app.py

app.py

@@ -1,523 +0,0 @@
-# app.py
-import os
-# 將 Matplotlib 與 Hugging Face 模型快取到可寫目錄（HF Spaces 常見做法）
-os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"
-os.environ.setdefault("TRANSFORMERS_CACHE", "/tmp/huggingface")
-os.makedirs(os.environ["TRANSFORMERS_CACHE"], exist_ok=True)
-
-import uuid
-import tempfile
-from datetime import datetime, timedelta, timezone
-
-from flask import Flask, request, abort, send_from_directory
-
-from linebot.v3 import WebhookHandler
-from linebot.v3.exceptions import InvalidSignatureError
-from linebot.v3.messaging import (
-    Configuration, ApiClient, MessagingApi,
-    ReplyMessageRequest, TextMessage
-)
-from linebot.v3.messaging.models import ImageMessage
-from linebot.v3.webhooks import MessageEvent, TextMessageContent
-
-import requests
-import pandas as pd
-
-# Matplotlib（無頭）
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-from matplotlib.colors import Normalize
-import matplotlib.cm as cm
-from matplotlib import font_manager as fm
-
-# ──────────────────────────────────────────────────────────────────────────────
-# CWA 地震預警：若外部模組存在就用；否則使用備援實作
-# ──────────────────────────────────────────────────────────────────────────────
-try:
-    from fetch_cwa_alarm_list import fetch_cwa_alarm_list  # type: ignore
-except Exception:
-    CWA_ALARM_API = "https://app-2.cwa.gov.tw/api/v1/earthquake/alarm/list"
-
-    def _parse_cwa_time(s: str):
-        if not s:
-            return ("未知", "未知")
-        try:
-            if "T" in s or s.endswith("Z") or "+" in s:
-                dt = datetime.fromisoformat(s.replace("Z", "+00:00"))
-            else:
-                dt = datetime.strptime(s, "%Y-%m-%d %H:%M:%S")
-                dt = dt.replace(tzinfo=timezone(timedelta(hours=8)))
-            tw = dt.astimezone(timezone(timedelta(hours=8))).strftime("%Y-%m-%d %H:%M")
-            utc = dt.astimezone(timezone.utc).strftime("%Y-%m-%d %H:%M")
-            return (tw, utc)
-        except Exception:
-            return (s, "未知")
-
-    def fetch_cwa_alarm_list(limit: int = 5) -> str:
-        try:
-            r = requests.get(CWA_ALARM_API, timeout=10)
-            r.raise_for_status()
-            payload = r.json()
-        except Exception as e:
-            return f"❌ 地震預警查詢失敗：{e}"
-
-        items = None
-        if isinstance(payload, dict):
-            items = payload.get("data") or payload.get("records") or payload.get("list") or payload.get("items")
-        if items is None and isinstance(payload, list):
-            items = payload
-        if not items:
-            return "✅ 目前沒有地震預警。"
-
-        def _key(it):
-            s = it.get("originTime") or ""
-            try:
-                if "T" in s or s.endswith("Z") or "+" in s:
-                    dt = datetime.fromisoformat(s.replace("Z", "+00:00"))
-                else:
-                    dt = datetime.strptime(s, "%Y-%m-%d %H:%M:%S").replace(tzinfo=timezone(timedelta(hours=8)))
-                return dt.astimezone(timezone.utc)
-            except Exception:
-                return datetime.min.replace(tzinfo=timezone.utc)
-
-        try:
-            items = sorted(items, key=_key, reverse=True)
-        except Exception:
-            pass
-
-        def _num(x):
-            xs = str(x)
-            ok = xs.replace(".", "", 1).replace("-", "", 1).isdigit()
-            return float(xs) if ok else None
-
-        out = ["🚨 地震預警（最新）:", "-" * 20]
-        for it in items[:limit]:
-            identifier = it.get("identifier") or it.get("eventId") or it.get("id") or "—"
-            status = it.get("status") or "—"
-            msg_type = it.get("msgType") or "—"
-            msg_no = it.get("msgNo") or it.get("msgSeq") or "—"
-
-            mag = _num(it.get("magnitudeValue") or it.get("magnitude") or it.get("ml") or it.get("mw"))
-            mag_str = f"{mag:.1f}" if mag is not None else "—"
-            depth = _num(it.get("depth"))
-            depth_str = f"{depth:.0f}" if depth is not None else "—"
-
-            lat = _num(it.get("epicenterLat") or it.get("latitude") or it.get("lat"))
-            lon = _num(it.get("epicenterLon") or it.get("longitude") or it.get("lon"))
-            lat_str = f"{lat:.2f}" if lat is not None else "—"
-            lon_str = f"{lon:.2f}" if lon is not None else "—"
-
-            tw_str, utc_str = _parse_cwa_time(it.get("originTime") or "")
-
-            areas = it.get("locationDesc") or it.get("areas") or it.get("alertAreas")
-            if isinstance(areas, list):
-                areas_txt = "、".join(str(a) for a in areas if a)
-            elif isinstance(areas, str):
-                areas_txt = areas
-            else:
-                areas_txt = "—"
-
-            out.append(
-                f"{status} | 類型: {msg_type}#{msg_no}\n"
-                f"震級/深度: M{mag_str} / {depth_str} km\n"
-                f"震中: lat {lat_str}, lon {lon_str}\n"
-                f"時間: {tw_str}（台灣） / {utc_str}（UTC）\n"
-                f"預警地區: {areas_txt}\n"
-                f"事件: {identifier}"
-            )
-            out.append("")
-        if len(items) > limit:
-            out.append(f"... 另有 {len(items) - limit} 筆。")
-        return "\n".join(out).strip()
-
-# ──────────────────────────────────────────────────────────────────────────────
-# AI 生成功能（Transformers）：懶載入 + 權限安全快取
-# ──────────────────────────────────────────────────────────────────────────────
-_LLM = {"loaded": False, "ok": False, "err": None, "model": None, "tokenizer": None, "device": "cpu"}
-
-def _ensure_llm():
-    if _LLM["loaded"]:
-        return _LLM["ok"], _LLM["err"]
-    _LLM["loaded"] = True
-    try:
-        import torch  # noqa
-        from transformers import AutoTokenizer, AutoModelForCausalLM
-
-        device = os.getenv("LLM_DEVICE")
-        if device not in ("cuda", "cpu"):
-            try:
-                import torch as _t
-                device = "cuda" if _t.cuda.is_available() else "cpu"
-                _t.set_num_threads(max(1, int(os.getenv("LLM_THREADS", "1"))))
-            except Exception:
-                device = "cpu"
-
-        model_name = os.getenv("LLM_MODEL", "ckiplab/gpt2-base-chinese")
-        cache_dir = os.environ["TRANSFORMERS_CACHE"]
-        tok = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
-        mdl = AutoModelForCausalLM.from_pretrained(model_name, cache_dir=cache_dir)
-        try:
-            mdl = mdl.to(device)
-        except Exception:
-            device = "cpu"
-            mdl = mdl.to(device)
-
-        _LLM.update({"ok": True, "model": mdl, "tokenizer": tok, "device": device})
-        return True, None
-    except Exception as e:
-        _LLM["err"] = f"{e}"
-        _LLM["ok"] = False
-        return False, _LLM["err"]
-
-def generate_ai_text(user_prompt: str) -> str:
-    ok, err = _ensure_llm()
-    if not ok:
-        return ("🤖 AI 尚未啟用：缺少依賴或模型未下載。\n"
-                "請在 requirements.txt 加入 transformers、torch、accelerate、safetensors、sentencepiece。\n"
-                f"詳細錯誤：{err}")
-
-    import torch  # type: ignore
-    tok = _LLM["tokenizer"]
-    mdl = _LLM["model"]
-    device = _LLM["device"]
-
-    max_new = int(os.getenv("LLM_MAX_NEW_TOKENS", "120"))
-    top_k = int(os.getenv("LLM_TOP_K", "50"))
-    temperature = float(os.getenv("LLM_TEMPERATURE", "0.7"))
-
-    sys_prefix = (
-        "你是一個地震資訊與一般問答的 LINE 助理。回答要精簡、清楚；"
-        "若與地震相關可加入注意事項；若無關則一般回覆。\n\n使用者："
-    )
-    prompt = sys_prefix + user_prompt
-
-    try:
-        inputs = tok(prompt, return_tensors="pt").to(device)
-        with torch.no_grad():
-            output = mdl.generate(
-                input_ids=inputs["input_ids"],
-                attention_mask=inputs.get("attention_mask"),
-                max_new_tokens=max_new,
-                do_sample=True,
-                top_k=top_k,
-                temperature=temperature,
-                pad_token_id=tok.eos_token_id,
-            )
-        text = tok.decode(output[0], skip_special_tokens=True)
-        if sys_prefix in text:
-            text = text.split(sys_prefix, 1)[-1]
-        if user_prompt in text:
-            text = text.split(user_prompt, 1)[-1].strip()
-        return (text or "（沒有產生內容）")[:1200]
-    except Exception as e:
-        return f"AI 產生發生錯誤：{e}"
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 中文字型（有就套用）
-# ──────────────────────────────────────────────────────────────────────────────
-for fp in [
-    "/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc",
-    "/usr/share/fonts/truetype/noto/NotoSansCJK-Regular.ttc",
-    "/usr/share/fonts/truetype/wqy/wqy-microhei.ttc",
-    "/usr/share/fonts/truetype/arphic/ukai.ttc",
-]:
-    if os.path.exists(fp):
-        matplotlib.rcParams["font.family"] = fm.FontProperties(fname=fp).get_name()
-        break
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 基本設定
-# ──────────────────────────────────────────────────────────────────────────────
-CHANNEL_ACCESS_TOKEN = os.getenv("CHANNEL_ACCESS_TOKEN")
-CHANNEL_SECRET = os.getenv("CHANNEL_SECRET")
-
-HF_SPACE_URL = os.getenv("SPACEURL")
-if not HF_SPACE_URL:
-    sid = os.getenv("SPACE_ID")
-    if sid and "/" in sid:
-        a, n = sid.split("/", 1)
-        HF_SPACE_URL = f"https://{a.replace('_','-')}-{n.replace('_','-')}.hf.space"
-    else:
-        HF_SPACE_URL = ""
-
-STATIC_DIR = os.getenv("STATIC_DIR", os.path.join(tempfile.gettempdir(), "static"))
-os.makedirs(STATIC_DIR, exist_ok=True)
-
-app = Flask(__name__)
-configuration = Configuration(access_token=CHANNEL_ACCESS_TOKEN)
-handler = WebhookHandler(CHANNEL_SECRET)
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 首頁（指令/Webhook/靜態檔說明）
-# ──────────────────────────────────────────────────────────────────────────────
-@app.route("/", methods=["GET"])
-def home():
-    base = (os.getenv("SPACEURL") or request.url_root).rstrip("/")
-    webhook_url = f"{base}/callback"
-    static_hint = f"{base}/static/<檔名>"
-    channel_ok = "✅" if os.getenv("CHANNEL_ACCESS_TOKEN") and os.getenv("CHANNEL_SECRET") else "⚠️"
-    space_ok = "✅" if os.getenv("SPACEURL") or os.getenv("SPACE_ID") else "ℹ️"
-
-    return f"""
-<!doctype html>
-<html lang="zh-Hant"><head>
-<meta charset="utf-8"/><meta name="viewport" content="width=device-width,initial-scale=1"/>
-<title>地震預警 dayichen – LINE Bot Server</title>
-<style>
-:root{{--bg:#0f1115;--card:#151821;--text:#e6e8ef;--muted:#9aa4b2;--border:rgba(255,255,255,.08)}}
-*{{box-sizing:border-box}} body{{margin:0;background:#0f1115;color:#e6e8ef;
-font:16px/1.6 -apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Noto Sans TC","PingFang TC",sans-serif;
-padding:32px 16px;display:flex;justify-content:center}}
-.wrap{{width:100%;max-width:980px}} .hero{{background:linear-gradient(135deg,#1f2937,#0f172a);
-border:1px solid var(--border);border-radius:16px;padding:28px;margin-bottom:20px;box-shadow:0 8px 30px rgba(0,0,0,.25)}}
-.title{{margin:0 0 6px;font-size:28px;font-weight:800}} .subtitle{{margin:0;color:#9aa4b2}}
-.grid{{display:grid;gap:16px;grid-template-columns:repeat(auto-fit,minmax(260px,1fr));margin-top:18px}}
-.card{{background:#151821;border:1px solid var(--border);border-radius:14px;padding:16px 18px}}
-h3{{margin:0 0 8px;font-size:18px}} .kbd{{padding:2px 6px;border:1px solid var(--border);border-radius:6px;background:#0b0e14}}
-.mono,code{{font-family:ui-monospace,SFMono-Regular,Menlo,Consolas,monospace;color:#e6e8ef}}
-a{{color:#60a5fa;text-decoration:none}} a:hover{{text-decoration:underline}}
-.badge{{display:inline-block;padding:2px 8px;border-radius:999px;background:#1f2937;border:1px solid var(--border);font-size:12px;color:#9aa4b2}}
-.foot{{color:#9aa4b2;font-size:13px;margin-top:18px;text-align:center}}
-</style></head>
-<body><div class="wrap"><section class="hero">
-<div class="badge">狀態：<span style="color:{'#86efac' if channel_ok=='✅' else '#fbbf24'}">{channel_ok}</span> LINE 金鑰　·　HF Space：{space_ok}</div>
-<h1 class="title">地震預警 dayichen – LINE Bot</h1>
-<p class="subtitle">指令：/help、地震/quake、臺灣地震/台灣地震、臺灣地震畫圖/台灣地震畫圖、地震預警、AI（ai + 問題）。</p>
-<div class="grid">
-<div class="card"><h3>🚀 快速開始</h3><ul>
-<li><span class="kbd">/help</span>：顯示所有指令</li>
-<li><span class="kbd">地震</span>/<span class="kbd">quake</span>：全球近 24 小時 M≥5.0</li>
-<li><span class="kbd">臺灣地震</span>/<span class="kbd">台灣地震</span>：今年台灣區域清單（含日期時間）</li>
-<li><span class="kbd">臺灣地震畫圖</span>/<span class="kbd">台灣地震畫圖</span>：回傳地圖圖片</li>
-<li><span class="kbd">地震預警</span>：CWA 地震預警（最新 5 筆）</li>
-<li><span class="kbd">ai 你的問題</span>：AI 對話（模型：<span class="mono">{os.getenv("LLM_MODEL","ckiplab/gpt2-base-chinese")}</span>）</li>
-</ul></div>
-<div class="card"><h3>🛠️ Webhook / 靜態檔</h3><ul>
-<li>Webhook：<span class="mono"><a href="{webhook_url}">{webhook_url}</a></span></li>
-<li>靜態圖片：<span class="mono">{static_hint}</span></li>
-<li>健康檢查：<span class="mono"><a href="{base}/healthz">{base}/healthz</a></span></li>
-</ul></div>
-<div class="card"><h3>ℹ️ 備註</h3><ul>
-<li>AI 快取位置：<span class="mono">{os.environ["TRANSFORMERS_CACHE"]}</span></li>
-<li>若 AI 未安裝依賴，機器人會提示安裝，不會影響其他功能。</li>
-</ul></div>
-</div>
-<p class="foot">© {datetime.now().year} dayichen · server: {base}</p>
-</section></div></body></html>"""
-
-@app.route("/healthz")
-def healthz():
-    return "ok"
-
-@app.route("/static/<path:filename>")
-def serve_static(filename):
-    return send_from_directory(STATIC_DIR, filename)
-
-# ──────────────────────────────────────────────────────────────────────────────
-# USGS 查詢
-# ──────────────────────────────────────────────────────────────────────────────
-USGS_API_BASE_URL = "https://earthquake.usgs.gov/fdsnws/event/1/query"
-
-def _iso(dt: datetime) -> str:
-    return dt.astimezone(timezone.utc).strftime("%Y-%m-%dT%H:%M:%S")
-
-def fetch_global_last24h_text(min_mag=5.0, limit=10) -> str:
-    now_utc = datetime.now(timezone.utc)
-    since = now_utc - timedelta(hours=24)
-    params = {
-        "format": "geojson",
-        "starttime": _iso(since),
-        "endtime": _iso(now_utc),
-        "minmagnitude": float(min_mag),
-        "limit": int(limit),
-        "orderby": "time",
-    }
-    try:
-        r = requests.get(USGS_API_BASE_URL, params=params, timeout=15)
-        r.raise_for_status()
-        features = r.json().get("features", [])
-        if not features:
-            return "✅ 過去 24 小時內，全球無規模 5.0 以上的顯著地震。"
-        lines = ["🚨 近 24 小時全球顯著地震 (M≥5.0):", "-" * 20]
-        for f in features:
-            p = f["properties"]
-            t_utc = datetime.fromtimestamp(p["time"] / 1000, tz=timezone.utc)
-            lines.append(f"震級: {p['mag']:.1f} | 日期時間: {t_utc.strftime('%Y-%m-%d %H:%M')} (UTC)\n地點: {p.get('place','')}")
-        return "\n\n".join(lines)
-    except Exception as e:
-        return f"❌ 查詢失敗: {e}"
-
-def fetch_taiwan_df_this_year(min_mag=5.0) -> pd.DataFrame | str:
-    now_utc = datetime.now(timezone.utc)
-    start_of_year_utc = datetime(now_utc.year, 1, 1, tzinfo=timezone.utc)
-    params = {
-        "format": "geojson", "starttime": _iso(start_of_year_utc), "endtime": _iso(now_utc),
-        "minmagnitude": float(min_mag),
-        "minlatitude": 21, "maxlatitude": 26,
-        "minlongitude": 119, "maxlongitude": 123,
-        "limit": 250, "orderby": "time",
-    }
-    try:
-        r = requests.get(USGS_API_BASE_URL, params=params, timeout=20)
-        r.raise_for_status()
-        features = r.json().get("features", [])
-        if not features:
-            return f"✅ 今年 ({now_utc.year} 年) 以來，台灣區域無 M≥{min_mag:.1f} 的顯著地震。"
-        rows = []
-        for f in features:
-            p = f["properties"]
-            lon, lat, *_ = f["geometry"]["coordinates"]
-            rows.append({
-                "latitude": lat, "longitude": lon, "magnitude": p["mag"],
-                "place": p.get("place", ""), "time_utc": datetime.fromtimestamp(p["time"]/1000, tz=timezone.utc)
-            })
-        return pd.DataFrame(rows)
-    except Exception as e:
-        return f"❌ 查詢失敗: {e}"
-
-# ──────────────────────────────────────────────────────────────────────────────
-# 製圖
-# ──────────────────────────────────────────────────────────────────────────────
-def create_and_save_map(df: pd.DataFrame) -> str:
-    fig, ax = plt.subplots(figsize=(9, 6), dpi=150)
-    ax.set_xlim(118.5, 123.5)
-    ax.set_ylim(20.5, 26.8)
-    ax.set_xlabel("Longitude (°E)")
-    ax.set_ylabel("Latitude (°N)")
-    ax.set_title(f"今年 ({datetime.now(timezone.utc).year}) 台灣區域顯著地震 (M≥5.0) — UTC")
-    ax.grid(True, linestyle="--", linewidth=0.5, alpha=0.4)
-
-    mags = df["magnitude"].astype(float).clip(lower=0)
-    norm = Normalize(vmin=max(4.5, mags.min()), vmax=max(6.5, mags.max()))
-    cmap = cm.get_cmap("YlOrRd")
-    colors = cmap(norm(mags.values))
-    sizes = 15 + (mags - mags.min()) * 25
-
-    ax.scatter(df["longitude"].values, df["latitude"].values,
-               s=sizes, c=colors, edgecolor="k", linewidths=0.4, alpha=0.9)
-
-    fig.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax, pad=0.02).set_label("Magnitude")
-
-    filename = f"map_{uuid.uuid4().hex}.png"
-    filepath = os.path.join(STATIC_DIR, filename)
-    fig.tight_layout()
-    fig.savefig(filepath)
-    plt.close(fig)
-    return filename
-
-def _base_url_for_images() -> str:
-    return HF_SPACE_URL.rstrip("/") if HF_SPACE_URL else request.url_root.rstrip("/")
-
-# ──────────────────────────────────────────────────────────────────────────────
-# LINE Webhook
-# ──────────────────────────────────────────────────────────────────────────────
-@app.route("/callback", methods=["POST"])
-def callback():
-    signature = request.headers.get("X-Line-Signature")
-    body = request.get_data(as_text=True)
-    try:
-        handler.handle(body, signature)
-    except InvalidSignatureError:
-        abort(400)
-    return "OK"
-
-@handler.add(MessageEvent, message=TextMessageContent)
-def handle_message(event):
-    user_message_raw = (event.message.text or "").strip()
-    user_message = user_message_raw.lower()
-
-    with ApiClient(configuration) as api_client:
-        line_bot_api = MessagingApi(api_client)
-
-        # 地震預警
-        if "地震預警" in user_message:
-            reply_text = fetch_cwa_alarm_list(limit=5)
-            line_bot_api.reply_message_with_http_info(
-                ReplyMessageRequest(reply_token=event.reply_token, messages=[TextMessage(text=reply_text)])
-            )
-            return
-
-        # 臺灣地震畫圖
-        if ("臺灣地震畫圖" in user_message) or ("台灣地震畫圖" in user_message):
-            result = fetch_taiwan_df_this_year()
-            if isinstance(result, pd.DataFrame):
-                filename = create_and_save_map(result)
-                image_url = f"{_base_url_for_images()}/static/{filename}"
-                reply = ReplyMessageRequest(
-                    reply_token=event.reply_token,
-                    messages=[
-                        TextMessage(text="🗺️ 已為您繪製今年台灣區域 M≥5.0 地震分佈圖（UTC）。"),
-                        ImageMessage(original_content_url=image_url, preview_image_url=image_url),
-                    ],
-                )
-            else:
-                reply = ReplyMessageRequest(reply_token=event.reply_token, messages=[TextMessage(text=result)])
-            line_bot_api.reply_message_with_http_info(reply)
-            return
-
-        # 說明
-        if user_message == "/help":
-            text = (
-                "📖 指令\n\n"
-                "• /help\n"
-                "• 地震 / quake（全球近24小時，含日期時間）\n"
-                "• 臺灣地震 / 台灣地震（今年台灣區域清單）\n"
-                "• 臺灣地震畫圖 / 台灣地震畫圖（今年台灣區域分佈圖）\n"
-                "• 地震預警（CWA 最新 5 筆）\n"
-                "• ai 你的問題（AI 對話）\n"
-                "• 你好"
-            )
-            line_bot_api.reply_message_with_http_info(
-                ReplyMessageRequest(reply_token=event.reply_token, messages=[TextMessage(text=text)])
-            )
-            return
-
-        # 台灣清單
-        if ("臺灣地震" in user_message) or ("台灣地震" in user_message):
-            result = fetch_taiwan_df_this_year()
-            if isinstance(result, pd.DataFrame):
-                count = len(result)
-                lines = [f"🇹🇼 今年 ({datetime.now(timezone.utc).year} 年) 台灣區域顯著地震 (M≥5.0)，共 {count} 筆:", "-" * 20]
-                for _, row in result.head(15).iterrows():
-                    t = row["time_utc"].strftime("%Y-%m-%d %H:%M")
-                    lines.append(f"震級: {row['magnitude']:.1f} | 日期時間: {t} (UTC)\n地點: {row['place']}")
-                if count > 15:
-                    lines.append(f"... (還有 {count - 15} 筆，可用「臺灣地震畫圖」查看全部)")
-                reply_text = "\n\n".join(lines)
-            else:
-                reply_text = result
-            line_bot_api.reply_message_with_http_info(
-                ReplyMessageRequest(reply_token=event.reply_token, messages=[TextMessage(text=reply_text)])
-            )
-            return
-
-        # 全球 24 小時
-        if ("地震" in user_message) or ("quake" in user_message):
-            reply_text = fetch_global_last24h_text()
-            line_bot_api.reply_message_with_http_info(
-                ReplyMessageRequest(reply_token=event.reply_token, messages=[TextMessage(text=reply_text)])
-            )
-            return
-
-        # AI 對話（明確觸發詞）
-        if user_message.startswith("ai ") or user_message.startswith("ai:") or user_message.startswith("ai："):
-            prompt = user_message_raw[2:].lstrip(" ：:").strip() or "請簡要介紹你的功能。"
-            ai_text = generate_ai_text(prompt)
-            line_bot_api.reply_message_with_http_info(
-                ReplyMessageRequest(reply_token=event.reply_token, messages=[TextMessage(text=ai_text)])
-            )
-            return
-
-        # 招呼
-        if ("你好" in user_message) or ("hi" in user_message):
-            line_bot_api.reply_message_with_http_info(
-                ReplyMessageRequest(reply_token=event.reply_token, messages=[TextMessage(text="👋 你好！輸入 /help 查看指令。")])
-            )
-            return
-
-        # 兜底：交給 AI 回覆，讓系統更智能
-        fallback = generate_ai_text(user_message_raw)
-        line_bot_api.reply_message_with_http_info(
-            ReplyMessageRequest(reply_token=event.reply_token, messages=[TextMessage(text=fallback)])
-        )