Classify tutorial [(#1120)](#1120)

* first version of classify_text tutorial

* addressing comments

* classify text tutorial

* update client version

* year first written

* use auto generated README

* add README.rst.in and README.rst

* addressing review comments

* add tests for index and query

* import order

* add numpy to requirements

Author: dizcology

language/snippets/classify_text/README.rst

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+.. This file is automatically generated. Do not edit this file directly.
+
+Google Cloud Natural Language API Python Samples
+===============================================================================
+
+This directory contains samples for Google Cloud Natural Language API. The `Google Cloud Natural Language API`_ provides natural language understanding technologies to developers.
+
+This tutorial demostrates how to use the `classify_text` method to classify content category of text files, and use the result to compare texts by their similarity to each other.  See the `tutorial page`_ for details about this sample.
+
+.. _tutorial page: https://cloud.google.com/natural-language/docs/classify-text-tutorial
+
+
+
+
+.. _Google Cloud Natural Language API: https://cloud.google.com/natural-language/docs/ 
+
+Setup
+-------------------------------------------------------------------------------
+
+
+Authentication
+++++++++++++++
+
+Authentication is typically done through `Application Default Credentials`_,
+which means you do not have to change the code to authenticate as long as
+your environment has credentials. You have a few options for setting up
+authentication:
+
+#. When running locally, use the `Google Cloud SDK`_
+
+    .. code-block:: bash
+
+        gcloud auth application-default login
+
+
+#. When running on App Engine or Compute Engine, credentials are already
+   set-up. However, you may need to configure your Compute Engine instance
+   with `additional scopes`_.
+
+#. You can create a `Service Account key file`_. This file can be used to
+   authenticate to Google Cloud Platform services from any environment. To use
+   the file, set the ``GOOGLE_APPLICATION_CREDENTIALS`` environment variable to
+   the path to the key file, for example:
+
+    .. code-block:: bash
+
+        export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service_account.json
+
+.. _Application Default Credentials: https://cloud.google.com/docs/authentication#getting_credentials_for_server-centric_flow
+.. _additional scopes: https://cloud.google.com/compute/docs/authentication#using
+.. _Service Account key file: https://developers.google.com/identity/protocols/OAuth2ServiceAccount#creatinganaccount
+
+Install Dependencies
+++++++++++++++++++++
+
+#. Install `pip`_ and `virtualenv`_ if you do not already have them.
+
+#. Create a virtualenv. Samples are compatible with Python 2.7 and 3.4+.
+
+    .. code-block:: bash
+
+        $ virtualenv env
+        $ source env/bin/activate
+
+#. Install the dependencies needed to run the samples.
+
+    .. code-block:: bash
+
+        $ pip install -r requirements.txt
+
+.. _pip: https://pip.pypa.io/
+.. _virtualenv: https://virtualenv.pypa.io/
+
+Samples
+-------------------------------------------------------------------------------
+
+Classify Text Tutorial
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+
+
+To run this sample:
+
+.. code-block:: bash
+
+    $ python classify_text_tutorial.py
+
+    usage: classify_text_tutorial.py [-h]
+                                     {classify,index,query,query-category} ...
+    
+    Using the classify_text method to cluster texts.
+    
+    positional arguments:
+      {classify,index,query,query-category}
+        classify            Classify the input text into categories.
+        index               Classify each text file in a directory and write the
+                            results to the index_file.
+        query               Find the indexed files that are the most similar to
+                            the query text.
+        query-category      Find the indexed files that are the most similar to
+                            the query label. The list of all available labels:
+                            https://cloud.google.com/natural-
+                            language/docs/categories
+    
+    optional arguments:
+      -h, --help            show this help message and exit
+
+
+
+
+The client library
+-------------------------------------------------------------------------------
+
+This sample uses the `Google Cloud Client Library for Python`_.
+You can read the documentation for more details on API usage and use GitHub
+to `browse the source`_ and  `report issues`_.
+
+.. _Google Cloud Client Library for Python:
+    https://googlecloudplatform.github.io/google-cloud-python/
+.. _browse the source:
+    https://github.com/GoogleCloudPlatform/google-cloud-python
+.. _report issues:
+    https://github.com/GoogleCloudPlatform/google-cloud-python/issues
+
+
+.. _Google Cloud SDK: https://cloud.google.com/sdk/

language/snippets/classify_text/README.rst.in

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+# This file is used to generate README.rst
+
+product:
+  name: Google Cloud Natural Language API
+  short_name: Cloud Natural Language API
+  url: https://cloud.google.com/natural-language/docs/
+  description: >
+    The `Google Cloud Natural Language API`_ provides natural language
+    understanding technologies to developers.
+
+
+    This tutorial demostrates how to use the `classify_text` method to classify content category of text files, and use the result to compare texts by their similarity to each other.  See the `tutorial page`_ for details about this sample.
+
+
+    .. _tutorial page: https://cloud.google.com/natural-language/docs/classify-text-tutorial
+
+setup:
+- auth
+- install_deps
+
+samples:
+- name: Classify Text Tutorial
+  file: classify_text_tutorial.py
+  show_help: true
+
+cloud_client_library: true