Update app.py

app.py

@@ -1,52 +1,101 @@
-import pandas as pd
-import gradio as gr
-import pickle
-
-# Load the XGBoost model from disk
-loaded_model = pickle.load(open("xgb_h.pkl", 'rb'))
-
-# Function to predict intent to stay
-def predict_intent_to_stay(Engagement, WorkEnvironment, UpperLevelManagement, RewardsBenefits, EmployeeWellBeing, Workload, LearningDevelopment):
-    # Create input DataFrame
-    input_data = pd.DataFrame({
-        'Engagement': [Engagement],
-        'WorkEnvironment': [WorkEnvironment],
-        'UpperLevelManagement': [UpperLevelManagement],
-        'RewardsBenefits': [RewardsBenefits],
-        'EmployeeWellBeing': [EmployeeWellBeing],
-        'Workload': [Workload],
-        'LearningDevelopment': [LearningDevelopment]
-    })
-
-    # Perform prediction using the loaded model
-    prediction = loaded_model.predict_proba(input_data)[0][1]  # Probability of staying
-
-    # Return the predicted probability
-    return prediction
-
-# Create sliders for user input
-engagement_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Engagement")
-work_environment_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Work Environment")
-upper_level_management_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Upper Level Management")
-rewards_benefits_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Rewards Benefits")
-employee_wellbeing_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Employee Well-Being")
-workload_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Workload")
-learning_development_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Learning & Development")
-
-# Create the interface
-interface = gr.Interface(
-    fn=predict_intent_to_stay,
-    inputs=[
-        engagement_slider,
-        work_environment_slider,
-        upper_level_management_slider,
-        rewards_benefits_slider,
-        employee_wellbeing_slider,
-        workload_slider,
-        learning_development_slider
-    ],
-    outputs=gr.outputs.Label(num_top_classes=2)
-)
-
-# Launch the interface
-interface.launch()
+
+import gradio as gr 
+
+import pickle 
+
+ 
+
+# Load the XGBoost model from disk 
+
+loaded_model = pickle.load(open("xgb_h.pkl", 'rb')) 
+
+ 
+
+# Function to predict intent to stay 
+
+def predict_intent_to_stay(Engagement, WorkEnvironment, UpperLevelManagement, RewardsBenefits, EmployeeWellBeing, Workload, LearningDevelopment): 
+
+# Create input DataFrame 
+
+input_data = pd.DataFrame({ 
+
+'Engagement': [Engagement], 
+
+'WorkEnvironment': [WorkEnvironment], 
+
+'UpperLevelManagement': [UpperLevelManagement], 
+
+'RewardsBenefits': [RewardsBenefits], 
+
+'EmployeeWellBeing': [EmployeeWellBeing], 
+
+'Workload': [Workload], 
+
+'LearningDevelopment': [LearningDevelopment] 
+
+}) 
+
+ 
+
+# Perform prediction using the loaded model 
+
+prediction = loaded_model.predict(input_data)[0] 
+
+ 
+
+return {"Leave": prediction, "Stay": 1 - prediction} 
+
+ 
+
+# Create sliders for user input 
+
+engagement_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Engagement") 
+
+work_environment_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Work Environment") 
+
+upper_level_management_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Upper Level Management") 
+
+rewards_benefits_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Rewards Benefits") 
+
+employee_wellbeing_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Employee Well-Being") 
+
+workload_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Workload") 
+
+learning_development_slider = gr.Slider(minimum=1, maximum=5, value=3, label="Learning & Development") 
+
+ 
+
+# Create the interface 
+
+interface = gr.Interface( 
+
+fn=predict_intent_to_stay, 
+
+inputs=[ 
+
+engagement_slider, 
+
+work_environment_slider, 
+
+upper_level_management_slider, 
+
+rewards_benefits_slider, 
+
+employee_wellbeing_slider, 
+
+workload_slider, 
+
+learning_development_slider 
+
+], 
+
+outputs="text" 
+
+) 
+
+ 
+
+# Launch the interface 
+
+interface.launch() 
+