Update app.py

app.py

@@ -9,13 +9,6 @@ import matplotlib.pyplot as plt
 from io import BytesIO
 
 
-
-# num_imputer = joblib.load('C:/Users/user/Desktop/Churn_Prediction_ML/models/numerical_imputer.joblib')
-# cat_imputer = joblib.load('C:/Users/user/Desktop/Churn_Prediction_ML/models/cat_imputer.joblib')
-# encoder = joblib.load('C:/Users/user/Desktop/Churn_Prediction_ML/models/encoder.joblib')
-# scaler = joblib.load('C:/Users/user/Desktop/Churn_Prediction_ML/models/scaler.joblib')
-# lr_model = joblib.load('C:/Users/user/Desktop/Churn_Prediction_ML/models/lr_smote_model.joblib')
-
 num_imputer = joblib.load('numerical_imputer.joblib')
 cat_imputer = joblib.load('cat_imputer.joblib')
 encoder = joblib.load('encoder.joblib')
@@ -57,7 +50,7 @@ original_feature_names = ['MONTANT', 'FREQUENCE_RECH', 'REVENUE', 'ARPU_SEGMENT'
 st.set_page_config(layout="wide")
 
 # Main page - Churn Prediction
-st.title('CUSTOMER CHURN PREDICTION APP ')
+st.title('CUSTOMER CHURN PREDICTION APP')
 
 # Main page - Churn Prediction
 st.markdown("Churn is a one of the biggest problem in the telecom industry. Research has shown that the average monthly churn rate among the top 4 wireless carriers in the US is 1.9% - 2%")
@@ -82,10 +75,10 @@ model_type = models[model_name]['type']
 
 
 # Collect input from the user
-st.sidebar.title('Enter Customer Details')
+st.sidebar.title('ENTER CUSTOMER DETAILS')
 input_features = {
     'MONTANT': st.sidebar.number_input('Top-up Amount (MONTANT)'),
-    'FREQUENCE_RECH': st.sidebar.number_input('Number of Times the Customer Refilled (FREQUENCE_RECH)'),
+    'FREQUENCE_RECH': st.sidebar.number_input('No. of Times the Customer Refilled (FREQUENCE_RECH)'),
     'REVENUE': st.sidebar.number_input('Monthly income of the client (REVENUE)'),
     'ARPU_SEGMENT': st.sidebar.number_input('Income over 90 days / 3 (ARPU_SEGMENT)'),
     'FREQUENCE': st.sidebar.number_input('Number of times the client has made an income (FREQUENCE)'),
@@ -208,7 +201,6 @@ if st.sidebar.button('Predict Churn'):
                     "- It\s crucial to genuinely address their concerns and provide value to rebuild their trust in your business\n" 
                     "- Regularly evaluate the effectiveness of your strategies and adjust them as needed based on customer responses and feedback\n")
         else:
-            #churn_index = churn_indices["No Churn"]
             churn_prob = churn_probability[churn_index]
             with col1:
                 st.success(f"This customer is not likely to churn with a probability of {churn_prob * 100:.2f}% 😀")
@@ -296,7 +288,7 @@ if st.sidebar.button('Predict Churn'):
             else:
                 churn_comparison = "equal"
 
-            explanation = f"This bar chart compares the churn probability of the selected customer " \
+            explanation = f"This compares the churn probability of the selected customer " \
                             f"with the average churn rate of all customers. It provides insights into how the " \
                             f"individual customer's churn likelihood ({predicted_churn_prob:.2f}) compares to the " \
                             f"overall trend. The 'Churn Probability' represents the likelihood of churn " \