Add system memory monitoring, Bedrock support, and Discord integrations

- Add CPU/GPU memory monitoring endpoint and frontend widget
- Add Amazon Bedrock model support (API key / inference profiles) plus model selector entries
- Add Discord weather/laundry notification job (JMA forecast + LLM-generated message)
- Add Discord bot that replies to mentions via the configured LLM, with weather-aware context
- Add scheduled "useless tips" job with separate webhook and quiet-hours skip
- Switch FastAPI startup to lifespan handler, log viewer, markdown rendering, and toast UI additions

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/app/core/weather.py

@@ -0,0 +1,87 @@
+"""気象庁の無料JSON APIから天気予報を取得し、洗濯指数を判定する。"""
+import logging
+from datetime import datetime, timedelta
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+JMA_FORECAST_URL = "https://www.jma.go.jp/bosai/forecast/data/forecast/{area_code}.json"
+
+
+async def fetch_tomorrow_weather(area_code: str, area_name_keyword: str) -> dict:
+    """指定エリアの明日の天気予報を取得する。
+
+    area_code: 気象庁の府県予報区コード（例: 千葉県 = 120000）
+    area_name_keyword: 府県内の地域区分名の一部（例: 市川市が属する「北西部」）
+    """
+    async with httpx.AsyncClient(timeout=10.0) as client:
+        response = await client.get(JMA_FORECAST_URL.format(area_code=area_code))
+        response.raise_for_status()
+        data = response.json()
+
+    # data[0] が短期予報（今日・明日・明後日）
+    short_term = data[0]
+    time_series = short_term["timeSeries"]
+
+    weather_series = time_series[0]
+    pop_series = time_series[1] if len(time_series) > 1 else None
+
+    tomorrow_index = _find_tomorrow_index(weather_series["timeDefines"])
+
+    weather_area = _find_area(weather_series["areas"], area_name_keyword)
+    weather_text = weather_area["weathers"][tomorrow_index] if weather_area else "不明"
+
+    pop_value = None
+    if pop_series:
+        pop_tomorrow_index = _find_tomorrow_index(pop_series["timeDefines"])
+        pop_area = _find_area(pop_series["areas"], area_name_keyword)
+        if pop_area and pop_area.get("pops"):
+            pops = pop_area["pops"]
+            # pops は3時間ごとの配列のことが多いため、明日に該当する値の最大値を取る
+            relevant_pops = [
+                int(p) for p, t in zip(pops, pop_series["timeDefines"])
+                if _is_same_date(t, pop_series["timeDefines"][pop_tomorrow_index]) and p
+            ]
+            if relevant_pops:
+                pop_value = max(relevant_pops)
+
+    return {
+        "weather": weather_text.strip(),
+        "pop": pop_value,
+    }
+
+
+def _find_tomorrow_index(time_defines: list[str]) -> int:
+    tomorrow = (datetime.now() + timedelta(days=1)).date()
+    for i, t in enumerate(time_defines):
+        dt = datetime.fromisoformat(t)
+        if dt.date() == tomorrow:
+            return i
+    return min(1, len(time_defines) - 1)
+
+
+def _is_same_date(t1: str, t2: str) -> bool:
+    return datetime.fromisoformat(t1).date() == datetime.fromisoformat(t2).date()
+
+
+def _find_area(areas: list[dict], keyword: str) -> dict | None:
+    for area in areas:
+        if keyword in area["area"]["name"]:
+            return area
+    return areas[0] if areas else None
+
+
+def judge_laundry(weather: str, pop: int | None) -> tuple[str, str]:
+    """天気と降水確率から洗濯指数を判定する。戻り値: (判定, 理由)"""
+    is_rainy = "雨" in weather or "雪" in weather
+
+    if is_rainy:
+        return "室内干し推奨", f"明日は「{weather}」のため"
+    if pop is None:
+        return "外干しOK（降水確率不明）", "降水確率データが取得できませんでした"
+    if pop <= 30:
+        return "外干しOK", f"降水確率{pop}%と低めです"
+    if pop <= 50:
+        return "室内干し推奨", f"降水確率{pop}%とやや高めです"
+    return "室内干し推奨", f"降水確率{pop}%と高いです"