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| 1 | +import * as tf from '@tensorflow/tfjs'; |
| 2 | +import * as brain from 'brain.js'; |
| 3 | +import { v4 as uuidv4 } from 'uuid'; |
| 4 | + |
| 5 | +class AIService { |
| 6 | + constructor() { |
| 7 | + this.models = {}; |
| 8 | + } |
| 9 | + |
| 10 | + async trainModel(data, options) { |
| 11 | + const modelId = uuidv4(); |
| 12 | + const model = tf.sequential(); |
| 13 | + model.add(tf.layers.dense({ units: 10, inputShape: [10] })); |
| 14 | + model.add(tf.layers.dense({ units: 10 })); |
| 15 | + model.compile({ optimizer: tf.optimizers.adam(), loss: 'meanSquaredError' }); |
| 16 | + await model.fit(data, options); |
| 17 | + this.models[modelId] = model; |
| 18 | + return modelId; |
| 19 | + } |
| 20 | + |
| 21 | + async predict(modelId, input) { |
| 22 | + const model = this.models[modelId]; |
| 23 | + if (!model) { |
| 24 | + throw new Error('Model not found'); |
| 25 | + } |
| 26 | + const output = model.predict(input); |
| 27 | + return output.dataSync(); |
| 28 | + } |
| 29 | + |
| 30 | + async classify(data, options) { |
| 31 | + const net = new brain.NeuralNetwork(); |
| 32 | + net.train(data, options); |
| 33 | + return net.run(data); |
| 34 | + } |
| 35 | + |
| 36 | + async generateText(prompt, options) { |
| 37 | + const model = await this.trainModel(prompt, options); |
| 38 | + const output = await this.predict(model, prompt); |
| 39 | + return output.join(' '); |
| 40 | + } |
| 41 | +} |
| 42 | + |
| 43 | +export default AIService; |
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