import os import os.path as osp import gradio as gr import spaces import torch from threading import Thread from transformers import AutoModelForCausalLM, AutoProcessor, TextIteratorStreamer HEADER = """ """ class VideoLLaMA3GradioInterface(object): def __init__(self, model_name, device="cpu", example_dir=None, **server_kwargs): self.device = device self.model = AutoModelForCausalLM.from_pretrained( model_name, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", ) self.model.to(self.device) self.processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True) self.server_kwargs = server_kwargs self.image_formats = ("png", "jpg", "jpeg") self.video_formats = ("mp4",) image_examples, video_examples = [], [] if example_dir is not None: example_files = [ osp.join(example_dir, f) for f in os.listdir(example_dir) ] for example_file in example_files: if example_file.endswith(self.image_formats): image_examples.append([example_file]) elif example_file.endswith(self.video_formats): video_examples.append([example_file]) with gr.Blocks() as self.interface: gr.Markdown(HEADER) with gr.Row(): chatbot = gr.Chatbot(type="messages", elem_id="chatbot", height=710) with gr.Column(): with gr.Tab(label="Input"): with gr.Row(): input_video = gr.Video(sources=["upload"], label="Upload Video") input_image = gr.Image(sources=["upload"], type="filepath", label="Upload Image") if len(image_examples): gr.Examples(image_examples, inputs=[input_image], label="Example Images") if len(video_examples): gr.Examples(video_examples, inputs=[input_video], label="Example Videos") input_text = gr.Textbox(label="Input Text", placeholder="Type your message here and press enter to submit") submit_button = gr.Button("Generate") with gr.Tab(label="Configure"): with gr.Accordion("Generation Config", open=True): do_sample = gr.Checkbox(value=True, label="Do Sample") temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, label="Temperature") top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.9, label="Top P") max_new_tokens = gr.Slider(minimum=0, maximum=4096, value=2048, step=1, label="Max New Tokens") with gr.Accordion("Video Config", open=True): fps = gr.Slider(minimum=0.0, maximum=10.0, value=1, label="FPS") max_frames = gr.Slider(minimum=0, maximum=256, value=180, step=1, label="Max Frames") input_video.change(self._on_video_upload, [chatbot, input_video], [chatbot, input_video]) input_image.change(self._on_image_upload, [chatbot, input_image], [chatbot, input_image]) input_text.submit(self._on_text_submit, [chatbot, input_text], [chatbot, input_text]) submit_button.click( self._predict, [ chatbot, input_text, do_sample, temperature, top_p, max_new_tokens, fps, max_frames ], [chatbot], ) def _on_video_upload(self, messages, video): if video is not None: # messages.append({"role": "user", "content": gr.Video(video)}) messages.append({"role": "user", "content": {"path": video}}) return messages, None def _on_image_upload(self, messages, image): if image is not None: # messages.append({"role": "user", "content": gr.Image(image)}) messages.append({"role": "user", "content": {"path": image}}) return messages, None def _on_text_submit(self, messages, text): messages.append({"role": "user", "content": text}) return messages, "" @spaces.GPU(duration=120) def _predict(self, messages, input_text, do_sample, temperature, top_p, max_new_tokens, fps, max_frames): if len(input_text) > 0: messages.append({"role": "user", "content": input_text}) new_messages = [] contents = [] for message in messages: if message["role"] == "assistant": if len(contents): new_messages.append({"role": "user", "content": contents}) contents = [] new_messages.append(message) elif message["role"] == "user": if isinstance(message["content"], str): contents.append(message["content"]) else: media_path = message["content"][0] if media_path.endswith(self.video_formats): contents.append({"type": "video", "video": {"video_path": media_path, "fps": fps, "max_frames": max_frames}}) elif media_path.endswith(self.image_formats): contents.append({"type": "image", "image": {"image_path": media_path}}) else: raise ValueError(f"Unsupported media type: {media_path}") if len(contents): new_messages.append({"role": "user", "content": contents}) if len(new_messages) == 0 or new_messages[-1]["role"] != "user": return messages generation_config = { "do_sample": do_sample, "temperature": temperature, "top_p": top_p, "max_new_tokens": max_new_tokens } inputs = self.processor( conversation=new_messages, add_system_prompt=True, add_generation_prompt=True, return_tensors="pt" ) inputs = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()} if "pixel_values" in inputs: inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16) streamer = TextIteratorStreamer(self.processor.tokenizer, skip_prompt=True, skip_special_tokens=True) generation_kwargs = { **inputs, **generation_config, "streamer": streamer, } thread = Thread(target=self.model.generate, kwargs=generation_kwargs) thread.start() messages.append({"role": "assistant", "content": ""}) for token in streamer: messages[-1]['content'] += token yield messages def launch(self): self.interface.launch(**self.server_kwargs) if __name__ == "__main__": interface = VideoLLaMA3GradioInterface( model_name="DAMO-NLP-SG/VideoLLaMA3-7B", device="cuda", example_dir="./examples", ) interface.launch()