chore(ml): improved logging (#3918)

* fixed `minScore` not being set correctly

* apply to init

* don't send `enabled`

* fix eslint warning

* added logger

* added logging

* refinements

* enable access log for info level

* formatting

* merged strings

---------

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

machine-learning/app/main.py

@@ -1,4 +1,5 @@
 import asyncio
+import logging
 import os
 from concurrent.futures import ThreadPoolExecutor
 from typing import Any
@@ -11,7 +12,7 @@ from starlette.formparsers import MultiPartParser
 
 from app.models.base import InferenceModel
 
-from .config import settings
+from .config import log, settings
 from .models.cache import ModelCache
 from .schemas import (
     MessageResponse,
@@ -20,14 +21,20 @@ from .schemas import (
 )
 
 MultiPartParser.max_file_size = 2**24  # spools to disk if payload is 16 MiB or larger
-
 app = FastAPI()
 
 
 def init_state() -> None:
     app.state.model_cache = ModelCache(ttl=settings.model_ttl, revalidate=settings.model_ttl > 0)
+    log.info(
+        (
+            "Created in-memory cache with unloading "
+            f"{f'after {settings.model_ttl}s of inactivity' if settings.model_ttl > 0 else 'disabled'}."
+        )
+    )
     # asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code
     app.state.thread_pool = ThreadPoolExecutor(settings.request_threads)
+    log.info(f"Initialized request thread pool with {settings.request_threads} threads.")
 
 
 @app.on_event("startup")
@@ -77,4 +84,6 @@ if __name__ == "__main__":
         port=settings.port,
         reload=is_dev,
         workers=settings.workers,
+        log_config=None,
+        access_log=log.isEnabledFor(logging.INFO),
     )