Update app.py

app.py

@@ -6,11 +6,7 @@ with open('./salifort_rf3.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'):
+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 = [['last_evaluation', 'number_project', 'tenure', 'work_accident',
        'promotion_last_5years', 'salary', 'department_IT', 'department_RandD',
        'department_accounting', 'department_hr', 'department_management',
@@ -41,7 +37,13 @@ salifort_ga = gr.Interface(fn=salifort,
                                             gr.Number(0, 1, label="department_technical: [0 1]"),
                                             gr.Number(0, 1, label="overworked: [0 1]")
                                         ]
-                               outputs="text", title="Data-driven suggestions for HR - Salifort Motors - Employee Retention", 
+                               outputs  = "text", title="Data-driven suggestions for HR - Salifort Motors - Employee Retention", 
+                               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",
                                theme='dark'
                                )