dosubot[bot]
commented
5 days ago Hello, @felipeliliti! I'm here to help you with any issues or questions you have. I can assist you with bug fixes, answer questions, and guide you on how to become a contributor. Feel free to ask me anything!
To handle the models' functionalities in the nlp.js, vision.js, and audio.js API endpoints, you need to add the specific training and prediction code. Here are the steps and code snippets for each endpoint:
nlp.js
- Import the model and necessary libraries.
- Load and preprocess the data.
- Train the model.
- Make predictions.
import createNLPModel from '../../models/nlpModel';
import * as tf from '@tensorflow/tfjs-node';
export default async (req, res) => {
const model = createNLPModel();
// Example data for training
const trainingData = tf.tensor2d([[0, 1, 2], [3, 4, 5]], [2, 3]);
const trainingLabels = tf.tensor2d([[1], [0]], [2, 1]);
// Train the model
await model.fit(trainingData, trainingLabels, {
epochs: 10,
batchSize: 2,
});
// Example data for prediction
const inputData = tf.tensor2d([[0, 1, 2]], [1, 3]);
const prediction = model.predict(inputData);
res.status(200).json({ message: 'NLP Model endpoint', prediction: prediction.arraySync() });
};vision.js
- Import the model and necessary libraries.
- Load and preprocess the data.
- Train the model.
- Make predictions.
import createVisionModel from '../../models/visionModel';
import * as tf from '@tensorflow/tfjs-node';
export default async (req, res) => {
const model = createVisionModel();
// Example data for training
const trainingData = tf.tensor4d([[[[0], [1]], [[2], [3]]]], [1, 2, 2, 1]);
const trainingLabels = tf.tensor2d([[1]], [1, 1]);
// Train the model
await model.fit(trainingData, trainingLabels, {
epochs: 10,
batchSize: 1,
});
// Example data for prediction
const inputData = tf.tensor4d([[[[0], [1]], [[2], [3]]]], [1, 2, 2, 1]);
const prediction = model.predict(inputData);
res.status(200).json({ message: 'Vision Model endpoint', prediction: prediction.arraySync() });
};audio.js
- Import the model and necessary libraries.
- Load and preprocess the data.
- Train the model.
- Make predictions.
import createAudioModel from '../../models/audioModel';
import * as tf from '@tensorflow/tfjs-node';
export default async (req, res) => {
const model = createAudioModel();
// Example data for training
const trainingData = tf.tensor2d([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]], [1, 20]);
const trainingLabels = tf.tensor2d([[1]], [1, 1]);
// Train the model
await model.fit(trainingData, trainingLabels, {
epochs: 10,
batchSize: 1,
});
// Example data for prediction
const inputData = tf.tensor2d([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]], [1, 20]);
const prediction = model.predict(inputData);
res.status(200).json({ message: 'Audio Model endpoint', prediction: prediction.arraySync() });
};These snippets provide a basic structure for training and prediction within each endpoint. Adjust the data and parameters as needed for your specific use case.
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felipeliliti
[26/6 14:38] h: aurora-ia ├── components ├── models │ ├── nlpModel.js │ ├── visionModel.js │ └── audioModel.js ├── pages │ ├── api │ │ ├── nlp.js │ │ ├── vision.js │ │ └── audio.js │ ├── index.js │ ├── nlp.js │ ├── vision.js │ └── audio.js ├── public ├── styles ├── Dockerfile ├── package.json └── README.md [26/6 14:38] h: { "name": "aurora-ia", "version": "1.0.0", "scripts": { "dev": "next dev", "build": "next build", "start": "next start" }, "dependencies": { "express": "^4.17.1", "next": "latest", "react": "latest", "react-dom": "latest", "@tensorflow/tfjs": "latest", "@tensorflow/tfjs-node": "latest" } } [26/6 14:38] h: FROM node:14
WORKDIR /app
COPY package*.json ./
RUN npm install
COPY . .
EXPOSE 3000
CMD ["npm", "run", "dev"] [26/6 14:38] h: const tf = require('@tensorflow/tfjs-node');
const createNLPModel = () => { const model = tf.sequential(); model.add(tf.layers.embedding({ inputDim: 10000, outputDim: 64 })); model.add(tf.layers.bidirectional({ layer: tf.layers.lstm({ units: 64 }) })); model.add(tf.layers.dense({ units: 64, activation: 'relu' })); model.add(tf.layers.dense({ units: 1 }));
model.compile({ loss: 'binaryCrossentropy', optimizer: 'adam', metrics: ['accuracy'] }); return model; };
module.exports = createNLPModel; [26/6 14:39] h: const tf = require('@tensorflow/tfjs-node');
const createAudioModel = () => { const model = tf.sequential(); model.add(tf.layers.dense({ inputShape: [20], units: 64, activation: 'relu' })); model.add(tf.layers.dense({ units: 64, activation: 'relu' })); model.add(tf.layers.dense({ units: 1 }));
model.compile({ loss: 'meanSquaredError', optimizer: 'adam' }); return model; };
module.exports = createAudioModel; [26/6 14:39] h: import createNLPModel from '../../models/nlpModel';
export default async (req, res) => { const model = createNLPModel(); // Adicione seu código de treinamento e predição aqui res.status(200).json({ message: 'NLP Model endpoint' }); }; [26/6 14:39] h: import createVisionModel from '../../models/visionModel';
export default async (req, res) => { const model = createVisionModel(); // Adicione seu código de treinamento e predição aqui res.status(200).json({ message: 'Vision Model endpoint' }); }; [26/6 14:42] h: import Link from 'next/link';
const Home = () => (
Welcome to AURORA I.A 3.6.9
);
export default Home; [26/6 14:42] h: const NLP = () => (
NLP Module
Integrate NLP functionalities here.
);
export default NLP; [26/6 14:43] h: const Vision = () => (
Vision Module
Integrate Vision functionalities here.
);
export default Vision; [26/6 14:43] h: const Audio = () => (
Audio Module
Integrate Audio functionalities here.
);
export default Audio;