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add label studio and ml backend #6
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| with open(csv_file, 'r', encoding='utf-8-sig') as f: | ||
| reader = csv.DictReader(f, delimiter=';') | ||
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| for row in reader: |
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Plutot qu'une grosse boucle on pourrait créer une fonction qui prend un row en entrée et appeler un map. Ca ferait un code plus organisé.
| except Exception: | ||
| return 'localhost' | ||
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| def convert_csv_to_labelstudio(csv_file, output_file, limit=None, force=False, host=None): |
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On a déjà une pipeline qui transforme le CSV (à terme un appel API) en un parquet propre. C'est peut être plus pertinent de partir dessus.
| print(f"Converted {len(tasks)} tasks to {output_file}") | ||
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser( |
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A la base j'aurais utilisé click que je trouve plus propre avec des decorateurs.
| from pathlib import Path | ||
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| def get_lan_ip(): |
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On est obligé de réécrire ca? Il n'y a pas une lib pour ca?
A lot of cleaning is probably needed. But basically there is a labelstudio and an mlbackend with yolo running.