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

Chapter 14: Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV

In the previous chapter, you learned how to create efficient REST API endpoints to make predictions with trained machine learning models. This approach covers a lot of use cases, given that we have a single observation we want to work on. In some cases, however, we may need to continuously perform predictions on a stream of input, for instance, a face detection system that works in real time with video input. This is exactly what we'll build in this chapter. How? If you remember, besides HTTP endpoints, FastAPI also has the ability to handle WebSockets endpoints, which allow us to send and receive streams of data. In this case, the browser will send into the WebSocket a stream of images from the webcam, and our application will run a face detection algorithm and send back the coordinates of the detected face in the image. For this face detection task, we'll rely on OpenCV, which...