gendesign/backend/app/api/v1/parcels.py

import json
import logging
from typing import Annotated, Any

from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy import text
from sqlalchemy.orm import Session

from app.core.db import get_db
from app.schemas.parcel import ParcelDetail, ParcelSearchRequest, ParcelSearchResponse

logger = logging.getLogger(__name__)

router = APIRouter()

# Веса POI-категорий для scoring (Максим: трамвай = минус)
_POI_WEIGHTS: dict[str, float] = {
    "school": 1.5,
    "kindergarten": 1.5,
    "pharmacy": 0.8,
    "hospital": 0.6,
    "shop_mall": 1.2,
    "shop_supermarket": 1.0,
    "shop_small": 0.5,
    "park": 1.8,
    "bus_stop": 0.3,
    "metro_stop": 1.5,
    "tram_stop": -0.5,  # негативный вес — шум / вибрация
}


@router.post("/search", response_model=ParcelSearchResponse)
async def search_parcels(payload: ParcelSearchRequest) -> ParcelSearchResponse:
    """Search parcels by filters + scoring.

    TODO Stage 2b: PostGIS query + scorer service.
    """
    return ParcelSearchResponse(items=[], total=0)


@router.get("/{parcel_id}", response_model=ParcelDetail)
async def get_parcel(parcel_id: str) -> ParcelDetail:
    """TODO Stage 2b: fetch parcel by id from DB."""
    raise HTTPException(status_code=501, detail="Not implemented yet")


@router.post("/{cad_num}/analyze")
def analyze_parcel(
    cad_num: str,
    db: Annotated[Session, Depends(get_db)],
) -> dict[str, Any]:
    """Анализ участка: близость к социалке + district context + конкуренты.

    Порядок поиска геометрии: cad_quarters_geom → cad_buildings.
    """
    # 1) Получить геометрию участка — GeoJSON строка через ST_AsGeoJSON
    row = (
        db.execute(
            text("""
            SELECT ST_AsGeoJSON(g.geom) AS geom_geojson,
                   g.geom              AS geom_wkb,
                   'cad_quarter'       AS source
              FROM cad_quarters_geom g
             WHERE g.cad_number = :c
            UNION ALL
            SELECT ST_AsGeoJSON(b.geom) AS geom_geojson,
                   b.geom               AS geom_wkb,
                   'cad_building'       AS source
              FROM cad_buildings b
             WHERE b.cad_num = :c
            UNION ALL
            SELECT ST_AsGeoJSON(p.geom) AS geom_geojson,
                   p.geom               AS geom_wkb,
                   'cad_parcel'         AS source
              FROM cad_parcels_geom p
             WHERE p.cad_num = :c
             LIMIT 1
        """),
            {"c": cad_num},
        )
        .mappings()
        .first()
    )

    if not row:
        raise HTTPException(
            status_code=404,
            detail=f"Геометрия для {cad_num} не найдена. Загрузи через NSPD geo.",
        )

    geom_geojson: str = row["geom_geojson"]
    source: str = row["source"]

    # Используем ST_AsText для передачи геометрии в последующие запросы.
    # Все PostGIS-запросы принимают текстовый WKT через ST_GeomFromText.
    geom_row = (
        db.execute(
            text("""
            SELECT ST_AsText(g.geom) AS wkt
              FROM (
                SELECT g.geom FROM cad_quarters_geom g WHERE g.cad_number = :c
                UNION ALL
                SELECT b.geom FROM cad_buildings b WHERE b.cad_num = :c
                UNION ALL
                SELECT p.geom FROM cad_parcels_geom p WHERE p.cad_num = :c
              ) g
             LIMIT 1
        """),
            {"c": cad_num},
        )
        .mappings()
        .first()
    )

    geom_wkt: str = geom_row["wkt"]  # type: ignore[index]

    # 2) District context — ближайший район ЕКБ
    district_row = (
        db.execute(
            text("""
            SELECT district_name,
                   median_price_per_m2,
                   ST_Distance(
                       d.geom::geography,
                       ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography
                   ) AS dist_to_center
              FROM ekb_districts d
             WHERE ST_DWithin(
                       d.geom::geography,
                       ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography,
                       5000
                   )
             ORDER BY dist_to_center ASC
             LIMIT 1
        """),
            {"wkt": geom_wkt},
        )
        .mappings()
        .first()
    )

    # 3) POI в радиусе 1 км — список с distance_m
    poi_rows = (
        db.execute(
            text("""
            SELECT category,
                   name,
                   lat,
                   lon,
                   ST_Distance(
                       p.geom::geography,
                       ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography
                   ) AS distance_m,
                   last_osm_edit_date
              FROM osm_poi_ekb p
             WHERE ST_DWithin(
                       p.geom::geography,
                       ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography,
                       1000
                   )
             ORDER BY distance_m ASC
        """),
            {"wkt": geom_wkt},
        )
        .mappings()
        .all()
    )

    # 4) Scoring: weighted sum с distance decay
    score = 0.0
    by_category: dict[str, list[dict[str, Any]]] = {}
    for p in poi_rows:
        cat: str = p["category"]
        w = _POI_WEIGHTS.get(cat, 0.0)
        # distance decay: 1.0 на 0м, 0.5 на ~500м, ~0 на 1000м
        decay = max(0.0, 1.0 - float(p["distance_m"]) / 1000.0)
        score += w * decay
        by_category.setdefault(cat, []).append(
            {
                "name": p["name"],
                "distance_m": round(float(p["distance_m"])),
                "lat": float(p["lat"]) if p["lat"] is not None else None,
                "lon": float(p["lon"]) if p["lon"] is not None else None,
                "last_edit": (
                    p["last_osm_edit_date"].isoformat() if p["last_osm_edit_date"] else None
                ),
            }
        )

    # 5) Конкуренты в радиусе 3 км из DOM.РФ.
    # NB: domrf_kn_objects имеет ~3 snapshot per obj_id → DISTINCT ON по
    # latest snapshot, иначе дубликаты ЖК в выдаче.
    competitor_rows = (
        db.execute(
            text("""
            WITH latest_obj AS (
                SELECT DISTINCT ON (obj_id) *
                  FROM domrf_kn_objects
                 WHERE latitude IS NOT NULL
                 ORDER BY obj_id, snapshot_date DESC NULLS LAST
            )
            SELECT obj_id,
                   comm_name,
                   dev_name,
                   obj_class,
                   flat_count,
                   district_name,
                   ST_Distance(
                       ST_SetSRID(ST_MakePoint(o.longitude, o.latitude), 4326)::geography,
                       ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography
                   ) AS distance_m
              FROM latest_obj o
             WHERE ST_DWithin(
                       ST_SetSRID(ST_MakePoint(o.longitude, o.latitude), 4326)::geography,
                       ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography,
                       3000
                   )
             ORDER BY o.flat_count DESC NULLS LAST
             LIMIT 20
        """),
            {"wkt": geom_wkt},
        )
        .mappings()
        .all()
    )

    return {
        "cad_num": cad_num,
        "source": source,
        "geom_geojson": json.loads(geom_geojson) if geom_geojson else None,
        "district": dict(district_row) if district_row else None,
        "score": round(score, 2),
        "score_breakdown": by_category,
        "poi_count": len(poi_rows),
        "competitors": [dict(c) for c in competitor_rows],
    }