​

async function trainChatbotModel() {

  // Define your training data

  const trainingData = [

    { input: "Hello", output: "Hello, human!" },

    // Add more training examples here

  ];

​

  // Preprocess the training data

  const sentences = trainingData.map((data) => data.input);

  const embeddings = await embedSentences(sentences);

​

  // Create the chatbot model

  chatbotModel = createModel();

  const inputs = tf.tensor2d(embeddings);

  const outputs = tf.tensor2d(trainingData.map((data) => [data.output]));

  await chatbotModel.fit(inputs, outputs, { epochs: 10 });

}

​

function createModel() {

  const model = tf.sequential();

  model.add(tf.layers.dense({ units: 64, inputShape: [512] }));

  model.add(tf.layers.dense({ units: 1 }));

  model.compile({ optimizer: "adam", loss: "meanSquaredError" });

  return model;

}

​

async function embedSentences(sentences) {

  const embedder = await tf.loadLayersModel(

    "https://tfhub.dev/tensorflow/universal-sentence-encoder/4/model.json"

  );

  const embeddings = await embedder.embed(sentences);

  embedder.dispose();

  return embeddings.arraySync();

}

​

function setup() {

  createCanvas(400, 200);

  trainChatbotModel();

  const inputField = createInput(); // Renamed to inputField

  inputField.position(20, 160);

  inputField.changed(generateResponse); // Changed the event handler function name

}

​

async function generateResponse() {

  const input = this.value();

  const embedding = await embedSentences([input]);

  const inputTensor = tf.tensor2d(embedding);

  const outputTensor = chatbotModel.predict(inputTensor);

  const response = outputTensor.arraySync()[0][0];

  outputTensor.dispose();

  createP(response).position(20, 20);

  this.value('');

}

​

function draw() {

  background(220);

}

​