June 20, 2020
ML with StreamLit

Building lightweight ML applications with python, pandas, streamlit, and scikit-learn is a awesome.

Let's run through a simple example app to illustrate this.

Install necessary requirements first.

pip install streamlit pandas scikit-learn

Here's the app with inline explanations.

# ml-app.py
# import libs
import streamlit as st
import pandas as pd
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier

# A Simple Iris Flower Prediction App
This app predicts the iris flower type!

st.sidebar.header("User Input Parameters")

# this basically is inputting features of a flower observation into a dataframe
def user_input_features():
    sepal_length = st.sidebar.slider("Sepal length", 4.3, 7.7, 5.4)
    sepal_width = st.sidebar.slider("Sepal width", 2.0, 4.3, 3.4)
    petal_length = st.sidebar.slider("Petal length", 1.0, 6.9, 1.3)
    petal_width = st.sidebar.slider("Petal width", 0.2, 2.5, 0.2)
    data = {"sepal_length": sepal_length,
            "sepal_width": sepal_width,
            "petal_length": petal_length,
            "petal_width": petal_width}
    features = pd.DataFrame(data, index=[0])
    return features

df = user_input_features()

st.subheader("User Input parameters")

# load in our datasets
iris = datasets.load_iris()
X = iris.data
Y = iris.target

# use a random forest classifier to fit our data to our target
clf = RandomForestClassifier()
clf.fit(X, Y)

# get the prediction and show our results in the ui
prediction = clf.predict(df)
prediction_prob = clf.predict_proba(df)

st.subheader("Class labels and their corresponding index number")


st.subheader("Prediction Probability")

Run the app 🚀

streamlit run ml-app.py

Once it's done starting up, visit http://localhost:8501 and you should see something like

Awesome right?? StreamLit is a great tool for building and examining your models with a minimal, accessible interface. If you're interested in learning more, checkout their website, streamlit.io.