quiabo

quiabo is a backend web service for running OCR jobs implemented as a Flask and Celery application.

Dependencies

Python dependencies are declared in pyproject.toml.

Development

Spin up the application using Docker Compose. There are number of dependencies (Postgres and Redis) as well as Flask/Celery app components (app, worker, and optionially flower). Redis serves as the Celery broker (source of jobs) and Postgres is the Celery results backend.

# Build the Docker image for app, worker, and flower
docker compose build

# Create the postgres database; only needed the first time
docker compose run --rm app bin/dbinit

# Start the Flask app, which will be running on http://localhost:8000/
docker compose up --detach

# Optionally start Flower, which is a dashboard for the Celery queue and
# will be running on http://localhost:5555/
docker compose up --profile flower --detach

Testing

Once the stack is started, execute the tests by running pytest in one of the running containers. Note that testing and linting dependencies are not installed by default, so you'll need to do that too.

# Install the testing and linting dependencies

docker compose exec app pip install --no-cache-dir -e .[test,lint]

# Run all the tests
docker compose exec app pytest

# Run tests with a specific marker
# Example: only run the unit tests
docker compose exec app pytest -m unit

Test results/reports are written to ./artifacts/pytest.

Configuration

quiabo's configuration is handled by environment variables using Flask's from_prefixed_env() method, using QUIABO as the prefix. Celery configuration is set using the same method. At a minimum, you will need to set the following:

Environment variable	Purpose	Example
QUIABO_CELERY__broker_url	Connection URL for the Celery broker (e.g. Redis)	redis://redis:6379
QUIABO_CELERY__result_backend	SQLAlchemy connection URL for the Celery result backend (e.g. Postgres)	db+postgresql://postgres:postgres@db:5432/quiabo