import argparse
import pathlib

import numpy as np
import onnxruntime as ort

# See https://scikit-learn.org/stable/datasets/toy_dataset.html#iris-dataset

# ['sepal length in cm', 'sepal width in cm', 'petal length in cm', 'petal width in cm']

DESCRIPTION = """
Makes Iris predictions using Onnx classier.

Inputs:

0: 'sepal length in cm'
1: 'sepal width in cm'
2: 'petal length in cm'
3: 'petal width in cm'

Pedicts:

Class
    - Iris-Setosa
    - Iris-Versicolour
    - Iris-Virginica
"""

EXAMPLE_DATA = np.array(
    [[5.4, 3.9, 1.7, 0.4], [6.1, 2.6, 5.6, 1.4], [5.2, 2.7, 3.9, 1.4]]
)


def main(args):
    inference_session = ort.InferenceSession(str(args.model_path))
    input_name = inference_session.get_inputs()[0].name
    label_name = inference_session.get_outputs()[0].name
    prediction = inference_session.run(
        [label_name], {input_name: EXAMPLE_DATA.astype(np.float32)}
    )[0]
    print(prediction)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description=DESCRIPTION
    )
    parser.add_argument(
        "model_path", type=pathlib.Path, help="Path to onnx classifier model."
    )
    args = parser.parse_args()
    main(args)