familienarchiv/ocr-service/test_metrics.py

"""Tests for Prometheus metrics exposed by the OCR service.

Each test that asserts on a counter/gauge value uses a fresh CollectorRegistry
(see decision #3 on issue #652) to keep the metrics isolated between tests.
"""

import io
import zipfile
from unittest.mock import AsyncMock, patch

import pytest
from httpx import ASGITransport, AsyncClient
from PIL import Image
from prometheus_client import CollectorRegistry

from main import app
from metrics import build_metrics


def _minimal_zip() -> bytes:
    """Return a ZIP containing one fake .xml so endpoint validation passes."""
    buf = io.BytesIO()
    with zipfile.ZipFile(buf, "w") as zf:
        zf.writestr("page_01.xml", "<PcGts/>")
    return buf.getvalue()


def _fake_training_result(accuracy: float = 0.91) -> dict:
    return {"loss": None, "accuracy": accuracy, "cer": round(1 - accuracy, 4), "epochs": 5}


@pytest.fixture
def fresh_metrics(monkeypatch):
    """Replace the module-level `main.metrics` with one bound to a fresh registry."""
    registry = CollectorRegistry()
    test_metrics = build_metrics(registry)
    monkeypatch.setattr("main.metrics", test_metrics)
    return test_metrics


@pytest.mark.asyncio
async def test_metrics_endpoint_returns_200():
    """`GET /metrics` returns 200 with Prometheus exposition content."""
    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            response = await client.get("/metrics")

    assert response.status_code == 200
    assert "text/plain" in response.headers.get("content-type", "")


@pytest.mark.asyncio
async def test_metrics_includes_http_request_metrics_after_ocr_call():
    """After a request to /ocr, `/metrics` exposes auto-instrumented http_* metrics."""
    mock_images = [Image.new("RGB", (100, 100))]
    mock_blocks = [{"pageNumber": 1, "x": 0.0, "y": 0.0, "width": 1.0, "height": 1.0,
                    "polygon": None, "text": "hi", "words": []}]

    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"), \
         patch("main._download_and_convert_pdf", new_callable=AsyncMock, return_value=mock_images), \
         patch("main.preprocess_page", side_effect=lambda img: img), \
         patch("main.surya_engine.extract_blocks", return_value=mock_blocks):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            # Models need to be loaded for /ocr to accept requests; force the flag.
            import main as main_module
            main_module._models_ready = True
            try:
                ocr_response = await client.post("/ocr", json={
                    "pdfUrl": "http://minio/doc.pdf",
                    "scriptType": "TYPEWRITER",
                    "language": "de",
                })
                assert ocr_response.status_code == 200, ocr_response.text

                metrics_response = await client.get("/metrics")
            finally:
                main_module._models_ready = False

    body = metrics_response.text
    assert "http_requests_total" in body
    assert "http_request_duration_seconds" in body


def test_build_metrics_registers_all_custom_metrics_on_given_registry():
    """`build_metrics` returns an OcrMetrics bound to the supplied registry."""
    registry = CollectorRegistry()
    metrics = build_metrics(registry)

    metric_names = {m.name for m in registry.collect()}
    expected = {
        "ocr_jobs",
        "ocr_pages",
        "ocr_skipped_pages",
        "ocr_words",
        "ocr_illegible_words",
        "ocr_processing_seconds",
        "ocr_training_runs",
        "ocr_model_accuracy",
        "ocr_models_ready",
    }
    assert expected <= metric_names, f"missing: {expected - metric_names}"

    # A second registry yields a separate container — no shared state.
    other_metrics = build_metrics(CollectorRegistry())
    assert metrics is not other_metrics


async def _drive_ocr(client: AsyncClient, *, script_type: str) -> None:
    """Helper — fires /ocr with a single mocked page and asserts a 200."""
    response = await client.post("/ocr", json={
        "pdfUrl": "http://minio/doc.pdf",
        "scriptType": script_type,
        "language": "de",
    })
    assert response.status_code == 200, response.text


@pytest.mark.asyncio
async def test_ocr_jobs_total_incremented_with_kraken_engine_label_for_kurrent(fresh_metrics):
    """A /ocr call with HANDWRITING_KURRENT increments engine=kraken."""
    mock_images = [Image.new("RGB", (100, 100))]
    mock_blocks = [{"pageNumber": 1, "x": 0.0, "y": 0.0, "width": 1.0, "height": 1.0,
                    "polygon": None, "text": "hi", "words": []}]

    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"), \
         patch("main.correct_text", side_effect=lambda t: t), \
         patch("main._download_and_convert_pdf", new_callable=AsyncMock, return_value=mock_images), \
         patch("main.preprocess_page", side_effect=lambda img: img), \
         patch("main.kraken_engine.is_available", return_value=True), \
         patch("main.kraken_engine.extract_blocks", return_value=mock_blocks):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            import main as main_module
            main_module._models_ready = True
            try:
                await _drive_ocr(client, script_type="HANDWRITING_KURRENT")
            finally:
                main_module._models_ready = False

    value = fresh_metrics.ocr_jobs_total.labels(
        engine="kraken", script_type="HANDWRITING_KURRENT"
    )._value.get()
    assert value == 1.0


@pytest.mark.asyncio
async def test_ocr_jobs_total_incremented_with_surya_engine_label_for_typewriter(fresh_metrics):
    """A /ocr call with TYPEWRITER increments engine=surya."""
    mock_images = [Image.new("RGB", (100, 100))]
    mock_blocks = [{"pageNumber": 1, "x": 0.0, "y": 0.0, "width": 1.0, "height": 1.0,
                    "polygon": None, "text": "hi", "words": []}]

    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"), \
         patch("main._download_and_convert_pdf", new_callable=AsyncMock, return_value=mock_images), \
         patch("main.preprocess_page", side_effect=lambda img: img), \
         patch("main.surya_engine.extract_blocks", return_value=mock_blocks):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            import main as main_module
            main_module._models_ready = True
            try:
                await _drive_ocr(client, script_type="TYPEWRITER")
            finally:
                main_module._models_ready = False

    value = fresh_metrics.ocr_jobs_total.labels(
        engine="surya", script_type="TYPEWRITER"
    )._value.get()
    assert value == 1.0


@pytest.mark.asyncio
async def test_ocr_pages_total_incremented_once_per_page_in_stream(fresh_metrics):
    """The /ocr/stream generator increments ocr_pages_total per successful page."""
    mock_images = [Image.new("RGB", (100, 100)) for _ in range(3)]
    mock_blocks = [{"pageNumber": 1, "x": 0.0, "y": 0.0, "width": 1.0, "height": 1.0,
                    "polygon": None, "text": "hi", "words": []}]

    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"), \
         patch("main._download_and_convert_pdf", new_callable=AsyncMock, return_value=mock_images), \
         patch("main.preprocess_page", side_effect=lambda img: img), \
         patch("main.surya_engine.extract_page_blocks", return_value=mock_blocks):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            import main as main_module
            main_module._models_ready = True
            try:
                async with client.stream("POST", "/ocr/stream", json={
                    "pdfUrl": "http://minio/doc.pdf",
                    "scriptType": "TYPEWRITER",
                    "language": "de",
                }) as response:
                    assert response.status_code == 200
                    # Drain the stream so all per-page increments fire.
                    async for _ in response.aiter_lines():
                        pass
            finally:
                main_module._models_ready = False

    value = fresh_metrics.ocr_pages_total.labels(engine="surya")._value.get()
    assert value == 3.0


@pytest.mark.asyncio
async def test_ocr_skipped_pages_total_incremented_when_engine_raises_for_a_page(fresh_metrics):
    """When the engine raises on a page, ocr_skipped_pages_total bumps and the stream finishes."""
    mock_images = [Image.new("RGB", (100, 100)) for _ in range(2)]
    good_blocks = [{"pageNumber": 1, "x": 0.0, "y": 0.0, "width": 1.0, "height": 1.0,
                    "polygon": None, "text": "ok", "words": []}]

    call_count = {"n": 0}

    def extract_side_effect(*args, **kwargs):
        call_count["n"] += 1
        if call_count["n"] == 1:
            raise RuntimeError("synthetic engine failure")
        return good_blocks

    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"), \
         patch("main._download_and_convert_pdf", new_callable=AsyncMock, return_value=mock_images), \
         patch("main.preprocess_page", side_effect=lambda img: img), \
         patch("main.surya_engine.extract_page_blocks", side_effect=extract_side_effect):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            import main as main_module
            main_module._models_ready = True
            try:
                async with client.stream("POST", "/ocr/stream", json={
                    "pdfUrl": "http://minio/doc.pdf",
                    "scriptType": "TYPEWRITER",
                    "language": "de",
                }) as response:
                    assert response.status_code == 200
                    saw_error = False
                    async for line in response.aiter_lines():
                        if line and '"type": "error"' in line:
                            saw_error = True
                    assert saw_error
            finally:
                main_module._models_ready = False

    assert fresh_metrics.ocr_skipped_pages_total._value.get() == 1.0
    # The second page still succeeds.
    assert fresh_metrics.ocr_pages_total.labels(engine="surya")._value.get() == 1.0


@pytest.mark.asyncio
async def test_ocr_words_and_illegible_words_total_sum_across_blocks(fresh_metrics):
    """Counters reflect totals summed over every block in the request.

    Threshold defaults to THRESHOLD_DEFAULT (0.3) for non-Kurrent scripts. Two
    blocks: 3 words above + 2 words below threshold across blocks.
    """
    mock_images = [Image.new("RGB", (100, 100))]
    mock_blocks = [
        {"pageNumber": 1, "x": 0.0, "y": 0.0, "width": 1.0, "height": 1.0,
         "polygon": None, "text": "ignored",
         "words": [{"text": "Lieber", "confidence": 0.9},
                   {"text": "Freund", "confidence": 0.1}]},
        {"pageNumber": 1, "x": 0.0, "y": 0.0, "width": 1.0, "height": 1.0,
         "polygon": None, "text": "ignored",
         "words": [{"text": "Gruss", "confidence": 0.8},
                   {"text": "verschmiert", "confidence": 0.05},
                   {"text": "Karl", "confidence": 0.95}]},
    ]

    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"), \
         patch("main._download_and_convert_pdf", new_callable=AsyncMock, return_value=mock_images), \
         patch("main.preprocess_page", side_effect=lambda img: img), \
         patch("main.surya_engine.extract_blocks", return_value=mock_blocks):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            import main as main_module
            main_module._models_ready = True
            try:
                await _drive_ocr(client, script_type="TYPEWRITER")
            finally:
                main_module._models_ready = False

    assert fresh_metrics.ocr_words_total._value.get() == 5.0
    assert fresh_metrics.ocr_illegible_words_total._value.get() == 2.0


def _histogram_count_sum(histogram, **labels) -> tuple[float, float]:
    """Read the per-label-set _count and _sum from a prometheus_client Histogram."""
    child = histogram.labels(**labels)
    return child._sum.get(), sum(b.get() for b in child._buckets)


@pytest.mark.asyncio
async def test_ocr_processing_seconds_histogram_observed_per_page_in_stream(fresh_metrics):
    """The streaming generator observes ocr_processing_seconds once per page."""
    mock_images = [Image.new("RGB", (100, 100)) for _ in range(2)]
    mock_blocks = [{"pageNumber": 1, "x": 0.0, "y": 0.0, "width": 1.0, "height": 1.0,
                    "polygon": None, "text": "ok", "words": []}]

    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"), \
         patch("main._download_and_convert_pdf", new_callable=AsyncMock, return_value=mock_images), \
         patch("main.preprocess_page", side_effect=lambda img: img), \
         patch("main.surya_engine.extract_page_blocks", return_value=mock_blocks):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            import main as main_module
            main_module._models_ready = True
            try:
                async with client.stream("POST", "/ocr/stream", json={
                    "pdfUrl": "http://minio/doc.pdf",
                    "scriptType": "TYPEWRITER",
                    "language": "de",
                }) as response:
                    assert response.status_code == 200
                    async for _ in response.aiter_lines():
                        pass
            finally:
                main_module._models_ready = False

    sum_seconds, count = _histogram_count_sum(
        fresh_metrics.ocr_processing_seconds, engine="surya"
    )
    assert count == 2.0
    assert sum_seconds >= 0.0


@pytest.mark.asyncio
async def test_ocr_training_runs_total_incremented_with_recognition_success_label(fresh_metrics):
    """/train success increments ocr_training_runs_total{kind=recognition, outcome=success}."""
    async def fake_to_thread(func, *args, **kwargs):
        return _fake_training_result()

    with patch("main.TRAINING_TOKEN", "secret-token"), \
         patch("main._models_ready", True), \
         patch("main.asyncio.to_thread", side_effect=fake_to_thread):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            response = await client.post(
                "/train",
                files={"file": ("training.zip", _minimal_zip(), "application/zip")},
                headers={"X-Training-Token": "secret-token"},
            )

    assert response.status_code == 200
    assert fresh_metrics.ocr_training_runs_total.labels(
        kind="recognition", outcome="success"
    )._value.get() == 1.0


@pytest.mark.asyncio
async def test_ocr_training_runs_total_incremented_with_recognition_error_label(fresh_metrics):
    """When /train's inner runner raises, the error counter bumps and the exception propagates."""
    async def fake_to_thread(func, *args, **kwargs):
        raise RuntimeError("ketos train failed (exit 1): synthetic")

    with patch("main.TRAINING_TOKEN", "secret-token"), \
         patch("main._models_ready", True), \
         patch("main.asyncio.to_thread", side_effect=fake_to_thread):
        transport = ASGITransport(app=app, raise_app_exceptions=False)
        async with AsyncClient(transport=transport, base_url="http://test") as client:
            response = await client.post(
                "/train",
                files={"file": ("training.zip", _minimal_zip(), "application/zip")},
                headers={"X-Training-Token": "secret-token"},
            )

    assert response.status_code == 500
    assert fresh_metrics.ocr_training_runs_total.labels(
        kind="recognition", outcome="error"
    )._value.get() == 1.0


@pytest.mark.asyncio
async def test_ocr_training_runs_total_incremented_with_segmentation_success_label(fresh_metrics):
    """/segtrain success increments ocr_training_runs_total{kind=segmentation, outcome=success}."""
    async def fake_to_thread(func, *args, **kwargs):
        return _fake_training_result(accuracy=0.83)

    with patch("main.TRAINING_TOKEN", "secret-token"), \
         patch("main._models_ready", True), \
         patch("main.asyncio.to_thread", side_effect=fake_to_thread):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            response = await client.post(
                "/segtrain",
                files={"file": ("training.zip", _minimal_zip(), "application/zip")},
                headers={"X-Training-Token": "secret-token"},
            )

    assert response.status_code == 200
    assert fresh_metrics.ocr_training_runs_total.labels(
        kind="segmentation", outcome="success"
    )._value.get() == 1.0


@pytest.mark.asyncio
async def test_ocr_training_runs_total_incremented_with_recognition_success_label_for_train_sender(fresh_metrics):
    """/train-sender success increments ocr_training_runs_total{kind=recognition, outcome=success}."""
    async def fake_to_thread(func, *args, **kwargs):
        return _fake_training_result()

    with patch("main.TRAINING_TOKEN", "secret-token"), \
         patch("main._models_ready", True), \
         patch("main.asyncio.to_thread", side_effect=fake_to_thread):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            response = await client.post(
                "/train-sender",
                files={"file": ("training.zip", _minimal_zip(), "application/zip")},
                data={"output_model_path": "/app/models/sender_test.mlmodel"},
                headers={"X-Training-Token": "secret-token"},
            )

    assert response.status_code == 200, response.text
    assert fresh_metrics.ocr_training_runs_total.labels(
        kind="recognition", outcome="success"
    )._value.get() == 1.0


@pytest.mark.asyncio
async def test_ocr_model_accuracy_gauge_stays_default_when_training_returns_no_accuracy(fresh_metrics):
    """When the runner returns accuracy=None, ocr_model_accuracy must remain at its default 0."""
    async def fake_to_thread(func, *args, **kwargs):
        return {"loss": None, "accuracy": None, "cer": None, "epochs": 5}

    with patch("main.TRAINING_TOKEN", "secret-token"), \
         patch("main._models_ready", True), \
         patch("main.asyncio.to_thread", side_effect=fake_to_thread):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            response = await client.post(
                "/train",
                files={"file": ("training.zip", _minimal_zip(), "application/zip")},
                headers={"X-Training-Token": "secret-token"},
            )

    assert response.status_code == 200
    # Gauge was never .set() — accessing the label child still creates it with default 0.0.
    assert fresh_metrics.ocr_model_accuracy.labels(
        kind="recognition"
    )._value.get() == 0.0


@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)


def test_ocr_models_ready_gauge_defaults_to_zero():
    """A freshly-built OcrMetrics has ocr_models_ready=0 before lifespan runs."""
    metrics = build_metrics(CollectorRegistry())
    assert metrics.ocr_models_ready._value.get() == 0.0


@pytest.mark.asyncio
async def test_ocr_models_ready_gauge_is_one_after_lifespan_startup(fresh_metrics):
    """The lifespan flips ocr_models_ready to 1 once load_models / load_spell_checker return.

    ASGITransport does not run lifespan by default, so the lifespan context
    manager is driven directly to exercise the startup code path.
    """
    assert fresh_metrics.ocr_models_ready._value.get() == 0.0
    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"):
        async with app.router.lifespan_context(app):
            assert fresh_metrics.ocr_models_ready._value.get() == 1.0


@pytest.mark.asyncio
async def test_ocr_processing_seconds_histogram_observed_per_page_in_guided_stream(fresh_metrics):
    """The guided streaming generator observes ocr_processing_seconds once per page."""
    mock_images = [Image.new("RGB", (100, 100)) for _ in range(2)]
    regions = [
        {"pageNumber": 1, "x": 0.0, "y": 0.0, "width": 0.5, "height": 0.5, "annotationId": "a1"},
        {"pageNumber": 2, "x": 0.0, "y": 0.0, "width": 1.0, "height": 1.0, "annotationId": "a2"},
    ]

    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"), \
         patch("main._download_and_convert_pdf", new_callable=AsyncMock, return_value=mock_images), \
         patch("main.preprocess_page", side_effect=lambda img: img), \
         patch("main.surya_engine.extract_region_text", return_value="text"):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            import main as main_module
            main_module._models_ready = True
            try:
                async with client.stream("POST", "/ocr/stream", json={
                    "pdfUrl": "http://minio/doc.pdf",
                    "scriptType": "TYPEWRITER",
                    "language": "de",
                    "regions": regions,
                }) as response:
                    assert response.status_code == 200
                    async for _ in response.aiter_lines():
                        pass
            finally:
                main_module._models_ready = False

    sum_seconds, count = _histogram_count_sum(
        fresh_metrics.ocr_processing_seconds, engine="surya"
    )
    assert count == 2.0
    assert sum_seconds >= 0.0


@pytest.mark.asyncio
async def test_ocr_processing_seconds_histogram_excludes_spell_check_time_in_guided_stream(fresh_metrics):
    """The guided observation must time engine work only, not the spell-check pass."""
    mock_images = [Image.new("RGB", (100, 100))]
    regions = [
        {"pageNumber": 1, "x": 0.0, "y": 0.0, "width": 0.5, "height": 0.5, "annotationId": "a1"},
        {"pageNumber": 1, "x": 0.5, "y": 0.0, "width": 0.5, "height": 0.5, "annotationId": "a2"},
    ]

    def slow_correct(text):
        import time as _time
        _time.sleep(0.05)
        return text

    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"), \
         patch("main._download_and_convert_pdf", new_callable=AsyncMock, return_value=mock_images), \
         patch("main.preprocess_page", side_effect=lambda img: img), \
         patch("main.kraken_engine.is_available", return_value=True), \
         patch("main.kraken_engine.extract_region_text", return_value="text"), \
         patch("main.correct_text", side_effect=slow_correct):
        async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
            import main as main_module
            main_module._models_ready = True
            try:
                async with client.stream("POST", "/ocr/stream", json={
                    "pdfUrl": "http://minio/doc.pdf",
                    "scriptType": "HANDWRITING_KURRENT",
                    "language": "de",
                    "regions": regions,
                }) as response:
                    assert response.status_code == 200
                    async for _ in response.aiter_lines():
                        pass
            finally:
                main_module._models_ready = False

    sum_seconds, _ = _histogram_count_sum(
        fresh_metrics.ocr_processing_seconds, engine="kraken"
    )
    # Spell-check sleeps 0.05s per region × 2 regions = 0.1s; engine work is instantaneous.
    # If timing included spell-check, sum_seconds would be >= 0.1s. Allow 30ms slack
    # for scheduler overhead.
    assert sum_seconds < 0.05, f"timing must exclude spell-check; got sum={sum_seconds}"


@pytest.mark.asyncio
async def test_ocr_jobs_total_not_incremented_when_pdf_download_fails_in_stream(fresh_metrics):
    """If `_download_and_convert_pdf` raises, ocr_jobs_total is NOT incremented.

    Mirrors the /ocr endpoint's semantics: the counter only records jobs that
    actually started OCR work, not failed downloads.
    """
    async def fail_download(url):
        raise RuntimeError("synthetic download failure")

    with patch("main.kraken_engine.load_models"), \
         patch("main.load_spell_checker"), \
         patch("main._download_and_convert_pdf", new=fail_download):
        transport = ASGITransport(app=app, raise_app_exceptions=False)
        async with AsyncClient(transport=transport, base_url="http://test") as client:
            import main as main_module
            main_module._models_ready = True
            try:
                response = await client.post("/ocr/stream", json={
                    "pdfUrl": "http://minio/doc.pdf",
                    "scriptType": "TYPEWRITER",
                    "language": "de",
                })
            finally:
                main_module._models_ready = False

    assert response.status_code == 500
    assert fresh_metrics.ocr_jobs_total.labels(
        engine="surya", script_type="TYPEWRITER"
    )._value.get() == 0.0


def test_uvicorn_access_log_filter_skips_metrics_path():
    """The MetricsPathFilter drops uvicorn.access log records that target /metrics."""
    import logging as _logging
    from main import MetricsPathFilter

    filt = MetricsPathFilter()
    metrics_record = _logging.LogRecord(
        name="uvicorn.access", level=_logging.INFO, pathname="", lineno=0,
        msg='%s - "%s %s HTTP/%s" %d',
        args=("127.0.0.1:1234", "GET", "/metrics", "1.1", 200),
        exc_info=None,
    )
    health_record = _logging.LogRecord(
        name="uvicorn.access", level=_logging.INFO, pathname="", lineno=0,
        msg='%s - "%s %s HTTP/%s" %d',
        args=("127.0.0.1:1234", "GET", "/health", "1.1", 200),
        exc_info=None,
    )
    ocr_record = _logging.LogRecord(
        name="uvicorn.access", level=_logging.INFO, pathname="", lineno=0,
        msg='%s - "%s %s HTTP/%s" %d',
        args=("127.0.0.1:1234", "POST", "/ocr", "1.1", 200),
        exc_info=None,
    )

    assert filt.filter(metrics_record) is False
    assert filt.filter(health_record) is False
    assert filt.filter(ocr_record) is True