Streamlit

Streamlit is an open-source app framework for machine learning and data science. It allows developers to build beautiful and interactive web applications with just a few lines of code, without having to deal with the low-level complexities of web development. Streamlit supports all the major Python libraries for data analysis and machine learning, making it a popular choice for data scientists who want to share their work with others in a visually appealing and interactive way.

Streamlit’s key features include:
- Easy to use and fast to develop
- Interactive widgets for data visualization and exploration
- Automated UI updates, so you can focus on writing the code
- Sharing and deploying to the cloud with just a few clicks

If you’re a data scientist or machine learning engineer and want to share your work with others in a beautiful, interactive, and scalable way, Streamlit is a great choice!

Getting started with Streamlit is easy! Here are the steps to get started:

1. Install Streamlit: To install Streamlit, you need to run the following command in your terminal or command prompt:

pip install streamlit

2. Create a new Streamlit script: To create a new Streamlit script, create a new Python file and add the following code to it:

import streamlit as st

st.title("Hello, Streamlit!")
st.write("This is your first Streamlit app")

3. Run the script: To run the script, go to the terminal or command prompt, navigate to the directory where the script is located, and run the following command:

streamlit run filename.py

4. View the app: After you run the script, Streamlit will automatically open a new browser window and show you your first Streamlit app.

And that’s it! You have successfully created your first Streamlit app. From here, you can start exploring the various components and functionalities of Streamlit and build more complex and interactive apps.

In the upcoming posts, I will be explaining Streamlit from scratch to advance.

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