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| 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) | |