#### A. **Frontend: Capture Selfie**

The frontend will handle capturing the selfie from the user's webcam and sending it to the backend for analysis.



## Step 1: **Set Up Webcam Access:**
    
    - Use the `getUserMedia()` API to access the webcam. This will allow users to capture their selfies in real-time.

```javascript
async function startWebcam() {
    const stream = await navigator.mediaDevices.getUserMedia({ video: true });
    const video = document.querySelector('#videoElement');
    video.srcObject = stream;
}

startWebcam();


```


Within the Flutter app: 

```html 
<video id="videoElement" autoplay></video>
<canvas id="canvasElement" style="display:none;"></canvas>


```




## Step 2: Capturing the Selfie program: 


```javascript
function captureSelfie() {
    const video = document.querySelector('#videoElement');
    const canvas = document.querySelector('#canvasElement');
    const context = canvas.getContext('2d');
    const width = video.videoWidth;
    const height = video.videoHeight;

    canvas.width = width;
    canvas.height = height;
    context.drawImage(video, 0, 0, width, height);

    return canvas.toDataURL('image/png');  // Returns the selfie as a base64-encoded image
}

```



## Step 3: Incorporating the ML Models for the Selfie 


1. Incorporate into the HTML first 


```html 
<script src="https://unpkg.com/face-api.js"></script>

```


2. Then write it into your javascript program 


**This is the part to actually research and improve on top of the current existing process**

Function for Loading the Model 
```javascript
async function loadFaceAPI() {
    await faceapi.nets.ssdMobilenetv1.loadFromUri('/models');
    await faceapi.nets.faceLandmark68Net.loadFromUri('/models');
    await faceapi.nets.faceRecognitionNet.loadFromUri('/models');
}

```


Function for Comparing the Model to the Captured Selfie: 
```javascript
async function detectFace(image) {
    const detections = await faceapi.detectSingleFace(image)
        .withFaceLandmarks()
        .withFaceDescriptor();
    return detections;
}

```


Remember to load the "faceapi" into a separate folder. 
You will need to upload the face recognition models (`face_landmark`, `ssd_mobilenet`, and `face_descriptor`) from a server or local file system for `face-api.js` to work properly.