<snapdata remixID="11506202"><project name="MachineLearningObjectRecognitionSnap" app="Snap! 7, https://snap.berkeley.edu" version="2"><notes></notes><thumbnail>data:image/png;base64,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</thumbnail><scenes select="1"><scene name="MachineLearningObjectRecognitionSnap"><notes></notes><hidden></hidden><headers></headers><code></code><blocks><block-definition s="Load Libraries" type="command" category="other"><header></header><code></code><translations></translations><inputs></inputs><script><block s="doRepeat"><l>2</l><script><block s="doRun"><block s="reportJSFunction"><list></list><l>   let script = document.createElement("script");&#xD;   script.type = "text/javascript"; &#xD;   script.src = "https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js";  &#xD;   document.head.appendChild(script);&#xD;   let script2 = document.createElement("script");&#xD;   script2.type = "text/javascript";&#xD;   script2.src = "https://cdn.jsdelivr.net/npm/@teachablemachine/image@0.8/dist/teachablemachine-image.min.js";&#xD;   document.head.appendChild(script2);  </l></block><list></list></block><block s="doWait"><l>1</l></block></script></block></script></block-definition><block-definition s="Load Model %&apos;URL&apos;" type="command" category="other"><header></header><code></code><translations></translations><inputs><input type="%txt"></input></inputs><script><block s="doRun"><block s="reportJSFunction"><list><l>URL</l></list><l>const when_loaded = (loaded_model) =&gt; {&#xD;  console.log("loading model")&#xD;  window.imageModel = loaded_model;&#xD;};&#xD;&#xD;const modelURL = URL + "model.json"; // model topology&#xD;const metadataURL = URL + "metadata.json"; // model metadata&#xD;&#xD;const recognizer = tmImage.load(modelURL, metadataURL).then(when_loaded);     </l></block><list><block var="URL"/></list></block></script></block-definition><block-definition s="Predict" type="command" category="other"><header></header><code></code><translations></translations><inputs></inputs><script><block s="doDeclareVariables"><list><l>predictionJS</l></list></block><block s="doDeclareVariables"><list><l>predictionList</l></list></block><block s="doDeclareVariables"><list><l>tempTable</l></list></block><block s="doUntil"><block s="reportKeyPressed"><l><option>x</option></l></block><script><block s="doRun"><block s="reportJSFunction"><list><l>videoCapture</l></list><l>const report_predictions = (prediction) =&gt; {&#xD;   let class_names = window.imageModel.getClassLabels();&#xD;   let names_and_scores = prediction.map((score, index) =&gt; [class_names[index], score.probability]);&#xD;   &#xD;   //console.log(class_names)&#xD;&#xD;   var predictionList = []&#xD;&#xD;   // This loop is for outer array&#xD;   for (var i = 0; i &lt; names_and_scores.length; i++) {&#xD;      if (names_and_scores[i].length &gt; 1) {&#xD;         predictionList.push([class_names[i],names_and_scores[i][1]]) &#xD;         //console.log([class_names[i], predictionList[i]])&#xD;      }&#xD;   }&#xD;&#xD;   window.prediction = predictionList&#xD;};&#xD;&#xD;const prediction = window.imageModel.predict(videoCapture.contents).then(report_predictions);   &#xD;//console.log(prediction)        </l></block><list><block s="reportVideo"><l><option>snap</option></l><l>Stage</l></block></list></block><block s="doSetVar"><l>predictionJS</l><block s="evaluate"><block s="reportJSFunction"><list></list><l>return window.prediction</l></block><list></list></block></block><block s="doSetVar"><l>predictionList</l><block s="reportTextSplit"><block s="reportJoinWords"><list><block var="predictionJS"/></list></block><l>,</l></block></block><block s="doSetVar"><l>tempTable</l><block s="reportNewList"><list></list></block></block><block s="doFor"><l>i</l><l>1</l><block s="reportQuotient"><block s="reportListAttribute"><l><option>length</option></l><block var="predictionList"/></block><l>2</l></block><script><block s="doAddToList"><block s="reportNewList"><list><block s="reportListItem"><block s="reportDifference"><block s="reportVariadicProduct"><list><l>2</l><block var="i"/></list></block><l>1</l></block><block var="predictionList"/></block><block s="reportListItem"><block s="reportVariadicProduct"><list><l>2</l><block var="i"/></list></block><block var="predictionList"/></block></list></block><block var="tempTable"/></block></script></block><block s="doSetVar"><l>PredictionTable</l><block var="tempTable"/></block><block s="doSetVar"><l>Prediction</l><block s="reportMap"><block s="reifyReporter"><autolambda><block s="reportListItem"><l>2</l><l/></block></autolambda><list></list></block><block var="PredictionTable"/></block></block></script></block></script></block-definition></blocks><stage name="Stage" width="480" height="360" costume="0" color="255,255,255,1" tempo="60" threadsafe="false" penlog="false" volume="100" pan="0" lines="round" ternary="true" hyperops="true" codify="false" inheritance="true" sublistIDs="false" id="149"><pentrails>data:image/png;base64,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</pentrails><costumes><list struct="atomic" id="150"></list></costumes><sounds><list struct="atomic" id="151"></list></sounds><variables></variables><blocks></blocks><scripts></scripts><sprites select="1"><sprite name="Sprite" idx="1" x="0" y="5.684341886080802e-14" heading="90" scale="1" volume="100" pan="0" rotation="1" draggable="true" costume="2" color="80,80,80,1" pen="tip" id="156"><costumes><list id="157"><item><ref mediaID="Sprite_cst_alonzo"></ref></item><item><ref mediaID="Sprite_cst_cat3"></ref></item></list></costumes><sounds><list struct="atomic" id="158"></list></sounds><blocks></blocks><variables></variables><scripts><script x="30" y="30"><block s="receiveKey"><l><option>s</option></l><list></list></block><block s="doSetVar"><l>URL</l><l>UPDATE WITH YOUR URL</l><comment w="90" collapsed="false">Insert the URL to your model, then run this code.</comment></block><custom-block s="Load Libraries"></custom-block><custom-block s="Load Model %txt"><block var="URL"/></custom-block></script><script x="33.59523809523813" y="288.5476190476188"><block s="receiveKey"><l><option>space</option></l><list></list></block><block s="doUntil"><block s="reportKeyPressed"><l><option>x</option></l></block><script><block s="doIfElse"><block s="reportGreaterThan"><block s="reportListItem"><l>1</l><block var="Prediction"/></block><l>.9</l></block><script><block s="doSwitchToCostume"><l>alonzo</l></block></script><script><block s="doSwitchToCostume"><l>cat3</l></block></script></block></script></block></script><script x="37.285714285714306" y="163.0238095238094"><block s="receiveKey"><l><option>space</option></l><list></list></block><custom-block s="Predict"><comment w="178" collapsed="false">When you run this the first time, it will take a minute to start predicting. Press x to stop predicting. </comment></custom-block></script></scripts></sprite><watcher var="URL" style="normal" x="10" y="10" color="243,118,29"/><watcher var="PredictionTable" style="normal" x="9" y="43" color="243,118,29" extX="128.5615234375" extY="137"/><watcher var="Prediction" style="normal" x="9" y="213" color="243,118,29" extX="80" extY="70"/></sprites></stage><variables><variable name="URL"><l>UPDATE WITH YOUR URL</l></variable><variable name="PredictionTable"><list id="207"><item><list struct="atomic" id="208">White kiss,0.47450387477874756</list></item><item><list struct="atomic" id="209">Red Kiss,0.35843604803085327</list></item><item><list struct="atomic" id="210">Green kiss,0.026005921885371208</list></item><item><list struct="atomic" id="211">Green Bubblegum,0.011478952132165432</list></item><item><list struct="atomic" id="212">Blue Bubblegum,0.011824271641671658</list></item><item><list struct="atomic" id="213">Purple Bubblegum,0.10616486519575119</list></item><item><list struct="atomic" id="214">Nothing,0.011586190201342106</list></item></list></variable><variable name="Prediction"><list struct="atomic" id="215">0.47450387477874756,0.35843604803085327,0.026005921885371208,0.011478952132165432,0.011824271641671658,0.10616486519575119,0.011586190201342106</list></variable></variables></scene></scenes></project><media name="MachineLearningObjectRecognitionSnap" app="Snap! 7, https://snap.berkeley.edu" version="2"><costume name="alonzo" center-x="45" center-y="60" image="data:image/png;base64,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" mediaID="Sprite_cst_alonzo"/><costume name="cat3" center-x="79" center-y="90" image="data:image/png;base64,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" mediaID="Sprite_cst_cat3"/></media></snapdata>