| """Suggest NCM positions for product descriptions with this model. |
| |
| Usage (from the downloaded model folder): |
| pip install -r requirements.txt |
| python inference/predict.py "Ureia agrícola saco 50 kg" "Arame farpado 500 m" |
| |
| Each description gets the three most likely positions, the calibrated |
| confidence of the first one, whether it should go to manual review and |
| whether the 31.05 small-package rule changed it. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import json |
| import sys |
| from pathlib import Path |
|
|
| from ncm_classifier.inference.predictor import NcmPredictor |
|
|
| EXAMPLES = ["Ureia agrícola granulada, saco 50 kg", "Ureia agrícola, pacote 1 kg"] |
|
|
|
|
| def main() -> None: |
| texts = sys.argv[1:] or EXAMPLES |
| predictor = NcmPredictor.from_directory(Path(__file__).resolve().parents[1]) |
| for text, prediction in zip(texts, predictor.predict(texts), strict=True): |
| print(json.dumps({"text": text, **prediction.to_dict()}, ensure_ascii=False, indent=2)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
