Fix AI Platform online prediction tests (#4791)

## Description

Fixes #4776 and fixes #4777 by using a new model version (created in the Cloud Console based on the same trained ML model) and updating code accordingly. It's unknown why the old model version that the test used stopped working.
Fixes #4778 by removing the code in question, which is no longer used in documentation.

## Checklist
- [x] I have followed [Sample Guidelines from AUTHORING_GUIDE.MD](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/AUTHORING_GUIDE.md)
- [ ] README is updated to include [all relevant information](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/AUTHORING_GUIDE.md#readme-file)
- [x] **Tests** pass:   `nox -s py-3.6` (see [Test Environment Setup](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/AUTHORING_GUIDE.md#test-environment-setup))
- [x] **Lint** pass:   `nox -s lint` (see [Test Environment Setup](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/AUTHORING_GUIDE.md#test-environment-setup))
- [ ] These samples need a new **API enabled** in testing projects to pass (let us know which ones)
- [ ] These samples need a new/updated **env vars** in testing projects set to pass (let us know which ones)
- [x] Please **merge** this PR for me once it is approved.
- [ ] This sample adds a new sample directory, and I updated the [CODEOWNERS file](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/.github/CODEOWNERS) with the codeowners for this sample

ml_engine/online_prediction/predict.py

@@ -15,12 +15,10 @@
 
 """Examples of using AI Platform's online prediction service."""
 import argparse
-import base64
 import json
 
 # [START import_libraries]
 import googleapiclient.discovery
-import six
 # [END import_libraries]
 
 
@@ -61,83 +59,7 @@ def predict_json(project, model, instances, version=None):
 # [END predict_json]
 
 
-# [START predict_tf_records]
-def predict_examples(project,
-                     model,
-                     example_bytes_list,
-                     version=None):
-    """Send protocol buffer data to a deployed model for prediction.
-
-    Args:
-        project (str): project where the AI Platform Model is deployed.
-        model (str): model name.
-        example_bytes_list ([str]): A list of bytestrings representing
-            serialized tf.train.Example protocol buffers. The contents of this
-            protocol buffer will change depending on the signature of your
-            deployed model.
-        version: str, version of the model to target.
-    Returns:
-        Mapping[str: any]: dictionary of prediction results defined by the
-            model.
-    """
-    service = googleapiclient.discovery.build('ml', 'v1')
-    name = 'projects/{}/models/{}'.format(project, model)
-
-    if version is not None:
-        name += '/versions/{}'.format(version)
-
-    response = service.projects().predict(
-        name=name,
-        body={'instances': [
-            {'b64': base64.b64encode(example_bytes).decode('utf-8')}
-            for example_bytes in example_bytes_list
-        ]}
-    ).execute()
-
-    if 'error' in response:
-        raise RuntimeError(response['error'])
-
-    return response['predictions']
-# [END predict_tf_records]
-
-
-# [START census_to_example_bytes]
-def census_to_example_bytes(json_instance):
-    """Serialize a JSON example to the bytes of a tf.train.Example.
-    This method is specific to the signature of the Census example.
-    See: https://cloud.google.com/ml-engine/docs/concepts/prediction-overview
-    for details.
-
-    Args:
-        json_instance (Mapping[str: Any]): Keys should be the names of Tensors
-            your deployed model expects to parse using it's tf.FeatureSpec.
-            Values should be datatypes convertible to Tensors, or (potentially
-            nested) lists of datatypes convertible to tensors.
-    Returns:
-        str: A string as a container for the serialized bytes of
-            tf.train.Example protocol buffer.
-    """
-    import tensorflow as tf
-    feature_dict = {}
-    for key, data in six.iteritems(json_instance):
-        if isinstance(data, six.string_types):
-            feature_dict[key] = tf.train.Feature(
-                bytes_list=tf.train.BytesList(value=[data.encode('utf-8')]))
-        elif isinstance(data, float):
-            feature_dict[key] = tf.train.Feature(
-                float_list=tf.train.FloatList(value=[data]))
-        elif isinstance(data, int):
-            feature_dict[key] = tf.train.Feature(
-                int64_list=tf.train.Int64List(value=[data]))
-    return tf.train.Example(
-        features=tf.train.Features(
-            feature=feature_dict
-        )
-    ).SerializeToString()
-# [END census_to_example_bytes]
-
-
-def main(project, model, version=None, force_tfrecord=False):
+def main(project, model, version=None):
     """Send user input to the prediction service."""
     while True:
         try:
@@ -148,16 +70,8 @@ def main(project, model, version=None, force_tfrecord=False):
         if not isinstance(user_input, list):
             user_input = [user_input]
         try:
-            if force_tfrecord:
-                example_bytes_list = [
-                    census_to_example_bytes(e)
-                    for e in user_input
-                ]
-                result = predict_examples(
-                    project, model, example_bytes_list, version=version)
-            else:
-                result = predict_json(
-                    project, model, user_input, version=version)
+            result = predict_json(
+                project, model, user_input, version=version)
         except RuntimeError as err:
             print(str(err))
         else:
@@ -183,16 +97,9 @@ def main(project, model, version=None, force_tfrecord=False):
         help='Name of the version.',
         type=str
     )
-    parser.add_argument(
-        '--force-tfrecord',
-        help='Send predictions as TFRecords rather than raw JSON',
-        action='store_true',
-        default=False
-    )
     args = parser.parse_args()
     main(
         args.project,
         args.model,
         version=args.version,
-        force_tfrecord=args.force_tfrecord
     )

ml_engine/online_prediction/predict_test.py

@@ -20,11 +20,10 @@
 import predict
 
 MODEL = 'census'
-JSON_VERSION = 'v1json'
-EXAMPLES_VERSION = 'v1example'
+JSON_VERSION = 'v2json'
 PROJECT = 'python-docs-samples-tests'
 EXPECTED_OUTPUT = {
-    u'confidence': 0.7760371565818787,
+    u'confidence': 0.7760370969772339,
     u'predictions': u' <=50K'
 }
 
@@ -37,10 +36,6 @@
     JSON = json.load(f)
 
 
-with open('resources/census_example_bytes.pb', 'rb') as f:
-    BYTESTRING = f.read()
-
-
 @pytest.mark.flaky
 def test_predict_json():
     result = predict.predict_json(
@@ -53,18 +48,3 @@ def test_predict_json_error():
     with pytest.raises(RuntimeError):
         predict.predict_json(
             PROJECT, MODEL, [{"foo": "bar"}], version=JSON_VERSION)
-
-
-@pytest.mark.flaky
-def test_census_example_to_bytes():
-    import tensorflow as tf
-    b = predict.census_to_example_bytes(JSON)
-    assert tf.train.Example.FromString(b) == tf.train.Example.FromString(
-        BYTESTRING)
-
-
-@pytest.mark.flaky(max_runs=6)
-def test_predict_examples():
-    result = predict.predict_examples(
-        PROJECT, MODEL, [BYTESTRING, BYTESTRING], version=EXAMPLES_VERSION)
-    assert [EXPECTED_OUTPUT, EXPECTED_OUTPUT] == result