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

Implementing a WebSocket to perform face detection on a stream of images

One of the main benefits of WebSockets, as we saw in Chapter 8, Defining WebSockets for Two-Way Interactive Communication in FastAPI, is that it opens a full-duplex communication channel between the client and the server. Once the connection is established, messages can be passed quickly without having to go through all the steps of the HTTP protocol. Therefore, it's much more suited to sending lots of messages in real time.

The point here will be to implement a WebSocket endpoint that is able to both accept image data and run OpenCV detection on it. The main challenge here will be to handle a phenomenon known as backpressure. Put simply, we'll receive more images from the browser than the server is able to handle, because of the time needed to run the detection algorithm. Thus, we'll have to work with a queue (or buffer) of limited size and drop some images along the way to handle the stream...