import spaces import os import shutil import torch import tempfile import gradio as gr from PIL import Image import sys sys.path.append('./') from videollama2.constants import MMODAL_TOKEN_INDEX, DEFAULT_MMODAL_TOKEN from videollama2.conversation import conv_templates, SeparatorStyle, Conversation from videollama2.model.builder import load_pretrained_model from videollama2.mm_utils import KeywordsStoppingCriteria, tokenizer_MMODAL_token, get_model_name_from_path, process_image, process_video title_markdown = ("""
VideoLLaMA2πŸš€

VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

If you like our project, please give us a star ✨ on Github for the latest update.
""") block_css = """ #buttons button { min-width: min(120px,100%); } """ tos_markdown = (""" ### Terms of use By using this service, users are required to agree to the following terms: The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research. Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator. For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality. """) learn_more_markdown = (""" ### License The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. """) class Chat: def __init__(self, model_path, conv_mode, model_base=None, load_8bit=False, load_4bit=False): # disable_torch_init() model_name = get_model_name_from_path(model_path) self.tokenizer, self.model, processor, context_len = load_pretrained_model( model_path, model_base, model_name, load_8bit, load_4bit, offload_folder="save_folder") self.processor = processor self.conv_mode = conv_mode self.conv = conv_templates[conv_mode].copy() def get_prompt(self, qs, state): state.append_message(state.roles[0], qs) state.append_message(state.roles[1], None) return state @spaces.GPU(duration=120) @torch.inference_mode() def generate(self, tensor: list, modals: list, prompt: str, first_run: bool, state): # TODO: support multiple turns of conversation. assert len(tensor) == len(modals) # 1. prepare model, tokenizer, and processor. tokenizer, model, processor = self.tokenizer, self.model, self.processor # 2. text preprocess (tag process & generate prompt). state = self.get_prompt(prompt, state) prompt = state.get_prompt() # print('\n\n\n') # print(prompt) input_ids = tokenizer_MMODAL_token(prompt, tokenizer, MMODAL_TOKEN_INDEX[modals[0]], return_tensors='pt').unsqueeze(0).to(self.model.device) # 3. generate response according to visual signals and prompts. stop_str = self.conv.sep if self.conv.sep_style in [SeparatorStyle.SINGLE] else self.conv.sep2 # keywords = ["", ""] keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, images_or_videos=tensor, modal_list=modals, do_sample=True, temperature=0.2, max_new_tokens=1024, use_cache=True, stopping_criteria=[stopping_criteria], ) outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] print(outputs) return outputs, state def save_image_to_local(image): filename = os.path.join('temp', next(tempfile._get_candidate_names()) + '.jpg') image = Image.open(image) image.save(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 @spaces.GPU(duration=120) def generate(image, video, first_run, state, state_, textbox_in, tensor, modals, dtype=torch.float16): 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" image = image if image else "none" video = video if video else "none" assert not (os.path.exists(image) and os.path.exists(video)) if type(state) is not Conversation: state = conv_templates[conv_mode].copy() state_ = conv_templates[conv_mode].copy() tensor = [] modals = [] first_run = False if len(state.messages) > 0 else True text_en_in = textbox_in.replace("picture", "image") processor = handler.processor if os.path.exists(image) and not os.path.exists(video): tensor.append(process_image(image, processor).to(handler.model.device, dtype=dtype)) modals.append('IMAGE') if not os.path.exists(image) and os.path.exists(video): tensor.append(process_video(video, processor).to(handler.model.device, dtype=dtype)) modals.append('VIDEO') if os.path.exists(image) and os.path.exists(video): raise NotImplementedError("Not support image and video at the same time") # BUG: Only support single video and image inference now. if os.path.exists(image) and not os.path.exists(video): text_en_in = text_en_in.replace(DEFAULT_MMODAL_TOKEN['IMAGE'], '').strip() text_en_in = DEFAULT_MMODAL_TOKEN['IMAGE'] + '\n' + text_en_in if not os.path.exists(image) and os.path.exists(video): text_en_in = text_en_in.replace(DEFAULT_MMODAL_TOKEN['VIDEO'], '').strip() text_en_in = DEFAULT_MMODAL_TOKEN['VIDEO'] + '\n' + text_en_in # if os.path.exists(image) and os.path.exists(video): # pass text_en_out, state_ = handler.generate(tensor, modals, 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(image): filename = save_image_to_local(image) 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) return (gr.update(value=image if os.path.exists(image) else None, interactive=True), gr.update(value=video if os.path.exists(video) else None, interactive=True), state.to_gradio_chatbot(), False, state, state_, gr.update(value=None, interactive=True), # tensor, modals ) def regenerate(state, state_, textbox, tensor, modals): state.messages.pop(-1) state_.messages.pop(-1) tensor.pop(-1) modals.pop(-1) textbox = gr.update(value=None, interactive=True) if len(state.messages) > 0: return state, state_, textbox, state.to_gradio_chatbot(), False, tensor, modals return state, state_, textbox, state.to_gradio_chatbot(), True, tensor, modals def clear_history(state, state_, tensor, modals): 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), \ state.to_gradio_chatbot(), \ True, state, state_, gr.update(value=None, interactive=True), [], []) conv_mode = "llama_2" model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B' def find_cuda(): # Check if CUDA_HOME or CUDA_PATH environment variables are set cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH') if cuda_home and os.path.exists(cuda_home): return cuda_home # Search for the nvcc executable in the system's PATH nvcc_path = shutil.which('nvcc') if nvcc_path: # Remove the 'bin/nvcc' part to get the CUDA installation path cuda_path = os.path.dirname(os.path.dirname(nvcc_path)) return cuda_path return None cuda_path = find_cuda() if cuda_path: print(f"CUDA installation found at: {cuda_path}") else: print("CUDA installation not found") device = torch.device("cuda") handler = Chat(model_path, conv_mode=conv_mode, load_8bit=False, load_4bit=True) # handler.model.to(dtype=torch.float16) # handler = handler.model.to(device) if not os.path.exists("temp"): os.makedirs("temp") textbox = gr.Textbox( show_label=False, placeholder="Enter text and press ENTER", container=False ) with gr.Blocks(title='VideoLLaMA2πŸš€', theme=gr.themes.Default(), css=block_css) as demo: gr.Markdown(title_markdown) state = gr.State() state_ = gr.State() first_run = gr.State() tensor = gr.State() modals = gr.State() with gr.Row(): with gr.Column(scale=3): image = 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. What’s happening in the desert?", ], ], inputs=[image, textbox], ) with gr.Column(scale=7): chatbot = gr.Chatbot(label="VideoLLaMA2", bubble_full_width=True, 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="πŸ‘ Upvote", interactive=True) downvote_btn = gr.Button(value="πŸ‘Ž Downvote", interactive=True) # flag_btn = gr.Button(value="⚠️ Flag", interactive=True) # stop_btn = gr.Button(value="⏹️ Stop Generation", interactive=False) regenerate_btn = gr.Button(value="πŸ”„ Regenerate", interactive=True) clear_btn = gr.Button(value="πŸ—‘οΈ Clear history", interactive=True) gr.Markdown(tos_markdown) gr.Markdown(learn_more_markdown) submit_btn.click(generate, [image, video, first_run, state, state_, textbox, tensor, modals], [image, video, chatbot, first_run, state, state_, textbox, # tensor, modals ]) regenerate_btn.click(regenerate, [state, state_, textbox, tensor, modals], [state, state_, textbox, chatbot, first_run, tensor, modals]).then( generate, [image, video, first_run, state, state_, textbox, tensor, modals], [image, video, chatbot, first_run, state, state_, textbox, tensor, modals]) clear_btn.click(clear_history, [state, state_, tensor, modals], [image, video, chatbot, first_run, state, state_, textbox, tensor, modals]) demo.launch()