First model version

app.py

@@ -1,8 +1,25 @@
 import streamlit as st
 from pybanking.churn_prediction import model_churn
-
+import pandas as pd
 x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+st.write('This is the input data')
 
 df = model_churn.get_data()
-st.dataframe(df)
+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'])

requirements.txt

@@ -1 +1,2 @@
-pybanking
+pybanking
+pandas