import torch import gradio as gr from gradio.themes.utils import colors, fonts, sizes from conversation import Chat # videochat from utils.config import Config from utils.easydict import EasyDict from models.videochat import VideoChat # ======================================== # Model Initialization # ======================================== def init_model(): print('Initializing VideoChat') config_file = "configs/config.json" cfg = Config.from_file(config_file) model = VideoChat(config=cfg.model) model = model.to(torch.device(cfg.device)) model = model.eval() chat = Chat(model) print('Initialization Finished') return chat # ======================================== # 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_img(gr_img, gr_video, chat_state, num_segments): # print(gr_img, gr_video) chat_state = EasyDict({ "system": "", "roles": ("Human", "Assistant"), "messages": [], "sep": "###" }) img_list = [] if gr_img is None and gr_video is None: return None, None, gr.update(interactive=True), chat_state, None if gr_video: llm_message, img_list, chat_state = chat.upload_video(gr_video, chat_state, img_list, num_segments) return gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list if gr_img: llm_message, img_list,chat_state = chat.upload_img(gr_img, chat_state, img_list) return gr.update(interactive=True), gr.update(interactive=True), 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 #print(chat_state) chat_state = chat.ask(user_message, chat_state) chatbot = chatbot + [[user_message, None]] return '', chatbot, chat_state def gradio_answer(gr_img, gr_video,chatbot, chat_state, img_list, num_beams, temperature): llm_message,llm_message_token, chat_state = chat.answer(conv=chat_state, img_list=img_list, max_new_tokens=1000, num_beams=num_beams, temperature=temperature) llm_message = llm_message.replace("", "") # handle chatbot[-1][1] = llm_message print(f"========{gr_img}####{gr_video}========") print(chat_state,flush=True) print(f"========{gr_img}####{gr_video}========") # print(f"Answer: {llm_message}") return chatbot, chat_state, img_list class OpenGVLab(gr.themes.base.Base): def __init__( self, *, primary_hue=colors.blue, secondary_hue=colors.sky, neutral_hue=colors.gray, spacing_size=sizes.spacing_md, radius_size=sizes.radius_sm, text_size=sizes.text_md, font=( fonts.GoogleFont("Noto Sans"), "ui-sans-serif", "sans-serif", ), font_mono=( fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace", ), ): super().__init__( primary_hue=primary_hue, secondary_hue=secondary_hue, neutral_hue=neutral_hue, spacing_size=spacing_size, radius_size=radius_size, text_size=text_size, font=font, font_mono=font_mono, ) super().set( body_background_fill="*neutral_50", ) gvlabtheme = OpenGVLab(primary_hue=colors.blue, secondary_hue=colors.sky, neutral_hue=colors.gray, spacing_size=sizes.spacing_md, radius_size=sizes.radius_sm, text_size=sizes.text_md, ) title = """

Ask-Anything

""" description ="""

VideoChat, an end-to-end chat-centric video understanding system powered by InternVideo. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal relationship inference.

""" with gr.Blocks(title="InternVideo-VideoChat!",theme=gvlabtheme,css="#chatbot {overflow:auto; height:500px;} #InputVideo {overflow:visible; height:320px;} footer {visibility: none}") as demo: gr.Markdown(title) gr.Markdown(description) with gr.Row(): with gr.Column(scale=0.5, visible=True) as video_upload: with gr.Column(elem_id="image") as img_part: with gr.Tab("Video", elem_id='video_tab'): up_video = gr.Video(interactive=True, include_audio=True, elem_id="video_upload")#.style(height=320) with gr.Tab("Image", elem_id='image_tab'): up_image = gr.Image(type="pil", interactive=True, elem_id="image_upload")#.style(height=320) upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary") 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", ) num_segments = gr.Slider( minimum=8, maximum=64, value=8, step=1, interactive=True, label="Video Segments", ) with gr.Column(visible=True) as input_raws: chat_state = gr.State(EasyDict({ "system": "", "roles": ("Human", "Assistant"), "messages": [], "sep": "###" })) img_list = gr.State() chatbot = gr.Chatbot(elem_id="chatbot",label='VideoChat') with gr.Row(): with gr.Column(scale=0.7): text_input = gr.Textbox(show_label=False, placeholder='Please upload your video first', interactive=False).style(container=False) with gr.Column(scale=0.15, min_width=0): run = gr.Button("💭Send") with gr.Column(scale=0.15, min_width=0): clear = gr.Button("🔄Clear️") chat = init_model() upload_button.click(upload_img, [up_image, up_video, chat_state, num_segments], [up_image, up_video, 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, [up_image, up_video, chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list] ) run.click(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then( gradio_answer, [up_image, up_video,chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list] ) run.click(lambda: "", None, text_input) clear.click(gradio_reset, [chat_state, img_list], [chatbot, up_image, up_video, text_input, upload_button, chat_state, img_list], queue=False) #demo.launch(server_name="0.0.0.0", favicon_path='bot_avatar.jpg', enable_queue=True,ssl_keyfile="vchat_cert/privkey1.pem",ssl_certfile="vchat_cert/cert1.pem",ssl_verify=False) demo.launch(server_name="0.0.0.0", enable_queue=True)