def test_predict_coreness(trained_pipeline):
assert path_for('data_itos').exists()
assert path_for('trained_classifier').exists()
output_dict = predict_coreness(title=TEST_TITLE, abstract=TEST_ABSTRACT)
assert set(output_dict.keys()) == {'prediction', 'scores'}
assert output_dict['prediction'] in {'rejected', 'non_core', 'core'}
assert set(
output_dict['scores'].keys()) == {'rejected', 'non_core', 'core'}
assert isclose(output_dict['scores']['rejected'] +
output_dict['scores']['non_core'] +
output_dict['scores']['core'],
1.0,
abs_tol=1e-2)
def core_classifier(**kwargs):
"""Endpoint for the CORE classifier."""
return predict_coreness(kwargs['title'], kwargs['abstract'])
def predict(title, abstract, base_path):
with click_spinner.spinner():
with current_app.app_context():
if base_path:
current_app.config['CLASSIFIER_BASE_PATH'] = base_path
click.echo(predict_coreness(title, abstract))