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 function dependency

In FastAPI, a dependency can be defined either as a function or as a callable class. In this section, we'll focus on the functions, which are the ones you'll probably work with most of the time.

As we said, a dependency is a way to wrap some logic that will retrieve some sub-values or sub-objects, make something with them, and finally return a value that will be injected into the endpoint calling it.

Let's look at a first example where we define a function dependency to retrieve the pagination query parameters, skip and limit:

chapter5_function_dependency_01.py

async def pagination(skip: int = 0, limit: int = 10) -> Tuple[int, int]:
    return (skip, limit)
@app.get("/items")
async def list_items(p: Tuple[int, int] = Depends(pagination)):
    skip, limit = p
    return {"skip": skip, "limit": limit}