Book Image

Building Data Science Applications with FastAPI

By : François Voron
5 (1)
Book Image

Building Data Science Applications with FastAPI

5 (1)
By: François Voron

Overview of this book

FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you’ll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you’ll cover best practices relating to testing and deployment to run a high-quality and robust application. You’ll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you’ll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you’ll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you’ll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI.
Table of Contents (19 chapters)
1
Section 1: Introduction to Python and FastAPI
7
Section 2: Build and Deploy a Complete Web Backend with FastAPI
13
Section 3: Build a Data Science API with Python and FastAPI

Creating and using a parameterized dependency with a class

In the previous section, we defined dependencies as regular functions, which works well in most cases. Still, you may need to set some parameters on a dependency to finely tune its behavior. Since the arguments of the function are set by the dependency injection system, we can't add an argument to the function.

In the pagination example, we added some logic to cap the limit value at 100. If we wanted to set this maximum limit dynamically, how would we do that?

The solution is to create a class that will be used as a dependency. This way, we can set class properties, with the __init__ method, for example, and use them in the logic of the dependency itself. This logic will be defined in the __call__ method of the class. If you remember what we learned in the Callable object section of Chapter 2, Python Programming Specificities, you know that it makes the object callable, meaning it can be called like a regular function...