#!/usr/bin/env python
#
# Copyright 2012 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.
#
# Author: Marc Cohen
#
'''Implementation of prediction request API.'''
import httplib2
import json
import logging
import urllib
from apiclient.discovery import build
from google.appengine.ext import webapp
from home import parse_json_file
from home import MODELS_FILE
from home import USER_AGENT
from oauth2client.appengine import CredentialsModel
from oauth2client.appengine import StorageByKeyName
from oauth2client.file import Storage
ERR_TAG = ''
ERR_END = ''
class PredictAPI(webapp.RequestHandler):
'''This class handles Ajax prediction requests, i.e. not user initiated
web sessions but remote procedure calls initiated from the Javascript
client code running the browser.
'''
def get(self):
try:
# Read server-side OAuth 2.0 credentials from datastore and
# raise an exception if credentials not found.
credentials = StorageByKeyName(CredentialsModel, USER_AGENT,
'credentials').locked_get()
if not credentials or credentials.invalid:
raise Exception('missing OAuth 2.0 credentials')
# Authorize HTTP session with server credentials and obtain
# access to prediction API client library.
http = credentials.authorize(httplib2.Http())
service = build('prediction', 'v1.4', http=http)
papi = service.trainedmodels()
# Read and parse JSON model description data.
models = parse_json_file(MODELS_FILE)
# Get reference to user's selected model.
model_name = self.request.get('model')
model = models[model_name]
# Build prediction data (csvInstance) dynamically based on form input.
vals = []
for field in model['fields']:
label = field['label']
val = str(self.request.get(label))
vals.append(val)
body = {'input' : {'csvInstance' : vals }}
logging.info('model:' + model_name + ' body:' + str(body))
# Make a prediction and return JSON results to Javascript client.
ret = papi.predict(id=model['model_id'], body=body).execute()
self.response.out.write(json.dumps(ret))
except Exception, err:
# Capture any API errors here and pass response from API back to
# Javascript client embedded in a special error indication tag.
err_str = str(err)
if err_str[0:len(ERR_TAG)] != ERR_TAG:
err_str = ERR_TAG + err_str + ERR_END
self.response.out.write(err_str)