modules/churn_analysis.py

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 demo:

    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)
        
demo.launch(share=True)