Call Cloud ML Engine from Google Cloud Functions

call-ml-engine-index.js

// Copyright 2018, 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.

'use strict'

// 
// How to do serverless machine learning prediction by calling
// Google Cloud Machine Learning Engine prediction.
//
// Designed to call the model built in this guide:
// https://cloud.google.com/ml-engine/docs/getting-started-training-prediction
//
// Usage: gcloud beta functions deploy testPrediction --trigger-http
// Call:
// curl -H "Content-Type: application/json" -d '{ "data": {"age": 25, "workclass": "Private", "education": " 11th", "education_num": 7, "marital_status": " Never-married", "occupation": " Machine-op-inspct", "relationship": " Own-child", "race": " Black", "gender": " Male", "capital_gain": 0, "capital_loss": 0, "hours_per_week": 40, "native_country": " United-States"}}' http://{CLOUD_FUNCTION_URL}

const google = require('googleapis');
const GoogleAuth = require('google-auth-library');
const authFactory = new GoogleAuth();

// Set to appropriate values, if necessary.
const modelName = `projects/${process.env.GCLOUD_PROJECT}/models/census`;
//const testData = {"age": 25, "workclass": " Private", "education": " 11th", "education_num": 7, "marital_status": " Never-married", "occupation": " Machine-op-inspct", "relationship": " Own-child", "race": " Black", "gender": " Male", "capital_gain": 0, "capital_loss": 0, "hours_per_week": 40, "native_country": " United-States"};

// testPrediction HTTP function
exports.testPrediction = function(req, res) {
  if(req.method === 'POST') {
    // Assume the body contains a JSON object:
    // {
    //   data: {...}
    // }
    let data = req.body.data;

    cmlePredict(data, (err, result) => {
      if (err) {
        console.error(new Error(err));
        return res.status(500).send(err)
      }
      console.log('Prediction: ', result);
      res.status(200).send(result);
    });
  } else {
    // Only accept POST.
    res.sendStatus(400);
  }
}

function cmlePredict(data, callback) {
  authFactory.getApplicationDefault(function (err, authClient, projectId) {
    if (err) {
      throw err;
    }

    // http://google.github.io/google-api-nodejs-client/21.2.0/ml.html
    var ml = google.ml({
      version: 'v1'
    });
    const params = {
      auth: authClient,
      name: modelName,
      resource: {
        instances: [
          data
        ]
      }
    };
    console.log(params.resource);
    ml.projects.predict(params, callback);
  });
}