31 lines
719 B
Python
31 lines
719 B
Python
#import json
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#import mlflow
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#import sys
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#input = sys.argv[1]
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#logged_model = 'mlruns/1/3630068c31924c05a9a04e70ef35e0b8/artifacts/s444409'
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#loaded_model = mlflow.pyfunc.load_model(logged_model)
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#with open(f'{logged_model}/'+input) as f:
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# data = json.load(f)
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#loaded_model.predict(data['inputs'])
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import json
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import mlflow
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import numpy as np
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input = sys.argv[1]
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logged_model = 'mlruns/1/296d6f314bb2451885fb7ae58988301e/artifacts/model'
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loaded_model = mlflow.pyfunc.load_model(logged_model)
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with open(f'{logged_model}/'+str(input)) as f:
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data = json.load(f)
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input_example = np.array([data['inputs'][0]], dtype=np.float64).reshape(-1, 2)
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print(f'Prediction: {loaded_model.predict(input_example)}') |