How to use React to implement image recognition app

 const tf = require('@tensorflow/tfjs-node')
const getData = require('./data')

const TRAIN_DIR = '../garbage classification/train'
const OUTPUT_DIR = '../outputDir'
const MOBILENET_URL = 'http://ai-sample.oss-cn-hangzhou.aliyuncs.com/pipcook/models/mobilenet/web_model/model.json'

const main = async () => {
  // Load data const { ds, classes} = await getData(TRAIN_DIR, OUTPUT_DIR)
  // Define the model const mobilenet = await tf.loadLayersModel(MOBILENET_URL)
  mobilenet.summary()
  // console.log(mobilenet.layers.map((l, i) => [l.name, i]))
  const model = tf.sequential()
  for (let i = 0; i <= 86; i += 1) {
    const layer = mobilenet.layers[i]
    layer.trainable = false
    model.add(layer)
  }
  model.add(tf.layers.flatten())
  model.add(tf.layers.dense({
    units: 10,
    activation: 'relu'
  }))
  model.add(tf.layers.dense({
    units: classes.length,
    activation: 'softmax'
  }))
  // Training model model.compile({
    loss: 'sparseCategoricalCrossentropy',
    optimizer: tf.train.adam(),
    metrics: ['acc']
  })
  await model.fitDataset(ds, { epochs: 20 })
  await model.save(`file://${process.cwd()}/${OUTPUT_DIR}`)
}
main()

 const fs = require('fs')
const tf = require('@tensorflow/tfjs-node')

const img2x = (imgPath) => {
  const buffer = fs.readFileSync(imgPath)
  return tf.tidy(() => {
    const imgTs = tf.node.decodeImage(new Uint8Array(buffer))
    const imgTsResized = tf.image.resizeBilinear(imgTs, [224, 224])
    return imgTsResized.toFloat().sub(255/2).div(255/2).reshape([1, 224, 224, 3])
  })
}

const getData = async (trainDir, outputDir) => {
  const classes = fs.readdirSync(trainDir)
  fs.writeFileSync(`${outputDir}/classes.json`, JSON.stringify(classes))

  const data = []
  classes.forEach((dir, dirIndex) => {
    fs.readdirSync(`${trainDir}/${dir}`)
      .filter(n => n.match(/jpg$/))
      .slice(0, 10)
      .forEach(filename => {
        console.log('read', dir, filename)
        const imgPath = `${trainDir}/${dir}/${filename}`
        data.push({ imgPath, dirIndex })
      })
  })

  tf.util.shuffle(data)

  const ds = tf.data.generator(function* () {
    const count = data.length
    const batchSize = 32
    for (let start = 0; start < count; start += batchSize) {
      const end = Math.min(start + batchSize, count)
      yield tf.tidy(() => {
        const inputs = []
        const labels = []
        for (let j = start; j < end; j += 1) {
          const { imgPath, dirIndex } = data[j]
          const x = img2x(imgPath)
          inputs.push(x)
          labels.push(dirIndex)
        }
        const xs = tf.concat(inputs)
        const ys = tf.tensor(labels)
        return { xs, ys }
      })
    }
  })

  return {
    ds,
    classes
  }
}

module.exports = getData

 import React, { PureComponent } from 'react'
import { Button, Progress, Spin, Empty } from 'antd'
import 'antd/dist/antd.css'
import * as tf from '@tensorflow/tfjs'
import { file2img, img2x } from './utils'
import intro from './intro'

const DATA_URL = 'http://127.0.0.1:8080/'
class App extends PureComponent {
  state = {}
  async componentDidMount() {
    this.model = await tf.loadLayersModel(DATA_URL + '/model.json')
    // this.model.summary()
    this.CLASSES = await fetch(DATA_URL + '/classes.json').then(res => res.json())
  }
  predict = async (file) => {
    const img = await file2img(file)

    this.setState({
      imgSrc: img.src,
      isLoading: true
    })
    setTimeout(() => {
      const pred = tf.tidy(() => {
        const x = img2x(img)
        return this.model.predict(x)
      })

      const results = pred.arraySync()[0]
        .map((score, i) => ({score, label: this.CLASSES[i]}))
        .sort((a, b) => b.score - a.score)
      this.setState({
        results,
        isLoading: false
      })
    }, 0)
  }

  renderResult = (item) => {
    const finalScore = Math.round(item.score * 100)
    return (
      <tr key={item.label}>
        <td style={{ width: 80, padding: '5px 0' }}>{item.label}</td>
        <td>
          <Progress percent={finalScore} status={finalScore === 100 ? 'success' : 'normal'} />
        </td>
      </tr>
    )
  }

  render() {
    const { imgSrc, results, isLoading } = this.state
    const finalItem = results && {...results[0], ...intro[results[0].label]}

    return (
      <div style={{padding: 20}}>
        <span
          style={{ color: '#cccccc', textAlign: 'center', fontSize: 12, display: 'block' }}
        >Recognition may not be accurate</span>
        <Button
          type="primary"
          size="large"
          style={{width: '100%'}}
          onClick={() => this.upload.click()}
        >
          Select Image Recognition</Button>
        <input
          type="file"
          onChange={e => this.predict(e.target.files[0])}
          ref={el => {this.upload = el}}
          style={{ display: 'none' }}
        />
        {
          !results && !imgSrc && <Empty style={{ marginTop: 40 }} />
        }
        {imgSrc && <div style={{ marginTop: 20, textAlign: 'center' }}>
          <img src={imgSrc} style={{ maxWidth: '100%' }} />
        </div>}
        {finalItem && <div style={{marginTop: 20}}>Recognition result: </div>}
        {finalItem && <div style={{display: 'flex', alignItems: 'flex-start', marginTop: 20}}>
          <img
            src={finalItem.icon}
            width={120}
          />
          <div>
            <h2 style={{color: finalItem.color}}>
              {finalItem.label}
            </h2>
            <div style={{color: finalItem.color}}>
              {finalItem.intro}
            </div>
          </div>
        </div>}
        {
          isLoading && <Spin size="large" style={{display: 'flex', justifyContent: 'center', alignItems: 'center', marginTop: 40 }} />
        }
        {results && <div style={{ marginTop: 20 }}>
          <table style={{width: '100%'}}>
            <tbody>
              <tr>
                <td>Category</td>
                <td>Matching degree</td>
              </tr>
              {results.map(this.renderResult)}
            </tbody>
          </table>
        </div>}
      </div>
    )
  }
}

export default App

 <!DOCTYPE html>
<html>
  <head>
    <title>Garbage Classification</title>
    <meta name="viewport" content="width=device-width, inital-scale=1">
  </head>
  <body>
    <div id="app"></div>
    <script src="./index.js"></script>
  </body>
</html>

 import React from 'react'
import ReactDOM from 'react-dom'
import App from './App'

ReactDOM.render(<App />, document.querySelector('#app'))

 export default {
  'Recyclables': {
    icon: 'https://lajifenleiapp.com/static/svg/1_3F6BA8.svg',
    color: '#3f6ba8',
    intro: 'Refers to items that are generated in daily life or in activities that provide services for daily life, have lost all or part of their original use value, and can be recycled and processed into production raw materials or can be sorted and reused, including waste paper, plastics, glass, metals, fabrics, etc. '
  },
  'Hazardous waste': {
    icon: 'https://lajifenleiapp.com/static/svg/2v_B43953.svg',
    color: '#b43953',
    intro: 'Refers to substances in domestic waste that cause direct or potential harm to human health or the natural environment, including waste rechargeable batteries, waste button batteries, waste light bulbs, discarded medicines, waste pesticides (containers), waste paints (containers), waste daily chemicals, waste mercury products, waste electrical appliances and electronic products, etc. '
  },
  'Kitchen waste': {
    icon: 'https://lajifenleiapp.com/static/svg/3v_48925B.svg',
    color: '#48925b',
    intro: 'Refers to the organic and perishable waste generated in residents' daily lives, including vegetable leaves, leftovers, fruit peels, egg shells, tea dregs, bones, etc. '
  },
  'Other garbage': {
    icon: 'https://lajifenleiapp.com/static/svg/4_89918B.svg',
    color: '#89918b',
    intro: 'Refers to other household waste that is mixed, contaminated and difficult to classify, except for recyclables, hazardous waste and kitchen waste. '
  }
}

 import * as tf from '@tensorflow/tfjs'

export const file2img = async (f) => {
  return new Promise(reslove => {
    const reader = new FileReader()
    reader.readAsDataURL(f)
    reader.onload = (e) => {
      const img = document.createElement('img')
      img.src = e.target.result
      img.width = 224
      img.height = 224
      img.onload = () => { reslove(img) }
    }
  })
}

export function img2x(imgEl) {
  return tf.tidy(() => {
    return tf.browser.fromPixels(imgEl)
        .toFloat().sub(255/2).div(255/2)
        .reshape([1, 224, 224, 3])
  })
}