app.py

import gradio as gr
import google.generativeai as genai
import cv2
import os
import shutil

# Set up your API key
genai.configure(api_key="YOUR_API_KEY")

# Constants
FRAME_EXTRACTION_DIRECTORY = "/content/frames"
FRAME_PREFIX = "_frame"

# Function to create/cleanup frame output directory
def create_frame_output_dir(output_dir):
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
    else:
        shutil.rmtree(output_dir)
        os.makedirs(output_dir)

# Function to extract frames from video
def extract_frame_from_video(video_file_path):
    create_frame_output_dir(FRAME_EXTRACTION_DIRECTORY)
    vidcap = cv2.VideoCapture(video_file_path)
    fps = vidcap.get(cv2.CAP_PROP_FPS)
    frame_duration = 1 / fps
    output_file_prefix = os.path.basename(video_file_path).replace('.', '_')
    frame_count = 0
    count = 0
    while vidcap.isOpened():
        success, frame = vidcap.read()
        if not success:
            break
        if int(count / fps) == frame_count:
            min = frame_count // 60
            sec = frame_count % 60
            time_string = f"{min:02d}:{sec:02d}"
            image_name = f"{output_file_prefix}{FRAME_PREFIX}{time_string}.jpg"
            output_filename = os.path.join(FRAME_EXTRACTION_DIRECTORY, image_name)
            cv2.imwrite(output_filename, frame)
            frame_count += 1
        count += 1
    vidcap.release()
    return frame_count

# Class to represent a file
class File:
    def __init__(self, file_path: str, display_name: str = None):
        self.file_path = file_path
        if display_name:
            self.display_name = display_name
        self.timestamp = self.get_timestamp(file_path)

    def set_file_response(self, response):
        self.response = response

    def get_timestamp(self, filename):
        parts = filename.split(FRAME_PREFIX)
        if len(parts) != 2:
            return None
        return parts[1].split('.')[0]

# Function to upload files to Gemini
def upload_files(files_to_upload):
    uploaded_files = []
    for file in files_to_upload:
        response = genai.upload_file(path=file.file_path)
        file.set_file_response(response)
        uploaded_files.append(file)
    return uploaded_files

# Function to generate description using Gemini
def generate_description(uploaded_files):
    prompt = "Describe this video."
    model = genai.GenerativeModel(model_name="models/gemini-1.5-pro-latest")
    request = [prompt]
    for file in uploaded_files:
        request.append(file.timestamp)
        request.append(file.response)
    response = model.generate_content(request, request_options={"timeout": 600})
    return response.text

# Function to delete files from Gemini
def delete_files(uploaded_files):
    for file in uploaded_files:
        genai.delete_file(file.response.name)

# Gradio interface
def process_video(video_file):
    try:
        # Extract frames
        frame_count = extract_frame_from_video(video_file.name)
        
        # Prepare files for upload
        files = os.listdir(FRAME_EXTRACTION_DIRECTORY)
        files = sorted(files)
        files_to_upload = []
        for file in files:
            files_to_upload.append(
                File(file_path=os.path.join(FRAME_EXTRACTION_DIRECTORY, file))
            )

        # Upload files to Gemini
        uploaded_files = upload_files(files_to_upload)
        
        # Generate description
        description = generate_description(uploaded_files)

        # Delete files from Gemini
        delete_files(uploaded_files)

        return f"Video processed successfully! Description:\n\n{description}"
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Create Gradio interface
iface = gr.Interface(
    fn=process_video,
    inputs=gr.Video(type="filepath"),
    outputs=gr.Textbox(),
    title="Video Description with Gemini",
    description="Upload a video to get a description using Google Gemini",
)

# Launch the interface
iface.launch()