app.py

import argparse
import os
import random

import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr

from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank
from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Chat, CONV_VISION

# imports modules for registration
from minigpt4.datasets.builders import *
from minigpt4.models import *
from minigpt4.processors import *
from minigpt4.runners import *
from minigpt4.tasks import *

def parse_args():
    """
    Parse command line arguments.

    Returns:
    argparse.Namespace: Parsed command line arguments.
    """
    parser = argparse.ArgumentParser(description="Demo")
    parser.add_argument("--cfg-path", type=str, default='eval_configs/minigpt4.yaml', help="path to configuration file.")
    parser.add_argument(
        "--options",
        nargs="+",
        help="override some settings in the used config, the key-value pair "
        "in xxx=yyy format will be merged into config file (deprecate), "
        "change to --cfg-options instead.",
    )
    args = parser.parse_args()
    return args

def setup_seeds(config):
    """
    Set up random seeds for reproducibility.

    Parameters:
    config (Config): Configuration object.
    """
    seed = config.run_cfg.seed + get_rank()

    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)

    cudnn.benchmark = False
    cudnn.deterministic = True

def initialize_chat():
    """
    Initialize the chat model.

    Returns:
    Chat: Initialized chat model.
    """
    print('Initializing Chat')
    config = Config(parse_args())

    model_config = config.model_cfg
    model_cls = registry.get_model_class(model_config.arch)
    model = model_cls.from_config(model_config).to('cuda:0')

    vis_processor_cfg = config.datasets_cfg.cc_align.vis_processor.train
    vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
    chat = Chat(model, vis_processor)
    print('Initialization Finished')

    return chat

def gradio_reset(chat_state, img_list):
    """
    Reset the Gradio interface.

    Parameters:
    chat_state (gr.State): The current state of the chat.
    img_list (gr.State): The current list of images.

    Returns:
    tuple: Updated Gradio interface elements.
    """
    if chat_state is not None:
        chat_state.messages = []
    if img_list is not None:
        img_list = []
    return None, gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your image first', interactive=False), gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list

def upload_img(gr_img, text_input, chat_state):
    """
    Upload an image and update the Gradio interface.

    Parameters:
    gr_img (gr.Image): The uploaded image.
    text_input (gr.Textbox): The text input box.
    chat_state (gr.State): The current state of the chat.

    Returns:
    tuple: Updated Gradio interface elements.
    """
    if gr_img is None:
        return None, None, gr.update(interactive=True), chat_state, None
    chat_state = CONV_VISION.copy()
    img_list = []
    llm_message = chat.upload_img(gr_img, chat_state, img_list)
    return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list

def gradio_ask(user_message, chatbot, chat_state):
    """
    Process user message and update the Gradio interface.

    Parameters:
    user_message (str): The message input by the user.
    chatbot (list): The current state of the chatbot.
    chat_state (gr.State): The current state of the chat.

    Returns:
    tuple: Updated Gradio interface elements.
    """
    if len(user_message) == 0:
        return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
    chat.ask(user_message, chat_state)
    chatbot = chatbot + [[user_message, None]]
    return '', chatbot, chat_state

def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature):
    """
    Generate a chatbot answer and update the Gradio interface.

    Parameters:
    chatbot (list): The current state of the chatbot.
    chat_state (gr.State): The current state of the chat.
    img_list (gr.State): The current list of images.
    num_beams (int): The number of beams for the beam search.
    temperature (float): The temperature for the generation.

    Returns:
    tuple: Updated Gradio interface elements.
    """
    llm_message = chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=300, num_beams=1, temperature=temperature, max_length=2000)[0]
    chatbot[-1][1] = llm_message
    return chatbot, chat_state, img_list

def main():
    """
    Main function to run the Gradio interface.
    """
    # Initialize the chat model
    chat = initialize_chat()

    # Set up the Gradio interface
    title = """<h1 align="center">Demo of MiniGPT-4</h1>"""
    description = """<h3>This is the demo of MiniGPT-4. Upload your images and start chatting!</h3>"""
    article = """<div style='display:flex; gap: 0.25rem; '><a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a><a href='https://github.com/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/Github-Code-blue'></a><a href='https://github.com/TsuTikgiau/blip2-llm/blob/release_prepare/MiniGPT_4.pdf'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></div>
    """

    with gr.Blocks() as demo:
        gr.Markdown(title)
        gr.Markdown(SHARED_UI_WARNING)
        gr.Markdown(description)
        gr.Markdown(article)

        with gr.Row():
            with gr.Column(scale=0.5):
                image = gr.Image(type="pil")
                upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary")
                clear = gr.Button("Restart")
                
                num_beams = gr.Slider(
                    minimum=1,
                    maximum=5,
                    value=1,
                    step=1,
                    interactive=True,
                    label="beam search numbers)",
                )
                
                temperature = gr.Slider(
                    minimum=0.1,
                    maximum=2.0,
                    value=1.0,
                                        step=0.1,
                    interactive=True,
                    label="Temperature",
                )

            with gr.Column():
                chat_state = gr.State()
                img_list = gr.State()
                chatbot = gr.Chatbot(label='MiniGPT-4')
                text_input = gr.Textbox(label='User', placeholder='Please upload your image first', interactive=False)
        
        upload_button.click(upload_img, [image, text_input, chat_state], [image, text_input, upload_button, chat_state, img_list])
        
        text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
            gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list]
        )
        clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, upload_button, chat_state, img_list], queue=False)

    demo.launch(enable_queue=True)

if __name__ == "__main__":
    main()