app.py

import spaces
import gradio as gr

import subprocess  # 🥲
subprocess.run(
    "pip install flash-attn --no-build-isolation",
    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
    shell=True,
)
# subprocess.run(
#     "pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git",
#     shell=True,
# )

import torch
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
from llava.conversation import conv_templates, SeparatorStyle
import copy
import warnings
from decord import VideoReader, cpu
import numpy as np
import tempfile
import os
import shutil
#warnings.filterwarnings("ignore")
title = "# Demo of VLM on Crime scenes"
description1 ="""The **🌋📹LLaVA-Video-7B-Qwen2** is a 7B parameter model  trained on the 🌋📹LLaVA-Video-178K dataset and the LLaVA-OneVision dataset. It is [based on the **Qwen2 language model**](https://huggingface.co/collections/Qwen/qwen2-6659360b33528ced941e557f), supporting a context window of up to 32K tokens. The model can process and interact with images, multi-images, and videos, with specific optimizations for video analysis.
This model leverages the **SO400M vision backbone** for visual input and Qwen2 for language processing, making it highly efficient in multi-modal reasoning, including visual and video-based tasks.
🌋📹LLaVA-Video has larger variants of [32B](https://huggingface.co/lmms-lab/LLaVA-NeXT-Video-32B-Qwen) and [72B](https://huggingface.co/lmms-lab/LLaVA-Video-72B-Qwen2) and with a [variant](https://huggingface.co/lmms-lab/LLaVA-Video-7B-Qwen2-Video-Only) only trained on the new synthetic data
For further details, please visit the [Project Page](https://github.com/LLaVA-VL/LLaVA-NeXT) or check out the corresponding [research paper](https://arxiv.org/abs/2410.02713).
- **Architecture**: `LlavaQwenForCausalLM`
- **Attention Heads**: 28
- **Hidden Layers**: 28
- **Hidden Size**: 3584
"""
description2 ="""
We have leveraged this VLM for Crime scene video description. The expected performance is achieved and we thank everyone who made this possible.
"""


def load_video(video_path, max_frames_num, fps=1, force_sample=False):
    if max_frames_num == 0:
        return np.zeros((1, 336, 336, 3))
    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
    total_frame_num = len(vr)
    video_time = total_frame_num / vr.get_avg_fps()
    fps = round(vr.get_avg_fps()/fps)
    frame_idx = [i for i in range(0, len(vr), fps)]
    frame_time = [i/fps for i in frame_idx]
    if len(frame_idx) > max_frames_num or force_sample:
        sample_fps = max_frames_num
        uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int)
        frame_idx = uniform_sampled_frames.tolist()
        frame_time = [i/vr.get_avg_fps() for i in frame_idx]
    frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
    spare_frames = vr.get_batch(frame_idx).asnumpy()
    return spare_frames, frame_time, video_time

# Load the model
pretrained = "lmms-lab/LLaVA-Video-7B-Qwen2"
model_name = "llava_qwen"
device = "cuda" if torch.cuda.is_available() else "cpu"
device_map = "auto"

print("Loading model...")
tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map)
model.eval()
print("Model loaded successfully!")

@spaces.GPU
def process_video(video_path, question):
    max_frames_num = 64
    video, frame_time, video_time = load_video(video_path, max_frames_num, 1, force_sample=True)
    video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].to(device).bfloat16()
    video = [video]

    conv_template = "qwen_1_5"
    time_instruction = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}. Please answer the following questions related to this video."
    
    full_question = DEFAULT_IMAGE_TOKEN + f"{time_instruction}\n{question}"
    
    conv = copy.deepcopy(conv_templates[conv_template])
    conv.append_message(conv.roles[0], full_question)
    conv.append_message(conv.roles[1], None)
    prompt_question = conv.get_prompt()
    
    input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
    
    with torch.no_grad():
        output = model.generate(
            input_ids,
            images=video,
            modalities=["video"],
            do_sample=False,
            temperature=0,
            max_new_tokens=4096,
        )
    
    response = tokenizer.batch_decode(output, skip_special_tokens=True)[0].strip()
    return response

def gradio_interface(video_file, question):
    if video_file is None:
        return "Please upload a video file."
    response = process_video(video_file, question)
    return response

with gr.Blocks() as demo:
    gr.Markdown(title)
    with gr.Row():
        with gr.Group():
            gr.Markdown(description1)
        with gr.Group():
            gr.Markdown(description2)
    with gr.Row():
        with gr.Column():
            video_input = gr.Video()
            question_input = gr.Textbox(label="🙋🏻‍♂️User Question", placeholder="Ask a question about the video... or Ask to describe the video")
            submit_button = gr.Button("Ask")
        output = gr.Textbox(label="VLM Bot")
    
    submit_button.click(
        fn=gradio_interface,
        inputs=[video_input, question_input],
        outputs=output
    )

if __name__ == "__main__":
    demo.launch(show_error=True, ssr_mode = False)