Neural Network Integration in Frontend

1. Choose a neural network framework

• TensorFlow.js: Allows machine learning models to be defined, trained, and run in the browser.
• ONNX.js: Supports running pre-built ONNX (Open Neural Network Exchange) models in the browser.
• Brain.js: A JavaScript library that simplifies working with neural networks.

2. Prepare Your Neural Network Model

• Train your model: You can train your model using frameworks like TensorFlow, PyTorch, or other machine learning frameworks.
• Export the model: After training, export the model to a format compatible with the chosen frontend framework (eg TensorFlow.js format or ONNX format).

3. Set Up Your Frontend Project

• Create Project: Create a new frontend project using a framework like meta, Vue or plain JavaScript / HTML.

• Build required libraries:

   npm @tensorflow/build tfjs
  

  or

   npm install onnxjs
  

  depending on the library you plan to use.

4. Load and Run the Model in the Frontend

• Load Model:

   // For TensorFlow.js
   * import as tf from @tensorflow/tfjs';
  
   async functionModel() {
     const model = await tf.loadLayersModel( 'path/to/model.json' );
     return model;
   }
  

For ONNX.js

 ```javascript
 // For ONNX.js
 const onnx = required ( 'onnxjs' );

 async functionModel() {
   const session = new onnx.InferenceSession();
   await session.loadModel("path/to/model.onnx");
   return;
 }
 ```

• How the model works:

   // For TensorFlow.js
   function async guess(input) {
     const model = await loadModel();
     const output = model.predict(tf.tensor(input));
     return;
   }
  

   // For ONNX.js
   function async guess(input) {
     const session = await loadModel();
     const tensor = onnx.new Tensor(input, 'float32');
     const outputMap = await session.run([tensor]);
     const outputTensor = outputMap.values (). Next(). price
     returnTensor;
   }
  

5. Integrate with the Frontend

• Create UI Components: Design login forms or other UI components to collect data from users.
• Prediction Triggers: Add event listeners or handlers to send user data to neural network for prediction.
• Result Display: Process the output of the neural network and display it in the UI.

Example: A simple React application with TensorFlow.js

1. Build the project

npx create-react-app neural-network-frontend
cd neural-network-front-end
npm @tensorflow/build tfjs

2. Create Components

App.js

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

const App = () => {
  const [input, setInput] = useState('');
  const [output, setOutput] = useState(null);

  const loadModel = async() => {
    const model = await tf.loadLayersModel('/path/to/model.json');
    return model;
  };

  const handlePredict = async() => {
    const model = await loadModel();
    const tensor = tf.tensor2d([parseFloat(input)], [1, 1]);
    const prediction = model.predict(tensor);
    setOutput(estimate.dataSync()[0]);
  };

  return (
    <div>
      <h1> Front Nervous System Integration</h1>
      <input type = "text" value = {input} onChange = {e => setInput(e.target.value)} />
      <button click={handlePredict}>prediction</button>
      {Log off! == null && <h2> Output: {output} </h2>}
    </div>
  );
};

export the main program;

3. Run the Project

npm start

Examples

1. TensorFlow.js Integration

Setup

1. Create a React Project

npx create-react-app tfjs-frontend
cd tfjs-frontend
npm install @tensorflow/tfjs

1. Create Components

App.js

import React, { useState } from 'react';
import * as tf from '@tensorflow/tfjs';

const App = () => {
  const [input, setInput] = useState('');
  const [output, setOutput] = useState(null);

  const loadModel = async () => {
    const model = await tf.loadLayersModel('/path/to/model.json'); // Update path to your model
    return model;
  };

  const handlePredict = async () => {
    const model = await loadModel();
    const tensor = tf.tensor2d([parseFloat(input)], [1, 1]);
    const prediction = model.predict(tensor);
    const result = prediction.dataSync()[0];
    setOutput(result);
  };

  return (
    <div>
      <h1>TensorFlow.js Integration</h1>
      <input type="text" value={input} onChange={e => setInput(e.target.value)} />
      <button onClick={handlePredict}>Predict</button>
      {output !== null && <h2>Output: {output}</h2>}
    </div>
  );
};

export default App;

Running the Application

npm start

2. ONNX.js Integration

Setup

1. Create a React Project

   npx create-react-app onnxjs-frontend
   cd onnxjs-frontend
   npm install onnxjs
   

2. Create Components

App.js

import React, { useState } from 'react';
import * as onnx from 'onnxjs';

const App = () => {
  const [input, setInput] = useState('');
  const [output, setOutput] = useState(null);

  const loadModel = async () => {
    const session = new onnx.InferenceSession();
    await session.loadModel('/path/to/model.onnx'); // Update path to your model
    return session;
  };

  const handlePredict = async () => {
    const session = await loadModel();
    const tensor = new onnx.Tensor([parseFloat(input)], 'float32', [1, 1]);
    const outputMap = await session.run([tensor]);
    const result = outputMap.values().next().value.data[0];
    setOutput(result);
  };

  return (
    <div>
      <h1>ONNX.js Integration</h1>
      <input type="text" value={input} onChange={e => setInput(e.target.value)} />
      <button onClick={handlePredict}>Predict</button>
      {output !== null && <h2>Output: {output}</h2>}
    </div>
  );
};

export default App;

Running the Application

npm start

3. Brain.js Integration

Setup

1. Create a React Project bash npx create-react-app brainjs-frontend cd brainjs-frontend npm install brain.js

1. Create Components

App.js

import React, { useState } from 'react';
import { NeuralNetwork } from 'brain.js';

const App = () => {
  const [input, setInput] = useState('');
  const [output, setOutput] = useState(null);

  const loadModel = () => {
    const net = new NeuralNetwork();
    net.fromJSON(require('./path/to/model.json')); // Update path to your model
    return net;
  };

  const handlePredict = () => {
    const net = loadModel();
    const result = net.run([parseFloat(input)])[0];
    setOutput(result);
  };

  return (
    <div>
      <h1>Brain.js Integration</h1>
      <input type="text" value={input} onChange={e => setInput(e.target.value)} />
      <button onClick={handlePredict}>Predict</button>
      {output !== null && <h2>Output: {output}</h2>}
    </div>
  );
};

export default App;

Running the Application

npm start

Note: If you have any Query DM me on Linkedin Syed Muhammad Ali Raza

Comments

[Brysen-Rom]

what a next level frontend
any microservice for this