fix formatting

src/embeddingbuddy/models/reducers.py

@@ -7,88 +7,89 @@ from .schemas import ReducedData
 
 
 class DimensionalityReducer(ABC):
-    
     def __init__(self, n_components: int = 3, random_state: int = 42):
         self.n_components = n_components
         self.random_state = random_state
         self._reducer = None
-    
+
     @abstractmethod
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
         pass
-    
+
     @abstractmethod
     def get_method_name(self) -> str:
         pass
 
 
 class PCAReducer(DimensionalityReducer):
-    
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
         self._reducer = PCA(n_components=self.n_components)
         reduced = self._reducer.fit_transform(embeddings)
         variance_explained = self._reducer.explained_variance_ratio_
-        
+
         return ReducedData(
             reduced_embeddings=reduced,
             variance_explained=variance_explained,
             method=self.get_method_name(),
-            n_components=self.n_components
+            n_components=self.n_components,
         )
-    
+
     def get_method_name(self) -> str:
         return "PCA"
 
 
 class TSNEReducer(DimensionalityReducer):
-    
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
-        self._reducer = TSNE(n_components=self.n_components, random_state=self.random_state)
+        self._reducer = TSNE(
+            n_components=self.n_components, random_state=self.random_state
+        )
         reduced = self._reducer.fit(embeddings)
-        
+
         return ReducedData(
             reduced_embeddings=reduced,
             variance_explained=None,
             method=self.get_method_name(),
-            n_components=self.n_components
+            n_components=self.n_components,
         )
-    
+
     def get_method_name(self) -> str:
         return "t-SNE"
 
 
 class UMAPReducer(DimensionalityReducer):
-    
     def fit_transform(self, embeddings: np.ndarray) -> ReducedData:
-        self._reducer = umap.UMAP(n_components=self.n_components, random_state=self.random_state)
+        self._reducer = umap.UMAP(
+            n_components=self.n_components, random_state=self.random_state
+        )
         reduced = self._reducer.fit_transform(embeddings)
-        
+
         return ReducedData(
             reduced_embeddings=reduced,
             variance_explained=None,
             method=self.get_method_name(),
-            n_components=self.n_components
+            n_components=self.n_components,
         )
-    
+
     def get_method_name(self) -> str:
         return "UMAP"
 
 
 class ReducerFactory:
-    
     @staticmethod
-    def create_reducer(method: str, n_components: int = 3, random_state: int = 42) -> DimensionalityReducer:
+    def create_reducer(
+        method: str, n_components: int = 3, random_state: int = 42
+    ) -> DimensionalityReducer:
         method_lower = method.lower()
-        
-        if method_lower == 'pca':
+
+        if method_lower == "pca":
             return PCAReducer(n_components=n_components, random_state=random_state)
-        elif method_lower == 'tsne':
+        elif method_lower == "tsne":
             return TSNEReducer(n_components=n_components, random_state=random_state)
-        elif method_lower == 'umap':
+        elif method_lower == "umap":
             return UMAPReducer(n_components=n_components, random_state=random_state)
         else:
             raise ValueError(f"Unknown reduction method: {method}")
-    
+
     @staticmethod
     def get_available_methods() -> list:
-        return ['pca', 'tsne', 'umap']
+        return ["pca", "tsne", "umap"]

src/embeddingbuddy/models/schemas.py

@@ -50,9 +50,11 @@ class PlotData:
     coordinates: np.ndarray
     prompts: Optional[List[Document]] = None
     prompt_coordinates: Optional[np.ndarray] = None
-    
+
     def __post_init__(self):
         if not isinstance(self.coordinates, np.ndarray):
             self.coordinates = np.array(self.coordinates)
-        if self.prompt_coordinates is not None and not isinstance(self.prompt_coordinates, np.ndarray):
-            self.prompt_coordinates = np.array(self.prompt_coordinates)
+        if self.prompt_coordinates is not None and not isinstance(
+            self.prompt_coordinates, np.ndarray
+        ):
+            self.prompt_coordinates = np.array(self.prompt_coordinates)