Update app.py

app.py

@@ -4,64 +4,64 @@ import pandas as pd
 import joblib
 import gradio as gr
 
-# Load saved files - make sure these are in the same folder as app.py
+# Load saved objects (make sure these files are in the same directory as app.py)
 feature_columns = joblib.load('feature_columns.pkl')
 num_cols = joblib.load('num_cols.pkl')
 scaler = joblib.load('scaler.pkl')
 best_model = joblib.load('best_model.pkl')
 
-# Define Gradio prediction function
 def predict_churn(SeniorCitizen, tenure, MonthlyCharges, TotalCharges,
                   gender, Partner, Dependents, PhoneService, MultipleLines,
                   InternetService, OnlineSecurity, OnlineBackup, DeviceProtection,
                   TechSupport, StreamingTV, StreamingMovies, Contract,
                   PaperlessBilling, PaymentMethod):
     try:
-        # Create a dict of inputs
+        # Prepare input data as a dictionary
         input_data = {
-    "SeniorCitizen": SeniorCitizen,
-    "tenure": float(tenure),
-    "MonthlyCharges": float(MonthlyCharges),
-    "TotalCharges": float(TotalCharges),
-    "gender": gender,
-    "Partner": Partner,
-    "Dependents": Dependents,
-    "PhoneService": PhoneService,
-    "MultipleLines": MultipleLines,
-    "InternetService": InternetService,
-    "OnlineSecurity": OnlineSecurity,
-    "OnlineBackup": OnlineBackup,
-    "DeviceProtection": DeviceProtection,
-    "TechSupport": TechSupport,
-    "StreamingTV": StreamingTV,
-    "StreamingMovies": StreamingMovies,
-    "Contract": Contract,
-    "PaperlessBilling": PaperlessBilling,
-    "PaymentMethod": PaymentMethod
-    }
+            "SeniorCitizen": SeniorCitizen,
+            "tenure": float(tenure),
+            "MonthlyCharges": float(MonthlyCharges),
+            "TotalCharges": float(TotalCharges),
+            "gender": gender,
+            "Partner": Partner,
+            "Dependents": Dependents,
+            "PhoneService": PhoneService,
+            "MultipleLines": MultipleLines,
+            "InternetService": InternetService,
+            "OnlineSecurity": OnlineSecurity,
+            "OnlineBackup": OnlineBackup,
+            "DeviceProtection": DeviceProtection,
+            "TechSupport": TechSupport,
+            "StreamingTV": StreamingTV,
+            "StreamingMovies": StreamingMovies,
+            "Contract": Contract,
+            "PaperlessBilling": PaperlessBilling,
+            "PaymentMethod": PaymentMethod
+        }
 
         # Convert to DataFrame
         df = pd.DataFrame([input_data])
 
-        # One-hot encode categorical features
-        df_encoded = pd.get_dummies(df)  
+        # One-hot encode categorical variables
+        df_encoded = pd.get_dummies(df)
 
-        # Align columns with training features (add missing cols with 0)
+        # Align with training features - fill missing columns with 0
         df_encoded = df_encoded.reindex(columns=feature_columns, fill_value=0)
 
-        # Scale numerical features
+        # Scale numerical columns
         df_encoded[num_cols] = scaler.transform(df_encoded[num_cols])
 
-        # Predict churn
+        # Make prediction
         pred = best_model.predict(df_encoded)[0]
+
         return "✅ Churn: Yes" if pred == 1 else "❎ Churn: No"
 
     except Exception as e:
         return f"❌ Error occurred: {str(e)}"
 
-# Define Gradio interface inputs  
+# Define Gradio inputs
 inputs = [
-    gr.Radio([0, 1], label="SeniorCitizen"),  
+    gr.Radio([0, 1], label="SeniorCitizen"),
     gr.Textbox(label="tenure"),
     gr.Textbox(label="MonthlyCharges"),
     gr.Textbox(label="TotalCharges"),
@@ -82,7 +82,7 @@ inputs = [
     gr.Dropdown(["Electronic check", "Mailed check", "Bank transfer (automatic)", "Credit card (automatic)"], label="PaymentMethod")
 ]
 
-# Launch Gradio Interface    
+# Create the Gradio interface
 interface = gr.Interface(
     fn=predict_churn,
     inputs=inputs,