Teachable machine with tensorflow.js model - jpg files for clasification instead of video stream from webcam

[boylucky]

Hi all. I would like to ask you if somebody have experience with Google Teachable machine model of tenworflow.js . The complete code to use is generated on Google Teachalbe machine, that is not a problem. But I tried to modify it to clasify image in form of jpg file instead of clasifying of webcam stream. I did not succeed. Do you have any experiences with it or any advice? I will appreciate it. Here is the sample. Here is how the standard javascript from Teachable Machine looks like and where I would like to modify the part which is clasifying the webcam stream to clasify the static jpg file which I would provide (with the webcam stream it works perfect). Thanks in advance for your help and comments:

`

Teachable Machine Image Model

Start

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.min.js"></script> <script src="https://cdn.jsdelivr.net/npm/@teachablemachine/image@latest/dist/teachablemachine-image.min.js"></script> <script type="text/javascript"> // More API functions here: // https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image

// the link to your model provided by Teachable Machine export panel
const URL = "./my_model/";

let model, webcam, labelContainer, maxPredictions;

// Load the image model and setup the webcam
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
    // or files from your local hard drive
    // Note: the pose library adds "tmImage" object to your window (window.tmImage)
    model = await tmImage.load(modelURL, metadataURL);
    maxPredictions = model.getTotalClasses();

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

    // append elements to the DOM
    document.getElementById("webcam-container").appendChild(webcam.canvas);
    labelContainer = document.getElementById("label-container");
    for (let i = 0; i < maxPredictions; i++) { // and class labels
        labelContainer.appendChild(document.createElement("div"));
    }
}

async function loop() {
    webcam.update(); // update the webcam frame
    await predict();
    window.requestAnimationFrame(loop);
}

// run the webcam image through the image model
async function predict() {
    // predict can take in an image, video or canvas html element
    const prediction = await model.predict(webcam.canvas);
    for (let i = 0; i < maxPredictions; i++) {
        const classPrediction =
            prediction[i].className + ": " + prediction[i].probability.toFixed(2);
        labelContainer.childNodes[i].innerHTML = classPrediction;
    }
}

</script>`