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(): 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): seed = config.run_cfg.seed + get_rank() random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) cudnn.benchmark = False cudnn.deterministic = True # ======================================== # Model Initialization # ======================================== print('Initializing Chat') cfg = Config(parse_args()) model_config = cfg.model_cfg model_cls = registry.get_model_class(model_config.arch) model = model_cls.from_config(model_config).to('cuda:0') vis_processor_cfg = cfg.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') # ======================================== # Gradio Setting # ======================================== def gradio_reset(chat_state, img_list): chat_state.messages = [] 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): if gr_img is None: return None, None, gr.update(interactive=True) 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): 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): llm_message = chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=1000, num_beams=num_beams, temperature=temperature)[0] chatbot[-1][1] = llm_message return chatbot, chat_state, img_list title = """

Demo of MiniGPT-4

""" description = """

This is the demo of MiniGPT-4. Upload your images and start chatting!

""" article = """Paper: Here Code: Here Project Page: Here """ #TODO show examples below with gr.Blocks() as demo: gr.Markdown(title) 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=16, value=5, 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(share=True, enable_queue=True)