mirror of https://github.com/snapotter-hq/SnapOtter.git synced 2026-08-03 07:46:42 +02:00

Files

T

Siddharth Kumar Sah 6668615750 docs: add VitePress documentation site with GitHub Pages deployment

Rewrites all documentation with accurate project details (Fastify, port
1349, single-container Docker, all 33+ tools, full database schema).
Adds getting started guide and configuration reference. Updates help and
settings dialogs to link to the docs site.

2026-03-22 21:00:37 +08:00

3.2 KiB

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Deployment

Stirling Image ships as a single Docker container. The frontend, API, and Python AI runtime all run inside one image.

Docker Compose (recommended)

services:
  stirling-image:
    image: siddharth123sk/stirling-image:latest
    container_name: stirling-image
    ports:
      - "1349:1349"
    volumes:
      - stirling-data:/data
      - stirling-workspace:/tmp/workspace
    environment:
      - AUTH_ENABLED=true
      - DEFAULT_USERNAME=admin
      - DEFAULT_PASSWORD=admin
    restart: unless-stopped

volumes:
  stirling-data:
  stirling-workspace:

docker compose up -d

The app is then available at http://localhost:1349.

What's inside the container

The Docker image uses a multi-stage build:

Build stage -- Installs Node.js dependencies and builds the React frontend with Vite.
Production stage -- Copies the built frontend and API source into a Node 22 image, installs system dependencies (Python 3, ImageMagick, Tesseract, potrace), sets up a Python virtual environment with all ML packages, and pre-downloads model weights.

Everything runs from a single process. The Fastify server handles API requests and serves the frontend SPA.

System dependencies installed in the image

Python 3 with pip
ImageMagick
Tesseract OCR
libraw (RAW image support)
potrace (bitmap to vector conversion)

Python packages

rembg with BiRefNet-Lite (background removal)
RealESRGAN (upscaling)
PaddleOCR (text recognition)
MediaPipe (face detection)
LaMa Cleaner (inpainting/object removal)
onnxruntime, opencv-python, Pillow, numpy

Model weights are downloaded at build time, so the container works fully offline.

Volumes

Mount these to persist data:

Mount point	Purpose
`/data`	SQLite database (users, API keys, pipelines, settings)
`/tmp/workspace`	Temporary image processing files

The /data volume is the important one. Without it, you lose all user accounts and saved pipelines on container restart. The workspace volume is optional but prevents the container's writable layer from growing.

Health check

The container includes a health check that hits GET /api/v1/health. Docker uses this to report container status:

docker inspect --format='{{.State.Health.Status}}' stirling-image

Reverse proxy

If you're running Stirling Image behind nginx or Caddy, point it at port 1349. Example nginx config:

server {
    listen 80;
    server_name images.example.com;

    client_max_body_size 200M;

    location / {
        proxy_pass http://localhost:1349;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
    }
}

Set client_max_body_size to match your MAX_UPLOAD_SIZE_MB value.

CI/CD

The GitHub repository has two workflows:

docker-publish.yml -- Builds and pushes the Docker image to Docker Hub on every push to main and on version tags. The image is published as siddharth123sk/stirling-image.
deploy-docs.yml -- Builds this documentation site and deploys it to GitHub Pages.

Both run automatically. No manual steps needed after merging to main.

3.2 KiB Raw Blame History