Gain insights into building 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

# The `Wordcloud` library
We’ll use the **`wordcloud`** library to create word clouds. First, we’ll create a default word cloud (see below) without any image mask.

The following piece of code creates a word cloud object:
```Python
wordcloud = WordCloud(width, height, repeat, 
max_words,max_font_size,background_color)
```

- **`width`:** It represents the width of the word cloud.
- **`height`:** It represents the height of the word cloud.
- **`repeat`:** A boolean value. If set to `True`, words will be repeated to fill up blank spaces. If set to `False`, blank spaces will be visible.
- **`max_words`:** The maximum number of words inside the word cloud.
- **`max_font_size`:** The maximum font size of a word, the word with the maximum weight will have the `max_font_size`.
- **`background_color`:** By default, it is set to black. However, we can change it.

To create fancier word clouds like those below, we need to create image masks.

Learn how to build word clouds using the wordcloud library.

Creating Word Clouds

Introduction

Build a UI for a Model Using Streamlit

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

Creating Word Clouds

The `Wordcloud` library

Creating Word Clouds

The Wordcloud library

The `Wordcloud` library