text: The fastest way to build custom ML tools
  Streamlit. 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
  GitHub
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