app.py

import argparse
import gradio as gr
from openai import OpenAI
import os

# 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()

model_url = os.getenv('MODEL_URL', 'http://localhost:8000/v1')
model_name = os.getenv('MODEL_NAME', 'default-model-name')  # Make sure to set this in the environment
temperature = float(os.getenv('TEMPERATURE', 0.8))
stop_token_ids = os.getenv('STOP_TOKEN_IDS', '')
host = os.getenv('HOST', '0.0.0.0')
port = os.getenv('PORT', 8001)

# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = 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=host, server_port=int(port), share=True)