def pre_inference(sample, metadata):
    """Prepare a sample before it is passed into the model.
​
    Args:
        sample: A sample from the request payload.
​
        metadata: Describes the expected shape and type of inputs to the model.
            If API model_format is tensorflow: map<string, SignatureDef>
                https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto
            If API model_format is onnx: list<onnxruntime.NodeArg>
                https://microsoft.github.io/onnxruntime/api_summary.html#onnxruntime.NodeArg
​
    Returns:
        A dictionary containing model input names as keys and python lists or numpy arrays as values. If the model only has a single input, then a python list or numpy array can be returned.
    """
    pass
​
def post_inference(prediction, metadata):
    """Modify a prediction from the model before responding to the request.
​
    Args:
        prediction: The output of the model.
​
        metadata: Describes the output shape and type of outputs from the model.
            If API model_format is tensorflow: map<string, SignatureDef>
                https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/meta_graph.proto
            If API model_format is onnx: list<onnxruntime.NodeArg>
                https://microsoft.github.io/onnxruntime/api_summary.html#onnxruntime.NodeArg
​
    Returns:
        A python dictionary or list.
    """

import numpy as np
​
labels = ["iris-setosa", "iris-versicolor", "iris-virginica"]
​
def pre_inference(sample, metadata):
    # Convert a dictionary of features to a flattened in list in the order expected by the model
    return {
        metadata[0].name : [
            sample["sepal_length"],
            sample["sepal_width"],
            sample["petal_length"],
            sample["petal_width"],
        ]
    }
​
​
def post_inference(prediction, metadata):
    # Update the model prediction to include the index and the label of the predicted class
    probabilites = prediction[0][0]
    predicted_class_id = int(np.argmax(probabilites))
    return {
        "class_label": labels[predicted_class_id],
        "class_index": predicted_class_id,
        "probabilities": probabilites,
    }

boto3==1.9.78
msgpack==0.6.1
numpy>=1.13.3,<2
requirements-parser==0.2.0
packaging==19.0.0
pillow==6.1.0
regex==2017.4.5
requests==2.21.0

from cortex.lib.log import get_logger
​
logger = get_logger()
​
def pre_inference(sample, metadata):
    logger.info(sample)
    logger.info(metadata)
    ...