How to put both webcam and youtube video in html

I want to put both webcam and youtube video in html. I can put webcam in html, but when I put both webcam and youtube video in html, webcam disappear in html. What should I do?

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <meta http-equiv="X-UA-Compatible" content="ie=edge">
    <link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/earlyaccess/notosanskr.css">
    <title>webcam and video</title>
    <link rel="stylesheet" href="style.css">
</head>

<body>


    <div>Teachable Machine Pose Model</div>
<button type="button" onclick="init()">Start</button>
<div><canvas id="canvas"></canvas></div>
<div id="label-container"></div>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/pose@0.8/dist/teachablemachine-pose.min.js"></script>





<script type="text/javascript">
    // More API functions here:
    // https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/pose

    // the link to your model provided by Teachable Machine export panel
    const URL = "./my_model/";
    let model, webcam, ctx, labelContainer, maxPredictions;

    async function init() {
        const modelURL = URL + "model.json";
        const metadataURL = URL + "metadata.json";

        // load the model and metadata
        // Refer to tmImage.loadFromFiles() in the API to support files from a file picker
        // Note: the pose library adds a tmPose object to your window (window.tmPose)
        model = await tmPose.load(modelURL, metadataURL);
        maxPredictions = model.getTotalClasses();

        // Convenience function to setup a webcam
        const size = 200;
        const flip = true; // whether to flip the webcam
        webcam = new tmPose.Webcam(size, size, flip); // width, height, flip
        await webcam.setup(); // request access to the webcam
        await webcam.play();
        window.requestAnimationFrame(loop);

        // append/get elements to the DOM
        const canvas = document.getElementById("canvas");
        canvas.width = size; canvas.height = size;
        ctx = canvas.getContext("2d");
        labelContainer = document.getElementById("label-container");
        for (let i = 0; i < maxPredictions; i++) { // and class labels
            labelContainer.appendChild(document.createElement("div"));
        }
    }

    async function loop(timestamp) {
        webcam.update(); // update the webcam frame
        await predict();
        window.requestAnimationFrame(loop);
    }
    var status = "good"
    var count_close = 0
    var count_foot = 0
    async function predict() {
        // Prediction #1: run input through posenet
        // estimatePose can take in an image, video or canvas html element
        const { pose, posenetOutput } = await model.estimatePose(webcam.canvas);
        // Prediction 2: run input through teachable machine classification model
        const prediction = await model.predict(posenetOutput);

        if (prediction[0].probability.toFixed(2) >= 0.9) {
            if (status == "good" || status == "foot") {
                count_close++

                $(".youtube-video").stopVideo();

                $('.close').css('width', count_close);
            }
            status = "close"
            // if (status == "close") {
                
            //     // var audio = new Audio('audio_file.mp3');
            //     // audio.play();
            // }
        } else if (prediction[1].probability.toFixed(2) >= 0.9) {
            if (status == "good" || status == "close") {
                count_foot++

                $(".youtube-video").stopVideo();

                $('.foot').css('width', count_foot);
            }
            status = "foot"
            // if (status == "foot") {
            //     count_foot++
            //     // var audio = new Audio('audio_file.mp3');
            //     // audio.play();
            //     // $('.progress').css('stroke-dashoffset', progress);
            //     // $('#counter').html(count_foot);
            // }
        } else if (prediction[0].probability.toFixed(2) < 0.9 && prediction[1].probability.toFixed(2) < 0.9) {
            status = "good"
        }

        for (let i = 0; i < maxPredictions; i++) {
            const classPrediction =
                prediction[i].className + ": " + prediction[i].probability.toFixed(2);
            labelContainer.childNodes[i].innerHTML = classPrediction;
        }

        // finally draw the poses
        drawPose(pose);
    }

    function drawPose(pose) {
        if (webcam.canvas) {
            ctx.drawImage(webcam.canvas, 0, 0);
            // draw the keypoints and skeleton
            if (pose) {
                const minPartConfidence = 0.5;
                tmPose.drawKeypoints(pose.keypoints, minPartConfidence, ctx);
                tmPose.drawSkeleton(pose.keypoints, minPartConfidence, ctx);
            }
        }
    }

    function docWrite(variable) {
    document.write(variable)
    }
    
</script>



<!-- <div><a href="#" class="play-video">Play Video</a></div>
<div><a href="#" class="stop-video">Stop Video</a></div>
<div><a href="#" class="pause-video">Pause Video</a></div> -->

<iframe class="youtube-video" width="560" height="315" src="https://www.youtube.com/embed/glEiPXAYE-U?enablejsapi=1&version=3&playerapiid=ytplayer" frameborder="0" allowfullscreen></iframe>




<div class="skills">
    <h3 class="skillsTop">가까움</h3>
   
   <div class="skillsBottom">
    <div class="progress">
      <div class="close progress-bar progress-bar-info" role="progressbar">
      </div>
    </div>
   </div>  

   <h3 class="skillsTop">발올림</h3>

   <div class="skillsBottom">
    <div class="progress">
      <div class="foot progress-bar progress-bar-info" role="progressbar">
      </div>
    </div>
  </div>
</div>


</body>
</html>