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import gradio as gr
from huggingface_hub import InferenceClient
import os
import json
api_key=os.environ.get('qwen_API_KEY')
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("Qwen/Qwen2.5-72B-Instruct",token=api_key)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p
):
token = message.choices[0].delta.content
response += token
yield response
example_prompts = [
["How to cook Kung Pao chicken the tastiest?"],
["Help me create an email expressing my greetings to an old friend."],
["写一篇关于青春的五言绝句"],
["你是谁？"]
]
demo = gr.ChatInterface(
respond,
examples=example_prompts,
title="千问2.5-72B",
description="千问2.5-72B聊天机器人",
additional_inputs=[
gr.Textbox(value="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.", label="System message"),
gr.Slider(minimum=1, maximum=8888, value=2048, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
],
chatbot=gr.Chatbot(show_label=True, show_copy_button=True)
)
if __name__ == "__main__":
demo.queue(default_concurrency_limit=40)
demo.launch(max_threads=40) |