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import argparse
import gradio as gr
from openai import OpenAI
# Argument parser setup
parser = argparse.ArgumentParser(
description='Chatbot Interface with Customizable Parameters')
parser.add_argument('--model-url',
type=str,
default=' https://55ad-165-204-156-250.ngrok-free.app/v1',
help='Model URL')
parser.add_argument('-m',
'--model',
type=str,
required=True,
help='Model name for the chatbot')
parser.add_argument('--temp',
type=float,
default=0.2,
help='Temperature for text generation')
parser.add_argument('--stop-token-ids',
type=str,
default='128001,128009',
help='Comma-separated stop token IDs')
parser.add_argument("--host", type=str, default=None)
parser.add_argument("--port", type=int, default=8001)
# Parse the arguments
args = parser.parse_args()
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = args.model_url
# Create an OpenAI client to interact with the API server
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
def predict(message, history):
# Convert chat history to OpenAI format
history_openai_format = [{
"role": "system",
"content": "You are a great ai assistant."
}]
for human, assistant in history:
history_openai_format.append({"role": "user", "content": human})
history_openai_format.append({
"role": "assistant",
"content": assistant
})
history_openai_format.append({"role": "user", "content": message})
# Create a chat completion request and send it to the API server
stream = client.chat.completions.create(
model="/app/model/llama3_8b_lora_indica_sft/", # Model name to use
messages=history_openai_format, # Chat history
temperature=args.temp, # Temperature for text generation
stream=True, # Stream response
extra_body={
'repetition_penalty':
1,
'stop_token_ids': [
int(id.strip()) for id in args.stop_token_ids.split(',')
if id.strip()
] if args.stop_token_ids else []
})
# Read and return generated text from response stream
partial_message = ""
for chunk in stream:
partial_message += (chunk.choices[0].delta.content or "")
yield partial_message
# Create and launch a chat interface with Gradio
gr.ChatInterface(predict).queue().launch() |