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import os | |
import gradio as gr | |
import aiohttp | |
import asyncio | |
import json | |
import urllib.parse | |
import traceback | |
LLM_API = os.environ.get("LLM_API") | |
LLM_URL = os.environ.get("LLM_URL") | |
USER_ID = "HuggingFace Space" | |
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) | |
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=180) | |
) as response: | |
if response.status != 200: | |
print(f"Error: {response.status}") | |
return f"Error: Status code {response.status}" | |
full_response = [] | |
async for line in response.content.iter_chunked(2048): | |
line = line.decode('utf-8').strip() | |
if not line or "data: " not in line: | |
continue | |
try: | |
data = json.loads(line.split("data: ")[1]) | |
if "answer" in data: | |
decoded_answer = urllib.parse.unquote(data["answer"]) | |
full_response.append(decoded_answer) | |
except (IndexError, json.JSONDecodeError) as e: | |
print(f"Skipping invalid line: {line}, error: {e}") | |
continue | |
if full_response: | |
return ''.join(full_response).strip() | |
else: | |
return "Error: No response found in the response" | |
except aiohttp.ClientConnectorError: | |
return "Error: Cannot connect to the API server. Please check the URL and server status." | |
except Exception as e: | |
print("Exception occurred in send_chat_message:") | |
print(traceback.format_exc()) | |
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) | |
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 | |
# 定義 Gradio 介面 | |
user_input = gr.Textbox(label='請輸入您想查詢的關鍵公司名稱') | |
examples = [ | |
["加密貨幣"], | |
# ["國泰金控"], | |
["中華電信"], | |
# ["台灣大哥大"], | |
["台積電"], | |
# ["BlockTempo"] | |
] | |
TITLE = """<h1>Social Media Trends 💬 分析社群相關資訊,並判斷其正、負、中立等評價及趨勢 (數據大會跑很久或失敗) </h1>""" | |
SUBTITLE = """<h2><a href='https://www.twman.org' target='_blank'>TonTon Huang Ph.D. @ 2024/11 </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://reurl.cc/g6GlZX' target='_blank'>手把手帶你一起踩AI坑</a> | <a href='https://blog.twman.org/2024/11/diffusion.html' target='_blank'>ComfyUI + Stable Diffuision</a><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> | <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><br> | |
<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> | <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><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> | <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><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> | <a href='https://blog.twman.org/2024/02/LLM.html' target='_blank'>那些大模型開發會踩的坑</a><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> | <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> | |
""" | |
# 使用 Gradio Blocks 設定頁面內容 | |
with gr.Blocks() as iface: | |
gr.HTML(TITLE) | |
gr.HTML(SUBTITLE) | |
gr.HTML(LINKS) | |
gr.Interface( | |
fn=lambda x: run_sync(handle_input, x), | |
inputs=user_input, | |
outputs="text", | |
examples=examples, | |
flagging_mode="never" # 停用範例加載 | |
) | |
iface.launch() | |