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))