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Building Data Science Applications with FastAPI

You're reading from   Building Data Science Applications with FastAPI Develop, manage, and deploy efficient machine learning applications with Python

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801079211
Length 426 pages
Edition 1st Edition
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Author (1):
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François Voron François Voron
Author Profile Icon François Voron
François Voron
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Python and FastAPI
2. Chapter 1: Python Development Environment Setup FREE CHAPTER 3. Chapter 2: Python Programming Specificities 4. Chapter 3: Developing a RESTful API with FastAPI 5. Chapter 4: Managing Pydantic Data Models in FastAPI 6. Chapter 5: Dependency Injections in FastAPI 7. Section 2: Build and Deploy a Complete Web Backend with FastAPI
8. Chapter 6: Databases and Asynchronous ORMs 9. Chapter 7: Managing Authentication and Security in FastAPI 10. Chapter 8: Defining WebSockets for Two-Way Interactive Communication in FastAPI 11. Chapter 9: Testing an API Asynchronously with pytest and HTTPX 12. Chapter 10: Deploying a FastAPI Project 13. Section 3: Build a Data Science API with Python and FastAPI
14. Chapter 11: Introduction to NumPy and pandas 15. Chapter 12: Training Machine Learning Models with scikit-learn 16. Chapter 13: Creating an Efficient Prediction API Endpoint with FastAPI 17. Chapter 14: Implement a Real-Time Face Detection System Using WebSockets with FastAPI and OpenCV 18. Other Books You May Enjoy

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...

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