import CognitiveBot from './CognitiveBot.js';

// Vocabulary size, embedding size, and hidden size
const vocabSize = 5000;
const embeddingSize = 128;
const hiddenSize = 128;

const bot = new CognitiveBot(vocabSize, embeddingSize, hiddenSize);

// Example training data
const inputTexts = ['hello', 'how are you'];
const targetTexts = ['hi', 'I am fine'];

async function trainBot() {
  await bot.train(inputTexts, targetTexts);
  console.log('Training completed.');
}

trainBot();

async function respondToInput(input) {
  const response = await bot.respond(input);
  console.log('Bot response:', response);
}

// Example interaction
respondToInput('hello');

import * as tf from '@tensorflow/tfjs';

class CognitiveBot {
  constructor(vocabSize, embeddingSize, hiddenSize) {
    this.vocabSize = vocabSize;
    this.embeddingSize = embeddingSize;
    this.hiddenSize = hiddenSize;

    this.embedding = tf.layers.embedding({ inputDim: vocabSize, outputDim: embeddingSize });
    this.encoder = tf.layers.lstm({ units: hiddenSize, returnState: true });
    this.decoder = tf.layers.lstm({ units: hiddenSize, returnSequences: true, returnState: true });
    this.output = tf.layers.dense({ units: vocabSize, activation: 'softmax' });

    this.optimizer = tf.train.adam();
    this.lossFunction = tf.losses.sparseCategoricalCrossentropy;

    this.conversationHistory = [];
  }

  async encode(inputText) {
    const embeddedInput = this.embedding.apply(inputText);
    const [, stateH, stateC] = await this.encoder.apply(embeddedInput);
    return [stateH, stateC];
  }

  async decode(stateH, stateC, targetText) {
    const embeddedTarget = this.embedding.apply(targetText);
    const [decoderOutputs, , ] = await this.decoder.apply(embeddedTarget, { initialState: [stateH, stateC] });
    const output = await this.output.apply(decoderOutputs);
    return output;
  }

  async train(inputTexts, targetTexts) {
    for (let i = 0; i < inputTexts.length; i++) {
      const inputText = inputTexts[i];
      const targetText = targetTexts[i];

      const gradientFunction = () => {
        const [stateH, stateC] = this.encode(inputText);
        const output = this.decode(stateH, stateC, targetText);
        const loss = this.lossFunction(targetText, output);
        return [loss, this.getTrainableWeights()];
      };

      const grads = await tf.tidy(() => this.optimizer.minimize(gradientFunction));
      this.optimizer.applyGradients(grads);
    }
  }

  async respond(inputText) {
    const [stateH, stateC] = await this.encode(inputText);
    const startToken = tf.tensor2d([this.vocabSize - 1], [1, 1]); // Start token
    let outputText = [startToken];

    while (true) {
      const [output, newStateH, newStateC] = await this.decoder.apply(tf.expand_dims(outputText[outputText.length - 1], 0), { initialState: [stateH, stateC] });
      const predictedToken = tf.argMax(output, -1);
      outputText.push(predictedToken);

      if (predictedToken.dataSync()[0] === this.vocabSize - 2) { // End token
        break;
      }

      [stateH, stateC] = [newStateH, newStateC];
    }

    this.conversationHistory.push([inputText, outputText]);
    return outputText.slice(1, -1).map((token) => this.vocabToText(token.dataSync()[0]));
  }

  vocabToText(token) {
    // Implement this function to convert tokens to text
  }
}

export default CognitiveBot;

const inputTexts = ['hello', 'how are you'];
const targetTexts = ['hi', 'I am fine'];

async function trainBot() {
  await bot.train(inputTexts, targetTexts);
  console.log('Training completed.');
}

trainBot();