Book Image

Getting Started with Streamlit for Data Science

By : Tyler Richards
Book Image

Getting Started with Streamlit for Data Science

By: Tyler Richards

Overview of this book

Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you’ll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you’ll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.
Table of Contents (17 chapters)
Section 1: Creating Basic Streamlit Applications
Section 2: Advanced Streamlit Applications
Section 3: Streamlit Use Cases

Training models inside Streamlit apps

Often, we may want to have the user input change how our model is trained. We may want to accept data from the user or ask the user what features they would like to use, or even allow the user to pick the type of machine learning algorithm they would like to use. All of these options are feasible in Streamlit, and in this section, we will cover the basics around using user input to affect the training process. As we discussed in the section above, if a model is going to be trained only once, it is probably best to train the model outside of Streamlit and import the model into Streamlit. But what if, in our example, the penguin researchers have the data stored locally, or do not know how to retrain the model but have the data in the correct format already? In cases like these, we can add the st.file_uploader() option and include a method for these users to input their own data, and get a custom model deployed for them without having to write any...