Update app.py

app.py

@@ -20,16 +20,17 @@ from deepface import DeepFace
 warnings.filterwarnings("ignore")
 
 # ==========================================
-# 0. 環境變數與 RAG 初始化 (維持不變)
+# 0. 環境變數與 RAG 初始化
 # ==========================================
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 HF_TOKEN = os.getenv("HF_TOKEN")
 
-# --- 資料讀取 ---
+# --- 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() 
+    df.columns = df.columns.str.strip() # 清理欄位空白
 
     def classify_mood(row):
         name_str = str(row.get('Name', ''))
@@ -37,10 +38,10 @@ try:
         text = (name_str + " " + rag_str).lower()
         tags = []
         rules = {
-            "開心/慶祝": ["牛排", "steak", "pizza", "炸", "甜點", "蛋糕", "buffet", "bistro"],
-            "傷心/疲憊": ["粥", "湯", "warm", "麵", "小吃", "豆花", "soup"],
-            "生氣/發洩": ["辣", "spicy", "麻辣", "鍋", "curry", "燒肉", "bbq", "fry"],
-            "平靜/放鬆": ["cafe", "coffee", "tea", "素食", "brunch", "sushi", "居酒屋"]
+            "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:
@@ -51,21 +52,26 @@ try:
         
     if 'Mood_Tags' not in df.columns:
         df['Mood_Tags'] = df.apply(classify_mood, axis=1)
-    print("✅ 成功讀取 restaurants.csv")
+    
+    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}") 
 
-# --- Prompt 資料庫 ---
+# --- 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: pass
+except Exception as e:
+    print(f"⚠️ 處理 Prompt 資料發生錯誤: {e}")
+
 
-# --- RAG ---
+# --- RAG 建置 ---
 retriever = None
 if not df.empty and 'RAG_Content' in df.columns:
     try:
@@ -76,14 +82,16 @@ if not df.empty and 'RAG_Content' in df.columns:
         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: pass
+    except Exception as e:
+        print(f"⚠️ RAG 初始化失敗: {e}")
 
 # ==========================================
-# 1. 核心功能函式 (餐廳推薦 - 維持不變)
+# 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"],
@@ -97,10 +105,11 @@ def get_restaurant_data(mood_score_str, food_choice):
     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)
-        if 'Mood_Tags' in candidates.columns:
-            candidates = candidates[candidates['Mood_Tags'].str.contains(pattern, regex=True, na=False)]
+        candidates = candidates[candidates['Mood_Tags'].str.contains(pattern, regex=True, na=False)]
     
     if food_keyword:
         candidates = candidates[
@@ -111,11 +120,23 @@ def get_restaurant_data(mood_score_str, food_choice):
     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"
+    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"���情：{mood_score}\n日記：{user_diary}\n餐廳：{restaurant_name}\n資料：{restaurant_detail}\n請推薦這家餐廳並給予安慰或慶祝。"
+    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", 
@@ -132,18 +153,24 @@ def generate_image_huggingface(prompt):
         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！", None, ""
+        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", 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:
@@ -151,17 +178,29 @@ def mood_agent_logic(score_input, food_input, diary_input, debug_mode):
             if docs: rag_info = "\n".join([d.page_content for d in docs])
         except: pass
 
-    img_prompt = f"Delicious food from {name}, cinematic lighting, 8k"
-    if not df_prompts.empty and name in df_prompts.index:
-        try:
-            val = df_prompts.loc[name, PROMPT_COL_NAME]
-            if isinstance(val, pd.Series): val = val.iloc[0]
-            if pd.notna(val): img_prompt = str(val)
-        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)
-    debug_text = f"\n\nPrompt: `{img_prompt}`" if debug_mode else ""
-    final_response = f"### 🍽️ 推薦：{name}\n\n{ai_text}{debug_text}"
+    
+    # 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
@@ -169,11 +208,16 @@ def mood_agent_logic(score_input, food_input, diary_input, debug_mode):
     yield final_response, image_output, map_html
 
 # ==========================================
-# 2. 情緒辨識整合模組
+# 2. 情緒辨識整合模組 (維持不變)
 # ==========================================
 
-emotion_text_obj = {'angry': '生氣', 'disgust': '噁心', 'fear': '害怕', 'happy': '開心', 'sad': '難過', 'surprise': '驚訝', 'neutral': '正常'}
+# 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'
@@ -184,69 +228,50 @@ def putText(img, x, y, text, size=50, color=(255, 255, 255)):
         displayText = emotion_text_obj.get(text, text)
         draw.text((x, y), displayText, fill=color, font=font)
         return np.array(imgPil)
-    except: return img
+    except:
+        return img
 
-# 核心辨識函式 (重構以供重複使用)
-def process_emotion(frame):
+# 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)
 
-        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: pass
-    return frame, mapped_score
+        # 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)"
 
-# 單次手動截圖
-def manual_detect(frame):
-    if frame is None: return None, None
-    return process_emotion(frame)
-
-# ★ 新增：切換錄製狀態按鈕邏輯
-def toggle_recording_state(is_recording):
-    # 切換 True/False
-    new_state = not is_recording
-    # 根據新狀態改變按鈕文字
-    if new_state:
-        return new_state, "⏹️ 停止錄製 (即時偵測中)"
-    else:
-        return new_state, "🔴 開始錄製 (即時偵測)"
-
-# ★ 新增：自動串流偵測 (包含限速邏輯)
-def auto_stream_detect(frame, is_recording, last_time):
-    # 如果沒開錄製，或畫面空的，就跳過不做事 (節省資源)
-    if not is_recording or frame is None:
-        return gr.skip(), gr.skip(), last_time
-    
-    # 限速：距離上次執行不到 1.0 秒，就跳過
-    current_time = time.time()
-    if current_time - last_time < 1.0:
-        return gr.skip(), gr.skip(), last_time
-        
-    # 執行辨識
-    processed_frame, score = process_emotion(frame)
+    except Exception as e:
+        print(f"DeepFace Error: {e}")
+        pass
     
-    # 回傳：更新圖片, 更新分數, 更新最後執行時間
-    return processed_frame, score, current_time
+    return frame, mapped_score
 
 # ==========================================
-# 3. Gradio 介面建構
+# 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:
     
-    # 定義狀態變數 (看不見的變數)
-    is_recording = gr.State(False) # 是否正在錄製
-    last_process_time = gr.State(0) # 上次偵測的時間
-
     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！")
@@ -254,63 +279,48 @@ with gr.Blocks(title="AI 心情食堂") as demo:
         with gr.Row():
             # 左側：輸入區
             with gr.Column(scale=1):
-                gr.Markdown("### 📸 步驟 1：心情偵測")
                 
-                with gr.Row():
-                    # 左邊：即時鏡頭
-                    webcam_input = gr.Image(sources=["webcam"], label="即時鏡頭", streaming=True)
-                    # 右邊：偵測結果
-                    captured_image = gr.Image(label="偵測結果", interactive=False)
+                # ★ 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():
-                    # 按鈕 1：錄製開關 (會變色變字)
-                    record_btn = gr.Button("🔴 開始錄製 (即時偵測)", variant="stop")
-                    # 按鈕 2：單次截圖 (維持不變)
-                    snapshot_btn = gr.Button("📸 單次截圖", variant="secondary")
+                    # 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 自動填入)", 
+                    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", placeholder="選填...")
+                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="🔧 除錯資訊", value=False)
+                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：輸出區
+            # 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")
 
-    # --- 事件綁定 ---
-
-    # 1. 錄製按鈕邏輯：點擊 -> 切換狀態 -> 改變按鈕文字
-    record_btn.click(
-        fn=toggle_recording_state,
-        inputs=[is_recording],
-        outputs=[is_recording, record_btn]
-    )
-
-    # 2. 串流偵測邏輯：鏡頭畫面改變 -> 檢查狀態 -> (如果錄製中) 執行偵測 -> 更新右邊圖片
-    webcam_input.stream(
-        fn=auto_stream_detect,
-        inputs=[webcam_input, is_recording, last_process_time],
-        outputs=[captured_image, score_input, last_process_time],
-        show_progress=False # 隱藏進度條，讓畫面更順暢
-    )
-
-    # 3. 單次截圖邏輯 (維持不變)
-    snapshot_btn.click(
-        fn=manual_detect,
+    # 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]
+        outputs=[captured_image, score_input] # 輸出到「右邊的截圖」和「分數選項」
     )
 
-    # 4. 送出 Agent
+    # 提交按鈕
     submit_btn.click(
         fn=mood_agent_logic,
         inputs=[score_input, food_input, diary_input, debug_mode_btn],