Update prediction sample to use sample_tools.

Reviewed in https://codereview.appspot.com/9325044/.

samples/prediction/prediction.py

@@ -37,68 +37,29 @@ To get detailed log output run:
 __author__ = ('jcgregorio@google.com (Joe Gregorio), '
               'marccohen@google.com (Marc Cohen)')
 
-import apiclient.errors
-import gflags
-import httplib2
-import logging
+import argparse
 import os
 import pprint
 import sys
 import time
 
-from apiclient.discovery import build
-from oauth2client.file import Storage
-from oauth2client.client import AccessTokenRefreshError
-from oauth2client.client import flow_from_clientsecrets
-from oauth2client.tools import run
+from apiclient import discovery
+from apiclient import sample_tools
+from oauth2client import client
 
-FLAGS = gflags.FLAGS
-
-# CLIENT_SECRETS, name of a file containing the OAuth 2.0 information for this
-# application, including client_id and client_secret, which are found
-# on the API Access tab on the Google APIs
-# Console <http://code.google.com/apis/console>
-CLIENT_SECRETS = 'samples/prediction/client_secrets.json'
-
-# Helpful message to display in the browser if the CLIENT_SECRETS file
-# is missing.
-MISSING_CLIENT_SECRETS_MESSAGE = """
-WARNING: Please configure OAuth 2.0
-
-To make this sample run you will need to populate the client_secrets.json file
-found at:
-
-   %s
-
-with information from the APIs Console <https://code.google.com/apis/console>.
-
-""" % os.path.join(os.path.dirname(__file__), CLIENT_SECRETS)
-
-# Set up a Flow object to be used if we need to authenticate.
-FLOW = flow_from_clientsecrets(CLIENT_SECRETS,
-  scope='https://www.googleapis.com/auth/prediction',
-  message=MISSING_CLIENT_SECRETS_MESSAGE)
-
-# The gflags module makes defining command-line options easy for
-# applications. Run this program with the '--help' argument to see
-# all the flags that it understands.
-gflags.DEFINE_enum('logging_level', 'ERROR',
-  ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],
-  'Set the level of logging detail.')
-
-gflags.DEFINE_string('object_name',
-                     None,
-                     'Full Google Storage path of csv data (ex bucket/object)')
-gflags.MarkFlagAsRequired('object_name')
-
-gflags.DEFINE_string('id',
-                     None,
-                     'Model Id of your choosing to name trained model')
-gflags.MarkFlagAsRequired('id')
 
 # Time to wait (in seconds) between successive checks of training status.
 SLEEP_TIME = 10
 
+
+# Declare command-line flags.
+argparser = argparse.ArgumentParser(add_help=False)
+argparser.add_argument('object_name',
+                     help='Full Google Storage path of csv data (ex bucket/object)')
+argparser.add_argument('id',
+                     help='Model Id of your choosing to name trained model')
+
+
 def print_header(line):
   '''Format and print header block sized to length of line'''
   header_str = '='
@@ -107,34 +68,14 @@ def print_header(line):
   print line
   print header_line
 
+
 def main(argv):
-  # Let the gflags module process the command-line arguments.
-  try:
-    argv = FLAGS(argv)
-  except gflags.FlagsError, e:
-    print '%s\\nUsage: %s ARGS\\n%s' % (e, argv[0], FLAGS)
-    sys.exit(1)
-
-  # Set the logging according to the command-line flag
-  logging.getLogger().setLevel(getattr(logging, FLAGS.logging_level))
-
-  # If the Credentials don't exist or are invalid run through the native client
-  # flow. The Storage object will ensure that if successful the good
-  # Credentials will get written back to a file.
-  storage = Storage('prediction.dat')
-  credentials = storage.get()
-  if credentials is None or credentials.invalid:
-    credentials = run(FLOW, storage)
-
-  # Create an httplib2.Http object to handle our HTTP requests and authorize it
-  # with our good Credentials.
-  http = httplib2.Http()
-  http = credentials.authorize(http)
+  service, flags = sample_tools.init(
+      argv, 'prediction', 'v1.5', __doc__, __file__, parents=[argparser],
+      scope='https://www.googleapis.com/auth/prediction')
 
   try:
-
     # Get access to the Prediction API.
-    service = build("prediction", "v1.5", http=http)
     papi = service.trainedmodels()
 
     # List models.
@@ -145,7 +86,7 @@ def main(argv):
 
     # Start training request on a data set.
     print_header('Submitting model training request')
-    body = {'id': FLAGS.id, 'storageDataLocation': FLAGS.object_name}
+    body = {'id': flags.id, 'storageDataLocation': flags.object_name}
     start = papi.insert(body=body).execute()
     print 'Training results:'
     pprint.pprint(start)
@@ -153,7 +94,7 @@ def main(argv):
     # Wait for the training to complete.
     print_header('Waiting for training to complete')
     while True:
-      status = papi.get(id=FLAGS.id).execute()
+      status = papi.get(id=flags.id).execute()
       state = status['trainingStatus']
       print 'Training state: ' + state
       if state == 'DONE':
@@ -171,25 +112,26 @@ def main(argv):
 
     # Describe model.
     print_header('Fetching model description')
-    result = papi.analyze(id=FLAGS.id).execute()
+    result = papi.analyze(id=flags.id).execute()
     print 'Analyze results:'
     pprint.pprint(result)
 
     # Make a prediction using the newly trained model.
     print_header('Making a prediction')
     body = {'input': {'csvInstance': ["mucho bueno"]}}
-    result = papi.predict(body=body, id=FLAGS.id).execute()
+    result = papi.predict(body=body, id=flags.id).execute()
     print 'Prediction results...'
     pprint.pprint(result)
 
     # Delete model.
     print_header('Deleting model')
-    result = papi.delete(id=FLAGS.id).execute()
+    result = papi.delete(id=flags.id).execute()
     print 'Model deleted.'
 
-  except AccessTokenRefreshError:
+  except client.AccessTokenRefreshError:
     print ("The credentials have been revoked or expired, please re-run"
       "the application to re-authorize")
 
+
 if __name__ == '__main__':
   main(sys.argv)