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/cache.py

@@ -0,0 +1,92 @@
+import asyncio
+
+from aiocache.backends.memory import SimpleMemoryCache
+from aiocache.lock import OptimisticLock
+from aiocache.plugins import BasePlugin, TimingPlugin
+
+from ..schemas import ModelType
+from .base import InferenceModel
+
+
+class ModelCache:
+    """Fetches a model from an in-memory cache, instantiating it if it's missing."""
+
+    def __init__(
+        self,
+        ttl: float | None = None,
+        revalidate: bool = False,
+        timeout: int | None = None,
+        profiling: bool = False,
+    ):
+        """
+        Args:
+            ttl: Unloads model after this duration. Disabled if None. Defaults to None.
+            revalidate: Resets TTL on cache hit. Useful to keep models in memory while active. Defaults to False.
+            timeout: Maximum allowed time for model to load. Disabled if None. Defaults to None.
+            profiling: Collects metrics for cache operations, adding slight overhead. Defaults to False.
+        """
+
+        self.ttl = ttl
+        plugins = []
+
+        if revalidate:
+            plugins.append(RevalidationPlugin())
+        if profiling:
+            plugins.append(TimingPlugin())
+
+        self.cache = SimpleMemoryCache(
+            ttl=ttl, timeout=timeout, plugins=plugins, namespace=None
+        )
+
+    async def get(
+        self, model_name: str, model_type: ModelType, **model_kwargs
+    ) -> InferenceModel:
+        """
+        Args:
+            model_name: Name of model in the model hub used for the task.
+            model_type: Model type or task, which determines which model zoo is used.
+
+        Returns:
+            model: The requested model.
+        """
+
+        key = self.cache.build_key(model_name, model_type.value)
+        model = await self.cache.get(key)
+        if model is None:
+            async with OptimisticLock(self.cache, key) as lock:
+                model = await asyncio.get_running_loop().run_in_executor(
+                    None,
+                    lambda: InferenceModel.from_model_type(
+                        model_type, model_name, **model_kwargs
+                    ),
+                )
+                await lock.cas(model, ttl=self.ttl)
+        return model
+
+    async def get_profiling(self) -> dict[str, float] | None:
+        if not hasattr(self.cache, "profiling"):
+            return None
+
+        return self.cache.profiling  # type: ignore
+
+
+class RevalidationPlugin(BasePlugin):
+    """Revalidates cache item's TTL after cache hit."""
+
+    async def post_get(self, client, key, ret=None, namespace=None, **kwargs):
+        if ret is None:
+            return
+        if namespace is not None:
+            key = client.build_key(key, namespace)
+        if key in client._handlers:
+            await client.expire(key, client.ttl)
+
+    async def post_multi_get(self, client, keys, ret=None, namespace=None, **kwargs):
+        if ret is None:
+            return
+
+        for key, val in zip(keys, ret):
+            if namespace is not None:
+                key = client.build_key(key, namespace)
+            if val is not None and key in client._handlers:
+                await client.expire(key, client.ttl)