%%writefile $script_file import json import joblib import numpy as np from azureml.core.model import Model # Called when the service is loaded def init(): global model # Get the path to the deployed model file and load it model_path = Model.get_model_path('diabetes_model') model = joblib.load(model_path) # Called when a request is received def run(raw_data): # Get the input data as a numpy array data = json.loads(raw_data)['data'] np_data = np.array(data) # Get a prediction from the model predictions = model.predict(np_data) # print the data and predictions (so they'll be logged!) log_text = 'Data:' + str(data) + ' - Predictions:' + str(predictions) print(log_text) # Get the corresponding classname for each prediction (0 or 1) classnames = ['not-diabetic', 'diabetic'] predicted_classes = [] for prediction in predictions: predicted_classes.append(classnames[prediction]) # Return the predictions as JSON return json.dumps(predicted_classes)
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