Developing and Deploying Applications with Streamlit

The fastest way to build and share data apps.

What you'll learn:

• Streamlit and its usefulness.
• Streamlit's features that help up build web , data and machine learning application
• Deploying streamlit applications on streamlit cloud
• Personal Portfolio page hosted on streamlit cloud

Streamlit is an open-source app framework for Machine Learning and Data Science teams.

Streamlit lets you turn data scripts into shareable web apps in minutes. It’s all Python, open-source, and free! And once you’ve created an app you can use our cloud platform to deploy, manage, and share your app!

In this course we will cover everything you need to know concerning streamlit such as

1. Installing Anaconda and create a virtual env

2. Installing Streamlit , pytube, firebase

3. Setting up GitHub account if you already don't have one

4. Display Information with Streamlit

5. Widgets with Streamlit

6. Working with data frames ( Loading , Displaying )

7. Creating a image filter ( we use popular Instagram filters)

8. Creating a YouTube video downloader (using pytube api)

   1. pytube is a lightweight, dependency-free Python library which is used for downloading videos from the web

9. Creating Interactive plots

   1. User selected input value for chart

   2. Animated Plot

10. Introduction to Multipage Apps

    1. Structuring multipage apps

    2. Run a multipage app

    3. Adding pages

11. Adding Authentication to your Streamlit app using Streamlit-Authenticator

    1. Authentication via Pickle File

    2. Authentication via Database

12. Build a Word Cloud App

13. Build a OCR - Image to text conversion with tesseract

14. Build a World Cloud App

15. ChatGPT + Streamlit

    1. Build a auto review response generator with chatGPT and Open AI

    2. Build a Leetcode problem solver with chatGPT and Open AI

16. Content in progress to be uploaded soon

    1. Creating a personal portfolio page with streamlit

    2. Deploy Application with Streamlit Cloud

    3. Concept of Sessions

    4. NTLKwith streamlit

    5. Working with SQLite

       1. Connecting to database

       2. Reading data from database

       3. Writing Data into database

    6. Additional Apps

       1. Static Code quality analyzer

       2. No SQLJob Board with Firebase API

       3. Converting random forest model into streamlit application