Delete app.py

app.py

@@ -1,405 +0,0 @@
-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)