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| 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("<s>", "") # handle <s> | |
| chatbot[-1][1] = llm_message | |
| print(f"========{gr_img}##<BOS>##{gr_video}========") | |
| print(chat_state,flush=True) | |
| print(f"========{gr_img}##<END>##{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 = """<h1 align="center"><a href="https://github.com/OpenGVLab/Ask-Anything"><img src="https://i.328888.xyz/2023/05/11/iqrAkZ.md.png" alt="Ask-Anything" border="0" style="margin: 0 auto; height: 100px;" /></a> </h1>""" | |
| description =""" | |
| <a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/OpenGVLab/VideoChatGPT?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> | |
| Due to the limited GPU vRAM, here is the Video Chat-7B-8BIT. You can duplicate and use it with a paid private GPU. Alternatively, you can also use the demo on our [project page](https://vchat.opengvlab.com) with vchat-13B. | |
| <p> VideoChat, an end-to-end chat-centric video understanding system powered by <a href='https://github.com/OpenGVLab/InternVideo'>InternVideo</a>. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal relationship inference.</p> | |
| <div style='display:flex; gap: 0.25rem; '> | |
| <a src="https://img.shields.io/badge/Github-Code-blue?logo=github" href="https://github.com/OpenGVLab/Ask-Anything"> <img src="https://img.shields.io/badge/Github-Code-blue?logo=github"> | |
| <a src="https://img.shields.io/badge/cs.CV-2305.06355-b31b1b?logo=arxiv&logoColor=red" href="https://arxiv.org/abs/2305.06355"> <img src="https://img.shields.io/badge/cs.CV-2305.06355-b31b1b?logo=arxiv&logoColor=red"> | |
| <a src="https://img.shields.io/badge/WeChat-Group-green?logo=wechat" href="https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/papers/media/wechat_group.jpg"> <img src="https://img.shields.io/badge/WeChat-Group-green?logo=wechat"> | |
| <a src="https://img.shields.io/discord/1099920215724277770?label=Discord&logo=discord" href="https://discord.gg/A2Ex6Pph6A"> <img src="https://img.shields.io/discord/1099920215724277770?label=Discord&logo=discord"> </div> | |
| """ | |
| 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") |