1st

app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+# Load Model
+model = pickle.load(open('logreg_model.pkl', 'rb'))
+st.title('Iris Variety Prediction')
+# Form
+with st.form(key='form_parameters'):
+   sepal_length = st.slider('Sepal Length', 4.0, 8.0, 4.0)
+   sepal_width = st.slider('Sepal Width', 2.0, 4.5, 2.0)
+   petal_length = st.slider('Petal Length', 1.0, 7.0, 1.0)
+   petal_width = st.slider('Petal Width', 0.1, 2.5, 0.1)
+   st.markdown('---')
+   submitted = st.form_submit_button('Predict')
+# Data Inference
+data_inf = {
+   'sepal.length': sepal_length,
+   'sepal.width': sepal_width,
+   'petal.length': petal_length,
+   'petal.width': petal_width
+}
+data_inf = pd.DataFrame([data_inf])
+if submitted:
+   # Predict using Logistic Regression
+   y_pred_inf = model.predict(data_inf)
+   st.write('## Iris Variety = '+ str(y_pred_inf))

logreg_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b4003b21b748e7ea2653ca442b58e0b7db51244bb23d9006b951b86faf475e7e
+size 1129

requirements.txt

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+streamlit
+pandas
+scikit-learn
+pickle