predict.py

#!/bin/python
# Copyright 2017 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Examples of using AI Platform's online prediction service."""
import argparse
import json

# [START import_libraries]
import googleapiclient.discovery
# [END import_libraries]


# [START predict_json]
# Create the AI Platform service object.
# To authenticate set the environment variable
# GOOGLE_APPLICATION_CREDENTIALS=<path_to_service_account_file>
service = googleapiclient.discovery.build('ml', 'v1')


def predict_json(project, model, instances, version=None):
    """Send json data to a deployed model for prediction.

    Args:
        project (str): project where the AI Platform Model is deployed.
        model (str): model name.
        instances ([Mapping[str: Any]]): Keys should be the names of Tensors
            your deployed model expects as inputs. Values should be datatypes
            convertible to Tensors, or (potentially nested) lists of datatypes
            convertible to tensors.
        version: str, version of the model to target.
    Returns:
        Mapping[str: any]: dictionary of prediction results defined by the
            model.
    """
    name = 'projects/{}/models/{}'.format(project, model)

    if version is not None:
        name += '/versions/{}'.format(version)

    response = service.projects().predict(
        name=name,
        body={'instances': instances}
    ).execute()

    if 'error' in response:
        raise RuntimeError(response['error'])

    return response['predictions']
# [END predict_json]


def main(project, model, version=None):
    """Send user input to the prediction service."""
    while True:
        try:
            user_input = json.loads(input("Valid JSON >>>"))
        except KeyboardInterrupt:
            return

        if not isinstance(user_input, list):
            user_input = [user_input]
        try:
            result = predict_json(
                project, model, user_input, version=version)
        except RuntimeError as err:
            print(str(err))
        else:
            print(result)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--project',
        help='Project in which the model is deployed',
        type=str,
        required=True
    )
    parser.add_argument(
        '--model',
        help='Model name',
        type=str,
        required=True
    )
    parser.add_argument(
        '--version',
        help='Name of the version.',
        type=str
    )
    args = parser.parse_args()
    main(
        args.project,
        args.model,
        version=args.version,
    )