listing named entitites according to spacy

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 
+from spacy import glossary
 from interfaces.cap import demo as cap_demo
 from interfaces.manifesto import demo as manifesto_demo
 from interfaces.sentiment import demo as sentiment_demo
@@ -7,13 +8,21 @@ from interfaces.emotion import demo as emotion_demo
 from interfaces.ner import demo as ner_demo
 from interfaces.ner import download_models as download_spacy_models
 
+entities = ["CARDINAL", "DATE", "EVENT", "FAC", "GPE", "LANGUAGE", "LAW", "LOC", "MONEY", "NORP", "ORDINAL", "ORG", "PERCENT", "PERSON", "PRODUCT", "QUANTITY", "TIME", "WORK_OF_ART"]
+ent_dict = glossary.GLOSSARY
+ent_sum = [f'{ent} = {ent_dict[ent]}' for ent in entities ]
+
 with gr.Blocks() as demo:
     gr.Markdown(
-        """
+        f"""
         <div style="display: block; text-align: left; padding:0; margin:0;">
             <h1 style="text-align: center">Babel Machine Demo</h1>
             <p>This is a demo for text classification using language models finetuned on data labeled by <a href="https://www.comparativeagendas.net/">CAP</a>, <a href="https://manifesto-project.wzb.eu/">Manifesto Project</a>, sentiment, and emotion coding systems.<br>
             For the coding of complete datasets, please visit the official <a href="https://babel.poltextlab.com/">Babel Machine</a> site.</p>
+            <p> For named entity recognition the following labels are used: </p>
+            <ul>
+            <li> {'</li> <li>'.join(ent_sum)} </li>
+            </ul>
         </div>
         """)