# Scripts

Setup-, import-, demo- en maintenance-scripts voor GeoIntel.

## Runtime verification

Verify the browser-facing Docker/LAN runtime:

```bash
bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202
bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202
```

Verify the explicit demo workflow plus export artifact path:

```bash
bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202
```

The demo/export smoke is intentionally mutating and idempotent: it seeds the
offline fixture demo if needed, verifies the project area GeoJSON, fixture
datasets, vector FeatureCollection content, vector feature summary, persisted
QA/QC metrics, creates metadata/report/vector GeoJSON exports, lists exports
and downloads the JSON/GeoJSON/HTML artifacts through the frontend proxy. The
persisted QA/QC result is compared against `fixtures/golden/expected_qa_metrics.json`
so runtime demo precision, recall, F1, mean IoU and false-positive/negative
counts cannot drift silently.

Verify the deterministic QA/QC golden benchmark:

```bash
bash scripts/verify_golden_qa_benchmark.sh
python scripts/run_golden_qa_benchmark.py --json
```

The benchmark uses only explicit local fixtures under `fixtures/golden`,
executes the existing QA/QC matching logic, verifies the expected precision,
recall, F1, mean IoU and false-positive/false-negative counts, and checks that
`QualityCheck` plus `Metric` rows would be persisted. The main readiness gate
runs this benchmark so QA metric drift fails before a release.

Verify a configured local YOLO model without running inference:

```bash
python scripts/yolo_preflight.py --model-path /absolute/path/to/model.pt --tile-manifest-path /absolute/path/to/manifest.json --check-model-load --json
```

Against the Docker runtime:

```bash
docker compose exec -T backend python scripts/yolo_preflight.py --model-path /absolute/path/to/model.pt --tile-manifest-path /absolute/path/to/manifest.json --check-model-load --json
```

The model-load smoke is opt-in, requires real optional AI dependencies, refuses
`--assume-dependencies`, loads only the supplied local file and does not download
weights or run prediction.

Clean old offline demo export artifacts without touching uploaded source data:

```bash
python scripts/cleanup_demo_artifacts.py
python scripts/cleanup_demo_artifacts.py --keep-latest 3 --apply
```

Against the Docker runtime, run the backend-container entrypoint:

```bash
docker compose exec -T backend python scripts/cleanup_demo_artifacts.py
docker compose exec -T backend python scripts/cleanup_demo_artifacts.py --keep-latest 3 --apply
```

The cleanup script is dry-run by default. It only targets the explicit
`GeoIntel Demo - Building QA` project unless `--project-name` is provided, keeps
the newest exports per matching project, deletes only `exports` rows/files when
`--apply` is set, and refuses to remove files outside the configured
`STORAGE_ROOT`.

## Tower deployment

Push the local branch to Gitea, then rebuild the Unraid/Tower Docker runtime:

```bash
bash scripts/deploy_tower.sh
```

From the Codex Windows workspace, use the PowerShell wrapper:

```powershell
.\scripts\deploy_tower.ps1
```

For the first deployment into an existing non-Git appdata folder, bootstrap the
checkout explicitly:

```bash
DEPLOY_BOOTSTRAP=1 bash scripts/deploy_tower.sh
```

```powershell
.\scripts\deploy_tower.ps1 -Bootstrap
```

Useful overrides:

```bash
REMOTE_HOST=root@192.168.10.150
REMOTE_PATH=/mnt/user/appdata/geointel
REMOTE_REPO=gitea-widefrog:NuklearRabbit/geointel.git
FRONTEND_URL=http://192.168.10.150:1202
```