Update src/streamlit_app.py

src/streamlit_app.py

@@ -62,15 +62,21 @@ st.markdown(
 
 # --- Page Configuration and UI Elements ---
 st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
-st.subheader("HR.ai", divider="orange")
+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", "Retire_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"      
+
 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.      
+
 **How to Use:** Type or paste your text into the text area below, then press Ctrl + Enter. Click the 'Results' button to extract and tag entities in your text data.      
+
 **Usage Limits:** You can request results unlimited times for one (1) month.      
+
 **Supported Languages:** English, German, French, Italian, Spanish, Portuguese      
+
 **Technical issues:** If your connection times out, please refresh the page or reopen the app's URL. 
+
 For any errors or inquiries, please contact us at [email protected]""")
 
 with st.sidebar:
@@ -87,7 +93,7 @@ with st.sidebar:
     st.text("")
     st.text("")
     st.divider()
-    st.subheader("Ready to build your own NER Web App? 🚀", divider="orange")
+    st.subheader("🚀 Ready to build your own NER Web App?", divider="green")
     st.link_button("NER Builder", "https://nlpblogs.com", type="primary")
 
 # --- Comet ML Setup ---
@@ -163,7 +169,7 @@ if st.button("Results"):
                     experiment.log_parameter("input_text", text)
                     experiment.log_table("predicted_entities", df)
                 
-                st.subheader("Extracted Entities", divider = "orange")
+                st.subheader("Grouped Entities by Category", divider = "green")
                 
                 # Create tabs for each category
                 category_names = sorted(list(category_mapping.keys()))
@@ -191,7 +197,7 @@ if st.button("Results"):
                 st.divider()
                                 
                 # Tree map
-                st.subheader("Tree map", divider = "orange")
+                st.subheader("Tree map", divider = "green")
                 fig_treemap = px.treemap(df, path=[px.Constant("all"), 'category', 'label', 'text'], values='score', color='category')
                 fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25), paper_bgcolor='#F5FFFA', plot_bgcolor='#F5FFFA')
                 st.plotly_chart(fig_treemap)
@@ -202,7 +208,7 @@ if st.button("Results"):
                 col1, col2 = st.columns(2)
                 
                 with col1:
-                    st.subheader("Pie chart", divider = "orange")
+                    st.subheader("Pie chart", divider = "green")
                     fig_pie = px.pie(grouped_counts, values='count', names='category', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted categories')
                     fig_pie.update_traces(textposition='inside', textinfo='percent+label')
                     fig_pie.update_layout(
@@ -212,7 +218,7 @@ if st.button("Results"):
                     st.plotly_chart(fig_pie)
                 
                 with col2:
-                    st.subheader("Bar chart", divider = "orange")
+                    st.subheader("Bar chart", divider = "green")
                     fig_bar = px.bar(grouped_counts, x="count", y="category", color="category", text_auto=True, title='Occurrences of predicted categories')
                     fig_pie.update_layout(
                         paper_bgcolor='#F5FFFA',
@@ -221,7 +227,7 @@ if st.button("Results"):
                     st.plotly_chart(fig_bar)
                 
                 # Most Frequent Entities
-                st.subheader("Most Frequent Entities", divider="orange")
+                st.subheader("Most Frequent Entities", divider="green")
                 word_counts = df['text'].value_counts().reset_index()
                 word_counts.columns = ['Entity', 'Count']
                 repeating_entities = word_counts[word_counts['Count'] > 1]