try loading ner_dict from label_dicts

interfaces/ner.py

@@ -9,6 +9,8 @@ from transformers import AutoModelForSequenceClassification
 from transformers import AutoTokenizer
 from huggingface_hub import HfApi
 
+from label_dicts import NER_DICT
+
 languages = [
     "English", "Hungarian", "Multilingual"
 ]
@@ -35,7 +37,7 @@ def named_entity_recognition(text, language):
     doc = pipeline(text)
     entities = [{"entity":ent.label_, "start":ent.start_char, "end":ent.end_char} for ent in doc.ents]
     labels_used = [ent.label_ for ent in doc.ents]
-    legend = "<ul>"+"".join([f"<li> <b>{label}</b> = <i>{spacy.glossary.GLOSSARY[label]}</i> </li>" for label in set(labels_used)])+"</ul>"
+    legend = "<p>Legend:</p><ul>"+"".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>'