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 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"      
+expander.write("""**Named Entities:** This HR.ai predicts thirty-six (36) labels: "Email", "Phone_number", "Street_address", "City", "Country", "Date_of_birth", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Terminated", "Retired", "Job_title", "Date", "Organization", "Role", "Performance_score", "Leave_of_absence", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Pay_rate", "Annual_salary", "Tax", "Deductions", "Interview_type", "Applicant", "Referral", "Job_board", "Recruiter", "Offer_letter", "Agreement", "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.      
 
@@ -106,24 +106,35 @@ 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", "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", "Country", "Date_of_birth", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Terminated", "Retired", "Job_title", "Date", "Organization", "Role", "Performance_score", "Leave_of_absence", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Pay_rate", "Annual_salary", "Tax", "Deductions", "Interview_type", "Applicant", "Referral", "Job_board", "Recruiter", "Offer_letter", "Agreement", "Certification", "Skill"]
+
+
 
 # Create a mapping dictionary for labels to categories
+
 category_mapping = {
-    "Contact Information": ["Email", "Phone_number", "Street_address", "City", "State", "Zip_code", "Country"],
-    "Personal Details": ["Date_of_birth", "Gender", "Marital_status", "Person"],
-    "Employment Status": ["Full_time", "Part_time", "Contract", "Temporary", "Terminated", "Active", "Retired"],
-    "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"],
-    "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"],
-    "Legal & Compliance": ["Contract", "Offer_letter", "Agreement"],
-    "Professional_Development": ["Training_course", "Certification", "Skill"]
+    "Contact Information": ["Email", "Phone_number", "Street_address", "City", "Country"],
+    "Personal Details": ["Date_of_birth", "Marital_status", "Person"],
+    "Employment Status": ["Full_time", "Part_time", "Contract", "Terminated", "Retired"],
+    "Employment Information" : ["Job_title", "Date", "Organization", "Role"],
+    "Performance": ["Performance_score"],
+    "Attendance": ["Leave_of_absence"],
+    "Benefits": ["Retirement_plan", "Bonus", "Stock_options", "Health_insurance"],
+    "Compensation": ["Pay_rate", "Annual_salary"],
+    "Deductions": ["Tax", "Deductions"],
+    "Recruitment & Sourcing": ["Interview_type", "Applicant", "Referral", "Job_board", "Recruiter"],
+    "Legal & Compliance": ["Offer_letter", "Agreement"],
+    "Professional_Development": [ "Certification", "Skill"]
 }
 
+
+
+
+
+
+
+
 # --- Model Loading ---
 @st.cache_resource
 def load_ner_model():