app.py

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

# 從環境變量中獲取 Hugging Face API 令牌和其他配置
HF_API_TOKEN = os.environ.get("HF_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(user_input):
    payload = {
        "inputs": {},
        "query": user_input,
        "response_mode": "streaming",
        "conversation_id": "",
        "user": USER_ID,
    }
    print("Sending chat message payload:", payload)

    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:
                        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}")
                        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(user_input)
    print("Chat response:", chat_response)
    return chat_response

def run_sync(user_input):
    print(f"Running sync with input: {user_input}")
    return asyncio.run(handle_input(user_input))

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["feedback"].add_item(new_data)
    dataset.push_to_hub(DATASET_NAME)

def handle_feedback(response, feedback_type, improvement):
    # 獲取最新的用戶輸入（假設用戶輸入保存在全局變量中）
    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(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")
        return feedbacks
    except Exception as e:
        return f"Error: {e}"

TITLE = """<h1 align="center">Large Language Model (LLM) Playground 💬 <a href='https://huggingface.co/spaces/DeepLearning101/High-Entropy-Alloys-FAQ/blob/main/reference.txt' target='_blank'>High-Entropy-Alloys-FAQ</a></h1>"""
SUBTITLE = """<h2 align="center"><a href='https://www.twman.org' target='_blank'>TonTon Huang Ph.D. @ 2024/06 </a><br></h2>"""
LINKS = """<a href='https://blog.twman.org/2021/04/ASR.html' target='_blank'>那些語音處理 (Speech Processing) 踩的坑</a> | <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> | <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> | <a href='https://blog.twman.org/2024/02/LLM.html' target='_blank'>那些大模型開發會踩的坑</a> | <a href='https://blog.twman.org/2023/04/GPT.html' target='_blank'>什麼是大語言模型，它是什麼？想要嗎？</a><br>
<a href='https://blog.twman.org/2023/07/wsl.html' target='_blank'>用PaddleOCR的PPOCRLabel來微調醫療診斷書和收據</a> | <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 = [
    ["AlCoCrFeNi HEA coating 可用怎樣的實驗方法做到 ?"],
    ["請問high entropy nitride coatings的形成，主要可透過那些元素來讓這個材料形成熱穩定?"]
]

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("送出")
    
    gr.Examples(examples=examples, inputs=user_input)
        
    with gr.Row():
        like_button = gr.Button("👍")
        dislike_button = gr.Button("👎")
        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', interactive=False)
    with gr.Row():
        show_feedback_button = gr.Button("查看所有反饋")
        feedback_display = gr.JSON(label='所有反饋')

    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()