The core image processing library built on [Sharp](https://sharp.pixelplumbing.com/). It handles all non-AI operations: resize, crop, rotate, flip, convert, compress, strip metadata, and color adjustments (brightness, contrast, saturation, grayscale, sepia, invert, color channels).
This package has no network dependencies and runs entirely in-process.
A bridge layer that calls Python scripts for ML operations. On first use, the bridge starts a persistent Python dispatcher process that pre-imports heavy libraries (rembg, OpenCV, NumPy) and keeps them warm in memory. Subsequent AI calls skip the import overhead entirely. If the dispatcher is unavailable, the bridge falls back to spawning a fresh Python subprocess per request.
The server handles graceful shutdown on SIGTERM/SIGINT: it drains HTTP connections, stops the worker pool, shuts down the Python dispatcher, and closes the database.
A React 19 single-page app built with Vite. Uses Zustand for state management, Tailwind CSS v4 for styling, and Lucide for icons. Communicates with the API over REST and SSE (for progress tracking).
3. The API route validates the input with Zod, then dispatches processing.
4. For standard tools, the request is offloaded to a Piscina worker thread pool so Sharp operations don't block the main event loop. The worker auto-orients the image based on EXIF metadata, runs the tool's process function, and returns the result. If the worker pool is unavailable, processing falls back to the main thread.
5. For AI tools, the TypeScript bridge sends a request to the persistent Python dispatcher (or spawns a fresh subprocess as fallback), waits for it to finish, and reads the output file.
6. Job progress is persisted to the `jobs` SQLite table so state survives container restarts. Real-time updates are delivered via SSE at `/api/v1/jobs/:jobId/progress`.
7. The API returns a `jobId` and `downloadUrl`. The user downloads the processed image from `/api/v1/download/:jobId/:filename`.
