Update app.py

app.py

@@ -1,157 +1,55 @@
-
-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(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, 
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- 
-
-work_environment_slider, 
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- 
-
-upper_level_management_slider, 
-
- 
-
-rewards_benefits_slider, 
-
- 
-
-employee_wellbeing_slider, 
-
- 
-
-workload_slider, 
-
- 
-
-learning_development_slider 
-
- 
-
-], 
-
- 
-
-outputs="text" 
-
- 
-
-) 
-
- 
-
- 
-
- 
-
-# Launch the interface 
-
-interface.launch() 
-
+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]
+
+    return {"Leave": prediction[0], "Stay": prediction[1]}
+
+
+# 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()