#import json
#import mlflow
#import sys

#input = sys.argv[1]

#logged_model = 'mlruns/1/3630068c31924c05a9a04e70ef35e0b8/artifacts/s444409'
#loaded_model = mlflow.pyfunc.load_model(logged_model)


#with open(f'{logged_model}/'+input) as f:
#    data = json.load(f)

#loaded_model.predict(data['inputs'])

import json
import mlflow
import numpy as np

logged_model = 'mlruns/1/296d6f314bb2451885fb7ae58988301e/artifacts/model'
loaded_model = mlflow.pyfunc.load_model(logged_model)


with open(f'{logged_model}/'+input) as f:
    data = json.load(f)
    input_example = np.array([data['inputs'][0]], dtype=np.float64).reshape(-1, 2)


print(f'Prediction: {loaded_model.predict(input_example)}')