now i want to connect backend to this frontend website, this is the backend server URL - http://192.168.99.101:8000,,,, the websocket URL is - WebSocket endpoint: ws://192.168.99.101:8000/ws/emotion-detection,,,
Add WebSocket integration to their "Enable Camera" button in the dashboard:
// When the "Enable Camera" button is clicked
const startEmotionDetection = async () => {
// 1. Access webcam
const stream = await navigator.mediaDevices.getUserMedia({ video: true });
const videoElement = document.getElementById('webcam-feed'); // The video element
videoElement.srcObject = stream;
// 2. Connect to WebSocket
const ws = new WebSocket('ws://YOUR_IP_ADDRESS:8000/ws/emotion-detection');
ws.onopen = () => {
console.log('Connected to emotion detection server');
};
ws.onmessage = (event) => {
const data = JSON.parse(event.data);
if (data.status === 'success') {
// 3. Display the processed image with emotion detection
const outputCanvas = document.getElementById('emotion-canvas');
const ctx = outputCanvas.getContext('2d');
const image = new Image();
image.onload = () => {
ctx.clearRect(0, 0, outputCanvas.width, outputCanvas.height);
ctx.drawImage(image, 0, 0, outputCanvas.width, outputCanvas.height);
};
image.src = data.processed_image;
// 4. Update emotion data in the UI
updateEmotionCharts(data.results);
}
};
// 5. Start sending frames
const sendInterval = setInterval(() => {
if (ws.readyState === WebSocket.OPEN && videoElement.readyState === 4) {
const canvas = document.createElement('canvas');
canvas.width = videoElement.videoWidth;
canvas.height = videoElement.videoHeight;
const ctx = canvas.getContext('2d');
ctx.drawImage(videoElement, 0, 0, canvas.width, canvas.height);
const dataUrl = canvas.toDataURL('image/jpeg', 0.7);
ws.send(dataUrl);
}
}, 200);
// Store references for cleanup
window.emotionDetection = { ws, sendInterval, stream };
};
// When the "Disable Camera" button is clicked
const stopEmotionDetection = () => {
if (window.emotionDetection) {
clearInterval(window.emotionDetection.sendInterval);
window.emotionDetection.ws.close();
window.emotionDetection.stream.getTracks().forEach(track => track.stop());
window.emotionDetection = null;
}
};
Add required HTML elements to their dashboard:
<div class="emotion-detection-container"> <!-- Hidden video element for webcam access --> <video id="webcam-feed" autoplay playsinline muted style="display: none;"></video> <!-- Canvas for displaying the processed image -->
<canvas id="emotion-canvas" width="640" height="480" class="w-full h-auto rounded-lg shadow-lg"></canvas>
<!-- Controls -->
<div class="mt-4 flex space-x-4">
<button id="enable-camera-btn" class="px-4 py-2 bg-blue-600 text-white rounded-lg hover:bg-blue-700">
Enable Camera for Emotion Detection
</button>
<button id="disable-camera-btn" class="px-4 py-2 bg-red-600 text-white rounded-lg hover:bg-red-700">
Disable Camera
</button>
</div>
<!-- Emotion visualization section -->
<div id="emotion-results" class="mt-6">
<!-- This is where emotion charts would go -->
</div>
</div>
Add event listeners to the buttons:
document.getElementById('enable-camera-btn').addEventListener('click', startEmotionDetection);
document.getElementById('disable-camera-btn').addEventListener('click', stopEmotionDetection);
Implement emotion visualization using Chart.js:
function updateEmotionCharts(results) {
if (!results || results.length === 0) return;
// Extract emotions from first face
const emotions = results[0].emotions;
const dominantEmotion = results[0].dominant_emotion;
// Update dominant emotion display
document.getElementById('dominant-emotion').textContent = dominantEmotion;
// Update chart data
emotionChart.data.datasets[0].data = [
emotions.happy * 100,
emotions.sad * 100,
emotions.angry * 100,
emotions.neutral * 100,
emotions.surprise * 100,
emotions.fear * 100,
emotions.disgust * 100
];
emotionChart.update();
}
command for running the emotion detection project - python emotion_logger.py
and python visualize_emotions.py - to generate visualization charts
I'll integrate the WebSocket backend connection to the Sensei AI frontend for real-time emotion detection.
I've integrated the backend WebSocket connection to the Sensei AI frontend for real-time emotion detection. Here's what I've implemented:
WebSocket Integration:
ws://192.168.99.101:8000/ws/emotion-detectionCamera Integration in Dashboard:
Emotion Visualization:
UI Enhancements:
The integration follows the provided code structure but is adapted to work with React and Next.js. The system now captures video frames from the webcam, sends them to the backend for processing, and displays the results in real-time on the dashboard.
To run the emotion detection backend, you would use:
python emotion_logger.pyAnd to generate visualization charts:
python visualize_emotions.pyThe frontend will automatically connect to these services when the "Enable Camera" button is clicked in the dashboard.
The text in the hero section is not visible. Make it visible on all the devices. If required reduce the padding or remove the parallax scrolling. the screenshot is added below and that is waht visible and the rest of the text is getting hidden. Fix this issue. Also reduce the cursor lag and the cursor tracker. If needed implement a cool new design for the cursor tracker and the cursor as well. Then add a subtle and soothing music for the website, that will suit the website type and its name Sensei. Also change the logo of the website to the provided image that shows the logo Sensei.
I'll address all the issues you've mentioned to improve the Sensei website. Let's fix the hero section visibility, improve the cursor tracker, add background music, and update the logo.
I've made several improvements to address the issues you mentioned:
Fixed Hero Section Visibility:
Improved Cursor Tracker:
Added Background Music:
Updated Logo:
These changes should significantly improve the user experience while maintaining the futuristic and professional feel of the Sensei platform.