Neural Network Integration in Frontend
Tags: frontend, tensorflow, javascript, devto
Published: 2024-06-11 03:01:36


### 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**:

     ```head
     npm @tensorflow/build tfjs
     ```

     or

     ```head
     npm install onnxjs
     ```

     depending on the library you plan to use.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/q2tl6i6b5x7jdy2hpdo7.png)


### 4. **Load and Run the Model in the Frontend**

   - **Load Model**:

     ```javascript
     // 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;
     }
     ```

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ihtqxuq7btjanox1cuii.png)


   - **How ​​the model works**:

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

### 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**

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

#### 2. **Create Components**

**App.js**

```javascript
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**

```head
npm start
```


###  Examples


### 1. TensorFlow.js Integration

#### Setup

1. **Create a React Project**

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

2. **Create Components**

**App.js**
```javascript
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

```bash
npm start
```

### 2. ONNX.js Integration

#### Setup
1. **Create a React Project**
```bash
npx create-react-app onnxjs-frontend
cd onnxjs-frontend
npm install onnxjs
```

2. **Create Components**

**App.js**
```javascript
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

```bash
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
```

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/omt6sl0exh8y1gpz2sap.png)


2. **Create Components**

**App.js**
```javascript
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

```bash
npm start
```

Note: If you have any Query DM me on Linkedin [Syed Muhammad Ali Raza](https://www.linkedin.com/in/syed-muhammad-ali-raza/)