Spaces:

My-Project-Team
/

MachineVision_DeepLearning_FinalProject

Sleeping

MachineVision_DeepLearning_FinalProject / app.py

Update app.py

0d1630d verified 11 months ago

9.85 kB

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

# --- [新增] 匯入組員的模組 ---
import teammate_logic 
# ---------------------------

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. 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

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。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}]
        )
        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 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') 
    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])

    # 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)
    
    # 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. Gradio 介面建構
# ==========================================

# 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:
    
    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"## 🍱 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...")
                        
                        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")

        # 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],
        outputs=[agent_output, image_output, map_output]
    )

if __name__ == "__main__":
    demo.launch(ssr_mode=False)

	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

	# --- [新增] 匯入組員的模組 ---
	import teammate_logic
	# ---------------------------

	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. 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

	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。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}]
	)
	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 ---
	\nSystem Prompt:\n{system_prompt}
	\nUser 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

	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')
	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])

	# 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)

	# 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. Gradio 介面建構
	# ==========================================

	# 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:

	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"## 🍱 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...")

	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")

	# 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],
	outputs=[agent_output, image_output, map_output]
	)

	if __name__ == "__main__":
	demo.launch(ssr_mode=False)