Update src/streamlit_app.py

src/streamlit_app.py

@@ -65,7 +65,7 @@ st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
 st.subheader("HR.ai", divider="green")
 st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 expander = st.expander("**Important notes**")
-expander.write("""**Named Entities:** This HR.ai predicts sixty (60) labels:"Email", "Phone_number", "Street_address", "City", "State", "Zip_code", "Country", "Date_of_birth", "Gender", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Temporary", "Terminated", "Active", "Retired", "Job_title", "Employment_type", "Year", "Date", "Company", "Organization", "Role", "Position","Performance_review", "Performance_rating", "Performance_score", "Sick_days", "Vacation_days", "Leave_of_absence", "Holidays", "Pension", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Retirement_date", "Pay_rate", "Hourly_wage", "Annual_salary", "Overtime_pay", "Tax", "Social_security", "Deductions", "Job_posting", "Job_description", "Interview_type", "Applicant", "Candidate", "Referral", "Job_board", "Recruiter","Contract", "Offer_letter", "Agreement", "Training_course", "Certification", "Skill"      
+expander.write("""**Named Entities:** This HR.ai predicts fifty-nine (59) labels:"Email", "Phone_number", "Street_address", "City", "State", "Zip_code", "Country", "Date_of_birth", "Gender", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Temporary", "Terminated", "Active", "Retired", "Job_title", "Employment_type", "Year", "Date", "Company", "Organization", "Role", "Position","Performance_review", "Performance_rating", "Performance_score", "Sick_days", "Vacation_days", "Leave_of_absence", "Holidays", "Pension", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Pay_rate", "Hourly_wage", "Annual_salary", "Overtime_pay", "Tax", "Social_security", "Deductions", "Job_posting", "Job_description", "Interview_type", "Applicant", "Candidate", "Referral", "Job_board", "Recruiter","Contract", "Offer_letter", "Agreement", "Training_course", "Certification", "Skill"      
 
 Results are presented in easy-to-read tables, visualized in an interactive tree map, pie chart and bar chart, and are available for download along with a Glossary of tags.      
 
@@ -106,7 +106,7 @@ if not comet_initialized:
     st.warning("Comet ML not initialized. Check environment variables.")
 
 # --- Label Definitions ---
-labels = ["Email", "Phone_number", "Street_address", "City", "State", "Zip_code", "Country", "Date_of_birth", "Gender", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Temporary", "Terminated", "Active", "Retired", "Job_title", "Employment_type", "Year", "Date", "Company", "Organization", "Role", "Position", "Performance_review", "Performance_rating", "Performance_score", "Sick_days", "Vacation_days", "Leave_of_absence", "Holidays", "Pension", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Retirement_date", "Pay_rate", "Hourly_wage", "Annual_salary", "Overtime_pay", "Tax", "Social_security", "Deductions", "Job_posting", "Job_description", "Interview_type", "Applicant", "Candidate", "Referral", "Job_board", "Recruiter", "Contract", "Offer_letter", "Agreement", "Training_course", "Certification", "Skill"]
+labels = ["Email", "Phone_number", "Street_address", "City", "State", "Zip_code", "Country", "Date_of_birth", "Gender", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Temporary", "Terminated", "Active", "Retired", "Job_title", "Employment_type", "Year", "Date", "Company", "Organization", "Role", "Position", "Performance_review", "Performance_rating", "Performance_score", "Sick_days", "Vacation_days", "Leave_of_absence", "Holidays", "Pension", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Pay_rate", "Hourly_wage", "Annual_salary", "Overtime_pay", "Tax", "Social_security", "Deductions", "Job_posting", "Job_description", "Interview_type", "Applicant", "Candidate", "Referral", "Job_board", "Recruiter", "Contract", "Offer_letter", "Agreement", "Training_course", "Certification", "Skill"]
 
 # Create a mapping dictionary for labels to categories
 category_mapping = {
@@ -116,7 +116,7 @@ category_mapping = {
     "Employment Information" : ["Job_title", "Employment_type", "Year", "Date", "Company", "Organization", "Role", "Position"],
     "Performance": ["Performance_review", "Performance_rating", "Performance_score"],
     "Attendance": ["Sick_days", "Vacation_days", "Leave_of_absence", "Holidays"],
-    "Benefits": ["Pension", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance","Retirement_date"],
+    "Benefits": ["Pension", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance"],
     "Compensation": ["Pay_rate", "Hourly_wage", "Annual_salary", "Overtime_pay"],
     "Deductions": ["Tax", "Social_security", "Deductions"],
     "Recruitment & Sourcing": ["Job_posting", "Job_description", "Interview_type", "Applicant", "Candidate", "Referral", "Job_board", "Recruiter"],