Update app.py

app.py

@@ -54,61 +54,63 @@ def main():
         years_since_last_promotion = st.number_input("Years Since Last Promotion")
         years_with_curr_manager = st.number_input("Years With Current Manager")
 
-    # Convert numerical features to strings
-    age = str(age)
-    monthly_income = str(monthly_income)
-    num_companies_worked = str(num_companies_worked)
-    percent_salary_hike = str(percent_salary_hike)
-    training_times_last_year = str(training_times_last_year)
-    years_since_last_promotion = str(years_since_last_promotion)
-    years_with_curr_manager = str(years_with_curr_manager)
-    
-    # Create a DataFrame to hold the user input data
-    input_data = pd.DataFrame({
-        'Age': [age],
-        'Department': [department],
-        'EnvironmentSatisfaction': [environment_satisfaction],
-        'JobRole': [job_role],
-        'JobSatisfaction': [job_satisfaction],
-        'MonthlyIncome': [monthly_income],
-        'NumCompaniesWorked': [num_companies_worked],
-        'OverTime': [over_time],
-        'PercentSalaryHike': [percent_salary_hike],
-        'RelationshipSatisfaction': [relationship_satisfaction],
-        'TrainingTimesLastYear': [training_times_last_year],
-        'WorkLifeBalance': [work_life_balance],
-        'YearsSinceLastPromotion': [years_since_last_promotion],
-        'YearsWithCurrManager': [years_with_curr_manager]
-    })
-    
-    # Reorder columns to match the expected order
-    input_data = input_data[['Age', 'Department', 'EnvironmentSatisfaction', 'JobRole', 'JobSatisfaction',
-                             'MonthlyIncome', 'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike',
-                             'RelationshipSatisfaction', 'TrainingTimesLastYear', 'WorkLifeBalance',
-                             'YearsSinceLastPromotion', 'YearsWithCurrManager']]
-
-    # Make predictions
-    prediction = model.predict(input_data)
-    probability = model.predict_proba(input_data)[:, 1]
-
-    # Display prediction
-    if prediction[0] == 0:
-        st.success("Employee is predicted to stay (Attrition = No)")
-    else:
-        st.error("Employee is predicted to leave (Attrition = Yes)")
-
-        # Offer recommendations for retaining the employee
-        st.subheader("Suggestions for retaining the employee:")
-        st.markdown("- Invest in orientation programs and career development for entry-level staff, which could contribute to higher retention.")
-        st.markdown("- Implement mentorship programs and career development initiatives aimed at engaging and retaining younger employees.")
-        st.markdown("- Offer robust training and development programs and regular promotions to foster career growth. This investment in skills and career advancement can contribute to higher job satisfaction and retention.")
-        st.markdown("- Recognize the diverse needs of employees based on marital status and consider tailoring benefits or support programs accordingly.")
-        st.markdown("- Consider offering benefits that cater to the unique needs of married, single, and divorced employees.")
-        st.markdown("- Introduce or enhance policies that support work-life balance for employees with families.")
-        st.markdown("- Recognize the unique challenges and opportunities within each department and tailor retention strategies accordingly.")
-
-    # Display probability
-    st.write(f"Probability of Attrition: {probability[0]*100:.2f}%")
+    # Predict button
+    if st.button("Predict"):
+        # Convert numerical features to strings
+        age = str(age)
+        monthly_income = str(monthly_income)
+        num_companies_worked = str(num_companies_worked)
+        percent_salary_hike = str(percent_salary_hike)
+        training_times_last_year = str(training_times_last_year)
+        years_since_last_promotion = str(years_since_last_promotion)
+        years_with_curr_manager = str(years_with_curr_manager)
+
+        # Create a DataFrame to hold the user input data
+        input_data = pd.DataFrame({
+            'Age': [age],
+            'Department': [department],
+            'EnvironmentSatisfaction': [environment_satisfaction],
+            'JobRole': [job_role],
+            'JobSatisfaction': [job_satisfaction],
+            'MonthlyIncome': [monthly_income],
+            'NumCompaniesWorked': [num_companies_worked],
+            'OverTime': [over_time],
+            'PercentSalaryHike': [percent_salary_hike],
+            'RelationshipSatisfaction': [relationship_satisfaction],
+            'TrainingTimesLastYear': [training_times_last_year],
+            'WorkLifeBalance': [work_life_balance],
+            'YearsSinceLastPromotion': [years_since_last_promotion],
+            'YearsWithCurrManager': [years_with_curr_manager]
+        })
+
+        # Reorder columns to match the expected order
+        input_data = input_data[['Age', 'Department', 'EnvironmentSatisfaction', 'JobRole', 'JobSatisfaction',
+                                 'MonthlyIncome', 'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike',
+                                 'RelationshipSatisfaction', 'TrainingTimesLastYear', 'WorkLifeBalance',
+                                 'YearsSinceLastPromotion', 'YearsWithCurrManager']]
+
+        # Make predictions
+        prediction = model.predict(input_data)
+        probability = model.predict_proba(input_data)[:, 1]
+
+        # Display prediction
+        if prediction[0] == 0:
+            st.success("Employee is predicted to stay (Attrition = No)")
+        else:
+            st.error("Employee is predicted to leave (Attrition = Yes)")
+
+            # Offer recommendations for retaining the employee
+            st.subheader("Suggestions for retaining the employee:")
+            st.markdown("- Invest in orientation programs and career development for entry-level staff, which could contribute to higher retention.")
+            st.markdown("- Implement mentorship programs and career development initiatives aimed at engaging and retaining younger employees.")
+            st.markdown("- Offer robust training and development programs and regular promotions to foster career growth. This investment in skills and career advancement can contribute to higher job satisfaction and retention.")
+            st.markdown("- Recognize the diverse needs of employees based on marital status and consider tailoring benefits or support programs accordingly.")
+            st.markdown("- Consider offering benefits that cater to the unique needs of married, single, and divorced employees.")
+            st.markdown("- Introduce or enhance policies that support work-life balance for employees with families.")
+            st.markdown("- Recognize the unique challenges and opportunities within each department and tailor retention strategies accordingly.")
+
+        # Display probability
+        st.write(f"Probability of Attrition: {probability[0]*100:.2f}%")
 
 if __name__ == "__main__":
     main()