Python 67%
TypeScript 18.2%
CSS 14.4%
1# -----------------------------------------------------------------------------2# House-Ka — Agrégateur de maisons à vendre (Canada hors Québec)3# Auteur : Simon-Pierre Boucher — contact@spboucher.ai4# commerces.py : grands commerces à proximité — API Mapbox Search Box,5# bannières ADAPTÉES À CHAQUE PROVINCE.6#7# Pour chaque grande bannière du panier provincial, on interroge l'API8# Search Box de Mapbox (jeton PUBLIC pk.… lu dans9# frontend/src/kamaps/config.ts) avec la position de l'annonce en10# `proximity`, et on retient le point de vente le plus proche. Cache par11# cellule d'environ 1 km (data/commerces.db, TTL 30 jours).12#13# Paniers par province (2026-08) — fondés sur la présence réelle des14# chaînes : Loblaw n°1 national (Superstore/No Frills, Zehrs ON, Dominion NL),15# Sobeys n°2 (dominant en Atlantique : Sobeys/Foodland/Lawtons), Metro n°316# (Ontario+Québec, Food Basics), Pattison/Save-On-Foods ~180 magasins dans17# l'Ouest + Yukon, Co-op (FCL) ~300 magasins en Saskatchewan, monopoles18# d'alcool provinciaux (LCBO, BC Liquor, Liquor Mart MB, NB Liquor, NSLC),19# Kent (Irving) en quincaillerie atlantique, London Drugs (BC/AB),20# Colemans (chaîne terre-neuvienne).21# -----------------------------------------------------------------------------22from __future__ import annotations2324import json25import math26import re27import sqlite328import time29import urllib.parse30import urllib.request31from concurrent.futures import ThreadPoolExecutor32from pathlib import Path3334ROOT = Path(__file__).resolve().parent.parent35DB_PATH = ROOT / "data" / "commerces.db"36UA = "HouseKaBot/1.0 (+https://www.house-ka.com; contact@spboucher.ai)"37TTL = 30 * 8640038API = "https://api.mapbox.com/search/searchbox/v1/forward"3940# id -> (libellé, requête Mapbox, mot-clé de validation — le nom du POI doit le41# contenir, sans accents ni casse ; écarte « Station Métro », « Super Qualité »…)42BRAND_DEFS = {43 # nationaux44 "costco": ("Costco", "Costco Wholesale", "costco"),45 "walmart": ("Walmart", "Walmart Supercentre", "walmart"),46 "canadiantire": ("Canadian Tire", "Canadian Tire", "canadian tire"),47 "dollarama": ("Dollarama", "Dollarama", "dollarama"),48 "homedepot": ("Home Depot", "Home Depot", "home depot"),49 "homehardware": ("Home Hardware", "Home Hardware", "home hardware"),50 "shoppers": ("Shoppers Drug Mart", "Shoppers Drug Mart", "shoppers"),51 "gianttiger": ("Giant Tiger", "Giant Tiger", "giant tiger"),52 # épicerie — Loblaw53 "loblaws": ("Loblaws", "Loblaws", "loblaws"),54 "superstore": ("Real Canadian Superstore", "Real Canadian Superstore", "superstore"),55 "atlanticsuperstore": ("Atlantic Superstore", "Atlantic Superstore", "superstore"),56 "nofrills": ("No Frills", "No Frills", "no frills"),57 "zehrs": ("Zehrs", "Zehrs", "zehrs"),58 "dominion": ("Dominion", "Dominion grocery", "dominion"),59 "independent": ("Your Independent Grocer", "Your Independent Grocer", "independent"),60 # épicerie — Empire/Sobeys61 "sobeys": ("Sobeys", "Sobeys", "sobeys"),62 "safeway": ("Safeway", "Safeway", "safeway"),63 "foodland": ("Foodland", "Foodland", "foodland"),64 "freshco": ("FreshCo", "FreshCo", "freshco"),65 # épicerie — Metro (Ontario)66 "metro": ("Metro", "Metro grocery", "metro"),67 "foodbasics": ("Food Basics", "Food Basics", "food basics"),68 # épicerie — Ouest / coopératives / régionales69 "saveon": ("Save-On-Foods", "Save-On-Foods", "save-on"),70 "coop": ("Co-op", "Co-op Food Store", "co-op"),71 "colemans": ("Colemans", "Colemans grocery", "colemans"),72 "iga": ("IGA", "IGA", "iga"),73 # alcool (monopoles/sociétés provinciales)74 "lcbo": ("LCBO", "LCBO", "lcbo"),75 "beerstore": ("The Beer Store", "The Beer Store", "beer store"),76 "bcliquor": ("BC Liquor", "BC Liquor Store", "liquor"),77 "liquormart": ("Liquor Mart", "Manitoba Liquor Mart", "liquor mart"),78 "nbliquor": ("NB Liquor", "NB Liquor Alcool NB", "liquor"),79 "nslc": ("NSLC", "NSLC", "nslc"),80 # pharmacies régionales81 "londondrugs": ("London Drugs", "London Drugs", "london drugs"),82 "rexall": ("Rexall", "Rexall", "rexall"),83 "lawtons": ("Lawtons", "Lawtons Drugs", "lawtons"),84 # quincaillerie85 "rona": ("RONA", "RONA", "rona"),86 "kent": ("Kent", "Kent Building Supplies", "kent"),87}8889_NATIONAL = ["costco", "walmart", "canadiantire", "dollarama", "homedepot",90 "shoppers"]9192# panier par province — les bannières LES PLUS PRÉSENTES dans chaque marché93PROVINCE_BRANDS = {94 "British Columbia": _NATIONAL + ["saveon", "superstore", "safeway",95 "nofrills", "iga", "londondrugs",96 "bcliquor", "homehardware"],97 "Alberta": _NATIONAL + ["superstore", "safeway", "saveon", "sobeys",98 "nofrills", "coop", "rexall", "homehardware"],99 "Saskatchewan": _NATIONAL + ["coop", "superstore", "sobeys", "safeway",100 "nofrills", "gianttiger", "homehardware"],101 "Manitoba": _NATIONAL + ["superstore", "coop", "sobeys", "safeway",102 "nofrills", "liquormart", "gianttiger",103 "homehardware"],104 "Ontario": _NATIONAL + ["loblaws", "nofrills", "foodbasics", "metro",105 "sobeys", "freshco", "zehrs", "lcbo", "beerstore",106 "rexall", "rona"],107 "New Brunswick": _NATIONAL + ["sobeys", "atlanticsuperstore", "foodland",108 "nofrills", "nbliquor", "kent", "lawtons",109 "gianttiger"],110 "Nova Scotia": _NATIONAL + ["sobeys", "atlanticsuperstore", "foodland",111 "nofrills", "nslc", "kent", "lawtons",112 "gianttiger"],113 "Prince Edward Island": _NATIONAL + ["sobeys", "atlanticsuperstore",114 "foodland", "kent", "lawtons",115 "gianttiger"],116 "Newfoundland and Labrador": _NATIONAL + ["sobeys", "dominion", "colemans",117 "foodland", "kent", "lawtons"],118 "Yukon": _NATIONAL + ["saveon", "superstore", "independent",119 "homehardware"],120 "Northwest Territories": _NATIONAL + ["independent", "coop",121 "homehardware"],122 "Nunavut": ["canadiantire", "independent", "coop", "homehardware"],123}124_DEFAULT_PROVINCE = "Ontario"125126# frontières longitudinales approximatives (repli quand la région est absente)127def _infer_province(lat: float, lng: float) -> str:128 if lat >= 60:129 if lng < -124:130 return "Yukon"131 return "Northwest Territories" if lng < -102 else "Nunavut"132 if lng < -120:133 return "British Columbia"134 if lng < -110:135 return "Alberta"136 if lng < -101.4:137 return "Saskatchewan"138 if lng < -95.15:139 return "Manitoba"140 if lng < -74.3:141 return "Ontario"142 if -64.5 <= lng <= -61.9 and 45.9 <= lat <= 47.1:143 return "Prince Edward Island"144 if lng >= -59.5 or lat >= 50.5:145 return "Newfoundland and Labrador"146 if lat < 46.05 or lng > -64.4:147 return "Nova Scotia"148 return "New Brunswick"149150151def _brands_for(region: str | None, lat: float, lng: float):152 prov = (region or "").strip()153 if prov not in PROVINCE_BRANDS:154 prov = _infer_province(lat, lng)155 ids = PROVINCE_BRANDS.get(prov, PROVINCE_BRANDS[_DEFAULT_PROVINCE])156 return [(bid, *BRAND_DEFS[bid]) for bid in ids if bid in BRAND_DEFS]157158_BAN = ("station", "stationnement", "kentucky", "kentville",159 "co-operators", "cooperators")160161162def _norm(s: str) -> str:163 import unicodedata164 s = unicodedata.normalize("NFD", s or "")165 return "".join(c for c in s if unicodedata.category(c) != "Mn").lower()166167_token_cache: list[str] = []168169170def _token() -> str:171 if not _token_cache:172 cfg = (ROOT / "frontend" / "src" / "kamaps" / "config.ts").read_text()173 m = re.search(r'"(pk\.[A-Za-z0-9._-]+)"', cfg)174 if not m:175 raise RuntimeError("jeton Mapbox introuvable (kamaps/config.ts)")176 _token_cache.append(m.group(1))177 return _token_cache[0]178179180def _connect() -> sqlite3.Connection:181 DB_PATH.parent.mkdir(parents=True, exist_ok=True)182 con = sqlite3.connect(DB_PATH, timeout=15)183 con.row_factory = sqlite3.Row184 con.execute("""CREATE TABLE IF NOT EXISTS commerces_cache (185 cellule TEXT, brand TEXT, nom TEXT, adresse TEXT,186 lat REAL, lng REAL, fetched_at REAL,187 PRIMARY KEY (cellule, brand))""")188 return con189190191def _dist_m(lat1, lng1, lat2, lng2) -> float:192 dlat = math.radians(lat2 - lat1)193 dlng = math.radians(lng2 - lng1)194 a = (math.sin(dlat / 2) ** 2 + math.cos(math.radians(lat1))195 * math.cos(math.radians(lat2)) * math.sin(dlng / 2) ** 2)196 return 6371000 * 2 * math.asin(math.sqrt(a))197198199def _fetch_brand(brand_q: str, lat: float, lng: float,200 match: str = "") -> dict | None:201 params = urllib.parse.urlencode({202 "q": brand_q, "proximity": f"{lng},{lat}", "limit": 5,203 "types": "poi", "language": "fr", "country": "CA",204 "access_token": _token()})205 req = urllib.request.Request(f"{API}?{params}",206 headers={"User-Agent": UA})207 try:208 with urllib.request.urlopen(req, timeout=12) as r:209 feats = json.load(r).get("features") or []210 except Exception:211 return None212 for f in feats:213 p = f.get("properties") or {}214 nom = _norm(p.get("name") or "")215 if match and match not in nom:216 continue217 if any(b in nom for b in _BAN):218 continue219 lng2, lat2 = f["geometry"]["coordinates"][:2]220 return {"nom": p.get("name") or brand_q,221 "adresse": p.get("full_address")222 or p.get("place_formatted") or "",223 "lat": lat2, "lng": lng2}224 return None225226227OVERPASS = ["https://overpass.kumi.systems/api/interpreter",228 "https://overpass-api.de/api/interpreter"]229230231def _fetch_transit(lat: float, lng: float) -> list[tuple[str, dict]]:232 """Station de métro et arrêt de bus les plus proches (OpenStreetMap)."""233 q = f"""[out:json][timeout:20];234(235 node["railway"="station"]["station"="subway"](around:3000,{lat},{lng});236 node["highway"="bus_stop"](around:1000,{lat},{lng});237);238out body;"""239 data = None240 for url in OVERPASS:241 try:242 req = urllib.request.Request(243 url, data=urllib.parse.urlencode({"data": q}).encode(),244 headers={"User-Agent": UA})245 with urllib.request.urlopen(req, timeout=25) as r:246 data = json.load(r)247 break248 except Exception:249 continue250 if not data:251 return []252 best: dict[str, tuple[float, dict]] = {}253 for el in data.get("elements", []):254 tags = el.get("tags") or {}255 kind = ("metro_station" if tags.get("railway") == "station"256 else "arret_bus")257 d = _dist_m(lat, lng, el["lat"], el["lon"])258 if kind not in best or d < best[kind][0]:259 best[kind] = (d, {"nom": tags.get("name")260 or ("Station de métro" if kind == "metro_station"261 else "Arrêt de bus"),262 "adresse": "", "lat": el["lat"],263 "lng": el["lon"]})264 return [(k, v[1]) for k, v in best.items()]265266267TRANSIT = [("metro_station", "Station de métro"),268 ("rem_station", "Station REM"),269 ("arret_bus", "Arrêt de bus"),270 ("gare_train", "Gare de train")]271272_DB_GENRE = {"metro": "metro_station", "rem": "rem_station",273 "bus": "arret_bus", "train": "gare_train"}274275276def _transit_from_db(lat: float, lng: float) -> list[tuple[str, dict]]:277 """Arrêts/stations depuis data/transit.db (extrait OpenStreetMap278 pré-calculé : ~37 000 arrêts de bus, métro, REM, gares du Québec)."""279 db = ROOT / "data" / "transit.db"280 if not db.exists():281 return []282 con = sqlite3.connect(f"file:{db}?mode=ro", uri=True)283 con.row_factory = sqlite3.Row284 out = []285 for genre, rayon in (("metro", 6000), ("rem", 6000), ("bus", 1500),286 ("train", 8000)):287 d = rayon / 111320.0288 rows = con.execute(289 "SELECT nom, lat, lng FROM arrets WHERE genre=? AND lat BETWEEN "290 "? AND ? AND lng BETWEEN ? AND ?",291 (genre, lat - d, lat + d, lng - d, lng + d)).fetchall()292 best = None293 for r in rows:294 dd = _dist_m(lat, lng, r["lat"], r["lng"])295 if dd <= rayon and (best is None or dd < best[0]):296 best = (dd, r)297 if best:298 out.append((_DB_GENRE[genre],299 {"nom": best[1]["nom"] or "", "adresse": "",300 "lat": best[1]["lat"], "lng": best[1]["lng"]}))301 con.close()302 return out303304_POI_CAT = {"metro": "metro_station", "bus": "arret_bus"}305306307def _transit_from_poi(lat: float, lng: float) -> list[tuple[str, dict]]:308 """Métro/bus depuis le cache POI du projet (louka.db, déjà calculé par309 immeuble) — la fiche interroge avec les mêmes coordonnées que poi.py."""310 db_main = ROOT / "data" / next(311 (n for n in ("louka.db", "immoka.db", "immo.db")312 if (ROOT / "data" / n).exists()), "louka.db")313 if not db_main.exists():314 return []315 try:316 con = sqlite3.connect(f"file:{db_main}?mode=ro", uri=True)317 con.row_factory = sqlite3.Row318 d = 300 / 111320.0319 row = con.execute(320 "SELECT pois, lat, lng FROM poi_cache WHERE lat BETWEEN ? AND ? "321 "AND lng BETWEEN ? AND ? ORDER BY (lat-?)*(lat-?)+(lng-?)*(lng-?) "322 "LIMIT 1", (lat - d, lat + d, lng - d, lng + d,323 lat, lat, lng, lng)).fetchone()324 con.close()325 except sqlite3.Error:326 return []327 if row is None:328 return []329 out = []330 for e in json.loads(row["pois"] or "[]"):331 k = _POI_CAT.get(e.get("cat"))332 if k:333 out.append((k, {"nom": e.get("name") or "", "adresse": "",334 "lat": lat, "lng": lng,335 "_dist": e.get("dist_m")}))336 return out337338339def nearby(lat: float, lng: float, region: str | None = None) -> dict:340 """Grand commerce le plus proche par bannière PROVINCIALE (cache ~1 km)."""341 brands = _brands_for(region, lat, lng)342 cell = f"{round(lat, 2)},{round(lng, 2)}"343 con = _connect()344 now = time.time()345 cached = {r["brand"]: r for r in con.execute(346 "SELECT * FROM commerces_cache WHERE cellule=? AND fetched_at>?",347 (cell, now - TTL))}348 manquants = [(bid, q, m) for bid, _, q, m in brands349 if bid not in cached]350 transit_manquant = any(k not in cached for k, _ in TRANSIT)351 if manquants or transit_manquant:352 res: list[tuple[str, dict | None]] = []353 if manquants:354 with ThreadPoolExecutor(max_workers=6) as ex:355 res = list(ex.map(356 lambda b: (b[0], _fetch_brand(b[1], lat, lng, b[2])),357 manquants))358 if transit_manquant:359 tr = (_transit_from_db(lat, lng)360 or _transit_from_poi(lat, lng) or _fetch_transit(lat, lng))361 res.extend(tr)362 with con:363 for bid, hit in res:364 if hit is None:365 continue366 con.execute(367 "INSERT OR REPLACE INTO commerces_cache VALUES "368 "(?,?,?,?,?,?,?)",369 (cell, bid, hit["nom"],370 hit.get("adresse") or (str(hit["_dist"])371 if hit.get("_dist") is not None372 else ""),373 hit["lat"], hit["lng"], now))374 cached = {r["brand"]: r for r in con.execute(375 "SELECT * FROM commerces_cache WHERE cellule=? AND fetched_at>?",376 (cell, now - TTL))}377 con.close()378379 items = []380 transit = []381 for bid, label in TRANSIT:382 r = cached.get(bid)383 if r is not None:384 # distance : celle du cache POI si disponible (adresse numérique)385 d = (float(r["adresse"]) if (r["adresse"] or "").replace(386 ".", "").isdigit() else _dist_m(lat, lng, r["lat"], r["lng"]))387 if d <= 5000:388 transit.append({"id": bid, "commerce": label,389 "nom": r["nom"], "adresse": "",390 "dist_m": round(d),391 "lat": r["lat"], "lng": r["lng"]})392 for bid, label, _q, _m in brands:393 r = cached.get(bid)394 if r is None:395 continue396 d = _dist_m(lat, lng, r["lat"], r["lng"])397 if d > 40000: # au-delà de 40 km : non pertinent398 continue399 items.append({"id": bid, "commerce": label, "nom": r["nom"],400 "adresse": r["adresse"], "dist_m": round(d),401 "lat": r["lat"], "lng": r["lng"]})402 items.sort(key=lambda x: x["dist_m"])403 transit.sort(key=lambda x: x["dist_m"])404 return {"n": len(items), "commerces": items, "transit": transit}405