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`
It is a best practice to create a virtual environment whenever you create a new project. You can follow one of the two options below:
* **Using the Anaconda Prompt:** 
   * Start the Anaconda Prompt.

   * Run the following command:
       ```
       conda create -n env_name python=3.7 
        ```
        You can specify any `env_name` you like and the desired `Python` version.
    * Activate your virtual environment by running the following command:
       ```
       conda activate env_name
       ```
   * Your environment is now activated and you can install whichever packages you need in this virtual environment.

* **Using venv package:**
   * Start the Command Prompt.

   * If you do not have `venv` installed, you need to first install it by running the command shown below:
        ```
        pip install venv
        ```
   * Then, run the following command to create the virtual environment:
       ```
       python3 -m venv path\env_name
        ```
        You can specify any `env_name` you like. You need to specify the path where you want to store the files for your environment. By default, it will take the current directory.
    * Activate your virtual environment by running the following command:
       ```a
       path\env_name\Scripts\activate
       ```
   * Your environment is now activated and you can install whichever packages you need in this virtual environment.
# Create the default `GET` route
The first step in creating our `REST API` is to define a default (home) route that performs a `GET` operation. Whenever anyone tries to go the URL:
```
http://localhost:8000/
```
we will just give a JSON response as shown below:
```a
{
   "message" : "Visit the endpoint: /api/v1/extract_text to perform OCR."
}
```
This means that the actual `OCR` operation is going to perform at the URL:
```
http://localhost:8000/api/v1/extract_text
```
with a request body containing the `multipart/form-data`, i.e., the images. 
> Note that we are going to pass multiple images in a single call, and our `API` should give the response in the form shown below:
```a
{
   "image_name": "extracted_text"
}
```
Let’s first create the default `GET` route.

Learn how to create a REST API that performs OCR on multiple images concurrently using FastAPI.

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 OCR API Using FastAPI - 1

Create a virtual environment for our `API`

Pop Quiz

Implement OCR API Using FastAPI - 1

Create a virtual environment for our API

Create a virtual environment for our `API`