# SpamLLM

A multilingual spam classifier for RSpamd using fine-tuned DistilBERT. Classifies emails in German and English, with automatic language detection that flags unexpected languages as suspicious.

## How it works

```
Incoming Mail → RSpamd → HTTP POST → SpamLLM Service → Score + Reason + Quote → RSpamd
```

RSpamd sends mail metadata (subject, body, sender) to the SpamLLM FastAPI service. The service runs the text through a fine-tuned `distilbert-base-multilingual-cased` model and returns:

- **score** (0-15, RSpamd-compatible)
- **reason** (human-readable explanation, e.g. "High spam confidence (94%)")
- **quote** (the most suspicious snippet from the mail)
- **language** detection with a score bonus for non-DE/EN mails

## Project structure

```
spamllm/
├── train.py                 # Fine-tune DistilBERT on spam/ham data
├── server.py                # FastAPI classification service
├── test_classify.py         # Local model validation (DE/EN/foreign samples)
├── export_rspamd_data.py    # Export Maildir folders to CSV training data
├── requirements.txt         # Python dependencies
└── rspamd/
    ├── local.d/
    │   └── external_services.conf   # RSpamd config
    └── lua/
        └── spamllm.lua              # RSpamd Lua plugin
```

## Setup

```bash
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```

## Training

### Option A: Demo dataset (quick start)

Uses the public SMS Spam Collection (~5,500 English messages):

```bash
python train.py --epochs 3
```

### Option B: Your own mail data (recommended for production)

1. Export your existing mails from Maildir:

```bash
python export_rspamd_data.py \
  --spam-dir /var/vmail/user/.Junk/cur \
  --ham-dir /var/vmail/user/.INBOX/cur \
  --max-per-class 5000
```

2. Train on the exported data:

```bash
python train.py --custom-data --epochs 5
```

You can also place a German-only dataset at `data/train_de.csv` (columns: `text`, `labels`) to supplement the English demo data when training without `--custom-data`.

## Running the service

```bash
uvicorn server:app --host 127.0.0.1 --port 8000
```

### API

**POST /classify**

```json
{
  "subject": "Sie haben gewonnen!",
  "body": "Klicken Sie hier um Ihren Preis abzuholen...",
  "from_addr": "spam@example.com"
}
```

Response:

```json
{
  "is_spam": true,
  "confidence": 0.94,
  "score": 14.1,
  "language": "de",
  "foreign_lang_bonus": 0.0,
  "reason": "High spam confidence (94%)",
  "quote": "...Klicken Sie hier um Ihren Preis abzuholen. Senden Sie uns Ihre Bankdaten..."
}
```

**GET /health** — returns `{"status": "ok", "model_loaded": true}`

## RSpamd integration

1. Copy `rspamd/lua/spamllm.lua` to `/etc/rspamd/plugins.d/`
2. Copy `rspamd/local.d/external_services.conf` to `/etc/rspamd/local.d/`
3. Reload RSpamd: `rspamadm reload`

The plugin registers three symbols:

| Symbol | Weight | Description |
|--------|--------|-------------|
| `SPAMLLM_SPAM` | +5.0 | Spam detected by classifier |
| `SPAMLLM_HAM` | -2.0 | Ham detected by classifier |
| `SPAMLLM_FOREIGN_LANG` | +4.0 | Unexpected language (not DE/EN) |

The Lua plugin only sends mails in the RSpamd grey zone (score 3-12) to the service, so obvious spam/ham is handled by RSpamd's built-in rules without extra latency.

## Language detection

Mails are classified by language using `langdetect`. Expected languages (German, English) are scored normally. All other languages receive a +4 point spam bonus and a lowered spam threshold (0.3 instead of 0.5), since non-DE/EN mails are almost always spam in this environment.

## Performance

- Inference: ~20-50ms per mail on CPU
- Model size: ~500MB (distilbert-base-multilingual-cased)
- Training on demo dataset: ~10-15 min on CPU, ~2 min on GPU