Update app.py

app.py

@@ -6,45 +6,51 @@ with open('./xgb_waze.pickle', 'rb') as file:
     model = pickle.load(file)
 
 # Define the function for making predictions
-def salifort(last_evaluation, number_project, tenure, work_accident, promotion_last_5years, salary, department_IT, department_RandD, department_accounting, department_hr, department_management, department_marketing, department_product_mng, department_sales, department_support, department_technical, overworked):
-    inputs = [[float(last_evaluation), float(number_project), float(tenure), float(work_accident), float(promotion_last_5years), float(salary), float(department_IT), float(department_RandD), float(department_accounting), float(department_hr), float(department_management), float(department_marketing), float(department_product_mng), float(department_sales), float(department_support), float(department_technical), float(overworked)]]
+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 = [['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']]
     prediction = model.predict(inputs)
     prediction_value = prediction[0]
     if prediction_value == 0:
-        label_text = 'Employee would not leave the company 🟢'
+        label_text = 'User Retained 🟢'
     else:
-        label_text = 'Employee will leave the company 🔴'
+        label_text = 'User Churned 🔴'
     return label_text
 
 # Create the Gradio interface
 salifort_ga = gr.Interface(fn=salifort, 
                                inputs = [
-                                            gr.Number(0, 1, label="last_evaluation: [0 1]"),
-                                            gr.Number(2, 7, label="number_project: [2 to 7]"),
-                                            gr.Number(2, 10, label="tenure: [2 to 10]"),
-                                            gr.Number(0, 1, label="work_accident: [0 1]"),
-                                            gr.Number(0, 1, label="promotion_last_5years: [0 1]"),
-                                            gr.Number(0, 2, label="salary: [0 1 2]"),
-                                            gr.Number(0, 1, label="department_IT: [0 1]"),
-                                            gr.Number(0, 1, label="department_RandD: [0 1]"),
-                                            gr.Number(0, 1, label="department_accounting: [0 1]"),
-                                            gr.Number(0, 1, label="department_hr: [0 1]"),
-                                            gr.Number(0, 1, label="department_management: [0 1]"),
-                                            gr.Number(0, 1, label="department_marketing: [0 1]"),
-                                            gr.Number(0, 1, label="department_product_mng: [0 1]"),
-                                            gr.Number(0, 1, label="department_sales: [0 1]"),
-                                            gr.Number(0, 1, label="department_support: [0 1]"),
-                                            gr.Number(0, 1, label="department_technical: [0 1]"),
-                                            gr.Number(0, 1, label="overworked: [0 1]")
+                                            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 HR - Salifort Motors - Employee Retention", 
+                               outputs  = "text", title="Data-driven suggestions for Waze - User Churn", 
                                examples = [
                                         [0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
                                         [0, 3, 3, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
                                         [0, 2, 3, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
                                         [0, 6, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1]
                                          ],
-                               description="Employee Retention Prediction Using Machine Learning",
+                               description="User Churn Prediction Using Machine Learning",
                                theme='dark'
                                )