README.md

react-native-machine-learning

allows the use of common machine learning frameworks in native mobile environments, bridged to React Native JS engine

Overview

this module allows react native apps on android to use tensorflow mobile (iOS coming soon). import a compiled model for image classification, regression, etc. and use the methods documented below to interface with it.

Getting started

$ npm install react-native-machine-learning --save

automatic installation

$ react-native link react-native-machine-learning

Manual installation

iOS

iOS coming soon!

Android

1. Open up android/app/src/main/java/[...]/MainActivity.java

• Add import com.reactlibrary.RNMachineLearningPackage; to the imports at the top of the file
• Add new RNMachineLearningPackage() to the list returned by the getPackages() method

2. Append the following lines to android/settings.gradle:

   include ':react-native-machine-learning'
   project(':react-native-machine-learning').projectDir = new File(rootProject.projectDir, 	'../node_modules/react-native-machine-learning/android')
   

3. Insert the following lines inside the dependencies block in android/app/build.gradle:

     compile project(':react-native-machine-learning')
   	```
   
   
   

Usage

note: example requires react-native-image-picker module to be installed.

import React, { Component } from 'react'
import { AppRegistry, Text, Button, ScrollView, Image } from 'react-native'
import RNML from 'react-native-machine-learning'
import ImagePicker from 'react-native-image-picker'

const clamp = (num, min, max) => Math.min(Math.max(num, min), max)

const getImage = (options = {}) =>
  new Promise((resolve, reject) => {
    ImagePicker.launchImageLibrary(options, response => {
      console.log('Response = ', response)

      if (response.didCancel) {
        reject(new Error('cancelled'))
      } else if (response.error) {
        reject(response.error)
      } else {
        // You can also display the image using data:
        // let source = { uri: 'data:image/jpeg;base64,' + response.data };
        resolve(response.uri)
      }
    })
  })
const getFromCamera = (options = {}) =>
  new Promise((resolve, reject) => {
    ImagePicker.launchCamera(options, response => {
      console.log('Response = ', response)

      if (response.didCancel) {
        reject(new Error('cancelled'))
      } else if (response.error) {
        reject(response.error)
      } else {
        // You can also display the image using data:
        // let source = { uri: 'data:image/jpeg;base64,' + response.data };
        resolve(response.uri)
      }
    })
  })
export default class MLtest extends Component {
  constructor (props) {
    super(props)
    this.runImage = this.runImage.bind(this)
    this.runCamera = this.runCamera.bind(this)
    this.state = { results: [] }
  }
  runImage () {
    getImage()
      .then(uri => {
        this.setState({ photo: uri })
        return RNML.recognizeFile(uri)
      })
      .then(results => this.setState({ results }))
      .catch(e => console.warn(e))
  }
  runCamera () {
    getFromCamera()
      .then(uri => {
        this.setState({ photo: uri })
        return RNML.recognizeFile(uri)
      })
      .then(results => this.setState({ results }))
      .catch(e => console.warn(e))
  }
  render () {
    return (
      <ScrollView style={{ flex: 1 }}>
        <Image
          source={{ uri: this.state.photo }}
          style={{ width: '100%', aspectRatio: 1 }}
        />
        {this.state.results.map(result => (
          <Text
            key={result.id + result.confidence}
            style={{
              fontSize: clamp(Math.round(result.confidence * 100), 16, 100),
              fontWeight: 'bold'
            }}>
            {result.title} ({Math.round(result.confidence * 100)}%)
          </Text>
        ))}
        <Button onPress={this.runImage} title={'pick from gallery'} />
        <Button onPress={this.runCamera} title={'take photo'} />
      </ScrollView>
    )
  }
}

AppRegistry.registerComponent('MLtest', () => MLtest)