The fastest way to build custom ML tools

Streamlit

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:

Installation

pip install streamlit
streamlit hello

Example

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)

A Bigger Example

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

GitHub