8000 Add sentiment analysis samples · mmmarklu/python-docs-samples@77fc490 · GitHub
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Add sentiment analysis samples
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‎automl/cloud-client/language_sentiment_analysis_create_dataset.py

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#!/usr/bin/env python
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# Copyright 2019 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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def create_dataset(project_id, display_name):
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"""Create a dataset."""
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# [START automl_language_sentiment_analysis_create_dataset]
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from google.cloud import automl
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# TODO(developer): Uncomment and set the following variables
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# project_id = 'YOUR_PROJECT_ID'
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# display_name = 'YOUR_DATASET_NAME'
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client = automl.AutoMlClient()
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# A resource that represents Google Cloud Platform location.
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project_location = client.location_path(project_id, 'us-central1')
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metadata = automl.types.TextSentimentDatasetMetadata(
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sentiment_max=4) # Possible max sentiment score: 1-10
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dataset = automl.types.Dataset(
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display_name=display_name,
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text_sentiment_dataset_metadata=metadata)
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# Create a dataset with the dataset metadata in the region.
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response = client.create_dataset(project_location, dataset)
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created_dataset = response.result()
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# Display the dataset information
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print(u'Dataset name: {}'.format(created_dataset.name))
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print(u'Dataset id: {}'.format(created_dataset.name.split('/')[-1]))
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# [END automl_language_sentiment_analysis_create_dataset]

‎automl/cloud-client/language_sentiment_analysis_create_model.py

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#!/usr/bin/env python
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# Copyright 2019 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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def create_model(project_id, dataset_id, display_name):
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"""Create a model."""
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# [START automl_language_sentiment_analysis_create_model]
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from google.cloud import automl
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# TODO(developer): Uncomment and set the following variables
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# project_id = 'YOUR_PROJECT_ID'
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# dataset_id = 'YOUR_DATASET_ID'
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# display_name = 'YOUR_MODEL_NAME'
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client = automl.AutoMlClient()
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# A resource that represents Google Cloud Platform location.
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project_location = client.location_path(project_id, 'us-central1')
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# Leave model unset to use the default base model provided by Google
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metadata = automl.types.TextSentimentModelMetadata()
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model = automl.types.Model(
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display_name=display_name,
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dataset_id=dataset_id,
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text_sentiment_model_metadata=metadata)
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# Create a model with the model metadata in the region.
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response = client.create_model(project_location, model)
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print(u'Training operation name: {}'.format(response.operation.name))
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print('Training started...')
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# [END automl_language_sentiment_analysis_create_model]

‎automl/cloud-client/language_sentiment_analysis_dataset_management_test.py

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#!/usr/bin/env python
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# Copyright 2019 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import datetime
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import os
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import pytest
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import language_sentiment_analysis_create_dataset
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import import_dataset
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import delete_dataset
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import list_datasets
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import get_dataset
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import export_dataset
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PROJECT_ID = os.environ['GCLOUD_PROJECT']
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BUCKET_ID = '{}-lcm'.format(PROJECT_ID)
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DATASET_ID = 'TST3960250460385409610'
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@pytest.mark.slow
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def test_create_import_delete_dataset(capsys):
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# create dataset
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dataset_name = 'test_' + datetime.datetime.now().strftime('%Y%m%d%H%M%S')
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language_sentiment_analysis_create_dataset.create_dataset(
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PROJECT_ID, dataset_name)
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out, _ = capsys.readouterr()
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assert 'Dataset id: ' in out
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# import data
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dataset_id = out.splitlines()[1].split()[2]
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data = 'gs://{}/do_not_delete_sentiment_dataset/sentiment_dataset.csv'\
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.format(BUCKET_ID)
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import_dataset.import_dataset(PROJECT_ID, dataset_id, data)
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out, _ = capsys.readouterr()
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assert 'Data imported.' in out
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# delete dataset
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delete_dataset.delete_dataset(PROJECT_ID, dataset_id)
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out, _ = capsys.readouterr()
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assert 'Dataset deleted.' in out
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def test_list_dataset(capsys):
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# list datasets
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list_datasets.list_datasets(PROJECT_ID)
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out, _ = capsys.readouterr()
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assert 'Dataset id: {}'.format(DATASET_ID) in out
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def test_get_dataset(capsys):
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get_dataset.get_dataset(PROJECT_ID, DATASET_ID)
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out, _ = capsys.readouterr()
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assert 'Dataset name: ' in out
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def test_export_dataset(capsys):
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export_dataset.export_dataset(
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PROJECT_ID,
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DATASET_ID,
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'gs://{}/TEST_EXPORT_OUTPUT/'.format(BUCKET_ID))
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out, _ = capsys.readouterr()
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assert 'Dataset exported' in out
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from google.cloud import storage
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storage_client = storage.Client()
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bucket = storage_client.get_bucket(BUCKET_ID)
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if len(list(bucket.list_blobs(prefix='TEST_EXPORT_OUTPUT'))) > 0:
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for blob in bucket.list_blobs(prefix='TEST_EXPORT_OUTPUT'):
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blob.delete()

‎automl/cloud-client/language_sentiment_analysis_model_management_text.py

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#!/usr/bin/env python
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# Copyright 2018 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import pytest
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import deploy_model
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import undeploy_model
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PROJECT_ID = os.environ['GCLOUD_PROJECT']
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MODEL_ID = 'TST8532792392862639819'
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@pytest.mark.slow
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def test_deploy_undeploy_model(capsys):
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undeploy_model.undeploy_model(PROJECT_ID, MODEL_ID)
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out, _ = capsys.readouterr()
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assert 'Model undeployment finished.' in out
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deploy_model.deploy_model(PROJECT_ID, MODEL_ID)
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out, _ = capsys.readouterr()
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assert 'Model deployment finished.' in out

‎automl/cloud-client/language_sentiment_analysis_predict.py

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#!/usr/bin/env python
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# Copyright 2019 Google LLC
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#
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# Lic 10000 ensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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def predict(project_id, model_id, content):
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"""Predict."""
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# [START automl_language_sentiment_analysis_predict]
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from google.cloud import automl
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# TODO(developer): Uncomment and set the following variables
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# project_id = 'YOUR_PROJECT_ID'
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# model_id = 'YOUR_MODEL_ID'
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# content = 'text to predict'
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prediction_client = automl.PredictionServiceClient()
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# Get the full path of the model.
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model_full_id = prediction_client.model_path(
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project_id, 'us-central1', model_id
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)
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text_snippet = automl.types.TextSnippet(
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content=content,
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mime_type='text/plain') # Types: 'text/plain', 'text/html'
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payload = automl.types.ExamplePayload(text_snippet=text_snippet)
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response = prediction_client.predict(model_full_id, payload)
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for annotation_payload in response.payload:
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print(u'Predicted class name: {}'.format(
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annotation_payload.display_name))
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print(u'Predicted sentiment score: {}'.format(
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annotation_payload.text_sentiment.sentiment))
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# [END automl_language_sentiment_analysis_predict]

‎automl/cloud-client/language_sentiment_analysis_predict_test.py

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#!/usr/bin/env python
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# Copyright 2018 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import language_sentiment_analysis_predict
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PROJECT_ID = os.environ['GCLOUD_PROJECT']
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MODEL_ID = 'TST8532792392862639819'
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def test_predict(capsys):
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text = 'Hopefully this Claritin kicks in soon'
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language_sentiment_analysis_predict.predict(PROJECT_ID, MODEL_ID, text)
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out, _ = capsys.readouterr()
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assert 'Predicted sentiment score: ' in out

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