InSpace: A new video conferencing platform that uses TensorFlow.js for toxicity filters in chat

A guest post by is a communication and virtual learning platform that gives people the ability to interact, collaborate, and educate in familiar physical ways, but in a virtual space. InSpace is built by educators for educators, putting education at the center of the platform.

• InSpace is designed to mirror the fluid, personal, and interactive nature of a real classroom. It allows participants to break free of detection in to separate message toxicity detection from the main application so the processes are independent and non-blocking.

  Below is the pseudocode for the main application, where we initialize the web worker by providing its path as an argument, then set the callback that will be called each time the worker sends a message, and also we declare the callback that will be called when the user submits a message.

  PYTHON Kopieren

  // main application
  // initializing the web worker
  const toxicityFilter = new Worker('toxicity-filter.worker.js'));
  // now we need to set the callback which will process the data from the worker
  worker.onMessage = ({ data: { message, isToxic } }) => {
   if (isToxic) {
     markAsToxic(message);
   } else {
     sendToAll(message);
   }
  }

  When the user sends the message, we pass it to the web worker:

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  onMessageSubmit = message => {
   worker.postMessage(message);
   addToQueue(message);
  }

  After the worker is initialized, it starts listening to the data messages from the main app, and handling them using the declared onmessage callback, which then sends a message back to the main app.

  PYTHON Kopieren

  // toxicity-filter worker
  // here we import dependencies
  importScripts(
   // the main library to run Tenser Flow in the browser
   'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs',
   // trained models for toxicity detection
   'https://cdn.jsdelivr.net/npm/@tensorflow-models/toxicity',
  );
  // threshold point for the decision
  const threshold = 0.9;
  // the main model promise which would be used to classify the message
  const modelPromise = toxicity.load(threshold);
  // registered callback to run when the main app sends the data message
  onmessage = ({ data: message }) => {
   modelPromise.then(model => {
     model.classify([message.body]).then(predictions => {
       // as we want to check the toxicity for all labels,
       // `predictions` will contain the results for all 7 labels
       // so we check, whether there is a match for any of them
       const isToxic = predictions.some(prediction => prediction.results[0].match);
       // here we send the data message back to the main app with the results
       postMessage({ message, isToxic });
     });
   });
  };
  

  As you can see, the toxicity detector is straightforward to integrate the package with an app, and does not require significant changes to existing architecture. The main application only needs a small "connector," and the logic of the filter is written in a separate file.

  To learn more about InSpace visit https://inspace.chat.

  Vollständiger Original-Bericht

  Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org.

  ↗ Original-Artikel auf blog.tensorflow.org lesen