Alot of people know how to build ML models, but surprisingly few are comfortable with the deployment process. Deploying models is a necessary skill in the industry, and the first step of deployment is running our ML models on the web during development for demos and testing. This can be done using a simple development server before deployment. A development server can be built with just a few lines of code using a framework like Flask. In this article, we will explore a very simple development server which will enable us to render the output predictions of our image classifier model (written using PyTorch) on a public webpage. This entire code can be run on Google Colab, without installing anything on our local machine.

Google Colab provides a virtual machine environment, so unlike when running Flask on our local machine, we cannot access localhost. Hence, we need to expose it to a public URL using the library flask-ngrok, which generates a temporary URL where the web app runs.

First, let’s create two folders called images and references in the current project directory, where we will store images (to be classified by our classifier) and a dictionary mapping class indices to human-understandable class names.

#developers corner #development server #flask #google colab #web application