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 load_dotenv() # 自動尋找並載入 .env 檔案中的變數 # ========================================== # 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') 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 ) global_retriever = vectorstore.as_retriever(search_kwargs={"k": 3}) 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. 小測驗與資源設定 # ========================================== custom_css = """ #hidden_tabs > .tab-nav { display: none !important; visibility: hidden !important; } #hidden_tabs > div > button { display: none !important; } .vertical-radio fieldset { display: flex !important; flex-direction: column !important; gap: 12px !important; } .vertical-radio label { width: 100% !important; margin: 0 !important; display: flex !important; } """ # 設定圖片路徑 img_path_1 = "images/image_0.png" img_path_2 = "images/image_1.png" img_path_3 = "images/image_2.png" # Q3 img_path_4 = "images/image_3.png" # Q4 img_path_5 = "images/image_4.png" # Q5 if not os.path.exists("images"): os.makedirs("images") for p in [img_path_1, img_path_2, img_path_3, img_path_4, img_path_5]: if not os.path.exists(p): Image.new('RGB', (400, 300), color='lightgray').save(p) # --- 選項設定 --- q1_options_map = { "不​要​再​問白癡​問題​了​我​只​想​吃飯​": "現實主義者 (只想吃飯)", "去​過隱​居​生活": "隱士 (嚮往平靜)", "加入​禁衛軍，​保衛​這個​世界": "守護者 (充滿正義感)", "神​羅​天​征，毀滅這個​世界​": "破壞神 (心情可能很差或很中二)" } q2_options_map = { "不​要​再​問白癡​問題​了​我​只​想​吃飯​": "無魔法 (飢餓度MAX)", "麵​包形狀​的​魔法​炸彈": "爆炸魔法 (喜歡刺激/重口味)", "毒氣​的​生化​魔法": "毒氣魔法 (可能想吃臭豆腐或特殊風味)", "領域​展開:​無量​空處​": "領域展開 (思緒混亂或想放空)" } q3_options_map = { "不​要​在​問白癡​問題​了​我​只​想​吃飯​": "現實主義者 (只想吃飯)", "歐洲": "嚮往歐洲 (浪漫/西式)", "日本": "嚮往日本 (精緻/日式)", "泰國": "嚮往泰國 (熱情/酸辣)", "轉身搭機捷回家": "戀家 (只想回家)" } q4_options_map = { "轉頭​回家​睡覺​": "獨行俠 (想睡覺)", "看​起來​很​會玩​的​帥潮​": "外向 (找帥潮)", "感覺​是​動漫宅​的​同好​": "御宅族 (找同好)", "有​興趣​的​異性​": "大膽 (找異性)" } q5_options_map = { "甚麼​都​不​想​​": "放空 (什麼都不想)", "來​自​星星​的​你​": "韓劇迷 (浪漫愛情)", "進擊​的​巨人​": "動漫迷 (熱血戰鬥)", "洛基": "美劇迷 (懸疑燒腦)" } # ========================================== # 3. 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 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 # [修改] 增加 debug_mode 參數 def generate_content_with_groq(restaurant_name, restaurant_detail, user_diary, mood_score, quiz_result, 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。請根據使用者的日記、心情以及「心情美食指南」來推薦餐廳。" user_msg = f""" 【狀態】心情分數：{mood_score}，日記：{user_diary} 【五題測驗結果】 1. 人設直覺：{quiz_result.get('q1', '未知')} 2. 魔法適性：{quiz_result.get('q2', '未知')} 3. 旅遊偏好：{quiz_result.get('q3', '未知')} 4. 社交選擇：{quiz_result.get('q4', '未知')} 5. 追劇偏好：{quiz_result.get('q5', '未知')} 【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上述 5 個測驗結果。 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}] ) content = response.choices[0].message.content # [修改] 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 Exception as e: print(f"❌ 生圖失敗: {e}") return None # [修改] 增加 debug_mode 參數 def mood_agent_logic(score_input, food_input, diary_input, quiz_state, 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') 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]) # [修改] 傳入 debug_mode ai_text = generate_content_with_groq(name, rag_info, diary_input, score_input, quiz_state, mood_reason, debug_mode) # [修改] 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'
🗺️ Google Map 導航
' yield final_response, None, map_html image_output = generate_image_huggingface(img_prompt) yield final_response, image_output, map_html # ========================================== # 4. 介面互動邏輯 # ========================================== def handle_q1_change(selected_label, current_state): if not selected_label: return current_state, gr.Button(interactive=False) current_state["q1"] = q1_options_map[selected_label] return current_state, gr.Button(interactive=True, variant="primary") def handle_q2_change(selected_label, current_state): if not selected_label: return current_state, gr.Button(interactive=False) current_state["q2"] = q2_options_map[selected_label] return current_state, gr.Button(interactive=True, variant="primary") def handle_q3_change(selected_label, current_state): if not selected_label: return current_state, gr.Button(interactive=False) current_state["q3"] = q3_options_map[selected_label] return current_state, gr.Button(interactive=True, variant="primary") def handle_q4_change(selected_label, current_state): if not selected_label: return current_state, gr.Button(interactive=False) current_state["q4"] = q4_options_map[selected_label] return current_state, gr.Button(interactive=True, variant="primary") def handle_q5_change(selected_label, current_state): if not selected_label: return current_state, gr.Button(interactive=False) current_state["q5"] = q5_options_map[selected_label] return current_state, gr.Button(interactive=True, variant="primary", value="完成測驗 (前往點餐) ➔") # ========================================== # 5. Gradio 介面建構 # ========================================== with gr.Blocks(title="AI 心情食堂") as demo: gr.HTML(f"") quiz_state = gr.State(value={}) with gr.Tabs(elem_id="hidden_tabs") as tabs: # Tab 0: Q1 with gr.TabItem("Q1", id=0): with gr.Column(): gr.Markdown("### 🔮 第一題：直覺測試") gr.Image(value=img_path_1, type="filepath", label="請觀察圖片", height=300) gr.Markdown("**問題：請觀察上方圖片，如果是你，你會怎麼做？**") radio_q1 = gr.Radio(choices=list(q1_options_map.keys()), label="請選擇", elem_classes="vertical-radio") btn_q1_next = gr.Button("下一頁 ➔", interactive=False) # Tab 1: Q2 with gr.TabItem("Q2", id=1): with gr.Column(): gr.Markdown("### 🔮 第二題：魔法適性") gr.Image(value=img_path_2, type="filepath", label="請觀察圖片", height=300) gr.Markdown("**問題：身為魔導士的你，會選擇哪一個法術？**") radio_q2 = gr.Radio(choices=list(q2_options_map.keys()), label="請選擇", elem_classes="vertical-radio") btn_q2_next = gr.Button("下一頁 ➔", interactive=False) # Tab 2: Q3 with gr.TabItem("Q3", id=2): with gr.Column(): gr.Markdown("### 🔮 第三題：旅遊直覺") gr.Image(value=img_path_3, type="filepath", label="請觀察圖片", height=300) gr.Markdown("**問題三：不考慮其他因素，假你在桃機你最想去哪裡玩？**") radio_q3 = gr.Radio(choices=list(q3_options_map.keys()), label="請選擇", elem_classes="vertical-radio") btn_q3_next = gr.Button("下一頁 ➔", interactive=False) # Tab 3: Q4 with gr.TabItem("Q4", id=3): with gr.Column(): gr.Markdown("### 🔮 第四題：社交場合") gr.Image(value=img_path_4, type="filepath", label="請觀察圖片", height=300) gr.Markdown("**問題四：你是小大一，班上還不熟，現在你正參加你們班上的認識彼此的活動，下列哪一個人是你會選擇搭話的人？**") radio_q4 = gr.Radio(choices=list(q4_options_map.keys()), label="請選擇", elem_classes="vertical-radio") btn_q4_next = gr.Button("下一頁 ➔", interactive=False) # Tab 4: Q5 with gr.TabItem("Q5", id=4): with gr.Column(): gr.Markdown("### 🔮 第五題：追劇時光") gr.Image(value=img_path_5, type="filepath", label="請觀察圖片", height=300) gr.Markdown("**問題五：假設失憶了忘記以下所列的劇的劇情，而你現在閒了下來剛好想看劇，你會想看哪一部？**") radio_q5 = gr.Radio(choices=list(q5_options_map.keys()), label="請選擇", elem_classes="vertical-radio") btn_q5_finish = gr.Button("完成測驗 ➔", interactive=False) # Tab 5: Main with gr.TabItem("Main", id=5): with gr.Column(): gr.Markdown(f"## 🍱 呷飽沒？？！") 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", 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...") # [新增] 除錯模式開關 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 (Events) radio_q1.change(fn=handle_q1_change, inputs=[radio_q1, quiz_state], outputs=[quiz_state, btn_q1_next]) btn_q1_next.click(fn=lambda: gr.Tabs(selected=1), outputs=tabs) radio_q2.change(fn=handle_q2_change, inputs=[radio_q2, quiz_state], outputs=[quiz_state, btn_q2_next]) btn_q2_next.click(fn=lambda: gr.Tabs(selected=2), outputs=tabs) radio_q3.change(fn=handle_q3_change, inputs=[radio_q3, quiz_state], outputs=[quiz_state, btn_q3_next]) btn_q3_next.click(fn=lambda: gr.Tabs(selected=3), outputs=tabs) radio_q4.change(fn=handle_q4_change, inputs=[radio_q4, quiz_state], outputs=[quiz_state, btn_q4_next]) btn_q4_next.click(fn=lambda: gr.Tabs(selected=4), outputs=tabs) radio_q5.change(fn=handle_q5_change, inputs=[radio_q5, quiz_state], outputs=[quiz_state, btn_q5_finish]) btn_q5_finish.click(fn=lambda: gr.Tabs(selected=5), outputs=tabs) # [修改] 加入 debug_mode_btn 到輸入 submit_btn.click( fn=mood_agent_logic, inputs=[score_input, food_input, diary_input, quiz_state, debug_mode_btn], outputs=[agent_output, image_output, map_output] ) if __name__ == "__main__": demo.launch(ssr_mode=False)