The Streamlit documentation recommends using the Pipenv environment manager for Linux/macOS. Creating a Streamlit appįirst of all we need to create a project folder and install Streamlit in a virtual environment. It’s API makes it very easy and quick to display data and create interactive widgets from just a regular Python script. Streamlit is a framework for creating interactive web apps for data visualisation in Python. In this post we will look at how to deploy a Streamlit application to RStudio Connect. RStudio Connect also supports a growing number of Python applications, API services including Flask and FastAPI and interactive web based apps such as Bokeh and Streamlit. However, despite the name, it is not just for R developers (hence their recent announcement). RStudio Connect is a platform which is well known for providing the ability to deploy and share R applications such as Shiny apps and Plumber APIs as well as plots, models and R Markdown reports. Part 3: Python API deployment with RStudio Connect: Streamlit (this post).
Part 2: Python API deployment with RStudio Connect: FastAPI.Part 1: Python API deployment with RStudio Connect: Flask.This is the final part of our three part series
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