feat: AR-House initial commit

location_agent/sub_agents/amenities_agent.py

@@ -0,0 +1,158 @@
+"""Sub-agente: Amenities y walkability.
+
+Fuente: Overpass API (OpenStreetMap) — gratuita, sin key requerida.
+"""
+
+from __future__ import annotations
+
+import math
+import time
+import requests
+from data_fetchers.base import USER_AGENT
+
+OVERPASS_URL = "https://overpass-api.de/api/interpreter"
+
+CATEGORIES = {
+    "supermarket": ["supermarket", "grocery"],
+    "hospital": ["hospital", "clinic", "doctors", "pharmacy"],
+    "restaurant": ["restaurant", "fast_food", "cafe"],
+    "park": ["park"],
+    "gym": ["fitness_centre", "sports_centre"],
+    "school": ["school", "kindergarten"],
+    "bank": ["bank", "atm"],
+    "gas_station": ["fuel"],
+}
+
+
+def run(lat: float, lon: float, address: str) -> dict:
+    result = {
+        "categories": {},
+        "nearest": {},
+        "walk_score_estimate": None,
+        "total_amenities": 0,
+        "sources": ["OpenStreetMap/Overpass"],
+        "errors": [],
+    }
+
+    try:
+        amenities = _overpass_amenities(lat, lon)
+        result["categories"] = amenities["by_category"]
+        result["nearest"] = amenities["nearest"]
+        result["total_amenities"] = amenities["total"]
+
+        # Walk score estimado (basado en densidad de amenities en 1 milla)
+        result["walk_score_estimate"] = _estimate_walk_score(amenities)
+    except Exception as e:
+        result["errors"].append(f"Overpass amenities: {e}")
+
+    return result
+
+
+def _overpass_amenities(lat: float, lon: float, radius_m: int = 3200) -> dict:
+    """Consulta Overpass API para amenities en radio de ~2 millas."""
+    amenity_values = "|".join(
+        v for values in CATEGORIES.values() for v in values
+    )
+
+    query = f"""
+    [out:json][timeout:30];
+    (
+      node["amenity"~"{amenity_values}"](around:{radius_m},{lat},{lon});
+    );
+    out body;
+    """
+    time.sleep(1)
+    r = requests.post(OVERPASS_URL, data={"data": query},
+                      headers={"User-Agent": USER_AGENT}, timeout=35)
+    r.raise_for_status()
+    elements = r.json().get("elements", [])
+
+    by_category: dict = {cat: [] for cat in CATEGORIES}
+    nearest: dict = {}
+
+    for el in elements:
+        tags = el.get("tags", {})
+        amenity = tags.get("amenity", "")
+        name = tags.get("name", amenity)
+        el_lat = el.get("lat", lat)
+        el_lon = el.get("lon", lon)
+        dist = _haversine(lat, lon, el_lat, el_lon)
+
+        for cat, values in CATEGORIES.items():
+            if amenity in values:
+                by_category[cat].append({"name": name, "dist_miles": round(dist, 2)})
+                if cat not in nearest or dist < nearest[cat]["dist_miles"]:
+                    nearest[cat] = {"name": name, "dist_miles": round(dist, 2)}
+                break
+
+    # Ordenar por distancia
+    for cat in by_category:
+        by_category[cat].sort(key=lambda x: x["dist_miles"])
+        by_category[cat] = by_category[cat][:5]
+
+    total = sum(len(v) for v in by_category.values())
+    return {"by_category": by_category, "nearest": nearest, "total": total}
+
+
+def _haversine(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
+    """Distancia en millas entre dos coordenadas."""
+    R = 3958.8  # radio Tierra en millas
+    dlat = math.radians(lat2 - lat1)
+    dlon = math.radians(lon2 - lon1)
+    a = math.sin(dlat/2)**2 + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.sin(dlon/2)**2
+    return R * 2 * math.asin(math.sqrt(a))
+
+
+def _estimate_walk_score(amenities: dict) -> int:
+    """Estima walk score 0-100 basado en densidad y diversidad de amenities."""
+    cats = amenities["by_category"]
+    nearest = amenities["nearest"]
+
+    score = 0
+
+    # Puntos por cercanía de supermercado (más importante)
+    sup = nearest.get("supermarket", {}).get("dist_miles", 99)
+    if sup <= 0.25:
+        score += 25
+    elif sup <= 0.5:
+        score += 18
+    elif sup <= 1.0:
+        score += 10
+    elif sup <= 2.0:
+        score += 5
+
+    # Restaurantes/cafes cercanos
+    rest_count = len([x for x in cats.get("restaurant", []) if x["dist_miles"] <= 1.0])
+    score += min(20, rest_count * 3)
+
+    # Diversidad de categorías con algo en <= 1 milla
+    cats_nearby = sum(
+        1 for cat, items in cats.items()
+        if any(x["dist_miles"] <= 1.0 for x in items)
+    )
+    score += cats_nearby * 5
+
+    # Hospitales
+    hosp = nearest.get("hospital", {}).get("dist_miles", 99)
+    if hosp <= 2.0:
+        score += 10
+
+    return min(100, max(0, score))
+
+
+def score(data: dict) -> int:
+    """Score 0-100 de amenities."""
+    ws = data.get("walk_score_estimate")
+    if ws is not None:
+        return ws
+
+    total = data.get("total_amenities", 0)
+    if total >= 50:
+        return 85
+    elif total >= 30:
+        return 70
+    elif total >= 15:
+        return 55
+    elif total >= 5:
+        return 40
+    return 25