7168920491
Signed-off-by: reidliu41 <reid201711@gmail.com> Co-authored-by: reidliu41 <reid201711@gmail.com>
126 lines
4.2 KiB
Python
126 lines
4.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Example for starting a Gradio OpenAI Chatbot Webserver
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Start vLLM API server:
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vllm serve meta-llama/Llama-2-7b-chat-hf
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Start Gradio OpenAI Chatbot Webserver:
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python examples/online_serving/gradio_openai_chatbot_webserver.py \
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-m meta-llama/Llama-2-7b-chat-hf
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Note that `pip install --upgrade gradio` is needed to run this example.
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More details: https://github.com/gradio-app/gradio
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If your antivirus software blocks the download of frpc for gradio,
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you can install it manually by following these steps:
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1. Download this file: https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_linux_amd64
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2. Rename the downloaded file to: frpc_linux_amd64_v0.3
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3. Move the file to this location: /home/user/.cache/huggingface/gradio/frpc
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"""
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import argparse
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import gradio as gr
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from openai import OpenAI
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def format_history_to_openai(history):
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history_openai_format = [{
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"role": "system",
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"content": "You are a great AI assistant."
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}]
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for human, assistant in history:
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history_openai_format.append({"role": "user", "content": human})
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history_openai_format.append({
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"role": "assistant",
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"content": assistant
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})
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return history_openai_format
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def predict(message, history, client, model_name, temp, stop_token_ids):
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# Format history to OpenAI chat format
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history_openai_format = format_history_to_openai(history)
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history_openai_format.append({"role": "user", "content": message})
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# Send request to OpenAI API (vLLM server)
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stream = client.chat.completions.create(
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model=model_name,
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messages=history_openai_format,
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temperature=temp,
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stream=True,
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extra_body={
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'repetition_penalty':
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1,
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'stop_token_ids':
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[int(id.strip())
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for id in stop_token_ids.split(',')] if stop_token_ids else []
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})
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# Collect all chunks and concatenate them into a full message
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full_message = ""
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for chunk in stream:
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full_message += (chunk.choices[0].delta.content or "")
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# Return the full message as a single response
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return full_message
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Chatbot Interface with Customizable Parameters')
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parser.add_argument('--model-url',
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type=str,
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default='http://localhost:8000/v1',
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help='Model URL')
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parser.add_argument('-m',
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'--model',
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type=str,
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required=True,
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help='Model name for the chatbot')
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parser.add_argument('--temp',
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type=float,
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default=0.8,
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help='Temperature for text generation')
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parser.add_argument('--stop-token-ids',
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type=str,
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default='',
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help='Comma-separated stop token IDs')
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parser.add_argument("--host", type=str, default=None)
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parser.add_argument("--port", type=int, default=8001)
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return parser.parse_args()
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def build_gradio_interface(client, model_name, temp, stop_token_ids):
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def chat_predict(message, history):
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return predict(message, history, client, model_name, temp,
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stop_token_ids)
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return gr.ChatInterface(fn=chat_predict,
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title="Chatbot Interface",
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description="A simple chatbot powered by vLLM")
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def main():
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# Parse the arguments
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args = parse_args()
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# Set OpenAI's API key and API base to use vLLM's API server
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openai_api_key = "EMPTY"
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openai_api_base = args.model_url
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# Create an OpenAI client
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client = OpenAI(api_key=openai_api_key, base_url=openai_api_base)
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# Define the Gradio chatbot interface using the predict function
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gradio_interface = build_gradio_interface(client, args.model, args.temp,
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args.stop_token_ids)
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gradio_interface.queue().launch(server_name=args.host,
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server_port=args.port,
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share=True)
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if __name__ == "__main__":
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main()
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