# ui

Real-time inference pipelines and demo interface. This package bridges the trained models with live webcam input, producing frame-by-frame focus predictions.

## Pipeline modes

FocusGuard supports five runtime modes, all sharing the same feature extraction backbone:

| Mode | Pipeline class | What it does |
|------|---------------|-------------|
| **Geometric** | `FaceMeshPipeline` | Deterministic scoring from head pose and eye state. No ML model needed. Fastest option. |
| **MLP** | `MLPPipeline` | 10 features through the PyTorch MLP (10-64-32-2). Threshold: 0.23 (LOPO Youden's J). |
| **XGBoost** | `XGBoostPipeline` | 10 features through XGBoost (600 trees). Threshold: 0.28 (LOPO Youden's J). |
| **Hybrid** | `HybridPipeline` | 30% MLP + 70% geometric ensemble (w_mlp=0.3, alpha=0.7). LOPO F1: 0.841. |
| **L2CS** | `L2CSPipeline` | Deep gaze estimation via L2CS-Net (ResNet50). Standalone focus scoring from gaze direction. |

Any mode can be combined with L2CS **Boost mode** (toggle in the UI), which fuses the base score (35%) with L2CS gaze score (65%) and applies gaze-based veto for off-screen looks.

## Output smoothing

All pipelines use asymmetric EMA (`_OutputSmoother`) to stabilise predictions:

| Parameter | Value | Effect |
|-----------|-------|--------|
| alpha_up | 0.55 | Fast rise: recognises focus quickly |
| alpha_down | 0.45 | Slower fall: avoids flicker on brief glances |
| grace_frames | 10 (~0.33s at 30fps) | Holds score steady when face is briefly occluded |

## Geometric scoring

`FaceMeshPipeline` computes:

- `s_face`: cosine-decay face orientation score from solvePnP (max_angle=22 deg, roll down-weighted 50%)
- `s_eye`: EAR-based eye openness score multiplied by iris gaze score
- Combined score: `0.7 * s_face + 0.3 * s_eye` (weights from LOPO grid search)
- MAR yawn veto: MAR > 0.55 overrides to unfocused

## L2CS Boost mode

When enabled alongside any base model:

1. L2CS-Net predicts gaze yaw/pitch from the face crop
2. Calibrated gaze is mapped to screen coordinates (if calibration was done)
3. Fusion: `0.35 * base_score + 0.65 * l2cs_score` with fused threshold 0.52
4. Off-screen gaze produces near-zero L2CS score via cosine decay, dragging fused score below threshold (soft veto)

This catches the key edge case where head faces the screen but eyes wander to a second monitor or phone.

## Files

| File | Purpose |
|------|---------|
| `pipeline.py` | All pipeline classes, feature clipping, output smoothing, hybrid config, runtime feature engine |
| `live_demo.py` | OpenCV webcam demo with real-time overlay (bounding box, mesh, gaze lines, score bar) |

## Local demo

```bash
python ui/live_demo.py          # MLP (default)
python ui/live_demo.py --xgb    # XGBoost
```

Controls: `m` cycle mesh overlay, `1-5` switch pipeline mode, `q` quit.

## Web application

The full web app (React frontend + FastAPI backend) runs from `main.py` in the project root:

- **WebSocket** (`/ws/video`): frame-slot architecture, only most recent frame processed, stale frames dropped
- **WebRTC** (`/api/webrtc/offer`): SDP exchange + ICE gathering for lower-latency streaming
- Inference offloaded to `ThreadPoolExecutor` (4 workers, per-pipeline locks)
- SQLite database persists sessions and per-frame events via `EventBuffer` (flushes every 2s)
- Frontend pages: Focus tracking with live overlays, session records, achievements/gamification, model customisation, 9-point gaze calibration, help documentation

Deployment via Docker: `docker-compose up` (port 7860). Vite builds the frontend statically into FastAPI's static directory. L2CS-Net weights are pulled at runtime via `huggingface_hub`.