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app.bundle.js
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app.bundle.js
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!function(e){var t={};function n(r){if(t[r])return t[r].exports;var i=t[r]={i:r,l:!1,exports:{}};return e[r].call(i.exports,i,i.exports,n),i.l=!0,i.exports}n.m=e,n.c=t,n.d=function(e,t,r){n.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:r})},n.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},n.t=function(e,t){if(1&t&&(e=n(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var r=Object.create(null);if(n.r(r),Object.defineProperty(r,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var i in e)n.d(r,i,function(t){return e[t]}.bind(null,i));return r},n.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return n.d(t,"a",t),t},n.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},n.p="",n(n.s=24)}([function(e,t,n){"use strict";e.exports=n(14)},function(e,t,n){"use strict";(function(e,r){n.d(t,"a",(function(){return C})),n.d(t,"b",(function(){return Ue})),n.d(t,"c",(function(){return Pe})),n.d(t,"d",(function(){return Xe})),n.d(t,"e",(function(){return re})),n.d(t,"f",(function(){return it})),n.d(t,"g",(function(){return D})),n.d(t,"h",(function(){return st})),n.d(t,"i",(function(){return jt})),n.d(t,"j",(function(){return At})),n.d(t,"k",(function(){return Ft})),n.d(t,"l",(function(){return Z})),n.d(t,"m",(function(){return Ze})),n.d(t,"n",(function(){return Qe})),n.d(t,"o",(function(){return dt})),n.d(t,"p",(function(){return ce}));
/*! *****************************************************************************
Copyright (c) Microsoft Corporation. All rights reserved.
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
THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
MERCHANTABLITY OR NON-INFRINGEMENT.
See the Apache Version 2.0 License for specific language governing permissions
and limitations under the License.
***************************************************************************** */
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/*!
Copyright (c) 2017 Jed Watson.
Licensed under the MIT License (MIT), see
http://jedwatson.github.io/classnames
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/*
object-assign
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/** @license React v0.18.0
* scheduler.production.min.js
*
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
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/*! *****************************************************************************
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
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KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
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See the Apache Version 2.0 License for specific language governing permissions
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ft(e,t,n,r){n&&Object.defineProperty(e,t,{enumerable:n.enumerable,configurable:n.configurable,writable:n.writable,value:n.initializer?n.initializer.call(r):void 0})}function dt(e,t,n,r,i){var a={};return Object.keys(r).forEach((function(e){a[e]=r[e]})),a.enumerable=!!a.enumerable,a.configurable=!!a.configurable,("value"in a||a.initializer)&&(a.writable=!0),a=n.slice().reverse().reduce((function(n,r){return r(e,t,n)||n}),a),i&&void 0!==a.initializer&&(a.value=a.initializer?a.initializer.call(i):void 0,a.initializer=void 0),void 0===a.initializer&&(Object.defineProperty(e,t,a),a=null),a}class pt{async _init(){}async _reset(){}}let ht=(ot=dt((at=class extends pt{constructor(...e){super(...e),ut(this,"className","UIStore"),ft(this,"modelName",ot,this),ft(this,"hasModelLoaded",lt,this),ft(this,"haveSpriteDataLoaded",st,this)}}).prototype,"modelName",[ve.l],{configurable:!0,enumerable:!0,writable:!0,initializer:function(){return""}}),lt=dt(at.prototype,"hasModelLoaded",[ve.l],{configurable:!0,enumerable:!0,writable:!0,initializer:function(){return!1}}),st=dt(at.prototype,"haveSpriteDataLoaded",[ve.l],{configurable:!0,enumerable:!0,writable:!0,initializer:function(){return!1}}),at);class vt extends pt{constructor(){super(),ut(this,"className","HistoryStore"),this.history=j()}push(e){this.getCurrentLocation()!==e&&this.history.push(e)}goBack(){this.history.goBack()}getCurrentLocation(){return this.history.location.pathname}}class mt{async _init(){}async _reset(){}}const yt=784,bt=10,gt=65e3,Pt=Math.floor(5/6*gt),wt=gt-Pt,Tt="https://storage.googleapis.com/learnjs-data/model-builder/mnist_images.png",Et="https://storage.googleapis.com/learnjs-data/model-builder/mnist_labels_uint8";class xt extends mt{constructor(){super(),ut(this,"className","MnistDatasetService"),this.shuffledTrainIndex=0,this.shuffledTestIndex=0}async load(){const e=new Image,t=document.createElement("canvas"),n=t.getContext("2d"),r=new Promise((r,i)=>{e.crossOrigin="",e.onload=()=>{e.width=e.naturalWidth,e.height=e.naturalHeight;const i=new ArrayBuffer(gt*yt*4);t.width=e.width,t.height=5e3;for(let r=0;r<gt/5e3;r++){const a=new Float32Array(i,r*yt*5e3*4,5e3*yt);n.drawImage(e,0,5e3*r,e.width,5e3,0,0,e.width,5e3);const o=n.getImageData(0,0,t.width,t.height);for(let e=0;e<o.data.length/4;e++)a[e]=o.data[4*e]/255}this.datasetImages=new Float32Array(i),r()},e.src=Tt}),i=fetch(Et),[a,o]=await Promise.all([r,i]);this.datasetLabels=new Uint8Array(await o.arrayBuffer()),this.trainIndices=tf.util.createShuffledIndices(Pt),this.testIndices=tf.util.createShuffledIndices(wt),this.trainImages=this.datasetImages.slice(0,yt*Pt),this.testImages=this.datasetImages.slice(yt*Pt),this.trainLabels=this.datasetLabels.slice(0,bt*Pt),this.testLabels=this.datasetLabels.slice(bt*Pt)}nextTrainBatch(e){return this.nextBatch(e,[this.trainImages,this.trainLabels],()=>(this.shuffledTrainIndex=(this.shuffledTrainIndex+1)%this.trainIndices.length,this.trainIndices[this.shuffledTrainIndex]))}nextTestBatch(e){return this.nextBatch(e,[this.testImages,this.testLabels],()=>(this.shuffledTestIndex=(this.shuffledTestIndex+1)%this.testIndices.length,this.testIndices[this.shuffledTestIndex]))}nextBatch(e,t,n){const r=new Float32Array(e*yt),i=new Uint8Array(e*bt);for(let a=0;a<e;a++){const e=n(),o=t[0].slice(e*yt,e*yt+yt);r.set(o,a*yt);const l=t[1].slice(e*bt,e*bt+bt);i.set(l,a*bt)}return{xs:tf.tensor2d(r,[e,yt]),labels:tf.tensor2d(i,[e,bt])}}}class St extends mt{constructor(...e){super(...e),ut(this,"className","ModelService"),ut(this,"_model",null)}predict(e){let t=tf.browser.fromPixels(e,1).reshape([1,28,28,1]).cast("float32").div(tf.scalar(255));const n=this._model.predict(t).dataSync(),r=Math.max(...n),i=n.indexOf(r);return{predictionResults:n,prediction:i.toString(),predictionProbability:r}}getDefaultModelName(){return"preTrainedModel"}async getModel(){const e=this.getDefaultModelName();let t;if(localStorage.getItem(`tensorflowjs_models/${e}/info`))t=await tf.loadLayersModel(`localstorage://${e}`);else{const n='{"format":"layers-model","generatedBy":"TensorFlow.js tfjs-layers 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this._down}set down(e){this._down=e}get x(){return this._x}get y(){return this._y}set x(e){this._x=e}set y(e){this._y=e}get px(){return this._px}get py(){return this._py}set px(e){this._px=e}set py(e){this._py=e}}class Wt{constructor(e,t,n,r){if(!document)throw new Error("no DOM found");if(e instanceof window.Node&&"CANVAS"===e.tagName)this.canvas=e;else{if("string"!=typeof e)throw new Error(`can't look for canvas based on '${e}'`);this.canvas=document.querySelector(e)}if(!this.canvas)throw new Error("canvas not found");this.canvas.width=t||100,this.canvas.height=n||100,this.canvas.style.cursor="crosshair",this.mouse=new Ut;const i=e=>{e.preventDefault();const t=this.canvas.getBoundingClientRect(),n=e.changedTouches&&e.changedTouches[0]||e;let r=n.offsetX,i=n.offsetY;void 0===r&&(r=n.clientX+document.documentElement.scrollLeft-t.left),void 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i.a.Component{constructor(e){super(e),ut(this,"_canvasModel",null),ut(this,"originalCanvasId","original"),ut(this,"boundedCanvasId","bounded"),ut(this,"mnistCenter","mnist_center"),ut(this,"mnist","mnist"),ut(this,"originalWidth",100),ut(this,"originalHeight",100),ut(this,"mnistWidth",28),ut(this,"mnistHeight",28),ut(this,"offset",2),ut(this,"originalCanvasRef",null),ut(this,"boundedCanvasRef",null),ut(this,"mnistCenterCanvasRef",null),ut(this,"mnistCanvasRef",null),this.clear=this.clear.bind(this),this.predict=this.predict.bind(this),this.originalCanvasRef=i.a.createRef(),this.boundedCanvasRef=i.a.createRef(),this.mnistCenterCanvasRef=i.a.createRef(),this.mnistCanvasRef=i.a.createRef()}componentDidMount(){this._canvasModel=new Wt("#original")}componentWillUnmount(){this._canvasModel.destroy(),this._canvasModel=null}clear(){const e=this.originalCanvasRef.current,t=this.boundedCanvasRef.current,n=this.mnistCenterCanvasRef.current,r=this.mnistCanvasRef.current;let i;(i=e.getContext("2d")).clearRect(-this.offset,-this.offset,this.originalWidth+this.offset,this.originalHeight+this.offset),(i=t.getContext("2d")).clearRect(-this.offset,-this.offset,this.originalWidth+this.offset,this.originalHeight+this.offset),(i=n.getContext("2d")).clearRect(-this.offset,-this.offset,this.mnistWidth+this.offset,this.mnistHeight+this.offset),(i=r.getContext("2d")).clearRect(-this.offset,-this.offset,this.mnistWidth+this.offset,this.mnistHeight+this.offset),this.props.onNewImage(""),this.props.resetPredictions()}predict(){const e=this.retrieveBase64EncodedImage();this.props.onNewImage(e)}retrieveBase64EncodedImage(){return document.getElementById("mnist").toDataURL()}render(){const e=this.props.isDebugMode?void 0:{display:"none"};return i.a.createElement(i.a.Fragment,null,i.a.createElement("div",null,i.a.createElement("h3",null,"Convert any size canvas based input into 20x20 pixel bounding box ",i.a.createElement("br",null),"centered on 28x28 canvas as per MNIST 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Output"))),i.a.createElement("div",{className:"toolbox"},i.a.createElement("button",{type:"button",onPointerDown:this.clear},i.a.createElement("span",null,"Clear")),i.a.createElement("button",{type:"button",onPointerDown:this.predict},i.a.createElement("span",null,"Predict"))),i.a.createElement("div",{className:"checkbox"},i.a.createElement("input",{type:"checkbox",id:"debug",name:"debug",value:this.props.isDebugMode,onChange:this.props.handleCheckbox}),i.a.createElement("label",{htmlFor:"debug"},"Debug Mode"))),i.a.createElement(Ht,{isDebugMode:this.props.isDebugMode,allPredictions:this.props.allPredictions}))}},ut(Xt,"propTypes",{onNewImage:u.a.func.isRequired,isDebugMode:u.a.bool.isRequired,digit:u.a.string.isRequired,digitProbability:u.a.number.isRequired,allPredictions:u.a.array.isRequired,handleCheckbox:u.a.func.isRequired,resetPredictions:u.a.func.isRequired}),qt=Zt))||qt;var $t,en,tn,nn,rn,an,on;let ln=et("services")($t=Xe((on=class extends i.a.Component{constructor(e){super(e),ut(this,"MNIST_SIZE",28),ft(this,"result",tn,this),ft(this,"resultProbability",nn,this),ft(this,"allPredictions",rn,this),ft(this,"isDebugMode",an,this),this.resetPredictions=this.resetPredictions.bind(this),this.onNewImage=this.onNewImage.bind(this),this.handleCheckbox=this.handleCheckbox.bind(this)}resetPredictions(){this.allPredictions=[]}async onNewImage(e){if(e){const t=await async function(e,t){const n=new Image,r=new Promise(e=>{n.onload=()=>{const r=document.createElement("canvas");r.width=t,r.height=t;const i=r.getContext("2d");i.drawImage(n,0,0,t,t);const a=i.getImageData(0,0,t,t);let o=[];for(let e=0,t=a.data.length/4;e<t;e+=1)o.push(a.data[4*e+3]),o.push(0),o.push(0),o.push(0);let l=new ImageData(new Uint8ClampedArray(o),28,28);e(l)}});return n.src=e,r}(e,this.MNIST_SIZE),{ModelService:n}=this.props.services,r=n.predict(t);Object(ve.m)(()=>{this.result=r.prediction,this.resultProbability=r.predictionProbability,this.allPredictions=Array.from(r.predictionResults)})}else Object(ve.m)(()=>{this.result=""})}handleCheckbox(){this.isDebugMode=!this.isDebugMode}render(){return i.a.createElement("div",{className:"flex"},i.a.createElement(Jt,{onNewImage:e=>this.onNewImage(e),isDebugMode:this.isDebugMode,digit:this.result,digitProbability:this.resultProbability,allPredictions:this.allPredictions,handleCheckbox:this.handleCheckbox,resetPredictions:this.resetPredictions}))}},tn=dt((en=on).prototype,"result",[ve.l],{configurable:!0,enumerable:!0,writable:!0,initializer:function(){return""}}),nn=dt(en.prototype,"resultProbability",[ve.l],{configurable:!0,enumerable:!0,writable:!0,initializer:function(){return 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