This is a sample Flask App that's intended to be easily configured and extended, with a dynamic data model in yaml format.
The model file is a list of tables and it's corresponding columns, defined with data types, data size and and additional doc attribute, which can configured with a Faker algorithm in the format of <algorithm name>-<locale>. Please check the Faker documentation for additional details about supported algorithms and locales.
Once you have the model file customized to your specific needs, just run the migrations files and it will take care of creating the data model and populating it with fake data.
Note: if you have a Postgres database ready to use, first edit the environment file and
source .env.file
If you don't have a Postgres database ready and just want to run some quick test, just edit the config file, comment the Postgres connection string and uncomment the Sqlite connection string like this:
SQLALCHEMY_DATABASE_URI = ('sqlite:///teste.db')
# SQLALCHEMY_DATABASE_URI = "postgresql+psycopg2://{}:{}@{}:{}/{}".format(database_user,database_password,database_host,database_port,database_name)
When you are ready, you can either run your own custom migration
python3 migrations_custom.py
Or run a fully automated migration with fake data based on what you defined in models.yaml
python3 migrations_faker.py
Once you have the data generated, just run the app and visit the the Flask Admin web app on http://127.0.0.1:5000/admin/ to easily view, edit and create data:
source .env.file
python3 app.py