app.py

import os
import time
import gradio as gr
from google import genai
from google.genai import types

# Retrieve API key from environment variables
GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
if not GOOGLE_API_KEY:
    raise ValueError("Please set the GOOGLE_API_KEY environment variable with your Google Cloud API key.")

# Initialize the Gemini API client
client = genai.Client(api_key=GOOGLE_API_KEY)
MODEL_NAME = "gemini-2.5-pro-exp-03-25"  # Model from the notebook that supports video analysis

def upload_and_process_video(video_file: str) -> types.File:
    """
    Upload a video file to the Gemini API and wait for processing.
    
    Args:
        video_file (str): Path to the video file
    
    Returns:
        types.File: Processed video file object
    """
    try:
        video_file_obj = client.files.upload(file=video_file)
        while video_file_obj.state == "PROCESSING":
            print(f"Processing {video_file}...")
            time.sleep(10)
            video_file_obj = client.files.get(name=video_file_obj.name)
        
        if video_file_obj.state == "FAILED":
            raise ValueError(f"Video processing failed: {video_file_obj.state}")
        
        print(f"Video processing complete: {video_file_obj.uri}")
        return video_file_obj
    except Exception as e:
        raise Exception(f"Error uploading video: {str(e)}")

def analyze_video(video_file: str, user_query: str) -> str:
    """
    Analyze the video using the Gemini API and return a summary.
    
    Args:
        video_file (str): Path to the video file
        user_query (str): Optional query to guide the analysis
    
    Returns:
        str: Markdown-formatted report
    """
    # Validate input
    if not video_file or not os.path.exists(video_file):
        return "Please upload a valid video file."
    if not video_file.lower().endswith('.mp4'):
        return "Please upload an MP4 video file."

    try:
        # Upload and process the video
        video_file_obj = upload_and_process_video(video_file)

        # Prepare prompt
        prompt = "Provide a detailed summary of this video."
        if user_query:
            prompt += f" Focus on: {user_query}"

        # Analyze video with Gemini API
        response = client.models.generate_content(
            model=MODEL_NAME,
            contents=[
                video_file_obj,  # Pass the processed video file object
                prompt
            ]
        )
        summary = response.text

        # Generate Markdown report
        markdown_report = (
            "## Video Analysis Report\n\n"
            f"**Summary:**\n{summary}\n"
        )
        return markdown_report

    except Exception as e:
        error_msg = (
            "## Video Analysis Report\n\n"
            f"**Error:** Unable to analyze video.\n"
            f"Details: {str(e)}\n"
        )
        return error_msg

# Define the Gradio interface
iface = gr.Interface(
    fn=analyze_video,
    inputs=[
        gr.Video(label="Upload Video File (MP4)"),  # Removed type="filepath"
        gr.Textbox(label="Analysis Query (optional)", 
                  placeholder="e.g., focus on main events or themes")
    ],
    outputs=gr.Markdown(label="Video Analysis Report"),
    title="AI Video Analysis Agent with Gemini",
    description=(
        "Upload an MP4 video to get a summary using Google's Gemini API. "
        "This tool analyzes the video content directly without audio or frame extraction. "
        "Optionally, provide a query to guide the analysis."
    )
)

if __name__ == "__main__":
    # Launch with share=True to create a public link
    iface.launch(share=True)