Build robust front-end Python web applications with Streamlit. Explore integrating Python libraries, creating visualizations, interactive widgets, and deploying on Streamlit cloud.

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Python is the premier programming language for data science and machine learning, but its native strengths for back-end development can leave developers scrambling for a front-end solution. Enter Streamlit, a robust open-source library for developing front-end applications with Python.

This course is a comprehensive introduction to building Streamlit applications integrated with different Python libraries. You’ll be walked through successive projects to create visualizations, display interactive widgets, and customize layouts. You won’t just display data but work on data transformations using Streamlit interfaces and become familiar with basic functionality for native Python applications like caching and placeholders. You’ll wrap up with deploying your application using the Streamlit cloud.

By the end of this course, you’ll be ready to give your Python-based data science and machine learning applications robust user interfaces using Streamlit.

Building the Frontend of Python Web Applications with Streamlit

## Add UI components
Firstly, we’ll add a title and description for the interface.
```python
import streamlit as st
# Title
st.title("Classifiers in Action")
# Description
st.text("Choose a Dataset and a Classifier in the sidebar. Input your values and get a prediction.")
```

### Add the sidebar
Let's add a sidebar to display the sliders on the Streamlit web interface.
```python
#sidebar
sideBar = st.sidebar
dataset = sideBar.selectbox('Which Dataset do you want to use?',('Wine' , 'Breast Cancer' , 'Iris'))
classifier = sideBar.selectbox('Which Classifier do you want to use?',('SVM' , 'KNN' , 'Random Forest'))
```
We use Streamlit’s `selectbox` component to create a drop-down menu for the user to select the dataset and the model.

Let's run the code below.




# Add UI components
Firstly, we’ll add a title and description for the interface.
```python
import streamlit as st
# Title
st.title("Classifiers in Action")
# Description
st.text("Choose a Dataset and a Classifier in the sidebar. Input your values and get a prediction.")
```

## Add the sidebar
Let's add a sidebar to display the sliders on the Streamlit web interface.
```python
#sidebar
sideBar = st.sidebar
dataset = sideBar.selectbox('Which Dataset do you want to use?',('Wine' , 'Breast Cancer' , 'Iris'))
classifier = sideBar.selectbox('Which Classifier do you want to use?',('SVM' , 'KNN' , 'Random Forest'))
```
We use Streamlit’s `selectbox` component to create a drop-down menu for the user to select the dataset and the model.

Let's run the code below.




Learn how to build a classifier UI using Streamlit.

Building UI using Streamlit

Introduction

Build a UI for a Model Using Streamlit

Sentiment Analysis Using Multinomial Logistic Regression

Build a Classification Web App using Streamlit and Sklearn

Build a Streamlit App to Scrape Github Profiles

Build a Web App to Transcribe Audio using Python and AssemblyAI

Build a Web App to Generate and Display a Word Cloud of Trending

Build a Web App to Download YouTube Videos in 30 Lines of Code

Deploy Streamlit Apps

Wrapping Up

Building UI using Streamlit

Add UI components

Add the sidebar