Add model asset detection workflow smoke
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Codex
@@ -123,6 +123,20 @@ cataloged file for that run. The backend resolves the ID to a local path and
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persists the selected asset metadata in Job/AnalysisRun parameters. GeoIntel
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does not download weights or accept arbitrary model paths from the browser.
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Operational runtime validation can be run against Docker/Tower with:
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```bash
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bash scripts/verify_model_asset_detection_workflow.sh http://192.168.10.150:1202
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```
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The smoke seeds the explicit offline demo raster, creates a tile manifest,
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selects a local model asset, checks read-only preflight, runs the existing
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configured-YOLO detection endpoint and verifies persisted AnalysisRun,
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Detection list and Detection GeoJSON outputs. It intentionally does not inject
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detector fixtures or download weights. A zero detection count is acceptable on
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the synthetic demo raster; production usefulness still requires validation on
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real georeferenced orthophotos and reference vectors.
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### Sprint 8C detection visualization and QA status
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Sprint 8C makes persisted detections reviewable:
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@@ -1,3 +1,40 @@
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## Sprint 120 Model asset detection workflow smoke (2026-07-06)
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Changed:
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- Added `scripts/verify_model_asset_detection_workflow.sh` to validate the configured-YOLO runtime path against a live Docker/Tower deployment.
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- The smoke seeds the explicit offline demo workflow, creates a raster tile manifest, selects the active local model asset from `GET /api/v1/detection/model-assets`, verifies read-only YOLO preflight, submits the existing detection run endpoint and checks persisted AnalysisRun, Detection list and Detection GeoJSON outputs.
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- Registered the script in `scripts/run_readiness_check.sh` as a syntax check only, so ordinary readiness runs remain valid on machines without optional AI dependencies or mounted model files.
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- Documented the smoke in `scripts/README.md`, `backend/README.md`, `docs/AI_PIPELINES.md`, `docs/TODO.md` and `CHANGELOG.md`.
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Validation:
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- RED: `python -m pytest backend/tests/test_sprint120_model_asset_detection_workflow_smoke.py -q` failed because `scripts/verify_model_asset_detection_workflow.sh` did not exist yet.
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- `python -m pytest backend/tests/test_sprint120_model_asset_detection_workflow_smoke.py -q` passed: 1 test.
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- `bash -n scripts/verify_model_asset_detection_workflow.sh` passed.
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- Live Tower smoke passed: `bash scripts/verify_model_asset_detection_workflow.sh http://192.168.10.150:1202`.
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- Live smoke selected `model_asset_id=yolov8n-building-segmentation-pt`, generated manifest `/app/storage/tiles/c0b00f1f-80bf-4992-be94-f5e5e6f6bf63/f9160f51-ee78-43b3-9353-d5390576fa1d/e9acd488-c376-45ed-b259-0dd79886f21e/manifest.json`, persisted analysis run `7f9e7ecb-c43d-4ed3-9f98-424bc0317805` and returned `detection_count=0`.
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- `python -m compileall backend/app` passed.
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- `cd backend && python -m pytest -q` passed: 384 tests with the existing Pydantic `model_*` namespace warnings.
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- `cd frontend && npm run typecheck` passed.
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- `cd frontend && npm run build` passed.
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- `cd backend && python -m alembic heads` passed: `202606120900 (head)`.
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- `cd backend && python -m alembic upgrade head --sql` passed.
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- `bash scripts/run_readiness_check.sh` passed: 384 backend tests, frontend typecheck/build, API contract audit, Alembic head and shell syntax checks.
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- Live browser/API smoke passed: `bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202`.
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- Live GIS capability smoke passed: `bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202`.
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- Live raster workflow smoke passed: `bash scripts/verify_demo_raster_workflow.sh http://192.168.10.150:1202`.
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- Live workbench default-state smoke passed: `bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202`.
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- Live workbench backing-state smoke passed: `bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202`.
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- Live demo/export workflow smoke passed: `bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202`.
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- `bash scripts/verify_ai_handoff_interactions.sh http://192.168.10.150:1202` could not run in this local Codex shell because Node cannot import Playwright; the script remains syntax-checked in readiness and the internal browser was used for live visual verification instead.
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- Internal browser validation passed on `http://192.168.10.150:1202`: AI Labs rendered Detection Lab and Segmentation Lab, selecting `yolo-configured` showed the Local model assets selector with `yolov8n-building-segmentation (active)` and `yolov8n`, no-download copy was visible and no console errors were emitted.
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Limitations:
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- The smoke proves the configured-YOLO runtime path, provenance and persistence. It does not prove production model quality because it runs against the synthetic demo raster.
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- Real operational validation still requires uploading a georeferenced Kempen orthophoto/GeoTIFF, running the configured building model on that raster and comparing persisted detections against reference building vectors through QA/QC.
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Next recommended pass:
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- Create the real-data validation path for orthophoto upload, tile generation, configured building-model run and reference-vector QA/QC.
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## Sprint 118 Local model and reference catalog clarity (2026-07-06)
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Changed:
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@@ -82,6 +82,7 @@ This file now starts with the current implementation status. Older preparation/b
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- [x] Add opt-in Docker/Unraid AI build/runtime path for local PyTorch/Ultralytics YOLO operation.
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- [x] Surface configured-YOLO runtime preflight status through the API and Detection Lab UI.
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- [x] Add read-only local model asset catalog and Detection Lab model-file selection.
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- [x] Add live model asset detection workflow smoke for configured-YOLO runtime/provenance validation.
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- [x] Add one-click full GIS workflow action for query, derived dataset, QA/QC and export handoff.
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- [x] Add QA/QC workspace result hierarchy and filter density polish.
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- [x] Add Change Detection panel hierarchy and analysis workspace density polish.
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@@ -90,6 +91,7 @@ This file now starts with the current implementation status. Older preparation/b
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- [x] Add QA/QC and Exports usability layout pass with calmer evidence review and handoff artifact scanning.
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- [x] Add AI Labs Detection/Segmentation hierarchy and result density polish.
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- [x] Add Export/System handoff hierarchy and provider registry density polish.
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- [ ] Validate the configured building model on a real georeferenced Kempen orthophoto/GeoTIFF with persisted reference vectors and QA/QC metrics.
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## Sprint 8 status
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