import json
from bratevalwrapper4nlp import evaluate

# Define document (ground truth and prediction)
doc_ground_truth = {
    "text": "This is a fine example.",
    "label": [
        (10, 14, "LABEL2"),
        (15, 22, "LABEL1"),
    ]
}
doc_prediction = {
    "text": "This is a fine example.",
    "label": [
        (10, 22, "LABEL1"),
    ]
}

# Verify the text spans
for src, doc in {"Ground Truth": doc_ground_truth, "Prediction": doc_prediction}.items():
    for lbl_start, lbl_stop, lbl_cls in doc.get("label", []):
        print("[{}] {} has label {}".format(
            src,
            repr(doc["text"][lbl_start:lbl_stop]),
            lbl_cls
        ))

# Run evaluation
score_response = evaluate(
    doc_ground_truth,     # or list of docs: [doc_ground_truth]
    doc_prediction,       # or list of docs: [doc_prediction]
    span_match="overlap", # "overlap", "exact", or float (overlap percentage between 1.0 and 0.0)
    type_match="exact"    # "exact", or "inexact" (ignores label classes)
)
scores = score_response["scores"]

print("Obtained scores:")
print(json.dumps(scores, indent=2))