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import argparse |
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import os |
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import random |
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import numpy as np |
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import torch |
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import torch.backends.cudnn as cudnn |
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import gradio as gr |
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import argparse |
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import torch |
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from llava.constants import ( |
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IMAGE_TOKEN_INDEX, |
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DEFAULT_IMAGE_TOKEN, |
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DEFAULT_IM_START_TOKEN, |
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DEFAULT_IM_END_TOKEN, |
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IMAGE_PLACEHOLDER, |
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) |
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from llava.conversation import conv_templates, SeparatorStyle |
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from llava.model.builder import load_pretrained_model |
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from llava.utils import disable_torch_init |
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from llava.mm_utils import ( |
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process_images, |
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tokenizer_image_token, |
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get_model_name_from_path, |
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) |
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from PIL import Image |
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from huggingface_hub import snapshot_download |
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import requests |
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from PIL import Image |
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from io import BytesIO |
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import re |
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from llava.chat import Chat, conv_llava_v1 |
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def parse_args(): |
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parser = argparse.ArgumentParser(description="Demo") |
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parser.add_argument("--model-path", type=str, default="gordonhu/MQT-LLaVA-7b") |
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parser.add_argument("--model-base", type=str, default=None) |
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parser.add_argument("--conv-mode", type=str, default='llava_v1') |
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parser.add_argument("--sep", type=str, default=",") |
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parser.add_argument("--temperature", type=float, default=0) |
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parser.add_argument("--top_p", type=float, default=None) |
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parser.add_argument("--num_beams", type=int, default=1) |
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parser.add_argument("--max_new_tokens", type=int, default=512) |
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parser.add_argument("--num-visual-tokens", type=int, default=256) |
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parser.add_argument("--gpu-id", type=int, default=0) |
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args = parser.parse_args() |
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return args |
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print('Initializing Chat') |
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args = parse_args() |
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if torch.cuda.is_available(): |
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device='cuda:{}'.format(args.gpu_id) |
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else: |
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device=torch.device('cpu') |
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disable_torch_init() |
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snapshot_download(repo_id="gordonhu/MQT-LLaVA-7b") |
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model_name = get_model_name_from_path(args.model_path) |
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tokenizer, model, image_processor, context_len = load_pretrained_model( |
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args.model_path, args.model_base, model_name, device_map=device, device=device |
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) |
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chat = Chat(model, tokenizer, image_processor, args, device=device) |
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print('Initialization Finished') |
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def gradio_reset(chat_state, img_list): |
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if chat_state is not None: |
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chat_state.messages = [] |
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if img_list is not None: |
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img_list = [] |
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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 |
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def upload_img(gr_img, text_input, chat_state): |
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if gr_img is None: |
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return None, None, gr.update(interactive=True), chat_state, None |
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chat_state = conv_llava_v1.copy() |
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img_list = [] |
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llm_message = chat.upload_img(gr_img, chat_state, img_list) |
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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 |
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def gradio_ask(user_message, chatbot, chat_state): |
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if len(user_message) == 0: |
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return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state |
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chat.ask(user_message, chat_state) |
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chatbot = chatbot + [[user_message, None]] |
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return '', chatbot, chat_state |
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def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature, num_visual_tokens): |
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llm_message = chat.answer(conv=chat_state, |
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img_list=img_list, |
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num_beams=num_beams, |
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temperature=temperature, |
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num_visual_tokens=num_visual_tokens, |
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) |
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chatbot[-1][1] = llm_message[0] |
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return chatbot, chat_state, img_list |
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title = """<h1 align="center">Demo of MQT-LLaVA</h1>""" |
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description = """<h3>This is the demo of MQT-LLaVA. Upload your images and start chatting! <br> To use |
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example questions, click example image, hit upload & start chat, and press enter on your keyboard in the chatbox. |
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<br> Due to limited memory constraint, we only support single turn conversation. To ask multiple questions, hit Restart and upload your image! </h3>""" |
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article = """<p><a href='https://gordonhu608.github.io/mqtllava/'><img src='https://img.shields.io/badge/Project-Page-Green'></a></p><p><a href='https://github.com/gordonhu608/MQT-LLaVA'><img src='https://img.shields.io/badge/Github-Code-blue'></a></p><p><a href='https://arxiv.org/abs/2405.19315'><img src='https://img.shields.io/badge/Paper-ArXiv-red'></a></p> |
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""" |
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with gr.Blocks() as demo: |
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gr.Markdown(title) |
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gr.Markdown(description) |
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gr.Markdown(article) |
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with gr.Row(): |
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with gr.Column(scale=0.5): |
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image = gr.Image(type="pil") |
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upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary") |
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clear = gr.Button("Restart 🔄") |
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num_visual_tokens = gr.Slider( |
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minimum=1, |
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maximum=256, |
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value=256, |
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step=1, |
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interactive=True, |
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label="Number of visual tokens", |
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) |
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temperature = gr.Slider( |
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minimum=0.1, |
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maximum=2.0, |
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value=1.0, |
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step=0.1, |
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interactive=True, |
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label="Temperature", |
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) |
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num_beams = gr.Slider( |
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minimum=1, |
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maximum=10, |
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value=5, |
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step=1, |
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interactive=True, |
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label="beam search numbers", |
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) |
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with gr.Column(): |
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chat_state = gr.State() |
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img_list = gr.State() |
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chatbot = gr.Chatbot(label='MQT-LLaVA') |
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text_input = gr.Textbox(label='User', placeholder='Please upload your image first', interactive=False) |
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gr.Examples(examples=[ |
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[f"images/extreme_ironing.jpg", "What is unusual about this image?"], |
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[f"images/waterview.jpg", "What are the things I should be cautious about when I visit here?"], |
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], inputs=[image, text_input]) |
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upload_button.click(upload_img, [image, text_input, chat_state], [image, text_input, upload_button, chat_state, img_list]) |
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text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then( |
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gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature, num_visual_tokens], [chatbot, chat_state, img_list] |
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) |
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clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, upload_button, chat_state, img_list], queue=False) |
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demo.launch() |