import streamlit as st
from pybanking.churn_prediction import model_churn
import pandas as pd
x = st.slider('Select a value')
st.write('This is the input data')

df = model_churn.get_data()
st.dataframe(df.head(5))

model_names = [
            "Logistic_Regression",
            "Support_Vector_Machine",
            "Support_Vector_Machine_Optimized",
            "Decision_Tree",
            "Neural_Network",
            "Random_Forest"
        ]
option = st.selectbox(
     'Which model would you like to use?',
     model_names)


option2 = st.selectbox(
     'Which dataset would you like to use for prediction?',
     ['Sample dataset','Upload custom'])