ium_444417/lab8/predictMlflow.py

# import mlflow
# import numpy as np
# import json
# 
# logged_model = '/mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model'
# 
# # Load model as a PyFuncModel.
# loaded_model = mlflow.pyfunc.load_model(logged_model)
# 
# with open(f'{loaded_model}/input_example.json') as f:
#     input_example_data = json.load(f)
# 
#     # Predictions
#     print(f'input: {input_example_data}')
#     print(f'predictions: {loaded_model.predict(input_example_data)}')

import mlflow
import numpy as np
import json

registry_path = '/mlruns/14/80fe21a0804844088147d15a3cebb3e5/artifacts/lego-model'
model = mlflow.pyfunc.load_model(registry_path)

with open(f'{registry_path}/input_example.json') as f:
    input_example_data = json.load(f)

input_example = np.array(input_example_data['inputs'])

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