AI Chatbots With TensorFlow.js: Training a Trivia Expert AI - CodeProject

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Mon Dec 19 2022 02:13:37 GMT+0000 (Coordinated Universal Time)

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<html>
    <head>
        <title>Trivia Know-It-All: Chatbots in the Browser with TensorFlow.js</title>
        <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script>
    </head>
    <body>
        <h1 id="status">Trivia Know-It-All Bot</h1>
        <label>Ask a trivia question:</label>
        <input id="question" type="text" />
        <button id="submit">Submit</button>
        <p id="bot-question"></p>
        <p id="bot-answer"></p>
        <script>
        function setText( text ) {
            document.getElementById( "status" ).innerText = text;
        }

        (async () => {
            // Load TriviaQA data
            let triviaData = await fetch( "web/verified-wikipedia-dev.json" ).then( r => r.json() );
            let data = triviaData.Data;

            // Process all QA to map to answers
            let questions = data.map( qa => qa.Question );

            let bagOfWords = {};
            let allWords = [];
            let wordReference = {};
            questions.forEach( q => {
                let words = q.replace(/[^a-z ]/gi, "").toLowerCase().split( " " ).filter( x => !!x );
                words.forEach( w => {
                    if( !bagOfWords[ w ] ) {
                        bagOfWords[ w ] = 0;
                    }
                    bagOfWords[ w ]++; // Counting occurrence just for word frequency fun
                });
            });

            allWords = Object.keys( bagOfWords );
            allWords.forEach( ( w, i ) => {
                wordReference[ w ] = i + 1;
            });

            // Create a tokenized vector for each question
            const maxSentenceLength = 30;
            let vectors = [];
            questions.forEach( q => {
                let qVec = [];
                // Use a regex to only get spaces and letters and remove any blank elements
                let words = q.replace(/[^a-z ]/gi, "").toLowerCase().split( " " ).filter( x => !!x );
                for( let i = 0; i < maxSentenceLength; i++ ) {
                    if( words[ i ] ) {
                        qVec.push( wordReference[ words[ i ] ] );
                    }
                    else {
                        // Add padding to keep the vectors the same length
                        qVec.push( 0 );
                    }
                }
                vectors.push( qVec );
            });

            let outputs = questions.map( ( q, index ) => {
                let output = [];
                for( let i = 0; i < questions.length; i++ ) {
                    output.push( i === index ? 1 : 0 );
                }
                return output;
            });

            // Define our RNN model with several hidden layers
            const model = tf.sequential();
            // Add 1 to inputDim for the "padding" character
            model.add(tf.layers.embedding( { inputDim: allWords.length + 1, outputDim: 128, inputLength: maxSentenceLength, maskZero: true } ) );
            model.add(tf.layers.simpleRNN( { units: 32 } ) );
            // model.add(tf.layers.bidirectional( { layer: tf.layers.simpleRNN( { units: 32 } ), mergeMode: "concat" } ) );
            model.add(tf.layers.dense( { units: 50 } ) );
            model.add(tf.layers.dense( { units: 25 } ) );
            model.add(tf.layers.dense( {
                units: questions.length,
                activation: "softmax"
            } ) );

            model.compile({
                optimizer: tf.train.adam(),
                loss: "categoricalCrossentropy",
                metrics: [ "accuracy" ]
            });

            const xs = tf.stack( vectors.map( x => tf.tensor1d( x ) ) );
            const ys = tf.stack( outputs.map( x => tf.tensor1d( x ) ) );
            await model.fit( xs, ys, {
                epochs: 20,
                shuffle: true,
                callbacks: {
                    onEpochEnd: ( epoch, logs ) => {
                        setText( `Training... Epoch #${epoch} (${logs.acc})` );
                        console.log( "Epoch #", epoch, logs );
                    }
                }
            } );

            setText( "Trivia Know-It-All Bot is Ready!" );

            document.getElementById( "question" ).addEventListener( "keyup", function( event ) {
                // Number 13 is the "Enter" key on the keyboard
                if( event.keyCode === 13 ) {
                    // Cancel the default action, if needed
                    event.preventDefault();
                    // Trigger the button element with a click
                    document.getElementById( "submit" ).click();
                }
            });

            document.getElementById( "submit" ).addEventListener( "click", async function( event ) {
                let text = document.getElementById( "question" ).value;
                document.getElementById( "question" ).value = "";

                // Run the calculation things
                let qVec = [];
                let words = text.replace(/[^a-z ]/gi, "").toLowerCase().split( " " ).filter( x => !!x );
                for( let i = 0; i < maxSentenceLength; i++ ) {
                    if( words[ i ] ) {
                        qVec.push( wordReference[ words[ i ] ] );
                    }
                    else {
                        // Add padding to keep the vectors the same length
                        qVec.push( 0 );
                    }
                }

                let prediction = await model.predict( tf.stack( [ tf.tensor1d( qVec ) ] ) ).data();
                // Get the index of the highest value in the prediction
                let id = prediction.indexOf( Math.max( ...prediction ) );

                document.getElementById( "bot-question" ).innerText = questions[ id ];
                document.getElementById( "bot-answer" ).innerText = data[ id ].Answer.Value;
            });
        })();
        </script>
    </body>
</html>
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https://www.codeproject.com/Articles/5282688/AI-Chatbots-With-TensorFlow-js-Training-a-Trivia-E