feat: Introduce a web frontend with new city API routes and refactor bot city query logic into a dedicated service.
This commit is contained in:
2569718930@qq.com
+3
-1
@@ -30,6 +30,9 @@ HTTP_PROXY=http://127.0.0.1:7890
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LOG_LEVEL=INFO
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ENV=production
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POLYWEATHER_MAP_URL=https://polyweather-pro.vercel.app/
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# Backend entitlement guard (for /api/cities, /api/city/*, /api/history/*)
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POLYWEATHER_REQUIRE_ENTITLEMENT=false
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POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN=
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# Polymarket P0 Read-Only Market Layer
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POLYMARKET_MARKET_SCAN_ENABLED=true
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@@ -74,4 +77,3 @@ POLYMARKET_WALLET_ACTIVITY_BOOTSTRAP_ALERT=false
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POLYMARKET_WALLET_ACTIVITY_AVG_PRICE_SHOW_MIN=0.01
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POLYMARKET_WALLET_ACTIVITY_AVG_PRICE_SHOW_MAX=0.99
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+12
-619
@@ -1,6 +1,5 @@
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import sys
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import os
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from typing import List
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import telebot # type: ignore
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from loguru import logger # type: ignore
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@@ -14,10 +13,11 @@ from src.utils.telegram_push import start_trade_alert_push_loop # type: ignore
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from src.onchain.polygon_wallet_watcher import start_polygon_wallet_watch_loop # type: ignore # noqa: E402
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from src.onchain.polymarket_wallet_activity_watcher import start_polymarket_wallet_activity_loop # type: ignore # noqa: E402
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from src.data_collection.weather_sources import WeatherDataCollector # type: ignore # noqa: E402
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from src.data_collection.city_registry import CITY_REGISTRY # noqa: E402
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from src.data_collection.city_risk_profiles import get_city_risk_profile # type: ignore # noqa: E402
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from src.analysis.deb_algorithm import calculate_dynamic_weights, update_daily_record # noqa: E402
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from src.database.db_manager import DBManager
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from src.analysis.city_query_service import (
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resolve_city_name,
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build_city_query_report,
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)
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MESSAGE_POINTS = 4
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MESSAGE_DAILY_CAP = 50
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@@ -27,13 +27,6 @@ CITY_QUERY_COST = 1
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DEB_QUERY_COST = 1
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def analyze_weather_trend(weather_data, temp_symbol, city_name=None):
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"""Thin wrapper 鈥?delegates to shared trend_engine module."""
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from src.analysis.trend_engine import analyze_weather_trend as _analyze
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display_str, ai_context, _structured = _analyze(weather_data, temp_symbol, city_name)
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return display_str, ai_context
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def start_bot():
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config = load_config()
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token = os.getenv("TELEGRAM_BOT_TOKEN")
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@@ -310,7 +303,6 @@ def start_bot():
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bot.reply_to(message, f"❌ 查询失败: {e}")
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@bot.message_handler(commands=["city"])
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def get_city_info(message):
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"""查询指定城市的天气详情"""
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try:
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@@ -323,38 +315,13 @@ def start_bot():
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)
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return
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from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
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city_input = parts[1].strip().lower()
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# --- 使用统一注册表解析城市 ---
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SUPPORTED_CITIES = list(CITY_REGISTRY.keys())
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# 1. 第一优先级：全称或别名完全匹配
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city_name = ALIASES.get(city_input)
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if not city_name and city_input in SUPPORTED_CITIES:
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city_name = city_input
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# 2. 第二优先级：前缀模糊匹配
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if not city_name and len(city_input) >= 2:
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# 搜别名
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for k, v in ALIASES.items():
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if k.startswith(city_input):
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city_name = v
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break
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# 搜城市全名
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if not city_name:
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for full_name in SUPPORTED_CITIES:
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if full_name.startswith(city_input):
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city_name = full_name
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break
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# 3. 未找到 ➔ 报错
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city_name, supported_cities = resolve_city_name(city_input)
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if not city_name:
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city_list = ", ".join(sorted(SUPPORTED_CITIES))
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city_list = ", ".join(supported_cities)
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bot.reply_to(
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message,
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f"❌ 未找到城市: <b>{city_input}</b>\n\n"
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f"支持的城市: {city_list}",
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f"❌ 未找到城市: <b>{city_input}</b>\n\n支持的城市: {city_list}",
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parse_mode="HTML",
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)
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return
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@@ -374,586 +341,12 @@ def start_bot():
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weather_data = weather.fetch_all_sources(
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city_name, lat=coords["lat"], lon=coords["lon"]
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)
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open_meteo = weather_data.get("open-meteo", {})
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metar = weather_data.get("metar", {})
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mgm = weather_data.get("mgm") or {}
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city_meta = CITY_REGISTRY.get(city_name.lower(), {})
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fallback_utc_offset = int(city_meta.get("tz_offset", 0))
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nws_periods = (weather_data.get("nws", {}) or {}).get("forecast_periods", []) or []
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if nws_periods:
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try:
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from datetime import datetime as _dt
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first_start = nws_periods[0].get("start_time")
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if first_start:
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maybe_dt = _dt.fromisoformat(str(first_start))
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if maybe_dt.utcoffset() is not None:
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fallback_utc_offset = int(maybe_dt.utcoffset().total_seconds())
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except Exception:
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pass
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# 鏁板€煎綊涓€鍖?
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def _sf(v):
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if v is None:
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return None
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try:
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return float(v)
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except Exception:
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return None
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city_is_fahrenheit = city_name.strip().lower() in ["dallas", "new york", "chicago", "miami", "atlanta", "seattle"]
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temp_symbol = "°F" if city_is_fahrenheit else "°C"
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# --- 1. 紧凑 Header (城市 + 时间 + 风险状态) ---
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local_time = open_meteo.get("current", {}).get("local_time", "")
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time_str = local_time.split(" ")[1][:5] if " " in local_time else "N/A"
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if time_str == "N/A":
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metar_obs = metar.get("observation_time", "") if metar else ""
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if "T" in metar_obs:
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try:
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from datetime import datetime, timezone, timedelta
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dt = datetime.fromisoformat(metar_obs.replace("Z", "+00:00"))
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utc_offset_for_view = open_meteo.get("utc_offset")
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if utc_offset_for_view is None:
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utc_offset_for_view = fallback_utc_offset
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local_dt = dt.astimezone(
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timezone(timedelta(seconds=int(utc_offset_for_view)))
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)
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time_str = local_dt.strftime("%H:%M")
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except Exception:
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time_str = metar_obs.split("T")[1][:5]
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elif " " in metar_obs:
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time_str = metar_obs.split(" ")[1][:5]
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elif metar_obs:
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time_str = str(metar_obs)[:5]
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else:
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try:
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from datetime import datetime, timezone, timedelta
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local_now = datetime.now(timezone.utc).astimezone(
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timezone(timedelta(seconds=int(fallback_utc_offset)))
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)
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time_str = local_now.strftime("%H:%M")
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except Exception:
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pass
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risk_profile = get_city_risk_profile(city_name)
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risk_emoji = risk_profile.get("risk_level", "⚠️") if risk_profile else "⚠️"
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msg_header = f"📍 <b>{city_name.title()}</b> ({time_str}) {risk_emoji}"
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msg_lines = [msg_header]
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# --- 2. 紧凑 风险提示 ---
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if risk_profile:
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bias = risk_profile.get("bias", "±0.0")
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msg_lines.append(
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f"⚠️ {risk_profile.get('airport_name', '')}: {bias}{temp_symbol} | {risk_profile.get('warning', '')}"
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)
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# --- 3. 紧凑 预测区 ---
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daily = open_meteo.get("daily", {})
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dates = daily.get("time", [])[:3]
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max_temps = daily.get("temperature_2m_max", [])[:3]
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nws_high = _sf(weather_data.get("nws", {}).get("today_high"))
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mgm_high = _sf(mgm.get("today_high"))
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mb_high = _sf(weather_data.get("meteoblue", {}).get("today_high"))
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metar_max_so_far = _sf(metar.get("current", {}).get("max_temp_so_far")) if metar else None
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# 今天对比
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today_t = _sf(max_temps[0]) if max_temps else None
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fallback_source = None
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metar_only_fallback = False
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if today_t is None:
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for source_name, candidate in (
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("MB", mb_high),
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("NWS", nws_high),
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("MGM", mgm_high),
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):
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if candidate is not None:
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today_t = candidate
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fallback_source = source_name
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break
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if today_t is None and metar_max_so_far is not None:
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# Last-resort display only: do not treat METAR as a forecast source
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today_t = metar_max_so_far
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metar_only_fallback = True
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today_t_display = (
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f"{today_t:.1f}" if isinstance(today_t, (int, float)) else "N/A"
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city_report = build_city_query_report(
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city_name=city_name,
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weather_data=weather_data,
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city_query_cost=CITY_QUERY_COST,
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)
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comp_parts = []
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sources = ["Open-Meteo"] if max_temps else []
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if mb_high is not None:
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if "MB" not in sources:
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sources.append("MB")
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# 只在非 fallback（即 Open-Meteo 存在）时显示为对比，否则作为 today_t 已显示
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if fallback_source != "MB":
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comp_parts.append(
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f"MB: {mb_high:.1f}{temp_symbol}"
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if isinstance(mb_high, (int, float))
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else f"MB: {mb_high}"
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)
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if nws_high is not None:
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if "NWS" not in sources:
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sources.append("NWS")
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if fallback_source != "NWS":
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comp_parts.append(
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f"NWS: {nws_high:.1f}{temp_symbol}"
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if isinstance(nws_high, (int, float))
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else f"NWS: {nws_high}"
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)
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if mgm_high is not None:
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if "MGM" not in sources:
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sources.append("MGM")
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if fallback_source != "MGM":
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comp_parts.append(
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f"MGM: {mgm_high:.1f}{temp_symbol}"
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if isinstance(mgm_high, (int, float))
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else f"MGM: {mgm_high}"
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)
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if fallback_source and fallback_source not in sources:
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sources.append(fallback_source)
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if metar_only_fallback:
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if not sources:
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sources = ["Model unavailable"]
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comp_parts.append(f"METAR实测回退: {metar_max_so_far:.1f}{temp_symbol}")
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if not sources:
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sources = ["N/A"]
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# 检查是否有显著分歧 (超过 5°F 或 2.5°C)
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divergence_warning = ""
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base_for_divergence = _sf(max_temps[0]) if max_temps else today_t
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if mb_high is not None and base_for_divergence is not None:
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diff = abs(mb_high - base_for_divergence)
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threshold = 5.0 if temp_unit == "fahrenheit" else 2.5
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if diff > threshold:
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divergence_warning = (
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f" ⚠️ <b>模型显著分歧 ({diff:.1f}{temp_symbol})</b>"
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)
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comp_str = f" ({' | '.join(comp_parts)})" if comp_parts else ""
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sources_str = " | ".join(sources)
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msg_lines.append(f"\n📊 <b>预报 ({sources_str})</b>")
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msg_lines.append(
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f"👉 <b>今天: {today_t_display}{temp_symbol}{comp_str}</b>{divergence_warning}"
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)
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# 明后天
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mgm_daily = mgm.get("daily_forecasts", {}) or {}
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mm_raw = weather_data.get("multi_model", {}) or {}
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mm_daily = mm_raw.get("daily_forecasts", {}) if isinstance(mm_raw, dict) else {}
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mb_daily = weather_data.get("meteoblue", {}).get("daily_highs", []) or []
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nws_periods = weather_data.get("nws", {}).get("forecast_periods", []) or []
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if len(dates) > 1:
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future_forecasts = []
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for d, t in zip(dates[1:], max_temps[1:]):
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# 检查 MGM 是否有该日期的预报
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mgm_f = mgm_daily.get(d)
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if mgm_f is not None:
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future_forecasts.append(
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f"{d[5:]}: {t}{temp_symbol} | 🇺🇸 <b>MGM: {mgm_f}{temp_symbol}</b>"
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)
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else:
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future_forecasts.append(f"{d[5:]}: {t}{temp_symbol}")
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msg_lines.append("📅 " + " | ".join(future_forecasts))
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elif mgm_daily:
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# Open-Meteo missing: still show next 2 days from MGM daily forecast
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from datetime import datetime, timezone, timedelta
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local_now = datetime.now(timezone.utc).astimezone(
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timezone(timedelta(seconds=int(fallback_utc_offset)))
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)
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today_local = local_now.strftime("%Y-%m-%d")
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future_forecasts = []
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for d in sorted(mgm_daily.keys()):
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if d <= today_local:
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continue
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t = mgm_daily.get(d)
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if t is None:
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continue
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future_forecasts.append(f"{d[5:]}: {t}{temp_symbol}")
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if len(future_forecasts) >= 2:
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break
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if future_forecasts:
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msg_lines.append("📅 " + " | ".join(future_forecasts))
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elif isinstance(mm_daily, dict) and mm_daily:
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# Open-Meteo missing: fallback to multi-model daily medians
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from datetime import datetime, timezone, timedelta
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local_now = datetime.now(timezone.utc).astimezone(
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timezone(timedelta(seconds=int(fallback_utc_offset)))
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)
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today_local = local_now.strftime("%Y-%m-%d")
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future_forecasts = []
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for d in sorted(mm_daily.keys()):
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if d <= today_local:
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continue
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day_models = mm_daily.get(d, {}) or {}
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vals = []
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for v in day_models.values():
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vv = _sf(v)
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if vv is not None:
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vals.append(vv)
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if not vals:
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continue
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vals.sort()
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median_v = vals[len(vals) // 2]
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future_forecasts.append(f"{d[5:]}: MM中位 {median_v:.1f}{temp_symbol}")
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if len(future_forecasts) >= 2:
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break
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if future_forecasts:
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msg_lines.append("📅 " + " | ".join(future_forecasts))
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elif isinstance(mb_daily, list) and len(mb_daily) > 1:
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# Open-Meteo missing: fallback to Meteoblue daily highs
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from datetime import datetime, timezone, timedelta
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local_now = datetime.now(timezone.utc).astimezone(
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timezone(timedelta(seconds=int(fallback_utc_offset)))
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)
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future_forecasts = []
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for idx in range(1, min(3, len(mb_daily))):
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t = _sf(mb_daily[idx])
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if t is None:
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continue
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d = (local_now + timedelta(days=idx)).strftime("%m-%d")
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future_forecasts.append(f"{d}: MB {t:.1f}{temp_symbol}")
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if future_forecasts:
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msg_lines.append("📅 " + " | ".join(future_forecasts))
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elif isinstance(nws_periods, list) and nws_periods:
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# US fallback: use next daytime NWS periods
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from datetime import datetime, timezone, timedelta
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local_now = datetime.now(timezone.utc).astimezone(
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timezone(timedelta(seconds=int(fallback_utc_offset)))
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)
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today_local = local_now.strftime("%Y-%m-%d")
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future_forecasts = []
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seen_days = set()
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for p in nws_periods:
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if not p.get("is_daytime"):
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continue
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temp_v = _sf(p.get("temperature"))
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start_time = str(p.get("start_time") or "")
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if temp_v is None or "T" not in start_time:
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continue
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day_str = start_time[:10]
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if day_str <= today_local or day_str in seen_days:
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continue
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seen_days.add(day_str)
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future_forecasts.append(f"{day_str[5:]}: NWS {temp_v:.0f}{temp_symbol}")
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if len(future_forecasts) >= 2:
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break
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if future_forecasts:
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msg_lines.append("📅 " + " | ".join(future_forecasts))
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# --- 3.5 日出日落 + 日照时长 ---
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sunrises = daily.get("sunrise", [])
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sunsets = daily.get("sunset", [])
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sunshine_durations = daily.get("sunshine_duration", [])
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if sunrises and sunsets:
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sunrise_t = (
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sunrises[0].split("T")[1][:5]
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if "T" in str(sunrises[0])
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else sunrises[0]
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)
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sunset_t = (
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sunsets[0].split("T")[1][:5]
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if "T" in str(sunsets[0])
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else sunsets[0]
|
||||
)
|
||||
sun_line = f"🌅 日出 {sunrise_t} | 🌇 日落 {sunset_t}"
|
||||
if sunshine_durations:
|
||||
sunshine_hours = sunshine_durations[0] / 3600 # 秒 -> 小时
|
||||
sun_line += f" | ☀️ 日照 {sunshine_hours:.1f}h"
|
||||
msg_lines.append(sun_line)
|
||||
|
||||
# --- 4. 核心 实测区 (合并 METAR 和 MGM) ---
|
||||
# 基础数据优先用 METAR
|
||||
cur_temp = _sf(
|
||||
metar.get("current", {}).get("temp")
|
||||
if metar
|
||||
else mgm.get("current", {}).get("temp")
|
||||
)
|
||||
max_p = _sf(
|
||||
metar.get("current", {}).get("max_temp_so_far") if metar else None
|
||||
)
|
||||
max_p_time = (
|
||||
metar.get("current", {}).get("max_temp_time") if metar else None
|
||||
)
|
||||
obs_t_str = "N/A"
|
||||
metar_age_min = None # METAR 数据年龄（分钟）
|
||||
main_source = "METAR" if metar else "MGM"
|
||||
|
||||
if metar:
|
||||
obs_t = metar.get("observation_time", "")
|
||||
try:
|
||||
if "T" in obs_t:
|
||||
from datetime import datetime, timezone, timedelta
|
||||
|
||||
dt = datetime.fromisoformat(obs_t.replace("Z", "+00:00"))
|
||||
utc_offset = open_meteo.get("utc_offset")
|
||||
if utc_offset is None:
|
||||
utc_offset = fallback_utc_offset
|
||||
local_dt = dt.astimezone(
|
||||
timezone(timedelta(seconds=int(utc_offset)))
|
||||
)
|
||||
obs_t_str = local_dt.strftime("%H:%M")
|
||||
# 计算数据年龄
|
||||
now_utc = datetime.now(timezone.utc)
|
||||
metar_age_min = int((now_utc - dt).total_seconds() / 60)
|
||||
elif " " in obs_t:
|
||||
obs_t_str = obs_t.split(" ")[1][:5]
|
||||
else:
|
||||
obs_t_str = obs_t
|
||||
except Exception:
|
||||
obs_t_str = obs_t[:16]
|
||||
elif mgm:
|
||||
m_time = mgm.get("current", {}).get("time", "")
|
||||
if "T" in m_time:
|
||||
from datetime import datetime, timezone, timedelta
|
||||
|
||||
dt = datetime.fromisoformat(m_time.replace("Z", "+00:00"))
|
||||
m_time = dt.astimezone(timezone(timedelta(hours=3))).strftime(
|
||||
"%H:%M"
|
||||
)
|
||||
elif " " in m_time:
|
||||
m_time = m_time.split(" ")[1][:5]
|
||||
obs_t_str = m_time
|
||||
|
||||
# 数据年龄标注
|
||||
age_tag = ""
|
||||
if metar_age_min is not None:
|
||||
if metar_age_min >= 60:
|
||||
age_tag = f" ⚠️{metar_age_min}分钟前"
|
||||
elif metar_age_min >= 30:
|
||||
age_tag = f" 🔔{metar_age_min}分钟前"
|
||||
|
||||
max_str = ""
|
||||
if max_p is not None:
|
||||
import math
|
||||
|
||||
settled_val = math.floor(max_p + 0.5)
|
||||
max_str = f" (最高: {max_p}{temp_symbol}"
|
||||
if max_p_time:
|
||||
max_str += f" @{max_p_time}"
|
||||
max_str += f" → WU {settled_val}{temp_symbol})"
|
||||
|
||||
# --- 天气状况总结 ---
|
||||
wx_summary = ""
|
||||
# 优先使用 METAR 天气现象
|
||||
metar_wx = metar.get("current", {}).get("wx_desc", "") if metar else ""
|
||||
metar_clouds = metar.get("current", {}).get("clouds", []) if metar else []
|
||||
mgm_cloud = mgm.get("current", {}).get("cloud_cover") if mgm else None
|
||||
|
||||
if metar_wx:
|
||||
wx_upper = metar_wx.upper().strip()
|
||||
wx_tokens = set(wx_upper.split())
|
||||
rain_codes = {
|
||||
"RA",
|
||||
"DZ",
|
||||
"-RA",
|
||||
"+RA",
|
||||
"-DZ",
|
||||
"+DZ",
|
||||
"TSRA",
|
||||
"SHRA",
|
||||
"FZRA",
|
||||
}
|
||||
snow_codes = {"SN", "GR", "GS", "-SN", "+SN", "BLSN"}
|
||||
fog_codes = {"FG", "BR", "HZ", "FZFG"}
|
||||
ts_codes = {"TS", "TSRA"}
|
||||
if ts_codes & wx_tokens:
|
||||
wx_summary = "⛈️ 雷暴"
|
||||
elif {"+RA", "+SN"} & wx_tokens:
|
||||
wx_summary = "🌧️ 大雨" if "+RA" in wx_tokens else "❄️ 大雪"
|
||||
elif rain_codes & wx_tokens:
|
||||
wx_summary = (
|
||||
"🌧️ 小雨" if {"-RA", "-DZ", "DZ"} & wx_tokens else "🌧️ 下雨"
|
||||
)
|
||||
elif snow_codes & wx_tokens:
|
||||
wx_summary = "❄️ 下雪"
|
||||
elif fog_codes & wx_tokens:
|
||||
wx_summary = "🌫️ 雾 / 霾"
|
||||
|
||||
# 如果 METAR 没有特殊现象，用云量推断
|
||||
if not wx_summary:
|
||||
# 优先 METAR 云层，回退 MGM
|
||||
cover_code = ""
|
||||
if metar_clouds:
|
||||
cover_code = metar_clouds[-1].get("cover", "")
|
||||
|
||||
if cover_code in ("SKC", "CLR") or (
|
||||
cover_code == "" and mgm_cloud is not None and mgm_cloud <= 1
|
||||
):
|
||||
wx_summary = "☀️ 晴"
|
||||
elif cover_code == "FEW" or (
|
||||
cover_code == "" and mgm_cloud is not None and mgm_cloud <= 2
|
||||
):
|
||||
wx_summary = "🌤️ 晴间少云"
|
||||
elif cover_code == "SCT" or (
|
||||
cover_code == "" and mgm_cloud is not None and mgm_cloud <= 4
|
||||
):
|
||||
wx_summary = "⛅ 晴间多云"
|
||||
elif cover_code == "BKN" or (
|
||||
cover_code == "" and mgm_cloud is not None and mgm_cloud <= 6
|
||||
):
|
||||
wx_summary = "🌥️ 多云"
|
||||
elif cover_code == "OVC" or (
|
||||
cover_code == "" and mgm_cloud is not None and mgm_cloud <= 8
|
||||
):
|
||||
wx_summary = "☁️ 阴天"
|
||||
elif mgm_cloud is not None:
|
||||
cloud_names = {
|
||||
0: "☀️ 晴",
|
||||
1: "☀️ 晴",
|
||||
2: "🌤️ 少云",
|
||||
3: "⛅ 散云",
|
||||
4: "⛅ 散云",
|
||||
5: "🌥️ 多云",
|
||||
6: "🌥️ 多云",
|
||||
7: "☁️ 阴",
|
||||
8: "☁️ 阴天",
|
||||
}
|
||||
wx_summary = cloud_names.get(mgm_cloud, "")
|
||||
|
||||
wx_display = f" {wx_summary}" if wx_summary else ""
|
||||
msg_lines.append(
|
||||
f"\n✈️ <b>实测 ({main_source}): {cur_temp}{temp_symbol}</b>{max_str} |{wx_display} | {obs_t_str}{age_tag}"
|
||||
)
|
||||
|
||||
if mgm:
|
||||
m_c = mgm.get("current", {})
|
||||
# 翻译风向
|
||||
wind_dir = m_c.get("wind_dir")
|
||||
wind_speed_ms = m_c.get("wind_speed_ms")
|
||||
dir_str = ""
|
||||
if wind_dir is not None:
|
||||
dirs = ["北", "东北", "东", "东南", "南", "西南", "西", "西北"]
|
||||
dir_str = dirs[int((float(wind_dir) + 22.5) % 360 / 45)] + "风"
|
||||
|
||||
# 体感和湿度（跳过缺失数据）
|
||||
feels_like = m_c.get("feels_like")
|
||||
humidity = m_c.get("humidity")
|
||||
if feels_like is not None or humidity is not None:
|
||||
parts = []
|
||||
if feels_like is not None:
|
||||
parts.append(f"🌡️ 体感: {feels_like}°C")
|
||||
|
||||
# 针对安卡拉，补充市区(Center)实测值
|
||||
ankara_center = next((s for s in weather_data.get("mgm_nearby", []) if "Bölge/Center" in s.get("name", "")), None)
|
||||
if ankara_center:
|
||||
parts.append(f"Ankara (Bölge/Center): <b>{ankara_center['temp']}°C</b>")
|
||||
|
||||
if humidity is not None:
|
||||
parts.append(f"💧 {humidity}%")
|
||||
msg_lines.append(f" [MGM] {' | '.join(parts)}")
|
||||
|
||||
# 风况（跳过缺失数据）
|
||||
if wind_dir is not None and wind_speed_ms is not None:
|
||||
msg_lines.append(
|
||||
f" [MGM] 🌬️ {dir_str}{wind_dir}° ({wind_speed_ms} m/s) | 💧 降水: {m_c.get('rain_24h') or 0}mm"
|
||||
)
|
||||
|
||||
# 新增：气压和云量
|
||||
extra_parts = []
|
||||
pressure = m_c.get("pressure")
|
||||
if pressure is not None:
|
||||
extra_parts.append(f"🌡 气压: {pressure}hPa")
|
||||
cloud_cover = m_c.get("cloud_cover")
|
||||
if cloud_cover is not None:
|
||||
cloud_desc_map = {
|
||||
0: "晴朗",
|
||||
1: "少云",
|
||||
2: "少云",
|
||||
3: "散云",
|
||||
4: "散云",
|
||||
5: "多云",
|
||||
6: "多云",
|
||||
7: "很多云",
|
||||
8: "阴天",
|
||||
}
|
||||
cloud_text = cloud_desc_map.get(cloud_cover, f"{cloud_cover}/8")
|
||||
extra_parts.append(f"☁️ 云量: {cloud_text}({cloud_cover}/8)")
|
||||
mgm_max = m_c.get("mgm_max_temp")
|
||||
if mgm_max is not None:
|
||||
extra_parts.append(f"🌡️ MGM最高: {mgm_max}°C")
|
||||
if extra_parts:
|
||||
msg_lines.append(f" [MGM] {' | '.join(extra_parts)}")
|
||||
|
||||
if metar:
|
||||
m_c = metar.get("current", {})
|
||||
wind = m_c.get("wind_speed_kt")
|
||||
wind_dir = m_c.get("wind_dir")
|
||||
vis = m_c.get("visibility_mi")
|
||||
clouds = m_c.get("clouds", [])
|
||||
|
||||
cloud_desc = ""
|
||||
if clouds:
|
||||
c_map = {
|
||||
"BKN": "多云",
|
||||
"OVC": "阴天",
|
||||
"FEW": "少云",
|
||||
"SCT": "散云",
|
||||
"SKC": "晴",
|
||||
"CLR": "晴",
|
||||
}
|
||||
main = clouds[-1]
|
||||
cloud_desc = f"☁️ {c_map.get(main.get('cover'), main.get('cover'))}"
|
||||
|
||||
prefix = "[METAR]" if mgm else " "
|
||||
if not mgm:
|
||||
msg_lines.append(
|
||||
f" {prefix} 🌪 {wind or 0}kt ({wind_dir or 0}°) | 👁️ {vis or 10}mi"
|
||||
)
|
||||
|
||||
if cloud_desc:
|
||||
msg_lines.append(
|
||||
f" {prefix} {cloud_desc} | 👁️ {vis or 10}mi | 🌪 {wind or 0}kt"
|
||||
)
|
||||
|
||||
# --- 5. 态势特征提取 ---
|
||||
feature_str, ai_context = analyze_weather_trend(
|
||||
weather_data, temp_symbol, city_name
|
||||
)
|
||||
if feature_str:
|
||||
# 仅将最核心的信息展示给用户作为"态势分析"
|
||||
# 但后面会把更全的数据传给 AI
|
||||
msg_lines.append("\n💡 <b>分析</b>:")
|
||||
for line in feature_str.split("\n"):
|
||||
if line.strip():
|
||||
msg_lines.append(f"- {line.strip()}")
|
||||
|
||||
# --- 6. Groq AI 深度分析 ---
|
||||
try:
|
||||
from src.analysis.ai_analyzer import get_ai_analysis
|
||||
# 构建更全的背景数据给 AI
|
||||
|
||||
# 补充多模型分歧
|
||||
mm = weather_data.get("multi_model", {}) or {}
|
||||
if not isinstance(mm, dict):
|
||||
mm = {}
|
||||
if mm.get("forecasts"):
|
||||
mm_str = " | ".join(
|
||||
[
|
||||
f"{k}:{v}{temp_symbol}"
|
||||
for k, v in mm["forecasts"].items()
|
||||
if v
|
||||
]
|
||||
)
|
||||
ai_context += f"\n模型分歧: {mm_str}"
|
||||
|
||||
ai_result = get_ai_analysis(ai_context, city_name, temp_symbol)
|
||||
if ai_result:
|
||||
msg_lines.append(f"\n{ai_result}")
|
||||
except Exception as e:
|
||||
logger.error(f"调用 Groq AI 分析失败: {e}")
|
||||
|
||||
msg_lines.append(f"\n💸 本次消耗 <b>{CITY_QUERY_COST}</b> 积分。")
|
||||
bot.send_message(message.chat.id, "\n".join(msg_lines), parse_mode="HTML")
|
||||
|
||||
bot.send_message(message.chat.id, city_report, parse_mode="HTML")
|
||||
except Exception as e:
|
||||
import traceback
|
||||
|
||||
|
||||
@@ -1 +1,6 @@
|
||||
POLYWEATHER_API_BASE_URL=http://127.0.0.1:8000
|
||||
# Optional dashboard guard (Next.js middleware)
|
||||
# If set, open dashboard with: /?access_token=<token>
|
||||
POLYWEATHER_DASHBOARD_ACCESS_TOKEN=
|
||||
# Shared secret forwarded by Next API routes to backend
|
||||
POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN=
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import { NextResponse } from "next/server";
|
||||
import { buildBackendRequestHeaders } from "@/lib/backend-auth";
|
||||
|
||||
const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
|
||||
|
||||
@@ -12,7 +13,7 @@ export async function GET() {
|
||||
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/cities`, {
|
||||
headers: { Accept: "application/json" },
|
||||
headers: buildBackendRequestHeaders(),
|
||||
next: { revalidate: 120 },
|
||||
});
|
||||
if (!res.ok) {
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import { NextRequest, NextResponse } from "next/server";
|
||||
import { buildBackendRequestHeaders } from "@/lib/backend-auth";
|
||||
|
||||
const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
|
||||
|
||||
@@ -26,7 +27,7 @@ export async function GET(
|
||||
|
||||
try {
|
||||
const res = await fetch(url, {
|
||||
headers: { Accept: "application/json" },
|
||||
headers: buildBackendRequestHeaders(),
|
||||
cache: "no-store",
|
||||
});
|
||||
if (!res.ok) {
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import { NextRequest, NextResponse } from "next/server";
|
||||
import { buildBackendRequestHeaders } from "@/lib/backend-auth";
|
||||
|
||||
const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
|
||||
|
||||
@@ -19,7 +20,7 @@ export async function GET(
|
||||
|
||||
try {
|
||||
const res = await fetch(url, {
|
||||
headers: { Accept: "application/json" },
|
||||
headers: buildBackendRequestHeaders(),
|
||||
cache: "no-store",
|
||||
});
|
||||
if (!res.ok) {
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import { NextRequest, NextResponse } from "next/server";
|
||||
import { buildBackendRequestHeaders } from "@/lib/backend-auth";
|
||||
|
||||
const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
|
||||
|
||||
@@ -19,7 +20,7 @@ export async function GET(
|
||||
|
||||
try {
|
||||
const res = await fetch(url, {
|
||||
headers: { Accept: "application/json" },
|
||||
headers: buildBackendRequestHeaders(),
|
||||
cache: "no-store",
|
||||
});
|
||||
if (!res.ok) {
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import { NextResponse } from "next/server";
|
||||
import { buildBackendRequestHeaders } from "@/lib/backend-auth";
|
||||
|
||||
const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
|
||||
|
||||
@@ -18,7 +19,7 @@ export async function GET(
|
||||
|
||||
try {
|
||||
const res = await fetch(url, {
|
||||
headers: { Accept: "application/json" },
|
||||
headers: buildBackendRequestHeaders(),
|
||||
cache: "no-store",
|
||||
});
|
||||
if (!res.ok) {
|
||||
|
||||
@@ -0,0 +1,46 @@
|
||||
type Props = {
|
||||
searchParams?: Promise<{ next?: string }>;
|
||||
};
|
||||
|
||||
export default async function EntitlementRequiredPage({ searchParams }: Props) {
|
||||
const params = (await searchParams) || {};
|
||||
const nextPath = params.next || "/";
|
||||
|
||||
return (
|
||||
<main
|
||||
style={{
|
||||
minHeight: "100vh",
|
||||
display: "grid",
|
||||
placeItems: "center",
|
||||
background:
|
||||
"radial-gradient(circle at 20% 20%, #13264f 0%, #071127 45%, #040812 100%)",
|
||||
color: "#d6e2ff",
|
||||
padding: "24px",
|
||||
}}
|
||||
>
|
||||
<section
|
||||
style={{
|
||||
width: "100%",
|
||||
maxWidth: 720,
|
||||
border: "1px solid rgba(68, 92, 140, 0.45)",
|
||||
borderRadius: 16,
|
||||
padding: 24,
|
||||
background: "rgba(9, 18, 36, 0.88)",
|
||||
boxShadow: "0 20px 50px rgba(0, 0, 0, 0.35)",
|
||||
}}
|
||||
>
|
||||
<h1 style={{ margin: 0, fontSize: 28, lineHeight: 1.2 }}>
|
||||
Entitlement Required
|
||||
</h1>
|
||||
<p style={{ marginTop: 12, color: "#9fb2da", lineHeight: 1.6 }}>
|
||||
This dashboard is protected. Append{" "}
|
||||
<code>?access_token=<your-token></code> to the URL once, and
|
||||
the session cookie will be set automatically.
|
||||
</p>
|
||||
<p style={{ marginTop: 12, color: "#9fb2da", lineHeight: 1.6 }}>
|
||||
Requested path: <code>{nextPath}</code>
|
||||
</p>
|
||||
</section>
|
||||
</main>
|
||||
);
|
||||
}
|
||||
@@ -0,0 +1,14 @@
|
||||
export const BACKEND_ENTITLEMENT_HEADER = "x-polyweather-entitlement";
|
||||
|
||||
export function buildBackendRequestHeaders(): HeadersInit {
|
||||
const headers: HeadersInit = {
|
||||
Accept: "application/json",
|
||||
};
|
||||
|
||||
const token = process.env.POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN?.trim();
|
||||
if (token) {
|
||||
headers[BACKEND_ENTITLEMENT_HEADER] = token;
|
||||
}
|
||||
|
||||
return headers;
|
||||
}
|
||||
@@ -0,0 +1,69 @@
|
||||
import { NextRequest, NextResponse } from "next/server";
|
||||
|
||||
const SESSION_COOKIE = "polyweather_entitlement";
|
||||
|
||||
function isStaticAsset(pathname: string) {
|
||||
return (
|
||||
pathname.startsWith("/_next/") ||
|
||||
pathname.startsWith("/favicon") ||
|
||||
pathname.startsWith("/robots.txt") ||
|
||||
pathname.startsWith("/sitemap.xml") ||
|
||||
pathname.startsWith("/icons/") ||
|
||||
pathname.startsWith("/images/") ||
|
||||
pathname.startsWith("/static/")
|
||||
);
|
||||
}
|
||||
|
||||
function isPublicPage(pathname: string) {
|
||||
return pathname === "/entitlement-required";
|
||||
}
|
||||
|
||||
export function middleware(request: NextRequest) {
|
||||
const requiredToken = process.env.POLYWEATHER_DASHBOARD_ACCESS_TOKEN?.trim();
|
||||
if (!requiredToken) {
|
||||
return NextResponse.next();
|
||||
}
|
||||
|
||||
const { pathname, searchParams } = request.nextUrl;
|
||||
if (isStaticAsset(pathname) || isPublicPage(pathname)) {
|
||||
return NextResponse.next();
|
||||
}
|
||||
|
||||
const cookieToken = request.cookies.get(SESSION_COOKIE)?.value;
|
||||
if (cookieToken && cookieToken === requiredToken) {
|
||||
return NextResponse.next();
|
||||
}
|
||||
|
||||
const queryToken = searchParams.get("access_token");
|
||||
if (queryToken && queryToken === requiredToken) {
|
||||
const cleanUrl = request.nextUrl.clone();
|
||||
cleanUrl.searchParams.delete("access_token");
|
||||
|
||||
const response = NextResponse.redirect(cleanUrl);
|
||||
response.cookies.set(SESSION_COOKIE, requiredToken, {
|
||||
httpOnly: true,
|
||||
sameSite: "lax",
|
||||
secure: cleanUrl.protocol === "https:",
|
||||
path: "/",
|
||||
maxAge: 60 * 60 * 12,
|
||||
});
|
||||
return response;
|
||||
}
|
||||
|
||||
if (pathname.startsWith("/api/")) {
|
||||
return NextResponse.json(
|
||||
{ error: "Unauthorized", detail: "Entitlement token required" },
|
||||
{ status: 401 },
|
||||
);
|
||||
}
|
||||
|
||||
const deniedUrl = request.nextUrl.clone();
|
||||
deniedUrl.pathname = "/entitlement-required";
|
||||
deniedUrl.search = "";
|
||||
deniedUrl.searchParams.set("next", pathname);
|
||||
return NextResponse.redirect(deniedUrl);
|
||||
}
|
||||
|
||||
export const config = {
|
||||
matcher: ["/((?!_next/static|_next/image).*)"],
|
||||
};
|
||||
@@ -0,0 +1,468 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime, timezone, timedelta
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from src.analysis.trend_engine import analyze_weather_trend
|
||||
from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
|
||||
from src.data_collection.city_risk_profiles import get_city_risk_profile
|
||||
|
||||
|
||||
FAHRENHEIT_CITIES = {
|
||||
"dallas",
|
||||
"new york",
|
||||
"chicago",
|
||||
"miami",
|
||||
"atlanta",
|
||||
"seattle",
|
||||
}
|
||||
|
||||
|
||||
def _sf(value: Any) -> Optional[float]:
|
||||
if value is None:
|
||||
return None
|
||||
try:
|
||||
return float(value)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def resolve_city_name(city_input: str) -> Tuple[Optional[str], List[str]]:
|
||||
city_input_norm = city_input.strip().lower()
|
||||
supported = list(CITY_REGISTRY.keys())
|
||||
|
||||
# 1) Exact alias/name
|
||||
city_name = ALIASES.get(city_input_norm)
|
||||
if not city_name and city_input_norm in supported:
|
||||
city_name = city_input_norm
|
||||
|
||||
# 2) Prefix match
|
||||
if not city_name and len(city_input_norm) >= 2:
|
||||
for alias, canonical in ALIASES.items():
|
||||
if alias.startswith(city_input_norm):
|
||||
city_name = canonical
|
||||
break
|
||||
if not city_name:
|
||||
for canonical in supported:
|
||||
if canonical.startswith(city_input_norm):
|
||||
city_name = canonical
|
||||
break
|
||||
|
||||
return city_name, sorted(supported)
|
||||
|
||||
|
||||
def _render_local_time(
|
||||
open_meteo: Dict[str, Any],
|
||||
metar: Dict[str, Any],
|
||||
fallback_utc_offset: int,
|
||||
) -> str:
|
||||
local_time = (open_meteo.get("current") or {}).get("local_time", "")
|
||||
if " " in str(local_time):
|
||||
return str(local_time).split(" ")[1][:5]
|
||||
|
||||
metar_obs = metar.get("observation_time", "") if metar else ""
|
||||
if "T" in str(metar_obs):
|
||||
try:
|
||||
dt = datetime.fromisoformat(str(metar_obs).replace("Z", "+00:00"))
|
||||
utc_offset = open_meteo.get("utc_offset")
|
||||
if utc_offset is None:
|
||||
utc_offset = fallback_utc_offset
|
||||
local_dt = dt.astimezone(timezone(timedelta(seconds=int(utc_offset))))
|
||||
return local_dt.strftime("%H:%M")
|
||||
except Exception:
|
||||
return str(metar_obs).split("T")[1][:5]
|
||||
|
||||
if " " in str(metar_obs):
|
||||
return str(metar_obs).split(" ")[1][:5]
|
||||
if metar_obs:
|
||||
return str(metar_obs)[:5]
|
||||
|
||||
try:
|
||||
local_now = datetime.now(timezone.utc).astimezone(
|
||||
timezone(timedelta(seconds=int(fallback_utc_offset)))
|
||||
)
|
||||
return local_now.strftime("%H:%M")
|
||||
except Exception:
|
||||
return "N/A"
|
||||
|
||||
|
||||
def _append_future_forecast_lines(
|
||||
lines: List[str],
|
||||
weather_data: Dict[str, Any],
|
||||
dates: List[str],
|
||||
max_temps: List[Any],
|
||||
temp_symbol: str,
|
||||
fallback_utc_offset: int,
|
||||
) -> None:
|
||||
mgm = weather_data.get("mgm") or {}
|
||||
mgm_daily = (mgm.get("daily_forecasts") or {}) if isinstance(mgm, dict) else {}
|
||||
mm_raw = weather_data.get("multi_model") or {}
|
||||
mm_daily = mm_raw.get("daily_forecasts", {}) if isinstance(mm_raw, dict) else {}
|
||||
mb_daily = (weather_data.get("meteoblue") or {}).get("daily_highs", []) or []
|
||||
nws_periods = (weather_data.get("nws") or {}).get("forecast_periods", []) or []
|
||||
|
||||
if len(dates) > 1:
|
||||
future_forecasts = []
|
||||
for d, t in zip(dates[1:], max_temps[1:]):
|
||||
mgm_value = mgm_daily.get(d) if isinstance(mgm_daily, dict) else None
|
||||
if mgm_value is not None:
|
||||
future_forecasts.append(
|
||||
f"{d[5:]}: {t}{temp_symbol} | 🇺🇸 <b>MGM: {mgm_value}{temp_symbol}</b>"
|
||||
)
|
||||
else:
|
||||
future_forecasts.append(f"{d[5:]}: {t}{temp_symbol}")
|
||||
lines.append("📅 " + " | ".join(future_forecasts))
|
||||
return
|
||||
|
||||
local_now = datetime.now(timezone.utc).astimezone(
|
||||
timezone(timedelta(seconds=int(fallback_utc_offset)))
|
||||
)
|
||||
today_local = local_now.strftime("%Y-%m-%d")
|
||||
|
||||
if isinstance(mgm_daily, dict) and mgm_daily:
|
||||
future = []
|
||||
for day in sorted(mgm_daily.keys()):
|
||||
if day <= today_local:
|
||||
continue
|
||||
day_temp = mgm_daily.get(day)
|
||||
if day_temp is None:
|
||||
continue
|
||||
future.append(f"{day[5:]}: {day_temp}{temp_symbol}")
|
||||
if len(future) >= 2:
|
||||
break
|
||||
if future:
|
||||
lines.append("📅 " + " | ".join(future))
|
||||
return
|
||||
|
||||
if isinstance(mm_daily, dict) and mm_daily:
|
||||
future = []
|
||||
for day in sorted(mm_daily.keys()):
|
||||
if day <= today_local:
|
||||
continue
|
||||
models = mm_daily.get(day, {}) or {}
|
||||
vals = [_sf(v) for v in models.values()]
|
||||
vals = [v for v in vals if v is not None]
|
||||
if not vals:
|
||||
continue
|
||||
vals.sort()
|
||||
median = vals[len(vals) // 2]
|
||||
future.append(f"{day[5:]}: MM中位 {median:.1f}{temp_symbol}")
|
||||
if len(future) >= 2:
|
||||
break
|
||||
if future:
|
||||
lines.append("📅 " + " | ".join(future))
|
||||
return
|
||||
|
||||
if isinstance(mb_daily, list) and len(mb_daily) > 1:
|
||||
future = []
|
||||
for idx in range(1, min(3, len(mb_daily))):
|
||||
day_temp = _sf(mb_daily[idx])
|
||||
if day_temp is None:
|
||||
continue
|
||||
day = (local_now + timedelta(days=idx)).strftime("%m-%d")
|
||||
future.append(f"{day}: MB {day_temp:.1f}{temp_symbol}")
|
||||
if future:
|
||||
lines.append("📅 " + " | ".join(future))
|
||||
return
|
||||
|
||||
if isinstance(nws_periods, list) and nws_periods:
|
||||
future = []
|
||||
seen_days = set()
|
||||
for period in nws_periods:
|
||||
if not period.get("is_daytime"):
|
||||
continue
|
||||
day_temp = _sf(period.get("temperature"))
|
||||
start_time = str(period.get("start_time") or "")
|
||||
if day_temp is None or "T" not in start_time:
|
||||
continue
|
||||
day = start_time[:10]
|
||||
if day <= today_local or day in seen_days:
|
||||
continue
|
||||
seen_days.add(day)
|
||||
future.append(f"{day[5:]}: NWS {day_temp:.0f}{temp_symbol}")
|
||||
if len(future) >= 2:
|
||||
break
|
||||
if future:
|
||||
lines.append("📅 " + " | ".join(future))
|
||||
|
||||
|
||||
def _build_wx_summary(
|
||||
metar_current: Dict[str, Any],
|
||||
metar_clouds: List[Dict[str, Any]],
|
||||
mgm_cloud: Optional[Any],
|
||||
) -> str:
|
||||
wx_desc = str(metar_current.get("wx_desc") or "").upper().strip()
|
||||
if wx_desc:
|
||||
tokens = set(wx_desc.split())
|
||||
rain_codes = {"RA", "DZ", "-RA", "+RA", "-DZ", "+DZ", "TSRA", "SHRA", "FZRA"}
|
||||
snow_codes = {"SN", "GR", "GS", "-SN", "+SN", "BLSN"}
|
||||
fog_codes = {"FG", "BR", "HZ", "FZFG"}
|
||||
ts_codes = {"TS", "TSRA"}
|
||||
if ts_codes & tokens:
|
||||
return "⛈️ 雷暴"
|
||||
if {"+RA", "+SN"} & tokens:
|
||||
return "🌧️ 大雨" if "+RA" in tokens else "❄️ 大雪"
|
||||
if rain_codes & tokens:
|
||||
return "🌧️ 小雨" if {"-RA", "-DZ", "DZ"} & tokens else "🌧️ 下雨"
|
||||
if snow_codes & tokens:
|
||||
return "❄️ 下雪"
|
||||
if fog_codes & tokens:
|
||||
return "🌫️ 雾 / 霾"
|
||||
|
||||
cover_code = ""
|
||||
if metar_clouds:
|
||||
cover_code = str((metar_clouds[-1] or {}).get("cover") or "")
|
||||
|
||||
if cover_code in ("SKC", "CLR") or (cover_code == "" and mgm_cloud is not None and mgm_cloud <= 1):
|
||||
return "☀️ 晴"
|
||||
if cover_code == "FEW" or (cover_code == "" and mgm_cloud is not None and mgm_cloud <= 2):
|
||||
return "🌤️ 晴间少云"
|
||||
if cover_code == "SCT" or (cover_code == "" and mgm_cloud is not None and mgm_cloud <= 4):
|
||||
return "⛅ 晴间多云"
|
||||
if cover_code == "BKN" or (cover_code == "" and mgm_cloud is not None and mgm_cloud <= 6):
|
||||
return "🌥️ 多云"
|
||||
if cover_code == "OVC" or (cover_code == "" and mgm_cloud is not None and mgm_cloud <= 8):
|
||||
return "☁️ 阴天"
|
||||
if mgm_cloud is not None:
|
||||
cloud_names = {
|
||||
0: "☀️ 晴",
|
||||
1: "☀️ 晴",
|
||||
2: "🌤️ 少云",
|
||||
3: "⛅ 散云",
|
||||
4: "⛅ 散云",
|
||||
5: "🌥️ 多云",
|
||||
6: "🌥️ 多云",
|
||||
7: "☁️ 阴",
|
||||
8: "☁️ 阴天",
|
||||
}
|
||||
return cloud_names.get(int(mgm_cloud), "")
|
||||
return ""
|
||||
|
||||
|
||||
def build_city_query_report(
|
||||
city_name: str,
|
||||
weather_data: Dict[str, Any],
|
||||
city_query_cost: int,
|
||||
) -> str:
|
||||
open_meteo = weather_data.get("open-meteo", {}) or {}
|
||||
metar = weather_data.get("metar", {}) or {}
|
||||
mgm = weather_data.get("mgm") or {}
|
||||
city_meta = CITY_REGISTRY.get(city_name.lower(), {})
|
||||
fallback_utc_offset = int(city_meta.get("tz_offset", 0))
|
||||
nws_periods = ((weather_data.get("nws") or {}).get("forecast_periods") or [])
|
||||
if nws_periods:
|
||||
try:
|
||||
first_start = nws_periods[0].get("start_time")
|
||||
if first_start:
|
||||
maybe_dt = datetime.fromisoformat(str(first_start))
|
||||
if maybe_dt.utcoffset() is not None:
|
||||
fallback_utc_offset = int(maybe_dt.utcoffset().total_seconds())
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
city_is_fahrenheit = city_name.strip().lower() in FAHRENHEIT_CITIES
|
||||
temp_symbol = "°F" if city_is_fahrenheit else "°C"
|
||||
|
||||
time_str = _render_local_time(open_meteo, metar, fallback_utc_offset)
|
||||
risk_profile = get_city_risk_profile(city_name)
|
||||
risk_emoji = risk_profile.get("risk_level", "⚠️") if risk_profile else "⚠️"
|
||||
|
||||
msg_lines = [f"📍 <b>{city_name.title()}</b> ({time_str}) {risk_emoji}"]
|
||||
if risk_profile:
|
||||
bias = risk_profile.get("bias", "±0.0")
|
||||
msg_lines.append(
|
||||
f"⚠️ {risk_profile.get('airport_name', '')}: {bias}{temp_symbol} | {risk_profile.get('warning', '')}"
|
||||
)
|
||||
|
||||
daily = open_meteo.get("daily", {}) or {}
|
||||
dates = (daily.get("time") or [])[:3]
|
||||
max_temps = (daily.get("temperature_2m_max") or [])[:3]
|
||||
|
||||
nws_high = _sf((weather_data.get("nws") or {}).get("today_high"))
|
||||
mgm_high = _sf((mgm.get("today_high") if isinstance(mgm, dict) else None))
|
||||
mb_high = _sf((weather_data.get("meteoblue") or {}).get("today_high"))
|
||||
metar_max_so_far = _sf((metar.get("current") or {}).get("max_temp_so_far"))
|
||||
|
||||
today_t = _sf(max_temps[0]) if max_temps else None
|
||||
fallback_source = None
|
||||
metar_only_fallback = False
|
||||
if today_t is None:
|
||||
for source_name, candidate in (("MB", mb_high), ("NWS", nws_high), ("MGM", mgm_high)):
|
||||
if candidate is not None:
|
||||
today_t = candidate
|
||||
fallback_source = source_name
|
||||
break
|
||||
if today_t is None and metar_max_so_far is not None:
|
||||
today_t = metar_max_so_far
|
||||
metar_only_fallback = True
|
||||
|
||||
today_t_display = f"{today_t:.1f}" if isinstance(today_t, (int, float)) else "N/A"
|
||||
sources = ["Open-Meteo"] if max_temps else []
|
||||
comp_parts: List[str] = []
|
||||
|
||||
if mb_high is not None:
|
||||
if "MB" not in sources:
|
||||
sources.append("MB")
|
||||
if fallback_source != "MB":
|
||||
comp_parts.append(f"MB: {mb_high:.1f}{temp_symbol}")
|
||||
if nws_high is not None:
|
||||
if "NWS" not in sources:
|
||||
sources.append("NWS")
|
||||
if fallback_source != "NWS":
|
||||
comp_parts.append(f"NWS: {nws_high:.1f}{temp_symbol}")
|
||||
if mgm_high is not None:
|
||||
if "MGM" not in sources:
|
||||
sources.append("MGM")
|
||||
if fallback_source != "MGM":
|
||||
comp_parts.append(f"MGM: {mgm_high:.1f}{temp_symbol}")
|
||||
if fallback_source and fallback_source not in sources:
|
||||
sources.append(fallback_source)
|
||||
if metar_only_fallback:
|
||||
if not sources:
|
||||
sources = ["Model unavailable"]
|
||||
comp_parts.append(f"METAR实测回退: {metar_max_so_far:.1f}{temp_symbol}")
|
||||
if not sources:
|
||||
sources = ["N/A"]
|
||||
|
||||
divergence_warning = ""
|
||||
base_for_divergence = _sf(max_temps[0]) if max_temps else today_t
|
||||
if mb_high is not None and base_for_divergence is not None:
|
||||
diff = abs(mb_high - base_for_divergence)
|
||||
threshold = 5.0 if city_is_fahrenheit else 2.5
|
||||
if diff > threshold:
|
||||
divergence_warning = f" ⚠️ <b>模型显著分歧 ({diff:.1f}{temp_symbol})</b>"
|
||||
|
||||
comp_str = f" ({' | '.join(comp_parts)})" if comp_parts else ""
|
||||
msg_lines.append(f"\n📊 <b>预报 ({' | '.join(sources)})</b>")
|
||||
msg_lines.append(
|
||||
f"👉 <b>今天: {today_t_display}{temp_symbol}{comp_str}</b>{divergence_warning}"
|
||||
)
|
||||
|
||||
_append_future_forecast_lines(
|
||||
lines=msg_lines,
|
||||
weather_data=weather_data,
|
||||
dates=dates,
|
||||
max_temps=max_temps,
|
||||
temp_symbol=temp_symbol,
|
||||
fallback_utc_offset=fallback_utc_offset,
|
||||
)
|
||||
|
||||
sunrises = daily.get("sunrise", []) or []
|
||||
sunsets = daily.get("sunset", []) or []
|
||||
sunshine_durations = daily.get("sunshine_duration", []) or []
|
||||
if sunrises and sunsets:
|
||||
sunrise_t = str(sunrises[0]).split("T")[1][:5] if "T" in str(sunrises[0]) else str(sunrises[0])
|
||||
sunset_t = str(sunsets[0]).split("T")[1][:5] if "T" in str(sunsets[0]) else str(sunsets[0])
|
||||
sun_line = f"🌅 日出 {sunrise_t} | 🌇 日落 {sunset_t}"
|
||||
if sunshine_durations:
|
||||
sun_line += f" | ☀️ 日照 {float(sunshine_durations[0]) / 3600:.1f}h"
|
||||
msg_lines.append(sun_line)
|
||||
|
||||
metar_current = metar.get("current", {}) if isinstance(metar, dict) else {}
|
||||
mgm_current = mgm.get("current", {}) if isinstance(mgm, dict) else {}
|
||||
cur_temp = _sf(metar_current.get("temp"))
|
||||
if cur_temp is None:
|
||||
cur_temp = _sf(mgm_current.get("temp"))
|
||||
max_p = _sf(metar_current.get("max_temp_so_far"))
|
||||
max_p_time = metar_current.get("max_temp_time")
|
||||
obs_t_str = "N/A"
|
||||
metar_age_min = None
|
||||
main_source = "METAR" if metar else "MGM"
|
||||
|
||||
if metar and metar.get("observation_time"):
|
||||
obs_t = str(metar.get("observation_time"))
|
||||
try:
|
||||
if "T" in obs_t:
|
||||
dt = datetime.fromisoformat(obs_t.replace("Z", "+00:00"))
|
||||
utc_offset = open_meteo.get("utc_offset")
|
||||
if utc_offset is None:
|
||||
utc_offset = fallback_utc_offset
|
||||
local_dt = dt.astimezone(timezone(timedelta(seconds=int(utc_offset))))
|
||||
obs_t_str = local_dt.strftime("%H:%M")
|
||||
metar_age_min = int((datetime.now(timezone.utc) - dt).total_seconds() / 60)
|
||||
elif " " in obs_t:
|
||||
obs_t_str = obs_t.split(" ")[1][:5]
|
||||
else:
|
||||
obs_t_str = obs_t
|
||||
except Exception:
|
||||
obs_t_str = obs_t[:16]
|
||||
elif mgm:
|
||||
mgm_time = str(mgm_current.get("time") or "")
|
||||
if "T" in mgm_time:
|
||||
dt = datetime.fromisoformat(mgm_time.replace("Z", "+00:00"))
|
||||
mgm_time = dt.astimezone(timezone(timedelta(hours=3))).strftime("%H:%M")
|
||||
elif " " in mgm_time:
|
||||
mgm_time = mgm_time.split(" ")[1][:5]
|
||||
obs_t_str = mgm_time or "N/A"
|
||||
|
||||
age_tag = ""
|
||||
if metar_age_min is not None:
|
||||
if metar_age_min >= 60:
|
||||
age_tag = f" ⚠️{metar_age_min}分钟前"
|
||||
elif metar_age_min >= 30:
|
||||
age_tag = f" 🔔{metar_age_min}分钟前"
|
||||
|
||||
max_str = ""
|
||||
if max_p is not None:
|
||||
settled_val = int(max_p + 0.5)
|
||||
max_str = f" (最高: {max_p}{temp_symbol}"
|
||||
if max_p_time:
|
||||
max_str += f" @{max_p_time}"
|
||||
max_str += f" → WU {settled_val}{temp_symbol})"
|
||||
|
||||
metar_clouds = metar_current.get("clouds", []) if isinstance(metar_current, dict) else []
|
||||
mgm_cloud = mgm_current.get("cloud_cover") if isinstance(mgm_current, dict) else None
|
||||
wx_summary = _build_wx_summary(metar_current, metar_clouds, mgm_cloud)
|
||||
wx_display = f" {wx_summary}" if wx_summary else ""
|
||||
msg_lines.append(
|
||||
f"\n✈️ <b>实测 ({main_source}): {cur_temp}{temp_symbol}</b>{max_str} |{wx_display} | {obs_t_str}{age_tag}"
|
||||
)
|
||||
|
||||
if metar:
|
||||
wind = metar_current.get("wind_speed_kt")
|
||||
wind_dir = metar_current.get("wind_dir")
|
||||
vis = metar_current.get("visibility_mi")
|
||||
if not mgm:
|
||||
msg_lines.append(f" [METAR] 🌪 {wind or 0}kt ({wind_dir or 0}°) | 👁️ {vis or 10}mi")
|
||||
if mgm:
|
||||
wind_dir = mgm_current.get("wind_dir")
|
||||
wind_speed_ms = mgm_current.get("wind_speed_ms")
|
||||
if wind_dir is not None and wind_speed_ms is not None:
|
||||
dirs = ["北", "东北", "东", "东南", "南", "西南", "西", "西北"]
|
||||
dir_str = dirs[int((float(wind_dir) + 22.5) % 360 / 45)] + "风"
|
||||
msg_lines.append(
|
||||
f" [MGM] 🌬️ {dir_str}{wind_dir}° ({wind_speed_ms} m/s) | 💧 降水: {mgm_current.get('rain_24h') or 0}mm"
|
||||
)
|
||||
|
||||
feature_str, ai_context, _structured = analyze_weather_trend(weather_data, temp_symbol, city_name)
|
||||
if feature_str:
|
||||
msg_lines.append("\n💡 <b>分析</b>:")
|
||||
for line in feature_str.split("\n"):
|
||||
if line.strip():
|
||||
msg_lines.append(f"- {line.strip()}")
|
||||
|
||||
try:
|
||||
from src.analysis.ai_analyzer import get_ai_analysis
|
||||
|
||||
mm = weather_data.get("multi_model", {}) or {}
|
||||
if not isinstance(mm, dict):
|
||||
mm = {}
|
||||
if mm.get("forecasts"):
|
||||
mm_parts = [
|
||||
f"{k}:{v}{temp_symbol}"
|
||||
for k, v in (mm.get("forecasts") or {}).items()
|
||||
if v is not None
|
||||
]
|
||||
if mm_parts:
|
||||
ai_context += f"\n模型分歧: {' | '.join(mm_parts)}"
|
||||
|
||||
ai_result = get_ai_analysis(ai_context, city_name, temp_symbol)
|
||||
if ai_result:
|
||||
msg_lines.append(f"\n{ai_result}")
|
||||
except Exception as exc:
|
||||
logger.error(f"调用 Groq AI 分析失败: {exc}")
|
||||
|
||||
msg_lines.append(f"\n💸 本次消耗 <b>{city_query_cost}</b> 积分。")
|
||||
return "\n".join(msg_lines)
|
||||
@@ -1013,6 +1013,154 @@ def _build_telegram_messages_mispricing(
|
||||
return {"zh": "\n".join(lines_zh), "en": "\n".join(lines_en)}
|
||||
|
||||
|
||||
def _select_rule_evidence(rule: Dict[str, Any], keys: List[str]) -> Dict[str, Any]:
|
||||
out: Dict[str, Any] = {}
|
||||
for key in keys:
|
||||
if key in rule:
|
||||
out[key] = rule.get(key)
|
||||
return out
|
||||
|
||||
|
||||
def _build_alert_evidence(
|
||||
city_weather: Dict[str, Any],
|
||||
rules: Dict[str, Dict[str, Any]],
|
||||
triggered: List[Dict[str, Any]],
|
||||
suppression: Dict[str, Any],
|
||||
market_snapshot: Dict[str, Any],
|
||||
temp_symbol: str,
|
||||
) -> Dict[str, Any]:
|
||||
current = city_weather.get("current") or {}
|
||||
deb = city_weather.get("deb") or {}
|
||||
|
||||
momentum = rules.get("momentum_spike") or {}
|
||||
breakthrough = rules.get("forecast_breakthrough") or {}
|
||||
advection = rules.get("advection") or {}
|
||||
ankara_center = rules.get("ankara_center_deb_hit") or {}
|
||||
|
||||
top_rows = []
|
||||
for row in (market_snapshot.get("top_bucket_rows") or [])[:4]:
|
||||
if not isinstance(row, dict):
|
||||
continue
|
||||
top_rows.append(
|
||||
{
|
||||
"label": row.get("label"),
|
||||
"probability": row.get("probability"),
|
||||
"yes_buy": row.get("yes_buy"),
|
||||
"yes_sell": row.get("yes_sell"),
|
||||
"market_url": row.get("market_url"),
|
||||
}
|
||||
)
|
||||
|
||||
trigger_types = [row.get("type") for row in triggered if row.get("type")]
|
||||
forecast_bucket = market_snapshot.get("forecast_bucket") or {}
|
||||
|
||||
return {
|
||||
"version": 1,
|
||||
"city": city_weather.get("name"),
|
||||
"generated_local_time": city_weather.get("local_time"),
|
||||
"observed_at": current.get("obs_time"),
|
||||
"temp_symbol": temp_symbol,
|
||||
"inputs": {
|
||||
"current_temp": _sf(current.get("temp")),
|
||||
"deb_prediction": _sf(deb.get("prediction")),
|
||||
"wu_settle": current.get("wu_settle"),
|
||||
"obs_age_min": current.get("obs_age_min"),
|
||||
},
|
||||
"trigger_summary": {
|
||||
"trigger_count": len(trigger_types),
|
||||
"trigger_types": trigger_types,
|
||||
"suppressed": bool(suppression.get("suppressed")),
|
||||
"suppression_reason": suppression.get("reason"),
|
||||
"suppression_snapshot": _select_rule_evidence(
|
||||
suppression,
|
||||
[
|
||||
"minutes_since_peak",
|
||||
"rollback",
|
||||
"rollback_threshold",
|
||||
"max_temp_time",
|
||||
"max_so_far",
|
||||
"current_temp",
|
||||
],
|
||||
),
|
||||
},
|
||||
"rules": {
|
||||
"momentum_spike": _select_rule_evidence(
|
||||
momentum,
|
||||
[
|
||||
"triggered",
|
||||
"direction",
|
||||
"delta_temp",
|
||||
"delta_minutes",
|
||||
"slope_30m",
|
||||
"threshold_30m",
|
||||
],
|
||||
),
|
||||
"forecast_breakthrough": _select_rule_evidence(
|
||||
breakthrough,
|
||||
[
|
||||
"triggered",
|
||||
"baseline_model",
|
||||
"baseline_high",
|
||||
"current_temp",
|
||||
"margin",
|
||||
"threshold",
|
||||
"model_coverage",
|
||||
],
|
||||
),
|
||||
"advection": _select_rule_evidence(
|
||||
advection,
|
||||
[
|
||||
"triggered",
|
||||
"lead_delta",
|
||||
"threshold_delta",
|
||||
"wind_now",
|
||||
"wind_prev",
|
||||
"turned_southerly",
|
||||
"wind_alignment_deg",
|
||||
"lead_window_minutes",
|
||||
],
|
||||
),
|
||||
"ankara_center_deb_hit": _select_rule_evidence(
|
||||
ankara_center,
|
||||
[
|
||||
"triggered",
|
||||
"deb_prediction",
|
||||
"airport_temp",
|
||||
"margin_vs_deb",
|
||||
"center_lead_vs_airport",
|
||||
],
|
||||
),
|
||||
},
|
||||
"market": {
|
||||
"available": bool(market_snapshot.get("available")),
|
||||
"market_prob": market_snapshot.get("market_prob"),
|
||||
"model_prob": market_snapshot.get("model_prob"),
|
||||
"edge_percent": market_snapshot.get("edge_percent"),
|
||||
"yes_buy": market_snapshot.get("yes_buy"),
|
||||
"yes_sell": market_snapshot.get("yes_sell"),
|
||||
"spread": market_snapshot.get("spread"),
|
||||
"signal_label": market_snapshot.get("signal_label"),
|
||||
"confidence": market_snapshot.get("confidence"),
|
||||
"top_bucket": market_snapshot.get("top_bucket"),
|
||||
"top_bucket_prob": market_snapshot.get("top_bucket_prob"),
|
||||
"open_meteo_today_high_c": market_snapshot.get("open_meteo_today_high_c"),
|
||||
"open_meteo_settlement": market_snapshot.get("open_meteo_settlement"),
|
||||
"forecast_bucket": {
|
||||
"label": forecast_bucket.get("label"),
|
||||
"probability": forecast_bucket.get("probability"),
|
||||
"yes_buy": forecast_bucket.get("yes_buy"),
|
||||
"yes_sell": forecast_bucket.get("yes_sell"),
|
||||
"market_url": forecast_bucket.get("market_url"),
|
||||
}
|
||||
if isinstance(forecast_bucket, dict)
|
||||
else None,
|
||||
"top4": top_rows,
|
||||
"market_url": market_snapshot.get("market_url"),
|
||||
"primary_market_url": market_snapshot.get("primary_market_url"),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def build_trading_alerts(
|
||||
city_weather: Dict[str, Any],
|
||||
map_url: Optional[str] = None,
|
||||
@@ -1065,6 +1213,14 @@ def build_trading_alerts(
|
||||
rules=rules,
|
||||
market_snapshot=market_snapshot,
|
||||
)
|
||||
evidence = _build_alert_evidence(
|
||||
city_weather=city_weather,
|
||||
rules=rules,
|
||||
triggered=triggered,
|
||||
suppression=suppression,
|
||||
market_snapshot=market_snapshot,
|
||||
temp_symbol=temp_symbol,
|
||||
)
|
||||
|
||||
return {
|
||||
"city": city,
|
||||
@@ -1076,6 +1232,7 @@ def build_trading_alerts(
|
||||
"market_snapshot": market_snapshot,
|
||||
"suppression": suppression,
|
||||
"triggered_alerts": triggered,
|
||||
"evidence": evidence,
|
||||
"telegram": telegram,
|
||||
}
|
||||
|
||||
|
||||
@@ -187,6 +187,53 @@ def _trigger_type_key(alert_payload: Dict[str, Any]) -> str:
|
||||
return "|".join(trigger_types)
|
||||
|
||||
|
||||
def _evidence_brief(alert_payload: Dict[str, Any]) -> str:
|
||||
evidence = alert_payload.get("evidence") or {}
|
||||
if not isinstance(evidence, dict):
|
||||
return "--"
|
||||
|
||||
trigger_summary = evidence.get("trigger_summary") or {}
|
||||
rules = evidence.get("rules") or {}
|
||||
market = evidence.get("market") or {}
|
||||
momentum = rules.get("momentum_spike") or {}
|
||||
advection = rules.get("advection") or {}
|
||||
breakthrough = rules.get("forecast_breakthrough") or {}
|
||||
|
||||
parts: List[str] = []
|
||||
trigger_types = trigger_summary.get("trigger_types")
|
||||
if isinstance(trigger_types, list) and trigger_types:
|
||||
parts.append(f"triggers={','.join(str(t) for t in trigger_types)}")
|
||||
|
||||
slope = momentum.get("slope_30m")
|
||||
if slope is not None:
|
||||
parts.append(f"slope_30m={slope}")
|
||||
|
||||
lead_delta = advection.get("lead_delta")
|
||||
if lead_delta is not None:
|
||||
parts.append(f"lead_delta={lead_delta}")
|
||||
|
||||
margin = breakthrough.get("margin")
|
||||
if margin is not None:
|
||||
parts.append(f"break_margin={margin}")
|
||||
|
||||
edge = market.get("edge_percent")
|
||||
if edge is not None:
|
||||
parts.append(f"edge_pct={edge}")
|
||||
|
||||
forecast_bucket = market.get("forecast_bucket") or {}
|
||||
if isinstance(forecast_bucket, dict):
|
||||
label = str(forecast_bucket.get("label") or "").strip()
|
||||
yes_buy = forecast_bucket.get("yes_buy")
|
||||
if label:
|
||||
parts.append(f"bucket={label}")
|
||||
if yes_buy is not None:
|
||||
parts.append(f"yes_buy={yes_buy}")
|
||||
|
||||
if not parts:
|
||||
return "--"
|
||||
return "; ".join(parts)
|
||||
|
||||
|
||||
def _alert_signature(alert_payload: Dict[str, Any]) -> str:
|
||||
rules = alert_payload.get("rules") or {}
|
||||
center_deb = rules.get("ankara_center_deb_hit") or {}
|
||||
@@ -321,11 +368,13 @@ def _maybe_send_alert(
|
||||
"severity": alert_payload.get("severity"),
|
||||
"ts": now_ts,
|
||||
"active": True,
|
||||
"evidence": alert_payload.get("evidence"),
|
||||
}
|
||||
state.setdefault("by_signature", {})[signature] = now_ts
|
||||
logger.info(
|
||||
f"trade alert pushed city={city} severity={alert_payload.get('severity')} "
|
||||
f"trigger_count={alert_payload.get('trigger_count')} trigger_key={trigger_key}"
|
||||
f"trigger_count={alert_payload.get('trigger_count')} trigger_key={trigger_key} "
|
||||
f"evidence={_evidence_brief(alert_payload)}"
|
||||
)
|
||||
return True
|
||||
|
||||
@@ -340,7 +389,13 @@ def start_trade_alert_push_loop(bot: Any, config: Dict[str, Any]) -> Optional[th
|
||||
logger.warning("telegram alert push loop skipped: TELEGRAM_CHAT_ID is not set")
|
||||
return None
|
||||
|
||||
interval_sec = max(60, _env_int("TELEGRAM_ALERT_PUSH_INTERVAL_SEC", 300))
|
||||
mispricing_only = _env_bool("TELEGRAM_ALERT_MISPRICING_ONLY", True)
|
||||
if mispricing_only:
|
||||
interval_sec = max(
|
||||
300, _env_int("TELEGRAM_ALERT_MISPRICING_INTERVAL_SEC", 7200)
|
||||
)
|
||||
else:
|
||||
interval_sec = max(60, _env_int("TELEGRAM_ALERT_PUSH_INTERVAL_SEC", 300))
|
||||
cooldown_sec = max(interval_sec, _env_int("TELEGRAM_ALERT_PUSH_COOLDOWN_SEC", 1800))
|
||||
min_trigger_count = max(1, _env_int("TELEGRAM_ALERT_MIN_TRIGGER_COUNT", 2))
|
||||
min_severity = os.getenv("TELEGRAM_ALERT_MIN_SEVERITY", "medium").strip().lower()
|
||||
@@ -353,7 +408,8 @@ def start_trade_alert_push_loop(bot: Any, config: Dict[str, Any]) -> Optional[th
|
||||
except Exception:
|
||||
logger.exception(f"failed to initialize telegram push state path={state_path}")
|
||||
logger.info(
|
||||
f"telegram alert push loop started cities={len(cities)} interval={interval_sec}s "
|
||||
f"telegram alert push loop started mode={'mispricing-only' if mispricing_only else 'full'} "
|
||||
f"cities={len(cities)} interval={interval_sec}s "
|
||||
f"cooldown={cooldown_sec}s min_triggers={min_trigger_count} min_severity={min_severity} "
|
||||
f"state_path={state_path}"
|
||||
)
|
||||
|
||||
+50
-5
@@ -21,7 +21,7 @@ if _root not in sys.path:
|
||||
if _file_dir not in sys.path:
|
||||
sys.path.insert(0, _file_dir)
|
||||
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from fastapi import FastAPI, HTTPException, Request
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from loguru import logger
|
||||
|
||||
@@ -77,6 +77,45 @@ CACHE_TTL = 300
|
||||
CACHE_TTL_ANKARA = 60 # Ankara measurement updates frequent, narrower cache
|
||||
|
||||
|
||||
def _env_bool(name: str, default: bool = False) -> bool:
|
||||
raw = os.getenv(name)
|
||||
if raw is None:
|
||||
return default
|
||||
return raw.strip().lower() in {"1", "true", "yes", "on"}
|
||||
|
||||
|
||||
_ENTITLEMENT_GUARD_ENABLED = _env_bool("POLYWEATHER_REQUIRE_ENTITLEMENT", False)
|
||||
_ENTITLEMENT_HEADER = "x-polyweather-entitlement"
|
||||
_ENTITLEMENT_TOKEN = (os.getenv("POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN") or "").strip()
|
||||
|
||||
|
||||
def _extract_bearer_token(auth_header: Optional[str]) -> Optional[str]:
|
||||
if not auth_header:
|
||||
return None
|
||||
parts = auth_header.strip().split()
|
||||
if len(parts) == 2 and parts[0].lower() == "bearer":
|
||||
return parts[1].strip()
|
||||
return None
|
||||
|
||||
|
||||
def _assert_entitlement(request: Request) -> None:
|
||||
if not _ENTITLEMENT_GUARD_ENABLED:
|
||||
return
|
||||
|
||||
if not _ENTITLEMENT_TOKEN:
|
||||
raise HTTPException(
|
||||
status_code=503,
|
||||
detail="Entitlement guard is enabled but backend token is not configured",
|
||||
)
|
||||
|
||||
token = request.headers.get(_ENTITLEMENT_HEADER)
|
||||
if not token:
|
||||
token = _extract_bearer_token(request.headers.get("authorization"))
|
||||
|
||||
if token != _ENTITLEMENT_TOKEN:
|
||||
raise HTTPException(status_code=401, detail="Unauthorized")
|
||||
|
||||
|
||||
def _sf(v) -> Optional[float]:
|
||||
"""Safe float conversion."""
|
||||
if v is None:
|
||||
@@ -672,8 +711,9 @@ def _analyze(city: str, force_refresh: bool = False) -> Dict[str, Any]:
|
||||
# Routes
|
||||
# ──────────────────────────────────────────────────────────
|
||||
@app.get("/api/cities")
|
||||
async def list_cities():
|
||||
async def list_cities(request: Request):
|
||||
"""Return all supported cities with coordinates and risk level."""
|
||||
_assert_entitlement(request)
|
||||
try:
|
||||
out = []
|
||||
for name, info in CITIES.items():
|
||||
@@ -701,8 +741,9 @@ async def list_cities():
|
||||
|
||||
|
||||
@app.get("/api/city/{name}")
|
||||
async def city_detail(name: str, force_refresh: bool = False):
|
||||
async def city_detail(request: Request, name: str, force_refresh: bool = False):
|
||||
"""Return full weather analysis for a single city."""
|
||||
_assert_entitlement(request)
|
||||
name = name.lower().strip().replace("-", " ")
|
||||
name = ALIASES.get(name, name)
|
||||
if name not in CITIES:
|
||||
@@ -819,8 +860,9 @@ def _build_city_detail_payload(
|
||||
|
||||
|
||||
@app.get("/api/history/{name}")
|
||||
async def city_history(name: str):
|
||||
async def city_history(request: Request, name: str):
|
||||
"""Return historical accuracy data (DEB, mu, actuals) for a city."""
|
||||
_assert_entitlement(request)
|
||||
name = name.lower().strip().replace("-", " ")
|
||||
name = ALIASES.get(name, name)
|
||||
|
||||
@@ -854,7 +896,8 @@ async def city_history(name: str):
|
||||
|
||||
|
||||
@app.get("/api/city/{name}/summary")
|
||||
async def city_summary(name: str, force_refresh: bool = False):
|
||||
async def city_summary(request: Request, name: str, force_refresh: bool = False):
|
||||
_assert_entitlement(request)
|
||||
city = _normalize_city_or_404(name)
|
||||
data = _analyze(city, force_refresh=force_refresh)
|
||||
return _build_city_summary_payload(data)
|
||||
@@ -862,10 +905,12 @@ async def city_summary(name: str, force_refresh: bool = False):
|
||||
|
||||
@app.get("/api/city/{name}/detail")
|
||||
async def city_detail_aggregate(
|
||||
request: Request,
|
||||
name: str,
|
||||
force_refresh: bool = False,
|
||||
market_slug: Optional[str] = None,
|
||||
):
|
||||
_assert_entitlement(request)
|
||||
city = _normalize_city_or_404(name)
|
||||
data = _analyze(city, force_refresh=force_refresh)
|
||||
return _build_city_detail_payload(data, market_slug=market_slug)
|
||||
|
||||
Reference in New Issue
Block a user