This page documents how to get the GeniePy boilerplate up and running for a new project.
Download
Purchase the boilerplate from Gumroad.
After the purchase, you'll be able to download an app-XX.YY.ZZ.zip file to your
machine. This is the base for setting up a new application.
Note that the XX.YY.ZZ in the file name will be different depending on the
version you end up purchasing. Once you've made a purchase, all future versions
will be made available to you at no additional cost.
Extract
As the artifact is a zip file, you need to extract it in the directory where
you want to initialize the project.
Assuming that you store your work projects under $HOME/projects and the name
of your new SaaS is GeniePy, the following commands should set everything up.
# create the project directory
$ mkdir -p ~/projects/geniepy
$ cd ~/projects/geniepy
# move and extract the downloaded file into the target directory
$ mv /path/to/app-22.01.01.zip .
$ unzip app-22.01.01.zip
# cleanup
$ rm app-22.01.01.zip
This should extract the application source code into the current working directory.
Configure
In line with the 12-factor methodology, GeniePy uses environment variables for application configuration.
We use the env_file configuration option to set the environment variables
for the main server process.
Make a copy of the sample.env file included in the codebase,
$ cp sample.env .env
... and edit the contents of this new file based on what your project needs.
All the available settings are defined under app/settings.py. If you'd like to
see what else is available, this is the file you should refer to.
Run
Run make init in the project directory. This command will install the latest
version of pip, pip-tools, and invoke, all three of which are the Python
dependencies that GeniePy is using. This will also initialize the Postgres
containers for Docker.
Next, run invoke app.server in the terminal.
That's it! You should now be able to visit the application in the browser at
http://localhost:9001.
Lint
Run invoke test.lint in the project directory.
At the time of this writing, the project's code is linted using three tools:
flake8, black, and isort.
flake8 is used to ensure quality checks on Python codebases. It can point out
issues related to style (eg. if some code is not conformant to PEP-8) or for
discovering errors that would otherwise only be discovered at runtime (eg.
undefined variables).
black is a code formatter for Python. If you've worked in a team of software
developers, you've most likely ran into formatting issues at one time or
another. black puts an end to such discussions by choosing a formatting style
for you.
isort is responsible for sorting imports in Python code. This keeps the
topmost section of the file clean and consistent across the entire codebase.
These three tools have also been installed as pre-commit hooks. If you set up
pre-commit as described in the previous section, all these linters will run
sequentially whenever you run git commit. Any violations will prevent you from
committing code.
Test
Run invoke test.pytest in the project directory.
This should run all the database migrations on the test database and then run
the entire test suite using pytest.
pytest is an excellent framework for writing better tests in Python. It is
widely used in the Python community and has a rich plugin system which end-users
can benefit from.