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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(): | |
| 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 | |
| # ======================================== | |
| SHARED_UI_WARNING = f'''### [NOTE] It is possible that you are waiting in a lengthy queue. | |
| You can duplicate and use it with a paid private GPU. | |
| <a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/Vision-CAIR/minigpt4?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-xl-dark.svg" alt="Duplicate Space"></a> | |
| Alternatively, you can also use the demo on our [project page](https://minigpt-4.github.io). | |
| ''' | |
| 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): | |
| 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) | |
| 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 ask(text, conv): | |
| if len(conv.messages) > 0 and conv.messages[-1][0] == conv.roles[0] \ | |
| and conv.messages[-1][1][-6:] == '</Img>': # last message is image. | |
| conv.messages[-1][1] = ' '.join([conv.messages[-1][1], text]) | |
| else: | |
| conv.append_message(conv.roles[0], text) | |
| 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=300, num_beams=1, temperature=temperature, max_length=2000)[0] | |
| chatbot[-1][1] = llm_message | |
| return chatbot, chat_state, img_list | |
| title = """<h1 align="center">Demo of Compositional-VLM</h1>""" | |
| description = """<h3>This is the demo of Compositional-VLM. Upload your images and start chatting!</h3>""" | |
| article = """<div style='display:flex; gap: 0.25rem; '><a href='https://compositionalvlm.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> | |
| """ | |
| # TODO show examples below | |
| 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='Compositional-VLM') | |
| 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) |