Flask API for Employee Data

This Flask API serves data from a SQLite database containing information about employees of a fictional company. The API allows for basic CRUD (Create, Read, Update, Delete) operations on the employee data, and also provides additional endpoints for data analysis purposes.

Endpoints

The Flask API has the following endpoints:

• GET /employees: Returns a list of all employees in the database.
• GET /employees/<int:id>: Returns the employee with the specified ID.
• POST /employees: Creates a new employee with the specified data (name, department, salary, hire_date). The API returns the ID of the newly created employee.
• PUT /employees/<int:id>: Updates the employee with the specified ID with the specified data (name, department, salary, hire_date).
• DELETE /employees/<int:id>: Deletes the employee with the specified ID.
• GET /departments: Returns a list of all unique departments in the database.
• GET /departments/<string:name>: Returns a list of all employees in the specified department.
• GET /average_salary/<string:department>: Returns the average salary of employees in the specified department.
• GET /top_earners: Returns a list of the top 10 earners in the company based on their salary.
• GET /most_recent_hires: Returns a list of the 10 most recently hired employees.
• POST /predict_salary: Takes in data for a new employee (department and hire date) and returns the predicted salary.

Commands

• flask generate-employees --count 1000: run this command to generate employees using faker
• flask train-salary-model: run this command to train salary prediction model

Models

The database is generated using the SQLAlchemy library and contains a table called "employees" with the following columns:

• id: an auto-incrementing integer and primary key
• name: a string with a maximum length of 50 characters
• department: a string with a maximum length of 50 characters
• salary: a float with a minimum value of 0 and maximum value of 1000000
• hire_date: a datetime object in the format of 'YYYY-MM-DD HH:MM:SS', with a range from 01-01-2020 00:00:00 to today.

Set-Up

1. Clone the repository:

git clone https://github.com/s3m3dov/flask-employee-api.git

2. Set-up poetry

poetry env use python3.10

poetry install

3. Run the development server

poetry run flask run

4. Run tests

poetry run pytest app/tests

5. Generate fake data for employees

poetry run flask generate-employees --count 1000

Documentation

The API documentation is available at:

• Swagger UI: http://localhost:5000/api/swagger
• Redoc UI: http://localhost:5000/api/redoc
• Rapidoc UI: http://localhost:5000/api/rapidoc

Dependencies

• Python 3.10
• Flask: a micro web framework for Python used to build the API endpoints
• Flask-Smorest: an extension for Flask that simplifies the creation of RESTful APIs
• Marshmallow: a Python library for serializing and deserializing data, which is used for validating input and output data in the API
• Flask-SQLAlchemy: an extension for Flask that adds support for SQLAlchemy, a SQL toolkit and ORM, which is used for communicating with the SQLite database
• SQLAlchemy-Utils: a library that provides various utility functions for working with SQLAlchemy
• Flask-Migrate: an extension for Flask that handles SQLAlchemy database migrations
• Flask-Testing: an extension for Flask that provides utilities for testing the API
• pandas: a library for data manipulation and analysis, used for loading and preprocessing data for the salary prediction model.
• scikit-learn: a popular machine learning library for Python, used for building and training the salary prediction model
• Faker: a library for generating fake data, used for populating the SQLite database with random employee data
• pytest: a framework for testing Python code
• colorlog: a library that provides colored logs