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

Using dependencies at a path, router, and global level

As we said, dependencies are the recommended way to create building blocks in a FastAPI project, allowing you to reuse logic across endpoints while maintaining maximum code readability. Until now, we've applied them on a single endpoint, but couldn't we expand this approach to a whole router? Or even a whole FastAPI application? Actually, we can!

The main motivation for this is to be able to apply some global request validation or perform side logic on several routes without the need to add the dependency on each endpoint. Typically, an authentication method or a rate-limiter could be very good candidates for this use case.

To show you how it works, we'll implement a simple dependency that we will use across all the following examples. You can see it in the following example:

chapter5_path_dependency_01.py

def secret_header(secret_header: Optional[str] = Header(None)) -> None:
    ...