@senseyeinc/react-native-senseye-sdk NPM

Senseye SDK

The Senseye SDK provides direct integration into the Senseye API on both iOS and Android for React Native apps. The JavaScript API is simple and cross-platform. Ready to get started? Follow the instructions for either local installation or stand-alone package installation.

What’s in this repo

.
+-- docs/           # See Documentation section of this README
+-- example/        # Contains an example app that demos usage of the SDK
+-- src/
|   +-- api/        # Modules for interfacing with the Senseye API
|   +-- components/ # Modules for serving Senseye frontend components
+-- package.json    # Defines package dependencies, build utilities, and pre-commit hook directives. See contributing guide for more information on these directives

Included Tasks

Calibration
PLR (Pupillary Light Response)
Smooth Pursuit

Requirements

Minimum iOS 12.0 or Android API Level 24. You may use Windows, macOS or Linux as your development operating system.

Installation

Installation of the stand-alone SDK package can be done using npm or yarn.

npm:

npm install @senseyeinc/react-native-senseye-sdk

yarn:

yarn add @senseyeinc/react-native-senseye-sdk

Local

If you prefer to build the SDK locally, clone this repo and run yarn install then yarn prepare from the root directory to get started. Optional: running yarn bootstrap runs prior commands and additionally installs dependencies for running examples in the demo section.

For local development, one of the dependencies is babel-plugin-module-resolver, for which your IDE may require a plugin to detect the alias defined and used throughout the codebase. The alias defined in babel.config.js will resolve paths so that you can write import { insert_SDK_components_here } from @senseyeinc/react-native-senseye-sdk as if it were importing modules from the stand-alone package.

Atom: You can install autocomplete-modules and enable "Babel Plugin Module Resolver" in its settings.

Usage

Execute Tasks and record Session data by utilizing TaskRunner.

import { TaskRunner, Task, Model } from '@senseyeinc/react-native-senseye-sdk';

export default function App() {
  return (
    <TaskRunner onEnd={myCallback(session: Model.Session)}>
      <Task.Calibration />
      <Task.Plr />
    </TaskRunner>
  );
}

Instantiate a SenseyeApiClient.

import { SenseyeApiClient, Constants } from '@senseyeinc/react-native-senseye-sdk';

const apiClient = new SenseyeApiClient(Constants.API_HOST, Constants.API_BASE_PATH, 'my_api_key');

Upload Session data.

const res = await session.uploadAll(apiClient)

let videoUrls: string[] = [];
res[0].forEach((v) => {
  videoUrls.push(v.s3_url);
});
console.log('uploaded videos: ' + videoUrls);
console.log('uploaded json: ' + res[1].s3_url);

Execute prediction on uploaded videos and poll for results.

const jobId = (await apiClient.startPrediction(videoUrls)).id;

setInterval(() => {
  apiClient
    .getPrediction(jobId)
    .then((job) => {
      if (
        job.status === Constants.JobStatus.COMPLETED ||
        job.status === Constants.JobStatus.FAILED
      ) {
        clearInterval(resultPollId);
        if (job.result !== undefined) {
          console.log('result: ' + JSON.stringify(job.result));
        }
      }
    })
    .catch((error) => {
      console.error(error);
    });
}, 3000);

Example App

An example React Native app is provided in the example/ directory. You can run yarn bootstrap to install its dependencies.

Instructions on how to run the app can be found in the example README.

Documentation

Run yarn docs to start a local server on port 9090 and automatically open a browser to the documentation, or simply open docs/index.html in a browser to view the same contents without running the server.