Gain insights into building REST APIs with FastAPI, explore parallel processing in Python, and discover how to deploy APIs on Microsoft Azure with a CI/CD pipeline using GitHub.

Build a REST API Using Python and Deploy It to Microsoft Azure.png

fastapi-docker.tar.gz

FastAPI-test

Python3

In this course, you will learn how to build a REST API using a lighter web framework for Python, i.e., FastAPI. You will learn the basics of FastAPI and learn how to implement parallel processing in Python APIs.

Finally, you will deploy your API on Microsoft Azure and build a Continuous Integration/Continuous Development (CI/CD) Pipeline using Github.

Build a REST API Using Python and Deploy it to Microsoft Azure

# Create a virtual environment for our `API`

We have already discussed how to create a virtual environment using *Anaconda* as well as the `venv` Python package. Before starting this project, it is advisable to create a virtual environment and activate it. 
> In this project, we will not use any `async` functionality for the sake of understanding the normal `API` building. Although, if you want, you can go ahead with the `async` functionality that we have already discussed. 

# Create the `call_text_analytics_api` function
We will first create a function `call_text_analytics_api()` that will accept three parameters: 
* **Header data:** This contains our key for the text analytics service.
* **Document:** This contains the text data in the format which is acceptable by the text analytics service. (*Remember, we discussed the format of the data that has to be sent to the service in the previous lesson*).
* **Endpoint:** This will contain either `sentiment` or `keyPhrases`, telling the service to perform the respective operation on our text data.
   > You can have the following values of `endpoint` to perform other operations on your text data:
   > * `languages`: This will be used to detect the language in the text. 
   > * `entities/recognition/general`: This will perform general Named Entity Recognition from your text data. 
   > * `entities/recognition/pii`: This will extract Personally Identifiable Information (PII) from the text. The PII feature is part of NER, and it can identify sensitive entities in the text that are associated with an individual person such as a phone number, email address, mailing address, or passport number.


Let us now create our function.


Create your REST API using the Azure Text Analytics Service.

Overview of the Course

Building Blocks of an API in Python

Learn the Basics of FastAPI

Optical Character Recognition API Using Tesseract - Project 1

Cloud and Microsoft Azure Services

Text Analytics API Using Azure Cognitive Services - Project 2

Deploy the REST API on Microsoft Azure

Pop Quiz

Appendix

Implement the Azure Text Analytics Service - 1

Create a virtual environment for our `API`

Create the `call_text_analytics_api` function

Pop Quiz

Implement the Azure Text Analytics Service - 1

Create a virtual environment for our API

Create the call_text_analytics_api function

Create a virtual environment for our `API`

Create the `call_text_analytics_api` function