import shutil import subprocess import torch import gradio as gr from fastapi import FastAPI import os from PIL import Image import tempfile from decord import VideoReader, cpu from transformers import TextStreamer from llava.constants import DEFAULT_X_TOKEN, X_TOKEN_INDEX from llava.conversation import conv_templates, SeparatorStyle, Conversation from llava.serve.gradio_utils import Chat, tos_markdown, learn_more_markdown, title_markdown, block_css def save_image_to_local(image): filename = os.path.join('temp', next(tempfile._get_candidate_names()) + '.jpg') image = Image.open(image) image.save(filename) # print(filename) return filename def save_video_to_local(video_path): filename = os.path.join('temp', next(tempfile._get_candidate_names()) + '.mp4') shutil.copyfile(video_path, filename) return filename def generate(image1, video, textbox_in, first_run, state, state_, images_tensor): flag = 1 if not textbox_in: if len(state_.messages) > 0: textbox_in = state_.messages[-1][1] state_.messages.pop(-1) flag = 0 else: return "Please enter instruction" image1 = image1 if image1 else "none" video = video if video else "none" # assert not (os.path.exists(image1) and os.path.exists(video)) if type(state) is not Conversation: state = conv_templates[conv_mode].copy() state_ = conv_templates[conv_mode].copy() images_tensor = [[], []] first_run = False if len(state.messages) > 0 else True text_en_in = textbox_in.replace("picture", "image") # images_tensor = [[], []] image_processor = handler.image_processor if os.path.exists(image1) and not os.path.exists(video): tensor = image_processor.preprocess(image1, return_tensors='pt')['pixel_values'][0] # print(tensor.shape) tensor = tensor.to(handler.model.device, dtype=dtype) images_tensor[0] = images_tensor[0] + [tensor] images_tensor[1] = images_tensor[1] + ['image'] print(torch.cuda.memory_allocated()) print(torch.cuda.max_memory_allocated()) video_processor = handler.video_processor if not os.path.exists(image1) and os.path.exists(video): tensor = video_processor(video, return_tensors='pt')['pixel_values'][0] # print(tensor.shape) tensor = tensor.to(handler.model.device, dtype=dtype) images_tensor[0] = images_tensor[0] + [tensor] images_tensor[1] = images_tensor[1] + ['video'] print(torch.cuda.memory_allocated()) print(torch.cuda.max_memory_allocated()) if os.path.exists(image1) and os.path.exists(video): tensor = video_processor(video, return_tensors='pt')['pixel_values'][0] # print(tensor.shape) tensor = tensor.to(handler.model.device, dtype=dtype) images_tensor[0] = images_tensor[0] + [tensor] images_tensor[1] = images_tensor[1] + ['video'] tensor = image_processor.preprocess(image1, return_tensors='pt')['pixel_values'][0] # print(tensor.shape) tensor = tensor.to(handler.model.device, dtype=dtype) images_tensor[0] = images_tensor[0] + [tensor] images_tensor[1] = images_tensor[1] + ['image'] print(torch.cuda.memory_allocated()) print(torch.cuda.max_memory_allocated()) if os.path.exists(image1) and not os.path.exists(video): text_en_in = DEFAULT_X_TOKEN['IMAGE'] + '\n' + text_en_in if not os.path.exists(image1) and os.path.exists(video): text_en_in = DEFAULT_X_TOKEN['VIDEO'] + '\n' + text_en_in if os.path.exists(image1) and os.path.exists(video): text_en_in = DEFAULT_X_TOKEN['VIDEO'] + '\n' + text_en_in + '\n' + DEFAULT_X_TOKEN['IMAGE'] text_en_out, state_ = handler.generate(images_tensor, text_en_in, first_run=first_run, state=state_) state_.messages[-1] = (state_.roles[1], text_en_out) text_en_out = text_en_out.split('#')[0] textbox_out = text_en_out show_images = "" if os.path.exists(image1): filename = save_image_to_local(image1) show_images += f'' if os.path.exists(video): filename = save_video_to_local(video) show_images += f'' if flag: state.append_message(state.roles[0], textbox_in + "\n" + show_images) state.append_message(state.roles[1], textbox_out) torch.cuda.empty_cache() return (state, state_, state.to_gradio_chatbot(), False, gr.update(value=None, interactive=True), images_tensor, gr.update(value=image1 if os.path.exists(image1) else None, interactive=True), gr.update(value=video if os.path.exists(video) else None, interactive=True)) def regenerate(state, state_): state.messages.pop(-1) state_.messages.pop(-1) if len(state.messages) > 0: return state, state_, state.to_gradio_chatbot(), False return (state, state_, state.to_gradio_chatbot(), True) def clear_history(state, state_): state = conv_templates[conv_mode].copy() state_ = conv_templates[conv_mode].copy() return (gr.update(value=None, interactive=True), gr.update(value=None, interactive=True),\ gr.update(value=None, interactive=True),\ True, state, state_, state.to_gradio_chatbot(), [[], []]) conv_mode = "llava_v1" model_path = 'LanguageBind/Video-LLaVA-7B' device = 'cpu' load_8bit = False load_4bit = True dtype = torch.get_default_dtype() handler = Chat(model_path, conv_mode=conv_mode, load_8bit=load_8bit, load_4bit=load_8bit, device=device) # handler.model.to(dtype=dtype) if not os.path.exists("temp"): os.makedirs("temp") ''' conv_mode = "llava_v1" model_path = 'LanguageBind/Video-LLaVA-7B' device = 'cuda' load_8bit = False load_4bit = True dtype = torch.float16 handler = Chat(model_path, conv_mode=conv_mode, load_8bit=load_8bit, load_4bit=load_8bit, device=device) # handler.model.to(dtype=dtype) if not os.path.exists("temp"): os.makedirs("temp") ''' print(torch.cuda.memory_allocated()) print(torch.cuda.max_memory_allocated()) app = FastAPI() textbox = gr.Textbox( show_label=False, placeholder="Enter text and press ENTER", container=False ) with gr.Blocks(title='Video-LLaVA๐Ÿš€', theme=gr.themes.Default(), css=block_css) as demo: gr.Markdown(title_markdown) state = gr.State() state_ = gr.State() first_run = gr.State() images_tensor = gr.State() with gr.Row(): with gr.Column(scale=3): image1 = gr.Image(label="Input Image", type="filepath") video = gr.Video(label="Input Video") cur_dir = os.path.dirname(os.path.abspath(__file__)) gr.Examples( examples=[ [ f"{cur_dir}/examples/extreme_ironing.jpg", "What is unusual about this image?", ], [ f"{cur_dir}/examples/waterview.jpg", "What are the things I should be cautious about when I visit here?", ], [ f"{cur_dir}/examples/desert.jpg", "If there are factual errors in the questions, point it out; if not, proceed answering the question. Whatโ€™s happening in the desert?", ], ], inputs=[image1, textbox], ) with gr.Column(scale=7): chatbot = gr.Chatbot(label="Video-LLaVA", bubble_full_width=True).style(height=750) with gr.Row(): with gr.Column(scale=8): textbox.render() with gr.Column(scale=1, min_width=50): submit_btn = gr.Button( value="Send", variant="primary", interactive=True ) with gr.Row(elem_id="buttons") as button_row: upvote_btn = gr.Button(value="๐Ÿ‘ Upvote", interactive=True) downvote_btn = gr.Button(value="๐Ÿ‘Ž Downvote", interactive=True) flag_btn = gr.Button(value="โš ๏ธ Flag", interactive=True) # stop_btn = gr.Button(value="โน๏ธ Stop Generation", interactive=False) regenerate_btn = gr.Button(value="๐Ÿ”„ Regenerate", interactive=True) clear_btn = gr.Button(value="๐Ÿ—‘๏ธ Clear history", interactive=True) with gr.Row(): gr.Examples( examples=[ [ f"{cur_dir}/examples/sample_img_8.png", f"{cur_dir}/examples/sample_demo_8.mp4", "Are the image and the video depicting the same place?", ], [ f"{cur_dir}/examples/sample_img_22.png", f"{cur_dir}/examples/sample_demo_22.mp4", "Are the instruments in the pictures used in the video?", ], [ f"{cur_dir}/examples/sample_img_13.png", f"{cur_dir}/examples/sample_demo_13.mp4", "Does the flag in the image appear in the video?", ], ], inputs=[image1, video, textbox], ) gr.Examples( examples=[ [ f"{cur_dir}/examples/sample_demo_1.mp4", "Why is this video funny?", ], '''[ f"{cur_dir}/examples/sample_demo_7.mp4", "Create a short fairy tale with a moral lesson inspired by the video.", ], [ f"{cur_dir}/examples/sample_demo_8.mp4", "Where is this video taken from? What place/landmark is shown in the video?", ], [ f"{cur_dir}/examples/sample_demo_12.mp4", "What does the woman use to split the logs and how does she do it?", ], [ f"{cur_dir}/examples/sample_demo_18.mp4", "Describe the video in detail.", ], [ f"{cur_dir}/examples/sample_demo_22.mp4", "Describe the activity in the video.", ],''' ], inputs=[video, textbox], ) gr.Markdown(tos_markdown) gr.Markdown(learn_more_markdown) submit_btn.click(generate, [image1, video, textbox, first_run, state, state_, images_tensor], [state, state_, chatbot, first_run, textbox, images_tensor, image1, video]) regenerate_btn.click(regenerate, [state, state_], [state, state_, chatbot, first_run]).then( generate, [image1, video, textbox, first_run, state, state_, images_tensor], [state, state_, chatbot, first_run, textbox, images_tensor, image1, video]) clear_btn.click(clear_history, [state, state_], [image1, video, textbox, first_run, state, state_, chatbot, images_tensor]) # app = gr.mount_gradio_app(app, demo, path="/") demo.launch() # uvicorn llava.serve.gradio_web_server:app