Kincses
451900ae1a
- Added centralized, self-learning GeoService (ZIP, City, Street) - Implemented Hybrid Address Management (Centralized table + Denormalized fields) - Fixed Gamification logic (PointsLedger field names & filtering) - Added address autocomplete and two-tier (Free/Premium) search API - Synchronized UserStats and PointsLedger schemas
86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
from fastapi import APIRouter, Depends, Form, Query, UploadFile, File
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import text
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from typing import Optional, List
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from app.db.session import get_db
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from app.services.geo_service import GeoService
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from app.services.gamification_service import GamificationService
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from app.services.config_service import config
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router = APIRouter()
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@router.get("/suggest-street")
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async def suggest_street(zip_code: str, q: str, db: AsyncSession = Depends(get_db)):
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"""Azonnali utca javaslatok gépelés közben."""
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return await GeoService.get_street_suggestions(db, zip_code, q)
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@router.post("/hunt")
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async def register_service_hunt(
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name: str = Form(...),
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zip_code: str = Form(...),
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city: str = Form(...),
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street_name: str = Form(...),
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street_type: str = Form(...),
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house_number: str = Form(...),
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parcel_id: Optional[str] = Form(None),
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latitude: float = Form(...),
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longitude: float = Form(...),
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user_latitude: float = Form(...),
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user_longitude: float = Form(...),
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current_user_id: int = 1,
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db: AsyncSession = Depends(get_db)
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):
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# 1. Hibrid címrögzítés
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addr_id = await GeoService.get_or_create_full_address(
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db, zip_code, city, street_name, street_type, house_number, parcel_id
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)
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# 2. Távolságmérés
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dist_query = text("""
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SELECT ST_Distance(
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ST_SetSRID(ST_MakePoint(:u_lon, :u_lat), 4326)::geography,
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ST_SetSRID(ST_MakePoint(:s_lon, :s_lat), 4326)::geography
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)
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""")
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distance = (await db.execute(dist_query, {
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"u_lon": user_longitude, "u_lat": user_latitude,
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"s_lon": longitude, "s_lat": latitude
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})).scalar() or 0.0
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# 3. Mentés (Denormalizált adatokkal a sebességért)
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await db.execute(text("""
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INSERT INTO data.organization_locations
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(name, address_id, coordinates, proposed_by, zip_code, city, street, house_number, sources, confidence_score)
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VALUES (:n, :aid, ST_SetSRID(ST_MakePoint(:lon, :lat), 4326)::geography, :uid, :z, :c, :s, :hn, jsonb_build_array(CAST('user_hunt' AS TEXT)), 1)
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"""), {
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"n": name, "aid": addr_id, "lon": longitude, "lat": latitude,
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"uid": current_user_id, "z": zip_code, "c": city, "s": f"{street_name} {street_type}", "hn": house_number
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})
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# 4. Jutalmazás
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await GamificationService.award_points(db, current_user_id, 50, f"Service Hunt: {city}")
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await db.commit()
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return {"status": "success", "address_id": str(addr_id), "distance_meters": round(distance, 2)}
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@router.get("/search")
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async def search_services(
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lat: float, lng: float,
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is_premium: bool = False,
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db: AsyncSession = Depends(get_db)
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):
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"""Kétlépcsős keresés: Free (Légvonal) vs Premium (Útvonal/Idő)"""
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query = text("""
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SELECT name, city, ST_Distance(coordinates, ST_SetSRID(ST_MakePoint(:lng, :lat), 4326)::geography) as dist
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FROM data.organization_locations WHERE is_verified = TRUE ORDER BY dist LIMIT 10
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""")
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res = (await db.execute(query, {"lat": lat, "lng": lng})).fetchall()
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results = []
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for row in res:
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item = {"name": row[0], "city": row[1], "distance_km": round(row[2]/1000, 2)}
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if is_premium:
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# PRÉMIUM: Itt jönne az útvonaltervező API integráció
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item["estimated_travel_time_min"] = round(row[2] / 700) # Becsült
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results.append(item)
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return results |