app.py

# app.py
import os
import uuid
import requests
import logging
import time  # 💡 導入時間模組進行效能測試
from fastapi import FastAPI, Request, HTTPException
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
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_api = LineBotApi(config.LINE_TOKEN)
handler = WebhookHandler(config.LINE_SECRET)

# ----------------------------------------------------------------
# 🔮 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"""
    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"""
    try:
        profile = line_bot_api.get_profile(user_id)
        return profile.display_name
    except:
        return "新朋友"

# ----------------------------------------------------------------
# 路由設定
# ----------------------------------------------------------------

@app.get("/")
async def root():
    return {"message": "16族智慧翻譯小幫手運行中", "base_url": config.BASE_URL}

@app.post("/callback")
async def callback(request: Request):
    signature = request.headers.get("X-Line-Signature")
    body = await request.body()
    try:
        handler.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)