Add governed VMM flood hazard scenarios

backend/app/services/geo_assistant_service.py

@@ -21,6 +21,9 @@ from app.schemas.assistant import (
     AssistantStatus,
     AssistantTemporalSeries,
 )
+from app.schemas.flood_hazard import FloodHazardSelectionRequest
+from app.services.flood_hazard_acquisition_service import FloodHazardAcquisitionService
+from app.services.flood_hazard_analysis_service import FloodHazardAnalysisService
 from app.services.vector_feature_service import VectorFeatureService
 
 
@@ -252,16 +255,22 @@ class GeoAssistantService:
             db.query(Dataset)
             .filter(Dataset.project_id == project_id)
             .filter(Dataset.status == "ready")
-            .filter(Dataset.dataset_type.in_(["vector", "geojson"]))
             .all()
         )
+        vector_datasets = [dataset for dataset in datasets if dataset.dataset_type in {"vector", "geojson"}]
+        flood_hazard_datasets = [
+            dataset
+            for dataset in datasets
+            if dataset.dataset_type == "raster" and dataset.source_name == FloodHazardAcquisitionService.PROVIDER
+            and (area is None or dataset.area_id is None or dataset.area_id == area.id)
+        ]
         warnings: list[str] = []
         context_metrics: list[AssistantContextMetric] = []
         source_dataset_ids: list[UUID] = []
         current_context: list[dict[str, Any]] = []
 
         if bbox is not None:
-            for dataset in self._current_datasets(datasets):
+            for dataset in self._current_datasets(vector_datasets):
                 kwargs: dict[str, Any] = {"dataset": dataset, "bbox": bbox}
                 if area is not None:
                     kwargs["selection_geometry"] = area.geometry
@@ -314,10 +323,60 @@ class GeoAssistantService:
                     }
                 )
 
+            for dataset in sorted(
+                flood_hazard_datasets,
+                key=lambda item: str((item.source_metadata or {}).get("product_key") or item.name),
+            ):
+                try:
+                    result = FloodHazardAnalysisService.analyze(
+                        db,
+                        project_id,
+                        dataset.id,
+                        FloodHazardSelectionRequest(bbox=bbox, area_id=area.id if area is not None else None),
+                        settings=self.settings,
+                    )
+                except AppError as exc:
+                    warnings.append(f"{dataset.name}: {exc.message}")
+                    continue
+                metadata = dataset.source_metadata if isinstance(dataset.source_metadata, dict) else {}
+                scenario_label = str(metadata.get("product_display_name") or result["product_key"])
+                serialized_metrics: list[dict[str, Any]] = []
+                for metric in result["summary"]["metrics"]:
+                    item = AssistantContextMetric(
+                        theme="flood_hazard",
+                        label=f"{metric['metric_label']} - {scenario_label}",
+                        value=float(metric["metric_value"]),
+                        unit=str(metric["metric_unit"]),
+                        source=FloodHazardAcquisitionService.ATTRIBUTION,
+                        dataset_id=dataset.id,
+                        is_estimate=False,
+                    )
+                    context_metrics.append(item)
+                    serialized_metrics.append(item.model_dump(mode="json"))
+                    serialized_metrics[-1]["measurement_quality"] = "exacte_berekening_binnen_gemodelleerd_scenario"
+                source_dataset_ids.append(dataset.id)
+                current_context.append(
+                    {
+                        "dataset_name": dataset.name,
+                        "dataset_id": str(dataset.id),
+                        "theme": "flood_hazard",
+                        "source": FloodHazardAcquisitionService.ATTRIBUTION,
+                        "scenario": {
+                            "label": scenario_label,
+                            "mechanism": result["mechanism"],
+                            "climate_context": result["climate_context"],
+                            "probability_class": result["probability_class"],
+                            "return_period_years": result["return_period_years"],
+                        },
+                        "metrics": serialized_metrics,
+                        "warning": result["limitation_message"],
+                    }
+                )
+
         temporal_series: list[AssistantTemporalSeries] = []
         temporal_context: list[dict[str, Any]] = []
         include_history = self.history_requested(payload.question)
-        for key, observations in self._series(datasets):
+        for key, observations in self._series(vector_datasets):
             first = observations[0]
             last = observations[-1]
             source_metadata = last.source_metadata if isinstance(last.source_metadata, dict) else {}
@@ -364,7 +423,9 @@ class GeoAssistantService:
             "available_temporal_series": temporal_context,
             "rules": {
                 "water_volume_available": False,
-                "water_volume_reason": "Geen gebiedsdekkende waterdiepte of bathymetrie gekoppeld.",
+                "water_volume_reason": "Geen bathymetrie gekoppeld voor de permanente inhoud van waterlichamen.",
+                "flood_hazard_scenarios_available": bool(flood_hazard_datasets),
+                "flood_depth_area_integral_is_concurrent_volume": False,
                 "object_counts_are_supporting_metrics": True,
                 "causal_explanations_available": False,
                 "forecast_available": False,
@@ -402,6 +463,7 @@ class GeoAssistantService:
             "Neem waarden en jaren letterlijk over en bereken zelf geen gemiddelde, tempo, oorzaak of afgeleide trend. "
             "Gebruik platte tekst met korte alinea's en opsommingen, zonder Markdown-symbolen. "
             "Bereken of suggereer nooit watervolume zonder gekoppelde diepte of bathymetrie. "
+            "Noem de VMM-diepte-oppervlakte-integraal nooit een werkelijk, permanent of gelijktijdig watervolume. "
             "Als de gevraagde informatie niet in de context staat, zeg precies welke bron of meting ontbreekt. "
             "CONTEXT_JSON:\n" + json.dumps(context, ensure_ascii=False, separators=(",", ":"))
         )