Update app.py

app.py

@@ -20,12 +20,25 @@ def preprocess_input(df):
     df['TENURE'] = (df['CURRENT DATE'] - df['DOJ']).dt.days // 365.25
     df['TOTAL_EXP'] = df['PAST_EXP'] + df['TENURE']
     df = df.drop(columns=['DOJ', 'CURRENT DATE', 'NAME', 'PAST_EXP', 'TENURE', 'AGE'])
-    return df
+    total_experience=int(df['TOTAL_EXP'][0]) 
+    # Provide salary increase recommendations based on total experience and rating
+    if total_experience >= 5 :
+        recommendation = "Your performance and experience suggest that you're well-positioned for a salary increase. Consider discussing this with your manager during your next performance review."
+    elif total_experience >= 3 :
+        recommendation = "You've gained valuable experience and have a solid performance rating. It might be a good time to explore opportunities for advancement within the company or discuss a salary review with your manager."
+    else:
+        recommendation = "Focus on enhancing your skills, gaining more experience, and improving your performance to increase your chances of a salary raise in the future."
+
+    return df,recommendation
+
+
 # Function to make prediction
 def predict_salary(data):
-    preprocessed_data = preprocess_input(data)
+    preprocessed_data,rec = preprocess_input(data)
+
     salary = model.predict(preprocessed_data)
-    return int(salary)
+    return salary,rec
+
 
 # Streamlit app
 def main():
@@ -55,8 +68,10 @@ def main():
             'RATINGS': [rating],
             'DOJ': [date_of_join]
         })
-        salary_prediction = predict_salary(input_data)
-        st.success(f'Predicted Salary: {salary_prediction}')
+        
+        salary_prediction,rec = predict_salary(input_data)
+
+        st.success(f'Predicted Salary: {salary_prediction[0]}\nRecommendation: {rec}')
 
 if __name__ == '__main__':
     main()