""" Adapted from: https://github.com/Vision-CAIR/MiniGPT-4/blob/main/demo.py """ import argparse import os import random import numpy as np import torch import torch.backends.cudnn as cudnn import gradio as gr from video_llama.common.config import Config from video_llama.common.dist_utils import get_rank from video_llama.common.registry import registry from video_llama.conversation.conversation_video import Chat, Conversation, default_conversation,SeparatorStyle import decord decord.bridge.set_bridge('torch') #%% # imports modules for registration from video_llama.datasets.builders import * from video_llama.models import * from video_llama.processors import * from video_llama.runners import * from video_llama.tasks import * #%% def parse_args(): parser = argparse.ArgumentParser(description="Demo") parser.add_argument("--cfg-path", default='eval_configs/video_llama_eval.yaml', 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 # ======================================== 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)) vis_processor_cfg = cfg.datasets_cfg.webvid.vis_processor.train vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg) chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id)) 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(value=None, interactive=True), gr.update(placeholder='Please upload your video first', interactive=False),gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list def upload_imgorvideo(gr_video, gr_img, text_input, chat_state): if gr_img is None and gr_video is None: return None, None, None, gr.update(interactive=True), chat_state, None elif gr_img is not None and gr_video is None: print(gr_img) chat_state = Conversation( system= "You are able to understand the visual content that the user provides." "Follow the instructions carefully and explain your answers in detail.", roles=("Human", "Assistant"), messages=[], offset=0, sep_style=SeparatorStyle.SINGLE, sep="###", ) img_list = [] llm_message = chat.upload_img(gr_img, chat_state, img_list) return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list elif gr_video is not None and gr_img is None: print(gr_video) chat_state = default_conversation.copy() chat_state = Conversation( system= "You are able to understand the visual content that the user provides." "Follow the instructions carefully and explain your answers in detail.", roles=("Human", "Assistant"), messages=[], offset=0, sep_style=SeparatorStyle.SINGLE, sep="###", ) img_list = [] llm_message = chat.upload_video(gr_video, chat_state, img_list) return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list else: # img_list = [] return gr.update(interactive=False), gr.update(interactive=False, placeholder='Currently, only one input is supported'), gr.update(value="Currently, only one input is supported", interactive=False), chat_state, None 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=1, temperature=temperature, max_new_tokens=300, max_length=2000)[0] chatbot[-1][1] = llm_message print(chat_state.get_prompt()) print(chat_state) return chatbot, chat_state, img_list title = """

Video-LLaMA

Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

""" #TODO show examples below with gr.Blocks() as demo: gr.Markdown(title) with gr.Row(): with gr.Column(scale=0.5): video = gr.Video() 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(): chat_state = gr.State() img_list = gr.State() chatbot = gr.Chatbot(label='Video-LLaMA') text_input = gr.Textbox(label='User', placeholder='Please upload your image/video first', interactive=False) upload_button.click(upload_imgorvideo, [video, image, text_input, chat_state], [video, 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, video, image, text_input, upload_button, chat_state, img_list], queue=False) demo.launch(share=False, enable_queue=True) # %%