Create app.py

app.py

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+import streamlit as st 
+import pandas as pd 
+import numpy as np 
+import joblib 
+import tensorflow 
+
+with open('full_pipeline.pkl', 'rb') as file_1:
+    model_pipeline = joblib.load(file_1)
+
+from tensorflow.keras.models import load_model
+model_ann = load_model('churn_model.h5')
+
+st.title("Customer Churn Prediction")
+
+membership_category = st.selectbox('Membership Category',('No Membership',
+                                                         'Basic Membership',
+                                                         'Silver Membership',
+                                                         'Premium Membership',
+                                                         'Gold Membership',
+                                                         'Platinum Membership'), index=1)
+avg_transaction_value = st.number_input('Average Transaction Value :',
+                                       min_value = 800.460000,
+                                       max_value = 99914.050000,
+                                       value = 800.460000)
+points_in_wallet = st.number_input('Points In Wallet :',
+                                  min_value = 0.000000,
+                                  max_value = 2069.069761,
+                                  value = 0.000000)
+feedback = st.selectbox('Feedback',('Poor Website',
+                                   'Poor Customer Service',
+                                   'Too Many Ads',
+                                   'Poor Product Quality',
+                                   'No Reason Specified',
+                                   'Products Always in Stock',
+                                   'Reasonable Price',
+                                   'Quality Customer Care',
+                                   'User Friendly Website'), index=1)
+
+df_inf = pd.DataFrame({
+    'membership_category' : [membership_category],
+    'avg_transaction_value' : [avg_transaction_value],
+    'points_in_wallet' : [points_in_wallet],
+    'feedback' : [feedback]
+})
+
+if st.button('Predict'):
+    data_inf_transform = model_pipeline.transform(df_inf)
+    y_pred_inf = model_ann.predict(data_inf_transform)
+    y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0)
+    churn_status = np.where(y_pred_inf == 0, "No", "Yes")
+
+    if churn_status == "No":
+        st.success(f"The customer is predicted to not churn.")
+    else:
+        st.error(f"The customer is predicted to churn.")