# Deployment (Jetson)

Repo: `https://git.proit.id/andrew/karung-counting-feedmill-semarang`
(`git remote set-url origin <url>` after the ervan → andrew transfer).

## Systemd services

| Unit | Runs | After |
|---|---|---|
| `karung-counter.service` | `/usr/bin/python3 predict.py` (cwd `/home/jetson/karung`, `QT_QPA_PLATFORM=offscreen`) | `network.target` |
| `karung-counter-dashboard.service` | `/usr/bin/python3 counter_dashboard.py` | `network.target` + counter |
| `mediamtx.service` | MediaMTX restream (see `zones.json:external_stream_url`) | — |

Both app units: `Restart=always`, `RestartSec=5`, load `EnvironmentFile=/home/jetson/karung/.env`.

```bash
sudo systemctl enable --now karung-counter karung-counter-dashboard
sudo systemctl restart karung-counter karung-counter-dashboard
systemctl status karung-counter karung-counter-dashboard --no-pager
```

## Deploy flow (`deploy_to_jetson.py`)
Paramiko sync of `templates/{operator,monitoring,base}.html`, `counter_dashboard.py`,
`predict.py`, `config.yaml`, `.env` **plus `models/*.engine`** (v4-best,
yolo11n-sack+box, best, truck-detector, model_karung_truk) →
`192.168.192.96:/home/jetson/karung/` (creates remote `models/` if missing,
skips missing local files), then restarts both services and checks status +
ports (5000/5721). Run from the dev machine. `.pt`/`.onnx` stay local-only
(dev/export).

## TensorRT export
On the Jetson (needs CUDA): `python3 export_model.py models/<name>.pt` exports to
FP16 `.engine` next to the `.pt` (default: karung-dimuat seg model). Production
loads `.engine` only — see `models/modelREADME.md` for which weights each mode uses.

## Runtime data files
- SQLite `jetson_counter.db`: `batches(counting_date, batch_number, camera_name,
  object_label, count, start/end_time, box_*, plate, do_numbers, expected_*,
  net_sack, net_box)`, `daily_summaries(...)`, `delivery_orders(...)` (DO photos).
- `current_batch.json` (crash recovery), `batch_mode.json` (batch flow mode only:
  `auto`|`do_manual`|`manual` — model mode lives in `config.yaml`),
  `do_settings.json` (require_plate/do + ocr_engine),
  `$OUTPUT_DIR/do_photos/YYYY-MM-DD/` (7-day retention),
  `batch_history_folder/batch_<ts>.json` + `hasil_perhitungan.json` (per-batch reports).
- Live frame: `/dev/shm/jetson-counter/live_frame.jpg` (written every 2nd frame,
  consumed by `/api/live-video` MJPEG).
- Helpers: `check_jetson_db.py` (root); retired ops scripts in `archive/`
  (`backup.py`, `dump_db.py`, `migrate_jetson_db.py`, `merge_batches_*.py`,
  `update_batches.py`, `diagnose_truck_jetson.py`).

## DO OCR packages (optional Paddle)
Primary path is system Tesseract (CPU):

```bash
sudo apt install tesseract-ocr tesseract-ocr-ind
pip install pytesseract
```

Backup engine (`ocr_engine: paddle` via office/operator UI toggle — no restart):

```bash
# optional; heavier — see paddleocr docs for Jetson wheels
pip install paddleocr
```

Missing paddle deps → upload returns explicit error; flip engine back to
`tesseract` from either dashboard. Operator page: `http://<jetson>:5000/operator`
(smartphone camera capture for DO photos). Photo dir under `output.dir` with
7-day retention (hourly purge in dashboard process).

## Dashboard (`counter_dashboard.py`)
Pages: `/` + `/monitoring`, `/operator` (manual start/stop; DO panel in
`do_manual`), `/history`, `/analytics`. Key APIs: `/api/live-video`,
`/api/current-batch`, `/api/previous-batch`,
`/api/batch/{start,stop,stop-preview,mode}`, `/api/model-modes`
(mode list is derived from `config.yaml`, so future modes appear automatically),
`/api/do/{upload,photo/<id>,staged,settings,retention}` +
`PUT/DELETE /api/do/<id>`, `/api/summary`, `/api/daily-data`,
`/api/day-detail/<date>`, `/api/recent-batches`, `/api/available-dates`,
`/api/export-daily-csv`, `/api/export-day-csv/<date>` (Excel via openpyxl).

Port split: mode / model_mode / require_plate / require_do POSTs → **office
5721 only** (403 on 5000). `ocr_engine` POST allowed on both ports (synced via
settings GET). Smartphones open `http://<host>:5000/operator` for camera capture.