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| import pickle | |
| import gradio as gr | |
| # Load the pickled model | |
| with open('./rf_cv_waze.pickle' , 'rb') as file: | |
| model = pickle.load(file) | |
| # Define the function for making predictions | |
| def salifort(sessions, drives, total_sessions, n_days_after_onboarding, total_navigations_fav1, total_navigations_fav2, driven_km_drives, duration_minutes_drives, activity_days, driving_days, km_per_driving_day, percent_sessions_in_last_month, professional_driver, total_sessions_per_day, km_per_hour, km_per_drive, percent_of_drives_to_favorite, device2): | |
| inputs = [[ float(sessions), float(drives), float(total_sessions), float(n_days_after_onboarding), | |
| float(total_navigations_fav1), float(total_navigations_fav2), float(driven_km_drives), | |
| float(duration_minutes_drives), float(activity_days), float(driving_days), | |
| float(km_per_driving_day), float(percent_sessions_in_last_month), | |
| float(professional_driver), float(total_sessions_per_day), float(km_per_hour), | |
| float(km_per_drive), float(percent_of_drives_to_favorite), float(device2) | |
| ]] | |
| prediction = model.predict(inputs) | |
| prediction_value = prediction[0] | |
| if prediction_value == 0: | |
| label_text = 'User Retained 🟢' | |
| else: | |
| label_text = 'User Churned 🔴' | |
| return label_text | |
| # Create the Gradio interface | |
| salifort_ga = gr.Interface(fn=salifort, | |
| inputs = [ | |
| gr.Number(0, 743, label="sessions: [0 to 743]"), | |
| gr.Number(0, 596, label="drives: [0 to 596]"), | |
| gr.Number(0, 1216, label="total_sessions: [0 to 1216]"), | |
| gr.Number(4, 3500, label="n_days_after_onboarding: [4 to 3500]"), | |
| gr.Number(0, 1236, label="total_navigations_fav1: [0 to 1236]"), | |
| gr.Number(0, 415, label="total_navigations_fav2: [0 to 415]"), | |
| gr.Number(60, 21183, label="driven_km_drives: [60 to 21183]"), | |
| gr.Number(18, 15852, label="duration_minutes_drives: [18 to 15852]"), | |
| gr.Number(0, 31, label="activity_days: [0 to 31]"), | |
| gr.Number(0, 30, label="driving_days: [0 to 30]"), | |
| gr.Number(0, 15420, label="km_per_driving_day: [0 to 15420]"), | |
| gr.Number(0, 1.5, label="percent_sessions_in_last_month: [0 to 1.5]"), | |
| gr.Number(0, 1, label="professional_driver: [0 to 1]"), | |
| gr.Number(0, 39, label="total_sessions_per_day: [0 to 39]"), | |
| gr.Number(72, 23642, label="km_per_hour: [72 to 23642]"), | |
| gr.Number(0, 15777, label="km_per_drive: [0 to 15777]"), | |
| gr.Number(0, 777, label="percent_of_drives_to_favorite: [0 to 777]"), | |
| gr.Number(0, 1, label="device2: [0 to 1]"), | |
| ], | |
| outputs = "text", title="Data-driven suggestions for Waze - User Churn", | |
| examples = [ | |
| [30, 30, 207, 2239, 11, 12, 3919, 2182, 26, 25, 157, 0, 0, 0, 108, 131, 0, 0], | |
| [47, 40, 144, 36, 353, 0, 10764, 4617, 18, 17, 633, 0, 0, 4, 140, 269, 2, 0], | |
| [75, 61, 87, 2825, 67, 94, 243, 183, 2, 0, 0, 1, 0, 0, 80, 4, 2, 0, 0, 0], | |
| [0, 0, 291, 1589, 447, 110, 1574, 1163, 17, 16, 98, 0, 0, 0, 81, 0, 2, 1, 0], | |
| [56, 45, 82, 701, 80, 32, 1515, 691, 20, 15, 101, 1, 0, 0, 132, 34, 1, 0, 0] | |
| ], | |
| description="User Churn Prediction Using Machine Learning", | |
| theme='dark' | |
| ) | |
| salifort_ga.launch(share=True) |