How to Build a Real-Time Emotion Detector in React Using a Pre-Trained ML Model

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In today’s AI-driven world, detecting emotions in real time can greatly enhance user experiences in applications like chatbots, video conferencing tools, or mental health platforms. In this tutorial, we’ll guide you through building a real-time emotion detector using React and a pre-trained machine learning (ML) model.

This project combines React.js for the frontend, TensorFlow.js for in-browser inference, and a pre-trained facial emotion recognition model to classify emotions from webcam input.

Table of Contents

  1. What Is a Real-Time Emotion Detector?

  2. Tools and Technologies Required

  3. Setting Up the React App

  4. Integrating Webcam Access

  5. Using a Pre-Trained ML Model with TensorFlow.js

  6. Performing Real-Time Emotion Detection

  7. Displaying Results in the UI

  8. Performance Optimization Tips

  9. Conclusion


What Is a Real-Time Emotion Detector?

A real-time emotion detector captures facial expressions via a webcam and classifies them into categories like happy, sad, angry, surprised, etc., using machine learning. It’s useful in user feedback systems, live customer service monitoring, and e-learning applications.


Tools and Technologies Required

To build this application, you will need:

  • React.js: Frontend JavaScript framework

  • TensorFlow.js: ML library to run models in the browser

  • Pre-trained emotion detection model: Such as fer2013

  • Webcam API: To capture video feed

  • HTML5 Canvas: For drawing overlays


Setting Up the React App

First, create your React application:

bash
npx create-react-app emotion-detector
cd emotion-detector
npm start

Install the necessary packages:

bash
npm install @tensorflow/tfjs @tensorflow-models/blazeface
npm install react-webcam

Integrating Webcam Access

Use the react-webcam library to access the webcam:

jsx
import React from "react";
import Webcam from "react-webcam";

const videoConstraints = {
width: 640,
height: 480,
facingMode: "user"
};

const CameraFeed = () => {
return <Webcam audio={false} height={480} width={640} videoConstraints={videoConstraints} />;
};

export default CameraFeed;


Using a Pre-Trained ML Model with TensorFlow.js

Download a pre-trained facial emotion recognition model or convert a Keras model to TensorFlow.js format using:

bash
tensorflowjs_converter --input_format=keras model.h5 model/

Load the model in your React app:

jsx
import * as tf from "@tensorflow/tfjs";

const loadModel = async () => {
const model = await tf.loadLayersModel('/path_to_model/model.json');
return model;
};


Performing Real-Time Emotion Detection

Extract frames from the webcam, preprocess them, and make predictions:

jsx
const detectEmotion = async (webcamRef, model) => {
if (
typeof webcamRef.current !== "undefined" &&
webcamRef.current !== null &&
webcamRef.current.video.readyState === 4
) {
const video = webcamRef.current.video;
const inputTensor = tf.browser.fromPixels(video)
.resizeNearestNeighbor([48, 48])
.mean(2)
.expandDims(2)
.expandDims(0)
.div(255.0);

const prediction = model.predict(inputTensor);
const emotion = EMOTION_LABELS[tf.argMax(prediction, 1).dataSync()[0]];
return emotion;
}
};


 Displaying Results in the UI

Update the UI with the detected emotion:

jsx
const [emotion, setEmotion] = useState("");

useEffect(() => {
const interval = setInterval(async () => {
const currentEmotion = await detectEmotion(webcamRef, model);
setEmotion(currentEmotion);
}, 1000);

return () => clearInterval(interval);
}, []);


Performance Optimization Tips

  • Use requestAnimationFrame instead of setInterval for better performance.

  • Reduce model size with quantization.

  • Use offscreen canvas or Web Workers for image preprocessing.

  • Limit prediction frequency to conserve CPU resources.


Conclusion

You’ve now built a basic real-time emotion detection app using React and TensorFlow.js. By leveraging a pre-trained machine learning model, you can classify user emotions directly in the browser—without needing server-side inference. This is a powerful addition to interactive applications across many industries.

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