import os
import gradio as gr
import aiohttp
import asyncio
import json
from functools import lru_cache
LLM_API = os.environ.get("LLM_API")
LLM_URL = os.environ.get("LLM_URL")
USER_ID = "HuggingFace Space" # Placeholder user ID
@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(user_input):
print(f"Running sync with input: {user_input}")
return asyncio.run(handle_input(user_input))
# 定義反饋處理函數
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}")
# 假設你有一個保存反饋的機制，可以是保存到文件或發送到服務器
# 這裡簡單打印出來，實際應用中應該保存反饋
return "感謝您的反饋！"
# 定義 Gradio 界面
user_input = gr.Textbox(label='歡迎問我加密貨幣交易所的各種疑難雜症', placeholder='在此輸入問題...')
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"]
]
TITLE = """"""
SUBTITLE = """"""
LINKS = """那些語音處理 (Speech Processing) 踩的坑 | 那些自然語言處理 (Natural Language Processing, NLP) 踩的坑 | 那些ASR和TTS可能會踩的坑 | 那些大模型開發會踩的坑 | 什麼是大語言模型，它是什麼？想要嗎？
用PaddleOCR的PPOCRLabel來微調醫療診斷書和收據 | 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
"""
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)
def chat(user_input, history):
response = run_sync(user_input)
history.append((user_input, response))
return history, history
def handle_feedback(response, feedback_type, improvement):
global last_user_input
feedback_message = save_feedback(last_user_input, response, feedback_type, improvement)
return feedback_message
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
)
iface.launch()