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/image_classification.py

@@ -0,0 +1,40 @@
+from pathlib import Path
+
+from PIL.Image import Image
+from transformers.pipelines import pipeline
+
+from ..config import settings
+from ..schemas import ModelType
+from .base import InferenceModel
+
+
+class ImageClassifier(InferenceModel):
+    _model_type = ModelType.IMAGE_CLASSIFICATION
+
+    def __init__(
+        self,
+        model_name: str,
+        min_score: float = settings.min_tag_score,
+        cache_dir: Path | None = None,
+        **model_kwargs,
+    ):
+        super().__init__(model_name, cache_dir)
+        self.min_score = min_score
+
+        self.model = pipeline(
+            self.model_type.value,
+            self.model_name,
+            model_kwargs={"cache_dir": self.cache_dir, **model_kwargs},
+        )
+
+    def predict(self, image: Image) -> list[str]:
+        predictions = self.model(image)
+        tags = list(
+            {
+                tag
+                for pred in predictions
+                for tag in pred["label"].split(", ")
+                if pred["score"] >= self.min_score
+            }
+        )
+        return tags