This base project contains a scaffold for building a Flask API with the following features:

• Logging
• Testing
• Caching
• Containerization
• Linting verification
• CI/CD Integration

• Logging: Configured to output logs to both the terminal and a log file.
• Testing: Uses pytest for testing, with fixtures and examples.
• Caching: Configured with Flask-Caching for efficient data handling.
• Containerization: Ready to be containerized with Docker.
• Linting: Integrated with flake8 for code style checking.
• CI/CD: Setup for continuous integration and deployment on AWS.

1. Set the local Python version:

   pyenv local 3.12

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   source ./venv/bin/activate

4. Install the required dependencies:

   pip install -r requirements.txt

5. Run the application:

   gunicorn --bind 0.0.0.0:8080 app:app

If pytest does not recognize the src folder as a module, use the following command to set the PYTHONPATH:

export PYTHONPATH=$(pwd)

Run the tests with:

pytest

Check the code style with flake8:

flake8 .

To run the application using Gunicorn:

gunicorn --bind 0.0.0.0:8000 app:app

To build and run the Docker container:

docker build -t template-api:v1 .
docker run -p 8000:8000 --env-file .env template-api:v1

For continuous integration and deployment, we utilize GitHub Actions. Our setup includes refreshing the AWS ECR image and triggering AWS App Runner to refresh its service source.

• AWS_ACCESS_KEY_ID
• AWS_ACCOUNT_ID
• AWS_REGION
• AWS_SECRET_ACCESS_KEY

To update the libraries requirements, run:

pip freeze > requirements.txt