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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, ImageFont, ImageDraw
import warnings
import cv2
import numpy as np
from deepface import DeepFace

# 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 - 使用 restaurants.csv) ---
df = pd.DataFrame()
try:
    # 確保你有 restaurants.csv 在專案根目錄
    df = pd.read_csv('restaurants.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)
    
    print("✅ 成功讀取 restaurants.csv 並建立 Mood_Tags")
except Exception as e:
    # 如果找不到檔案或讀取失敗，這裡會提醒
    print(f"⚠️ 讀取 restaurants.csv 失敗: {e}") 

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

try:
    if not df.empty:
        df_prompts = df.copy()
        if 'Name' in df_prompts.columns:
            df_prompts.set_index('Name', inplace=True)
except Exception as e:
    print(f"⚠️ 處理 Prompt 資料發生錯誤: {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. 核心功能函式 (餐廳推薦邏輯)
# ==========================================

def get_restaurant_data(mood_score_str, food_choice):
    if df.empty: return None, True
    # 定義分數與 Mood_Tags 的對應關係 (與舊版維持一致)
    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

# 注意：因為移除了測驗，這裡我們用 '無測驗' 作為預設結果
def generate_content_with_groq(restaurant_name, restaurant_detail, user_diary, mood_score):
    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和心情來推薦餐廳。"
    user_msg = f"""
    【使用者狀態】
    心情分數：{mood_score}
    心情日記：{user_diary}
    【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「心情」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 ---
# 移除了 quiz_state 參數
def mood_agent_logic(score_input, food_input, diary_input, debug_mode):
    # 確保 score_input 有值
    if not score_input:
        yield "⚠️ 請先選擇心情分數，或使用上方相機偵測！", None, ""
        return

    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'])
    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
    img_prompt = f"Delicious food from {name}, cinematic lighting, 8k, photorealistic"
    prompt_source = "⚠️ 預設生成"

    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"
            except: pass

    # 3. 呼叫 LLM (移除了 quiz_state)
    ai_text = generate_content_with_groq(name, rag_info, diary_input, score_input)
    
    # 4. 組合回應
    debug_text = ""
    if debug_mode:
        debug_text = f"\n\n---\n**🛠️ Prompt 來源**: {prompt_source}\n**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>'
    
    yield final_response, None, map_html
    image_output = generate_image_huggingface(img_prompt)
    yield final_response, image_output, map_html

# ==========================================
# 2. 情緒辨識整合模組 (維持不變)
# ==========================================

# 定義中文字典 (繪圖用)
emotion_text_obj = {
    'angry': '生氣', 'disgust': '噁心', 'fear': '害怕',
    'happy': '開心', 'sad': '難過', 'surprise': '驚訝', 'neutral': '正常'
}

# 繪圖函式 (維持不變)
def putText(img, x, y, text, size=50, color=(255, 255, 255)):
    try:
        fontpath = 'NotoSansTC-VariableFont_wght.ttf'
        if not os.path.exists(fontpath): return img
        font = ImageFont.truetype(fontpath, size)
        imgPil = Image.fromarray(img)
        draw = ImageDraw.Draw(imgPil)
        displayText = emotion_text_obj.get(text, text)
        draw.text((x, y), displayText, fill=color, font=font)
        return np.array(imgPil)
    except:
        return img

# 關鍵功能：偵測情緒並回傳「分數選項」 (維持不變)
def detect_emotion_and_map(frame):
    if frame is None:
        return frame, None
    
    detected_emotion = "neutral" 
    mapped_score = "3 (普通)"    

    try:
        # 1. 辨識情緒
        analyze = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
        if isinstance(analyze, list): analyze = analyze[0]
        detected_emotion = analyze['dominant_emotion']
        
        # 2. 畫在圖片上
        frame = putText(frame, 20, 40, detected_emotion)

        # 3. 橋樑：將情緒轉換為餐廳系統的分數
        if detected_emotion == 'happy':
            mapped_score = "5 (超棒)"
        elif detected_emotion == 'surprise':
            mapped_score = "4 (不錯)"
        elif detected_emotion == 'neutral':
            mapped_score = "3 (普通)"
        elif detected_emotion in ['sad', 'fear']:
            mapped_score = "2 (不太好)"
        elif detected_emotion in ['angry', 'disgust']:
            mapped_score = "1 (心情差)"

    except Exception as e:
        print(f"DeepFace Error: {e}")
        pass
    
    return frame, mapped_score

# ==========================================
# 5. Gradio 介面建構 (修改版：鏡頭與結果分離)
# ==========================================

with gr.Blocks(title="AI 心情食堂") as demo:
    
    with gr.Column(visible=True) as main_app_col: 
        gr.Markdown(f"## 🍱 AI 心情食堂導航")
        gr.Markdown("請看著鏡頭，讓 AI 幫你判斷今天的心情分數！")

        with gr.Row():
            # 左側：輸入區
            with gr.Column(scale=1):
                
                # ★ 修改重點：將鏡頭與結果分開顯示 ★
                gr.Markdown("### 📸 步驟 1：心情偵測 (選用)")
                
                # 這裡用 Row 把兩個影像並排 (左邊鏡頭，右邊截圖)
                with gr.Row():
                    # 左邊：永遠是即時鏡頭 (不設為 output)
                    webcam_input = gr.Image(sources=["webcam"], label="即時鏡頭 (請看這裡)", streaming=True)
                    
                    # 右邊：顯示偵測後的「靜態截圖」
                    captured_image = gr.Image(label="偵測結果截圖", interactive=False)
                
                detect_btn = gr.Button("📸 截圖並偵測心情 👇", variant="secondary")
                
                gr.Markdown("### 📝 步驟 2：確認與補充")
                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 (AI 會自動填入，也可手動改)", 
                    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=3, label="3. 心情日記 (選填)", placeholder="例如：今天被老闆罵了，想吃點好料的...")
                
                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")

            # 右側：Agent 輸出區 (維持不變)
            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 
    
    # ★ 修改重點：按鈕點擊後，輸出目標改為 captured_image，不再覆蓋 webcam_input
    detect_btn.click(
        fn=detect_emotion_and_map,
        inputs=[webcam_input],
        outputs=[captured_image, score_input] # 輸出到「右邊的截圖」和「分數選項」
    )

    # 提交按鈕
    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()

	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, ImageFont, ImageDraw
	import warnings
	import cv2
	import numpy as np
	from deepface import DeepFace

	# 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 - 使用 restaurants.csv) ---
	df = pd.DataFrame()
	try:
	# 確保你有 restaurants.csv 在專案根目錄
	df = pd.read_csv('restaurants.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)

	print("✅ 成功讀取 restaurants.csv 並建立 Mood_Tags")
	except Exception as e:
	# 如果找不到檔案或讀取失敗，這裡會提醒
	print(f"⚠️ 讀取 restaurants.csv 失敗: {e}")

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

	try:
	if not df.empty:
	df_prompts = df.copy()
	if 'Name' in df_prompts.columns:
	df_prompts.set_index('Name', inplace=True)
	except Exception as e:
	print(f"⚠️ 處理 Prompt 資料發生錯誤: {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. 核心功能函式 (餐廳推薦邏輯)
	# ==========================================

	def get_restaurant_data(mood_score_str, food_choice):
	if df.empty: return None, True
	# 定義分數與 Mood_Tags 的對應關係 (與舊版維持一致)
	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

	# 注意：因為移除了測驗，這裡我們用 '無測驗' 作為預設結果
	def generate_content_with_groq(restaurant_name, restaurant_detail, user_diary, mood_score):
	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和心情來推薦餐廳。"
	user_msg = f"""
	【使用者狀態】
	心情分數：{mood_score}
	心情日記：{user_diary}
	【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「心情」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 ---
	# 移除了 quiz_state 參數
	def mood_agent_logic(score_input, food_input, diary_input, debug_mode):
	# 確保 score_input 有值
	if not score_input:
	yield "⚠️ 請先選擇心情分數，或使用上方相機偵測！", None, ""
	return

	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'])
	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
	img_prompt = f"Delicious food from {name}, cinematic lighting, 8k, photorealistic"
	prompt_source = "⚠️ 預設生成"

	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"
	except: pass

	# 3. 呼叫 LLM (移除了 quiz_state)
	ai_text = generate_content_with_groq(name, rag_info, diary_input, score_input)

	# 4. 組合回應
	debug_text = ""
	if debug_mode:
	debug_text = f"\n\n---\n🛠️ Prompt 來源: {prompt_source}\nPrompt: `{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>'

	yield final_response, None, map_html
	image_output = generate_image_huggingface(img_prompt)
	yield final_response, image_output, map_html

	# ==========================================
	# 2. 情緒辨識整合模組 (維持不變)
	# ==========================================

	# 定義中文字典 (繪圖用)
	emotion_text_obj = {
	'angry': '生氣', 'disgust': '噁心', 'fear': '害怕',
	'happy': '開心', 'sad': '難過', 'surprise': '驚訝', 'neutral': '正常'
	}

	# 繪圖函式 (維持不變)
	def putText(img, x, y, text, size=50, color=(255, 255, 255)):
	try:
	fontpath = 'NotoSansTC-VariableFont_wght.ttf'
	if not os.path.exists(fontpath): return img
	font = ImageFont.truetype(fontpath, size)
	imgPil = Image.fromarray(img)
	draw = ImageDraw.Draw(imgPil)
	displayText = emotion_text_obj.get(text, text)
	draw.text((x, y), displayText, fill=color, font=font)
	return np.array(imgPil)
	except:
	return img

	# 關鍵功能：偵測情緒並回傳「分數選項」 (維持不變)
	def detect_emotion_and_map(frame):
	if frame is None:
	return frame, None

	detected_emotion = "neutral"
	mapped_score = "3 (普通)"

	try:
	# 1. 辨識情緒
	analyze = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False)
	if isinstance(analyze, list): analyze = analyze[0]
	detected_emotion = analyze['dominant_emotion']

	# 2. 畫在圖片上
	frame = putText(frame, 20, 40, detected_emotion)

	# 3. 橋樑：將情緒轉換為餐廳系統的分數
	if detected_emotion == 'happy':
	mapped_score = "5 (超棒)"
	elif detected_emotion == 'surprise':
	mapped_score = "4 (不錯)"
	elif detected_emotion == 'neutral':
	mapped_score = "3 (普通)"
	elif detected_emotion in ['sad', 'fear']:
	mapped_score = "2 (不太好)"
	elif detected_emotion in ['angry', 'disgust']:
	mapped_score = "1 (心情差)"

	except Exception as e:
	print(f"DeepFace Error: {e}")
	pass

	return frame, mapped_score

	# ==========================================
	# 5. Gradio 介面建構 (修改版：鏡頭與結果分離)
	# ==========================================

	with gr.Blocks(title="AI 心情食堂") as demo:

	with gr.Column(visible=True) as main_app_col:
	gr.Markdown(f"## 🍱 AI 心情食堂導航")
	gr.Markdown("請看著鏡頭，讓 AI 幫你判斷今天的心情分數！")

	with gr.Row():
	# 左側：輸入區
	with gr.Column(scale=1):

	# ★ 修改重點：將鏡頭與結果分開顯示 ★
	gr.Markdown("### 📸 步驟 1：心情偵測 (選用)")

	# 這裡用 Row 把兩個影像並排 (左邊鏡頭，右邊截圖)
	with gr.Row():
	# 左邊：永遠是即時鏡頭 (不設為 output)
	webcam_input = gr.Image(sources=["webcam"], label="即時鏡頭 (請看這裡)", streaming=True)

	# 右邊：顯示偵測後的「靜態截圖」
	captured_image = gr.Image(label="偵測結果截圖", interactive=False)

	detect_btn = gr.Button("📸 截圖並偵測心情 👇", variant="secondary")

	gr.Markdown("### 📝 步驟 2：確認與補充")
	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 (AI 會自動填入，也可手動改)",
	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=3, label="3. 心情日記 (選填)", placeholder="例如：今天被老闆罵了，想吃點好料的...")

	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")

	# 右側：Agent 輸出區 (維持不變)
	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

	# ★ 修改重點：按鈕點擊後，輸出目標改為 captured_image，不再覆蓋 webcam_input
	detect_btn.click(
	fn=detect_emotion_and_map,
	inputs=[webcam_input],
	outputs=[captured_image, score_input] # 輸出到「右邊的截圖」和「分數選項」
	)

	# 提交按鈕
	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()