app.py

import gradio as gr
from gpt4all import GPT4All
from urllib.request import urlopen
import json
import time
from load_llms import model_choices, llm_intro, load_model


# Construct chatbot
def generate_response(model_name, message, chat_history):
    model = load_model(model_name)
    chat_history = []
    if len(chat_history) > 0:
        past_chat = ", ".join(chat_history)
        input_text = past_chat + " " + message
    else:
        input_text = message
    response = model.generate(input_text, max_tokens=100)
    chat_history.append((input_text, response))
    return chat_history, response

# Create Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# GPT4All Chatbot")
    with gr.Row():
        with gr.Column(scale=1):
            model_dropdown = gr.Dropdown(
                choices=model_choices(),  
                multiselect=False,
                type="value",
                value="orca-mini-3b-gguf2-q4_0.gguf",
                label="LLMs to choose from"
            )
            explanation = gr.Textbox(label="Model Description", lines=3, interactive=False, value=llm_intro("orca-mini-3b-gguf2-q4_0.gguf"))
    
            # Link the dropdown with the textbox to update the description based on the selected model
            model_dropdown.change(fn=llm_intro, inputs=model_dropdown, outputs=explanation)
            
        with gr.Column(scale=4):
            chatbot = gr.Chatbot(label="Chatroom", value=[(None, "How may I help you today?")])
            
    message = gr.Textbox(label="Message")
    state = gr.State()

    message.submit(generate_response, inputs=[model_dropdown, message, state], outputs=[chatbot, state])

# Launch the Gradio app
demo.launch()

#     clear = gr.ClearButton([input_text, chatbot])