02267d0306
- FastAPI backend: PostGIS estimator + 3 scrapers (Avito/Cian/Yandex)
- Next.js 15 frontend: tradein.html mockup design, basePath=/trade-in
- WeasyPrint PDF (Брусника-style 4-page report)
- Address autocomplete с typo-tolerance + 6 EKB presets
- Изолированный docker stack gendesign-tradein (отдельная postgres БД)
- Caddy inline routes: gendsgn.ru/trade-in/* и /trade-in/api/v1/*
- Forgejo Actions: .forgejo/workflows/deploy-tradein.yml (shell-based GHCR login)
- Триггер только по paths: tradein-mvp/** (не пересекается с deploy.yml)
- Образы: ghcr.io/lekss361/gendesign-tradein-{backend,frontend}:latest
Первый запуск на сервере (вручную, один раз):
- создать /opt/gendesign/tradein-mvp/.env.runtime (postgres pwd, contact email)
- docker network create gendesign_shared (если нет)
- docker compose -p gendesign-tradein up -d
- docker compose -p gendesign exec caddy caddy reload
411 lines
14 KiB
Python
411 lines
14 KiB
Python
"""Trade-In Estimator — endpoints (TI-1 mock + TI-2 PDF).
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MOCK implementation: returns realistic ЕКБ price bands by rooms/floor/repair.
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TODO TI-1b: заменить _mock_estimate() на реальный SQL aggregation из
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objective_lots + rosreestr_deals после OBJ-1/2 merge.
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"""
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from __future__ import annotations
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import json
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import logging
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import random
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from datetime import UTC, datetime, timedelta
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from typing import Annotated
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from uuid import UUID, uuid4
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from fastapi import APIRouter, Depends, HTTPException, Response
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from sqlalchemy import text
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from sqlalchemy.orm import Session
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from app.core.db import get_db
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from app.schemas.trade_in import AggregatedEstimate, AnalogLot, TradeInEstimateInput
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from app.services.exporters.trade_in_pdf import generate_trade_in_pdf
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logger = logging.getLogger(__name__)
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router = APIRouter()
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# ЕКБ-адреса для фейковых аналогов (реальные улицы центра)
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_EKB_STREETS = [
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"ул. Малышева",
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"ул. Куйбышева",
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"ул. 8 Марта",
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"ул. Белинского",
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"пр. Ленина",
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"ул. Толмачёва",
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"ул. Радищева",
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"ул. Мамина-Сибиряка",
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"ул. Луначарского",
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"ул. Первомайская",
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]
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# Базовые ценовые диапазоны по комнатности (ЕКБ, 2026)
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_PRICE_BANDS: dict[int, dict[str, int | float | str]] = {
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0: { # студия ~25 м²
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"median": 6_500_000,
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"low": 5_800_000,
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"high": 7_500_000,
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"ppm2": 260_000,
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"ref_area": 25.0,
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},
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1: { # 1к ~40 м²
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"median": 9_000_000,
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"low": 8_000_000,
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"high": 10_500_000,
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"ppm2": 225_000,
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"ref_area": 40.0,
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},
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2: { # 2к ~60 м²
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"median": 12_500_000,
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"low": 11_000_000,
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"high": 14_000_000,
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"ppm2": 208_000,
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"ref_area": 60.0,
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},
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3: { # 3к ~80 м²
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"median": 17_000_000,
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"low": 15_000_000,
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"high": 19_000_000,
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"ppm2": 213_000,
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"ref_area": 80.0,
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},
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}
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def _floor_factor(floor: int, total_floors: int) -> float:
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"""±5% поправка за этаж: 1й и последний этаж снижают цену."""
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if floor == 1:
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return 0.95
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if floor == total_floors:
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return 0.97
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return 1.0
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def _repair_factor(repair_state: str | None) -> float:
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"""±10% поправка за состояние отделки."""
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factors = {
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"needs_repair": 0.90,
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"standard": 1.00,
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"good": 1.05,
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"excellent": 1.10,
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}
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return factors.get(repair_state or "standard", 1.0)
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def _confidence(rooms: int) -> str:
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if 1 <= rooms <= 3:
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return "high"
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return "medium"
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def _gen_analogs(
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rooms: int,
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area_m2: float,
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base_ppm2: int,
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n: int,
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*,
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is_listing: bool,
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) -> list[AnalogLot]:
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"""Генерирует список фейковых аналогов (объявления или сделки)."""
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rng = random.Random(42 + rooms + n)
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result: list[AnalogLot] = []
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today = datetime.now(tz=UTC).date()
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for i in range(n):
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street = _EKB_STREETS[i % len(_EKB_STREETS)]
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building_no = rng.randint(1, 120)
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apt_no = rng.randint(1, 300)
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addr = f"г. Екатеринбург, {street}, {building_no}, кв. {apt_no}"
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area_jitter = area_m2 * rng.uniform(0.85, 1.15)
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ppm2_jitter = int(base_ppm2 * rng.uniform(0.90, 1.10))
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price = int(area_jitter * ppm2_jitter)
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floor_val = rng.randint(2, 16)
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total_fl = rng.randint(floor_val, 20)
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if is_listing:
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dom = rng.randint(5, 120)
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listing_dt = today - timedelta(days=dom)
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else:
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dom = rng.randint(10, 60)
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listing_dt = today - timedelta(days=rng.randint(30, 365))
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result.append(
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AnalogLot(
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address=addr,
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area_m2=round(area_jitter, 1),
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rooms=rooms if rooms > 0 else 0,
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floor=floor_val,
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total_floors=total_fl,
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price_rub=price,
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price_per_m2=ppm2_jitter,
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listing_date=listing_dt,
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days_on_market=dom,
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photo_url=None,
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)
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)
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return result
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def _mock_estimate(payload: TradeInEstimateInput) -> AggregatedEstimate:
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"""Возвращает mock-оценку на основе диапазонов ЕКБ 2026.
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Логика:
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- Берём базовый band по rooms (0-3+).
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- Масштабируем на фактическую площадь относительно референсной.
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- Применяем поправку за этаж (±5%) и отделку (±10%).
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- Генерируем 7-10 аналогов (листинги) и 3-5 actual_deals.
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"""
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rooms_key = min(payload.rooms, 3) # 4к+ → диапазон 3к
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band = _PRICE_BANDS[rooms_key]
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# Масштаб по площади
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ref_area: float = band["ref_area"] # type: ignore[assignment]
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area_scale = payload.area_m2 / ref_area
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ff = _floor_factor(payload.floor, payload.total_floors)
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rf = _repair_factor(payload.repair_state)
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combined = area_scale * ff * rf
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median = int(band["median"] * combined) # type: ignore[operator]
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low = int(band["low"] * combined) # type: ignore[operator]
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high = int(band["high"] * combined) # type: ignore[operator]
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ppm2 = int(band["ppm2"] * ff * rf) # type: ignore[operator]
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n_analogs = random.randint(7, 10)
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n_deals = random.randint(3, 5)
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analogs = _gen_analogs(rooms_key, payload.area_m2, ppm2, n_analogs, is_listing=True)
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actual_deals = _gen_analogs(
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rooms_key, payload.area_m2, int(ppm2 * 0.93), n_deals, is_listing=False
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)
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now = datetime.now(tz=UTC)
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return AggregatedEstimate(
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estimate_id=uuid4(),
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median_price_rub=median,
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range_low_rub=low,
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range_high_rub=high,
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median_price_per_m2=ppm2,
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confidence=_confidence(payload.rooms),
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n_analogs=n_analogs + n_deals,
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period_months=24,
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analogs=analogs,
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actual_deals=actual_deals,
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expires_at=now + timedelta(hours=24),
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)
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@router.post("/estimate", response_model=AggregatedEstimate)
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async def estimate(
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payload: TradeInEstimateInput,
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db: Annotated[Session, Depends(get_db)],
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) -> AggregatedEstimate:
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"""Реальная оценка через SQL aggregation поверх listings + deals.
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1. Geocode address → lat/lon
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2. PostGIS ST_DWithin радиус 800м (или 2км fallback)
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3. Tukey IQR outlier filter
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4. Median + Q1 + Q3 + confidence с explanation
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"""
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from app.services.estimator import estimate_quality
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return await estimate_quality(payload, db)
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# OLD MOCK PATH — оставлен как fallback для отладки; не вызывается из router.
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def _estimate_legacy_mock(
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payload: TradeInEstimateInput,
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db: Annotated[Session, Depends(get_db)],
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) -> AggregatedEstimate:
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"""Старая mock-реализация. НЕ используется в роутере, удалить когда стабилизируем новый estimator."""
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result = _mock_estimate(payload)
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analogs_json = json.dumps(
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[a.model_dump(mode="json") for a in result.analogs],
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ensure_ascii=False,
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)
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deals_json = json.dumps(
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[a.model_dump(mode="json") for a in result.actual_deals],
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ensure_ascii=False,
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)
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db.execute(
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text(
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"""
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INSERT INTO trade_in_estimates (
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id, address, area_m2, rooms, floor, total_floors,
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year_built, house_type, repair_state, has_balcony,
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median_price, range_low, range_high, median_price_per_m2,
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confidence, n_analogs, analogs, actual_deals, expires_at
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) VALUES (
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CAST(:id AS uuid),
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:address, :area_m2, :rooms, :floor, :total_floors,
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:year_built, :house_type, :repair_state, :has_balcony,
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:median_price, :range_low, :range_high, :median_price_per_m2,
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:confidence, :n_analogs,
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CAST(:analogs AS jsonb),
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CAST(:actual_deals AS jsonb),
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:expires_at
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)
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"""
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),
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{
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"id": str(result.estimate_id),
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"address": payload.address,
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"area_m2": payload.area_m2,
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"rooms": payload.rooms,
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"floor": payload.floor,
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"total_floors": payload.total_floors,
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"year_built": payload.year_built,
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"house_type": payload.house_type,
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"repair_state": payload.repair_state,
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"has_balcony": payload.has_balcony,
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"median_price": result.median_price_rub,
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"range_low": result.range_low_rub,
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"range_high": result.range_high_rub,
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"median_price_per_m2": result.median_price_per_m2,
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"confidence": result.confidence,
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"n_analogs": result.n_analogs,
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"analogs": analogs_json,
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"actual_deals": deals_json,
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"expires_at": result.expires_at,
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},
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)
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db.commit()
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logger.info(
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"trade_in estimate saved id=%s rooms=%d area=%.1f confidence=%s",
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result.estimate_id,
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payload.rooms,
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payload.area_m2,
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result.confidence,
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)
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return result
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@router.get("/estimate/{estimate_id}", response_model=AggregatedEstimate)
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def get_estimate(
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estimate_id: UUID,
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db: Annotated[Session, Depends(get_db)],
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) -> AggregatedEstimate:
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"""Получить сохранённую оценку по UUID (для генерации PDF).
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Возвращает 404 если оценка не найдена или TTL истёк.
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"""
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row = db.execute(
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text(
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"""
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SELECT id, median_price, range_low, range_high, median_price_per_m2,
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confidence, n_analogs, analogs, actual_deals, expires_at,
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address, area_m2, rooms
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FROM trade_in_estimates
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WHERE id = CAST(:id AS uuid)
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AND expires_at > NOW()
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"""
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),
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{"id": str(estimate_id)},
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).fetchone()
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if row is None:
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raise HTTPException(status_code=404, detail="estimate not found or expired")
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analogs = [AnalogLot(**a) for a in (row.analogs or [])]
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actual_deals = [AnalogLot(**a) for a in (row.actual_deals or [])]
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return AggregatedEstimate(
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estimate_id=row.id,
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median_price_rub=row.median_price,
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range_low_rub=row.range_low,
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range_high_rub=row.range_high,
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median_price_per_m2=row.median_price_per_m2,
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confidence=row.confidence,
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n_analogs=row.n_analogs,
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period_months=24,
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analogs=analogs,
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actual_deals=actual_deals,
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expires_at=row.expires_at,
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)
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@router.get("/estimate/{estimate_id}/pdf")
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def estimate_pdf(
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estimate_id: UUID,
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db: Annotated[Session, Depends(get_db)],
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brand: str | None = None,
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) -> Response:
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"""Скачать 4-страничный PDF-отчёт для оценки trade-in.
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Возвращает application/pdf attachment.
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404 — оценка не найдена.
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410 — оценка просрочена (TTL 24ч).
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"""
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row = db.execute(
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text(
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"""
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SELECT id, median_price, range_low, range_high, median_price_per_m2,
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confidence, confidence_explanation, n_analogs,
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analogs, actual_deals, sources_used, data_freshness_minutes,
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expires_at,
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address, lat, lon, area_m2, rooms, floor, total_floors,
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year_built, house_type, repair_state, has_balcony
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FROM trade_in_estimates
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WHERE id = CAST(:id AS uuid)
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"""
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),
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{"id": str(estimate_id)},
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).fetchone()
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if row is None:
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raise HTTPException(status_code=404, detail="estimate not found")
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if row.expires_at.replace(tzinfo=UTC) < datetime.now(tz=UTC):
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raise HTTPException(status_code=410, detail="estimate expired (24h TTL)")
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analogs = [AnalogLot(**a) for a in (row.analogs or [])]
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actual_deals = [AnalogLot(**a) for a in (row.actual_deals or [])]
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estimate = AggregatedEstimate(
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estimate_id=row.id,
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median_price_rub=row.median_price,
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range_low_rub=row.range_low,
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range_high_rub=row.range_high,
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median_price_per_m2=row.median_price_per_m2,
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confidence=row.confidence,
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confidence_explanation=row.confidence_explanation,
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n_analogs=row.n_analogs,
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period_months=24,
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analogs=analogs,
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actual_deals=actual_deals,
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expires_at=row.expires_at,
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target_address=row.address,
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target_lat=row.lat,
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target_lon=row.lon,
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sources_used=row.sources_used or [],
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data_freshness_minutes=row.data_freshness_minutes,
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)
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input_snapshot = {
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"address": row.address,
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"area_m2": row.area_m2,
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"rooms": row.rooms,
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"floor": row.floor,
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"total_floors": row.total_floors,
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"year_built": row.year_built,
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"house_type": row.house_type,
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"repair_state": row.repair_state,
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"has_balcony": row.has_balcony,
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}
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from app.services.brand import get_brand as _resolve_brand
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brand_obj = _resolve_brand(brand, db)
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pdf_bytes = generate_trade_in_pdf(estimate, input_snapshot, brand=brand_obj)
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filename = f"trade-in-{brand_obj.slug}-{estimate_id}.pdf"
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logger.info(
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"PDF generated estimate_id=%s brand=%s size=%d",
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estimate_id, brand_obj.slug, len(pdf_bytes),
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)
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return Response(
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content=pdf_bytes,
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media_type="application/pdf",
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headers={"Content-Disposition": f'attachment; filename="{filename}"'},
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)
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