web_app/STARTUP.md

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:

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

Or run in background:

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

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