from transformers import AutoProcessor, AutoModelForCausalLM
import torch
import gradio as gr

# Ensure you use the latest version of transformers!
# For example, in your requirements.txt, you might include:
# transformers>=4.31.0

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

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."
    # The processor is expected to handle both text and video input.
    inputs = processor(text=prompt, video=video_path, return_tensors="pt")
    inputs = {k: v.to(device) for k, v in inputs.items()}
    outputs = model.generate(**inputs, max_new_tokens=100)
    answer = processor.decode(outputs[0], skip_special_tokens=True)
    return answer

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()