Implement the Azure Text Analytics Service - 1

Explore how to implement the Azure Text Analytics Service in a FastAPI Python application. Learn to create a virtual environment, build a function to call various text analytics endpoints, and set up a POST route to handle text data for sentiment and key phrase extraction.

We'll cover the following...

- Create a virtual environment for our API
- Create the call_text_analytics_api function
- Create the POST route

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

...

1.Overview of the Course

2.Building Blocks of an API in Python

3.Learn the Basics of FastAPI

4.Optical Character Recognition API Using Tesseract - Project 1

5.Cloud and Microsoft Azure Services

6.Text Analytics API Using Azure Cognitive Services - Project 2

7.Deploy the REST API on Microsoft Azure

Assessment

8.Appendix

Implement the Azure Text Analytics Service - 1

Create a virtual environment for our `API`

Create the `call_text_analytics_api` function

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