chore(ml): added testing and github workflow (#2969)

* added testing

* github action for python, made mypy happy

* formatted with black

* minor fixes and styling

* test model cache

* cache test dependencies

* narrowed model cache tests

* moved endpoint tests to their own class

* cleaned up fixtures

* formatting

* removed unused dep

machine-learning/app/conftest.py

@@ -0,0 +1,119 @@
+from types import SimpleNamespace
+from typing import Any, Iterator, TypeAlias
+from unittest import mock
+
+import numpy as np
+import pytest
+from fastapi.testclient import TestClient
+from PIL import Image
+
+from .main import app, init_state
+
+ndarray: TypeAlias = np.ndarray[int, np.dtype[np.float32]]
+
+
+@pytest.fixture
+def pil_image() -> Image.Image:
+    return Image.new("RGB", (600, 800))
+
+
+@pytest.fixture
+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:
+        yield mocked
+
+
+@pytest.fixture(scope="session")
+def deployed_app() -> TestClient:
+    init_state()
+    return TestClient(app)