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83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
import argparse
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import gradio as gr
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from openai import OpenAI
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# Argument parser setup
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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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# Parse the arguments
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args = parser.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 to interact with the API server
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client = OpenAI(
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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def predict(message, history):
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# Convert chat history to OpenAI format
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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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history_openai_format.append({"role": "user", "content": message})
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# Create a chat completion request and send it to the API server
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stream = client.chat.completions.create(
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model=args.model, # Model name to use
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messages=history_openai_format, # Chat history
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temperature=args.temp, # Temperature for text generation
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stream=True, # Stream response
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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()) for id in args.stop_token_ids.split(',')
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if id.strip()
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] if args.stop_token_ids else []
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})
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# Read and return generated text from response stream
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partial_message = ""
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for chunk in stream:
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partial_message += (chunk.choices[0].delta.content or "")
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yield partial_message
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# Create and launch a chat interface with Gradio
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gr.ChatInterface(predict).queue().launch(server_name=args.host,
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server_port=args.port,
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share=True)
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