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 (修改：回傳分開的除錯資訊) # ========================================== 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}" # 準備除錯資訊 debug_log = "" if debug_mode: debug_log = f""" ### 🔧 Groq Prompt Debug **System Prompt:** {system_prompt} **User Message:** {user_msg} """ try: 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 return content, debug_log # 回傳 (內容, 除錯資訊) except Exception as e: return f"Groq Error: {str(e)}", debug_log 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, "", gr.update(); 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]) # 呼叫 Groq 生成內容與取得除錯資訊 ai_text, groq_debug_log = generate_content_with_groq(name, rag_info, diary_input, score_input, mood_reason, debug_mode) # 準備完整的除錯資訊 (包含圖片 Prompt) full_debug_log = "" if debug_mode: img_debug_log = f""" ### 🎨 Image Prompt Debug **Visual Prompt:** {img_prompt} """ full_debug_log = groq_debug_log + "\n\n" + img_debug_log # 設定除錯視窗的狀態 (有勾選才顯示) debug_output_update = gr.update(value=full_debug_log, visible=debug_mode) final_response = f"### 🍽️ 推薦：{name}\n\n{ai_text}" map_html = f'
🗺️ Google Map 導航
' # 先回傳文字與除錯資訊 yield final_response, None, map_html, debug_output_update # 再回傳圖片 image_output = generate_image_huggingface(img_prompt) yield final_response, image_output, map_html, debug_output_update # ========================================== # 3. 橋接邏輯 (Bridge Functions) # ========================================== 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)"} try: score = int(score) except: score = 3 return mapping.get(score, "3 (普通)") def bridge_start_click(st): res = emotion.on_restart(st) return res[1], res[0], gr.update(visible=True), res[5], res[4] def bridge_stop_click(st): res = emotion.on_stop(st) return res[1], res[0], gr.update(visible=False), res[5], res[4] def bridge_predict_frame(frame, st): res = emotion.on_stream(frame, st) out_cam = res[0]; out_result = res[1]; out_st = res[4]; out_btn_start = res[5] score_update = gr.update(); tabs_update = gr.update(); out_btn_stop = gr.update() if out_st.finished and hasattr(out_st, 'final_score'): 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 # ========================================== # 4. Gradio 介面建構 # ========================================== # 定義強制黑色文字與 Tab 修復的 CSS force_black_css = """ /* 1. 針對 Tab 區域的背景容器：設為淺灰，確保黑字能被看見 */ .tab-nav { background-color: #e2e8f0 !important; border-radius: 8px 8px 0 0 !important; } /* 2. 【關鍵修正】針對「未選中」的 Tab 按鈕 */ .tab-nav button { color: #000000 !important; /* 強制文字黑色 */ background-color: transparent !important; /* 背景透明，透出下方的淺灰 */ opacity: 1 !important; /* 防止透明度造成看不清 */ text-shadow: none !important; /* 移除陰影 */ } /* 3. 針對「已選中」的 Tab 按鈕 (圖片中左邊那個) */ .tab-nav button.selected { color: #000000 !important; /* 文字黑色 (如果你想保留橘色，改成 #ff7f00) */ background-color: #ffffff !important; /* 背景純白，做出區隔 */ font-weight: bold !important; /* 加粗 */ border-bottom: 2px solid #ff7f00 !important; /* 底部橘線 */ } /* 4. 強制 Tab 內的 Icon/Emoji 也變色 */ .tab-nav button span { color: inherit !important; } /* 5. 確保內容的大標題也是黑色 (H1-H3) */ h1, h2, h3, h4, h5, h6, .prose h1, .prose h2, .prose h3 { color: #000000 !important; } """ # 合併原本的 CSS 與 新的強制黑色 CSS combined_css = emotion.css + force_black_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」並允許瀏覽器使用相機。")) # 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): 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") # --- [新增] 除錯資訊顯示區 (位於按鈕下方) --- debug_output = gr.Markdown(label="除錯資訊 (Debug Log)", visible=False) # ---------------------------------------- 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" ) submit_btn.click( fn=mood_agent_logic, inputs=[score_input, food_input, diary_input, debug_mode_btn], outputs=[agent_output, image_output, map_output, debug_output] # 加入 debug_output ) if __name__ == "__main__": demo.launch(ssr_mode=False)