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 an HTTP endpoint to perform face detection on a single image

Before working with WebSockets, we'll start simple and implement, using FastAPI, a classic HTTP endpoint for accepting image uploads and performing face detection on them. As you'll see, the main difference from the previous example is in how we acquire the image: instead of streaming it from the webcam, we get it from a file upload that we have to convert into an OpenCV image object.

You can see the whole implementation in the following code:

chapter14_api.py

from typing import List, Tuple
import cv2
import numpy as np
from fastapi import FastAPI, File, UploadFile
from pydantic import BaseModel
app = FastAPI()
cascade_classifier = cv2.CascadeClassifier()
class Faces(BaseModel):
    faces: List[Tuple[int, int, int, int]]
@app.post("/face-detection", response_model=Faces)
async def face_detection(image: UploadFile = File(...)) -> Faces:
    ...