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Generic Flask skeleton set up the way I like it so I can get running quick on projects

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generic-flask-skeleton

This Flask skeleton is set up in the particular way that my team and I like so that we can get moving quickly on projects.

This skeleton includes some utilites for both GitHub and GitLab. Repository specific files should be removed as necessary.

Poetry is used to manage dependencies and the virtual environment. Please note that poetry shell does not work correctly. Additionally, note that when installing during deployments, dependencies should be installed with poetry install --no-dev.

Pre-Commit

This project uses pre-commit hooks to ensure consistent code style throughout the repo. We use black for Python files and Python code within documentation. We use prettier for all other filetypes.

Make sure you've installed all the packages listed in requirements.txt. This will install pre-commit for you. Then run pre-commit install to set up the local pre-commit environment.

Pre-commit will run each time you attempt to commit staged changes. You can run the pre-commit checks at any time using pre-commit run.

Running Tests

To run the test suite, make sure you've run poetry install and activated your virtual environment. Then run pytest --cov=application_name

Pull requests that cause the repository's overall test coverage to drop below X% or cause a decrease in coverage of %Y or more will be rejected. Please make sure to update tests in accordance with your changes.

Configuring the Application

The application expects environment variables to be available in order to run.

To facilitate local development we use python-dotenv to load environment variables from a .env file in the project. To get started, create a .env file from the template.

cp .env.sample .env`

Adjust the SQLALCHEMY_DATABASE_URI to point to an actual running postgres instance.

Running the Application

To run the application locally, use the run.py script:

python run.py

Use the status check endpoint located at localhost:5000/status in order to check the status of the database connection.

Production Deployment

Use gunicorn to run the application behind a production-grade WSGI server. The Flask development server is not suitable for production use.

gunicorn -w 4 -b 127.0.0.1:5000 application_name.app:app

This command produces 4 workers and binds to port 5000.

Use the status checkpoint, localhost:5000/status to verify the service is healthy.

Adding Resources: Models, Views, Routes, and Blueprints

All models should extend application_name.database.BaseModel. Models, views, and Blueprints should all be added to the respective resource module in the application_name/ directory.

New Blueprints will need to be registered in application_name.app:

from application_name.resource_name.views import new_blueprint

app.register_blueprint(new_blueprint)

See application_name/resource_name/ for an example.

Database Migrations

Flask-Migrate is used to manage database migrations. An initial migration with the example models is available in migrations/versions/

In order to run the migration, you'll first need to configure the application to connect to your database. Next, run the following command to create the table:

flask db upgrade

In your own project, after adding a new model or updating the columns, you can create the migration and apply it with following commands:

flask db migrate -m "second migration"
flask db upgrade

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Generic Flask skeleton set up the way I like it so I can get running quick on projects

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