Update app.py

app.py

@@ -13,10 +13,15 @@ loaded_model = pickle.load(open("h25Team12_xgb.pkl", 'rb'))
 explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
 
 # Create the main function for server
-def main_func(ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance):
-    new_row = pd.DataFrame.from_dict({'ValueDiversity':ValueDiversity,'AdequateResources':AdequateResources,
-              'Voice':Voice,'GrowthAdvancement':GrowthAdvancement,'Workload':Workload,
-              'WorkLifeBalance':WorkLifeBalance}, orient = 'index').transpose()
+def main_func(GM3, WorkEnv3, WellBeing2, GM2, JobSecurity, WellBeing1):
+    new_row = pd.DataFrame.from_dict({
+        'GM3': GM3,
+        'WorkEnv3': WorkEnv3,
+        'WellBeing2': WellBeing2,
+        'GM2': GM2,
+        'JobSecurity': JobSecurity,
+        'WellBeing1': WellBeing1
+    }, orient='index').transpose()
     
     prob = loaded_model.predict_proba(new_row)
     
@@ -35,7 +40,8 @@ def main_func(ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,
 # Create the UI
 title = "**Employee Turnover Predictor & Interpreter** 🪐"
 description1 = """
-This app takes six inputs about employees' satisfaction with different aspects of their work (such as work-life balance, ...) and predicts whether the employee intends to stay with the employer or leave. There are two outputs from the app: 1- the predicted probability of stay or leave, 2- Shapley's force-plot which visualizes the extent to which each factor impacts the stay/ leave prediction.   
+This app predicts whether an employee is likely to stay or leave based on selected workplace factors. 
+It also provides a SHAP visualization to show how each factor influences the prediction.
 """
 
 description2 = """
@@ -51,26 +57,27 @@ with gr.Blocks(title=title) as demo:
     gr.Markdown("""---""")
     with gr.Row():        
         with gr.Column():
-            ValueDiversity = gr.Slider(label="ValueDiversity Score", minimum=1, maximum=5, value=4, step=.1)
-            AdequateResources = gr.Slider(label="AdequateResources Score", minimum=1, maximum=5, value=4, step=.1)
-            Voice = gr.Slider(label="Voice Score", minimum=1, maximum=5, value=4, step=.1)
-            GrowthAdvancement = gr.Slider(label="GrowthAdvancement Score", minimum=1, maximum=5, value=4, step=.1)
-            Workload = gr.Slider(label="Workload Score", minimum=1, maximum=5, value=4, step=.1)
-            WorkLifeBalance = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=.1)
+            GM3 = gr.Slider(label="GM3", minimum=1, maximum=5, value=4, step=0.1)
+            WorkEnv3 = gr.Slider(label="Work Environment 3", minimum=1, maximum=5, value=4, step=0.1)
+            WellBeing2 = gr.Slider(label="Well-Being 2", minimum=1, maximum=5, value=4, step=0.1)
+            GM2 = gr.Slider(label="GM2", minimum=1, maximum=5, value=4, step=0.1)
+            JobSecurity = gr.Slider(label="Job Security", minimum=1, maximum=5, value=4, step=0.1)
+            WellBeing1 = gr.Slider(label="Well-Being 1", minimum=1, maximum=5, value=4, step=0.1)
             submit_btn = gr.Button("Analyze")
-        with gr.Column(visible=True,scale=1, min_width=600) as output_col:
-            label = gr.Label(label = "Predicted Label")
-            local_plot = gr.Plot(label = 'Shap:')
         
+        with gr.Column(visible=True, scale=1, min_width=600) as output_col:
+            label = gr.Label(label="Predicted Intent")
+            local_plot = gr.Plot(label='SHAP Analysis')
+
             submit_btn.click(
                 main_func,
-                [ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance],
-                [label,local_plot], api_name="Employee_Turnover"
+                [GM3, WorkEnv3, WellBeing2, GM2, JobSecurity, WellBeing1],
+                [label, local_plot], api_name="IntentToStay_Predictor"
             )
     
     gr.Markdown("### Click on any of the examples below to see how it works:")
     gr.Examples([[4,4,4,4,5,5], [5,4,5,4,4,4]], 
-                [ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance], 
-                [label,local_plot], main_func, cache_examples=True)
+                [GM3, WorkEnv3, WellBeing2, GM2, JobSecurity, WellBeing1], 
+                [label, local_plot], main_func, cache_examples=True)
 
 demo.launch()