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import scispacy
import spacy
from scispacy.umls_linking import UmlsEntityLinker
from spacy import displacy

nlp = spacy.load("en_ner_bc5cdr_md")

linker = UmlsEntityLinker(resolve_abbreviations=True)
nlp.add_pipe(linker)

text = """
Data collection may include questions about groups of symptoms, such as
  GI symptoms (nausea, vomiting, diarrhea)
  Cough (non-productive, productive, or haemoptisis)
"""

doc = nlp(text)

entities = doc.ents
for entity in entities:
    print(entity.text, entity.start_char, entity.end_char, entity.label_)

    for umls_ent in entity._.umls_ents:
        # tuple with 2 values
        conceptId, score = umls_ent

        print(f"Name: {entity}")
        print(f"CUI: {conceptId}, Score {score}")
        print(linker.umls.cui_to_entity[umls_ent[0]])
        print()

colors = {
    'CHEMICAL': 'lightpink',
    'DISEASE': 'lightorange',
}

# show NER
displacy.serve(doc, style="ent", host="127.0.0.1", options={'colors': colors})
displacy.serve(doc, style="dep", host="127.0.0.1")

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