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

Utilizing a pre-trained ML model in Streamlit

Now that we have our model, we want to load it (along with our mapping function as well) into Streamlit. In our file,, that we created before, we will again use the pickle library to load our files using the following code. We use the same functions as before, but instead of wb, we use the rb parameter, which stands for read bytes. To make sure these are the same Python objects that we used before, we will use the st.write() function that we are so familiar with already to check: 

import streamlit as st
import pickle
rf_pickle = open('random_forest_penguin.pickle', 'rb')
map_pickle = open('output_penguin.pickle', 'rb')
rfc = pickle.load(rf_pickle)
unique_penguin_mapping = pickle.load(map_pickle)

As with our previous Streamlit apps, we run the following code in the terminal to run our app: 

streamlit run penguins_streamlit...