# Streamlit Overview

## Streamlit: The Basics

[Streamlit](https://www.streamlit.io/) is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science. In just a few minutes you can build and deploy powerful data apps.&#x20;

In practice, a Streamlit application is just a `.py` file where you add a few Streamlit commands and then you run it with `streamlit run`:

```
streamlit run your_script.py [-- script args]
```

As soon as you run the script as shown above, a local Streamlit server will spin up and your app will open in a new tab in your default web browser. This app is your canvas, where you'll draw charts, text, widgets, tables, and more. This `.py` file is all you need to build a fully-featured web-app!

What gets drawn in the app is up to you. For example [`st.text`](https://docs.streamlit.io/library/api-reference/text/st.text) writes raw text to your Streamlit app, and [`st.line_chart`](https://docs.streamlit.io/library/api-reference/charts/st.line_chart) draws a line chart. Refer to Streamlit's [API documentation](https://docs.streamlit.io/library/api-reference) to see all commands that are available to you.

## Core Concepts in Streamlit

New to writing Streamlit applications and looking to understand more? Check out the Streamlit docs to learn more [core concepts](https://docs.streamlit.io/library/get-started/main-concepts).&#x20;


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