test(ocr): assert ocr_model_accuracy gauge is set per kind on success

Hits /train then /segtrain through the same test, each with a distinct
mocked accuracy, and asserts the labelled gauges reflect the two values.
Locks down the kind-label separation between recognition and segmentation
accuracy (decision #2).

Refs #652 (AC6)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

ocr-service/test_metrics.py

@@ -388,3 +388,37 @@ async def test_ocr_training_runs_total_incremented_with_segmentation_success_lab
     assert fresh_metrics.ocr_training_runs_total.labels(
         kind="segmentation", outcome="success"
     )._value.get() == 1.0
+
+
+@pytest.mark.asyncio
+async def test_ocr_model_accuracy_gauge_set_per_kind_after_successful_training(fresh_metrics):
+    """After /train and /segtrain succeed, ocr_model_accuracy{kind=...} reflects the result."""
+    recognition_accuracy = 0.917
+    segmentation_accuracy = 0.834
+
+    async def fake_recognition_to_thread(func, *args, **kwargs):
+        return _fake_training_result(accuracy=recognition_accuracy)
+
+    async def fake_segmentation_to_thread(func, *args, **kwargs):
+        return _fake_training_result(accuracy=segmentation_accuracy)
+
+    with patch("main.TRAINING_TOKEN", "secret-token"), \
+         patch("main._models_ready", True):
+        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
+            with patch("main.asyncio.to_thread", side_effect=fake_recognition_to_thread):
+                rec_resp = await client.post(
+                    "/train",
+                    files={"file": ("training.zip", _minimal_zip(), "application/zip")},
+                    headers={"X-Training-Token": "secret-token"},
+                )
+            assert rec_resp.status_code == 200
+            with patch("main.asyncio.to_thread", side_effect=fake_segmentation_to_thread):
+                seg_resp = await client.post(
+                    "/segtrain",
+                    files={"file": ("training.zip", _minimal_zip(), "application/zip")},
+                    headers={"X-Training-Token": "secret-token"},
+                )
+            assert seg_resp.status_code == 200
+
+    assert fresh_metrics.ocr_model_accuracy.labels(kind="recognition")._value.get() == pytest.approx(recognition_accuracy)
+    assert fresh_metrics.ocr_model_accuracy.labels(kind="segmentation")._value.get() == pytest.approx(segmentation_accuracy)