# CEFR Auto-Grader Web App - Quick Start Guide

## Application Status
✅ **RUNNING** - Fully functional

## Quick Access
- **Web Interface**: http://localhost:5000
- **LAN Access**: http://192.168.1.11:5000

## Starting the Application

If the app is not running, start it from the project root:

```bash
cd /home/fwl/src/textmining
source .venv/bin/activate
python web_app/app.py
```

Or run in background:
```bash
nohup python web_app/app.py > web_app/flask.log 2>&1 &
```

## Model Information

- **Architecture**: Metric Proto K3
- **Base Model**: KB/bert-base-swedish-cased
- **Device**: CUDA (GPU)
- **Performance**: 84.1% macro F1, 87.3% accuracy

## Testing Examples

| Sentence | Predicted Level | Confidence |
|----------|----------------|------------|
| "Hej." | A1 | 98.9% |
| "Jag heter Anna." | A1 | 98.9% |
| "Jag studerar svenska." | A1 | 99.1% |
| "Den komplexa algoritmen..." | B2 | 99.0% |
| "Det metodologiska ramverket..." | C1 | 99.1% |

## Features

- 📝 Large text input area
- 🔍 Automatic sentence segmentation
- 🎨 Color-coded CEFR levels (A1-C2)
- 📊 Statistics dashboard
- 📈 Level distribution visualization
- 📋 Detailed results table
- ⚡ Real-time processing

## Files

- `app.py` - Flask application
- `model.py` - Model loading & inference
- `templates/index.html` - Web interface
- `static/css/style.css` - Styling
- `static/js/app.js` - Frontend logic

## Troubleshooting

If predictions are all the same level:
1. Check model loaded: `grep "Loading model" web_app/flask.log`
2. Verify model path: `ls runs/metric-proto-k3/metric_proto.pt`
3. Restart from project root: `cd /home/fwl/src/textmining`

## API Endpoint

```bash
curl -X POST http://localhost:5000/assess \
  -H "Content-Type: application/json" \
  -d '{"text": "Jag heter Anna."}'
```