app.py

import gradio as gr
from openai import OpenAI
from huggingface_hub import InferenceClient
import os
import sys
import subprocess
import time
import requests
import urllib.parse
import pandas as pd
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from PIL import Image
from dotenv import load_dotenv

# ==========================================
# Step 0: 環境安裝檢查 (僅保留 Playwright)
# ==========================================
def install_playwright():
    """確保 Playwright 瀏覽器核心有安裝"""
    try:
        import playwright
    except ImportError:
        print("⚠️ 偵測到缺少 playwright，正在強制安裝...")
        subprocess.check_call([sys.executable, "-m", "pip", "install", "playwright"])
    
    # 設定瀏覽器路徑
    os.environ["PLAYWRIGHT_BROWSERS_PATH"] = os.path.join(os.getcwd(), "playwright_browsers")
    
    print("🔄 檢查 Chromium 瀏覽器...")
    try:
        # 檢查是否已安裝，若無則安裝
        if not os.path.exists(os.environ["PLAYWRIGHT_BROWSERS_PATH"]):
             subprocess.run([sys.executable, "-m", "playwright", "install", "chromium"], check=True)
    except Exception as e:
        print(f"⚠️ 瀏覽器安裝警告: {e}")

# 執行安裝
install_playwright()

# 延遲匯入
from playwright.sync_api import sync_playwright

load_dotenv()

# ==========================================
# 模組載入 (emotion 模組)
# ==========================================
import emotion

# ==========================================
# 0. 環境變數 & 1. 資料初始化
# ==========================================
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
HF_TOKEN = os.getenv("HF_TOKEN")

global_df = None
global_mood_df = None
global_retriever = None
rag_initialized = False

def init_static_data():
    """初始化靜態的心情資料：讀取外部 CSV"""
    global global_mood_df
    if global_mood_df is None:
        csv_path = 'mood_food_guide.csv'
        if os.path.exists(csv_path):
            try:
                # 讀取 CSV，確保欄位正確
                global_mood_df = pd.read_csv(csv_path)
                print(f"✅ 成功載入心情指南：{csv_path}")
            except Exception as e:
                print(f"❌ 讀取 CSV 失敗: {e}")
                global_mood_df = pd.DataFrame() # 建立空表防止後續報錯
        else:
            print(f"⚠️ 警告：找不到 {csv_path}，請確認檔案已上傳至 Space。")
            global_mood_df = pd.DataFrame()

# ==========================================
# API 金鑰檢查工具函式
# ==========================================
def check_api_key_status(name, key):
    """檢查 API Key 是否存在，並回傳部分內容以供辨識"""
    if not key:
        return "❌ 未設定 (Not Set)"
    
    # 遮罩處理，只顯示前後幾碼
    if len(key) > 8:
        masked = f"{key[:4]}...{key[-4:]}"
    else:
        masked = "******"
    return f"✅ 已設定 ({masked})"

# ==========================================
# 2. Google Maps 爬蟲功能
# ==========================================
def sync_google_maps(url):
    global global_df, global_retriever, rag_initialized
    clean_url = url.strip() if url else ""
    if not clean_url or "http" not in clean_url.lower():
        yield "❌ 請輸入有效的 Google Maps 分享連結。"
        return

    try:
        yield "🚀 [1/4] 啟動瀏覽器..."
        with sync_playwright() as p:
            browser = p.chromium.launch(headless=True)
            page = browser.new_page()
            yield "🌐 [2/4] 連線中..."
            page.goto(clean_url, wait_until="domcontentloaded", timeout=60000)
            yield "⏳ [3/4] 等待列表加載 (約 10 秒)..."
            time.sleep(10) 
            yield "📄 [4/4] 解析餐廳資訊..."
            
            titles = page.locator('div.fontHeadlineSmall').all_inner_texts()
            details = page.locator('div.fontBodyMedium').all_inner_texts()
            
            restaurant_list = []
            for i, name in enumerate(titles):
                name = name.strip()
                if name:
                    addr = details[i].strip() if i < len(details) else ""
                    search_query = f"{name} {addr}"
                    restaurant_list.append({
                        "Name": name,
                        "Address": addr,
                        "Category": "未分類",
                        "RAG_Content": f"餐廳：{name}，資訊：{addr}",
                        "URL": f"https://www.google.com/maps/search/?api=1&query={urllib.parse.quote(search_query)}"
                    })
            browser.close()

            if not restaurant_list:
                yield "⚠️ 找不到餐廳。請確認連結格式正確且已公開。"
                return

            global_df = pd.DataFrame(restaurant_list)
            
            try:
                embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
                vectorstore = FAISS.from_texts(global_df['RAG_Content'].tolist(), embeddings, metadatas=global_df.to_dict('records'))
                global_retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
            except Exception as e:
                print(f"RAG 初始化警告: {e}")

            rag_initialized = True
            yield f"✅ 同步成功！已載入 {len(restaurant_list)} 間餐廳。"
    except Exception as e:
        yield f"❌ 系統錯誤：{str(e)}"

# ==========================================
# 3. 核心功能 (AI Agent)
# ==========================================
def get_restaurant_data(mood_score_str, food_choice):
    init_static_data() # 確保心情指南已載入
    
    if global_df is None or global_df.empty: 
        return None, True, "", "資料庫未載入，無建議"
    
    # 解析分數 (例如 "3 (普通)" -> 3)
    try: score = int(str(mood_score_str).split(' ')[0])
    except: score = 3
    
    # 從 CSV 資料中查找對應的推薦類別與原因
    rec_categories = ""
    mood_reason = "隨意探索"
    
    if global_mood_df is not None and not global_mood_df.empty:
        mood_info = global_mood_df[global_mood_df['分數'] == score]
        if not mood_info.empty:
            rec_categories = mood_info.iloc[0]['推薦料理類別']
            mood_reason = mood_info.iloc[0]['原因']
    
    candidates = global_df.copy()
    food_keyword = "飯" if food_choice == "吃飯" else "麵" if food_choice == "吃麵" else ""
    
    is_random = False
    if food_keyword:
        filtered = candidates[candidates['Name'].str.contains(food_keyword, case=False, na=False) | candidates['RAG_Content'].str.contains(food_keyword, case=False, na=False)]
        if not filtered.empty:
            candidates = filtered
        else:
            is_random = True 
    
    if candidates.empty:
        result = global_df.sample(1).iloc[0]; is_random = True
    else:
        result = candidates.sample(1).iloc[0]
        
    return result, is_random, rec_categories, mood_reason

def generate_content_with_groq(restaurant_name, restaurant_detail, user_diary, mood_score, mood_guide_reason, debug_mode=False):
    if not GROQ_API_KEY: return "⚠️ 請設定 GROQ_API_KEY", ""
    
    client = OpenAI(api_key=GROQ_API_KEY, base_url="https://api.groq.com/openai/v1")
    
    system_prompt = "你是一個幽默、懂吃且善解人意的 AI 朋友。請根據使用者的日記、心情以及「心情美食指南」來推薦餐廳。"
    user_msg = f"""
        【狀態】心情分數：{mood_score}，日記：{user_diary}
        【心情美食指南建議】
        因為分數是 {mood_score}，建議吃這類食物的原因是：「{mood_guide_reason}」。
        
        【推薦餐廳】
        名稱：{restaurant_name}
        資料：{restaurant_detail}
        
        任務：
        請用繁體中文寫一段溫暖有趣的回覆：
        1. 先回應他的日記與測驗人設。
        2. 引用「心情美食指南」的原因，告訴他為什麼現在適合吃這家餐廳。
        3. 介紹這家餐廳的特色。
    """
    
    debug_log = ""
    if debug_mode:
        debug_log = f"### 🔧 Groq Prompt Debug\n**System:** {system_prompt}\n**User:** {user_msg}"

    try:
        response = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_msg}])
        return response.choices[0].message.content, debug_log
    except Exception as e: 
        return f"Groq Error: {str(e)}", debug_log

def generate_image_huggingface(prompt):
    if not HF_TOKEN: 
        return None, "HF_TOKEN 未設定"
    
    try:
        hf_client = InferenceClient(token=HF_TOKEN)
        image = hf_client.text_to_image(
            prompt=prompt, 
            negative_prompt="blurry, low quality, distortion, text, watermark",
            model="stabilityai/stable-diffusion-xl-base-1.0"
        )
        return image, None # 成功時，錯誤訊息為 None
    except Exception as e:
        # 回傳具體錯誤訊息
        return None, str(e)

def mood_agent_logic(score_input, food_input, diary_input, debug_mode):
    if not rag_initialized:
        yield "⚠️ 請先同步地圖清單！", None, "", gr.update()
        return
    
    restaurant, is_random, rec_categories, mood_reason = get_restaurant_data(score_input, food_input)
    if restaurant is None: 
        yield "資料庫讀取錯誤", None, "", gr.update()
        return
    
    name = restaurant['Name']; url = restaurant['URL']
    info = restaurant.get('RAG_Content', '')
    rag_info = str(restaurant.get('RAG_Content', ''))
    
    img_prompt = f"Delicious food photography of {name}, {info}. high quality, photorealistic, 8k, cinematic lighting, appetizing, restaurant atmosphere, 50mm lens"
    
    ai_text, groq_debug_log = generate_content_with_groq(name, rag_info, diary_input, score_input, mood_reason, debug_mode)
    
    prefix = ""
    if is_random and food_input != "隨便":
        prefix = f"> 💡 **溫馨提示**：清單中暫無『{food_input}』，已從現有名單挑選最適合的店！\n\n"

    # 組合完整的除錯資訊，包含 API Key 狀態與錯誤訊息
    full_debug_log = ""
    if debug_mode:
        # 1. 檢查 Key 狀態
        groq_status = check_api_key_status("GROQ_API_KEY", GROQ_API_KEY)
        hf_status = check_api_key_status("HF_TOKEN", HF_TOKEN)
        
        api_debug_block = f"""
### 🔑 API 金鑰與系統狀態
- **GROQ_API_KEY**: {groq_status}
- **HF_TOKEN**: {hf_status}
- **RAG 狀態**: {"✅ 已初始化" if rag_initialized else "❌ 未初始化"}
"""
        full_debug_log = api_debug_block + "\n" + groq_debug_log + f"\n\n### 🎨 Image Prompt Debug\n{img_prompt}"

    debug_output_update = gr.update(value=full_debug_log, visible=debug_mode)
    
    final_response = f"{prefix}### 🍽️ 推薦：{name}\n\n{ai_text}"
    map_html = f'<div style="text-align:center"><a href="{url}" target="_blank" style="background:#4CAF50;color:white;padding:8px 16px;border-radius:20px;text-decoration:none">🗺️ Google Map 導航</a></div>'
    
    # 先回傳文字
    yield final_response, None, map_html, debug_output_update
    
    # 呼叫圖片生成並捕捉錯誤
    image_output, img_error = generate_image_huggingface(img_prompt)
    
    # 如果有圖片錯誤且在除錯模式，追加錯誤訊息到 log
    if img_error and debug_mode:
        full_debug_log += f"\n\n⚠️ **Hugging Face 圖片生成失敗:**\n{img_error}"
        debug_output_update = gr.update(value=full_debug_log)
        
    yield final_response, image_output, map_html, debug_output_update
    
# ==========================================
# 4. 橋接邏輯
# ==========================================
def _score_to_radio_value(score):
    mapping = {1: "1 (心情差)", 2: "2 (不太好)", 3: "3 (普通)", 4: "4 (不錯)", 5: "5 (超棒)"}
    try: score = int(score)
    except: score = 3
    return mapping.get(score, "3 (普通)")

def bridge_start_click(st):
    try:
        res = emotion.on_restart(st)
        return res[1], res[0], gr.update(visible=True), res[5], res[4], gr.update(visible=False)
    except: return gr.update(), gr.update(), gr.update(), gr.update(), st, gr.update()

def bridge_stop_click(st):
    try:
        res = emotion.on_stop(st)
        return res[1], res[0], gr.update(visible=False), res[5], res[4]
    except: return gr.update(), gr.update(), gr.update(), gr.update(), st

def bridge_predict_frame(frame, st):
    try:
        res = emotion.on_stream(frame, st)
        out_cam = res[0]; out_result = res[1]; out_st = res[4]; out_btn_start = res[5]
        score_update = gr.update(); btn_go_visible = gr.update(visible=False)
        out_btn_stop = gr.update()

        if hasattr(out_st, 'finished') and out_st.finished and hasattr(out_st, 'final_score'):
            new_val = _score_to_radio_value(out_st.final_score)
            score_update = gr.update(value=new_val)
            out_btn_stop = gr.update(visible=False)
            btn_go_visible = gr.update(visible=True)
            
        return out_cam, out_result, out_st, out_btn_stop, out_btn_start, score_update, btn_go_visible
    except Exception as e:
        return frame, gr.update(), st, gr.update(), gr.update(), gr.update(), gr.update()

def bridge_predict_upload(img, st):
    try:
        res = emotion.on_upload(img, st)
        out_result, out_st = res[0], res[2]
        score_update = gr.update()
        if hasattr(out_st, 'finished') and out_st.finished and hasattr(out_st, 'final_score'):
            new_val = _score_to_radio_value(out_st.final_score)
            score_update = gr.update(value=new_val)
        return out_result, score_update, gr.Tabs(selected=1), out_st
    except: return gr.update(), gr.update(), gr.Tabs(), st

# ==========================================
# 5. Gradio 介面
# ==========================================
css_ = "#app_container { max-width: 960px; margin: 0 auto; }" 
if hasattr(emotion, 'css'): css_ += "\n" + emotion.css

with gr.Blocks(title="AI 心情食堂", css=css_) as demo:
    st_state = gr.State(emotion.AppState())

    with gr.Tabs() as tabs:
        # Tab 1
        with gr.TabItem("😊 情緒辨識 (Step 1)", id=0):
            with gr.Column(elem_id="app_container"):
                gr.Markdown("### 第一步：測測你的心情能量\n讓 AI 看看你的表情，自動幫你決定心情分數！")
                with gr.Row():
                    btn_start = gr.Button("📸 開啟攝影機辨識", variant="primary")
                    btn_stop  = gr.Button("⏹️ 停止", variant="secondary", visible=False)
                cam = gr.Image(sources=["webcam"], streaming=True, type="numpy", label="攝影機畫面", visible=False)
                result_markdown = gr.Markdown(emotion._hint_html("請按「開啟攝影機辨識」並允許瀏覽器使用相機。"))
                btn_go_dining = gr.Button("🚀 確定心情，來找餐廳！", variant="primary", visible=False, size="lg")

        # Tab 2
        with gr.TabItem("🍽️ AI 心情食堂 (Step 2)", id=1):
            with gr.Column():
                gr.Markdown(f"## 🔗 載入你的口袋名單")
                with gr.Row():
                    map_url = gr.Textbox(label="Google Maps Saved Lists 連結", placeholder="請貼上清單的分享連結...", scale=3)
                    sync_btn = gr.Button("🔄 同步清單", variant="secondary", scale=1)
                sync_msg = gr.Markdown("ℹ️ 尚未同步資料庫")
                gr.Markdown("---")
                gr.Markdown(f"## 🍱 今天想吃點什麼？")
                with gr.Row():
                    with gr.Column(scale=1):
                        score_input = gr.Radio(["1 (心情差)", "2 (不太好)", "3 (普通)", "4 (不錯)", "5 (超棒)"], label="1. 心情分數 (由 Tab 1 自動填入)", value="3 (普通)")
                        food_input = gr.Radio(["吃飯", "吃麵", "隨便"], label="2. 想吃什麼", value="隨便")
                        diary_input = gr.Textbox(lines=4, label="3. 心情日記", placeholder="寫下今天發生的事...")
                        
                        debug_mode_btn = gr.Checkbox(label="🔧 開啟除錯模式", value=False)
                        submit_btn = gr.Button("🍱 送出給 Agent", variant="primary")
                        debug_output = gr.Markdown(label="除錯資訊 (Debug Log)", visible=False)
                    
                    with gr.Column(scale=1):
                        agent_output = gr.Markdown(label="AI 回應")
                        image_output = gr.Image(label="AI 推薦美食圖", type="pil", width=400)
                        map_output = gr.HTML(label="地圖導航")

    # 事件
    sync_btn.click(fn=sync_google_maps, inputs=[map_url], outputs=[sync_msg])
    btn_start.click(fn=bridge_start_click, inputs=[st_state], outputs=[result_markdown, cam, btn_stop, btn_start, st_state, btn_go_dining])
    btn_stop.click(fn=bridge_stop_click, inputs=[st_state], outputs=[result_markdown, cam, btn_stop, btn_start, st_state])
    cam.stream(fn=bridge_predict_frame, inputs=[cam, st_state], outputs=[cam, result_markdown, st_state, btn_stop, btn_start, score_input, btn_go_dining])
    btn_go_dining.click(fn=lambda: gr.Tabs(selected=1), inputs=None, outputs=tabs)
    
    # debug_mode
    submit_btn.click(
        fn=mood_agent_logic, 
        inputs=[score_input, food_input, diary_input, debug_mode_btn], 
        outputs=[agent_output, image_output, map_output, debug_output]
    )

if __name__ == "__main__":
    demo.launch(ssr_mode=False)