app.py

import gradio as gr
import torch
from transformers import AutoProcessor

# Import the custom model class directly from the remote code.
# Note: The import path here is based on the repository structure. If this fails,
# check the model repository's files to confirm the correct import path and class name.
from transformers.models.llava.modeling_llava import LlavaForCausalLM

# Load the processor and model while trusting remote code.
processor = AutoProcessor.from_pretrained(
    "lmms-lab/LLaVA-Video-7B-Qwen2",
    trust_remote_code=True
)
model = LlavaForCausalLM.from_pretrained(
    "lmms-lab/LLaVA-Video-7B-Qwen2",
    trust_remote_code=True
)

# Set device to GPU if available.
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

def analyze_video(video_path):
    """
    This function accepts the path to a video file,
    then uses the LLaVA-Video model to analyze it for the moment
    when the crowd is most engaged.
    """
    prompt = "Analyze this video of a concert and determine the moment when the crowd is most engaged."
    
    # Process the text and video input.
    # (Make sure that the processor handles video inputs as expected.)
    inputs = processor(text=prompt, video=video_path, return_tensors="pt")
    
    # Move tensors to the device.
    inputs = {key: value.to(device) for key, value in inputs.items()}
    
    # Generate a response.
    outputs = model.generate(**inputs, max_new_tokens=100)
    
    # Decode the generated tokens to a string.
    answer = processor.decode(outputs[0], skip_special_tokens=True)
    return answer

# Create the Gradio interface.
iface = gr.Interface(
    fn=analyze_video,
    inputs=gr.Video(label="Upload Concert/Event Video", type="filepath"),
    outputs=gr.Textbox(label="Engagement Analysis"),
    title="Crowd Engagement Analyzer",
    description=(
        "Upload a video of a concert or event and the model will analyze "
        "the video to identify the moment when the crowd is most engaged."
    )
)

if __name__ == "__main__":
    iface.launch()