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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()
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