Deploying an Application to Streamlit Cloud

In this chapter, we developed two Streamlit applications based on PyCaret models. The next step is to deploy them on Streamlit Cloud. In the rest of this chapter, we’ll walk through the steps of deploying the Insurance Charges Prediction App on the Community tier of Streamlit Cloud. Because the process is practically identical for every application, we’ll skip it for the Iris Classification App. However, you’re welcome to deploy it yourself!

Creating a GitHub repository

Firstly, we’ll create a GitHub repository for the application, including the source code, the regression model, and the requirements.txt the dependencies file. In this case, requirements.txt contains the pycaret==2.3.4 dependency only which specifies the PyCaret version to be installed. If we want to fork or clone the Insurance Charges Prediction App repository, it is available at this link. The complete code of this chapter is available at this repository as well.