Update Demo.py

Demo.py

@@ -143,10 +143,14 @@ output = fit_data(pipeline, text_to_analyze)
 # Display matched sentence
 st.subheader("Processed output:")
 
+# Define the abbreviation mapping
+abbreviation_mapping = dict(zip('RGC', ['LOC', 'PER', 'MISC', 'ORG']))
+
+# Extract the NER results
 results = {
     'Document': output[0]['document'][0].result,
     'NER Chunk': [n.result for n in output[0]['ner_chunk']],
-    "NER Label": [n.metadata['entity'] for n in output[0]['ner_chunk']]
+    'NER Label': [abbreviation_mapping.get(n.metadata['entity'], 'UNKNOWN') for n in output[0]['ner_chunk']]
 }
 
 annotate(results)