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.

“Building web appsUIs with Python and Streamlit”.png

finalStreamlitDocker1.0.tar.gz

streamlit

streamlitcode

train

SPAUIModel

Challenge1

Challenge1Code

Chapter2SPA

chapter2SPAplot

SPAPLOT

classifierStreamlit

Challenge2

SPAHTML

Chapter3SPA

AssemblyAI

WordCloudsSPA

WordCloudsSPA-copy

YoutubeSTreamlitSPA

YoutubeSTreamlit

YoutubeSTreamlitSPA-copy

YoutubeCode

streamlitHello

WordCloud

WordCloudsSPAFinal

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 apps
To run a Streamlit app, type the following command: `streamlit run app.py`.

Since we don’t have any components yet, we should see a blank screen.. At the top right corner in the interface of the output, the “Rerun” button in the drop-down menu tells Streamlit to reload each time we change our code.





# Streamlit apps
To run a Streamlit app, type the following command: `streamlit run app.py`.

Since we don’t have any components yet, we should see a blank screen.. At the top right corner in the interface of the output, the “Rerun” button in the drop-down menu tells Streamlit to reload each time we change our code.





Learn how to run a logistic regression model in Streamlit UI.

Build the DataFrame and Model Performance UI with Streamlit

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

Build the DataFrame and Model Performance UI with Streamlit

Streamlit apps