Update src/streamlit_app.py

src/streamlit_app.py

@@ -25,7 +25,7 @@ st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 st.markdown(':rainbow[**Supported Languages: English**]')
 
 expander = st.expander("**Important notes**")
-expander.write("""**Named Entities:** This DataHarvest web app predicts nine (9) labels: "person", "country", "city", "organization", "date", "seconds", "money", "percent value", "position"
+expander.write("""**Named Entities:** This DataHarvest web app predicts nine (9) labels: "person", "country", "city", "organization", "date", "time", "money", "percent value", "position"
 
 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.      
 
@@ -66,7 +66,7 @@ if not comet_initialized:
     st.warning("Comet ML not initialized. Check environment variables.")
 
 # --- Label Definitions ---
-labels = ["person", "country", "city", "organization", "date", "seconds", "money", "percent value", "position"]
+labels = ["person", "country", "city", "organization", "date", "time", "money", "percent_value", "position"]
 
 # Corrected mapping dictionary
 
@@ -74,8 +74,8 @@ labels = ["person", "country", "city", "organization", "date", "seconds", "money
 category_mapping = {
     "People": ["person", "organization", "position"],
     "Locations": ["country", "city"],
-    "Time": ["date", "seconds"],
-    "Numbers": ["money", "percent value"]
+    "Time": ["date", "time"],
+    "Numbers": ["money", "percent_value"]
 }