I'm interested in building a ChatGPT clone using TensorFlow.js, and I'm looking for guidance on how to get started. I want to develop a conversational AI model that can generate responses based on user input, similar to the capabilities of OpenAI's ChatGPT.
Asked by: BinaryNinjaThis code demonstrates the training and prediction process using a sequence-to-sequence model.
HTML (index.html)
<!DOCTYPE html>
<html>
<head>
<title>Chatbot Demo</title>
<link rel="stylesheet" type="text/css" href="styles.css">
</head>
<body>
<div id="chat-container">
<div id="chat-log"></div>
<input type="text" id="user-input" placeholder="Type your message...">
<button id="send-btn">Send</button>
</div>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@3.13.0/dist/tf.min.js"></script>
<script src="model.js"></script>
<script src="script.js"></script>
</body>
</html>
CSS (styles.css)
body {
font-family: Arial, sans-serif;
}
#chat-container {
width: 400px;
margin: 20px auto;
}
#chat-log {
height: 200px;
overflow-y: scroll;
border: 1px solid #ccc;
padding: 10px;
}
#user-input {
width: 300px;
padding: 5px;
margin-right: 5px;
}
#send-btn {
padding: 5px 10px;
}
JavaScript (script.js)
const chatLog = document.getElementById('chat-log');
const userInput = document.getElementById('user-input');
const sendBtn = document.getElementById('send-btn');
// Load the pre-trained model
async function loadModel() {
const model = await tf.loadLayersModel('model/model.json');
return model;
}
// Generate a response from the model
async function generateResponse(model, message) {
const inputTensor = tf.tensor2d([message.map(char => char.charCodeAt(0))]);
const outputTensor = model.predict(inputTensor);
const outputArray = Array.from(outputTensor.dataSync()[0]);
const response = outputArray.map(code => String.fromCharCode(code)).join('');
return response;
}
// Add message to the chat log
function addMessageToChatLog(message, isUser) {
const messageContainer = document.createElement('div');
messageContainer.classList.add('message');
if (isUser) {
messageContainer.classList.add('user');
} else {
messageContainer.classList.add('bot');
}
messageContainer.textContent = message;
chatLog.appendChild(messageContainer);
chatLog.scrollTop = chatLog.scrollHeight;
}
// Handle user input and generate response
async function handleUserInput() {
const userMessage = userInput.value;
userInput.value = '';
addMessageToChatLog(userMessage, true);
const response = await generateResponse(model, userMessage);
addMessageToChatLog(response, false);
}
// Load the model and set up event listeners
let model;
loadModel().then(loadedModel => {
model = loadedModel;
sendBtn.addEventListener('click', handleUserInput);
userInput.addEventListener('keydown', (event) => {
if (event.key === 'Enter') {
handleUserInput();
}
});
});
This code assumes that you have a trained TensorFlow.js model saved in the "model" directory. The model should be saved in TensorFlow.js's "Layers Model" format, which consists of a model.json file and associated weight files.
model.js
// Define the model architecture
function createModel() {
const model = tf.sequential();
// Encoder
model.add(tf.layers.lstm({
units: 128,
inputShape: [null, 1],
returnSequences: false
}));
// Repeat vector to match decoder input shape
model.add(tf.layers.repeatVector({ n: 10 }));
// Decoder
model.add(tf.layers.lstm({
units: 128,
returnSequences: true
}));
model.add(tf.layers.timeDistributed({
layer: tf.layers.dense({ units: 1 })
}));
return model;
}
// Prepare the data for training
function prepareData() {
const data = [
{ input: "Hello", output: "Hi" },
{ input: "What's your name?", output: "I am a chatbot" },
// Add more training examples here
];
const inputSeqs = data.map(entry => entry.input.split("").map(char => char.charCodeAt(0)));
const outputSeqs = data.map(entry => entry.output.split("").map(char => char.charCodeAt(0)));
const maxInputLength = Math.max(...inputSeqs.map(seq => seq.length));
const maxOutputLength = Math.max(...outputSeqs.map(seq => seq.length));
const inputTensor = tf.tensor2d(inputSeqs, [inputSeqs.length, maxInputLength, 1]);
const outputTensor = tf.tensor2d(outputSeqs, [outputSeqs.length, maxOutputLength, 1]);
return { inputTensor, outputTensor };
}
// Train the model
async function trainModel(model, inputTensor, outputTensor) {
const learningRate = 0.01;
const optimizer = tf.train.adam(learningRate);
model.compile({
optimizer,
loss: 'meanSquaredError'
});
const batchSize = 2;
const epochs = 100;
const history = await model.fit(inputTensor, outputTensor, {
batchSize,
epochs,
shuffle: true,
validationSplit: 0.2,
callbacks: tf.node.tensorBoard('./logs')
});
console.log('Training complete.');
}
// Save the model
async function saveModel(model) {
await model.save('model');
console.log('Model saved.');
}
// Create and train the model
async function main() {
const model = createModel();
const { inputTensor, outputTensor } = prepareData();
await trainModel(model, inputTensor, outputTensor);
await saveModel(model);
}
main().catch(console.error);
I define a simple sequence-to-sequence model using LSTM layers. The model takes character sequences as input and tries to predict the corresponding output sequences. The createModel() function creates the model architecture, which consists of an encoder LSTM layer, a repeat vector layer to match the decoder input shape, and a decoder LSTM layer followed by a time-distributed dense layer.
The prepareData() function prepares the training data by converting the input and output sequences into tensors. The sequences are split into individual characters, and their ASCII values are used for training.
The trainModel() function compiles and trains the model using the provided input and output tensors. It uses the Adam optimizer and mean squared error loss.
The saveModel() function saves the trained model in the "model" directory.
The main() function is responsible for creating the model, preparing the data, training the model, and saving it.
Answered by: CopyManWe'll be using a sequence-to-sequence LSTM model. We'll need to set up the file structure as described and then write the code for training and predicting using the model.
Create the HTML structure in index.html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>ChatGPT Clone</title>
<link rel="stylesheet" href="style.css">
</head>
<body>
<div class="chat-container">
<div class="chat-output" id="chat-output"></div>
<input type="text" id="user-input" placeholder="Type your message...">
<button id="send-button">Send</button>
</div>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@3.8.0/dist/tf.esm.js"></script>
<script src="model.js"></script>
<script src="script.js"></script>
</body>
</html>
Define the styles in style.css
body {
font-family: Arial, sans-serif;
background-color: #f0f0f0;
display: flex;
justify-content: center;
align-items: center;
height: 100vh;
margin: 0;
}
.chat-container {
background-color: #fff;
border-radius: 5px;
box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
padding: 20px;
width: 400px;
}
.chat-output {
border: 1px solid #ccc;
border-radius: 5px;
height: 300px;
overflow-y: scroll;
padding: 10px;
}
#user-input {
width: 100%;
margin-top: 10px;
padding: 5px;
}
#send-button {
margin-top: 10px;
padding: 5px 10px;
}
Implement the model in model.js
// Global variables
const maxInputLength = 20; // Maximum length of the input sequence
const embeddingSize = 64;
const lstmUnits = 128;
let model;
async function createModel() {
model = tf.sequential();
model.add(tf.layers.embedding({
inputDim: maxInputLength,
outputDim: embeddingSize,
inputLength: maxInputLength,
}));
model.add(tf.layers.lstm({ units: lstmUnits }));
model.add(tf.layers.repeatVector({ n: maxInputLength }));
model.add(tf.layers.lstm({ units: lstmUnits, returnSequences: true }));
model.add(tf.layers.timeDistributed({ layer: tf.layers.dense({ units: maxInputLength, activation: 'softmax' }) }));
model.compile({
optimizer: 'adam',
loss: 'categoricalCrossentropy',
metrics: ['accuracy'],
});
}
async function trainModel(inputs, outputs, epochs = 100) {
const inputTensor = tf.tensor2d(inputs);
const outputTensor = tf.tensor3d(outputs);
await model.fit(inputTensor, outputTensor, { epochs });
console.log('Training completed.');
}
async function saveModel() {
await model.save('localstorage://model');
console.log('Model saved.');
}
async function loadModel() {
model = await tf.loadLayersModel('localstorage://model');
console.log('Model loaded.');
}
async function predict(inputData) {
const inputTensor = tf.tensor2d(inputData, [1, maxInputLength]);
const prediction = model.predict(inputTensor);
const predictionData = await prediction.array();
return predictionData[0];
}
Write the code for training and predicting in script.js
// Sample training data
const trainingData = [
{ input: 'Hello', output: 'Hi!' },
{ input: 'How are you?', output: 'I am good, thank you!' },
// Add more training data here
];
// Convert training data to numerical representation
function prepareTrainingData(data) {
const inputs = [];
const outputs = [];
data.forEach(({ input, output }) => {
const inputArray = input.split('').map(char => char.charCodeAt(0));
const outputArray = output.split('').map(char => char.charCodeAt(0));
inputs.push([...inputArray, ...Array(maxInputLength - inputArray.length).fill(0)]);
outputs.push(outputArray);
});
return { inputs, outputs };
}
// Training and saving the model
async function trainAndSaveModel() {
const { inputs, outputs } = prepareTrainingData(trainingData);
await createModel();
await trainModel(inputs, outputs);
await saveModel();
}
// Loading the trained model and making predictions
async function initializeModelAndPredict() {
await loadModel();
const userInput = document.getElementById('user-input');
const sendButton = document.getElementById('send-button');
const chatOutput = document.getElementById('chat-output');
sendButton.addEventListener('click', async () => {
const inputText = userInput.value;
if (inputText.trim() !== '') {
const inputData = inputText.split('').map(char => char.charCodeAt(0));
const predictionData = await predict([...inputData, ...Array(maxInputLength - inputData.length).fill(0)]);
const predictionText = String.fromCharCode(...predictionData);
chatOutput.innerHTML += `User: ${inputText}
`;
chatOutput.innerHTML += `Bot: ${predictionText}
`;
userInput.value = '';
chatOutput.scrollTop = chatOutput.scrollHeight;
}
});
}
// Start the training and prediction process
trainAndSaveModel().then(initializeModelAndPredict);
The web page will display a simple chat interface where you can interact with the ChatGPT clone by entering your messages in the input box and clicking the "Send" button.
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