AI-powered screenshot manager with intelligent classification, search, and analytics.
- Smart Classification: CLIP (ViT-B/32) automatically categorizes screenshots into Person, Documents, Code, Browser, Chat, Games, and Other
- OCR Search: Extract and search text from screenshots (English + Arabic)
- Analytics: Detailed statistics about your screenshot collection
- Privacy-First: Everything runs locally, completely offline after setup
- Fast: ONNX runtime for efficient CPU inference
Download Release (Recommended)
# Linux
wget https://github.com/Tojan-Naiem/snapy/releases/latest/download/snapy-v1.1.0-linux-x64.zip
unzip snapy-v1.1.0-linux-x64.zip
cd linux-x64
./setup.sh
# Windows
# Download snapy-v1.1.0-win-x64.zip from releases
# Extract, install Tesseract OCR, run setup.ps1Build from Source
git clone https://github.com/Tojan-Naiem/snapy.git
cd snapy
./setup.shsnapy organize ~/Screenshots # Categorize screenshots
snapy search "invoice" # Search by text content
snapy stats ~/Screenshots # View statistics
snapy info screenshot.png # File metadata
snapy restart ~/Screenshots # Undo categorization- Classification: CLIP model converts images to embeddings and matches against category embeddings
- Search: Tesseract OCR extracts text, stores in SQLite for fast full-text search
- Performance: ~100-200ms per classification, near-instant search
- Linux: Ubuntu/Debian, Python 3.8+
- Windows: Windows 10/11, Python 3.8+, Tesseract OCR
- .NET 8.0 SDK (if building from source)
C# (.NET 8.0) • CLIP (ONNX) • Tesseract OCR • SQLite • ImageSharp
Snapy.Cli/ # CLI interface
Snapy.Core/ # Domain entities
Snapy.Infrastructure/ # AI models, database, services
Models/ # ONNX models and embeddings
- Categories fixed at compile-time
- No progress bars for batch operations
- Primary testing on Ubuntu/Debian
Contributions welcome! Submit issues or pull requests.
MIT License - see LICENSE file for details.
Built with CLIP, Tesseract OCR, and ONNX Runtime
Privacy Notice: All processing happens locally. No data sent to external servers.