Add AI lab run readiness panels

CHANGELOG.md

@@ -1000,3 +1000,11 @@ Added:
 - Added responsive styling and regression coverage for the Map quick-action grid.
 - Verified locally against the live demo state that the empty map state exposes two dataset actions and opens `demo_predicted_buildings.geojson` as a 2-feature map layer.
 - No API contracts, migrations, product capabilities, live provider fetching or AI/model dependency changes were introduced.
+
+## Sprint 103 AI Lab run readiness (2026-06-24)
+
+- Added compact run-readiness panels to Detection Lab and Segmentation Lab.
+- Detection readiness now shows raster dataset, model availability and the configured-YOLO tile manifest requirement before submitting a run.
+- Segmentation readiness now shows raster dataset, model availability and tile manifest provenance state before submitting a run.
+- Added regression coverage for the AI Lab readiness UI contract and styling.
+- No API contracts, migrations, product capabilities, live provider fetching or AI/model dependency changes were introduced.

backend/tests/test_sprint103_ai_lab_run_readiness.py

@@ -0,0 +1,51 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+
+ROOT = Path(__file__).resolve().parents[2]
+
+
+def test_detection_lab_exposes_run_readiness_contract() -> None:
+    lab = (ROOT / "frontend" / "src" / "components" / "detection" / "DetectionLab.tsx").read_text(
+        encoding="utf-8"
+    )
+
+    assert "selectedDetectionModel = detectionModels.find" in lab
+    assert "detectionRequiresTileManifest = selectedDetectionModelId === 'yolo-configured'" in lab
+    assert "detectionTileManifestPath.trim().length > 0" in lab
+    assert "detectionRunReady" in lab
+    assert 'aria-label="Detection run readiness"' in lab
+    assert "Run readiness" in lab
+    assert "Raster dataset" in lab
+    assert "Model availability" in lab
+    assert "Tile manifest" in lab
+    assert "Ready to submit" in lab
+
+
+def test_segmentation_lab_exposes_run_readiness_contract() -> None:
+    lab = (ROOT / "frontend" / "src" / "components" / "segmentation" / "SegmentationLab.tsx").read_text(
+        encoding="utf-8"
+    )
+
+    assert "segmentationHasDataset" in lab
+    assert "segmentationHasTileManifest = segmentationTileManifestPath.trim().length > 0" in lab
+    assert "segmentationRunReady" in lab
+    assert "selectedSegmentationModelConfigured" in lab
+    assert "selectedSegmentationModelLimitation" in lab
+    assert 'aria-label="Segmentation run readiness"' in lab
+    assert "Run readiness" in lab
+    assert "Raster dataset" in lab
+    assert "Model availability" in lab
+    assert "Tile manifest" in lab
+    assert "Ready to submit" in lab
+
+
+def test_ai_lab_run_readiness_css_contract() -> None:
+    css = (ROOT / "frontend" / "src" / "styles" / "app.css").read_text(encoding="utf-8")
+
+    assert ".lab-readiness-panel" in css
+    assert ".lab-readiness-panel-ready" in css
+    assert ".lab-readiness-grid" in css
+    assert ".lab-readiness-item" in css
+    assert ".lab-readiness-item-ready" in css

docs/CODEX_EXECUTION_LOG.md

@@ -3722,3 +3722,28 @@ Limitations:
 
 Next recommended pass:
 - After live validation, continue with the next V1 usability gap from the workbench flow rather than adding new model/provider scope.
+
+## Sprint 103 AI Lab run readiness (2026-06-24)
+
+Changed:
+- Added compact run-readiness panels to Detection Lab and Segmentation Lab.
+- Detection readiness now checks selected raster dataset, selected model availability and the configured-YOLO tile manifest requirement before a run is submitted.
+- Segmentation readiness now checks selected raster dataset, configured segmentation model state and whether a tile manifest is present for provenance.
+- Added shared AI Lab readiness styling and regression coverage in `backend/tests/test_sprint103_ai_lab_run_readiness.py`.
+- Updated `CHANGELOG.md` and `docs/TODO.md`.
+
+Tested:
+- Red step: `python -m pytest backend\tests\test_sprint103_ai_lab_run_readiness.py -q` failed while the readiness panels and CSS contracts were absent.
+- `python -m pytest backend\tests\test_sprint103_ai_lab_run_readiness.py -q` (`3 passed`)
+- `python -m compileall backend/app`
+- `cd frontend && npm run typecheck`
+- `cd frontend && npm run build`
+
+Open:
+- Full repository validation, commit, deploy and live browser verification are still pending for this pass.
+
+Limitations:
+- Frontend readiness guidance only; no backend API, persistence, migration, provider fetching, AI dependency or model execution behavior changed.
+
+Next recommended pass:
+- After deploy validation, continue with V1 usability work that reduces operator confusion without expanding frozen product scope.

docs/TODO.md

@@ -369,3 +369,4 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Add raster pipeline readiness and guardrail surfaces for metadata, CRS, preview, tile manifest and clip-AOI handoff.
 - [x] Add useful default dataset context so Data, Map and Exports are immediately usable after project/demo load.
 - [x] Make raster tile handoff to Detection Lab auto-select the configured YOLO run form.
+- [x] Add AI Lab run-readiness checks for Detection and Segmentation before job submission.

frontend/src/components/detection/DetectionLab.tsx

@@ -84,6 +84,16 @@ export function DetectionLab({
   onSelectReferenceDataset,
   onRunQa,
 }: DetectionLabProps): JSX.Element {
+  const selectedDetectionModel = detectionModels.find((model) => model.model_id === selectedDetectionModelId) ?? null
+  const detectionRequiresTileManifest = selectedDetectionModelId === 'yolo-configured'
+  const detectionHasDataset = selectedDetectionDatasetId.length > 0
+  const detectionHasModel = selectedDetectionModel !== null
+  const detectionModelReady = Boolean(selectedDetectionModel?.configured)
+  const detectionHasTileManifest =
+    !detectionRequiresTileManifest || detectionTileManifestPath.trim().length > 0
+  const detectionRunReady =
+    Boolean(selectedProjectId) && detectionHasDataset && detectionHasModel && detectionModelReady && detectionHasTileManifest
+
   return (
     <section className="workspace-panel ai-lab-shell detection-lab-shell">
       <div className="panel-title-row">
@@ -143,6 +153,44 @@ export function DetectionLab({
       <div className="lab-block">
         <div className="ai-lab-run-surface" aria-label="Detection run controls">
           <h3>Run detection</h3>
+          <div
+            className={detectionRunReady ? 'lab-readiness-panel lab-readiness-panel-ready' : 'lab-readiness-panel'}
+            aria-label="Detection run readiness"
+          >
+            <div className="ai-lab-section-header">
+              <div>
+                <h3>Run readiness</h3>
+                <p>Checks the selected dataset, model and tile manifest before submitting a detection job.</p>
+              </div>
+              <span className={detectionRunReady ? 'status-badge status-badge-ready' : 'status-badge'}>
+                {detectionRunReady ? 'Ready to submit' : 'Blocked'}
+              </span>
+            </div>
+            <div className="lab-readiness-grid">
+              <div className={detectionHasDataset ? 'lab-readiness-item lab-readiness-item-ready' : 'lab-readiness-item'}>
+                <span>Raster dataset</span>
+                <strong>{detectionHasDataset ? 'Selected' : 'Select a raster dataset'}</strong>
+              </div>
+              <div className={detectionModelReady ? 'lab-readiness-item lab-readiness-item-ready' : 'lab-readiness-item'}>
+                <span>Model availability</span>
+                <strong>
+                  {detectionModelReady
+                    ? `${selectedDetectionModel?.display_name ?? selectedDetectionModelId} is configured`
+                    : selectedDetectionModel?.limitation_message ?? 'Select a configured model'}
+                </strong>
+              </div>
+              <div className={detectionHasTileManifest ? 'lab-readiness-item lab-readiness-item-ready' : 'lab-readiness-item'}>
+                <span>Tile manifest</span>
+                <strong>
+                  {detectionRequiresTileManifest
+                    ? detectionHasTileManifest
+                      ? 'Provided for configured YOLO'
+                      : 'Required for configured YOLO'
+                    : 'Not required for this model'}
+                </strong>
+              </div>
+            </div>
+          </div>
           {rasterDatasets.length === 0 ? (
             <div className="result-state result-state-empty">
               <strong>No raster datasets available for detection.</strong>

frontend/src/components/segmentation/SegmentationLab.tsx

@@ -88,6 +88,11 @@ export function SegmentationLab({
   onSelectReferenceDataset,
   onRunQa,
 }: SegmentationLabProps): JSX.Element {
+  const segmentationHasDataset = selectedSegmentationDatasetId.length > 0
+  const segmentationHasTileManifest = segmentationTileManifestPath.trim().length > 0
+  const segmentationRunReady =
+    Boolean(selectedProjectId) && segmentationHasDataset && selectedSegmentationModelConfigured
+
   return (
     <section className="workspace-panel ai-lab-shell segmentation-lab-shell">
       <div className="panel-title-row">
@@ -147,6 +152,38 @@ export function SegmentationLab({
       <div className="lab-block">
         <div className="ai-lab-run-surface" aria-label="Segmentation run controls">
           <h3>Run segmentation</h3>
+          <div
+            className={segmentationRunReady ? 'lab-readiness-panel lab-readiness-panel-ready' : 'lab-readiness-panel'}
+            aria-label="Segmentation run readiness"
+          >
+            <div className="ai-lab-section-header">
+              <div>
+                <h3>Run readiness</h3>
+                <p>Checks the selected raster and segmenter state before submitting a segmentation job.</p>
+              </div>
+              <span className={segmentationRunReady ? 'status-badge status-badge-ready' : 'status-badge'}>
+                {segmentationRunReady ? 'Ready to submit' : 'Blocked'}
+              </span>
+            </div>
+            <div className="lab-readiness-grid">
+              <div className={segmentationHasDataset ? 'lab-readiness-item lab-readiness-item-ready' : 'lab-readiness-item'}>
+                <span>Raster dataset</span>
+                <strong>{segmentationHasDataset ? 'Selected' : 'Select a raster dataset'}</strong>
+              </div>
+              <div className={selectedSegmentationModelConfigured ? 'lab-readiness-item lab-readiness-item-ready' : 'lab-readiness-item'}>
+                <span>Model availability</span>
+                <strong>
+                  {selectedSegmentationModelConfigured
+                    ? 'Selected model is configured'
+                    : selectedSegmentationModelLimitation ?? 'Select a configured segmentation model'}
+                </strong>
+              </div>
+              <div className={segmentationHasTileManifest ? 'lab-readiness-item lab-readiness-item-ready' : 'lab-readiness-item'}>
+                <span>Tile manifest</span>
+                <strong>{segmentationHasTileManifest ? 'Provided for provenance' : 'Optional for the fixture segmenter'}</strong>
+              </div>
+            </div>
+          </div>
           {rasterDatasets.length === 0 ? (
             <div className="result-state result-state-empty">
               <strong>No raster datasets available for segmentation.</strong>

frontend/src/styles/app.css

@@ -2667,6 +2667,58 @@ button.entity-card {
   min-width: 0;
 }
 
+.lab-readiness-panel {
+  display: grid;
+  gap: 0.6rem;
+  min-width: 0;
+  border-left: 4px solid #c9d8d1;
+  border-radius: 8px;
+  padding: 0.64rem 0.68rem;
+  background: #f8fbf9;
+}
+
+.lab-readiness-panel-ready {
+  border-left-color: #2f7d56;
+  background: #f5fbf6;
+}
+
+.lab-readiness-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(9.5rem, 1fr));
+  gap: 0.5rem;
+  min-width: 0;
+}
+
+.lab-readiness-item {
+  display: grid;
+  gap: 0.18rem;
+  min-width: 0;
+  border: 1px solid #d8e3de;
+  border-radius: 7px;
+  padding: 0.52rem 0.58rem;
+  background: #ffffff;
+}
+
+.lab-readiness-item-ready {
+  border-color: #b8dcc9;
+  background: #fbfffc;
+}
+
+.lab-readiness-item span {
+  color: var(--muted);
+  font-size: 0.72rem;
+  font-weight: 700;
+  text-transform: uppercase;
+}
+
+.lab-readiness-item strong {
+  min-width: 0;
+  color: var(--text);
+  font-size: 0.88rem;
+  line-height: 1.28;
+  overflow-wrap: anywhere;
+}
+
 .ai-lab-shell .model-list {
   grid-template-columns: repeat(auto-fit, minmax(12.5rem, 1fr));
   gap: 0.55rem;