Upload app.py

app.py

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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_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from PIL import Image
+from dotenv import load_dotenv
+
+# --- 匯入組員的模組 ---
+import emotion 
+# ---------------------------
+
+load_dotenv()
+
+# ==========================================
+# 0. 環境變數 & 1. 系統初始化 (保持不變)
+# ==========================================
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+global_df = None
+global_mood_df = None
+global_retriever = None
+rag_initialized = False
+
+def init_rag_system():
+    global global_df, global_mood_df, global_retriever, rag_initialized
+    if rag_initialized: return
+    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
+    if os.path.exists("faiss_index"):
+        try:
+            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})
+        except Exception: pass
+    rag_initialized = True
+
+# ==========================================
+# 2. 核心功能 (保持不變)
+# ==========================================
+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..."
+    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}"
+    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
+
+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')
+    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])
+    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}"
+    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)
+# ==========================================
+
+# 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 
+
+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.Column():
+                gr.Markdown(f"## 🍱 今天想吃點什麼？")
+                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)"
+                        )
+                        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")
+                    
+                    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"
+    )
+
+    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)