdgFdqHm","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011sxTpRyefo2PWZYU1KfigN","name":"Bash","input":{"command":"npm run build 2>&1 | grep -E \"error|Error|✓ Compiled|Generating\" | head -5; echo \"build exit: $?\"","description":"Rebuild the app","timeout":600000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1164,"cache_read_input_tokens":149461,"cache_creation":{"ephemeral_5m_input_tokens":1164,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"e87f76cf-a187-489d-89fa-459eac988fcd","request_id":"req_011CeJx1WLULsiEfXoLL7L8q"}}
{"type":"claude","event":{"type":"system","subtype":"task_started","task_id":"bw3oyber8","tool_use_id":"toolu_011sxTpRyefo2PWZYU1KfigN","description":"Rebuild the app","task_type":"local_bash","uuid":"1dea567e-aeed-4503-812c-c30409638765","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"system","subtype":"task_notification","task_id":"bw3oyber8","tool_use_id":"toolu_011sxTpRyefo2PWZYU1KfigN","status":"completed","output_file":"","summary":"Rebuild the app","uuid":"6070a14b-9b1b-486b-b9f1-f3299b0889ed","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011sxTpRyefo2PWZYU1KfigN","type":"tool_result","content":"✓ Compiled successfully in 753ms\n  Generating static pages using 27 workers (0/115) ...\n  Generating static pages using 27 workers (28/115) \r\n  Generating static pages using 27 workers (57/115) \r\n  Generating static pages using 27 workers (86/115) \r\nbuild exit: 0","is_error":false}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"f5ea8359-9eb4-4db8-ab1b-aefa0ca6433b","timestamp":"2026-08-23T03:12:36.013Z","tool_use_result":{"stdout":"✓ Compiled successfully in 753ms\n  Generating static pages using 27 workers (0/115) ...\n  Generating static pages using 27 workers (28/115) \r\n  Generating static pages using 27 workers (57/115) \r\n  Generating static pages using 27 workers (86/115) \r\nbuild exit: 0","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"1357382f-5ee1-4f15-9fa4-7ee7e138262b","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJx2JZq6LyMy5CWdSWqC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WN2S4W2y54WrTjhYnjcPBE","name":"Bash","input":{"command":"pm2 restart vrai-prix 2>&1 | grep -c restarted; sleep 4; curl -s -o /dev/null -w \"home %{http_code}\\n\" http://localhost:8090/ && ID=$(sqlite3 data/vraiprix.db \"SELECT id_provinc FROM transactions WHERE id_provinc IS NOT NULL ORDER BY date DESC LIMIT 1;\") && node -e \"\nconst { chromium } = require('playwright');\n(async () => {\n  const b = await chromium.launch();\n  const m = await b.newPage({ viewport: { width: 390, height: 844 } });\n  await m.goto('http://localhost:8090/estimation/$ID', { waitUntil: 'networkidle' });\n  await m.waitForTimeout(1200);\n  await m.screenshot({ path: '/tmp/vp-est-mobile2.png', clip: { x: 0, y: 0, width: 390, height: 1400 } });\n  await b.close();\n})().catch(e => { console.error(e); process.exit(1); });\n\" && echo SHOT","description":"Restart, healthcheck, re-screenshot mobile hero","timeout":180000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":277,"cache_read_input_tokens":150625,"cache_creation":{"ephemeral_5m_input_tokens":277,"ephemeral_1h_input_tokens":0},"output_tokens":17,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"d0f4e574-edb1-47af-a519-70e373c29a7c","request_id":"req_011CeJx2GBTHwrEFojSxbyKq"}}
{"type":"claude","event":{"type":"system","subtype":"task_started","task_id":"b1jwzawkr","tool_use_id":"toolu_01WN2S4W2y54WrTjhYnjcPBE","description":"Restart, healthcheck, re-screenshot mobile hero","task_type":"local_bash","uuid":"29d6f4cf-2c49-49da-9d92-40a1e67921a6","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"system","subtype":"task_notification","task_id":"b1jwzawkr","tool_use_id":"toolu_01WN2S4W2y54WrTjhYnjcPBE","status":"completed","output_file":"","summary":"Restart, healthcheck, re-screenshot mobile hero","uuid":"0f1d1c3e-358d-42de-b007-a94ea8d5cb84","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WN2S4W2y54WrTjhYnjcPBE","type":"tool_result","content":"0\nhome 200\nSHOT","is_error":false}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"59f98bb7-954e-4384-a74e-9dcf8d18c833","timestamp":"2026-08-23T03:12:50.214Z","tool_use_result":{"stdout":"0\nhome 200\nSHOT","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"415fb699-304e-4c19-8daf-f79a26081039","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"system","subtype":"thinking_tokens","estimated_tokens":50,"estimated_tokens_delta":50,"uuid":"4afe21b8-f3a9-42c5-b158-0628f169b894","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"system","subtype":"thinking_tokens","estimated_tokens":128,"estimated_tokens_delta":78,"uuid":"328275bf-ede7-41b3-be86-13b315b4c4f1","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
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{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJx3MuGu416fQaRJg8Xk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011efKfE4EH7DueXv1M5Shvd","name":"Bash","input":{"command":"pm2 ls | grep \"vrai-prix \" ","description":"Confirm vrai-prix online"},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":487,"cache_read_input_tokens":150902,"cache_creation":{"ephemeral_5m_input_tokens":487,"ephemeral_1h_input_tokens":0},"output_tokens":2,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"52f4d832-5283-4acb-a542-a1a44a282a7b","request_id":"req_011CeJx3JwRo6ZT18VxTDKou"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011efKfE4EH7DueXv1M5Shvd","type":"tool_result","content":"│ 7  │ vrai-prix               │ default     │ 0.1.0   │ fork    │ 49396    │ 12s    │ 57   │ online    │ 0%       │ 175.0mb  │ simon-p… │ disabled │","is_error":false}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"27822a00-21d8-48a5-ad94-1794ff8381ba","timestamp":"2026-08-23T03:12:56.424Z","tool_use_result":{"stdout":"│ 7  │ vrai-prix               │ default     │ 0.1.0   │ fork    │ 49396    │ 12s    │ 57   │ online    │ 0%       │ 175.0mb  │ simon-p… │ disabled │","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Biz19EfkVrgCuD49FtCVZo","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"ae663d86-f01a-4bf5-87e0-3554bd2c9d19","timestamp":"2026-08-23T03:13:35.323Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","oldString":"/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];","newString":"/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Type de propriété du sujet (clé i18n). */\n  typeProp: string;\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\n/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */\nexport interface SectorStats {\n  nSales12m: number;\n  medianPrice: number | null;\n  medianPpm2: number | null;\n  radiusKm: number;\n}\n\n/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];\n  index12mPct: number | null;\n  indexSince2021Pct: number | null;\n  sector: SectorStats | null;\n  /** % des propriétés de la municipalité valant moins que le sujet. */\n  muniPercentile: number | null;\n  muniTotal: number | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n  context?: MarketContext;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nfunction median(xs: number[]): number | null {\n  if (!xs.length) return null;\n  const s = [...xs].sort((a, b) => a - b);\n  const m = Math.floor(s.length / 2);\n  return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;\n}\n\n/** Stats du secteur à partir des candidats déjà chargés (aucune requête en plus). */\nfunction sectorStats(lat: number, lng: number, rows: TxRow[]): SectorStats | null {\n  const cutoff = since(12);\n  const recent = rows.filter((r) => r.date >= cutoff);\n  for (const radiusKm of [2, 5, 10]) {\n    const within = recent.filter(\n      (r) => haversineM(lat, lng, r.lat, r.lng) <= radiusKm * 1000\n    );\n    if (within.length >= 8 || radiusKm === 10) {\n      if (within.length === 0) return null;\n      return {\n        nSales12m: within.length,\n        medianPrice: median(within.map((r) => r.amount)),\n        medianPpm2: median(\n          within\n            .filter((r) => r.floor_area != null && r.floor_area > 20)\n            .map((r) => r.amount / r.floor_area!)\n        ),\n        radiusKm,\n      };\n    }\n  }\n  return null;\n}\n\nfunction buildContext(\n  lat: number,\n  lng: number,\n  candidates: TxRow[],\n  index: { month: string; idx: number }[],\n  municipalite: string | null,\n  estimateValue: number\n): MarketContext {\n  const last = index.length ? index[index.length - 1].idx : null;\n  const yearAgo = index.length >= 13 ? index[index.length - 13].idx : null;\n  const first = index.length >= 2 ? index[0].idx : null;\n  const pr =\n    municipalite && Number.isFinite(estimateValue) && estimateValue > 0\n      ? municipalityPercentile(municipalite, estimateValue)\n      : null;\n  return {\n    index,\n    index12mPct:\n      last != null && yearAgo != null && yearAgo > 0\n        ? (last / yearAgo - 1) * 100\n        : null,\n    indexSince2021Pct:\n      last != null && first != null && first > 0 ? (last / first - 1) * 100 : null,\n    sector: sectorStats(lat, lng, candidates),\n    muniPercentile: pr ? (pr.below / pr.total) * 100 : null,\n    muniTotal: pr ? pr.total : null,\n  };\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const candidates = candidatesAround(u.lat, u.lng);\n  const index = getMarketIndex(indexType(u.type_prop));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  const context = buildContext(\n    u.lat,\n    u.lng,\n    candidates,\n    index,\n    u.municipalite,\n    result.estimate\n  );\n  return {\n    context,\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const candidates = candidatesAround(center.lat, center.lng);\n  const index = getMarketIndex(indexType(p.typeProp));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  if (!Number.isFinite(result.estimate)) return null;\n  const context = buildContext(\n    center.lat,\n    center.lng,\n    candidates,\n    index,\n    p.municipality,\n    result.estimate\n  );\n  return { unit: null, result, context };\n}\n","structuredPatch":[{"oldStart":48,"oldLines":6,"newStart":48,"newLines":8,"lines":[" "," /** Contexte marché affiché avec l'estimation — même transparence que le calcul. */"," export interface MarketContext {","+  /** Type de propriété du sujet (clé i18n). */","+  typeProp: string;","   /** Indice de marché mensuel du type (1.0 = niveau courant). */","   index: { month: string; idx: number }[];","   index12mPct: number | null;"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CumFR91Zfxm15AAE2mnU2a","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"c7dace51-a8a4-4324-a839-9e3428885009","timestamp":"2026-08-23T03:13:41.043Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","oldString":"function buildContext(\n  lat: number,\n  lng: number,\n  candidates: TxRow[],\n  index: { month: string; idx: number }[],\n  municipalite: string | null,\n  estimateValue: number\n): MarketContext {","newString":"function buildContext(\n  typeProp: string,\n  lat: number,\n  lng: number,\n  candidates: TxRow[],\n  index: { month: string; idx: number }[],\n  municipalite: string | null,\n  estimateValue: number\n): MarketContext {","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\n/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */\nexport interface SectorStats {\n  nSales12m: number;\n  medianPrice: number | null;\n  medianPpm2: number | null;\n  radiusKm: number;\n}\n\n/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Type de propriété du sujet (clé i18n). */\n  typeProp: string;\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];\n  index12mPct: number | null;\n  indexSince2021Pct: number | null;\n  sector: SectorStats | null;\n  /** % des propriétés de la municipalité valant moins que le sujet. */\n  muniPercentile: number | null;\n  muniTotal: number | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n  context?: MarketContext;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nfunction median(xs: number[]): number | null {\n  if (!xs.length) return null;\n  const s = [...xs].sort((a, b) => a - b);\n  const m = Math.floor(s.length / 2);\n  return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;\n}\n\n/** Stats du secteur à partir des candidats déjà chargés (aucune requête en plus). */\nfunction sectorStats(lat: number, lng: number, rows: TxRow[]): SectorStats | null {\n  const cutoff = since(12);\n  const recent = rows.filter((r) => r.date >= cutoff);\n  for (const radiusKm of [2, 5, 10]) {\n    const within = recent.filter(\n      (r) => haversineM(lat, lng, r.lat, r.lng) <= radiusKm * 1000\n    );\n    if (within.length >= 8 || radiusKm === 10) {\n      if (within.length === 0) return null;\n      return {\n        nSales12m: within.length,\n        medianPrice: median(within.map((r) => r.amount)),\n        medianPpm2: median(\n          within\n            .filter((r) => r.floor_area != null && r.floor_area > 20)\n            .map((r) => r.amount / r.floor_area!)\n        ),\n        radiusKm,\n      };\n    }\n  }\n  return null;\n}\n\nfunction buildContext(\n  lat: number,\n  lng: number,\n  candidates: TxRow[],\n  index: { month: string; idx: number }[],\n  municipalite: string | null,\n  estimateValue: number\n): MarketContext {\n  const last = index.length ? index[index.length - 1].idx : null;\n  const yearAgo = index.length >= 13 ? index[index.length - 13].idx : null;\n  const first = index.length >= 2 ? index[0].idx : null;\n  const pr =\n    municipalite && Number.isFinite(estimateValue) && estimateValue > 0\n      ? municipalityPercentile(municipalite, estimateValue)\n      : null;\n  return {\n    index,\n    index12mPct:\n      last != null && yearAgo != null && yearAgo > 0\n        ? (last / yearAgo - 1) * 100\n        : null,\n    indexSince2021Pct:\n      last != null && first != null && first > 0 ? (last / first - 1) * 100 : null,\n    sector: sectorStats(lat, lng, candidates),\n    muniPercentile: pr ? (pr.below / pr.total) * 100 : null,\n    muniTotal: pr ? pr.total : null,\n  };\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const candidates = candidatesAround(u.lat, u.lng);\n  const index = getMarketIndex(indexType(u.type_prop));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  const context = buildContext(\n    u.lat,\n    u.lng,\n    candidates,\n    index,\n    u.municipalite,\n    result.estimate\n  );\n  return {\n    context,\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const candidates = candidatesAround(center.lat, center.lng);\n  const index = getMarketIndex(indexType(p.typeProp));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  if (!Number.isFinite(result.estimate)) return null;\n  const context = buildContext(\n    center.lat,\n    center.lng,\n    candidates,\n    index,\n    p.municipality,\n    result.estimate\n  );\n  return { unit: null, result, context };\n}\n","structuredPatch":[{"oldStart":155,"oldLines":6,"newStart":155,"newLines":7,"lines":[" }"," "," function buildContext(","+  typeProp: string,","   lat: number,","   lng: number,","   candidates: TxRow[],"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Q3Ubcf47zhRSVHuJCD5a7m","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"288c06df-19f3-4aa1-a411-ad69bb6e96f9","timestamp":"2026-08-23T03:13:45.675Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","oldString":"  return {\n    index,\n    index12mPct:","newString":"  return {\n    typeProp,\n    index,\n    index12mPct:","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\n/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */\nexport interface SectorStats {\n  nSales12m: number;\n  medianPrice: number | null;\n  medianPpm2: number | null;\n  radiusKm: number;\n}\n\n/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Type de propriété du sujet (clé i18n). */\n  typeProp: string;\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];\n  index12mPct: number | null;\n  indexSince2021Pct: number | null;\n  sector: SectorStats | null;\n  /** % des propriétés de la municipalité valant moins que le sujet. */\n  muniPercentile: number | null;\n  muniTotal: number | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n  context?: MarketContext;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nfunction median(xs: number[]): number | null {\n  if (!xs.length) return null;\n  const s = [...xs].sort((a, b) => a - b);\n  const m = Math.floor(s.length / 2);\n  return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;\n}\n\n/** Stats du secteur à partir des candidats déjà chargés (aucune requête en plus). */\nfunction sectorStats(lat: number, lng: number, rows: TxRow[]): SectorStats | null {\n  const cutoff = since(12);\n  const recent = rows.filter((r) => r.date >= cutoff);\n  for (const radiusKm of [2, 5, 10]) {\n    const within = recent.filter(\n      (r) => haversineM(lat, lng, r.lat, r.lng) <= radiusKm * 1000\n    );\n    if (within.length >= 8 || radiusKm === 10) {\n      if (within.length === 0) return null;\n      return {\n        nSales12m: within.length,\n        medianPrice: median(within.map((r) => r.amount)),\n        medianPpm2: median(\n          within\n            .filter((r) => r.floor_area != null && r.floor_area > 20)\n            .map((r) => r.amount / r.floor_area!)\n        ),\n        radiusKm,\n      };\n    }\n  }\n  return null;\n}\n\nfunction buildContext(\n  typeProp: string,\n  lat: number,\n  lng: number,\n  candidates: TxRow[],\n  index: { month: string; idx: number }[],\n  municipalite: string | null,\n  estimateValue: number\n): MarketContext {\n  const last = index.length ? index[index.length - 1].idx : null;\n  const yearAgo = index.length >= 13 ? index[index.length - 13].idx : null;\n  const first = index.length >= 2 ? index[0].idx : null;\n  const pr =\n    municipalite && Number.isFinite(estimateValue) && estimateValue > 0\n      ? municipalityPercentile(municipalite, estimateValue)\n      : null;\n  return {\n    index,\n    index12mPct:\n      last != null && yearAgo != null && yearAgo > 0\n        ? (last / yearAgo - 1) * 100\n        : null,\n    indexSince2021Pct:\n      last != null && first != null && first > 0 ? (last / first - 1) * 100 : null,\n    sector: sectorStats(lat, lng, candidates),\n    muniPercentile: pr ? (pr.below / pr.total) * 100 : null,\n    muniTotal: pr ? pr.total : null,\n  };\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const candidates = candidatesAround(u.lat, u.lng);\n  const index = getMarketIndex(indexType(u.type_prop));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  const context = buildContext(\n    u.lat,\n    u.lng,\n    candidates,\n    index,\n    u.municipalite,\n    result.estimate\n  );\n  return {\n    context,\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const candidates = candidatesAround(center.lat, center.lng);\n  const index = getMarketIndex(indexType(p.typeProp));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  if (!Number.isFinite(result.estimate)) return null;\n  const context = buildContext(\n    center.lat,\n    center.lng,\n    candidates,\n    index,\n    p.municipality,\n    result.estimate\n  );\n  return { unit: null, result, context };\n}\n","structuredPatch":[{"oldStart":171,"oldLines":6,"newStart":171,"newLines":7,"lines":["       ? municipalityPercentile(municipalite, estimateValue)","       : null;","   return {","+    typeProp,","     index,","     index12mPct:","       last != null && yearAgo != null && yearAgo > 0"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014yfwX8v1NUmJtTx6aAvPmw","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"d366dec0-0816-41b6-9356-f131e0f497f5","timestamp":"2026-08-23T03:13:50.887Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","oldString":"  const context = buildContext(\n    u.lat,\n    u.lng,\n    candidates,\n    index,\n    u.municipalite,\n    result.estimate\n  );","newString":"  const context = buildContext(\n    u.type_prop,\n    u.lat,\n    u.lng,\n    candidates,\n    index,\n    u.municipalite,\n    result.estimate\n  );","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\n/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */\nexport interface SectorStats {\n  nSales12m: number;\n  medianPrice: number | null;\n  medianPpm2: number | null;\n  radiusKm: number;\n}\n\n/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Type de propriété du sujet (clé i18n). */\n  typeProp: string;\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];\n  index12mPct: number | null;\n  indexSince2021Pct: number | null;\n  sector: SectorStats | null;\n  /** % des propriétés de la municipalité valant moins que le sujet. */\n  muniPercentile: number | null;\n  muniTotal: number | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n  context?: MarketContext;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nfunction median(xs: number[]): number | null {\n  if (!xs.length) return null;\n  const s = [...xs].sort((a, b) => a - b);\n  const m = Math.floor(s.length / 2);\n  return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;\n}\n\n/** Stats du secteur à partir des candidats déjà chargés (aucune requête en plus). */\nfunction sectorStats(lat: number, lng: number, rows: TxRow[]): SectorStats | null {\n  const cutoff = since(12);\n  const recent = rows.filter((r) => r.date >= cutoff);\n  for (const radiusKm of [2, 5, 10]) {\n    const within = recent.filter(\n      (r) => haversineM(lat, lng, r.lat, r.lng) <= radiusKm * 1000\n    );\n    if (within.length >= 8 || radiusKm === 10) {\n      if (within.length === 0) return null;\n      return {\n        nSales12m: within.length,\n        medianPrice: median(within.map((r) => r.amount)),\n        medianPpm2: median(\n          within\n            .filter((r) => r.floor_area != null && r.floor_area > 20)\n            .map((r) => r.amount / r.floor_area!)\n        ),\n        radiusKm,\n      };\n    }\n  }\n  return null;\n}\n\nfunction buildContext(\n  typeProp: string,\n  lat: number,\n  lng: number,\n  candidates: TxRow[],\n  index: { month: string; idx: number }[],\n  municipalite: string | null,\n  estimateValue: number\n): MarketContext {\n  const last = index.length ? index[index.length - 1].idx : null;\n  const yearAgo = index.length >= 13 ? index[index.length - 13].idx : null;\n  const first = index.length >= 2 ? index[0].idx : null;\n  const pr =\n    municipalite && Number.isFinite(estimateValue) && estimateValue > 0\n      ? municipalityPercentile(municipalite, estimateValue)\n      : null;\n  return {\n    typeProp,\n    index,\n    index12mPct:\n      last != null && yearAgo != null && yearAgo > 0\n        ? (last / yearAgo - 1) * 100\n        : null,\n    indexSince2021Pct:\n      last != null && first != null && first > 0 ? (last / first - 1) * 100 : null,\n    sector: sectorStats(lat, lng, candidates),\n    muniPercentile: pr ? (pr.below / pr.total) * 100 : null,\n    muniTotal: pr ? pr.total : null,\n  };\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const candidates = candidatesAround(u.lat, u.lng);\n  const index = getMarketIndex(indexType(u.type_prop));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  const context = buildContext(\n    u.lat,\n    u.lng,\n    candidates,\n    index,\n    u.municipalite,\n    result.estimate\n  );\n  return {\n    context,\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const candidates = candidatesAround(center.lat, center.lng);\n  const index = getMarketIndex(indexType(p.typeProp));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  if (!Number.isFinite(result.estimate)) return null;\n  const context = buildContext(\n    center.lat,\n    center.lng,\n    candidates,\n    index,\n    p.municipality,\n    result.estimate\n  );\n  return { unit: null, result, context };\n}\n","structuredPatch":[{"oldStart":203,"oldLines":6,"newStart":203,"newLines":7,"lines":["   const index = getMarketIndex(indexType(u.type_prop));","   const result = estimate(subject, toComps(candidates), index, nowISO());","   const context = buildContext(","+    u.type_prop,","     u.lat,","     u.lng,","     candidates,"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BwSerLyyJjcVFePwYFYrML","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"480cb781-11ef-43a1-995c-fd55b57b14e1","timestamp":"2026-08-23T03:13:56.117Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/lib/estimator.ts","oldString":"  const context = buildContext(\n    center.lat,\n    center.lng,\n    candidates,\n    index,\n    p.municipality,\n    result.estimate\n  );","newString":"  const context = buildContext(\n    p.typeProp,\n    center.lat,\n    center.lng,\n    candidates,\n    index,\n    p.municipality,\n    result.estimate\n  );","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n// Service d'estimation : relie la base SQLite au moteur (engine.ts).\nimport {\n  getCandidates,\n  getMarketIndex,\n  getUnit,\n  municipalityCenter,\n  municipalityPercentile,\n  type TxRow,\n  type UnitRow,\n} from \"./db\";\nimport {\n  estimate,\n  haversineM,\n  type CompInput,\n  type EstimateResult,\n  type Subject,\n} from \"./engine\";\n\nexport interface UnitSpecs {\n  cubf: number | null;\n  cubfLibelle: string | null;\n  anneeEstimee: string | null;\n  frontTerrainM: number | null;\n  nbEtages: number | null;\n  nbLocauxNonResid: number | null;\n  nbChambresLocatives: number | null;\n  lienPhysique: string | null;\n  genreConstruction: string | null;\n  matricule: string | null;\n  uniteVoisinage: string | null;\n  nAdresses: number | null;\n  datCondMarche: string | null;\n  valeurTerrain: number | null;\n  valeurBatiment: number | null;\n  valeurAnterieure: number | null;\n  arrond: string | null;\n  apt: string | null;\n}\n\n/** Ventes réelles des 12 derniers mois autour du sujet (rayon adaptatif). */\nexport interface SectorStats {\n  nSales12m: number;\n  medianPrice: number | null;\n  medianPpm2: number | null;\n  radiusKm: number;\n}\n\n/** Contexte marché affiché avec l'estimation — même transparence que le calcul. */\nexport interface MarketContext {\n  /** Type de propriété du sujet (clé i18n). */\n  typeProp: string;\n  /** Indice de marché mensuel du type (1.0 = niveau courant). */\n  index: { month: string; idx: number }[];\n  index12mPct: number | null;\n  indexSince2021Pct: number | null;\n  sector: SectorStats | null;\n  /** % des propriétés de la municipalité valant moins que le sujet. */\n  muniPercentile: number | null;\n  muniTotal: number | null;\n}\n\nexport interface UnitEstimate {\n  unit: {\n    id: string;\n    adresse: string | null;\n    municipalite: string | null;\n    typeProp: string;\n    cubfLibelle: string | null;\n    anneeConstruction: number | null;\n    aireEtagesM2: number | null;\n    superficieTerrainM2: number | null;\n    nbLogements: number | null;\n    valeurRole: number | null;\n    lat: number;\n    lng: number;\n    history: { year: number; value: number | null }[];\n    specs: UnitSpecs;\n  } | null;\n  result: EstimateResult;\n  context?: MarketContext;\n}\n\n/** Type de comparable attendu pour le type de tx du marché de l'indice. */\nexport function indexType(typeProp: string): string {\n  if (typeProp === \"condo_ou_multi\") return \"condo\";\n  if (typeProp === \"plex\") return \"plex\";\n  return \"unifamilial\";\n}\n\nfunction toComps(rows: TxRow[]): CompInput[] {\n  return rows.map((r) => ({\n    id: r.id,\n    date: r.date,\n    amount: r.amount,\n    lat: r.lat,\n    lng: r.lng,\n    propertyType: r.property_type,\n    yearBuilt: r.year_built,\n    floorArea: r.floor_area,\n    street: r.street,\n    city: r.city,\n  }));\n}\n\nfunction nowISO(): string {\n  return new Date().toISOString().slice(0, 10);\n}\n\nfunction since(months: number): string {\n  const d = new Date();\n  d.setMonth(d.getMonth() - months);\n  return d.toISOString().slice(0, 10);\n}\n\nfunction candidatesAround(lat: number, lng: number): TxRow[] {\n  // ~2,2 km puis élargit à ~11 km si marché mince\n  for (const halfDeg of [0.02, 0.1]) {\n    const rows = getCandidates(lat, lng, halfDeg, since(30), 600);\n    if (rows.length >= 30) return rows;\n  }\n  return getCandidates(lat, lng, 0.25, since(36), 600);\n}\n\nfunction median(xs: number[]): number | null {\n  if (!xs.length) return null;\n  const s = [...xs].sort((a, b) => a - b);\n  const m = Math.floor(s.length / 2);\n  return s.length % 2 ? s[m] : (s[m - 1] + s[m]) / 2;\n}\n\n/** Stats du secteur à partir des candidats déjà chargés (aucune requête en plus). */\nfunction sectorStats(lat: number, lng: number, rows: TxRow[]): SectorStats | null {\n  const cutoff = since(12);\n  const recent = rows.filter((r) => r.date >= cutoff);\n  for (const radiusKm of [2, 5, 10]) {\n    const within = recent.filter(\n      (r) => haversineM(lat, lng, r.lat, r.lng) <= radiusKm * 1000\n    );\n    if (within.length >= 8 || radiusKm === 10) {\n      if (within.length === 0) return null;\n      return {\n        nSales12m: within.length,\n        medianPrice: median(within.map((r) => r.amount)),\n        medianPpm2: median(\n          within\n            .filter((r) => r.floor_area != null && r.floor_area > 20)\n            .map((r) => r.amount / r.floor_area!)\n        ),\n        radiusKm,\n      };\n    }\n  }\n  return null;\n}\n\nfunction buildContext(\n  typeProp: string,\n  lat: number,\n  lng: number,\n  candidates: TxRow[],\n  index: { month: string; idx: number }[],\n  municipalite: string | null,\n  estimateValue: number\n): MarketContext {\n  const last = index.length ? index[index.length - 1].idx : null;\n  const yearAgo = index.length >= 13 ? index[index.length - 13].idx : null;\n  const first = index.length >= 2 ? index[0].idx : null;\n  const pr =\n    municipalite && Number.isFinite(estimateValue) && estimateValue > 0\n      ? municipalityPercentile(municipalite, estimateValue)\n      : null;\n  return {\n    typeProp,\n    index,\n    index12mPct:\n      last != null && yearAgo != null && yearAgo > 0\n        ? (last / yearAgo - 1) * 100\n        : null,\n    indexSince2021Pct:\n      last != null && first != null && first > 0 ? (last / first - 1) * 100 : null,\n    sector: sectorStats(lat, lng, candidates),\n    muniPercentile: pr ? (pr.below / pr.total) * 100 : null,\n    muniTotal: pr ? pr.total : null,\n  };\n}\n\nexport function estimateByUnitId(id: string): UnitEstimate | null {\n  const u = getUnit(id);\n  if (!u) return null;\n  const subject: Subject = {\n    lat: u.lat,\n    lng: u.lng,\n    typeProp: u.type_prop,\n    floorArea: u.aire_etages_m2,\n    yearBuilt: u.annee_construction,\n    landArea: u.superficie_terrain_m2,\n    modelEstimate: u.est_2026,\n    modelP10: u.p10,\n    modelP90: u.p90,\n  };\n  const candidates = candidatesAround(u.lat, u.lng);\n  const index = getMarketIndex(indexType(u.type_prop));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  const context = buildContext(\n    u.type_prop,\n    u.lat,\n    u.lng,\n    candidates,\n    index,\n    u.municipalite,\n    result.estimate\n  );\n  return {\n    context,\n    unit: {\n      id: u.id_provinc,\n      adresse: u.adresse,\n      municipalite: u.municipalite,\n      typeProp: u.type_prop,\n      cubfLibelle: u.cubf_libelle,\n      anneeConstruction: u.annee_construction,\n      aireEtagesM2: u.aire_etages_m2,\n      superficieTerrainM2: u.superficie_terrain_m2,\n      nbLogements: u.nb_logements,\n      valeurRole: u.valeur_role,\n      lat: u.lat,\n      lng: u.lng,\n      history: ([2021, 2022, 2023, 2024, 2025, 2026] as const).map((y) => ({\n        year: y,\n        value: u[`est_${y}` as keyof UnitRow] as number | null,\n      })),\n      specs: {\n        cubf: u.cubf,\n        cubfLibelle: u.cubf_libelle,\n        anneeEstimee: u.annee_estimee,\n        frontTerrainM: u.front_terrain_m,\n        nbEtages: u.nb_etages,\n        nbLocauxNonResid: u.nb_locaux_non_resid,\n        nbChambresLocatives: u.nb_chambres_locatives,\n        lienPhysique: u.lien_physique,\n        genreConstruction: u.genre_construction,\n        matricule: u.matricule,\n        uniteVoisinage: u.unite_voisinage,\n        nAdresses: u.n_adresses,\n        datCondMarche: u.dat_cond_marche,\n        valeurTerrain: u.valeur_terrain,\n        valeurBatiment: u.valeur_batiment,\n        valeurAnterieure: u.valeur_anterieure,\n        arrond: u.arrond,\n        apt: u.apt,\n      },\n    },\n    result,\n  };\n}\n\nexport interface PortfolioAggregates {\n  count: number;\n  totalEstimate: number;\n  totalLow: number;\n  totalHigh: number;\n  totalRole: number;\n  ecartRolePct: number | null;\n  confidencePct: number; // moyenne pondérée par valeur\n  confidenceLevel: \"A\" | \"B\" | \"C\" | \"D\";\n  totalFloorArea: number;\n  totalLandArea: number;\n  totalDwellings: number;\n  avgYearBuilt: number | null;\n  municipalities: { name: string; count: number; total: number }[];\n  types: { type: string; count: number; total: number }[];\n  history: { year: number; total: number; nCovered: number }[];\n  growthPct: number | null; // croissance du parc 2021→2026 (unités couvertes)\n}\n\nexport interface PortfolioResult {\n  items: UnitEstimate[];\n  aggregates: PortfolioAggregates;\n}\n\nexport function estimatePortfolio(ids: string[]): PortfolioResult {\n  const items = ids\n    .slice(0, 40)\n    .map((id) => estimateByUnitId(id))\n    .filter((x): x is UnitEstimate => x !== null && x.unit !== null);\n\n  const val = (n: number | null | undefined) => n ?? 0;\n  const totalEstimate = items.reduce((s, i) => s + val(i.result.estimate), 0);\n  const totalRole = items.reduce((s, i) => s + val(i.unit!.valeurRole), 0);\n  const wConf =\n    totalEstimate > 0\n      ? items.reduce((s, i) => s + i.result.confidencePct * val(i.result.estimate), 0) /\n        totalEstimate\n      : 0;\n  const level = wConf >= 75 ? \"A\" : wConf >= 60 ? \"B\" : wConf >= 45 ? \"C\" : \"D\";\n\n  const byKey = (key: (i: UnitEstimate) => string) => {\n    const m = new Map<string, { count: number; total: number }>();\n    for (const i of items) {\n      const k = key(i);\n      const e = m.get(k) ?? { count: 0, total: 0 };\n      e.count += 1;\n      e.total += val(i.result.estimate);\n      m.set(k, e);\n    }\n    return [...m.entries()]\n      .map(([name, v]) => ({ name, ...v }))\n      .sort((a, b) => b.total - a.total);\n  };\n\n  const years = [2021, 2022, 2023, 2024, 2025, 2026] as const;\n  const history = years.map((y) => {\n    let total = 0;\n    let n = 0;\n    for (const i of items) {\n      const h = i.unit!.history.find((hh) => hh.year === y);\n      if (h?.value != null) {\n        total += h.value;\n        n += 1;\n      }\n    }\n    return { year: y, total, nCovered: n };\n  });\n  // croissance : somme 2026 vs 2021 sur les unités couvertes aux deux bornes\n  let g21 = 0;\n  let g26 = 0;\n  for (const i of items) {\n    const a = i.unit!.history.find((h) => h.year === 2021)?.value;\n    const b = i.unit!.history.find((h) => h.year === 2026)?.value;\n    if (a != null && b != null) {\n      g21 += a;\n      g26 += b;\n    }\n  }\n\n  const yb = items\n    .map((i) => i.unit!.anneeConstruction)\n    .filter((v): v is number => v != null && v > 1600);\n\n  return {\n    items,\n    aggregates: {\n      count: items.length,\n      totalEstimate,\n      totalLow: items.reduce((s, i) => s + val(i.result.low), 0),\n      totalHigh: items.reduce((s, i) => s + val(i.result.high), 0),\n      totalRole,\n      ecartRolePct: totalRole > 0 ? (totalEstimate / totalRole - 1) * 100 : null,\n      confidencePct: Math.round(wConf),\n      confidenceLevel: level,\n      totalFloorArea: Math.round(items.reduce((s, i) => s + val(i.unit!.aireEtagesM2), 0)),\n      totalLandArea: Math.round(items.reduce((s, i) => s + val(i.unit!.superficieTerrainM2), 0)),\n      totalDwellings: items.reduce((s, i) => s + val(i.unit!.nbLogements), 0),\n      avgYearBuilt: yb.length ? Math.round(yb.reduce((s, v) => s + v, 0) / yb.length) : null,\n      municipalities: byKey((i) => i.unit!.municipalite ?? \"—\"),\n      types: byKey((i) => i.unit!.typeProp).map(({ name, ...v }) => ({ type: name, ...v })),\n      history,\n      growthPct: g21 > 0 ? (g26 / g21 - 1) * 100 : null,\n    },\n  };\n}\n\nexport interface ManualParams {\n  municipality: string;\n  typeProp: string;\n  floorArea?: number;\n  yearBuilt?: number;\n  landArea?: number;\n}\n\nexport function estimateManual(p: ManualParams): UnitEstimate | null {\n  const center = municipalityCenter(p.municipality);\n  if (!center || !center.n) return null;\n  const subject: Subject = {\n    lat: center.lat,\n    lng: center.lng,\n    typeProp: p.typeProp,\n    floorArea: p.floorArea ?? null,\n    yearBuilt: p.yearBuilt ?? null,\n    landArea: p.landArea ?? null,\n    modelEstimate: null,\n    modelP10: null,\n    modelP90: null,\n  };\n  const candidates = candidatesAround(center.lat, center.lng);\n  const index = getMarketIndex(indexType(p.typeProp));\n  const result = estimate(subject, toComps(candidates), index, nowISO());\n  if (!Number.isFinite(result.estimate)) return null;\n  const context = buildContext(\n    center.lat,\n    center.lng,\n    candidates,\n    index,\n    p.municipality,\n    result.estimate\n  );\n  return { unit: null, result, context };\n}\n","structuredPatch":[{"oldStart":388,"oldLines":6,"newStart":388,"newLines":7,"lines":["   const result = estimate(subject, toComps(candidates), index, nowISO());","   if (!Number.isFinite(result.estimate)) return null;","   const context = buildContext(","+    p.typeProp,","     center.lat,","     center.lng,","     candidates,"]}],"userModified":false,"replaceAll":false}}}
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{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GTxfQ5io3sG6H9wyXnsB4y","type":"tool_result","content":"The file /Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx has been updated successfully. (file state is current in your context — no need to Read it back)"}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"9f04acbe-10c4-4ab0-ad44-56d5b5037bbb","timestamp":"2026-08-23T03:14:02.813Z","tool_use_result":{"filePath":"/Users/simon-pierreboucher/apps/vrai-prix/src/components/ResultView.tsx","oldString":"                    {fr\n                      ? `Indice de marché — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 en ${ctx.index[0].month.slice(0, 4)})`\n                      : `Market index — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 in ${ctx.index[0].month.slice(0, 4)})`}","newString":"                    {fr\n                      ? `Indice de marché — ${t(ctx.typeProp as TKey)} (base 100 en ${ctx.index[0].month.slice(0, 4)})`\n                      : `Market index — ${t(ctx.typeProp as TKey)} (base 100 in ${ctx.index[0].month.slice(0, 4)})`}","originalFile":"// Auteur : Simon-Pierre Boucher — contact@spboucher.ai\n\"use client\";\nimport { useState } from \"react\";\nimport Link from \"next/link\";\nimport { useLang } from \"./LangContext\";\nimport LeadForm from \"./LeadForm\";\nimport RadarMap from \"./RadarMap\";\nimport {\n  BuildingGlyph,\n  ConfidenceDial,\n  CountUp,\n  EraLine,\n  locNum,\n  LotDiagram,\n  MarketIndexChart,\n  PercentileBar,\n  RangeBar,\n  ValueSplit,\n} from \"./MetricViz\";\nimport type { EstimateResult } from \"@/lib/engine\";\nimport type { UnitEstimate } from \"@/lib/estimator\";\nimport type { TKey } from \"@/lib/i18n\";\n\nconst fmt = (v: number | null | undefined, lang: string) =>\n  v == null\n    ? \"—\"\n    : new Intl.NumberFormat(lang === \"fr\" ? \"fr-CA\" : \"en-CA\", {\n        style: \"currency\",\n        currency: \"CAD\",\n        maximumFractionDigits: 0,\n      }).format(v);\n\nconst signed = (v: number, lang: string) =>\n  `${v >= 0 ? \"+\" : \"−\"}${fmt(Math.abs(Math.round(v)), lang)}`;\n\nfunction Chip({ k, v }: { k: string; v: string | number }) {\n  return (\n    <div className=\"kv-cell\">\n      <p className=\"k\">{k}</p>\n      <p className=\"v break-words\">{v}</p>\n    </div>\n  );\n}\n\nexport default function ResultView({ data }: { data: UnitEstimate }) {\n  const { lang, t } = useLang();\n  const [activeComp, setActiveComp] = useState<string | null>(null);\n  const r: EstimateResult = data.result;\n  const u = data.unit;\n  const ctx = data.context;\n  const s = u?.specs;\n  const fr = lang === \"fr\";\n  const histMax = Math.max(...(u?.history.map((h) => h.value ?? 0) ?? [0]), 1);\n  const moneyFmt = (v: number) =>\n    new Intl.NumberFormat(fr ? \"fr-CA\" : \"en-CA\", {\n      style: \"currency\",\n      currency: \"CAD\",\n      maximumFractionDigits: 0,\n    }).format(Math.round(v / 100) * 100);\n\n  return (\n    <div className=\"space-y-10 py-8\">\n      {/* fil d'ariane */}\n      <nav className=\"vp-mono flex flex-wrap items-center gap-2 text-[11.5px] uppercase tracking-[0.06em] text-ink-3\">\n        <Link href=\"/\" className=\"border-b-[1.5px] border-transparent hover:border-green hover:text-green\">\n          Vrai-Prix\n        </Link>\n        <span>/</span>\n        <span>{u ? `${u.adresse ?? \"\"} · ${u.municipalite ?? \"\"}` : t(\"manualTitle\")}</span>\n      </nav>\n\n      {/* ---- Panneau prix ---- */}\n      <section className=\"vp-card rise p-6 sm:p-8\">\n        <div className=\"flex flex-wrap items-start justify-between gap-6\">\n          {/* basis large : sous ~380px de large la jauge passe dessous au lieu d'écraser la colonne */}\n          <div className=\"min-w-0 flex-1 basis-[300px]\">\n            <span className=\"kicker\">{t(\"estimatedValue\")}</span>\n            {u && (\n              <h1 className=\"vp-display mt-2 text-[clamp(20px,3vw,28px)] font-bold uppercase leading-tight tracking-[-0.02em]\">\n                {u.adresse}\n                {s?.apt ? ` app. ${s.apt}` : \"\"}\n                {u.municipalite ? <span className=\"text-ink-3\"> · {u.municipalite}</span> : null}\n              </h1>\n            )}\n            <p className=\"vp-display mt-3 text-[clamp(44px,7vw,72px)] font-bold leading-none tracking-[-0.04em]\">\n              <CountUp value={r.estimate} format={moneyFmt} />\n            </p>\n            {/* faits rapides */}\n            <div className=\"mt-4 flex flex-wrap gap-2\">\n              {[\n                u ? t(u.typeProp as TKey) : null,\n                u?.aireEtagesM2 && r.estimate > 0\n                  ? `${locNum(Math.round(r.estimate / u.aireEtagesM2), lang, { maxFrac: 0 })} $/m²`\n                  : null,\n                u?.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : null,\n                u?.anneeConstruction ? `${fr ? \"constr.\" : \"built\"} ${u.anneeConstruction}` : null,\n                u?.superficieTerrainM2\n                  ? `${fr ? \"terrain\" : \"lot\"} ${locNum(u.superficieTerrainM2, lang, { unit: \"m²\" })}`\n                  : null,\n              ]\n                .filter((x): x is string => x != null)\n                .map((x) => (\n                  <span\n                    key={x}\n                    className=\"vp-mono whitespace-nowrap rounded-[5px] border-[1.5px] border-ink bg-surface-2 px-2.5 py-1 text-[11px] font-bold uppercase tracking-[0.04em]\"\n                  >\n                    {x}\n                  </span>\n                ))}\n            </div>\n            {/* fourchette visuelle P10-P90 avec position du rôle */}\n            <div className=\"mt-6 max-w-2xl\">\n              <p className=\"klabel mb-1\">{t(\"range\")}</p>\n              <RangeBar\n                low={r.low}\n                estimate={r.estimate}\n                high={r.high}\n                role={u?.valeurRole ?? null}\n                lang={lang}\n              />\n            </div>\n            <div className=\"mt-5 flex flex-wrap items-center gap-x-6 gap-y-3\">\n              {u?.valeurRole != null && (\n                <div>\n                  <p className=\"klabel\">{t(\"assessed\")}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-ink-2\">{fmt(u.valeurRole, lang)}</p>\n                </div>\n              )}\n              {u?.valeurRole != null && u.valeurRole > 0 && r.estimate > 0 && (\n                <div>\n                  <p className=\"klabel\">{fr ? \"Écart vs rôle\" : \"vs assessment\"}</p>\n                  <p className=\"vp-display text-[19px] font-bold text-green-deep\">\n                    {r.estimate >= u.valeurRole ? \"+\" : \"\"}\n                    {((r.estimate / u.valeurRole - 1) * 100).toFixed(0)} %\n                  </p>\n                </div>\n              )}\n            </div>\n            {u && (\n              <div className=\"mt-6 flex flex-wrap gap-2.5\">\n                <a\n                  href={`/api/report?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-primary\"\n                  download\n                >\n                  {fr ? \"Rapport PDF\" : \"PDF report\"} ↓\n                </a>\n                <a\n                  href={`/api/report/pro?id=${encodeURIComponent(u.id)}`}\n                  className=\"btn btn-ghost\"\n                  download\n                  title={fr ? \"Structure de rapport d'évaluation — présentation bancaire\" : \"Appraisal-style structure — bank presentation\"}\n                >\n                  {fr ? \"Rapport détaillé format professionnel (banque)\" : \"Detailed professional report (bank)\"} ↓\n                </a>\n              </div>\n            )}\n          </div>\n          <div className=\"shrink-0\">\n            <ConfidenceDial\n              pct={r.confidencePct}\n              level={r.confidenceLevel}\n              label={`${t(\"confidence\")} — ${t(`conf${r.confidenceLevel}` as TKey)}`}\n            />\n          </div>\n        </div>\n        <p className=\"vp-mono mt-5 border-t-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-3\">\n          {t(\"disclaimer\")}\n        </p>\n      </section>\n\n      {/* ---- Portrait de la propriété (bento du registre) ---- */}\n      {u && s && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Registre officiel — dessiné par les données\" : \"Official record — drawn by the data\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Portrait de la propriété\" : \"Property portrait\"}\n          </h2>\n          <div className=\"mt-4 grid gap-4 lg:grid-cols-3\">\n            {/* Bâtiment */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"60ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Bâtiment\" : \"Building\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <BuildingGlyph\n                  floors={s.nbEtages}\n                  dwellings={u.nbLogements}\n                  genre={s.genreConstruction}\n                  yearBuilt={u.anneeConstruction}\n                  floorAreaM2={u.aireEtagesM2}\n                  lang={lang}\n                />\n              </div>\n              <EraLine year={u.anneeConstruction} lang={lang} />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Aire d'étages\" : \"Floor area\"} v={u.aireEtagesM2 ? locNum(u.aireEtagesM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Étages × logements\" : \"Storeys × dwellings\"} v={`${s.nbEtages ?? \"—\"} × ${u.nbLogements ?? \"—\"}`} />\n                <Chip k={fr ? \"Genre\" : \"Style\"} v={s.genreConstruction ?? \"—\"} />\n                <Chip k={fr ? \"Lien physique\" : \"Physical link\"} v={s.lienPhysique ?? \"—\"} />\n              </div>\n            </div>\n            {/* Terrain */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"140ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Terrain — à l'échelle\" : \"Lot — to scale\"}\n              </p>\n              <div className=\"flex justify-center py-2\">\n                <LotDiagram\n                  areaM2={u.superficieTerrainM2}\n                  frontageM={s.frontTerrainM}\n                  footprintM2={\n                    u.aireEtagesM2 && s.nbEtages ? u.aireEtagesM2 / Math.max(1, s.nbEtages) : u.aireEtagesM2\n                  }\n                  lang={lang}\n                  isCondo={!!s.apt || (u.nbLogements != null && u.nbLogements >= 1 && !u.superficieTerrainM2)}\n                />\n              </div>\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Superficie\" : \"Area\"} v={u.superficieTerrainM2 ? locNum(u.superficieTerrainM2, lang, { unit: \"m²\" }) : \"—\"} />\n                <Chip k={fr ? \"Frontage\" : \"Frontage\"} v={s.frontTerrainM ? locNum(s.frontTerrainM, lang, { unit: \"m\" }) : \"—\"} />\n                <Chip k=\"CUBF\" v={s.cubf ?? \"—\"} />\n                <Chip k={fr ? \"Usage\" : \"Use\"} v={s.cubfLibelle ?? \"—\"} />\n              </div>\n            </div>\n            {/* Valeur au rôle */}\n            <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"220ms\" }}>\n              <p className=\"klabel mb-1 border-b-[1.5px] border-dashed border-[rgba(20,24,20,0.14)] pb-2\">\n                {fr ? \"Valeur au rôle 2026 — composition\" : \"2026 assessed value — split\"}\n              </p>\n              <p className=\"vp-display py-2 text-[26px] font-bold tracking-[-0.02em]\">\n                {fmt(u.valeurRole, lang)}\n              </p>\n              <ValueSplit\n                land={s.valeurTerrain}\n                building={s.valeurBatiment}\n                previous={s.valeurAnterieure}\n                total={u.valeurRole}\n                lang={lang}\n                labels={{\n                  land: fr ? \"Terrain\" : \"Land\",\n                  building: fr ? \"Bâtiment\" : \"Building\",\n                  previous: fr ? \"Rôle antérieur\" : \"Previous roll\",\n                }}\n              />\n              <div className=\"mt-3 grid grid-cols-2 gap-2\">\n                <Chip k={fr ? \"Cond. marché au\" : \"Market cond.\"} v={s.datCondMarche ?? \"—\"} />\n                <Chip k={fr ? \"Matricule\" : \"Roll no.\"} v={s.matricule ?? \"—\"} />\n              </div>\n            </div>\n          </div>\n          {/* registre brut complet */}\n          <details className=\"vp-card mt-4 p-5\">\n            <summary className=\"vp-mono cursor-pointer text-[11px] font-bold uppercase tracking-[0.08em] text-green-deep\">\n              {fr ? \"▸ Toutes les données du registre (brutes)\" : \"▸ All registry data (raw)\"}\n            </summary>\n            <div className=\"mt-4 grid grid-cols-2 gap-2 sm:grid-cols-4\">\n              <Chip k={fr ? \"Année construction\" : \"Year built\"} v={u.anneeConstruction ? `${u.anneeConstruction}${s.anneeEstimee === \"E\" ? (fr ? \" (estimée)\" : \" (est.)\") : \"\"}` : \"—\"} />\n              <Chip k={fr ? \"Locaux non résid.\" : \"Non-res. units\"} v={s.nbLocauxNonResid ?? \"—\"} />\n              <Chip k={fr ? \"Chambres locatives\" : \"Rental rooms\"} v={s.nbChambresLocatives ?? \"—\"} />\n              <Chip k={fr ? \"Unité de voisinage\" : \"Neighbourhood\"} v={s.uniteVoisinage || \"—\"} />\n              <Chip k={fr ? \"Arrondissement\" : \"Borough\"} v={s.arrond || \"—\"} />\n              <Chip k={fr ? \"Adresses sur l'unité\" : \"Addresses\"} v={s.nAdresses ?? \"—\"} />\n              <Chip k={fr ? \"Valeur terrain\" : \"Land value\"} v={fmt(s.valeurTerrain, lang)} />\n              <Chip k={fr ? \"Valeur bâtiment\" : \"Building value\"} v={fmt(s.valeurBatiment, lang)} />\n              <Chip k={fr ? \"Rôle antérieur\" : \"Previous roll\"} v={fmt(s.valeurAnterieure, lang)} />\n              <Chip k=\"ID provincial\" v={u.id} />\n            </div>\n          </details>\n        </section>\n      )}\n\n      {/* ---- Transparence du calcul ---- */}\n      <section>\n        <span className=\"kicker\">{fr ? \"Transparence\" : \"Transparency\"}</span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"whyTitle\")}\n        </h2>\n        <div className=\"mt-4 grid gap-4 sm:grid-cols-2\">\n          <div className=\"vp-card vp-card-hover rise p-5\">\n            <p className=\"klabel\">{t(\"modelPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.modelEstimate != null ? (\n                <CountUp value={r.modelEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightModel\")} : {(r.modelWeight * 100).toFixed(0)} %\n            </p>\n          </div>\n          <div className=\"vp-card vp-card-hover rise p-5\" style={{ animationDelay: \"100ms\" }}>\n            <p className=\"klabel\">{t(\"compsPart\")}</p>\n            <p className=\"vp-display mt-2 text-[30px] font-bold tracking-[-0.03em]\">\n              {r.compsEstimate != null ? (\n                <CountUp value={r.compsEstimate} format={moneyFmt} />\n              ) : (\n                \"—\"\n              )}\n            </p>\n            <p className=\"vp-mono mt-1 text-[11px] uppercase tracking-[0.06em] text-green-deep\">\n              {t(\"weightComps\")} : {((1 - r.modelWeight) * 100).toFixed(0)} % · {r.nCompsUsed} comps\n              {r.compsDispersionPct != null ? ` · ±${r.compsDispersionPct} %` : \"\"}\n            </p>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Marché local ---- */}\n      {ctx && (ctx.index.length >= 6 || ctx.sector || ctx.muniPercentile != null) && (\n        <section>\n          <span className=\"kicker\">{fr ? \"Contexte de marché\" : \"Market context\"}</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {fr ? \"Le marché autour de cette propriété\" : \"The market around this property\"}\n          </h2>\n          <div className=\"mt-4 grid items-stretch gap-4 lg:grid-cols-[1.35fr_1fr]\">\n            {/* trajectoire du marché */}\n            {ctx.index.length >= 6 && (\n              <div className=\"vp-card rise p-5\">\n                <div className=\"flex flex-wrap items-center justify-between gap-2\">\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Indice de marché — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 en ${ctx.index[0].month.slice(0, 4)})`\n                      : `Market index — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 in ${ctx.index[0].month.slice(0, 4)})`}\n                  </p>\n                  <div className=\"flex gap-2\">\n                    {ctx.index12mPct != null && (\n                      <span className={`vp-mono rounded-[4px] border border-ink px-1.5 py-0.5 text-[10px] font-bold ${ctx.index12mPct >= 0 ? \"bg-lime text-ink\" : \"bg-danger text-white\"}`}>\n                        12 {fr ? \"mois\" : \"mo\"} {ctx.index12mPct >= 0 ? \"+\" : \"\"}\n                        {ctx.index12mPct.toFixed(1)} %\n                      </span>\n                    )}\n                    {ctx.indexSince2021Pct != null && (\n                      <span className=\"vp-mono rounded-[4px] border border-ink bg-surface-2 px-1.5 py-0.5 text-[10px] font-bold text-ink-2\">\n                        {ctx.index[0].month.slice(0, 4)}→ {ctx.indexSince2021Pct >= 0 ? \"+\" : \"\"}\n                        {ctx.indexSince2021Pct.toFixed(0)} %\n                      </span>\n                    )}\n                  </div>\n                </div>\n                <div className=\"mt-3\">\n                  <MarketIndexChart series={ctx.index} lang={lang} />\n                </div>\n                <p className=\"vp-mono mt-2 text-[10px] uppercase tracking-[0.05em] text-ink-3\">\n                  {fr\n                    ? \"Indice utilisé pour l'ajustement « marché » des comparables — même donnée, même calcul.\"\n                    : \"Index used for the comparables' market adjustment — same data, same math.\"}\n                </p>\n              </div>\n            )}\n            <div className=\"flex flex-col gap-4\">\n              {/* ventes du secteur */}\n              {ctx.sector && (\n                <div className=\"vp-card rise p-5\" style={{ animationDelay: \"80ms\" }}>\n                  <p className=\"klabel\">\n                    {fr\n                      ? `Ventes réelles — rayon ${ctx.sector.radiusKm} km · 12 mois`\n                      : `Real sales — ${ctx.sector.radiusKm} km radius · 12 months`}\n                  </p>\n                  <div className=\"mt-3 grid grid-cols-3 gap-2\">\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Ventes\" : \"Sales\"}</p>\n                      <p className=\"v\">{ctx.sector.nSales12m}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">{fr ? \"Prix médian\" : \"Median price\"}</p>\n                      <p className=\"v\">{ctx.sector.medianPrice != null ? fmt(Math.round(ctx.sector.medianPrice), lang) : \"—\"}</p>\n                    </div>\n                    <div className=\"kv-cell\">\n                      <p className=\"k\">$/m² {fr ? \"médian\" : \"median\"}</p>\n                      <p className=\"v\">\n                        {ctx.sector.medianPpm2 != null\n                          ? locNum(Math.round(ctx.sector.medianPpm2), lang, { maxFrac: 0 })\n                          : \"—\"}\n                      </p>\n                    </div>\n                  </div>\n                  {ctx.sector.medianPrice != null && r.estimate > 0 && (\n                    <p className=\"vp-mono mt-3 text-[10.5px] uppercase tracking-[0.05em] text-ink-2\">\n                      {fr ? \"Cette propriété\" : \"This property\"} :{\" \"}\n                      <b className=\"text-ink\">\n                        {r.estimate >= ctx.sector.medianPrice ? \"+\" : \"−\"}\n                        {Math.abs((r.estimate / ctx.sector.medianPrice - 1) * 100).toFixed(0)} %\n                      </b>{\" \"}\n                      {fr ? \"vs médiane du secteur\" : \"vs sector median\"}\n                    </p>\n                  )}\n                </div>\n              )}\n              {/* position dans la municipalité */}\n              {ctx.muniPercentile != null && u?.municipalite && (\n                <div className=\"vp-card rise flex-1 p-5\" style={{ animationDelay: \"160ms\" }}>\n                  <p className=\"klabel\">\n                    {fr ? `Position dans ${u.municipalite}` : `Position in ${u.municipalite}`}\n                  </p>\n                  <div className=\"mt-3\">\n                    <PercentileBar pct={ctx.muniPercentile} lang={lang} />\n                  </div>\n                  <p className=\"mt-3 text-[13px] text-ink-2\">\n                    {fr ? (\n                      <>\n                        Vaut plus que{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> des{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        propriétés estimées de la municipalité.\n                      </>\n                    ) : (\n                      <>\n                        Worth more than{\" \"}\n                        <b className=\"text-ink\">{Math.round(ctx.muniPercentile)} %</b> of the{\" \"}\n                        {ctx.muniTotal != null ? locNum(ctx.muniTotal, lang, { maxFrac: 0 }) : \"\"}{\" \"}\n                        estimated properties in the municipality.\n                      </>\n                    )}\n                  </p>\n                </div>\n              )}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Historique ---- */}\n      {u && u.history.some((h) => h.value != null) && (\n        <section>\n          <span className=\"kicker\">2021 → 2026</span>\n          <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n            {t(\"history\")}\n          </h2>\n          <div className=\"vp-card rise mt-4 p-5 sm:p-6\">\n            <div className=\"flex flex-col gap-2\">\n              {u.history.map((h, i) => {\n                const prev = i > 0 ? u.history[i - 1].value : null;\n                const yoy =\n                  h.value != null && prev != null && prev > 0\n                    ? ((h.value / prev - 1) * 100)\n                    : null;\n                return (\n                  <div key={h.year} className=\"hbar-row grid grid-cols-[44px_1fr_auto] items-center gap-2 sm:grid-cols-[52px_1fr_auto] sm:gap-3\">\n                    <span className=\"vp-mono text-[12px] font-bold\">{h.year}</span>\n                    <span className=\"hbar-track\">\n                      <span\n                        className=\"hbar-fill grow-x\"\n                        style={{\n                          width: `${((h.value ?? 0) / histMax) * 100}%`,\n                          animationDelay: `${i * 90}ms`,\n                          background: h.year === 2026 ? \"var(--ink)\" : undefined,\n                        }}\n                      />\n                    </span>\n                    <span className=\"vp-mono flex items-center justify-end gap-2 text-[12px] font-bold\">\n                      {fmt(h.value, lang)}\n                      {yoy != null && (\n                        <span className={`rounded-[4px] border border-ink px-1 py-px text-[9px] ${yoy >= 0 ? \"bg-lime-soft text-green-deep\" : \"bg-[var(--danger-soft)] text-[var(--danger)]\"}`}>\n                          {yoy >= 0 ? \"+\" : \"\"}\n                          {yoy.toFixed(0)} %\n                        </span>\n                      )}\n                    </span>\n                  </div>\n                );\n              })}\n            </div>\n          </div>\n        </section>\n      )}\n\n      {/* ---- Plan des comparables (carte maison) ---- */}\n      <section>\n        <span className=\"kicker\">\n          {r.nCompsUsed} {fr ? \"ventes réelles\" : \"real sales\"}\n        </span>\n        <h2 className=\"vp-display mt-2 text-[22px] font-bold uppercase tracking-[-0.02em]\">\n          {t(\"compsTitle\")}\n        </h2>\n        <div className=\"mt-4 grid items-start gap-4 lg:grid-cols-[1.05fr_1fr]\">\n          <div className=\"overflow-hidden rounded-[10px] border-[1.5px] border-ink shadow-[8px_8px_0_rgba(20,24,20,0.08)]\">\n            <RadarMap\n              subject={{\n                lat: u?.lat ?? r.comps[0]?.lat ?? 46.81,\n                lng: u?.lng ?? r.comps[0]?.lng ?? -71.21,\n                label: u?.adresse ?? (fr ? \"Propriété estimée\" : \"Subject property\"),\n              }}\n              comps={r.comps.map((c) => ({\n                id: c.id,\n                lat: c.lat,\n                lng: c.lng,\n                label: `${c.street ?? \"\"}, ${c.city ?? \"\"}`,\n                price: fmt(c.amount, lang),\n                adjusted: fmt(Math.round(c.adjustedPrice), lang),\n                date: c.date,\n                distanceM: c.distanceM,\n                weight: c.weight,\n              }))}\n              highlight={activeComp}\n              onHover={setActiveComp}\n            />\n          </div>\n          <div className=\"src-wrap max-h-[560px] overflow-y-auto\">\n            <table className=\"src-table\">\n              <thead className=\"sticky top-0 z-10\">\n                <tr>\n                  <th>#</th>\n                  <th>{fr ? \"Vente\" : \"Sale\"}</th>\n                  <th className=\"text-right\">{t(\"adjusted\")}</th>\n                </tr>\n              </thead>\n              <tbody>\n                {r.comps.map((c, i) => (\n                  <tr\n                    key={c.id}\n                    onMouseEnter={() => setActiveComp(c.id)}\n                    onMouseLeave={() => setActiveComp(null)}\n                    className={activeComp === c.id ? \"!bg-lime-soft\" : \"\"}\n                  >\n                    <td>\n                      <span\n                        className={`vp-mono inline-flex h-6 w-6 items-center justify-center rounded-full border-[1.5px] border-ink text-[11px] font-bold ${\n                          activeComp === c.id ? \"bg-lime text-ink\" : \"bg-green text-paper\"\n                        }`}\n                      >\n                        {i + 1}\n                      </span>\n                    </td>\n                    <td>\n                      <p className=\"font-semibold leading-tight\">\n                        {c.street}, {c.city}\n                      </p>\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                        {c.date} · {c.distanceM < 1000 ? `${Math.round(c.distanceM)} m` : `${(c.distanceM / 1000).toFixed(1)} km`} ·{\" \"}\n                        {t(\"sold\")} {fmt(c.amount, lang)}\n                        {c.floorArea && c.floorArea > 20\n                          ? ` (${locNum(Math.round(c.amount / c.floorArea), lang, { maxFrac: 0 })} $/m²)`\n                          : \"\"}\n                      </p>\n                      {(c.floorArea || c.yearBuilt) && (\n                        <p className=\"vp-mono mt-0.5 text-[10.5px] uppercase tracking-[0.04em] text-ink-3\">\n                          {[\n                            c.floorArea ? locNum(c.floorArea, lang, { unit: \"m²\" }) : null,\n                            c.yearBuilt ? `${fr ? \"constr.\" : \"built\"} ${c.yearBuilt}` : null,\n                          ]\n                            .filter(Boolean)\n                            .join(\" · \")}\n                        </p>\n                      )}\n                      <p className=\"vp-mono mt-0.5 text-[10.5px] text-ink-2\">\n                        {t(\"adjTime\")} {signed(c.adjTime, lang)} · {t(\"adjArea\")} {signed(c.adjArea, lang)} ·{\" \"}\n                        {t(\"adjAge\")} {signed(c.adjAge, lang)}\n                      </p>\n                    </td>\n                    <td className=\"vp-display text-right font-bold\">\n                      {fmt(Math.round(c.adjustedPrice), lang)}\n                      <p className=\"vp-mono text-[10px] font-normal text-ink-3\">\n                        {t(\"weight\")} {(c.weight * 100).toFixed(0)} %\n                      </p>\n                    </td>\n                  </tr>\n                ))}\n              </tbody>\n            </table>\n          </div>\n        </div>\n      </section>\n\n      {/* ---- Lead ---- */}\n      <section className=\"rounded-[10px] border-[1.5px] border-ink bg-ink p-6 text-paper shadow-[8px_8px_0_rgba(20,24,20,0.2)] sm:p-8\">\n        <h2 className=\"vp-display text-[22px] font-bold uppercase tracking-[-0.02em] text-lime\">\n          {t(\"leadTitle\")}\n        </h2>\n        <div className=\"mt-4 max-w-xl\">\n          <LeadForm unitId={u?.id ?? null} estimate={r.estimate} />\n        </div>\n      </section>\n    </div>\n  );\n}\n","structuredPatch":[{"oldStart":321,"oldLines":8,"newStart":321,"newLines":8,"lines":["                 <div className=\"flex flex-wrap items-center justify-between gap-2\">","                   <p className=\"klabel\">","                     {fr","-                      ? `Indice de marché — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 en ${ctx.index[0].month.slice(0, 4)})`","-                      : `Market index — ${t((u?.typeProp ?? \"unifamilial\") as TKey)} (base 100 in ${ctx.index[0].month.slice(0, 4)})`}","+                      ? `Indice de marché — ${t(ctx.typeProp as TKey)} (base 100 en ${ctx.index[0].month.slice(0, 4)})`","+                      : `Market index — ${t(ctx.typeProp as TKey)} (base 100 in ${ctx.index[0].month.slice(0, 4)})`}","                   </p>","                   <div className=\"flex gap-2\">","                     {ctx.index12mPct != null && ("]}],"userModified":false,"replaceAll":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"fafc9143-9a00-477c-bc9c-6800a70c0f87","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"system","subtype":"thinking_tokens","estimated_tokens":50,"estimated_tokens_delta":50,"uuid":"6c56b8dc-98c6-404b-9317-286be42e6981","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"system","subtype":"thinking_tokens","estimated_tokens":150,"estimated_tokens_delta":100,"uuid":"4598180e-bdeb-4e68-b9f8-6bb3e1e6d5cb","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"system","subtype":"thinking_tokens","estimated_tokens":171,"estimated_tokens_delta":21,"uuid":"113226fc-79cc-441e-80fe-3145aab73a35","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJx8iDJC6psfuJT5D8jy","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":478,"cache_read_input_tokens":155954,"cache_creation":{"ephemeral_5m_input_tokens":478,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"c9eeeb97-0ce9-4ca8-b4b0-9176187f0f0d","request_id":"req_011CeJx8fP8muSRVp919RPcD"}}
{"type":"claude","event":{"type":"assistant","message":{"model":"claude-fable-5","id":"msg_011CeJx8iDJC6psfuJT5D8jy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01V76BRghkJSQWBqo1mPrA4R","name":"Bash","input":{"command":"npm run build 2>&1 | grep -E \"✓|error\" | head -5 && pm2 restart vrai-prix 2>&1 >/dev/null; sleep 4; ID=$(sqlite3 data/vraiprix.db \"SELECT id_provinc FROM transactions WHERE id_provinc IS NOT NULL ORDER BY date DESC LIMIT 1;\"); curl -s -o /dev/null -w \"home %{http_code} · \" http://localhost:8090/; curl -s -o /dev/null -w \"estimation %{http_code} · \" \"http://localhost:8090/estimation/$ID\"; curl -s -X POST http://localhost:8090/api/estimate -H 'Content-Type: application/json' -d '{\"municipality\":\"Lévis\",\"typeProp\":\"condo_ou_multi\",\"floorArea\":95,\"yearBuilt\":2010}' | python3 -c \"import json,sys; d=json.load(sys.stdin); c=d.get('context'); print('api/estimate OK — type:', c and c.get('typeProp'), '· sector:', bool(c and c.get('sector')), '· percentile:', c and round(c.get('muniPercentile') or -1))\"","description":"Rebuild, restart, verify pages and manual-estimate API context","timeout":600000},"caller":{"type":"direct"}}],"stop_reason":null,"stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":478,"cache_read_input_tokens":155954,"cache_creation":{"ephemeral_5m_input_tokens":478,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard","inference_geo":"global"},"diagnostics":null,"context_management":null},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"fd07e279-5dfa-48cb-870f-07e63ebde2ec","request_id":"req_011CeJx8fP8muSRVp919RPcD"}}
{"type":"claude","event":{"type":"system","subtype":"task_started","task_id":"b7tuwwbpw","tool_use_id":"toolu_01V76BRghkJSQWBqo1mPrA4R","description":"Rebuild, restart, verify pages and manual-estimate API context","task_type":"local_bash","uuid":"6ddc244f-25ee-4d03-aca4-8bc3cfc4c41a","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"system","subtype":"task_notification","task_id":"b7tuwwbpw","tool_use_id":"toolu_01V76BRghkJSQWBqo1mPrA4R","status":"completed","output_file":"","summary":"Rebuild, restart, verify pages and manual-estimate API context","uuid":"e4cee54b-45b4-4e74-8e90-1b87480fdf36","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01V76BRghkJSQWBqo1mPrA4R","type":"tool_result","content":"✓ Running next.config.ts took 11ms\n✓ Compiled successfully in 647ms\n✓ Generating static pages using 27 workers (115/115) in 1296ms\nhome 200 · estimation 200 · api/estimate OK — type: condo_ou_multi · sector: True · percentile: 14","is_error":false}]},"parent_tool_use_id":null,"session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86","uuid":"f54c8932-5aa2-4057-8ff5-5342eb857068","timestamp":"2026-08-23T03:14:26.506Z","tool_use_result":{"stdout":"✓ Running next.config.ts took 11ms\n✓ Compiled successfully in 647ms\n✓ Generating static pages using 27 workers (115/115) in 1296ms\nhome 200 · estimation 200 · api/estimate OK — type: condo_ou_multi · sector: True · percentile: 14","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false}}}
{"type":"claude","event":{"type":"system","subtype":"status","status":"requesting","uuid":"ff57bf99-d872-4d45-b798-30ee15138377","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
{"type":"claude","event":{"type":"system","subtype":"thinking_tokens","estimated_tokens":50,"estimated_tokens_delta":50,"uuid":"d7d8603c-5594-4295-949a-2b61b67ab5a7","session_id":"537090c3-73e4-4172-b3a6-008b35dd7a86"}}
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