YOUR LINK

Once UPLOADED you will receive Javascript Code that we can add to our web pages hosted, similar to our Max & Node.js examples (from Class 11).

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The code we are interested in is it the link offered to you once yoru model has been uploaded.

(see the arrow in the image above)

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my model link looks like this:

 

https://teachablemachine.withgoogle.com/models/lXMwFdqKx/

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We will add this link to the index.html file that is in our "node4max_TeachableMachine_BASIC" folder of our Class 12 files.

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find this section of the Teachable Machine <script> tag:

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    ///////CHANGE THE BELOW URL TO THE ONE PROVIDED BY EXPORTING YOUR MODEL!!!!!!!!!!!!
  // the link to your model provided by Teachable Machine export panel
  const URL = "https://teachablemachine.withgoogle.com/models/lXMwFdqKx/";

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and replace this URL with the one from your uploaded Model

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Now START the Max Node.js script by opening the "node4max-TM_Basic.maxpat" and navigate to the web page linked on the Max Basic Patcher. (make sure to install the required packages the first time you run the patcher)

 

http://localhost:3000/

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Here's what I did to make this work by adding a line to use socket.io and node.js:

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The exported JavaScript code from Google's TM will look something like this:

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 <div>Teachable Machine Image Model</div>
<button type="button" onclick="init()">Start</button>
<div id="webcam-container"></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/image@0.8/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 = "https://teachablemachine.withgoogle.com/models/XX_YOUR_MODEL_XX/";

    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>