| | 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 |
| |
|
| | |
| | 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 |
| | |
| | |
| | |
| | |
| |
|
| | 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') |
| |
|
| | |
| | |
| | |
| |
|
| | 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 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 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) |
