FastGen - A FastAPI auto generator

FastGen acts as an automatic boilerplate generator to create basic CRUD apps given a configuration file. Idea is to reduce the unneccessary time spent required scaffolding application while avoiding typically human error pitfall.

This will generate the bare necessities and produce a functioning CRUD app. However, several key features of a production ready webapp are not present and should be adjusted accordingly. Features such as:

Security features (Authenticated + Authorised access to endpoints)
Unique business logic functions
Enforcement of constraints necessary to various more complex database
Reverse Proxy
Using any other enterprise database management system (PostgreSQL, MySQL, SQL Server, etc.)
- SQLAlchemy does provide several configurable options for different DBMS.

Notable Files and Directories

`generators/`

The directory contains all the generators which programmatically update the templates to reflect the configuration file

`templates/`

The directory contains all the templates of standard fastAPI classes and code required to create the tables

`generator.sh`

The main trigger file to call and generate the project

Setup

The script depends on providing a configuration file containing the structure of the models that are to be generated. See the following example (snippet of of test_config.json provided).

{
	"project": {
		"name": "Pets FastAPI",
		"desc": "Project for FastAPI to test",
		"version": "0.0.1",
		"database_name": "pets"
	},
	"tables": [
		{ 
			"name": "Pet",
			"tablename": "pets",
			"metadata": {
				"name": "Pet",
				"desc": "Operations with Pets Owner"
			},
			"columns": [
				{ 
					"column_name": "name",
					"column_type": "str"
				},
				// ** Other column types **
			]
		},
		{ 
			"name": "Owner",
			"tablename": "owners",
			"metadata": {
				"name": "Owner",
				"desc": "Operations with Pets Owners"
			},
			"columns": [
				{ 
					"column_name": "name",
					"column_type": "str"
				},
				// ** Other column types **
			]
		},
		{ 
			"name": "Trainer",
			"tablename": "trainers",
			"metadata": {
				"name": "Trainer",
				"desc": "Operations with Pets Trainers"
			},
			"columns": [
				{ 
					"column_name": "name",
					"column_type": "str"
				},
				// ** Other column types **
			]
		},
		{ 
			"name": "NutritionPlan",
			"tablename": "nutrition_plans",
			"metadata": {
				"name": "NutritionPlan",
				"desc": "Operations with Pets Nutrition Plan"
			},
			"columns": [
				{ 
					"column_name": "name",
					"column_type": "str"
				},
				// ** Other column types **
			]
		}
	],
	"relationships": [
		{
			"table_1": "Pet",
			"table_2": "Owner",
			"type": "m2o"
		},
		{
			"table_1": "Pet",
			"table_2": "Trainer",
			"type": "m2m"
		},
		{
			"table_1": "Pet",
			"table_2": "NutritionPlan",
			"type": "o2o"
		},
		// ** Other possible relationships **
	]
}

As of now, only 4 data types are partially supported (more may come in the future):

int: Integer

str: String

datetime: Date time

json: JSON

Relationships are supported and represented as such

// Usage: table_1 has a type relationship with table_2

{
	"table_1": "Pet",
	"table_2": "Owner",
	"type": "m2o"
}

// Pet has a many-to-one type relationship with Owner

Only 3 relationships are supported:

Many to Many -> m2m

Many to One -> m2o

One to One -> o2o

Usage

Generating

After a configuration json is created to match the model. Run the following command

./generator.sh -p <PROJECT_NAME> -c <CONFIGURATION_JSON_FILE>

This will generate a directory <PROJECT_NAME> using the the provided <CONFIGURATION_JSON_FILE>

The following are generated:

Model Files: Models files required to create tables in the database
Schema Files: Schema files for enforced data typing
Crud Files: Interface files to the database layer
Endpoint Files: The endpoint triggers for each model
Database Files: The engine and creation system for the database (uses SQLAlchemy and defaults to using SQLITE configuration)
Metadata Files: Files related to the swagger documentation
Test Files: Files regarding unit testing and configuration

A test configuration test_config.json has already a sample setup to have a look

Running the project

Setup Up

While in the root project directory using the bash terminal, assuming your have not yet created a virtual environment start a virtual environment (.venv). You only need to be created on the first time.

python3 -m venv .venv

To start the environment, run the following command

source .venv/bin/activate

Install the dependencies

pip3 install -r requirements.txt

To deactivate the environment after finishing, run the following command

deactivate

Starting the project

After setting up the environment and activating it. Run the following command to start the web server

python3 -m uvicorn app.main:app --reload --port 9876

This will start the webserver pointing at port 9876.

Afterwards you can access the generated documentation on the following webpage: http:/127.0.0.1:9876/docs

Testing

Unit test will automatically generate. However, test data must be provided in the test/unit/test_data.py file. Test data should reflect the what is to be expected to be used in production. 3 should be sufficient.

Set up

Setup process is identical to the project setup. Navigate to Usage -> Running Project -> Setup

Running the test

After setting up the environment and activating it. Run the following command to start the web server

python3 -m pytest test/unit/*_tests.py

This will start the run all generated basic unit test.

Sanity Check

A Sanity Test Script has been created to ensure the application works correctly. To check, run the following command

./sanity_test.sh test_project test_config.json sample_data.txt

The script will create a test project using the provided test_config.json and start the application on http://127.0.0.1:9876 and on exit, will run the generated unit tests.

HaoCheong/FastGen