Book Image

Building Data Science Applications with FastAPI - Second Edition

By : François Voron
3 (1)
Book Image

Building Data Science Applications with FastAPI - Second Edition

3 (1)
By: François Voron

Overview of this book

Building Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects – a real-time object detection system and a text-to-image generation platform using Stable Diffusion. The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications. Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios. By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements.
Table of Contents (21 chapters)
1
Part 1: Introduction to Python and FastAPI
7
Part 2: Building and Deploying a Complete Web Backend with FastAPI
13
Part 3: Building Resilient and Distributed Data Science Systems with FastAPI

Using dependencies at the 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 to 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 a 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:

chapter05_path_dependency_01.py

def secret_header(secret_header: str | None = Header(None)) -> None:   ...