یکپارچه سازی شبکه عصبی در فرانت اند

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

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

 ```javascript
 // 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;
 }
 ```

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

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;

npm start

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

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;

npm start

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

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;

npm start

npx create-react-app brainjs-frontend
cd brainjs-frontend
npm install brain.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;

npm start