feat(services): add logger to classifier

src/emotion_analysis/services/classifier.py

@@ -1,3 +1,4 @@
+from logging import Logger, getLogger
 from pathlib import Path
 from typing import Literal, Self
 
@@ -7,7 +8,6 @@ from torch import Tensor
 from torch import device as torch_device
 from torch import inference_mode as torch_inferece_mode
 from torch.cuda import is_available as cuda_is_available
-from torch.types import Number as TorchNumber
 from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
 from transformers.modeling_outputs import SequenceClassifierOutput
 
@@ -22,10 +22,21 @@ class Classifier:
         /,
         audio_max_duration: int | None = 30,
     ):
+        self._log: Logger = getLogger(__name__)
+
+        self._log.debug("Selecting model device")
         self._device = torch_device("cuda" if cuda_is_available() else "cpu")
+        self._log.info(f"Model will be initiated in {self._device}")
+
+        self._log.debug(f"Get pretrained {settings.MODEL_ID} to {self._device}")
         self._model = AutoModelForAudioClassification.from_pretrained(
             settings.MODEL_ID
         ).to(self._device)
+        self._log.info(f"Model {settings.MODEL_ID} available in {self._device}")
+
+        self._log.debug(
+            f"Get feature extractor {settings.FEATURE_EXTRACTOR_ID or settings.MODEL_ID}"
+        )
         self._feat_extractor = AutoFeatureExtractor.from_pretrained(
             settings.FEATURE_EXTRACTOR_ID or settings.MODEL_ID
         )
@@ -47,6 +58,8 @@ class Classifier:
         truncation: bool = True,
         return_tensors_type: Literal["pt", "tf"] = "pt",
     ) -> dict[str, Tensor]:
+        self._log.debug(f"Preprocessing audio: {audio_path}")
+
         audio, _ = librosa_load(audio_path, sr=None)
         audio = (
             audio[: self._max_length]
@@ -65,6 +78,7 @@ class Classifier:
     def _predict(self: Self, audio_sample: dict[str, Tensor]) -> Tensor:
         with torch_inferece_mode():
             prediction: SequenceClassifierOutput = self._model(**audio_sample)
+            self._log.debug(f"Prediction logits: {prediction.logits}")
         return prediction.logits
 
     def predict(
@@ -84,6 +98,8 @@ class Classifier:
             .softmax(dim=-1)[0]
         )
 
+        self._log.info(f"Prediction probabilities: {probs}")
+
         return (
             {idx: prob.item() for idx, prob in enumerate(probs)}
             if return_labeled_probs