Video Summarization Service

Overview

This project enables video summarization for both YouTube videos and uploaded video files.
It uses a Python backend powered by Hugging Face models for processing and summarization, and a Spring Boot application to interact with the backend.

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Features

• Summarizes YouTube videos via URL.
• Summarizes uploaded video files.
• Uses AI-based summarization models from Hugging Face.
• Integrates seamlessly between Python backend and Spring Boot application.
• Returns concise summaries.

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How It Works

1. The Spring Boot application sends either:
   • A YouTube video link, or
   • An uploaded video file.
2. The Python service:
   • Downloads or receives the video.
   • Extracts and transcribes audio.
   • Generates a summary using a Hugging Face model.
3. The Spring Boot application receives and returns the summary to the user.

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Components

• Python Backend
  • Handles video/audio extraction, transcription, and summarization.
  • Uses Hugging Face transformer models.
• Spring Boot Application
  • Provides REST API endpoints for clients.
  • Forwards requests to the Python backend and returns the responses.

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Workflow

• YouTube URL flow: Spring Boot → Python Service → Video Download → Transcription → Summary → Response.
• Upload flow: Spring Boot → Python Service → Audio Extraction → Transcription → Summary → Response.

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Requirements

• Java 17+ for Spring Boot.
• Python 3.10+ for backend service.
• Internet connection for model loading and YouTube downloads.

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Usage

1. Start the Python backend service.
2. Start the Spring Boot application.
3. Use the Spring Boot API endpoints to:
   • Summarize a YouTube video by providing its link.
   • Summarize a locally uploaded video file.

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Output

The output is a concise, AI-generated summary of the video content.
Responses are returned in structured JSON format for easy integration.

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Security Considerations

• Validate input URLs to prevent misuse.
• Limit the size of uploaded files.
• Handle potentially long processing times for large videos.

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Future Enhancements

• Add multi-language transcription and summarization.(now supported kannada hindi english)
• Offer both extractive and abstractive summary options.
• Implement caching to speed up repeated requests.