refactor(ml): model downloading (#3545)

* download facial recognition models

* download hf models

* simplified logic

* updated `predict` for facial recognition

* ensure download method is called

* fixed repo_id for clip

* fixed download destination

* use st's own `snapshot_download`

* conditional download

* fixed predict method

* check if loaded

* minor fixes

* updated mypy overrides

* added pytest-mock

* updated tests

* updated lock

machine-learning/app/conftest.py

@@ -1,5 +1,4 @@
-from types import SimpleNamespace
-from typing import Any, Iterator, TypeAlias
+from typing import Iterator, TypeAlias
 from unittest import mock
 
 import numpy as np
@@ -22,91 +21,6 @@ def cv_image(pil_image: Image.Image) -> ndarray:
     return np.asarray(pil_image)[:, :, ::-1]  # PIL uses RGB while cv2 uses BGR
 
 
-@pytest.fixture
-def mock_classifier_pipeline() -> Iterator[mock.Mock]:
-    with mock.patch("app.models.image_classification.pipeline") as model:
-        classifier_preds = [
-            {"label": "that's an image alright", "score": 0.8},
-            {"label": "well it ends with .jpg", "score": 0.1},
-            {"label": "idk, im just seeing bytes", "score": 0.05},
-            {"label": "not sure", "score": 0.04},
-            {"label": "probably a virus", "score": 0.01},
-        ]
-
-        def forward(
-            inputs: Image.Image | list[Image.Image], **kwargs: Any
-        ) -> list[dict[str, Any]] | list[list[dict[str, Any]]]:
-            if isinstance(inputs, list) and not all([isinstance(img, Image.Image) for img in inputs]):
-                raise TypeError
-            elif not isinstance(inputs, Image.Image):
-                raise TypeError
-
-            if isinstance(inputs, list):
-                return [classifier_preds] * len(inputs)
-
-            return classifier_preds
-
-        model.return_value = forward
-        yield model
-
-
-@pytest.fixture
-def mock_st() -> Iterator[mock.Mock]:
-    with mock.patch("app.models.clip.SentenceTransformer") as model:
-        embedding = np.random.rand(512).astype(np.float32)
-
-        def encode(inputs: Image.Image | list[Image.Image], **kwargs: Any) -> ndarray | list[ndarray]:
-            #  mypy complains unless isinstance(inputs, list) is used explicitly
-            img_batch = isinstance(inputs, list) and all([isinstance(inst, Image.Image) for inst in inputs])
-            text_batch = isinstance(inputs, list) and all([isinstance(inst, str) for inst in inputs])
-            if isinstance(inputs, list) and not any([img_batch, text_batch]):
-                raise TypeError
-
-            if isinstance(inputs, list):
-                return np.stack([embedding] * len(inputs))
-
-            return embedding
-
-        mocked = mock.Mock()
-        mocked.encode = encode
-        model.return_value = mocked
-        yield model
-
-
-@pytest.fixture
-def mock_faceanalysis() -> Iterator[mock.Mock]:
-    with mock.patch("app.models.facial_recognition.FaceAnalysis") as model:
-        face_preds = [
-            SimpleNamespace(  # this is so these fields can be accessed through dot notation
-                **{
-                    "bbox": np.random.rand(4).astype(np.float32),
-                    "kps": np.random.rand(5, 2).astype(np.float32),
-                    "det_score": np.array([0.67]).astype(np.float32),
-                    "normed_embedding": np.random.rand(512).astype(np.float32),
-                }
-            ),
-            SimpleNamespace(
-                **{
-                    "bbox": np.random.rand(4).astype(np.float32),
-                    "kps": np.random.rand(5, 2).astype(np.float32),
-                    "det_score": np.array([0.4]).astype(np.float32),
-                    "normed_embedding": np.random.rand(512).astype(np.float32),
-                }
-            ),
-        ]
-
-        def get(image: np.ndarray[int, np.dtype[np.float32]], **kwargs: Any) -> list[SimpleNamespace]:
-            if not isinstance(image, np.ndarray):
-                raise TypeError
-
-            return face_preds
-
-        mocked = mock.Mock()
-        mocked.get = get
-        model.return_value = mocked
-        yield model
-
-
 @pytest.fixture
 def mock_get_model() -> Iterator[mock.Mock]:
     with mock.patch("app.models.cache.InferenceModel.from_model_type", autospec=True) as mocked: