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HTML

              
                <div class="jumbotron text-center">
    <h1>Language detection with TensorFlow.js</h1>
    <p>PowerCoders AI workshop - 2019 EPFL EdTech Collider</p> 
</div>

<div class="container">
    <div class="row">
        <div class="col-sm-4">
            <div class="card">
                <div class="card-body">
                    <h5 class="card-title">Languages</h5>
                    <textarea id="languages">["Deutsch", "Fran\u00e7ais", "Itialiano", "Rumantsch", "English"]</textarea>
                </div>
            </div>
        </div>
        <div class="col-sm-4">
            <div class="card">
                <div class="card-body">
                    <h5 class="card-title">Vocabulary</h5>
                    <textarea id="vocabulary">[" a ", "s e", " in", "cha", "the", "e. ", "n s", "ers", "e f", "ti ", "st ", "azi", " l\u2019", "s. ", "n i", "erl", "er ", "de ", " re", "ur ", "da ", "es ", "i c", "uns", "lic", "ons", "to ", "ent", "pli", " su", "gen", "con", "for", " al", "e c", "e l", "von", "e p", "ni ", "izr", "el ", "ada", "ch ", "una", "uis", "l 1", " ch", "des", "tsc", " le", "ted", "hwe", "a. ", "ant", "en ", "ono", "s s", " as", "n t", "si ", " tu", "lan", "ed ", "sa ", "di ", "ung", "les", "sta", "nta", "a c", "del", "au ", " mo", "che", "ver", " se", " pr", "ra ", "nge", "ist", "ize", "swi", "cht", "a s", "of ", "tra", " we", "cun", "en.", "her", "ro ", "ss ", " ei", "ral", "e a", "one", "d i", "ns ", " fo", "aus", "sch", "zer", " da", "und", " im", "son", "zze", "a e", "ica", " sw", "dal", " la", "iss", "all", "ate", "nce", "a l", "nd ", "ala", "im ", "ne ", " is", "nen", " ne", "ils", " to", "n d", "zio", "us ", "wit", " so", "is ", "ue ", "svi", "ont", "et ", " th", "eur", "t l", "ta ", ", a", " st", "ach", "te ", " ca", "ll'", "a t", ". l", "ell", "s p", "itz", "oun", "le ", "s o", " un", "nde", " au", "e e", "las", "me ", "ziu", "ts ", ". d", " ce", "dan", "mit", "ter", "a p", "no ", "ind", "s l", "e, ", "den", "par", "re ", " vo", "ina", " be", "ion", "a d", "ura", "he ", "tze", " es", " ar", " et", "nto", " pa", " ge", " po", " qu", "chw", "o a", "tio", "iqu", "la ", "tad", "li ", "on ", " \u00e0 ", " an", " co", " wa", " mi", "tre", " ha", "ls ", "han", "s a", "ich", "our", " of", "n l", "un ", "ren", "a, ", "nel", " te", "sen", "il ", "s c", "ng ", "du ", "iun", " si", "ha ", "ert", "n. ", "ste", "era", "na ", " \u00e8 ", "der", "eme", "men", "lla", "sui", "rla", "a m", "an ", "t d", "s, ", "rei", "r s", "ing", "rde", "ran", "ber", "viz", " zu", ", d", "lle", "ali", "ern", "eiz", "nte", "in ", "and", " l'", "ine", "s t", "s d", "e t", " il", "zra", "se ", "i a", " sc", "ans", " cu", "e d", " fr", "ere", "e o", "th ", "ede", "que", "are", "das", " de", "it ", "hen", "man", " pe", "f t", "r l", "\u00e9ra", "ly ", "oni", "n a", "or ", "ie ", " en", " e ", "o d", "nt ", "iz ", " d'", "ire", " sv", ", l", "ale", "ia ", "ein", "per", "ato", "ten", "i s", "sse", "wei", "ati", "al ", " ve", "die", "e i", "eit", "isc", "i p", "o s", "i d", "d t", "res", "est", " pl", " di", "izz", " du", "as ", "wis", "e s"]</textarea>
                </div>
            </div>
        </div>
        <div class="col-sm-4">
            <div class="card">
                <div class="card-body">
                    <h5 class="card-title">Text</h5>
                    <textarea id="text">il n’y a pas le feu au lac</textarea>
                </div>
            </div>
        </div>
    </div>

    <div class="row mt-3"></div>

    <div class="row">
        <div class="col-sm-12">
            <div class="card">
                <div class="card-body">
                    <h5 class="card-title">Predictions</h5>
                    <p class="card-text">Neural network output</p>
                    <pre id="prediction"></pre>
                    <button type="button" class="btn btn-primary" onclick="guess()">Guess language</button>
                </div>
            </div>
        </div>
    </div>
</div>
              
            
!

CSS

              
                textarea {
    min-width: 100%;
    height: 120px;
}
              
            
!

JS

              
                async function guess() {

    // Load language, vocabulary and text
    var languages = JSON.parse(document.getElementById("languages").value)
    var vocabulary = JSON.parse(document.getElementById("vocabulary").value)
    var text = document.getElementById("text").value

    // Encode text
    var text_encoded = new Array(vocabulary.length).fill(0);
    for (var i = 0; i < text.length; i++) {
        for (var j = 0; j < vocabulary.length; j++) {
            ngram = vocabulary[j]
            if (text.substr(i, ngram.length) === ngram) {
                text_encoded[j] += 1
            }
        }
    }

    // Make predictions!
    const model = await tf.loadLayersModel('https://raw.githubusercontent.com/epfl-exts/powercoders-ai/master/python-code/tfjs-model/model.json');
    const preds_tensor = model.predict(tf.tensor(text_encoded, shape=[1, vocabulary.length]))

    // Print them
    preds_tensor.data().then(function(probs) {
        output = ''
        for (var i = 0; i < languages.length; i++) {
            var spaces = ' '.repeat(15 - languages[i].length)
            output += languages[i] + spaces + (100*probs[i]).toFixed(2) + '%<br />'
        }
        document.getElementById("prediction").innerHTML = output
    });
}
              
            
!
999px

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