helpers.py

from transformers import pipeline

baseline_classifier = pipeline("ner",
    model="Dagobert42/biored-finetuned",
    aggregation_strategy="simple"
    )
augmented_classifier = pipeline("ner",
    model="Dagobert42/biored-augmented",
    aggregation_strategy="simple"
    )

def annotate_sentence(sentence, predictions):
    colors = {
        'null': '#bfbfbf',  # Pastel gray
        'GeneOrGeneProduct': '#aad4aa',  # Pastel green
        'DiseaseOrPhenotypicFeature': '#f8b400',  # Pastel orange
        'ChemicalEntity': '#a4c2f4',  # Pastel blue
        'OrganismTaxon': '#ffb6c1',  # Pastel pink
        'SequenceVariant': '#e2b0ff',  # Pastel purple
        'CellLine': '#ffcc99'  # Pastel peach
    }
    output = []
    i = 0
    for p in predictions:
        if sentence[i:p['start']] != '':
            output.append(sentence[i:p['start']])
        output.append((p['word'], p['entity_group'], colors[p['entity_group']]))
        i = p['end']
    if sentence[p['end']:]:
        output.append(sentence[p['end']:])
    return output