Update app.py

app.py

@@ -1,4 +1,59 @@
 import streamlit as st 
 import numpy as np 
+import pickle as pkl
+
+
+model = pickle.load(open("model.pkl","rb"))
 
 st.title("Churn Modelling")
+
+st.sidebar.header("Enter the customer details:")
+
+# Credit Score
+credit_score = st.sidebar.number_input("Credit Score", min_value=0, max_value=1000)
+
+# Age
+age = st.sidebar.number_input("Age", min_value=0, max_value=100)
+
+# Tenure
+tenure = st.sidebar.number_input("Tenure", min_value=0, max_value=100)
+
+# Balance
+balance = st.sidebar.number_input("Balance", min_value=0, max_value=100000)
+
+# Num of Products
+num_of_products = st.sidebar.number_input("Num of Products", min_value=0, max_value=10)
+
+# Has Cr Card
+has_cr_card = st.sidebar.radio("Has Cr Card", ("Yes", "No"))
+
+# Is Active Member
+is_active_member = st.sidebar.radio("Is Active Member", ("Yes", "No"))
+
+# Estimated Salary
+estimated_salary = st.sidebar.number_input("Estimated Salary", min_value=0, max_value=100000)
+
+# Female
+gender = st.sidebar.selectbox("Enter your gender", ("Male", "Female"))
+country = st.sidebar.selectbox("Enter your Country", ("france", "Spain","germany"))
+
+# Predict the customer's churn status
+if st.sidebar.button("Predict"):
+
+    if gender=="Male":
+      female, male = 0,1
+    elif gender =="Female":
+      female, male = 1,0  
+    
+    if country == "france":
+      france, germany, spain = 1,0,0
+    elif country == "germany":
+      france, germany, spain = 0,1,0
+    else:
+      france, germany, spain = 0,0,1
+
+    features = [credit_score, age, tenure, balance, num_of_products, has_cr_card, is_active_member, estimated_salary, female, male, france, germany, spain]
+    prediction = model.predict([features])
+
+    # Display the prediction
+    st.write(f"The customer is predicted to churn: {prediction}")