Streamlit is the first application development framework specifically for machine learning and data science teams. It is the fastest way to develop custom machine learning tools.
$ pip install --upgrade streamlit
$ streamlit hello
UI
import streamlit as st
x = st.slider('x')
st.write(x, 'squared is', x * x)
cache
import streamlit as st
import pandas as pd
#Reuse this data across runs!
read_and_cache_csv = st.cache(pd.read_csv)
BUCKET = "https://streamlit-self-driving.s3-us-west-2.amazonaws.com/"
data = read_and_cache_csv(BUCKET + "labels.csv.gz", nrows=1000)
desired_label = st.selectbox('Filter to:', ['car', 'truck'])
st.write(data[data.label == desired_label])
example
The Streamlit application example allows you to perform semantic search in the entire Udacity self-driving vehicle photo data set, visualize manual annotation, and run a YOLO target detector in real time
$ pip install --upgrade streamlit opencv-python
$ streamlit run https://raw.githubusercontent.com/streamlit/demo-self-driving/master/app.py
