Upgrade prediction api sample code to v 1.6 and fix training data.
diff --git a/samples/prediction/prediction.py b/samples/prediction/prediction.py
index a130f2a..50d4b5e 100644
--- a/samples/prediction/prediction.py
+++ b/samples/prediction/prediction.py
@@ -22,7 +22,7 @@
 the setup.sh script to load the sample data to Google Storage.
 
 Usage:
-  $ python prediction.py --object_name="bucket/object" --id="model_id"
+  $ python prediction.py "bucket/object" "model_id" "project_id"
 
 You can also get help on all the command-line flags the program understands
 by running:
@@ -43,8 +43,8 @@
 import sys
 import time
 
-from googleapiclient import discovery
-from googleapiclient import sample_tools
+from apiclient import discovery
+from apiclient import sample_tools
 from oauth2client import client
 
 
@@ -55,9 +55,11 @@
 # 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')
+    help='Full Google Storage path of csv data (ex bucket/object)')
+argparser.add_argument('model_id',
+    help='Model Id of your choosing to name trained model')
+argparser.add_argument('project_id',
+    help='Model Id of your choosing to name trained model')
 
 
 def print_header(line):
@@ -70,9 +72,15 @@
 
 
 def main(argv):
+  # If you previously ran this app with an earlier version of the API
+  # or if you change the list of scopes below, revoke your app's permission
+  # here: https://accounts.google.com/IssuedAuthSubTokens
+  # Then re-run the app to re-authorize it.
   service, flags = sample_tools.init(
-      argv, 'prediction', 'v1.5', __doc__, __file__, parents=[argparser],
-      scope='https://www.googleapis.com/auth/prediction')
+      argv, 'prediction', 'v1.6', __doc__, __file__, parents=[argparser],
+      scope=(
+          'https://www.googleapis.com/auth/prediction',
+          'https://www.googleapis.com/auth/devstorage.read_only'))
 
   try:
     # Get access to the Prediction API.
@@ -80,21 +88,21 @@
 
     # List models.
     print_header('Fetching list of first ten models')
-    result = papi.list(maxResults=10).execute()
+    result = papi.list(maxResults=10, project=flags.project_id).execute()
     print 'List results:'
     pprint.pprint(result)
 
     # Start training request on a data set.
     print_header('Submitting model training request')
-    body = {'id': flags.id, 'storageDataLocation': flags.object_name}
-    start = papi.insert(body=body).execute()
+    body = {'id': flags.model_id, 'storageDataLocation': flags.object_name}
+    start = papi.insert(body=body, project=flags.project_id).execute()
     print 'Training results:'
     pprint.pprint(start)
 
     # 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.model_id, project=flags.project_id).execute()
       state = status['trainingStatus']
       print 'Training state: ' + state
       if state == 'DONE':
@@ -112,25 +120,27 @@
 
     # Describe model.
     print_header('Fetching model description')
-    result = papi.analyze(id=flags.id).execute()
+    result = papi.analyze(id=flags.model_id, project=flags.project_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()
-    print 'Prediction results...'
-    pprint.pprint(result)
+    # Make some predictions using the newly trained model.
+    print_header('Making some predictions')
+    for sample_text in ['mucho bueno', 'bonjour, mon cher ami']:
+      body = {'input': {'csvInstance': [sample_text]}}
+      result = papi.predict(
+        body=body, id=flags.model_id, project=flags.project_id).execute()
+      print 'Prediction results for "%s"...' % sample_text
+      pprint.pprint(result)
 
     # Delete model.
     print_header('Deleting model')
-    result = papi.delete(id=flags.id).execute()
+    result = papi.delete(id=flags.model_id, project=flags.project_id).execute()
     print 'Model deleted.'
 
   except client.AccessTokenRefreshError:
-    print ("The credentials have been revoked or expired, please re-run"
-      "the application to re-authorize")
+    print ('The credentials have been revoked or expired, please re-run '
+           'the application to re-authorize.')
 
 
 if __name__ == '__main__':