Add project README and reason/quote fields to classifier response

- README.md: full project overview with setup, training, API, and RSpamd integration docs - server.py: add reason (human-readable explanation) and quote (suspicious snippet) to response - spamllm.lua: pass reason and quote through to RSpamd symbol description for logs/UI Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-19 22:32:54 +01:00 · 2026-03-19 22:32:54 +01:00 · f05320a8cb
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+# 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