refactor(ml): modularization and styling (#2835)

* basic refactor and styling

* removed batching

* module entrypoint

* removed unused imports

* model superclass,  model cache now in app state

* fixed cache dir and enforced abstract method

---------

Co-authored-by: Alex Tran <alex.tran1502@gmail.com>

machine-learning/app/models/facial_recognition.py

@@ -0,0 +1,59 @@
+from pathlib import Path
+from typing import Any
+
+import cv2
+from insightface.app import FaceAnalysis
+
+from ..config import settings
+from ..schemas import ModelType
+from .base import InferenceModel
+
+
+class FaceRecognizer(InferenceModel):
+    _model_type = ModelType.FACIAL_RECOGNITION
+
+    def __init__(
+        self,
+        model_name: str,
+        min_score: float = settings.min_face_score,
+        cache_dir: Path | None = None,
+        **model_kwargs,
+    ):
+        super().__init__(model_name, cache_dir)
+        self.min_score = min_score
+        model = FaceAnalysis(
+            name=self.model_name,
+            root=self.cache_dir.as_posix(),
+            allowed_modules=["detection", "recognition"],
+            **model_kwargs,
+        )
+        model.prepare(
+            ctx_id=0,
+            det_thresh=self.min_score,
+            det_size=(640, 640),
+        )
+        self.model = model
+
+    def predict(self, image: cv2.Mat) -> list[dict[str, Any]]:
+        height, width, _ = image.shape
+        results = []
+        faces = self.model.get(image)
+
+        for face in faces:
+            x1, y1, x2, y2 = face.bbox
+
+            results.append(
+                {
+                    "imageWidth": width,
+                    "imageHeight": height,
+                    "boundingBox": {
+                        "x1": round(x1),
+                        "y1": round(y1),
+                        "x2": round(x2),
+                        "y2": round(y2),
+                    },
+                    "score": face.det_score.item(),
+                    "embedding": face.normed_embedding.tolist(),
+                }
+            )
+        return results