Update modules/churn_analysis.py

modules/churn_analysis.py

@@ -0,0 +1,143 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib, os
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+pipeline_path = os.path.join(script_dir, 'toolkit', 'pipeline.joblib')
+model_path = os.path.join(script_dir, 'toolkit', 'Random Forest Classifier.joblib')
+
+# Load transformation pipeline and model
+pipeline = joblib.load(pipeline_path)
+model = joblib.load(model_path)
+
+# Create a function to calculate TotalCharges
+def calculate_total_charges(tenure, monthly_charges):
+    return tenure * monthly_charges
+
+# Create a function that applies the ML pipeline and makes predictions
+def predict(SeniorCitizen, Partner, Dependents, tenure,
+            InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,
+            StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod,
+            MonthlyCharges):
+    
+    # Calculate TotalCharges
+    TotalCharges = calculate_total_charges(tenure, MonthlyCharges)
+
+    # Create a dataframe with the input data
+    input_df = pd.DataFrame({
+        'SeniorCitizen': [SeniorCitizen],
+        'Partner': [Partner],
+        'Dependents': [Dependents],
+        'tenure': [tenure],
+        'InternetService': [InternetService],
+        'OnlineSecurity': [OnlineSecurity],
+        'OnlineBackup': [OnlineBackup],
+        'DeviceProtection': [DeviceProtection],
+        'TechSupport': [TechSupport],
+        'StreamingTV': [StreamingTV],
+        'StreamingMovies': [StreamingMovies],
+        'Contract': [Contract],
+        'PaperlessBilling': [PaperlessBilling],
+        'PaymentMethod': [PaymentMethod],
+        'MonthlyCharges': [MonthlyCharges],
+        'TotalCharges': [TotalCharges]
+    })
+
+    # Selecting categorical and numerical columns separately
+    cat_cols = [col for col in input_df.columns if input_df[col].dtype == 'object']
+    num_cols = [col for col in input_df.columns if input_df[col].dtype != 'object']
+    
+    X_processed = pipeline.transform(input_df)
+
+    # Extracting feature names for categorical columns after one-hot encoding
+    cat_encoder = pipeline.named_steps['preprocessor'].named_transformers_['cat'].named_steps['onehot']
+    cat_feature_names = cat_encoder.get_feature_names_out(cat_cols)
+
+    # Concatenating numerical and categorical feature names
+    feature_names = num_cols + list(cat_feature_names)
+
+    # Convert X_processed to DataFrame
+    final_df = pd.DataFrame(X_processed, columns=feature_names)
+
+    # Extract the first three columns and remaining columns, then merge
+    first_three_columns = final_df.iloc[:, :3]
+    remaining_columns = final_df.iloc[:, 3:]
+    final_df = pd.concat([remaining_columns, first_three_columns], axis=1)
+
+    # Make predictions using the model
+    prediction_probs = model.predict_proba(final_df)[0]
+    prediction_label = {
+        "Prediction: CHURN 🔴": prediction_probs[1],
+        "Prediction: STAY ✅": prediction_probs[0]
+    }
+
+    return prediction_label
+
+input_interface = []
+
+with gr.Blocks(theme=gr.themes.Soft()) as app:
+
+    Title = gr.Label('Customer Churn Prediction App')
+
+    with gr.Row():
+        Title
+
+    with gr.Row():
+        gr.Markdown("This app predicts likelihood of a customer to leave or stay with the company")
+
+    with gr.Row():
+        with gr.Column():
+            input_interface_column_1 = [
+                gr.components.Radio(['Yes', 'No'], label="Are you a Seniorcitizen?"),
+                gr.components.Radio(['Yes', 'No'], label='Do you have Partner?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have any Dependents?'),
+                gr.components.Slider(label='Enter lenghth of Tenure in Months', minimum=1, maximum=73, step=1),
+                gr.components.Radio(['DSL', 'Fiber optic', 'No Internet'], label='What is your Internet Service?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have Online Security?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have Online Backup?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have Device Protection?')
+            ]
+
+        with gr.Column():
+            input_interface_column_2 = [
+                gr.components.Radio(['No', 'Yes'], label='Do you have Tech Support?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have Streaming TV?'),
+                gr.components.Radio(['No', 'Yes'], label='Do you have Streaming Movies?'),
+                gr.components.Radio(['Month-to-month', 'One year', 'Two year'], label='What is your Contract Type?'),
+                gr.components.Radio(['Yes', 'No'], label='Do you prefer Paperless Billing?'),
+                gr.components.Radio(['Electronic check', 'Mailed check', 'Bank transfer (automatic)', 'Credit card (automatic)'], label='Which PaymentMethod do you prefer?'),
+                gr.components.Slider(label="Enter monthly charges", minimum=18.40, maximum=118.65)
+            ]
+
+    with gr.Row():
+        input_interface.extend(input_interface_column_1)
+        input_interface.extend(input_interface_column_2)
+
+    with gr.Row():
+        predict_btn = gr.Button('Predict')
+        output_interface = gr.Label(label="churn")
+    
+    with gr.Accordion("Open for information on inputs", open=False):
+        gr.Markdown("""This app receives the following as inputs and processes them to return the prediction on whether a customer, will churn or not.
+                    
+                    - SeniorCitizen: Whether a customer is a senior citizen or not
+                    - Partner: Whether the customer has a partner or not (Yes, No)
+                    - Dependents: Whether the customer has dependents or not (Yes, No)
+                    - Tenure: Number of months the customer has stayed with the company
+                    - InternetService: Customer's internet service provider (DSL, Fiber Optic, No)
+                    - OnlineSecurity: Whether the customer has online security or not (Yes, No, No Internet)
+                    - OnlineBackup: Whether the customer has online backup or not (Yes, No, No Internet)
+                    - DeviceProtection: Whether the customer has device protection or not (Yes, No, No internet service)
+                    - TechSupport: Whether the customer has tech support or not (Yes, No, No internet)
+                    - StreamingTV: Whether the customer has streaming TV or not (Yes, No, No internet service)
+                    - StreamingMovies: Whether the customer has streaming movies or not (Yes, No, No Internet service)
+                    - Contract: The contract term of the customer (Month-to-Month, One year, Two year)
+                    - PaperlessBilling: Whether the customer has paperless billing or not (Yes, No)
+                    - Payment Method: The customer's payment method (Electronic check, mailed check, Bank transfer(automatic), Credit card(automatic))
+                    - MonthlyCharges: The amount charged to the customer monthly
+                    """)
+        
+    predict_btn.click(fn=predict, inputs=input_interface, outputs=output_interface)
+        
+app.launch(share=True)