Deployment using Streamlit Sharing

In previous chapters, we built a GitHub web scraper and a KNN classification model from scratch and used Streamlit for UI.

However, if we can’t show our web apps in a visually impressive way, then what would be the point?

In this chapter, we’ll learn how to deploy our machine learning web app using Streamlit Sharing.

Streamlit sharing

Streamlit Sharing is a service provided by Streamlit to easily deploy our application. Below, we will walk over the steps in the deployment process in the “Build a Classification Web App using Streamlit and Sklearn” chapter.

Create a text file with the necessary libraries

Create a requirements.txt file with the dependencies. Use the following command to create the file:

pip freeze > requirements.txt

Note: Make sure your virtual environment is activated before you type the above command.

Upload the files to GitHub

Create a public repository on GitHub and upload the .py files and the requirements.txt file.