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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='http://134.28.190.100:8000/v1',
help='Model URL')
parser.add_argument('-m',
'--model',
type=str,
required=True,
default='TheBloke/Mistral-7B-Instruct-v0.2-AWQ',
help='Model name for the chatbot')
parser.add_argument('--temp',
type=float,
default=0.8,
help='Temperature for text generation')
parser.add_argument('--stop-token-ids',
type=str,
default='',
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 add_document():
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=args.model, # 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
with gr.Blocks(title="MethodAI 0.15", theme="Soft") as demo:
with gr.Row():
with gr.Column(scale=1):
gr.UploadButton("Click to upload PDFs",file_types=[".pdf"])
with gr.Column(scale=4):
# Create and launch a chat interface with Gradio
gr.ChatInterface(predict).queue()
# with demo:
# btn.upload(render_file, inputs=[btn], outputs=[show_img])
demo.launch(server_name=args.host, server_port=args.port, share=True)
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