import gradio as gr
import aiohttp
import asyncio
import json, os
LLM_API = os.environ.get("LLM_API")
LLM_URL = os.environ.get("LLM_URL")
USER_ID = "HuggingFace Space" # Placeholder user ID
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:
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}"
# Handle the stream of events
full_response = []
async for line in response.content:
line = line.decode('utf-8')
if line:
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 thought found in the response"
async def handle_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):
return asyncio.run(handle_input(user_input))
# Define Gradio interface
user_input = gr.Textbox(label='歡迎問我加密貨幣交易所的各種疑難雜症')
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)
gr.Interface(
fn=run_sync,
inputs=user_input,
outputs="text",
examples=examples,
allow_flagging="never"
)
iface.launch()