FastAPI Tutorial: Construct APIs with Python in Minutes

FastAPI Tutorial: Construct APIs with Python in Minutes
FastAPI Tutorial: Construct APIs with Python in Minutes

FastAPI Tutorial: Construct APIs with Python in Minutes
Picture by Creator


FastAPI is a well-liked net framework for constructing APIs with Python. It is tremendous easy to be taught and is cherished by builders.

FastAPI leverages Python kind hints and is predicated on Pydantic. This makes it easy to outline information fashions and request/response schemas. The framework mechanically validates request information in opposition to these schemas, lowering potential errors. It additionally natively helps asynchronous endpoints, making it simpler to construct performant APIs that may deal with I/O-bound operations effectively.

This tutorial will train you find out how to construct your first API with FastAPI. From organising your improvement surroundings to constructing an API for a easy machine studying app, this tutorial takes you thru all of the steps: defining information fashions, API endpoints, dealing with requests, and extra. By the tip of this tutorial, you’ll have a superb understanding of find out how to use FastAPI to construct APIs shortly and effectively. So let’s get began.


Step 1: Set Up the Setting


FastAPI requires Python 3.7 or later. So be sure you have a current model of Python put in. Within the mission listing, create and activate a devoted digital surroundings for the mission:

$ python3 -m venv v1
$ supply v1/bin/activate


The above command to activate the digital surroundings works should you’re on Linux or MacOS. Should you’re a Home windows person, check the docs to create and activate digital environments.

Subsequent, set up the required packages. You may set up FastAPI and uvicorn utilizing pip:

$ pip3 set up fastapi uvicorn


This installs FastAPI and all of the required dependencies as nicely uvicorn, the server that we’ll use to run and check the API that we construct. As a result of we’ll construct a easy machine studying mannequin utilizing scikit-learn, set up it in your mission surroundings as nicely:

$ pip3 set up scikit-learn


With the installations out of the way in which, we will get to coding! You could find the code on GitHub.


Step 2: Create a FastAPI App


Create a file within the mission listing. Step one is to create a FastAPI app occasion like so:

# Create a FastAPI app
# Root endpoint returns the app description

from fastapi import FastAPI

app = FastAPI()


The Iris dataset is likely one of the toy datasets that you simply work with when beginning out with information science. It has 150 information information, 4 options, and a goal label (species of Iris flowers). To maintain issues easy, let’s create an API to foretell the Iris species.

Within the coming steps, we’ll construct a logistic regression mannequin and create an API endpoint for prediction. After you’ve constructed the mannequin and outlined the /predict/ API endpoint, it is best to be capable of make a POST request to the API with the enter options and obtain the expected species as a response.


Iris Prediction API | Picture by Creator


Simply so it’s useful, let’s additionally outline a root endpoint which returns the outline of the app that we’re constructing. To take action, we outline the get_app_description perform and create the foundation endpoint with the @app decorator like so:

# Outline a perform to return an outline of the app
def get_app_description():
	return (
    	"Welcome to the Iris Species Prediction API!"
    	"This API means that you can predict the species of an iris flower based mostly on its sepal and petal measurements."
    	"Use the '/predict/' endpoint with a POST request to make predictions."
    	"Instance utilization: POST to '/predict/' with JSON information containing sepal_length, sepal_width, petal_length, and petal_width."

# Outline the foundation endpoint to return the app description
async def root():
	return {"message": get_app_description()}


Sending a GET request to the foundation endpoint returns the outline.


Step 3: Construct a Logistic Regression Classifier


To this point we’ve instantiated a FastAPI app and have outlined a root endpoint. It’s now time to do the next:

  • Construct a machine studying mannequin. We’ll use a logistic regression classifier. Should you’d prefer to be taught extra about logistics regression, learn Building Predictive Models: Logistic Regression in Python.
  • Outline a prediction perform that receives the enter options and makes use of the machine studying mannequin to make a prediction for the species (one among setosa, versicolor, and virginica).


Logistic Regression Classifier | Picture by Creator


We construct a easy logistic regression classifier from scikit-learn and outline the predict_species perform as proven:

# Construct a logistic regression classifier
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

# Load the Iris dataset
iris = load_iris()
X, y = iris.information, iris.goal

# Prepare a logistic regression mannequin
mannequin = LogisticRegression()
mannequin.match(X, y)

# Outline a perform to foretell the species
def predict_species(sepal_length, sepal_width, petal_length, petal_width):
	options = [[sepal_length, sepal_width, petal_length, petal_width]]
	prediction = mannequin.predict(options)
	return iris.target_names[prediction[0]]



Step 4: Outline Pydantic Mannequin for Enter Information


Subsequent, we should always mannequin the info that we ship within the POST request. Right here the enter options are the size and width of the sepals and petals—all floating level values. To mannequin this, we create an IrisData class that inherits from the Pydantic BaseModel class like so:

# Outline the Pydantic mannequin to your enter information
from pydantic import BaseModel

class IrisData(BaseModel):
	sepal_length: float
	sepal_width: float
	petal_length: float
	petal_width: float


Should you want a fast tutorial on utilizing Pydantic for information modeling and validation, learn Pydantic Tutorial: Data Validation in Python Made Super Simple.


Step 5: Create an API Endpoint


Now that we’ve constructed the classifier and have outlined the predict_species perform prepared, we will create the API endpoint for prediction. Like earlier, we will use the @app decorator to outline the /predict/ endpoint that accepts a POST request and returns the expected species:

# Create API endpoint
async def predict_species_api(iris_data: IrisData):
	species = predict_species(iris_data.sepal_length, iris_data.sepal_width, iris_data.petal_length, iris_data.petal_width)
	return {"species": species}


And it’s time to run the app!


Step 6: Run the App


You may run the app with the next command:

$ uvicorn foremost:app --reload


Right here foremost is the title of the module and app is the FastAPI occasion. The --reload flag ensures that the app reloads if there are any modifications within the supply code.

Upon working the command, it is best to see comparable INFO messages:

INFO: 	Will look ahead to modifications in these directories: ['/home/balapriya/fastapi-tutorial']
INFO: 	Uvicorn working on (Press CTRL+C to stop)
INFO: 	Began reloader course of [11243] utilizing WatchFiles
INFO: 	Began server course of [11245]
INFO: 	Ready for utility startup.
INFO: 	Utility startup full.


Should you navigate to “″(localhost), it is best to see the app description:


App Operating on localhost


Step 7: Check the API


Now you can ship POST requests to the /predict/ endpoint with the sepal and petal measurements—with legitimate values—and get the expected species. You need to use a command-line utility like cURL. Right here’s an instance:

curl -X 'POST' 
  -H 'Content material-Sort: utility/json' 
  -d '{
  "sepal_length": 5.1,
  "sepal_width": 3.5,
  "petal_length": 1.4,
  "petal_width": 0.2


For this instance request that is the anticipated output:


Wrapping Up


On this tutorial, we went over constructing an API with FastAPI for a easy classification mannequin. We went by modeling the enter information for use within the requests, defining API endpoints, working the app, and querying the API.

As an train, take an current machine studying mannequin and construct an API on high of it utilizing FastAPI. Glad coding!



Bala Priya C is a developer and technical author from India. She likes working on the intersection of math, programming, information science, and content material creation. Her areas of curiosity and experience embrace DevOps, information science, and pure language processing. She enjoys studying, writing, coding, and low! At present, she’s engaged on studying and sharing her information with the developer group by authoring tutorials, how-to guides, opinion items, and extra. Bala additionally creates participating useful resource overviews and coding tutorials.

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