Blame for app.py

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_community.document_loaders import DataFrameLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from PIL import Image
import warnings

# 忽略 pandas 的一些警告
warnings.filterwarnings("ignore")

# ==========================================
# 0. 環境變數與 RAG 初始化
# ==========================================
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
HF_TOKEN = os.getenv("HF_TOKEN")

# --- 1. 資料讀取 (主要資料庫) ---
df = pd.DataFrame() 
try:
    df = pd.read_csv('google_maps_rag_data.csv', engine='python')
    df.columns = df.columns.str.strip() # 清理欄位空白

    def classify_mood(row):
        name_str = str(row.get('Name', ''))
        rag_str = str(row.get('RAG_Content', ''))
        text = (name_str + " " + rag_str).lower()
        tags = []
        rules = {
            "開心/慶祝": ["牛排", "steak", "pizza", "炸", "雞排", "甜點", "蛋糕", "cake", "冰", "waffle", "吃到飽", "buffet", "burger", "bistro", "餐酒館"],
            "傷心/疲憊": ["粥", "湯", "warm", "congee", "麵", "noodle", "小吃", "comfort food", "豆花", "關東煮", "soup"],
            "生氣/發洩": ["辣", "spicy", "麻辣", "鍋", "curry", "咖哩", "燒肉", "bbq", "臭豆腐", "fry"],
            "平靜/放鬆": ["cafe", "coffee", "tea", "茶", "素食", "vegetable", "早午餐", "brunch", "壽司", "sushi", "居酒屋"]
        }
        for mood, keywords in rules.items():
            for kw in keywords:
                if kw in text:
                    tags.append(mood)
        if not tags: tags.append("隨意/探索")
        return ", ".join(list(set(tags)))
        
    if 'Mood_Tags' not in df.columns:
        df['Mood_Tags'] = df.apply(classify_mood, axis=1)
except Exception as e:
    print(f"⚠️ 讀取 google_maps_rag_data.csv 失敗: {e}")

# --- 2. 資料讀取 (Prompt 資料庫) ---
df_prompts = pd.DataFrame()
PROMPT_COL_NAME = 'Image_Prompt' 

try:
    df_prompts = pd.read_csv('restaurants_prompts_v6.csv', engine='python')
    df_prompts.columns = df_prompts.columns.str.strip()
    
    if 'Name' in df_prompts.columns:
        df_prompts.set_index('Name', inplace=True)
    else:
        print(f"⚠️ Prompts CSV 警告: 找不到 'Name' 欄位")

    if PROMPT_COL_NAME not in df_prompts.columns:
        print(f"⚠️ Prompts CSV 警告: 找不到 '{PROMPT_COL_NAME}' 欄位")

except FileNotFoundError:
    print("⚠️ 找不到 restaurants_prompts_v6.csv")
except Exception as e:
    print(f"⚠️ 讀取 Prompt CSV 發生錯誤: {e}")


# --- RAG 建置 ---
retriever = None
if not df.empty and 'RAG_Content' in df.columns:
    try:
        loader = DataFrameLoader(df, page_content_column="RAG_Content")
        documents = loader.load()
        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
        docs = text_splitter.split_documents(documents)
        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
        vectorstore = FAISS.from_documents(docs, embeddings)
        retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
    except Exception as e:
        print(f"⚠️ RAG 初始化失敗: {e}")

# ==========================================
# 1. 小測驗設定
# ==========================================
custom_css = """
.vertical-radio fieldset {
    display: flex !important;
    flex-direction: column !important;
    align-items: flex-start !important;
    gap: 12px !important;
}
.vertical-radio label {
    width: 100% !important;
    margin: 0 !important;
    display: flex !important;
}
"""

img_path_1 = "images/image_0.png"
img_path_2 = "images/image_1.png"
if not os.path.exists("images"): os.makedirs("images")
try:
    if not os.path.exists(img_path_1): Image.new('RGB', (400, 300), color='gray').save(img_path_1)
    if not os.path.exists(img_path_2): Image.new('RGB', (400, 300), color='lightblue').save(img_path_2)
except: pass

q1_options_map = {
    "不​要​再​問白癡​問題​了​我​只​想​吃飯​": "現實主義者 (只想吃飯)",
    "去​過隱​居​生活": "隱士 (嚮往平靜)",
    "加入​禁衛軍，​保衛​這個​世界": "守護者 (充滿正義感)",
    "神​羅​天​征，毀滅這個​世界​": "破壞神 (心情可能很差或很中二)"
}

q2_options_map = {
    "不​要​再​問白癡​問題​了​我​只​想​吃飯​": "無魔法 (飢餓度MAX)",
    "麵​包形狀​的​魔法​炸彈": "爆炸魔法 (喜歡刺激/重口味)",
    "毒氣​的​生化​魔法": "毒氣魔法 (可能想吃臭豆腐或特殊風味)",
    "領域​展開:​無量​空處​": "領域展開 (思緒混亂或想放空)"
}

# ==========================================
# 2. 核心功能函式
# ==========================================

def get_restaurant_data(mood_score_str, food_choice):
    if df.empty: return None, True
    score_map = {
        "1 (心情差)": ["傷心/疲憊", "生氣/發洩"],
        "2 (不太好)": ["傷心/疲憊", "生氣/發洩"],
        "3 (普通)": ["平靜/放鬆", "隨意/探索"],
        "4 (不錯)": ["開心/慶祝", "平靜/放鬆"],
        "5 (超棒)": ["開心/慶祝"]
    }
    target_moods = score_map.get(mood_score_str, [])
    food_keyword = ""
    if food_choice == "吃飯": food_keyword = "飯"
    elif food_choice == "吃麵": food_keyword = "麵"

    candidates = df.copy()
    if 'Mood_Tags' not in candidates.columns: return df.sample(1).iloc[0], True

    if target_moods:
        pattern = "|".join(target_moods)
        candidates = candidates[candidates['Mood_Tags'].str.contains(pattern, regex=True, na=False)]
    
    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: return df.sample(1).iloc[0], True
    return candidates.sample(1).iloc[0], False

def generate_content_with_groq(restaurant_name, restaurant_detail, user_diary, mood_score, quiz_result):
    if not GROQ_API_KEY: return "⚠️ 請設定 GROQ_API_KEY (系統變數)"
    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「中二病測驗結果」來推薦餐廳。"
    user_msg = f"""
    【使用者狀態】
    心情分數：{mood_score}
    心情日記：{user_diary}
    【前測小遊戲結果】
    隱藏人設：{quiz_result.get('q1', '未知')}
    魔法：{quiz_result.get('q2', '未知')}
    【推薦餐廳】
    名稱：{restaurant_name}
    資料：{restaurant_detail}
    請完成任務：
    【回應內容】：用繁體中文寫一段溫暖有趣的回覆。
       - 結合「日記」與「前測人設」給予回應。
       - 推薦上述餐廳，說明為什麼這家店適合他。
    """
    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
    except Exception as e:
        return f"Groq Error: {str(e)}"

def generate_image_huggingface(prompt):
    if not HF_TOKEN: return None
    try:
        hf_client = InferenceClient(token=HF_TOKEN)
        return hf_client.text_to_image(prompt=prompt, model="stabilityai/stable-diffusion-xl-base-1.0")
    except: return None

# --- 主邏輯 Agent (新增 debug_mode 參數) ---
def mood_agent_logic(score_input, food_input, diary_input, quiz_state, debug_mode):
    restaurant, is_random = get_restaurant_data(score_input, food_input)
    if restaurant is None:
        yield "資料庫讀取錯誤或為空", None, ""
        return

    name = str(restaurant['Name'])
    address = str(restaurant.get('Address', '未知地址'))
    url = str(restaurant.get('URL', f'https://www.google.com/maps/search/?api=1&query={urllib.parse.quote(name)}'))
    note = "(隨機推薦)" if is_random else ""

    # 1. 處理 RAG
    rag_info = str(restaurant.get('RAG_Content', ''))
    if retriever:
        try:
            docs = retriever.invoke(name)
            if docs: rag_info = "\n".join([d.page_content for d in docs])
        except: pass

    # 2. 準備圖片 Prompt (提前執行以便顯示除錯資訊)
    img_prompt = f"Delicious food from {name}, cinematic lighting, 8k, photorealistic" # 預設值
    prompt_source = "⚠️ 預設生成 (未找到 CSV 對應)"

    if not df_prompts.empty and PROMPT_COL_NAME in df_prompts.columns:
        if name in df_prompts.index:
            try:
                csv_prompt = df_prompts.loc[name, PROMPT_COL_NAME]
                if isinstance(csv_prompt, pd.Series): csv_prompt = csv_prompt.iloc[0]
                
                if pd.notna(csv_prompt) and str(csv_prompt).strip() != "":
                    img_prompt = str(csv_prompt)
                    prompt_source = "✅ CSV 檔案 (restaurants_prompts_v6.csv)"
                else:
                    prompt_source = "⚠️ CSV 有此餐廳但 Prompt 空白"
            except Exception as e:
                prompt_source = f"❌ 讀取錯誤: {e}"
        else:
            prompt_source = "ℹ️ CSV 中無此餐廳"

    # 3. 呼叫 LLM
    ai_text = generate_content_with_groq(name, rag_info, diary_input, score_input, quiz_state)
    
    # 4. 組合回應 (包含除錯資訊)
    debug_text = ""
    if debug_mode:
        debug_text = f"""
        \n\n---
        **🛠️ Prompt 除錯模式**
        * **餐廳名稱**: `{name}`
        * **Prompt 來源**: {prompt_source}
        * **實際使用 Prompt**: 
        ```
        {img_prompt}
        ```
        """
    
    final_response = f"### 🍽️ 推薦：{name} {note}\n\n{ai_text}{debug_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>'
    
    # 5. 先回傳文字
    yield final_response, None, map_html

    # 6. 生成圖片
    image_output = generate_image_huggingface(img_prompt)
    yield final_response, image_output, map_html

# ==========================================
# 3. 頁面切換邏輯
# ==========================================

def handle_q1_change(selected_label, current_state):
    if not selected_label: return current_state, gr.Button(interactive=False)
    current_state["q1"] = q1_options_map.get(selected_label, selected_label)
    return current_state, gr.Button(interactive=True, variant="primary")

def handle_q2_change(selected_label, current_state):
    if not selected_label: return current_state, gr.Button(interactive=False)
    current_state["q2"] = q2_options_map.get(selected_label, selected_label)
    return current_state, gr.Button(interactive=True, variant="primary", value="完成測驗 (前往點餐) ➔")

def go_to_page_2(): return gr.update(visible=False), gr.update(visible=True)
def go_to_main_app(): return gr.update(visible=False), gr.update(visible=True)

# ==========================================
# 4. Gradio 介面建構
# ==========================================

with gr.Blocks(title="AI 心情食堂") as demo:
    gr.HTML(f"<style>{custom_css}</style>")
    quiz_state = gr.State(value={})

    # Page 1
    with gr.Column(visible=True) as page_1_col:
        gr.Markdown("### 🔮 第一題：直覺測試")
        gr.Image(value=img_path_1, type="filepath", label="請觀察圖片", height=300)
        gr.Markdown("**問題：請觀察上方圖片，如果是你，你會怎麼做？**")
        radio_q1 = gr.Radio(choices=list(q1_options_map.keys()), label="請選擇", elem_classes="vertical-radio")
        btn_q1_next = gr.Button("下一頁 ➔", interactive=False)

    # Page 2
    with gr.Column(visible=False) as page_2_col:
        gr.Markdown("### 🔮 第二題：魔法適性")
        gr.Image(value=img_path_2, type="filepath", label="請觀察圖片", height=300)
        gr.Markdown("**問題：身為魔導士的你，會選擇哪一個法術？**")
        radio_q2 = gr.Radio(choices=list(q2_options_map.keys()), label="請選擇", elem_classes="vertical-radio")
        btn_q2_finish = gr.Button("完成測驗 ➔", interactive=False)

    # Page 3 (Main)
    with gr.Column(visible=False) as main_app_col:
        gr.Markdown(f"## 🍱 AI 心情食堂導航")
        gr.Markdown("測驗結束！現在，告訴我你真正想吃什麼吧！")

        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...")
                
                # [新增] 除錯模式開關
                debug_mode_btn = gr.Checkbox(label="🔧 顯示 Prompt 除錯資訊", 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")

    # Events
    radio_q1.change(fn=handle_q1_change, inputs=[radio_q1, quiz_state], outputs=[quiz_state, btn_q1_next])
    btn_q1_next.click(fn=go_to_page_2, outputs=[page_1_col, page_2_col])
    radio_q2.change(fn=handle_q2_change, inputs=[radio_q2, quiz_state], outputs=[quiz_state, btn_q2_finish])
    btn_q2_finish.click(fn=go_to_main_app, outputs=[page_2_col, main_app_col])

    # [修改] 加入 debug_mode_btn 到 inputs
    submit_btn.click(
        fn=mood_agent_logic,
        inputs=[score_input, food_input, diary_input, quiz_state, debug_mode_btn],
        outputs=[agent_output, image_output, map_output]
    )

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