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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 Jul 2023
Publisher Packt
ISBN-13 9781837632749
Length 422 pages
Edition 2nd 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 (21) Chapters Close

Preface 1. Part 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 Injection in FastAPI 7. Part 2: Building and Deploying 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. Part 3: Building Resilient and Distributed Data Science Systems with FastAPI
14. Chapter 11: Introduction to Data Science in Python 15. Chapter 12: Creating an Efficient Prediction API Endpoint with FastAPI 16. Chapter 13: Implementing a Real-Time Object Detection System Using WebSockets with FastAPI 17. Chapter 14: Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model 18. Chapter 15: Monitoring the Health and Performance of a Data Science System 19. Index 20. Other Books You May Enjoy

Implementing a Real-Time Object Detection System Using WebSockets with FastAPI

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, an object 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 an object detection algorithm and send back the coordinates and label of each detected object in the image. For this task, we’ll rely on Hugging Face...

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