Update app.py

app.py

@@ -7,16 +7,16 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 # load the model from disk
-loaded_model = pickle.load(open("h22_xgb.pkl", 'rb'))
+loaded_model = pickle.load(open("clf.pkl", 'rb'))
 
 # Setup SHAP
 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(RewardsBenefit2,JobSatisfaction,RecommendToWork,Tenure,Generation,LearningDevelopment1):
+    new_row = pd.DataFrame.from_dict({'RewardsBenefit2':RewardsBenefit2,'JobSatisfaction':JobSatisfaction,
+              'RecommendToWork':RecommentToWork,'Tenure':Tenure,'Generation':Generation,
+              'LearningDevelopment1':LearningDevelopment1}, orient = 'index').transpose()
     
     prob = loaded_model.predict_proba(new_row)
     
@@ -33,7 +33,7 @@ def main_func(ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,
     return {"Leave": float(prob[0][0]), "Stay": 1-float(prob[0][0])}, local_plot
 
 # Create the UI
-title = "**Employee Turnover Predictor & Interpreter** 🪐"
+title = "Worlds Greatest Employee Predictor of Turnover"
 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.   
 """
@@ -51,12 +51,12 @@ 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)
+            RewardsBenefit2 = gr.Slider(label="Rewards Benefit 2 Score", minimum=1, maximum=5, value=4, step=.1)
+            JobSatisfaction = gr.Slider(label="Job Satisfaction Score", minimum=1, maximum=5, value=4, step=.1)
+            RecommendToWork = gr.Slider(label="Recommend to Work Score", minimum=1, maximum=5, value=4, step=.1)
+            Tenure = gr.Slider(label="Tenure Score", minimum=1, maximum=5, value=4, step=.1)
+            Generation = gr.Slider(label="Generation Score", minimum=1, maximum=5, value=4, step=.1)
+            LearningDevelopment1 = gr.Slider(label="Learning Development Score", minimum=1, maximum=5, value=4, step=.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")
@@ -64,13 +64,13 @@ with gr.Blocks(title=title) as demo:
         
             submit_btn.click(
                 main_func,
-                [ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance],
+                [RewardsBenefit2,JobSatisfcation,RecommendToWork,Tenure,Generation,LearningDevelopment1],
                 [label,local_plot], api_name="Employee_Turnover"
             )
     
     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], 
+                [RewardsBenefit2,JobSatisfcation,RecommendToWork,Tenure,Generation,LearningDevelopment1], 
                 [label,local_plot], main_func, cache_examples=True)
 
 demo.launch(share=True)