InsightX

A powerful image analysis platform that provides automated insights through advanced computer vision and machine learning capabilities.

Project Overview

InsightX is a distributed system that provides real-time image analysis with the following key features:

• NSFW Detection: Advanced content moderation using deep learning to identify potentially inappropriate content
• Object Detection: Real-time object detection with bounding boxes using YOLOv8
• Visual Insights: Detailed analysis of image contents with confidence scores
• Modern UI: Clean and intuitive interface for viewing and managing image analysis results
• Flexible Storage: MinIO for local development and AWS S3 for production environments

Key Features

• Real-time Processing: Asynchronous processing pipeline for quick image analysis
• Multiple Detection Models:
  • YOLOv8 for object detection
  • NudeNet for NSFW content detection
• Visual Annotations: Automatic generation of annotated images with bounding boxes
• Detailed Analytics: Comprehensive object detection results with confidence scores
• Content Moderation: Built-in NSFW detection for content safety
• Scalable Architecture: Distributed system design for handling high volumes of images

Prerequisites

• Docker and Docker Compose
• Node.js (for local development)
• Python 3.8+ (for local development)

Quick Start

1. Clone the repository:

git clone [repository-url]
cd insightx

2. Start the services using Docker Compose:

docker-compose up -d

3. Access the application:

• Frontend: http://localhost:3000
• API: http://localhost:8000
• Worker: Running in background

Project Structure

.
├── client/          # Next.js frontend application
├── api/            # FastAPI backend service
├── worker/         # Background processing service
├── storage/        # Image storage and processing
└── docker-compose.yml

Development

Client

• Built with Next.js and TypeScript
• Modern UI components using shadcn/ui
• Real-time image analysis results display
• Located in client/ directory
• See Client README for detailed setup instructions

API

• FastAPI-based backend service
• Handles image uploads and analysis requests
• Manages detection results and storage
• Supports both MinIO, AWS S3 for image storage
• Located in api/ directory
• See API README for detailed setup instructions

Worker

• Background processing service for image analysis
• Implements YOLOv8 and NudeNet models
• Generates annotated images with detection results
• Supports both MinIO, AWS S3 for image storage
• Located in worker/ directory
• See Worker README for detailed setup instructions

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0) - see the LICENSE file for details.

The AGPL-3.0 license requires that any modifications to this software must be made available under the same license when the software is run over a network. This ensures that improvements to the software remain open source and available to the community.

Copyright (c) 2025 Y. Siva Sai Krishna

Support

For support, please open an issue in the repository or contact the maintainers.