app.py

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

# HERE IS WHERE THE MODEL NAME GOES ⬇️
model_name = "DAMO-NLP-SG/VideoRefer-VideoLLaMA3-7B"

# Load the model function
def load_model():
    try:
        # Use the model name here ⬇️
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForCausalLM.from_pretrained(
            model_name,  # And here ⬇️
            torch_dtype=torch.float16,
            device_map="auto",
            trust_remote_code=True  # May be needed for some models
        )
        return tokenizer, model
    except Exception as e:
        return None, None

# Initialize model (this happens when the Space starts)
print(f"Loading model: {model_name}")  # And you can use it here ⬇️
tokenizer, model = load_model()

def process_video_question(video_file, question):
    """Process video and answer questions about it"""
    if model is None:
        return "Sorry, the model failed to load. Please try again later."
    
    if video_file is None:
        return "Please upload a video file first."
    
    if not question.strip():
        return "Please enter a question about the video."
    
    try:
        # Your video processing logic would go here
        # This is a placeholder - you'll need to implement the actual VideoLLaMA3 pipeline
        
        # For now, just return a simple response
        response = f"I received your video and question: '{question}'. Video processing with {model_name} would happen here."
        return response
        
    except Exception as e:
        return f"Error processing video: {str(e)}"

# Create the Gradio interface
with gr.Blocks(title="VideoLLaMA3 Demo") as demo:
    gr.Markdown("# 🎥 VideoLLaMA3 Interactive Demo")
    gr.Markdown(f"**Model:** `{model_name}`")  # Display the model name ⬇️
    gr.Markdown("Upload a video and ask questions about its content!")
    
    with gr.Row():
        with gr.Column(scale=1):
            video_input = gr.Video(
                label="📹 Upload Video",
                height=300
            )
            question_input = gr.Textbox(
                label="❓ Ask a question about the video",
                placeholder="What is happening in this video?",
                lines=2
            )
            submit_btn = gr.Button("🚀 Analyze Video", variant="primary")
            
        with gr.Column(scale=1):
            output_text = gr.Textbox(
                label="🤖 AI Response",
                lines=10,
                placeholder="The AI response will appear here..."
            )
    
    # Examples section
    gr.Markdown("### 💡 Example Questions:")
    gr.Markdown("""
    - "What objects can you see in this video?"
    - "Describe the main action happening"
    - "What is the setting or location?"
    - "How many people are in the video?"
    """)
    
    # Connect the button to the function
    submit_btn.click(
        fn=process_video_question,
        inputs=[video_input, question_input],
        outputs=output_text
    )

# Launch the app
if __name__ == "__main__":
    demo.launch()