Delete app.py

app.py

@@ -1,255 +0,0 @@
-import gradio as gr
-from openai import OpenAI
-from huggingface_hub import InferenceClient
-import os
-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
-
-# --- [新增] 匯入組員的模組 ---
-import teammate_logic 
-# ---------------------------
-
-load_dotenv()  # 自動尋找並載入 .env 檔案中的變數
-
-# ==========================================
-# 0. 環境變數
-# ==========================================
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-# 全域變數
-global_df = None
-global_mood_df = None
-global_retriever = None
-rag_initialized = False
-
-# ==========================================
-# 1. 系統初始化 (讀取 CSV + 讀取 FAISS)
-# ==========================================
-def init_rag_system():
-    global global_df, global_mood_df, global_retriever, rag_initialized
-    
-    if rag_initialized: return
-
-    print("⏳ 正在初始化系統...")
-    
-    # --- A. 讀取 CSV (用於篩選與對照) ---
-    try:
-        global_df = pd.read_csv('restaurants.csv')
-        global_df['RAG_Content'] = global_df['RAG_Content'].fillna("")
-        global_df['Category'] = global_df['Category'].fillna("其他")
-        
-        global_mood_df = pd.read_csv('mood_food_guide.csv')
-        print(f"✅ CSV 資料讀取成功 (餐廳: {len(global_df)} 筆)")
-    except Exception as e:
-        print(f"❌ CSV 讀取失敗: {e}")
-        return
-
-    # --- B. 讀取預先建立好的 FAISS 索引 ---
-    if os.path.exists("faiss_index"):
-        try:
-            print("⏳ 正在載入 FAISS 向量資料庫...")
-            embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-            
-            vectorstore = FAISS.load_local(
-                "faiss_index", 
-                embeddings, 
-                allow_dangerous_deserialization=True
-            )
-            global_retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
-            print("✅ FAISS 資料庫載入完成！")
-        except Exception as e:
-            print(f"❌ FAISS 載入失敗: {e}")
-    else:
-        print("⚠️ 警告：找不到 'faiss_index' 資料夾，RAG 功能將無法使用。")
-        print("請先執行 build_index.py 並上傳資料夾。")
-
-    rag_initialized = True
-
-# ==========================================
-# 2. 核心功能
-# ==========================================
-
-def get_restaurant_data(mood_score_str, food_choice):
-    init_rag_system()
-    if global_df is None or global_df.empty: return None, True, "資料庫未載入", "無建議"
-    
-    try:
-        score = int(str(mood_score_str).split(' ')[0])
-    except:
-        score = 3
-    
-    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']
-    else:
-        rec_categories = ""
-        mood_reason = "隨意探索"
-
-    candidates = global_df.copy()
-    
-    if rec_categories:
-        candidates = candidates[candidates['Category'].apply(lambda x: str(x) in str(rec_categories) or str(rec_categories) in str(x))]
-        
-    food_keyword = "飯" if food_choice == "吃飯" else "麵" if food_choice == "吃麵" else ""
-    if food_keyword:
-        candidates = candidates[
-            candidates['Name'].str.contains(food_keyword, case=False, na=False) | 
-            candidates['RAG_Content'].str.contains(food_keyword, case=False, na=False)
-        ]
-    
-    if candidates.empty:
-        result = global_df.sample(1).iloc[0]
-        is_random = True
-    else:
-        result = candidates.sample(1).iloc[0]
-        is_random = False
-        
-    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。"
-    
-    # 移除了測驗相關的 user_msg 內容
-    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. 綜合回應他的日記與心情。
-    2. 引用「心情美食指南」的原因。
-    3. 介紹這家餐廳的特色。
-    (只��要回覆文字內容)
-    """
-    
-    try:
-        response = client.chat.completions.create(
-            model="llama-3.3-70b-versatile", messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_msg}]
-        )
-        content = response.choices[0].message.content
-        
-        # 如果開啟除錯模式，附加 Prompt 資訊
-        if debug_mode:
-            debug_info = f"""
-            \n\n--- 🛠️ [DEBUG] LLM Prompt 檢查 ---
-            \n**System Prompt:**\n{system_prompt}
-            \n**User Message:**\n{user_msg}
-            \n-----------------------------------
-            """
-            return content + debug_info
-            
-        return content
-    except Exception as e:
-        return f"Groq Error: {str(e)}"
-
-def generate_image_huggingface(prompt):
-    if not HF_TOKEN: return None
-    if not prompt or pd.isna(prompt): prompt = "Delicious gourmet food, photorealistic, 8k"
-        
-    try:
-        hf_client = InferenceClient(token=HF_TOKEN)
-        return hf_client.text_to_image(prompt=prompt, model="stabilityai/stable-diffusion-xl-base-1.0")
-    except Exception as e:
-        print(f"❌ 生圖失敗: {e}")
-        return None
-
-def mood_agent_logic(score_input, food_input, diary_input, debug_mode):
-    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, ""
-        return
-
-    name = restaurant['Name']
-    address = restaurant['Address']
-    url = restaurant['URL']
-    img_prompt = restaurant.get('Visual_prompt') 
-    note = "(隨機推薦)" if is_random else ""
-
-    # RAG 檢索
-    rag_info = str(restaurant.get('RAG_Content', ''))
-    if global_retriever:
-        docs = global_retriever.invoke(name)
-        if docs:
-            rag_info = "\n".join([d.page_content for d in docs])
-
-    # 呼叫生成函式 (已移除 quiz_state 參數)
-    ai_text = generate_content_with_groq(name, rag_info, diary_input, score_input, mood_reason, debug_mode)
-    
-    # 如果開啟除錯模式，附加圖片 Prompt 資訊
-    if debug_mode:
-        img_debug_info = f"""
-        \n\n--- 🛠️ [DEBUG] 圖片生成檢查 ---
-        \n**使用的 Visual Prompt:**\n{img_prompt}
-        \n(如果上方圖片為空白，可能是 HF API 忙碌或 Prompt 無效)
-        \n-----------------------------------
-        """
-        ai_text += img_debug_info
-
-    final_response = f"### 🍽️ 推薦：{name} {note}\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>'
-    
-    yield final_response, None, map_html
-    image_output = generate_image_huggingface(img_prompt)
-    yield final_response, image_output, map_html
-
-# ==========================================
-# 3. Gradio 介面建構
-# ==========================================
-
-# 結合組員的 CSS
-combined_css = teammate_logic.css 
-
-with gr.Blocks(title="AI 心情食堂", css=combined_css) as demo:
-    
-    with gr.Tabs():
-        # Tab 1: 主功能區 (已移除 Q1-Q5，直接顯示點餐介面)
-        with gr.TabItem("🍽️ AI 心情食堂 (點餐推薦)", id=0):
-            with gr.Column():
-                gr.Markdown(f"## 🍱 呷飽沒？？！")
-                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", 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 (顯示 Prompt 與圖片資訊)", value=False)
-                        
-                        submit_btn = gr.Button("送出給 Agent", variant="primary")
-                    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")
-
-        # Tab 2: 組員功能
-        with gr.TabItem("情緒辨識 (組員功能)", id=1):
-            teammate_logic.render_ui()  # 呼叫組員的介面渲染函式
-
-    # 事件綁定 (只保留主功能)
-    submit_btn.click(
-        fn=mood_agent_logic,
-        inputs=[score_input, food_input, diary_input, debug_mode_btn],
-        outputs=[agent_output, image_output, map_output]
-    )
-
-if __name__ == "__main__":
-    demo.launch(ssr_mode=False)