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let chatbotModel;
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);
}