Update app.py

app.py

@@ -21,32 +21,6 @@ class FastAIWrapper(BaseEstimator, RegressorMixin):
 df = pd.read_csv('City_Employee_Payroll__Current__20240915.csv', low_memory=False)
 ensemble = joblib.load('ensemble_model.joblib')
 
-def predict_total_pay(gender, job_title, ethnicity):
-    # Create a sample input DataFrame
-    sample = pd.DataFrame({
-        'GENDER': [gender],
-        'JOB_TITLE': [job_title],
-        'ETHNICITY': [ethnicity],
-    })
-    
-    # Fill in other required features (you may need to adjust this based on your model's requirements)
-    sample['EMPLOYMENT_TYPE'] = df['EMPLOYMENT_TYPE'].mode().iloc[0]
-    sample['JOB_STATUS'] = df['JOB_STATUS'].mode().iloc[0]
-    sample['MOU'] = df['MOU'].mode().iloc[0]
-    sample['DEPARTMENT_NO'] = df['DEPARTMENT_NO'].mode().iloc[0]
-    sample['PAY_YEAR'] = df['PAY_YEAR'].max()
-    sample['REGULAR_PAY'] = df['REGULAR_PAY'].mean()
-    sample['OVERTIME_PAY'] = df['OVERTIME_PAY'].mean()
-    sample['ALL_OTHER_PAY'] = df['ALL_OTHER_PAY'].mean()
-    
-    # Calculate derived features
-    sample['PAY_RATIO'] = sample['REGULAR_PAY'] / (sample['OVERTIME_PAY'] + sample['ALL_OTHER_PAY'] + 1)
-    sample['TOTAL_NON_REGULAR_PAY'] = sample['OVERTIME_PAY'] + sample['ALL_OTHER_PAY']
-    
-    # Make prediction
-    prediction = ensemble.predict(sample)[0]
-    return prediction
-
 def predict_total_pay(gender, job_title, ethnicity):
     # Function to predict total pay based on input parameters
     # Parameters:
@@ -120,6 +94,10 @@ def predict_total_pay(gender, job_title, ethnicity):
     # Return the predicted total pay
     return prediction
 
+def gradio_predict(gender, ethnicity, job_title):
+    predicted_pay = predict_total_pay(gender, job_title, ethnicity)
+    return f"${predicted_pay:.2f}"
+
 # Prepare dropdown options
 genders = df['GENDER'].dropna().unique().tolist()
 ethnicities = df['ETHNICITY'].dropna().unique().tolist()
@@ -127,7 +105,7 @@ job_titles = sorted(df['JOB_TITLE'].dropna().unique().tolist())
 
 # Create Gradio interface
 iface = gr.Interface(
-    fn=predict_total_pay,
+    fn=gradio_predict,
     inputs=[
         gr.Dropdown(choices=genders, label="Gender"),
         gr.Dropdown(choices=ethnicities, label="Ethnicity"),
@@ -138,4 +116,5 @@ iface = gr.Interface(
     description="Predict the total pay for LA City employees based on gender, ethnicity, and job title."
 )
 
+# Launch the interface
 iface.launch()