app.py

import os
import torch
import gradio as gr

from strings import TITLE, ABSTRACT 
from gen import get_pretrained_models, get_output, setup_model_parallel

os.environ["RANK"] = "0"
os.environ["WORLD_SIZE"] = "1"
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "50505"

local_rank, world_size = setup_model_parallel()
generator = get_pretrained_models("7B", "tokenizer", local_rank, world_size)

history = []
simple_history = []

def chat(user_input):
    bot_response = get_output(generator, user_input)[0]

    history.append({
        "role": "user",
        "content": user_input
    })
    history.append({
        "role": "system",
        "content": bot_response
    })    

    simple_history.append((user_input, None))
    
    response = ""
    for word in bot_response.split(" "):
        time.sleep(0.1)
        response += word + " "
        current_pair = (user_input, response)
        simple_history[-1] = current_pair
        yield simple_history

with gr.Blocks(css = """#col_container {width: 700px; margin-left: auto; margin-right: auto;}
                #chatbot {height: 400px; overflow: auto;}""") as demo:
    
    with gr.Column(elem_id='col_container'):
        gr.Markdown(f"## {TITLE}\n\n\n\n{ABSTRACT}")
        chatbot = gr.Chatbot(elem_id='chatbot')
        textbox = gr.Textbox(placeholder="Enter a prompt")
    
        textbox.submit(chat, textbox, chatbot)

demo.queue(api_open=False).launch()