16 lines
379 B
Python
16 lines
379 B
Python
import json
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import mlflow
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import sys
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import numpy as np
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input = sys.argv[1]
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logged_model = 'mlruns/1/70439eb482b54d56b54b0ecc6f1ca96f/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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input_example = np.array([data['inputs']], dtype=np.float64)
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loaded_model.predict(data['inputs']) |