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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 transformers import StoppingCriteriaList | |
| 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_Vicuna0, CONV_VISION_LLama2, StoppingCriteriaSub | |
| # 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", required=True, help="path to configuration file.") | |
| parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.") | |
| 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 | |
| # ======================================== | |
| conv_dict = {'pretrain_vicuna0': CONV_VISION_Vicuna0, | |
| 'pretrain_llama2': CONV_VISION_LLama2} | |
| print('Initializing Chat') | |
| args = parse_args() | |
| cfg = Config(args) | |
| model_config = cfg.model_cfg | |
| model_config.device_8bit = args.gpu_id | |
| model_cls = registry.get_model_class(model_config.arch) | |
| model = model_cls.from_config(model_config).to('cuda:{}'.format(args.gpu_id)) | |
| CONV_VISION = conv_dict[model_config.model_type] | |
| vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train | |
| vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg) | |
| stop_words_ids = [[835], [2277, 29937]] | |
| stop_words_ids = [torch.tensor(ids).to(device='cuda:{}'.format(args.gpu_id)) for ids in stop_words_ids] | |
| stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)]) | |
| chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id), stopping_criteria=stopping_criteria) | |
| print('Initialization Finished') | |
| # ======================================== | |
| # Gradio Setting | |
| # ======================================== | |
| def gradio_reset(chat_state, img_list): | |
| 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): | |
| 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) | |
| chat.encode_img(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, | |
| num_beams=num_beams, | |
| temperature=temperature, | |
| max_new_tokens=300, | |
| max_length=2000)[0] | |
| chatbot[-1][1] = llm_message | |
| return chatbot, chat_state, img_list | |
| 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 = """<p><a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a></p><p><a href='https://github.com/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/Github-Code-blue'></a></p><p><a href='https://raw.githubusercontent.com/Vision-CAIR/MiniGPT-4/main/MiniGPT_4.pdf'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></p> | |
| """ | |
| #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=1): | |
| 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=10, | |
| 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(scale=2): | |
| 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) | |