Add samples for natural language api.

.coveragerc

@@ -9,6 +9,7 @@ include =
  dns/*
  datastore/*
  error_reporting/*
+ language/*
  managed_vms/*
  monitoring/*
  speech/*

conftest.py

@@ -15,9 +15,10 @@
 import os
 
 import pytest
+import requests
 
 
-class Namespace:
+class Namespace(object):
  def __init__(self, **kwargs):
  self.__dict__.update(kwargs)
 
@@ -48,3 +49,24 @@ def resource(request):
  testing resource"""
  local_path = os.path.dirname(request.module.__file__)
  return lambda *args: get_resource_path(args, local_path)
+
+
+def fetch_gcs_resource(resource, tmpdir, _chunk_size=1024):
+ resp = requests.get(resource, stream=True)
+ dest_file = str(tmpdir.join(os.path.basename(resource)))
+ with open(dest_file, 'wb') as f:
+ for chunk in resp.iter_content(_chunk_size):
+ f.write(chunk)
+
+ return dest_file
+
+
+@pytest.fixture(scope='module')
+def remote_resource(cloud_config):
+ """Provides a function that downloads the given resource from Cloud
+ Storage, returning the path to the downloaded resource."""
+ remote_uri = 'http://storage.googleapis.com/{}/'.format(
+ cloud_config.storage_bucket)
+
+ return lambda path, tmpdir: fetch_gcs_resource(
+ remote_uri + path.strip('/'), tmpdir)

language/README.md

@@ -0,0 +1,14 @@
+# Google Cloud Natural Language API examples
+
+This directory contains Python examples that use the
+[Google Cloud Natural Language API](https://cloud.google.com/natural-language/).
+
+- [api](api) has a simple command line tool that shows off the API's features.
+
+- [ocr_nl](ocr_nl) uses the [Cloud Vision API](https://cloud.google.com/vision/)
+to extract text from images, then uses the NL API to extract entity information
+from those texts, and stores the extracted information in a database in support
+of further analysis and correlation.
+
+- [syntax_triples](syntax_triples) uses syntax analysis to find
+subject-verb-object triples in a given piece of text.

language/api/README.md

@@ -0,0 +1,87 @@
+
+# Google Cloud Natural Language API Sample
+
+This Python sample demonstrates the use of the [Google Cloud Natural Language API][NL-Docs]
+for sentiment, entity, and syntax analysis.
+
+[NL-Docs]: https://cloud.google.com/natural-language/docs/
+
+## Setup
+
+Please follow the [Set Up Your Project](https://cloud.google.com/natural-language/docs/getting-started#set_up_your_project)
+steps in the Quickstart doc to create a project and enable the
+Cloud Natural Language API. Following those steps, make sure that you
+[Set Up a Service Account](https://cloud.google.com/natural-language/docs/common/auth#set_up_a_service_account),
+and export the following environment variable:
+
+```
+export GOOGLE_APPLICATION_CREDENTIALS=/path/to/your-project-credentials.json
+```
+
+## Run the sample
+
+Install [pip](https://pip.pypa.io/en/stable/installing) if not already installed.
+
+To run the example, install the necessary libraries using pip:
+
+```sh
+$ pip install -r requirements.txt
+```
+
+Then, run the script:
+
+```sh
+$ python analyze.py <command> <text-string>
+```
+
+where `<command>` is one of: `entities`, `sentiment`, or `syntax`.
+
+The script will write to STDOUT the json returned from the API for the requested feature.
+
+For example, if you run:
+
+```sh
+$ python analyze.py entities "Tom Sawyer is a book written by a guy known as Mark Twain."
+```
+
+You will see something like the following returned:
+
+```
+{
+ "entities": [
+ {
+ "salience": 0.49785897,
+ "mentions": [
+ {
+ "text": {
+ "content": "Tom Sawyer",
+ "beginOffset": 0
+ }
+ }
+ ],
+ "type": "PERSON",
+ "name": "Tom Sawyer",
+ "metadata": {
+ "wikipedia_url": "http://en.wikipedia.org/wiki/The_Adventures_of_Tom_Sawyer"
+ }
+ },
+ {
+ "salience": 0.12209519,
+ "mentions": [
+ {
+ "text": {
+ "content": "Mark Twain",
+ "beginOffset": 47
+ }
+ }
+ ],
+ "type": "PERSON",
+ "name": "Mark Twain",
+ "metadata": {
+ "wikipedia_url": "http://en.wikipedia.org/wiki/Mark_Twain"
+ }
+ }
+ ],
+ "language": "en"
+}
+```

language/api/analyze.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python
+
+# Copyright 2016 Google, Inc
+#
+# 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.
+
+"""Analyzes text using the Google Cloud Natural Language API."""
+
+import argparse
+import json
+import sys
+
+from googleapiclient import discovery
+import httplib2
+from oauth2client.client import GoogleCredentials
+
+
+def get_service():
+ credentials = GoogleCredentials.get_application_default()
+ scoped_credentials = credentials.create_scoped(
+ ['https://www.googleapis.com/auth/cloud-platform'])
+ http = httplib2.Http()
+ scoped_credentials.authorize(http)
+ return discovery.build('language', 'v1beta1', http=http)
+
+
+def get_native_encoding_type():
+ """Returns the encoding type that matches Python's native strings."""
+ if sys.maxunicode == 65535:
+ return 'UTF16'
+ else:
+ return 'UTF32'
+
+
+def analyze_entities(text, encoding='UTF32'):
+ body = {
+ 'document': {
+ 'type': 'PLAIN_TEXT',
+ 'content': text,
+ },
+ 'encodingType': encoding,
+ }
+
+ service = get_service()
+
+ request = service.documents().analyzeEntities(body=body)
+ response = request.execute()
+
+ return response
+
+
+def analyze_sentiment(text):
+ body = {
+ 'document': {
+ 'type': 'PLAIN_TEXT',
+ 'content': text,
+ }
+ }
+
+ service = get_service()
+
+ request = service.documents().analyzeSentiment(body=body)
+ response = request.execute()
+
+ return response
+
+
+def analyze_syntax(text, encoding='UTF32'):
+ body = {
+ 'document': {
+ 'type': 'PLAIN_TEXT',
+ 'content': text,
+ },
+ 'features': {
+ 'extract_syntax': True,
+ },
+ 'encodingType': encoding,
+ }
+
+ service = get_service()
+
+ request = service.documents().annotateText(body=body)
+ response = request.execute()
+
+ return response
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(
+ description=__doc__,
+ formatter_class=argparse.RawDescriptionHelpFormatter)
+ parser.add_argument('command', choices=[
+ 'entities', 'sentiment', 'syntax'])
+ parser.add_argument('text')
+
+ args = parser.parse_args()
+
+ if args.command == 'entities':
+ result = analyze_entities(args.text, get_native_encoding_type())
+ elif args.command == 'sentiment':
+ result = analyze_sentiment(args.text)
+ elif args.command == 'syntax':
+ result = analyze_syntax(args.text, get_native_encoding_type())
+
+ print(json.dumps(result, indent=2))