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

# Date formatting
The website mentioned in the previous lesson follows the following format.

`https://us.trend-calendar.com/trend/{date}.html`

The `{date}` has to be replaced by the date that we want a word cloud of. It has to be in the `YYYY-MM-DD` format. For ease, we’llll scrape the data in intervals of seven days in the following way, `[2020–01–01, 2020–01–08, 2020–01–15, 2020–01–22 …… ]`

# Generate the dates
The pandas library has a function `date_range()`, which is like the `range()` function but for dates. The function takes the `start date`, `end date`, and `frequency` as parameters.
```Python
def get_dates():
    dates = pd.date_range('2020-01-01','2020-12-27',freq='7d')
    dates = [d.strftime('%Y-%m-%d') for d in dates]
    return dates
```
## Define a function to get data for a given day

We’ll only store the top 10 keywords and hashtags.
The following figure illustrates the tags for Twitter hashtags.



Learn how to acquire data from the website in a specified form.

Acquiring the Data

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

Acquiring the Data

Date formatting

Generate the dates