Update src/streamlit_app.py

src/streamlit_app.py

@@ -82,10 +82,10 @@ expander.write("""
 
 -Type or paste your text into the text area, then press Ctrl + Enter. Click the 'Results' button to extract and tag entities in your text data.  
 -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.      
--This HR.ai web app predicts thirty-seven (37) labels: "Job_with_Date", "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"
+-This HR.ai web app predicts thirty-seven (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"
 
 
-**How to Use the InfoFinder web app:**
+**How to Use the Question-Answering feature:**
 1. Type or paste your text into the text area, then press Ctrl + Enter. 
 2. Click the 'Add Question' button to add your question to the Record of Questions. You can manage your questions by deleting them one by one.
 3. Click the 'Extract Answers' button to extract the answer to your question.
@@ -141,13 +141,13 @@ def load_gliner_model(model_name):
         st.stop()
 
 # --- HR_AI Model Labels and Mappings ---
-labels = ["Job_with_Date", "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"]
+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"]
 
 category_mapping = {
     "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_with_Date", "Job_title", "Date", "Organization", "Role"],
+    "Employment Information": ["Job_title", "Date", "Organization", "Role"],
     "Performance": ["Performance_score"],
     "Attendance": ["Leave_of_absence"],
     "Benefits": ["Retirement_plan", "Bonus", "Stock_options", "Health_insurance"],
@@ -169,10 +169,10 @@ def get_stable_color(label):
     return '#' + hex_dig[:6]
 
 # --- Main App with Tabs ---
-tab1, tab2 = st.tabs(["HR.ai", "InfoFinder"])
+tab1, tab2 = st.tabs(["HR.ai", "Question-Answering"])
 
 with tab1:
-    st.subheader("HR.ai")
+    
     
     # Load model for this tab
     model_hr = load_gliner_model("HR_AI")
@@ -266,7 +266,7 @@ with tab1:
                     st.download_button(
                         label="Download results and glossary (zip)",
                         data=buf.getvalue(),
-                        file_name="nlpblogs_hr_results.zip",
+                        file_name="nlpblogs_results.zip",
                         mime="application/zip",
                     )
                     
@@ -283,7 +283,7 @@ with tab1:
             st.info(f"Results processed in **{elapsed_time:.2f} seconds**.")
 
 with tab2:
-    st.subheader("InfoFinder")
+    
 
     # Load model for this tab
     model_qa = load_gliner_model("InfoFinder")
@@ -295,7 +295,7 @@ with tab2:
 
     st.button("Clear text", on_click=clear_text_qa, key="clear_qa")
 
-    st.subheader("Question-Answering", divider="violet")
+    st.subheader("Question-Answering", divider="green")
     question_input = st.text_input("Ask wh-questions. **Wh-questions begin with what, when, where, who, whom, which, whose, why and how. We use them to ask for specific information.**")
 
     if st.button("Add Question"):
@@ -363,7 +363,7 @@ with tab2:
                         st.download_button(
                             label="Download CSV",
                             data=csv_data,
-                            file_name="nlpblogs_infofinder_results.csv",
+                            file_name="nlpblogs_questions_answers.csv",
                             mime="text/csv",
                         )