The fastest way to build data apps.
Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file. No need to mess with HTTP requests, HTML, JavaScript, etc. All you need is your favorite editor and a browser. Take a look at Streamlit in action:
Check out our launch blog post!!
pip install streamlit
streamlit helloWe also have suggestions for installing Streamlit in a virtual environment in Windows, Mac, and Linux.
Streamlit lets you build interactive apps ridiculously easily:
import streamlit as st
x = st.slider('Select a value')
st.write(x, 'squared is', x * x)
Despite its simplicity Streamlit lets you build incredibly rich and powerful tools. This demo project lets you browse the entire Udacity self-driving-car dataset and run inference in real time using the YOLO object detection net.
The complete demo is implemented in less than 300 lines of Python. In fact, the app contains only 23 Streamlit calls which illustrates all the major building blocks of Streamlit. You can try it right now with:
pip install --upgrade streamlit opencv-python
streamlit run https://raw.githubusercontent.com/streamlit/demo-self-driving/master/app.py- Our launch post
- Our lovely community
- Streamlit documentation
- More demo projects
- If you would like to contribute, see instructions here
Streamlit for Teams is our enterprise edition, with single-click deploy, authentication, web editing, versioning, and more. Please contact us if you would like to learn more.
Streamlit is completely free and open source and licensed under the Apache 2.0 license.
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!