This project is managed using a Makefile. The Makefile simplifies the setup and management of various tasks, including installing dependencies, linting, testing, and more.
To set up the project for the first time, you need to perform some initial steps:
-
Install Poetry: Poetry is used for dependency management. You can install it by following the instructions at https://python-poetry.org/docs/#installation.
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Install Google Cloud CLI: This project uses Google Cloud services (such as Firestore), so you need to install the Google Cloud CLI. Instructions can be found here.
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Install Make: Make sure
makeis installed on your system. On most Unix-based systems, it should be available by default. For Windows, you can use tools likechocoorscoopto installmake. -
Environment Variables: Create a
.envfile in the root directory and add the necessary environment variables. An example.envfile:JWT__SECRET_KEY="secret" FIRESTORE__DATABASE="eu-dev"
Once the initial setup is complete, you can use the Makefile to manage the project.
To install all necessary dependencies and set up pre-commit hooks, run:
make installPre-commit hooks are used to check your code before committing. By default, the following tools are configured:
- black: Formats your code.
- mypy: Validates types.
- isort: Sorts imports in all files.
- flake8: Spots possible bugs.
To install the pre-commit hooks, use the make install command as mentioned above.
To lint and format your code, run:
make lint
make formatTo run tests and generate a coverage report, use:
make testTo run tests automatically on every change, run:
make watchTo update poetry dependencies and export them to requirements.txt, run:
make poetry-updateSimple export without updating dependencies:
make poetry-export$ tree "persony_admin"
.
├── bigquery
│ └── schema_views # Configuration and views for BigQuery data analysis
├── functions
│ └── src # Source code for serverless functions
├── modelmind
│ ├── _mocker # Utilities for mocking data and functionalities in tests
│ ├── api # API endpoints, presentation, permissions, dependencies
│ ├── clients # Integrations with third-party APIs
│ ├── community # Domain knowledge external to core questionnaires
│ │ ├── engines
│ │ │ └── persony # Custom engine using community-based knowledge
│ │ └── theory
│ │ ├── jung # Jungian personality theory implementation
│ │ └── mbti # MBTI personality theory implementation
│ ├── db # Persistence layer and database interactions
│ ├── models # Core business logic and data manipulation
│ │ ├── analytics # Calculations based on questionnaire results
│ │ ├── engines # Logic for selecting questions from a questionnaire
│ │ ├── questionnaires # (Main) Core questionnaire models
│ │ ├── questions # Questions models that belong to a questionnaire
│ │ └── results # Management of results from questionnaires
│ ├── services # Auxiliary services, including event notification
│ │ └── event_notifier # Service to handle event notifications within the system
│ └── utils # Utility functions and helpers
├── terraform # Infrastructure as code configurations
└── tests
├── endpoints # Tests for API endpoints
└── models # Tests for core business logic and modelsThis project uses Terraform for managing infrastructure as code on Google Cloud. We also use a Cloud Build trigger (deploy_cloud_run_prod.yml) for CI/CD to deploy our application to Google Cloud Run.
- Poetry: Read more about Poetry here.
- Pre-commit: Learn more about pre-commit here.
- Pydantic BaseSettings: Read more about the BaseSettings class here.