app.py

import gradio as gr
import google.generativeai as genai
from pathlib import Path
import tempfile



def summarize_video(video_path):
    if video_path is None:
        return "Please upload a video file."
    
    try:
        # Create a temporary file to store the video
        with tempfile.NamedTemporaryFile(suffix=Path(video_path.name).suffix, delete=False) as tmp_file:
            tmp_file.write(video_path.read())
            video_file_path = tmp_file.name

        # Create the prompt
        prompt = "Summarize this video"

        # Set up the model
        model = genai.GenerativeModel(model_name="models/gemini-1.5-pro", api_key=os.environ['GOOGLE_API_KEY'])

        # Make the LLM request
        print("Making LLM inference request...")
        response = model.generate_content([prompt, video_file_path],
                                        request_options={"timeout": 2000})
        
        return response.text

    except Exception as e:
        return f"An error occurred: {str(e)}"
    
    finally:
        # Clean up temporary file
        if 'video_file_path' in locals():
            Path(video_file_path).unlink(missing_ok=True)

# Create Gradio interface
iface = gr.Interface(
    fn=summarize_video,
    inputs=gr.Video(label="Upload Video"),
    outputs=gr.Textbox(label="Summary", lines=10),
    title="Video Summarizer",
    description="Upload a video to get an AI-generated summary using Gemini 1.5 Pro.",
    examples=[],
    cache_examples=False
)

# Launch the interface
if __name__ == "__main__":
    iface.launch(share=True)