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Add intent classifier example (#268)
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‎intent-classifier/.babelrc

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[
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"env",
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}

‎intent-classifier/.eslintrc.json

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"browser": true,
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"es6": true,
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"node": true
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},
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}

‎intent-classifier/.gitignore

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node_modules
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dist*
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.cache
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yarn-error.log
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*.tgz
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*-ubyte
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*.pyc
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.yalc
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.DS_STORE
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yalc.lock
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model.json
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weights.bin
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training/models
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training/data

‎intent-classifier/README.md

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# Text Classifer Example
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This example uses the universal sentence encoder to train two text
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classification models.
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1. An 'intent' classifier that classifies sentences into categories representing
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user intent for a query.
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2. A token tagger, that classifies tokens within a weather releated query to
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identify location related tokens.
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## Setup and Installation
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Note: These instructions use `yarn`, but you can use `npm run` instead if you
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do not have `yarn` installed.
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Install dependencies
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```
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yarn
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```
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## Preparing training data
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There are four npm/yarn scripts listed in package.json for preparing the training data. Each writes out one of more new files.
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The two scripts needed to train the intent classifier are:
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1. `yarn convert-raw-to-csv`: Converts the raw data into a csv format
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2. `yarn convert-csv-to-tensors`: Converts the strings in the CSV created in step 1 into tensors.
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The two scripts needed to train the token tagger are:
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1. `yarn convert-raw-to-tagged-tokens`: Extracts tokens from sentences in the original data and tags each token with a category
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2. `yarn convert-tokens-to-embeddings`: embeds the tokens from the queries using the universal sentence encoder and writes out a look-up-table.
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You can run all four of these commands with
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```
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yarn prep-data
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```
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You only need to do this once. This process can take 15-25 mins. The output of these scripts will be written to the `training/data` folder.
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## Train the models
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To train the intent classifier model run:
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```
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yarn train-intent
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```
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To train the token tagging model run:
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```
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yarn train-tagger
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```
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Each of these scripts take multiple options, look at `training/train-intent.js` and `training/train-tagger.js` for details.
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These scripts will output model artifacts in the `training/models` folder.
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## Run the apps
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Once the models are trained you can use the following commands
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to run the demo apps for each model.
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```
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yarn intent-app
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```
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```
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yarn tagger-app
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```

‎intent-classifier/app/index.html

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<!-- Copyright 2019 Google LLC. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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============================================================================== -->
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<html>
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<link rel="stylesheet" href="./index.scss">
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<style>
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</style>
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</head>
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<body>
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<div class="container">
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<div class="app">
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<div class="title">
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<p>Chatty McChatterson</p>
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</div>
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<div id='message-area' class="messages">
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<div class="message bot">
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👋! Hello, I can:
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<b>get the weather (⛅)</b>,
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<!-- <br /> -->
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&nbsp;<b>play Music (🎵🎺🎵)</b>,
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<!-- <br /> -->
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&nbsp;and <b>put things on your playlist (💿➡️📇)</b>
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</div>
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</div>
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<div class="controls">
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<form id="textentry">
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<input id="textbox" type="text" />
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<input id="submit" type="submit" value="💬">
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</form>
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</div>
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</div>
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</div>
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<script src="./index.js"></script>
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</body>
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</html>

‎intent-classifier/app/index.js

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/**
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* @license
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* Copyright 2019 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import * as useLoader from '@tensorflow-models/universal-sentence-encoder';
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import * as tf from '@tensorflow/tfjs';
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tf.ENV.set('WEBGL_PACK', false);
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const DENSE_MODEL_URL = './models/intent/model.json';
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const METADATA_URL = './models/intent/intent_metadata.json';
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let use;
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async function loadUSE() {
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if (use == null) {
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use = await useLoader.load();
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}
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return use;
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}
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let intent;
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async function loadIntentClassifer(url) {
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if (intent == null) {
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intent = await tf.loadLayersModel(url);
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}
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return intent;
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}
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let metadata;
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async function loadMetadata() {
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if (metadata == null) {
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const resp = await fetch(METADATA_URL);
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metadata = resp.json();
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}
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return metadata;
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}
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async function classify(sentences) {
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const [use, intent, metadata] = await Promise.all(
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loadUSE(), loadIntentClassifer(DENSE_MODEL_URL), loadMetadata());
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const {labels} = metadata;
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console.log('classifying', sentences);
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console.time(`Embedding ${sentences.length} sentences`);
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const activations = await use.embed(sentences);
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console.timeEnd(`Embedding ${sentences.length} sentences`);
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const prediction = intent.predict(activations);
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const predsArr = await prediction.array();
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const preview = [predsArr[0].slice()];
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preview.unshift(labels);
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console.table(preview);
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tf.dispose([activations, prediction]);
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return predsArr[0];
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}
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const THRESHOLD = 0.90;
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async function getClassificationMessage(softmaxArr) {
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const {labels} = await loadMetadata();
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const max = Math.max(...softmaxArr);
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const maxIndex = softmaxArr.indexOf(max);
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const intentLabel = labels[maxIndex];
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if (max < THRESHOLD) {
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return '¯\\_(ツ)_/¯';
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} else {
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let response;
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switch (intentLabel) {
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case 'AddToPlaylist':
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response = '💿➡️📇';
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break;
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case 'GetWeather':
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response = '⛅';
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break;
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case 'PlayMusic':
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response = '🎵🎺🎵';
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break;
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default:
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response = '?';
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break;
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}
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return response;
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}
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}
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async function onSendMessage(inputText) {
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if (inputText != null && inputText.length > 0) {
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// Add the input text to the chat window
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const msgId = appendMessage(inputText, 'input');
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// Classify the text
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const classification = await classify([inputText]);
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// Add the response to the chat window
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const response = await getClassificationMessage(classification);
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appendMessage(response, 'bot', msgId);
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}
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}
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let messageId = 0;
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function appendMessage(message, sender, appendAfter) {
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const messageDiv = document.createElement('div');
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messageDiv.classList = `message ${sender}`;
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messageDiv.innerHTML = message;
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messageDiv.dataset.messageId = messageId++;
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const messageArea = document.getElementById('message-area');
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if (appendAfter == null) {
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messageArea.appendChild(messageDiv);
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} else {
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const inputMsg =
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document.querySelector(`.message[data-message-id="${appendAfter}"]`);
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inputMsg.parentNode.insertBefore(messageDiv, inputMsg.nextElementSibling);
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}
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// Scroll the message area to the bottom.
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messageArea.scroll({top: messageArea.scrollHeight, behavior: 'smooth'});
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// Return this message id so that a reply can be posted to it later
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return messageDiv.dataset.messageId;
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}
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function setupListeners() {
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const form = document.getElementById('textentry');
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const textbox = document.getElementById('textbox');
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form.addEventListener('submit', event => {
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event.preventDefault();
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event.stopPropagation();
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const inputText = textbox.value;
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onSendMessage(inputText);
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textbox.value = '';
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}, false);
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}
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function warmup() {
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classify('hello there');
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}
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window.addEventListener('load', function() {
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setupListeners();
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warmup();
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});

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