Update app.py

app.py

@@ -11,48 +11,80 @@ from langchain_community.vectorstores import FAISS
 from PIL import Image
 from dotenv import load_dotenv
 
-# --- 匯入組員的模組 ---
-import emotion 
+# --- [新增] 匯入組員的模組 ---
+import teammate_logic 
 # ---------------------------
 
-load_dotenv()
+load_dotenv()  # 自動尋找並載入 .env 檔案中的變數
 
 # ==========================================
-# 0. 環境變數 & 1. 系統初始化 (保持不變)
+# 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')
-    except Exception: pass
+        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)
+            
+            vectorstore = FAISS.load_local(
+                "faiss_index", 
+                embeddings, 
+                allow_dangerous_deserialization=True
+            )
             global_retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
-        except Exception: pass
+            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 (保持不變)
+# 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
+    
+    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']
@@ -60,203 +92,159 @@ def get_restaurant_data(mood_score_str, food_choice):
     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)]
+        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
+        result = global_df.sample(1).iloc[0]
+        is_random = True
     else:
-        result = candidates.sample(1).iloc[0]; is_random = False
+        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..."
-    user_msg = f"【狀態】心情分數：{mood_score}\n【日記】{user_diary}\n【建議原因】{mood_guide_reason}\n【餐廳】{restaurant_name}\n資料：{restaurant_detail}"
+    
+    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。
+    (只需要回覆文字內容)
+    """
+    
     try:
-        response = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_msg}])
-        return response.choices[0].message.content
-    except Exception as e: return f"Groq Error: {str(e)}"
+        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: return None
+    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')
+    
+    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])
+        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)
-    final_response = f"### 🍽️ 推薦：{name}\n\n{ai_text}"
+    
+    # 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. [修正版] 橋接邏輯 (Bridge Functions)
+# 3. Gradio 介面建構
 # ==========================================
 
-# 這是 app.py 自己的格式 (注意：這裡是半形括號)
-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)"}
-    # 預防萬一 score 是 None 或其他怪怪的值
-    try:
-        score = int(score)
-    except:
-        score = 3
-    return mapping.get(score, "3 (普通)")
-
-# 橋接 1：開始
-def bridge_start_click(st):
-    res = emotion.on_restart(st)
-    return res[1], res[0], gr.update(visible=True), res[5], res[4]
-
-# 橋接 2：停止
-def bridge_stop_click(st):
-    res = emotion.on_stop(st)
-    return res[1], res[0], gr.update(visible=False), res[5], res[4]
-
-# 橋接 3：Webcam 串流 (修正分數填入問題)
-def bridge_predict_frame(frame, st):
-    res = emotion.on_stream(frame, st)
-    
-    out_cam = res[0]
-    out_result = res[1]
-    # out_mood = res[2] # ❌ 我們不使用隊友回傳的 UI 更新，因為括號格式不同
-    out_st = res[4]
-    out_btn_start = res[5]
-    
-    score_update = gr.update()
-    tabs_update = gr.update()
-    out_btn_stop = gr.update()
-
-    # 如果辨識完成，我們自己產生一個符合 app.py 格式的 UI 更新
-    if out_st.finished and hasattr(out_st, 'final_score'):
-        # ✅ 使用 _score_to_radio_value 將整數轉換為半形括號字串
-        new_val = _score_to_radio_value(out_st.final_score)
-        score_update = gr.update(value=new_val)
-        
-        tabs_update = gr.Tabs(selected=1)
-        out_btn_stop = gr.update(visible=False)
-        
-    return out_cam, out_result, out_st, out_btn_stop, out_btn_start, score_update, tabs_update
-
-# 橋接 4：圖片上傳 (修正分數填入問題)
-def bridge_predict_upload(img, st):
-    res = emotion.on_upload(img, st)
-    
-    out_result = res[0]
-    # out_mood = res[1] # ❌ 同樣忽略隊友的格式
-    out_st = res[2]
-    
-    score_update = gr.update()
-    tabs_update = gr.update()
-
-    if out_st.finished and hasattr(out_st, 'final_score'):
-        # ✅ 使用 _score_to_radio_value 將整數轉換為半形括號字串
-        new_val = _score_to_radio_value(out_st.final_score)
-        score_update = gr.update(value=new_val)
-        
-        tabs_update = gr.Tabs(selected=1)
-    
-    return out_result, score_update, tabs_update, out_st
-
-
-# ==========================================
-# 4. Gradio 介面建構
-# ==========================================
-
-combined_css = emotion.css 
+# 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:
     
-    st_state = gr.State(emotion.AppState())
-
-    with gr.Tabs() as tabs:
-        
-        # Tab 1: 情緒辨識
-        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！(辨識完畢會自動跳轉)")
-                
-                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」或下方上傳照片。"))
-                
-                gr.Markdown("---")
-                gr.Markdown("### 或者：上傳照片")
-                upload_img = gr.Image(sources=["upload"], type="numpy", label="上傳照片")
-
-        # Tab 2: 主功能區
-        with gr.TabItem("🍽️ AI 心情食堂 (Step 2)", id=1):
+    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"## 🍱 今天想吃點什麼？")
+                gr.Markdown(f"## 🍱 呷飽沒？？！")
                 with gr.Row():
                     with gr.Column(scale=1):
-                        # 这里的 Radio 选项使用的是半形括号
-                        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)"
-                        )
+                        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", value=False)
-                        submit_btn = gr.Button("🍱 送出給 Agent", variant="primary")
-                    
+                        
+                        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")
 
-    # ==========================================
-    # 事件綁定
-    # ==========================================
-
-    btn_start.click(
-        fn=bridge_start_click,
-        inputs=[st_state],
-        outputs=[result_markdown, cam, btn_stop, btn_start, st_state],
-        show_progress="minimal"
-    )
-
-    btn_stop.click(
-        fn=bridge_stop_click,
-        inputs=[st_state],
-        outputs=[result_markdown, cam, btn_stop, btn_start, st_state],
-        show_progress="minimal"
-    )
-
-    cam.stream(
-        fn=bridge_predict_frame,
-        inputs=[cam, st_state],
-        outputs=[cam, result_markdown, st_state, btn_stop, btn_start, score_input, tabs], 
-        show_progress="minimal"
-    )
-
-    upload_img.change(
-        fn=bridge_predict_upload,
-        inputs=[upload_img, st_state],
-        outputs=[result_markdown, score_input, tabs, st_state], 
-        show_progress="minimal"
-    )
+        # 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],