app.py

import gradio as gr
import torch
from transformers import AutoConfig, AutoProcessor
# Import the custom model class directly.
from transformers.models.llava.modeling_llava import LlavaQwenForCausalLM

# --- Diagnostic Print (Optional) ---
config = AutoConfig.from_pretrained(
    "lmms-lab/LLaVA-Video-7B-Qwen2",
    trust_remote_code=True
)
print("Configuration type:", type(config))
print("Configuration architectures:", config.architectures)
# --- End Diagnostic ---

# Load the processor and the model using the custom model class.
processor = AutoProcessor.from_pretrained(
    "lmms-lab/LLaVA-Video-7B-Qwen2",
    trust_remote_code=True
)
model = LlavaQwenForCausalLM.from_pretrained(
    "lmms-lab/LLaVA-Video-7B-Qwen2",
    trust_remote_code=True
)

# Move model to the appropriate device.
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

def analyze_video(video_path):
    prompt = "Analyze this video of a concert and determine the moment when the crowd is most engaged."
    # Process the text and video.
    inputs = processor(text=prompt, video=video_path, return_tensors="pt")
    inputs = {k: v.to(device) for k, v in inputs.items()}
    # Generate output (assumes the custom model has a generate method).
    outputs = model.generate(**inputs, max_new_tokens=100)
    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 moment when the crowd is most engaged."
)

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