app.py

# app.py
import os
import ssl
import uuid
import requests
import logging
import time  # 💡 導入時間模組進行效能測試

# 💡 全域繞過 Linux Docker 容器內的 SSL 證書查驗 (防止 ai-labs.ilrdf.org.tw CERTIFICATE_VERIFY_FAILED)
ssl._create_default_https_context = ssl._create_unverified_context

from fastapi import FastAPI, Request, HTTPException, UploadFile, File, Form
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
from pydantic import BaseModel
from typing import Optional
from linebot import LineBotApi, WebhookHandler
from linebot.exceptions import InvalidSignatureError
from linebot.models import (
    MessageEvent, TextMessage, TextSendMessage, 
    AudioSendMessage, FlexSendMessage, VideoMessage, 
    ImageMessage, AudioMessage, ImageSendMessage, PostbackEvent, FollowEvent
)

# 💡 導入專案模組
import image_processor 
import config
import translator
import audio_processor
import video_processor
import ui_templates
import db_manager
import deep_analyzer 
import visual_renderer 

# 設定日誌
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI()

# 確保靜態資料夾存在
if not os.path.exists("static"):
    os.makedirs("static")
app.mount("/static", StaticFiles(directory="static"), name="static")

# 💡 LINE Bot 防呆初始化 (避免純網頁模式無 LINE Secrets 時崩潰)
line_bot_api = None
handler_instance = None

if config.LINE_TOKEN and config.LINE_SECRET:
    try:
        line_bot_api = LineBotApi(config.LINE_TOKEN)
        handler_instance = WebhookHandler(config.LINE_SECRET)
    except Exception as e:
        logger.warning(f"LINE Bot 初始化提醒: {e}")

class DummyHandler:
    def add(self, *args, **kwargs):
        def decorator(func):
            return func
        return decorator

handler = handler_instance if handler_instance else DummyHandler()

# ----------------------------------------------------------------
# Pydantic 資料結構
# ----------------------------------------------------------------
class TranslateRequest(BaseModel):
    text: Optional[str] = None
    source_text: Optional[str] = None
    tribe: Optional[str] = "太魯閣"

class DeepTranslateRequest(BaseModel):
    source_text: Optional[str] = None
    target_text: Optional[str] = None
    tribe: Optional[str] = "太魯閣"
    api_key: Optional[str] = None

class FeedbackRequest(BaseModel):
    content: Optional[str] = ""
    tribe: Optional[str] = "太魯閣"

# ----------------------------------------------------------------
# 🔮 智慧翻譯快取攔截器 (Priority 2: LRU Cache)
# ----------------------------------------------------------------
from functools import lru_cache

@lru_cache(maxsize=1024)
def cached_translate(text: str, tribe: str):
    """記憶體快取：如果相同的句子與族語查過，直接秒回，不再去遠端 API 排隊 5 秒"""
    logger.info(f"🔮 [Cache Miss] 正在向遠端 API 請求全新翻譯: {text[:10]}...")
    return translator.translate(text, tribe)

# ----------------------------------------------------------------
# 輔助功能
# ----------------------------------------------------------------

@app.get("/download_file/{filename}")
async def download_file(filename: str):
    """供使用者下載生成的音檔或檔案"""
    file_path = os.path.join("static", filename)
    if os.path.exists(file_path):
        return FileResponse(path=file_path, filename=f"TRK_AI_{filename}", media_type='application/octet-stream')
    return {"error": "檔案不存在"}

def show_loading_animation(user_id, loading_seconds=20):
    """發送 LINE 官方的讀取中動畫"""
    if not config.LINE_TOKEN:
        return
    url = "https://api.line.me/v2/bot/chat/loading/start"
    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {config.LINE_TOKEN}"}
    data = {"chatId": user_id, "loadingSeconds": loading_seconds}
    try:
        requests.post(url, headers=headers, json=data, timeout=5)
    except Exception as e:
        logger.error(f"Loading Animation Error: {e}")

def get_line_nickname(user_id):
    """小工具：向 LINE 伺服器請求使用者暱稱"""
    if not line_bot_api:
        return "新朋友"
    try:
        profile = line_bot_api.get_profile(user_id)
        return profile.display_name
    except:
        return "新朋友"

# ----------------------------------------------------------------
# 路由設定 (Web API + LINE Webhook)
# ----------------------------------------------------------------

@app.get("/")
async def root():
    index_path = os.path.join("static", "index.html")
    if os.path.exists(index_path):
        return FileResponse(index_path, headers={"Cache-Control": "no-cache, no-store, must-revalidate"})
    return {"message": "16族智慧翻譯小幫手運行中", "base_url": config.BASE_URL}

# 🌐 Web 端點：文字翻譯
@app.post("/api/translate")
async def api_translate(req: TranslateRequest):
    query_text = (req.text or req.source_text or "").strip()
    tribe_name = (req.tribe or "太魯閣").strip()
    if not query_text:
        return {"error": "請輸入欲翻譯之內容"}

    start_time = time.time()
    res = cached_translate(query_text, tribe_name)
    duration = time.time() - start_time
    if isinstance(res, dict):
        res["duration"] = round(duration, 2)
    return res

# 🌐 Web 端點：深度文化潤飾
@app.post("/api/deep_translate")
async def api_deep_translate(req: DeepTranslateRequest):
    try:
        refined = deep_analyzer.deep_translation(
            source_text=req.source_text,
            target_text=req.target_text,
            tribe=req.tribe,
            user_api_key=req.api_key
        )
        # 🎯 回傳 is_api_key_error 旗標，讓前端精準判斷是否需要顯示「更換 API Key」按鈕
        is_api_key_error = (
            "API Key 驗證失敗" in refined or
            "API key not valid" in refined or
            "無效或已過期" in refined or
            "需要輸入 API Key" in refined
        )
        return {"result": refined, "is_api_key_error": is_api_key_error}
    except Exception as e:
        logger.error(f"Web deep_translate 錯誤: {e}")
        return {"result": f"⚠️ 深度翻譯無法回應 ({str(e)})", "is_api_key_error": False}

# 🌐 Web 端點：意見回饋
@app.post("/api/feedback")
async def api_feedback(req: FeedbackRequest):
    db_manager.log_interaction("web_user", req.tribe, "text_feedback", req.content)
    return {"status": "ok"}

@app.post("/callback")
async def callback(request: Request):
    if not handler_instance:
        raise HTTPException(status_code=400, detail="LINE Bot 服務未設定")
    signature = request.headers.get("X-Line-Signature")
    body = await request.body()
    try:
        handler_instance.handle(body.decode("utf-8"), signature)
    except InvalidSignatureError:
        raise HTTPException(status_code=400)
    return "OK"

# ----------------------------------------------------------------
# 1. 處理初次加入好友 (迎賓與暱稱紀錄)
# ----------------------------------------------------------------

@handler.add(FollowEvent)
def handle_follow(event):
    user_id = event.source.user_id
    
    # 抓取暱稱並存入資料庫
    user_nickname = get_line_nickname(user_id)
    db_manager.set_user_tribe(user_id, "太魯閣", display_name=user_nickname)
    
    # 合成 static 資料夾內的圖片網址 (對齊最新視覺設計)
    image_filename = "welcome_hero.png" 
    full_image_url = f"{config.BASE_URL}/static/{image_filename}"
    
    # 創意總監潤色版歡迎文案
    welcome_text = (
        f"Embiyax su hug！{user_nickname} 您好 ❤️\n\n"
        "我是您的專屬「16族智慧翻譯小幫手」。\n"
        "不僅能翻譯文字，還能聽懂語音、看懂圖片！\n\n"
        "👇 請點擊下方「快速選單」，開啟您的南島語言探索之旅。"
    )
    
    carousel_card = ui_templates.create_welcome_carousel(hero_image_url=full_image_url)
    
    line_bot_api.reply_message(event.reply_token, [
        TextSendMessage(text=welcome_text),
        FlexSendMessage(alt_text="歡迎使用16族智慧翻譯小幫手", contents=carousel_card)
    ])

# ----------------------------------------------------------------
# 2. 處理互動按鈕與回饋
# ----------------------------------------------------------------

@handler.add(PostbackEvent)
def handle_postback(event):
    user_id = event.source.user_id
    current_tribe = db_manager.get_user_tribe(user_id)
    data = event.postback.data
    
    if data == "feedback_good":
        db_manager.log_feedback(user_id, current_tribe, "good")
        line_bot_api.reply_message(event.reply_token, TextSendMessage(text="✅ 感謝您的肯定！這將幫助我們將模型訓練得更好。"))
    elif data == "feedback_bad":
        db_manager.log_feedback(user_id, current_tribe, "bad")
        line_bot_api.reply_message(event.reply_token, TextSendMessage(text="📝 感謝您的回饋！我們已將此翻譯標記，將持續優化品質。"))

# ----------------------------------------------------------------
# 3. 處理多媒體 (影片/圖片/語音)
# ----------------------------------------------------------------

@handler.add(MessageEvent, message=VideoMessage)
def handle_video(event):
    user_id = event.source.user_id
    msg_id = event.message.id
    current_tribe = db_manager.get_user_tribe(user_id)
    show_loading_animation(user_id, 30)
    
    video_path = f"static/{msg_id}.mp4"
    audio_path = f"static/{msg_id}_extracted.wav"

    try:
        message_content = line_bot_api.get_message_content(msg_id)
        with open(video_path, 'wb') as fd:
            for chunk in message_content.iter_content(): fd.write(chunk)

        if video_processor.extract_audio(video_path, audio_path):
            recognized_text = audio_processor.speech_to_text(audio_path, current_tribe)
            if recognized_text:
                res = cached_translate(recognized_text, current_tribe)
                reply_text = f"🎬 【{current_tribe}語影片辨識】\n━━━━━━━━━━━━━━\n原文：\n{recognized_text}\n\n翻譯結果：\n{res.get('target', '翻譯失敗')}"
                db_manager.log_interaction(user_id, current_tribe, "video", recognized_text)
            else:
                reply_text = f"⚠️ 抱歉，影片中的{current_tribe}語音量太小或不夠清晰。"
        else:
            reply_text = "❌ 影片音軌提取失敗，請檢查檔案格式。"
        
        line_bot_api.reply_message(event.reply_token, TextSendMessage(text=reply_text))
    except Exception as e:
        line_bot_api.reply_message(event.reply_token, TextSendMessage(text=f"影片處理錯誤: {e}"))
    finally:
        for p in [video_path, audio_path]:
            if os.path.exists(p): os.remove(p)

@handler.add(MessageEvent, message=ImageMessage)
def handle_image(event):
    user_id = event.source.user_id
    msg_id = event.message.id
    current_tribe = db_manager.get_user_tribe(user_id)
    show_loading_animation(user_id, 10)
    
    image_path = f"static/{msg_id}.jpg"
    try:
        message_content = line_bot_api.get_message_content(msg_id)
        with open(image_path, 'wb') as fd:
            for chunk in message_content.iter_content(): fd.write(chunk)

        # ⏱️ 啟動唯一的一次計時
        start_time = time.time()
        
        # 💡 呼叫一體化模組
        result = image_processor.extract_and_translate(image_path, current_tribe)
        
        duration = time.time() - start_time

        if result and "items" in result:
            originals = [i['original'] for i in result['items']]
            translates = [i['translated'] for i in result['items']]
            
            reply_text = (
                f"👁️ 【視覺辨識結果】總耗時 {duration:.2f}s\n"
                f"━━━━━━━━━━━━━━\n"
                f"📝 擷取原文：\n" + "\n".join(originals) + "\n\n"
                f"🏹 【{current_tribe}語翻譯】：\n" + "\n".join(translates)
            )
        else:
            reply_text = f"⚠️ 辨識結束 (耗時 {duration:.2f}s)，未能偵測文字。"

        line_bot_api.reply_message(event.reply_token, TextSendMessage(text=reply_text))
        
    except Exception as e:
        line_bot_api.reply_message(event.reply_token, TextSendMessage(text="❌ 處理失敗"))
    finally:
        if os.path.exists(image_path): os.remove(image_path)

@handler.add(MessageEvent, message=AudioMessage)
def handle_audio(event):
    user_id = event.source.user_id
    msg_id = event.message.id
    current_tribe = db_manager.get_user_tribe(user_id)
    show_loading_animation(user_id, 15)
    
    audio_path = f"static/{msg_id}.m4a"
    try:
        message_content = line_bot_api.get_message_content(msg_id)
        with open(audio_path, 'wb') as fd:
            for chunk in message_content.iter_content(): fd.write(chunk)

        recognized_text = audio_processor.speech_to_text(audio_path, current_tribe)
        if recognized_text:
            res = cached_translate(recognized_text, current_tribe)
            reply_text = (
                f"🎤 【{current_tribe}語音辨識】\n━━━━━━━━━━━━━━\n"
                f"聽寫原文：\n{recognized_text}\n\n"
                f"中文翻譯：\n{res.get('target', '翻譯失敗')}"
            )
            db_manager.log_interaction(user_id, current_tribe, "audio", recognized_text)
        else:
            reply_text = f"⚠️ 無法聽辨{current_tribe}語。"
        
        line_bot_api.reply_message(event.reply_token, TextSendMessage(text=reply_text))
    except Exception as e:
        line_bot_api.reply_message(event.reply_token, TextSendMessage(text=f"語音處理失敗: {e}"))
    finally:
        if os.path.exists(audio_path): os.remove(audio_path)

# ----------------------------------------------------------------
# 4. 文字訊息處理 (核心邏輯)
# ----------------------------------------------------------------

@handler.add(MessageEvent, message=TextMessage)
def handle_message(event):
    user_text = event.message.text.strip()
    user_id = event.source.user_id
    current_tribe = db_manager.get_user_tribe(user_id)

    # ---------------------------------------------------------
    # 💡 意見回饋處理邏輯
    # ---------------------------------------------------------
    if user_text.startswith("建議："):
        feedback_content = user_text.replace("建議：", "").strip()
        db_manager.log_interaction(user_id, current_tribe, "text_feedback", feedback_content)
        reply_msg = "💖 感謝您的寶貴建議！\n開發團隊已經收到您的留言，我們會持續讓翻譯官變得更好！"
        line_bot_api.reply_message(event.reply_token, TextSendMessage(text=reply_msg))
        return

    if user_text == "我要留言":
        reply_msg = "💡 請直接在對話框輸入您的建議！\n\n(請記得以「建議：」開頭，例如：建議：字體可以大一點嗎？)"
        line_bot_api.reply_message(event.reply_token, TextSendMessage(text=reply_msg))
        return
        
# ---------------------------------------------------------
    # 💡 執行長最新版 Rich Menu (2x3 六項配置對接)
    # ---------------------------------------------------------
    
    # 1. 切換族語 (上排左)
    if user_text in ["切換族語", "選單", "切換專屬族語"]:
        show_loading_animation(user_id, 3)
        menu = ui_templates.create_tribe_menu() 
        line_bot_api.reply_message(event.reply_token, FlexSendMessage(alt_text="請選擇族語模式", contents=menu))
        return

    # 2. 意見回饋 (上排中)
    if user_text in ["意見回饋", "填寫意見回饋"]:
        feedback_ui = ui_templates.create_feedback_card()
        line_bot_api.reply_message(event.reply_token, FlexSendMessage(alt_text="意見回饋", contents=feedback_ui))
        return

# 4. 構詞語法分析 (下排left)
    if user_text in ["構詞語法分析", "語法分析", "什麼是構詞語法分析", "什麼是構詞語法分析？"]:
        # 🎯 解決 UnboundLocalError：直接從已經通車的 deep_analyzer 借調 supabase 客戶端
        from deep_analyzer import supabase
        current_tribe = "太魯閣"  # 安全防禦預設值
        try:
            pref_res = supabase.table("user_preferences").select("tribe").eq("user_id", user_id).execute()
            if pref_res.data:
                current_tribe = pref_res.data[0]["tribe"]
        except Exception as e:
            logger.error(f"選單介紹圖卡即時撈取族語偏好失敗: {e}")
            
        card = ui_templates.create_glossing_intro_card(current_tribe)
        line_bot_api.reply_message(event.reply_token, FlexSendMessage(alt_text="構詞語法分析介紹", contents=card))
        return

    # 5. 多媒體翻譯 (下排中)
    if user_text in ["多媒體翻譯", "拍照翻譯", "什麼是多媒體翻譯", "什麼是多媒體翻譯？"]:
        # 🎯 解決 UnboundLocalError
        from deep_analyzer import supabase
        current_tribe = "太魯閣"
        try:
            pref_res = supabase.table("user_preferences").select("tribe").eq("user_id", user_id).execute()
            if pref_res.data:
                current_tribe = pref_res.data[0]["tribe"]
        except Exception as e:
            logger.error(f"選單介紹圖卡即時撈取族語偏好失敗: {e}")
            
        card = ui_templates.create_multimedia_intro_card(current_tribe)
        line_bot_api.reply_message(event.reply_token, FlexSendMessage(alt_text="多媒體翻譯教學", contents=card))
        return

    # 6. 深度文化翻譯 (下排right)
    if user_text in ["深度文化翻譯", "深度翻譯", "什麼是深度文化翻譯", "什麼是深度文化翻譯？"]:
        # 🎯 解決 UnboundLocalError
        from deep_analyzer import supabase
        current_tribe = "太魯閣"
        try:
            pref_res = supabase.table("user_preferences").select("tribe").eq("user_id", user_id).execute()
            if pref_res.data:
                current_tribe = pref_res.data[0]["tribe"]
        except Exception as e:
            logger.error(f"選單介紹圖卡即時撈取族語偏好失敗: {e}")
            
        card = ui_templates.create_deep_translation_intro_card(current_tribe)
        line_bot_api.reply_message(event.reply_token, FlexSendMessage(alt_text="深度文化翻譯介紹", contents=card))
        return

    # ---------------------------------------------------------
    # 💡 處理多媒體測試引導 (亮點功能導覽)
    # ---------------------------------------------------------
    if user_text == "🎙️ 我要測試語音/影片解析":
        reply_msg = (
            "👇 請直接使用 LINE 的內建功能傳送：\n\n"
            "1️⃣ 【錄製語音】：按住右下角的「🎤 麥克風」，說一段太魯閣語（例如：Embiyax su hug?）。\n\n"
            "2️⃣ 【上傳影片】：點擊左下角「➕」>「相簿」，選取一段有族語對話的短影片。\n\n"
            "傳送後，我會立刻幫您「聽寫原文」並「翻譯內容」喔！🚀"
        )
        line_bot_api.reply_message(event.reply_token, [
            TextSendMessage(text=reply_msg),
            TextSendMessage(text="💡 小提示：錄音時記得大聲一點，AI 老師才聽得清楚喔！")
        ])
        return
        
    # ---------------------------------------------------------
    # 💡 處理指令式功能
    # ---------------------------------------------------------

    if user_text.startswith("📍 切換族語："):
        tribe = user_text.replace("📍 切換族語：", "").strip()
        nickname = get_line_nickname(user_id) 
        db_manager.set_user_tribe(user_id, tribe, display_name=nickname)
        line_bot_api.reply_message(event.reply_token, TextSendMessage(text=f"✅ 已成功切換至【{tribe}語】模式！"))
        return

    if user_text.startswith("🔊 聽發音："):
        target_native = user_text.replace("🔊 聽發音：", "").strip()
        show_loading_animation(user_id, 5)
        db_manager.log_interaction(user_id, current_tribe, "tts", target_native)
        try:
            u_id = str(uuid.uuid4())
            rel_path = audio_processor.text_to_speech(target_native, current_tribe, f"tts_{u_id}")
            if config.BASE_URL and rel_path:
                audio_url = f"{config.BASE_URL}/{rel_path}"
                download_url = f"{config.BASE_URL}/download_file/tts_{u_id}.wav"
                line_bot_api.reply_message(event.reply_token, [
                    AudioSendMessage(original_content_url=audio_url, duration=2000),
                    FlexSendMessage(alt_text="儲存音檔", contents=ui_templates.create_download_button(download_url))
                ])
        except Exception as e:
            logger.error(f"TTS Error: {e}")
            line_bot_api.reply_message(event.reply_token, TextSendMessage(text="⚠️ 語音生成失敗。"))
        return

    # =================================================================
    # 💡 語法分析： 階段一 (純文字快速版 + 觸發繪圖按鈕)
    # =================================================================
    if user_text.startswith("💡 語法分析："):
        target_sentence = user_text.replace("💡 語法分析：", "").strip()
        show_loading_animation(user_id, 10) 

        try:
            start_time = time.time()  
            analysis_data = deep_analyzer.get_structured_analysis(target_sentence, current_tribe)
            duration = time.time() - start_time 
            
            logger.info(f"⏱️ [語法分析文字版] 耗時: {duration:.2f}s")
            
            # 1. 建立純文字回覆內容
            reply_text = f"📖 【{current_tribe}語 語法深度解析】\n━━━━━━━━━━━━━━\n"

            if not analysis_data.get("is_long"):
                glossing = analysis_data.get("glossing", {})
                l1 = glossing.get("line1", [])
                l2 = glossing.get("line2", [])
                l3 = glossing.get("line3", [])

                comp_text = "🎯 【逐詞分析】\n"
                for i in range(len(l1)):
                    if l1[i] and l1[i].strip():
                        # 🎴 這裡精準替換成「構詞語法(族語)」與「構詞語法(華語)」
                        comp_text += f"▪️ {l1[i]}\n   ├ 族語: {l2[i] if i < len(l2) else ''}\n   └ 華語: {l3[i] if i < len(l3) else ''}\n"
                reply_text += comp_text + "\n"
            
            reply_text += f"💡 【深入解說】\n{analysis_data.get('explanation', '無詳細說明')}\n\n"
            reply_text += f"🏹 【翻譯結果】\n{analysis_data.get('translation', target_sentence)}"

            # 2. 建立「需要四行分析圖」的按鈕卡片 (Flex Message)
            if not analysis_data.get("is_long"):
                button_card = {
                    "type": "bubble",
                    "size": "kilo",
                    "body": {
                        "type": "box", "layout": "vertical", "contents": [
                            {"type": "text", "text": "需要將以上分析轉為精美圖卡嗎？", "wrap": True, "size": "sm", "color": "#666666"}
                        ]
                    },
                    "footer": {
                        "type": "box", "layout": "vertical", "contents": [
                            {
                                "type": "button",
                                "style": "primary",
                                "color": "#2B579A",
                                "action": {
                                    "type": "message",
                                    "label": "🖼️ 生成四行分析圖卡",
                                    "text": f"🎨 繪製圖卡：{target_sentence}"
                                }
                            }
                        ]
                    }
                }
                
                # 同時發送「文字」與「按鈕」
                line_bot_api.reply_message(event.reply_token, [
                    TextSendMessage(text=reply_text),
                    FlexSendMessage(alt_text="生成圖卡選項", contents=button_card)
                ])
            else:
                # 若為長句，就不顯示繪圖按鈕（避免畫出來擠在一起）
                line_bot_api.reply_message(event.reply_token, TextSendMessage(text=reply_text))
            
            db_manager.log_interaction(user_id, current_tribe, "analysis_text", target_sentence)
            
        except Exception as e:
            logger.error(f"語法分析邏輯錯誤: {e}")
            line_bot_api.reply_message(event.reply_token, TextSendMessage(text="⚠️ 系統解析中，請稍後再試。"))
        return

    # =================================================================
    # 🎨 繪製圖卡： 階段二 (接收繪圖指令並產出圖片)
    # =================================================================
    if user_text.startswith("🎨 繪製圖卡："):
        target_sentence = user_text.replace("🎨 繪製圖卡：", "").strip()
        show_loading_animation(user_id, 15) # 畫圖需要比較久
        
        try:
            # 重新獲取一次分析資料 (為了畫圖)
            analysis_data = deep_analyzer.get_structured_analysis(target_sentence, current_tribe)
            
            # 準備圖片路徑
            img_filename = f"analysis_{uuid.uuid4().hex[:8]}.png"
            img_path = os.path.join("static", img_filename)
            
            # 呼叫美術部門畫圖
            visual_renderer.draw_glossing_card(analysis_data, img_path)
            image_url = f"{config.BASE_URL}/{img_path}"
            
            # 發送圖片
            line_bot_api.reply_message(
                event.reply_token, 
                ImageSendMessage(original_content_url=image_url, preview_image_url=image_url)
            )
            
        except Exception as e:
            logger.error(f"圖卡繪製錯誤: {e}")
            line_bot_api.reply_message(event.reply_token, TextSendMessage(text="⚠️ 抱歉，圖卡生成失敗，請確認您的 BASE_URL 網址設定是否正確。"))
        return

    # =================================================================
    # 🧠 【新增關鍵路由】核心處理：執行「深度文化翻譯潤飾」
    # =================================================================
    if "深度翻譯" in user_text and "|" in user_text:
        # 1. 移除非必要的 Emoji 標題前綴
        clean_text = user_text.replace("🧠 深度翻譯：", "").replace("深度翻譯：", "").strip()
        
        # 2. 切開中文字串與一般翻譯初稿
        parts = clean_text.split("|")
        if len(parts) == 2:
            source_text = parts[0].strip()   # 華語原文
            target_text = parts[1].strip()   # 族語初稿
            
            show_loading_animation(user_id, loading_seconds=15)
            
            try:
                # 3. 呼叫大腦進行潤飾
                refined_result = deep_analyzer.deep_translation(source_text, target_text, current_tribe)
                db_manager.log_interaction(user_id, current_tribe, "deep_translation", user_text)
                
                # 4. 直接回覆經過深度潤飾與說明的結果
                line_bot_api.reply_message(event.reply_token, TextSendMessage(text=refined_result))
                return
            except Exception as e:
                logger.error(f"深度翻譯處理失敗: {e}")
                line_bot_api.reply_message(event.reply_token, TextSendMessage(text="⚠️ 深度翻譯功能暫時無回應，請稍後再試。"))
                return

    # ---------------------------------------------------------
    # 💡 保險限制：攔截一般翻譯的過長文本 (只對一般翻譯生效)
    # ---------------------------------------------------------
    if len(user_text) > 180:
        limit_msg = (
            "⚠️ 您要翻譯的句子太長了（超過 180 字），\n"
            "AI 會沒辦法快速思考，\n"
            "麻煩您將句子調整在 180 個字以內，感謝您。❤️"
        )
        line_bot_api.reply_message(event.reply_token, TextSendMessage(text=limit_msg))
        return

    # ---------------------------------------------------------
    # 一般文字翻譯 (NLLB 模型 - 極速版)
    # ---------------------------------------------------------
    # 在開始運算前「立刻」告訴 LINE 要顯示動畫
    show_loading_animation(user_id, loading_seconds=15)

    start_time = time.time()
    res = cached_translate(user_text, current_tribe)  # 🎯 這裡已經被精準修正為 user_text 了！
    duration = time.time() - start_time
    
    logger.info(f"⏱️ [NLLB 一般翻譯效能] 輸入字數: {len(user_text)} | 耗時: {duration:.2f}s")
    db_manager.log_interaction(user_id, current_tribe, "text", user_text)
    
    target_output = res.get('target', '翻譯失敗')
    
    # 雖已有 180 字輸入限制，此處仍保留 200 字輸出保險，避免極端狀況燒毀 Token
    if len(target_output) > 200:
        line_bot_api.reply_message(
            event.reply_token, 
            TextSendMessage(text=f"🏹 【{current_tribe}語結果】\n\n{target_output}")
        )
        return

    try:
        card = ui_templates.create_translation_card(
            tribe=current_tribe, 
            source_text=user_text, 
            target_text=target_output,
            confidence_level=res.get("confidence_level", "high"),
            confidence_desc=f"耗時 {duration:.1f}s"  # 💡 讓使用者也看到我們的極速成果
        )
        line_bot_api.reply_message(event.reply_token, FlexSendMessage(alt_text="翻譯結果", contents=card))
    except Exception as e:
        logger.error(f"Flex Message 發送失敗: {e}")
        line_bot_api.push_message(user_id, TextSendMessage(text=f"🏹 【{current_tribe}語】\n{target_output}"))

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)