label legend

interfaces/ner.py

@@ -9,7 +9,7 @@ from transformers import AutoModelForSequenceClassification
 from transformers import AutoTokenizer
 from huggingface_hub import HfApi
 
-from label_dicts import NER_DICT
+from spacy.glossary import GLOSSARY as NER_DICT
 
 languages = [
     "English", "Hungarian", "Multilingual"
@@ -40,7 +40,7 @@ def named_entity_recognition(text, language):
     legend = '<p style="text-align: left; display: block">Legend:</p><ul style="text-align: left; display: block">'+"".join([f"<li> <b>{label}</b> = <i>{NER_DICT[label]}</i> </li>" for label in set(labels_used)])+"</ul>"
     output = {"text":text, "entities":entities}
     model_id_hf = f"huspacy/{model_id}" if model_id.startswith("hu") else f"spacy/{model_id}"
-    output_info = legend + f'<p style="text-align: center; display: block">Prediction was made using the <a href="https://huggingface.co/{model_id_hf}">{model_id_hf}</a> model.</p> <ul> {" ".join(legend)} </ul>'
+    output_info = legend + f'<p style="text-align: center; display: block">Prediction was made using the <a href="https://huggingface.co/{model_id_hf}">{model_id_hf}</a> model.</p> <ul>'
     return output, output_info
 
 demo = gr.Interface(