app.py

import os
import gradio as gr
import aiohttp
import asyncio
import json
from functools import lru_cache
from datasets import Dataset, DatasetDict, load_dataset
from huggingface_hub import HfFolder

import subprocess

def upgrade_pip():
    try:
        subprocess.check_call([os.sys.executable, "-m", "pip", "install", "--upgrade", "pip"])
        print("pip 升級成功")
    except subprocess.CalledProcessError:
        print("pip 升級失敗")

# 呼叫升級函數
upgrade_pip()

# 從環境變量中獲取 Hugging Face API 令牌和其他配置
HF_API_TOKEN = os.environ.get("Feedback_API_TOKEN")
LLM_API = os.environ.get("LLM_API")
LLM_URL = os.environ.get("LLM_URL")
USER_ID = "HuggingFace Space"
DATASET_NAME = os.environ.get("DATASET_NAME")

# 確保令牌不為空
if HF_API_TOKEN is None:
    raise ValueError("HF_API_TOKEN 環境變量未設置。請在 Hugging Face Space 的設置中添加該環境變量。")

# 設置 Hugging Face API 令牌
HfFolder.save_token(HF_API_TOKEN)

# 定義數據集特徵
features = {
    "user_input": "string",
    "response": "string",
    "feedback_type": "string",
    "improvement": "string"
}

# 加載或創建數據集
try:
    dataset = load_dataset(DATASET_NAME)
except:
    dataset = DatasetDict({
        "feedback": Dataset.from_dict({
            "user_input": [],
            "response": [],
            "feedback_type": [],
            "improvement": []
        })
    })

@lru_cache(maxsize=32)
async def send_chat_message(LLM_URL, LLM_API, user_input):
    payload = {
        "inputs": {},
        "query": user_input,
        "response_mode": "streaming",
        "conversation_id": "",
        "user": USER_ID,
    }
    print("Sending chat message payload:", payload)  # Debug information

    async with aiohttp.ClientSession() as session:
        try:
            async with session.post(
                url=f"{LLM_URL}/chat-messages",
                headers={"Authorization": f"Bearer {LLM_API}"},
                json=payload,
                timeout=aiohttp.ClientTimeout(total=60)
            ) as response:
                if response.status != 200:
                    print(f"Error: {response.status}")
                    return f"Error: {response.status}"

                full_response = []
                async for line in response.content:
                    line = line.decode('utf-8').strip()
                    if not line:
                        continue
                    if "data: " not in line:
                        continue
                    try:
                        print("Received line:", line)  # Debug information
                        data = json.loads(line.split("data: ")[1])
                        if "answer" in data:
                            full_response.append(data["answer"])
                    except (IndexError, json.JSONDecodeError) as e:
                        print(f"Error parsing line: {line}, error: {e}")  # Debug information
                        continue

                if full_response:
                    return ''.join(full_response).strip()
                else:
                    return "Error: No response found in the response"
        except Exception as e:
            print(f"Exception: {e}")
            return f"Exception: {e}"

async def handle_input(user_input):
    print(f"Handling input: {user_input}")
    chat_response = await send_chat_message(LLM_URL, LLM_API, user_input)
    print("Chat response:", chat_response)  # Debug information
    return chat_response

def run_sync(func, *args):
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    result = loop.run_until_complete(func(*args))
    loop.close()
    return result

def save_feedback(user_input, response, feedback_type, improvement):
    feedback = {
        "user_input": user_input,
        "response": response,
        "feedback_type": feedback_type,
        "improvement": improvement
    }
    print(f"Saving feedback: {feedback}")
    # Append to the dataset
    new_data = {
        "user_input": [user_input],
        "response": [response],
        "feedback_type": [feedback_type],
        "improvement": [improvement]
    }
    global dataset
    dataset["feedback"] = Dataset.from_dict({
        "user_input": dataset["feedback"]["user_input"] + [user_input],
        "response": dataset["feedback"]["response"] + [response],
        "feedback_type": dataset["feedback"]["feedback_type"] + [feedback_type],
        "improvement": dataset["feedback"]["improvement"] + [improvement]
    })
    dataset.push_to_hub(DATASET_NAME)

def handle_feedback(response, feedback_type, improvement):
    global last_user_input
    save_feedback(last_user_input, response, feedback_type, improvement)
    return "感謝您的反饋！"

def handle_user_input(user_input):
    print(f"User input: {user_input}")
    global last_user_input
    last_user_input = user_input  # 保存最新的用戶輸入
    return run_sync(handle_input, user_input)

# 讀取並顯示反饋內容的函數
def show_feedback():
    try:
        feedbacks = dataset["feedback"].to_pandas().to_dict(orient="records")
        print(f"Feedbacks: {feedbacks}")  # Debug information
        return feedbacks
    except Exception as e:
        print(f"Error: {e}")  # Debug information
        return {"error": str(e)}

TITLE = """<h1>Large Language Model (LLM) Playground 💬 <a href='https://support.maicoin.com/zh-TW/support/home' target='_blank'>Cryptocurrency Exchange FAQ</a></h1>"""
SUBTITLE = """<h2><a href='https://www.twman.org' target='_blank'>TonTon Huang Ph.D.</a> | <a href='https://blog.twman.org/p/deeplearning101.html' target='_blank'>手把手帶你一起踩AI坑</a><br></h2>"""
LINKS = """
<a href='https://github.com/Deep-Learning-101' target='_blank'>Deep Learning 101 Github</a> | <a href='http://deeplearning101.twman.org' target='_blank'>Deep Learning 101</a> | <a href='https://www.facebook.com/groups/525579498272187/' target='_blank'>台灣人工智慧社團 FB</a> | <a href='https://www.youtube.com/c/DeepLearning101' target='_blank'>YouTube</a><br>
<a href='https://blog.twman.org/2025/03/AIAgent.html' target='_blank'>那些 AI Agent 要踩的坑</a>：探討多種 AI 代理人工具的應用經驗與挑戰，分享實用經驗與工具推薦。<br>
<a href='https://blog.twman.org/2024/08/LLM.html' target='_blank'>白話文手把手帶你科普 GenAI</a>：淺顯介紹生成式人工智慧核心概念，強調硬體資源和數據的重要性。<br>
<a href='https://blog.twman.org/2024/09/LLM.html' target='_blank'>大型語言模型直接就打完收工？</a>：回顧 LLM 領域探索歷程，討論硬體升級對 AI 開發的重要性。<br>
<a href='https://blog.twman.org/2024/07/RAG.html' target='_blank'>那些檢索增強生成要踩的坑</a>：探討 RAG 技術應用與挑戰，提供實用經驗分享和工具建議。<br>
<a href='https://blog.twman.org/2024/02/LLM.html' target='_blank'>那些大型語言模型要踩的坑</a>：探討多種 LLM 工具的應用與挑戰，強調硬體資源的重要性。<br>
<a href='https://blog.twman.org/2023/04/GPT.html' target='_blank'>Large Language Model，LLM</a>：探討 LLM 的發展與應用，強調硬體資源在開發中的關鍵作用。。<br>
<a href='https://blog.twman.org/2024/11/diffusion.html' target='_blank'>ComfyUI + Stable Diffuision</a>：深入探討影像生成與分割技術的應用，強調硬體資源的重要性。<br>
<a href='https://blog.twman.org/2024/02/asr-tts.html' target='_blank'>那些ASR和TTS可能會踩的坑</a>：探討 ASR 和 TTS 技術應用中的問題，強調數據質量的重要性。<br>
<a href='https://blog.twman.org/2021/04/NLP.html' target='_blank'>那些自然語言處理 (Natural Language Processing, NLP) 踩的坑</a>：分享 NLP 領域的實踐經驗，強調數據質量對模型效果的影響。<br>
<a href='https://blog.twman.org/2021/04/ASR.html' target='_blank'>那些語音處理 (Speech Processing) 踩的坑</a>：分享語音處理領域的實務經驗，強調資料品質對模型效果的影響。<br>
<a href='https://blog.twman.org/2023/07/wsl.html' target='_blank'>用PPOCRLabel來幫PaddleOCR做OCR的微調和標註</a><br>
<a href='https://blog.twman.org/2023/07/HugIE.html' target='_blank'>基於機器閱讀理解和指令微調的統一信息抽取框架之診斷書醫囑資訊擷取分析</a><br>
"""
# 添加示例
examples = [
    ["MAX 帳號刪除關戶後，又重新註冊 MAX 後要怎辦？"],
    ["手機APP怎麼操作掛單交易？"],
    ["USDT 怎樣換新台幣？"],
    ["新台幣入金要怎操作"]
]

with gr.Blocks() as iface:
    gr.HTML(TITLE)
    gr.HTML(SUBTITLE)
    gr.HTML(LINKS)
    with gr.Row():
        chatbot = gr.Chatbot()
    
    with gr.Row():
        user_input = gr.Textbox(label='輸入您的問題', placeholder="在此輸入問題...")
        submit_button = gr.Button("問題輸入好，請點我送出")
    
    gr.Examples(examples=examples, inputs=user_input)
        
    with gr.Row():
        # like_button = gr.Button(" 👍 覺得答案很棒，請按我；或者直接繼續問新問題亦可")
        dislike_button = gr.Button(" 👎 覺得答案待改善，請輸入改進建議，再按我送出保存")
        improvement_input = gr.Textbox(label='請輸入改進建議', placeholder='請輸入如何改進模型回應的建議')

    with gr.Row():
        feedback_output = gr.Textbox(label='反饋結果執行狀態', interactive=False)
    with gr.Row():
        show_feedback_button = gr.Button("查看目前所有反饋記錄")
        feedback_display = gr.JSON(label='所有反饋記錄')

    def chat(user_input, history):
        response = handle_user_input(user_input)
        history.append((user_input, response))
        return history, history

    submit_button.click(fn=chat, inputs=[user_input, chatbot], outputs=[chatbot, chatbot])

    # like_button.click(
    #     fn=lambda response, improvement: handle_feedback(response, "like", improvement),
    #     inputs=[chatbot, improvement_input],
    #     outputs=feedback_output
    # )

    dislike_button.click(
        fn=lambda response, improvement: handle_feedback(response, "dislike", improvement),
        inputs=[chatbot, improvement_input],
        outputs=feedback_output
    )

    show_feedback_button.click(fn=show_feedback, outputs=feedback_display)

iface.launch()