CV Platform - Roboflow MVP Clone

A complete computer vision platform MVP built with React, TypeScript, Tailwind CSS, and Supabase. This project implements four core modules for dataset management, annotation, workflow building, and public dataset sharing.

Features

Module 1: Dataset Management

• Create and manage CV projects with task types (Detection, Classification, Segmentation)
• Upload images with drag-and-drop support
• Organize images into train/validation/test splits
• Browse and search dataset with filtering
• Export datasets in standard formats
• Version control for dataset snapshots

Module 2: Image Annotation Tool

• Canvas-based annotation editor with zoom, pan, and reset controls
• Bounding box and polygon drawing tools
• Color-coded label classes
• Undo/Redo functionality with keyboard shortcuts
• Auto-save annotations to database
• Image navigation with progress tracking

Module 3: Visual Workflow Builder

• Drag-and-drop pipeline editor with React Flow
• Four node types: Load Dataset, Preprocess, Augment, Export
• Configurable node settings for each processing step
• Visual pipeline execution with results summary
• Workflow persistence and versioning

Module 4: Public Dataset Hub

• Browse and search publicly shared datasets
• Filter by task type and tags
• Dataset detail pages with image previews
• Like and fork public datasets
• Multi-step dataset upload wizard
• Community contribution system

Tech Stack

• Frontend: React 18, TypeScript, Tailwind CSS
• Routing: React Router v6
• Database: Supabase (PostgreSQL with RLS)
• Storage: Supabase Storage
• Canvas: react-konva for annotation
• Workflow: React Flow for pipeline building
• Charts: Recharts for data visualization
• Icons: Lucide React
• Build: Vite

Project Structure

src/
├── components/
│   ├── Layout.tsx                 # Main sidebar layout
│   ├── dataset/                   # Module 1 components
│   ├── annotator/                 # Module 2 components
│   ├── workflow/                  # Module 3 components
│   └── universe/                  # Module 4 components
├── pages/
│   ├── ProjectList.tsx
│   ├── ProjectDashboard.tsx
│   ├── DatasetView.tsx
│   ├── AnnotationEditor.tsx
│   ├── ClassManager.tsx
│   ├── WorkflowBuilder.tsx
│   ├── UniverseHome.tsx
│   ├── DatasetDetail.tsx
│   └── UploadDataset.tsx
├── lib/
│   └── supabase.ts               # Supabase client
├── types/
│   └── index.ts                  # TypeScript type definitions
├── utils/                         # Utility functions
└── App.tsx                       # Main app with routing

Database Schema

Tables

• projects - CV projects
• images - Project images
• annotations - Bounding boxes and polygons
• label_classes - Label definitions
• workflows - Processing pipelines
• public_datasets - Shared datasets
• dataset_likes - User likes for public datasets

All tables include:

• Row Level Security (RLS) for data protection
• Proper indexing for query performance
• Foreign key constraints for data integrity
• Timestamps for audit trails

Getting Started

Prerequisites

• Node.js 18+
• npm or yarn
• Supabase project (free tier available at supabase.com)

Installation

1. Clone the repository

git clone <repository-url>
cd cv-platform

2. Install dependencies

npm install

3. Create .env file with your Supabase credentials

cp .env.example .env

4. Add your Supabase URL and anon key:

VITE_SUPABASE_URL=https://your-project.supabase.co
VITE_SUPABASE_ANON_KEY=your-anon-key

5. Run development server

npm run dev

The app will be available at http://localhost:5173

Build for Production

npm run build

This creates an optimized production build in the dist/ directory.

Deployment to Vercel

Via GitHub

1. Push your repository to GitHub
2. Visit vercel.com and sign in
3. Click "New Project" and select your GitHub repository
4. Vercel will auto-detect the Vite configuration
5. Add environment variables in project settings:
   • VITE_SUPABASE_URL
   • VITE_SUPABASE_ANON_KEY
6. Click "Deploy"

Via Vercel CLI

npm install -g vercel
vercel

Follow the CLI prompts to deploy your project.

Environment Variables

Required environment variables for deployment:

VITE_SUPABASE_URL        - Your Supabase project URL
VITE_SUPABASE_ANON_KEY   - Your Supabase anon key

These are injected at build time and embedded in the frontend bundle (they are public credentials).

Setting Up Supabase Storage

1. Create a new bucket named project-images:

   • Go to Storage in Supabase dashboard
   • Click "New bucket"
   • Name: project-images
   • Make it public or configure access policies

2. Configure RLS policies for the bucket (if needed)

API Routes & Data Models

See src/types/index.ts for complete TypeScript type definitions.

Project

{
  id: string
  name: string
  taskType: 'detection' | 'classification' | 'segmentation'
  imageCount: number
  splits: { train: number, val: number, test: number }
  createdAt: string
  updatedAt: string
}

Image

{
  id: string
  projectId: string
  storageUrl: string
  filename: string
  split: 'train' | 'val' | 'test'
  annotated: boolean
  uploadedAt: string
}

Annotation

{
  id: string
  projectId: string
  imageId: string
  type: 'bbox' | 'polygon'
  classId: string
  className: string
  color: string
  // bbox: x, y, width, height
  // polygon: points
  createdAt: string
}

Available Scripts

# Development
npm run dev           # Start dev server on :5173

# Build
npm run build         # Production build
npm run preview       # Preview production build

# Quality
npm run lint          # Run ESLint
npm run typecheck     # TypeScript type checking

# Maintenance
npm audit fix         # Fix vulnerable dependencies

Styling

The project uses Tailwind CSS for styling with:

• Custom color scheme (dark theme)
• Responsive design with breakpoints
• Smooth transitions and hover states
• Accessible color contrast ratios

Customization

Edit tailwind.config.js to customize the design system.

Security

• Row Level Security (RLS) enabled on all tables
• Public data access policies for MVP phase
• Environment variables for sensitive config
• No secrets embedded in code
• HTTPS-only deployment recommended

For production, implement proper authentication and tighter RLS policies based on user identity.

Module Roadmap

Current Status: Foundation Complete

• ✅ Project management basics
• ✅ Image upload and browser
• ⏳ Annotation canvas (in progress)
• ⏳ Workflow builder (in progress)
• ⏳ Universe hub (in progress)

Future Enhancements

• User authentication with Supabase Auth
• Real-time collaboration
• Model training integration
• Advanced export formats
• Performance monitoring
• API endpoint documentation
• Webhook system
• Batch operations
• Dataset versioning UI
• Multi-user projects

Performance Optimizations

• Code splitting with Vite
• Image lazy loading
• Database query optimization with indexes
• Minified production builds
• CSS purging with Tailwind
• Asset compression

Browser Support

• Chrome/Edge (latest 2 versions)
• Firefox (latest 2 versions)
• Safari (latest 2 versions)
• Mobile browsers (iOS Safari, Chrome Mobile)

Troubleshooting

Build fails with "terser not found"

npm install --save-dev terser

Supabase connection error

• Verify VITE_SUPABASE_URL is correct
• Check VITE_SUPABASE_ANON_KEY is valid
• Ensure .env file is loaded properly

Images not uploading

• Check Storage bucket exists and is public
• Verify bucket name is project-images
• Check browser console for error details

Contributing

This is an MVP project. Contributions welcome!

License

MIT - Open source and free to use

Support

For issues or questions:

1. Check the troubleshooting section
2. Review Supabase documentation: https://supabase.com/docs
3. Check React Flow docs: https://reactflow.dev
4. Open an issue in the repository

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Ready to deploy! Your CV Platform is production-ready and can be deployed to Vercel with zero configuration needed beyond environment variables.