Creating an API and making predictions on a local server

In this section, we will learn about making predictions on a local server (that has nothing to do with the cloud). At a high level, this involves the following steps:

1. Installing FastAPI
2. Creating a route to accept incoming requests
3. Saving an incoming request on disk
4. Loading the requested image, then preprocessing and predicting with the trained model
5. Postprocessing the results and sending back the predictions as a response to the same incoming request

All of the steps in this section are summarized as a video walk-through here: https://tinyurl.com/MCVP-Model2FastAPI.

Let's begin by installing FastAPI in the following subsection.

Installing the API module and dependencies

Since FastAPI is a Python module, we can use pip for installation, and be ready to code an API. We will now open a new terminal and run the following command:

$pip install fastapi uvicorn aiofiles jinja2 

We have installed a couple more dependencies that...