├── .gitignore ├── LICENSE ├── README.md ├── data └── patientdataV6.csv ├── doc └── images │ ├── api-1.png │ ├── api-2.png │ ├── application.png │ ├── architecture.png │ ├── auto-1.png │ ├── auto-2.png │ ├── auto-3.png │ ├── auto-4.png │ ├── auto-5.png │ ├── auto-6.png │ ├── auto-7.png │ ├── auto-8.png │ ├── auto-9.png │ ├── cf-1.png │ ├── cf-2.png │ ├── cp4d.png │ ├── deploy-1.png │ ├── deploy-2.png │ ├── deploy-3.png │ ├── deploy-4.png │ ├── deploy-5.png │ ├── deploy-6.png │ ├── deploy-7.png │ ├── pro-1.png │ ├── pro-2.png │ └── pro-3.png ├── manifest.yml └── server ├── app.js ├── env.example ├── package.json ├── public ├── favicon.ico ├── index.html ├── javascripts │ └── index.js └── stylesheets │ └── style.css └── routes └── classify.js /.gitignore: -------------------------------------------------------------------------------- 1 | # See http://help.github.com/ignore-files/ for more about ignoring files. 2 | 3 | # compiled output 4 | client/dist 5 | /tmp 6 | /out-tsc 7 | # Only exists if Bazel was run 8 | /bazel-out 9 | 10 | # server bits 11 | server/node_modules 12 | server/package-lock.json 13 | server/.env 14 | 15 | # dependencies 16 | /node_modules 17 | 18 | # profiling files 19 | chrome-profiler-events.json 20 | speed-measure-plugin.json 21 | 22 | # IDEs and editors 23 | /.idea 24 | .project 25 | .classpath 26 | .c9/ 27 | *.launch 28 | .settings/ 29 | *.sublime-workspace 30 | 31 | # IDE - VSCode 32 | .vscode/* 33 | !.vscode/settings.json 34 | !.vscode/tasks.json 35 | !.vscode/launch.json 36 | !.vscode/extensions.json 37 | .history/* 38 | 39 | # misc 40 | /.sass-cache 41 | /connect.lock 42 | /coverage 43 | /libpeerconnection.log 44 | npm-debug.log 45 | yarn-error.log 46 | testem.log 47 | /typings 48 | 49 | # System Files 50 | .DS_Store 51 | Thumbs.db 52 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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A predictive model will be built using AutoAI on IBM Cloud Pak for Data. The model is then deployed to the Watson Machine Learning service, where it can be accessed via a REST API. 8 | 9 | 2. A Node.js web app that allows a user to input some data to be scored against the previous model. 10 | 11 | When the reader has completed this Code Pattern, they will understand how to: 12 | 13 | * Build a predictive model with AutoAI on Cloud Pak for Data 14 | * Deploy the model to the IBM Watson Machine Learning service 15 | * Via a Node.js app, score some data against the model via an API call to the Watson Machine Learning service 16 | 17 | **Sample output** 18 | 19 | Here's an example of what the final web app looks like 20 | 21 | ![form](doc/images/application.png) 22 | 23 | ## Architecture 24 | 25 | 1. The developer creates a [Cloud Pak for Data](https://www.ibm.com/cloud/watson-studio) project. 26 | 1. A model is created with AutoAI by uploading some data. 27 | 1. Data is backed up and stored on Cloud Object Storage. 28 | 1. The model is deployed using the Watson Machine Learning service. 29 | 1. A [Node.js](https://nodejs.org/) web app is deployed on IBM Cloud. It calls the predictive model hosted on the Watson Machine Learning service. 30 | 1. A user visits the web app, enters their information, and the predictive model returns a response. 31 | 32 | !["architecture diagram"](doc/images/architecture.png) 33 | 34 | ## Prerequisites 35 | 36 | * An [IBM Cloud Account](https://cloud.ibm.com) 37 | * An account on [IBM Cloud Pak for Data](https://dataplatform.cloud.ibm.com/). 38 | 39 | > **NOTE**: As of 10/16/2020, the Watson Machine Learning service on IBM Cloud is only available in the Dallas, London, Frankfurt, or Tokyo regions. Not the Seoul, Frankfurt, or Sydney regions. 40 | 41 | ## Steps 42 | 43 | 1. [Create an IBM Cloud API key](#1-create-an-ibm-cloud-api-key) 44 | 1. [Create a new Cloud Pak for Data project](#2-create-a-new-cloud-pak-for-data-project) 45 | 1. [Build a model with AutoAI](#3-build-a-model-with-autoai) 46 | 1. [Deploy the model with WML](#4-deploy-the-model-with-wml) 47 | 1. [Run the Node.js application](#5-the-client-side) 48 | 49 | ### 1. Create an IBM Cloud API key 50 | 51 | To use the Watson Machine Learning service programmatically we'll need an API key. Even though this isn't used until later on, let's create one now. 52 | 53 | Navigate to and choose to create a new API key. 54 | 55 | ![create api key](doc/images/api-1.png) 56 | 57 | Give it a name and description, hit OK. Write down the API key somewhere. 58 | 59 | ![generated api key](doc/images/api-2.png) 60 | 61 | ### 2. Create a new Cloud Pak for Data project 62 | 63 | Log into IBM's [Cloud Pak for Data](https://dataplatform.cloud.ibm.com) service (formally known as Watson Studio). Once in, you'll land on the dashboard. 64 | 65 | Create a new project by clicking `Create a project`. 66 | 67 | ![new project](doc/images/cp4d.png) 68 | 69 | Choose an `Empty project`. 70 | 71 | ![empty project](doc/images/pro-1.png) 72 | 73 | Enter a `Name` and associate the project with a `Cloud Object Storage` service. 74 | 75 | ![empty project](doc/images/pro-2.png) 76 | 77 | > **NOTE**: By creating a project in Watson Studio a free tier `Object Storage` service will be created in your IBM Cloud account. Select the `Free` storage type to avoid fees. 78 | 79 | At the project dashboard click on the `Assets` tab and upload the data set associated with this repo. [`patientdataV6.csv`](https://raw.githubusercontent.com/IBM/predictive-model-on-watson-ml/master/data/patientdataV6.csv) 80 | 81 | ![upload data](doc/images/pro-3.png) 82 | 83 | ### 3. Build a model with AutoAI 84 | 85 | Now we're going to build a model from the data using IBM's AutoAI. A tool that will automatically create multiple models and test them, giving us the best result. Data science made easy! 86 | 87 | Start by clicking on `Add to project` and choosing `AutoAI experiment`. 88 | 89 | ![Add to project](doc/images/auto-1.png) 90 | 91 | Give it a `Name` and specify a `Watson Machine Learning` instance. 92 | 93 | ![WML](doc/images/auto-2.png) 94 | 95 | Choose to use data from your project. 96 | 97 | ![Choose data](doc/images/auto-3.png) 98 | 99 | Choose the `patientdataV6.csv` option. 100 | 101 | ![data set](doc/images/auto-4.png) 102 | 103 | For the "What do you want to predict?" option, choose `HEARTFAILURE`. 104 | 105 | ![right column](doc/images/auto-5.png) 106 | 107 | The experiment will take a few minutes to run. Once completed hover over the top option to make the `Save as` button appear. Click it. 108 | 109 | ![experiment](doc/images/auto-6.png) 110 | 111 | Choose to save the experiment as a `Model`. You can optionally download a generated Jupyter Notebook that can be used to re-create the steps that were taken to create the model. 112 | 113 | ![save](doc/images/auto-7.png) 114 | 115 | You model will be saved. Click the dialog to view it in your project. 116 | 117 | ![dialog](doc/images/auto-8.png) 118 | 119 | Once you're at the model overview choose the button `Promote to deployment space`. 120 | 121 | ![promote](doc/images/auto-9.png) 122 | 123 | ### 4. Deploy the model with WML 124 | 125 | To promote the model to deployment you must specify a deployment space. If no space is created choose the `New space +` option to create one. This action will associate the model with the space. 126 | 127 | ![specify space](doc/images/deploy-1.png) 128 | 129 | Navigate to the space using the hamburger menu (`☰`) on the top right and choose to `View all spaces`. 130 | 131 | ![hamburger](doc/images/deploy-2.png) 132 | 133 | Click on the space you saved the model to. 134 | 135 | ![space](doc/images/deploy-3.png) 136 | 137 | Choose the deploy the model by clicking the rocket ship icon. 138 | 139 | ![deploy](doc/images/deploy-4.png) 140 | 141 | Choose the `Online` deployment option and give it a name. 142 | 143 | ![online](doc/images/deploy-5.png) 144 | 145 | Your new deployment will appear. 146 | 147 | ![new deployment](doc/images/deploy-6.png) 148 | 149 | Click on the `API reference` tab and save the `Endpoint`. We'll be using this in our application. 150 | 151 | ![endpoint](doc/images/deploy-7.png) 152 | 153 | ### 5. Run the Node.js application 154 | 155 | You can deploy this application as a Cloud Foundry application to IBM Cloud by simply clicking the button below. This option will create a deployment pipeline, complete with a hosted Git lab project and devops toolchain. 156 | 157 |

158 | 159 | Deploy to IBM Cloud 160 | 161 |

162 | 163 | You may be prompted for an *IBM Cloud API Key* during this process. Use the `Create (+)` button to auto-fill this field and the others. Click on the `Deploy` button to deploy the application. 164 | 165 | ![pipeline](doc/images/cf-1.png) 166 | 167 | Before using the application go to the `Runtime` section of the application and in the `Environment variables` tab add in your `API_KEY` and `DEPLOYMENT_URL` values from steps 1 and 4. 168 | 169 | > **TIP** Do *NOT* wrap these values with double quotes. 170 | 171 | Once updated your application will restart and you can visit the application by clicking on `Visit App URL`. 172 | 173 | ![env vars](doc/images/cf-2.png) 174 | 175 | The app is fairly self-explantory, simply fill in the data you want to score and click on the `Classify` button to test how those figures would score against our model. The model predicts that the risk of heart failure for a patient with these medical characteristics. 176 | 177 | ![risk](doc/images/application.png) 178 | 179 | ## License 180 | 181 | This code pattern is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the [Developer Certificate of Origin, Version 1.1 (DCO)](https://developercertificate.org/) and the [Apache Software License, Version 2](http://www.apache.org/licenses/LICENSE-2.0.txt). 182 | 183 | [Apache Software License (ASL) FAQ](http://www.apache.org/foundation/license-faq.html#WhatDoesItMEAN) 184 | -------------------------------------------------------------------------------- /doc/images/api-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IBM/predictive-model-on-watson-ml/416b67e58b39fd8bdf5b78f7f452c0cfafa20ff9/doc/images/api-1.png -------------------------------------------------------------------------------- /doc/images/api-2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IBM/predictive-model-on-watson-ml/416b67e58b39fd8bdf5b78f7f452c0cfafa20ff9/doc/images/api-2.png 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-------------------------------------------------------------------------------- /manifest.yml: -------------------------------------------------------------------------------- 1 | --- 2 | applications: 3 | - path: ./server 4 | name: watson-ml-tester 5 | command: npm start 6 | instances: 1 7 | memory: 256M 8 | env: 9 | API_KEY: placeholder 10 | DEPLOYMENT_URL: placeholder 11 | -------------------------------------------------------------------------------- /server/app.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright 2017 IBM Corp. All Rights Reserved. 3 | * 4 | * Licensed under the Apache License, Version 2.0 (the "License"); 5 | * you may not use this file except in compliance with the License. 6 | * You may obtain a copy of the License at 7 | * 8 | * http://www.apache.org/licenses/LICENSE-2.0 9 | * 10 | * Unless required by applicable law or agreed to in writing, software 11 | * distributed under the License is distributed on an "AS IS" BASIS, 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | * See the License for the specific language governing permissions and 14 | * limitations under the License. 15 | */ 16 | 17 | /*jslint node: true*/ 18 | /*jslint es6 */ 19 | "use strict"; 20 | 21 | var express = require('express'); 22 | var path = require('path'); 23 | var favicon = require('serve-favicon'); 24 | var logger = require('morgan'); 25 | var cookieParser = require('cookie-parser'); 26 | var bodyParser = require('body-parser'); 27 | 28 | var classify = require('./routes/classify'); 29 | 30 | var app = express(); 31 | 32 | app.use(favicon(__dirname + '/public/favicon.ico')); 33 | app.use(logger('dev')); 34 | app.use(bodyParser.json()); 35 | app.use(bodyParser.urlencoded({ extended: false })); 36 | app.use(cookieParser()); 37 | app.use(express.static(path.join(__dirname, 'public'))); 38 | 39 | app.use('/classify', classify); 40 | 41 | // catch 404 and forward to error handler 42 | app.use(function(req, res, next) { 43 | var err = new Error('Not Found'); 44 | err.status = 404; 45 | next(err); 46 | }); 47 | 48 | // development error handler 49 | // will print stacktrace 50 | if (app.get('env') === 'development') { 51 | app.use(function(err, req, res, next) { 52 | res.status(err.status || 500); 53 | res.json({ 54 | message: err.message, 55 | error: err 56 | }); 57 | }); 58 | } 59 | 60 | // production error handler 61 | // no stacktraces leaked to user 62 | app.use(function(err, req, res, next) { 63 | res.status(err.status || 500); 64 | res.json({ 65 | message: err.message, 66 | error: {} 67 | }); 68 | }); 69 | 70 | 71 | const port = process.env.PORT || process.env.VCAP_APP_PORT || 3000; 72 | 73 | app.listen(port, function () { 74 | console.log("Server running on port: %d", port); 75 | }); 76 | module.exports = app; 77 | -------------------------------------------------------------------------------- /server/env.example: -------------------------------------------------------------------------------- 1 | # Replace the credentials here with your own. 2 | # Rename this file to .env before running 'npm start'. 3 | 4 | API_KEY= 5 | DEPLOYMENT_URL= 6 | -------------------------------------------------------------------------------- /server/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "wml-heart-risk", 3 | "description": "An application using Watson Machine Learning to classify heart risk given some data", 4 | "version": "0.0.1", 5 | "main": "app.js", 6 | "scripts": { 7 | "start": "node app.js" 8 | }, 9 | "dependencies": { 10 | "body-parser": "^1.18.3", 11 | "cookie-parser": "~1.3.4", 12 | "debug": "^3.1.0", 13 | "dotenv": "^2.0.0", 14 | "express": "^4.16.3", 15 | "morgan": "^1.9.1", 16 | "request": "^2.61.0", 17 | "serve-favicon": "^2.5.0", 18 | "vcap_services": "^0.2.0", 19 | "xmlhttprequest": "^1.8.0" 20 | } 21 | } 22 | -------------------------------------------------------------------------------- /server/public/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IBM/predictive-model-on-watson-ml/416b67e58b39fd8bdf5b78f7f452c0cfafa20ff9/server/public/favicon.ico -------------------------------------------------------------------------------- /server/public/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | IBM Watson Machine Learning Heart Risk Example 8 | 9 | 10 | 11 | 12 | 15 | 16 | 17 | 18 | 19 | 20 | 21 |
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IBM Watson Machine Learning Heart Risk Example 26 | By the IBM Developer Advocacy team 27 |

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31 | Use Watson Machine Learning to predict heart risk. Enter information below and see how it is classified. 32 |

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34 | The Training Data: The data set used in this example was mocked up and is only for demonstrative and illustrative purposes only and does not constitute an offering that has gone through regulatory review. 35 |

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37 | Fork the code in GitHub | 38 | WML Product Page | 39 | View the docs 40 |

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125 | 126 | 127 | 128 | 129 | 130 | -------------------------------------------------------------------------------- /server/public/javascripts/index.js: -------------------------------------------------------------------------------- 1 | (function() { 2 | 3 | $('.answers').hide(); 4 | $('.loading').hide(); 5 | $('#form').submit(onFormSubmit); 6 | $('#age').val('40'); 7 | $('#gender').val('M'); 8 | $('#familyhistory').val('N'); 9 | $('#smoker').val('N'); 10 | $('#exercise').val('60'); 11 | $('#cholesterol').val('10'); 12 | $('#bmi').val('20'); 13 | $('#heartbeats').val('60'); 14 | $('#palpitations').val('1'); 15 | 16 | function onFormSubmit() { 17 | var age = $('#age').val(); 18 | var gender = $('#gender').val(); 19 | var familyhistory = $('#familyhistory').val(); 20 | var smoker = $('#smoker').val(); 21 | var exercise = $('#exercise').val(); 22 | var cholesterol = $('#cholesterol').val(); 23 | var bmi = $('#bmi').val(); 24 | var heartbeats = $('#heartbeats').val(); 25 | var palpitations = $('#palpitations').val(); 26 | $('.loading').show(); 27 | $('.answers').hide(); 28 | $('.classify-btn').prop('disabled', true); 29 | $.post("/classify", {age: age, 30 | gender: gender, 31 | familyhistory: familyhistory, 32 | smoker: smoker, 33 | exercise: exercise, 34 | cholesterol: cholesterol, 35 | bmi: bmi, 36 | heartbeats: heartbeats, 37 | palpitations: palpitations 38 | }, function(data) { 39 | renderAnswer(data) 40 | }).fail(function(err) { 41 | renderAnswer(err); 42 | }); 43 | return false; 44 | } 45 | 46 | function renderAnswer(parsedResponse) { 47 | console.log(parsedResponse); 48 | 49 | if (parsedResponse.errors) { 50 | $('.answer').html('Something went wrong :-( ' + parsedResponse.errors[0].message); 51 | } else { 52 | var data = parsedResponse.predictions[0].values[0] 53 | var risk = data[0] 54 | var prediction = data[1][0] 55 | var probability = data[1][1] 56 | $('.risk').html('Heart Risk: '+risk); 57 | $('.prediction').html('Prediction: '+Math.floor(prediction*100 ).toFixed(0)+'%'); 58 | $('.probability').html('Probability: '+Math.floor(probability*100 ).toFixed(0)+'%'); 59 | } 60 | 61 | $('.classify-btn').prop('disabled', false); 62 | $('.answers').show(); 63 | $('.loading').hide(); 64 | } 65 | }()); 66 | -------------------------------------------------------------------------------- /server/public/stylesheets/style.css: -------------------------------------------------------------------------------- 1 | 2 | .container { 3 | margin-right: auto; 4 | margin-left: auto; 5 | padding-left: 15px; 6 | padding-right: 15px; 7 | } 8 | 9 | @media (min-width: 992px) { 10 | .container { 11 | width: 970px; 12 | } 13 | } 14 | 15 | .header { 16 | background: #F2FAFC; 17 | border-radius:none; 18 | border-bottom:1px solid #00B2EF; 19 | padding-top: 10px; 20 | padding-bottom:30px; 21 | margin:0 0 30px 0; 22 | } 23 | 24 | .page-header { 25 | padding-bottom: 8px; 26 | margin: 36px 0 18px; 27 | border-bottom: 1px solid #eeeeee; 28 | } 29 | 30 | .header h1 { 31 | color: #00B2EF; 32 | font-size: 50px; 33 | } 34 | 35 | .input-lg { 36 | height: 44px; 37 | } 38 | 39 | .btn-lg { 40 | border: solid 1px; 41 | } 42 | 43 | table { 44 | max-width: 100%; 45 | background-color: transparent; 46 | } 47 | th { 48 | text-align: left; 49 | } 50 | .table { 51 | width: 100%; 52 | margin-bottom: 18px; 53 | } 54 | td { 55 | padding: 8px; 56 | line-height: 1.428571429; 57 | vertical-align: top; 58 | border-top: 1px solid #eeeeee; 59 | } 60 | 61 | .row { 62 | padding-bottom: 20px; 63 | margin-left: -15px; 64 | margin-right: -15px; 65 | } 66 | 67 | .search-dropdown-menu { 68 | left: inherit; 69 | position: absolute; 70 | top: 100%; 71 | right: 0 !important; 72 | z-index: 1000; 73 | display: none; 74 | float: left; 75 | width: 800px; 76 | padding: 5px 0; 77 | margin: 2px 0 0; 78 | font-size: 14px; 79 | text-align: left; 80 | list-style: none; 81 | background-color: #fff; 82 | -webkit-background-clip: padding-box; 83 | background-clip: padding-box; 84 | border: 1px solid #ccc; 85 | border: 1px solid rgba(0,0,0,.15); 86 | border-radius: 4px; 87 | -webkit-box-shadow: 0 6px 12px rgba(0,0,0,.175); 88 | box-shadow: 0 6px 12px rgba(0,0,0,.175); 89 | } 90 | .search-dropdown-menu li { 91 | border-bottom: 1px solid #667 92 | } 93 | .search-dropdown-menu li, .search-dropdown-menu li a { 94 | overflow-wrap: break-word; 95 | word-wrap: break-word; 96 | white-space: normal; 97 | } 98 | 99 | .jumbotron { 100 | padding-top: 30px; 101 | padding-bottom: 30px; 102 | margin-bottom: 30px; 103 | padding-left: 20px; 104 | color: inherit; 105 | background-color: #eee; 106 | } 107 | 108 | @media screen and (min-width: 768px) { 109 | .container .jumbotron, .container-fluid .jumbotron { 110 | /* padding-right: 60px; */ 111 | padding-left: 20px; 112 | } 113 | } -------------------------------------------------------------------------------- /server/routes/classify.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright 2020 IBM Corp. All Rights Reserved. 3 | * 4 | * Licensed under the Apache License, Version 2.0 (the 'License'); you may not 5 | * use this file except in compliance with the License. You may obtain a copy of 6 | * the License at 7 | * 8 | * http://www.apache.org/licenses/LICENSE-2.0 9 | * 10 | * Unless required by applicable law or agreed to in writing, software 11 | * distributed under the License is distributed on an 'AS IS' BASIS, WITHOUT 12 | * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the 13 | * License for the specific language governing permissions and limitations under 14 | * the License. 15 | */ 16 | 17 | 'use strict'; 18 | 19 | require('dotenv').config({ 20 | silent: true, 21 | }); 22 | 23 | const express = require('express'); 24 | const router = express.Router(); 25 | const XMLHttpRequest = require("xmlhttprequest").XMLHttpRequest; 26 | 27 | const apiKey = process.env.API_KEY 28 | const deploymentURL = process.env.DEPLOYMENT_URL 29 | 30 | router.post('/', function(req, res, next) { 31 | classify(req, res); 32 | }); 33 | 34 | function classify(req, res) { 35 | console.log("Classifying:") 36 | console.log(req.body); 37 | 38 | getToken((req, res, err) => console.log(err), function () { 39 | let tokenResponse; 40 | try { 41 | tokenResponse = JSON.parse(this.responseText); 42 | } catch(ex) { 43 | console.log(ex) 44 | res.json({errors: [{message: "Cannot parse API token"}]}) 45 | } 46 | 47 | let values; 48 | const fields = ["AVGHEARTBEATSPERMIN","PALPITATIONSPERDAY","CHOLESTEROL","BMI","AGE","SEX","FAMILYHISTORY","SMOKERLAST5YRS","EXERCISEMINPERWEEK"] 49 | values = [[req.body.heartbeats, 50 | req.body.palpitations, 51 | req.body.cholesterol, 52 | req.body.bmi, 53 | req.body.age, 54 | req.body.gender, 55 | req.body.familyhistory, 56 | req.body.smoker, 57 | req.body.exercise]] 58 | let payload_json = {"input_data": [{"fields": fields, "values": values}]} 59 | let payload = JSON.stringify(payload_json); 60 | 61 | console.log("input data"); 62 | console.log(payload); 63 | 64 | apiPost(deploymentURL, tokenResponse.access_token, payload, function (resp) { 65 | let parsedPostResponse; 66 | try { 67 | parsedPostResponse = JSON.parse(this.responseText); 68 | } catch (ex) { 69 | console.log(ex) 70 | res.json({errors: [{message: "Cannot parse API token"}]}) 71 | } 72 | console.log("Scoring response"); 73 | res.json(parsedPostResponse) 74 | }, function (error) { 75 | console.log(error); 76 | res.json({errors: [{message: error}]}) 77 | }); 78 | }); 79 | 80 | } 81 | 82 | function getToken(errorCallback, loadCallback) { 83 | const req = new XMLHttpRequest(); 84 | req.addEventListener("load", loadCallback); 85 | req.addEventListener("error", errorCallback); 86 | req.open("POST", "https://iam.ng.bluemix.net/identity/token"); 87 | req.setRequestHeader("Content-Type", "application/x-www-form-urlencoded"); 88 | req.setRequestHeader("Accept", "application/json"); 89 | req.send("grant_type=urn:ibm:params:oauth:grant-type:apikey&apikey=" + apiKey); 90 | } 91 | 92 | function apiPost(deploymentURL, token, payload, loadCallback, errorCallback){ 93 | const oReq = new XMLHttpRequest(); 94 | oReq.addEventListener("load", loadCallback); 95 | oReq.addEventListener("error", errorCallback); 96 | oReq.open("POST", deploymentURL); 97 | oReq.setRequestHeader("Accept", "application/json"); 98 | oReq.setRequestHeader("Authorization", "Bearer " + token); 99 | oReq.setRequestHeader("Content-Type", "application/json;charset=UTF-8"); 100 | oReq.send(payload); 101 | } 102 | 103 | module.exports = router; 104 | --------------------------------------------------------------------------------