fix(counts): refresh deployment cached counts in pipeline.save_results

ami/ml/models/pipeline.py

@@ -995,6 +995,10 @@ def save_results(
     event_ids = [img.event_id for img in source_images]  # type: ignore
     update_calculated_fields_for_events(pks=event_ids)
 
+    deployment_ids = {img.deployment_id for img in source_images if img.deployment_id}
+    for deployment in Deployment.objects.filter(pk__in=deployment_ids):
+        deployment.update_calculated_fields(save=True)
+
     total_time = time.time() - start_time
     job_logger.info(f"Saved results from pipeline {pipeline} in {total_time:.2f} seconds")
 

ami/ml/tests.py

@@ -10,7 +10,9 @@
 from ami.base.serializers import reverse_with_params
 from ami.main.models import (
     Classification,
+    Deployment,
     Detection,
+    Event,
     Project,
     SourceImage,
     SourceImageCollection,
@@ -1404,3 +1406,110 @@ def test_update_state_returns_none_when_state_genuinely_missing(self):
         # Do NOT call initialize_job — the total key doesn't exist.
         progress = self.manager.update_state({"img1", "img2"}, "process")
         self.assertIsNone(progress)
+
+
+class TestSaveResultsRefreshesDeploymentCounts(TestCase):
+    """save_results must refresh Deployment cached counts, not just Event counts.
+
+    Reproduces the "Station counts for occurrences and taxa are not always
+    getting updated" report: prior to the fix, save_results refreshed
+    update_calculated_fields_for_events but never the parent Deployment, so
+    deployment.occurrences_count / taxa_count stayed at the pre-job value
+    until something else (a manual deployment.save) ran.
+    """
+
+    def setUp(self):
+        self.project = Project.objects.create(name="Refresh Counts Project")
+        self.deployment = Deployment.objects.create(name="d1", project=self.project)
+        event_time = datetime.datetime(2026, 4, 16, 22, 0, 0)
+        self.event = Event.objects.create(
+            project=self.project,
+            deployment=self.deployment,
+            group_by="2026-04-16",
+            start=event_time,
+            end=event_time,
+        )
+        self.image = SourceImage.objects.create(
+            deployment=self.deployment,
+            project=self.project,
+            event=self.event,
+            timestamp=event_time,
+            path="refresh_counts_test.jpg",
+        )
+        self.collection = SourceImageCollection.objects.create(project=self.project, name="c")
+        self.collection.images.add(self.image)
+
+        self.pipeline = Pipeline.objects.create(name="Refresh Counts Pipeline (Random)")
+        self.algorithms = {
+            key: get_or_create_algorithm_and_category_map(val) for key, val in ALGORITHM_CHOICES.items()
+        }
+        self.pipeline.algorithms.set(
+            [
+                self.algorithms["random-detector"],
+                self.algorithms["random-binary-classifier"],
+                self.algorithms["random-species-classifier"],
+            ]
+        )
+
+        self.deployment.update_calculated_fields(save=True)
+        self.deployment.refresh_from_db()
+        self.assertEqual(self.deployment.occurrences_count, 0)
+        self.assertEqual(self.deployment.taxa_count, 0)
+
+    def _fake_results(self):
+        detector = ALGORITHM_CHOICES["random-detector"]
+        binary_classifier = ALGORITHM_CHOICES["random-binary-classifier"]
+        species_classifier = ALGORITHM_CHOICES["random-species-classifier"]
+        assert binary_classifier.category_map and species_classifier.category_map
+
+        detection = DetectionResponse(
+            source_image_id=self.image.pk,
+            bbox=BoundingBox(x1=0.0, y1=0.0, x2=1.0, y2=1.0),
+            inference_time=0.1,
+            algorithm=AlgorithmReference(name=detector.name, key=detector.key),
+            timestamp=self.image.timestamp,
+            classifications=[
+                ClassificationResponse(
+                    classification=binary_classifier.category_map.labels[0],
+                    labels=binary_classifier.category_map.labels,
+                    scores=[0.95],
+                    algorithm=AlgorithmReference(name=binary_classifier.name, key=binary_classifier.key),
+                    timestamp=self.image.timestamp,
+                    terminal=False,
+                ),
+                ClassificationResponse(
+                    classification=species_classifier.category_map.labels[0],
+                    labels=species_classifier.category_map.labels,
+                    scores=[0.85],
+                    algorithm=AlgorithmReference(name=species_classifier.name, key=species_classifier.key),
+                    timestamp=self.image.timestamp,
+                    terminal=True,
+                ),
+            ],
+        )
+        return PipelineResultsResponse(
+            pipeline=self.pipeline.slug,
+            algorithms={
+                detector.key: detector,
+                binary_classifier.key: binary_classifier,
+                species_classifier.key: species_classifier,
+            },
+            total_time=0.01,
+            source_images=[SourceImageResponse(id=self.image.pk, url=self.image.path)],
+            detections=[detection],
+        )
+
+    def test_deployment_counts_refresh_after_save_results(self):
+        save_results(self._fake_results())
+
+        self.deployment.refresh_from_db()
+        self.assertGreater(
+            self.deployment.occurrences_count,
+            0,
+            "Deployment.occurrences_count should reflect occurrences created by save_results",
+        )
+        self.assertGreater(
+            self.deployment.taxa_count,
+            0,
+            "Deployment.taxa_count should reflect taxa from occurrences created by save_results",
+        )