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

## Introduction
To generate a word cloud we’ll use data from the following website:


https://us.trend-calendar.com/trend/2020-01-01.html

The above website stores archives of the trending keywords and hashtags each day. We’ll use Beautiful Soup to scrape data and build an application based on the following features:

* A word cloud for the year 2020.
* Provide the user an option to select a date and generate a word cloud for it.

* Provide the user an option to change the image mask.


## Prerequisites

* Basic familiarity with web scraping using Beautiful Soup.
* Knowledge of Streamlit is not necessary for generating the word clouds but a basic understanding of Streamlit is required to build the web app.

## Install the required libraries

We need to install the following libraries:
- Streamlit
- Matplotlib
- Beautiful Soup
- pandas
- wordcloud

To install the above libraries, run the following line of code:

```
pip install pandas,streamlit,wordcloud,matplotlib,bs4
```

# Introduction
To generate a word cloud we’ll use data from the following website:


https://us.trend-calendar.com/trend/2020-01-01.html

The above website stores archives of the trending keywords and hashtags each day. We’ll use Beautiful Soup to scrape data and build an application based on the following features:

* A word cloud for the year 2020.
* Provide the user an option to select a date and generate a word cloud for it.

* Provide the user an option to change the image mask.


# Prerequisites

* Basic familiarity with web scraping using Beautiful Soup.
* Knowledge of Streamlit is not necessary for generating the word clouds but a basic understanding of Streamlit is required to build the web app.

# Install the required libraries

We need to install the following libraries:
- Streamlit
- Matplotlib
- Beautiful Soup
- pandas
- wordcloud

To install the above libraries, run the following line of code:

```
pip install pandas,streamlit,wordcloud,matplotlib,bs4
```

Learn how to install required libraries to generate and display a word cloud.

Prerequisites and Required Libraries

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

Prerequisites and Required Libraries

Introduction