FastAPI Template

This is a basic template of FastAPI. The App can also be deployed on docker (cf Docker).

The App predicts the class of the iris dataset.

Getting started

Important: Once you clone the project, all the following commands have to be executed from the root directory of the project. If you have any troubles don't hesitate to reach me at : anas.rabhi.hakim@gmail.com or my linkedin

Local run

Warning : If you are using python 32-bit it may not work. Use docker instead, or go to src -> main.py and rename iris_predict.pkl into iris_predict_32b.pkl

Install all package in requirements.txt

pip install -r requirements/api_requirements.txt

Then run the server with :

uvicorn src.main:app

or

python src/main_local.py

Once the API is running you can send requests --> How to send requests

For more information visit : https://fastapi.tiangolo.com/

Docker run

Use the docker-compose.yml file to build the container and run the App inside using the following command :

docker-compose up

Once the container is running you can send requests --> How to send requests

For more information about Docker visit: https://www.docker.com/resources/what-container

API testing

Method 1 : Using the browser

Once the API is running go to : http://127.0.0.1:8000/docs

Method 2 : Using python

import json
import requests

# Respect the following structure.
data = {
  "sepal_length": 5,
  "sepal_width": 1,
  "petal_length": 0,
  "petal_width": 1
}

url = "http://127.0.0.1:8000/predictions"
response = requests.get(url, data=json.dump(data))

print(f"Predicted class : {response.json()['predicted']}")

Method 3 : Using request_sender file.

Run request_sender.py.

python src/tests/request_sender.py

Then enter the required data.