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

## Streamlit app
Now that we know how to scrape Github profile data, let’s see that in action in a Streamlit app.. Let’s run the terminal below to verify the Streamlit installation.

### Create a file
Create a file named `app.py`. This will have the necessary code for Streamlit.

Before we do anything else, we need to import Streamlit and the relevant functions to get user data..

```python
import streamlit as st
from scrape import getData
```
We’ll use the `.title()` method to display a title and the `.text_input()` method to get the user name.
```python
st.title("Github Scraper")
userName = st.text_input('Enter Github Username')
```
Whenever a change is made to any of the variables, Streamlit reruns the app. Therefore, in our case, when the user inputs a username, the app is run again.

Let's run the Streamlit app below.

# Streamlit app
Now that we know how to scrape Github profile data, let’s see that in action in a Streamlit app.. Let’s run the terminal below to verify the Streamlit installation.

## Create a file
Create a file named `app.py`. This will have the necessary code for Streamlit.

Before we do anything else, we need to import Streamlit and the relevant functions to get user data..

```python
import streamlit as st
from scrape import getData
```
We’ll use the `.title()` method to display a title and the `.text_input()` method to get the user name.
```python
st.title("Github Scraper")
userName = st.text_input('Enter Github Username')
```
Whenever a change is made to any of the variables, Streamlit reruns the app. Therefore, in our case, when the user inputs a username, the app is run again.

Let's run the Streamlit app below.

Learn how to scrape Github profile information using Streamlit UI.

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

Streamlit Scraping Application

Streamlit app

Create a file