Update src/streamlit_app.py

src/streamlit_app.py

@@ -21,7 +21,7 @@ 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 ten (10) labels: "person", "country", "city", "organization", "date", "time", "percent, "cardinal", "money", "position"
+expander.write("""**Named Entities:** This DataHarvest web app predicts nine (9) labels: "person", "country", "city", "organization", "date", "time", "cardinal", "money", "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.
 
@@ -59,7 +59,7 @@ if not comet_initialized:
 
 # --- Label Definitions ---
 
-labels = ["person", "country", "city", "organization", "date", "time", "percent", "cardinal", "money", "position"]
+labels = ["person", "country", "city", "organization", "date", "time", "cardinal", "money", "position"]
 
 # Corrected mapping dictionary
 # Create a mapping dictionary for labels to categories
@@ -67,7 +67,7 @@ category_mapping = {
     "People": ["person", "organization", "position"],
     "Locations": ["country", "city"],
     "Time": ["date", "time"],
-    "Numbers": ["money", "percent", "cardinal"]
+    "Numbers": ["money", "cardinal"]
 }
 
 # --- Model Loading ---
@@ -75,7 +75,7 @@ category_mapping = {
 def load_ner_model():
     """Loads the GLiNER model and caches it."""
     try:
-        return GLiNER.from_pretrained("gliner-community/gliner_large-v2.5", nested_ner=True, num_gen_sequences=2, gen_constraints= labels)
+        return GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5", nested_ner=True, num_gen_sequences=2, gen_constraints= labels)
     except Exception as e:
         st.error(f"Failed to load NER model. Please check your internet connection or model availability: {e}")
         st.stop()