style: run python linter & prettifier on backend code

server/analysis/nlp.py

@@ -6,6 +6,7 @@ from typing import Any
 from transformers import pipeline
 from sentence_transformers import SentenceTransformer
 
+
 class NLP:
     _topic_models: dict[str, SentenceTransformer] = {}
     _emotion_classifiers: dict[str, Any] = {}
@@ -32,7 +33,7 @@ class NLP:
             )
             self.entity_recognizer = self._get_entity_recognizer(
                 self.device_str, self.pipeline_device
-            )           
+            )
         except RuntimeError as exc:
             if self.use_cuda and "out of memory" in str(exc).lower():
                 torch.cuda.empty_cache()
@@ -90,7 +91,7 @@ class NLP:
             )
             cls._emotion_classifiers[device_str] = classifier
         return classifier
-    
+
     @classmethod
     def _get_entity_recognizer(cls, device_str: str, pipeline_device: int) -> Any:
         recognizer = cls._entity_recognizers.get(device_str)
@@ -207,8 +208,7 @@ class NLP:
         self.df.drop(columns=existing_drop, inplace=True)
 
         remaining_emotion_cols = [
-            c for c in self.df.columns
-            if c.startswith("emotion_")
+            c for c in self.df.columns if c.startswith("emotion_")
         ]
 
         if remaining_emotion_cols:
@@ -227,8 +227,6 @@ class NLP:
 
             self.df[remaining_emotion_cols] = normalized.values
 
-        
-
     def add_topic_col(self, confidence_threshold: float = 0.3) -> None:
         titles = self.df[self.title_col].fillna("").astype(str)
         contents = self.df[self.content_col].fillna("").astype(str)
@@ -257,7 +255,7 @@ class NLP:
         self.df.loc[self.df["topic_confidence"] < confidence_threshold, "topic"] = (
             "Misc"
         )
-        
+
     def add_ner_cols(self, max_chars: int = 512) -> None:
         texts = (
             self.df[self.content_col]
@@ -302,8 +300,4 @@ class NLP:
 
         for label in all_labels:
             col_name = f"entity_{label}"
-            self.df[col_name] = [
-                d.get(label, 0) for d in entity_count_dicts
-            ]
-
-
+            self.df[col_name] = [d.get(label, 0) for d in entity_count_dicts]