Added Prediction sample
diff --git a/samples/prediction/main.py b/samples/prediction/main.py
new file mode 100644
index 0000000..5ae8d36
--- /dev/null
+++ b/samples/prediction/main.py
@@ -0,0 +1,79 @@
+#!/usr/bin/python2.4
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright 2011 Google Inc. All Rights Reserved.
+
+"""Simple command-line example for Google Prediction API.
+
+Command-line application that trains on some data. This
+sample does the same thing as the Hello Prediction! example.
+
+ http://code.google.com/apis/predict/docs/hello_world.html
+"""
+
+__author__ = 'jcgregorio@google.com (Joe Gregorio)'
+
+import httplib2
+import pprint
+import time
+
+from apiclient.discovery import build
+from oauth2client.client import OAuth2WebServerFlow
+from oauth2client.file import Storage
+from oauth2client.tools import run
+
+# Uncomment to get low level HTTP logging
+#httplib2.debuglevel = 4
+
+# Name of Google Storage bucket/object that contains the training data
+OBJECT_NAME = "apiclient-prediction-sample/prediction_models/languages"
+
+
+def main():
+ storage = Storage('prediction.dat')
+ credentials = storage.get()
+
+ if credentials is None or credentials.invalid == True:
+ flow = OAuth2WebServerFlow(
+ # You MUST put in your client id and secret here for this sample to
+ # work. Visit https://code.google.com/apis/console to get your client
+ # credentials.
+ client_id='<Put Your Client ID Here>',
+ client_secret='<Put Your Client Secret Here>',
+ scope='https://www.googleapis.com/auth/prediction',
+ user_agent='prediction-cmdline-sample/1.0',
+ xoauth_displayname='Prediction Example App')
+
+ credentials = run(flow, storage)
+
+ http = httplib2.Http()
+ http = credentials.authorize(http)
+
+ service = build("prediction", "v1.1", http=http)
+
+ # Start training on a data set
+ train = service.training()
+ start = train.insert(data=OBJECT_NAME, body={}).execute()
+
+ print 'Started training'
+ pprint.pprint(start)
+
+ # Wait for the training to complete
+ while 1:
+ status = train.get(data=OBJECT_NAME).execute()
+ pprint.pprint(status)
+ if 'accuracy' in status['modelinfo']:
+ break
+ print 'Waiting for training to complete.'
+ time.sleep(10)
+ print 'Training is complete'
+
+ # Now make a prediction using that training
+ body = {'input': {'mixture':["mucho bueno"]}}
+ prediction = service.predict(body=body, data=OBJECT_NAME).execute()
+ print 'The prediction is:'
+ pprint.pprint(prediction)
+
+if __name__ == '__main__':
+ main()