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| import gradio as gr | |
| import joblib | |
| import pandas as pd | |
| from PIL import Image | |
| best_model = joblib.load("best_model.pkl") | |
| roc_img = Image.open("roc_curve_rf_tuned.png") | |
| def churn_prediction(age, gender, tenure, usage_frequency, support_calls, payment_delay, last_interaction, total_spend, subscription_type, contract_length): | |
| try: | |
| input_data = { | |
| "Age": age, | |
| "Gender_Male": 1 if gender == "Male" else 0, | |
| "Gender_Female": 1 if gender == "Female" else 0, | |
| "Usage Frequency": usage_frequency, | |
| "Support Calls": support_calls, | |
| "Contract Length_Monthly": 1 if contract_length == "Monthly" else 0, | |
| "Contract Length_Quarterly": 1 if contract_length == "Quarterly" else 0, | |
| "Contract Length_Annual": 1 if contract_length == "Annual" else 0, | |
| "Payment Delay": payment_delay, | |
| "Last Interaction": last_interaction, | |
| "Total Spend": total_spend, | |
| "Tenure": tenure, | |
| "Subscription Type_Basic": 1 if subscription_type == "Basic" else 0, | |
| "Subscription Type_Premium": 1 if subscription_type == "Premium" else 0, | |
| "Subscription Type_Standard": 1 if subscription_type == "Standard" else 0, | |
| } | |
| input_df = pd.DataFrame([input_data]) | |
| # Predict churn and probability | |
| prediction = best_model.predict(input_df) | |
| prediction_proba = best_model.predict_proba(input_df)[:, 1] | |
| churn_probability = f"{prediction_proba[0]:.2f}" | |
| if prediction_proba[0] < 0.8: | |
| churn_result = "No" | |
| else: | |
| churn_result = "Yes" if prediction[0] == 1 else "No" | |
| return churn_result, churn_probability, roc_img | |
| except Exception as e: | |
| return f"Error: {str(e)}", "N/A", None | |
| inputs = [ | |
| gr.Slider(18, 100, value=40, label="Age"), | |
| gr.Dropdown(["Female", "Male"], value="Male", label="Gender"), | |
| gr.Slider(1, 60, value=30, label="Tenure (months)"), | |
| gr.Slider(1, 30, value=15, label="Usage Frequency"), | |
| gr.Slider(0, 10, value=4, label="Support Calls"), | |
| gr.Slider(0, 30, value=15, label="Payment Delay"), | |
| gr.Slider(1, 30, value=15, label="Last Interaction (days ago)"), | |
| gr.Slider(100, 1000, value=620, label="Total Spend"), | |
| gr.Dropdown(["Premium", "Standard", "Basic"], value="Standard", label="Subscription Type"), | |
| gr.Dropdown(["Monthly", "Quarterly", "Annual"], value="Annual", label="Contract Length") | |
| ] | |
| outputs = [ | |
| gr.Textbox(label="Churn Prediction"), | |
| gr.Textbox(label="Churn Probability"), | |
| gr.Image(label="ROC Curve") | |
| ] | |
| gr.Interface( | |
| fn=churn_prediction, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title="Customer Churn Prediction" | |
| ).launch() |