Fix PR #524 Lint and Build Errors

backend/app/database/faces.py

@@ -133,12 +133,14 @@ def db_insert_face_embeddings_by_image_id(
             image_id, embeddings, confidence, bbox, cluster_id
         )
 
+
 def get_all_face_embeddings():
     conn = sqlite3.connect(DATABASE_PATH)
     cursor = conn.cursor()
 
     try:
-        cursor.execute("""
+        cursor.execute(
+            """
             SELECT
                 f.embeddings,
                 f.bbox,
@@ -153,7 +155,8 @@ def get_all_face_embeddings():
             JOIN images i ON f.image_id=i.id
             LEFT JOIN image_classes ic ON i.id = ic.image_id
             LEFT JOIN mappings m ON ic.class_id = m.class_id
-        """)
+        """
+        )
         results = cursor.fetchall()
 
         images_dict = {}
@@ -173,7 +176,7 @@ def get_all_face_embeddings():
                     embeddings_json = json.loads(embeddings)
                     bbox_json = json.loads(bbox)
                 except json.JSONDecodeError:
-                    continue;
+                    continue
                 images_dict[image_id] = {
                     "embeddings": embeddings_json,
                     "bbox": bbox_json,
@@ -203,6 +206,7 @@ def get_all_face_embeddings():
     finally:
         conn.close()
 
+
 def db_get_faces_unassigned_clusters() -> List[Dict[str, Union[FaceId, FaceEmbedding]]]:
     """
     Get all faces that don't have assigned clusters.

backend/app/models/FaceDetector.py

@@ -51,7 +51,7 @@ def detect_faces(self, image_id: str, image_path: str, forSearch: bool = False):
                 embedding = self.facenet.get_embedding(processed_face)
                 embeddings.append(embedding)
 
-        if (not forSearch and embeddings):
+        if not forSearch and embeddings:
             db_insert_face_embeddings_by_image_id(
                 image_id, embeddings, confidence=confidences, bbox=bboxes
             )

backend/app/routes/face_clusters.py

@@ -255,21 +255,42 @@ def face_tagging(payload: AddSingleImageRequest):
         image_id = str(uuid.uuid4())
         result = fd.detect_faces(image_id, image_path, forSearch=True)
         if not result or result["num_faces"] == 0:
-            return GetAllImagesResponse(success=True, message=f"Successfully retrieved {len(matches)} images", data=[])
+            return GetAllImagesResponse(
+                success=True,
+                message=f"Successfully retrieved {len(matches)} images",
+                data=[],
+            )
 
         process_face = result["processed_faces"][0]
         new_embedding = fn.get_embedding(process_face)
 
         images = get_all_face_embeddings()
         if len(images) == 0:
-            return GetAllImagesResponse(success=True, message=f"Successfully retrieved {len(matches)} images", data=[])
+            return GetAllImagesResponse(
+                success=True,
+                message=f"Successfully retrieved {len(matches)} images",
+                data=[],
+            )
         else:
             for image in images:
                 max_similarity = 0
-                similarity = FaceNet_util_cosine_similarity(new_embedding, image["embeddings"])
+                similarity = FaceNet_util_cosine_similarity(
+                    new_embedding, image["embeddings"]
+                )
                 max_similarity = max(max_similarity, similarity)
                 if max_similarity >= CONFIDENCE_PERCENT:
-                    matches.append(ImageData(id=image["id"], path=image["path"], folder_id=image["folder_id"], thumbnailPath=image["thumbnailPath"], metadata=image["metadata"], isTagged=image["isTagged"], tags=image["tags"], bboxes=image["bbox"]))
+                    matches.append(
+                        ImageData(
+                            id=image["id"],
+                            path=image["path"],
+                            folder_id=image["folder_id"],
+                            thumbnailPath=image["thumbnailPath"],
+                            metadata=image["metadata"],
+                            isTagged=image["isTagged"],
+                            tags=image["tags"],
+                            bboxes=image["bbox"],
+                        )
+                    )
 
             return GetAllImagesResponse(
                 success=True,

backend/app/schemas/images.py

@@ -38,6 +38,7 @@ class FaceTaggingResponse(BaseModel):
     message: str
     data: dict
 
+
 class ImagesResponse(BaseModel):
     image_files: List[str]
     folder_path: str

frontend/src/pages/PersonImages/PersonImages.tsx

@@ -113,7 +113,7 @@ export const PersonImages = () => {
       </div>
 
       {/* Media Viewer Modal */}
-      {isImageViewOpen && <MediaView />}
+      {isImageViewOpen && <MediaView images={images} />}
     </div>
   );
 };