Refactor: Address Sourcery Bot and reviewer feedback for stability and testability

- Fixed frame index calculation in capturer.py to prevent invalid -1 index
- Removed side-effect of automatic settings load in globals.py; added explicit init()
- Refactored functions to accept config params directly, reducing global settings reliance:
  - find_cluster_centroids() now takes max_k and kmeans_init
  - NSFW prediction functions now accept threshold
- Updated documentation for all affected functions
- Verified all changes against existing test suite — all tests pass

modules/capturer.py

@@ -32,7 +32,9 @@ def get_video_frame(video_path: str, frame_number: int = 0) -> Optional[Any]:
             capture.set(cv2.CAP_PROP_CONVERT_RGB, 0)  # Explicitly disable if not needed
         
         frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
-        capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
+        # Ensure frame_number is valid (0-based index)
+        target_frame = max(0, min(frame_total - 1, frame_number))
+        capture.set(cv2.CAP_PROP_POS_FRAMES, target_frame)
         has_frame, frame = capture.read()
 
         # Only convert manually if color_correction is enabled but capture didn't handle it

modules/cluster_analysis.py

@@ -7,13 +7,14 @@ import modules.globals
 
 logger = logging.getLogger(__name__)
 
-def find_cluster_centroids(embeddings, max_k=10) -> Any:
+def find_cluster_centroids(embeddings, max_k=None, kmeans_init=None) -> Any:
     """
     Identifies optimal face clusters using KMeans and silhouette scoring
     
     Args:
         embeddings: Face embedding vectors
-        max_k: Maximum number of clusters to consider
+        max_k: Maximum number of clusters to consider (default: from globals)
+        kmeans_init: KMeans initialization method (default: from globals)
         
     Returns:
         Array of optimal cluster centroids
@@ -23,9 +24,11 @@ def find_cluster_centroids(embeddings, max_k=10) -> Any:
             logger.warning("Not enough embeddings for clustering analysis")
             return embeddings  # Return the single embedding as its own cluster
             
-        # Use settings from globals if available
+        # Use settings from globals if not explicitly provided
-        max_k = getattr(modules.globals, 'max_cluster_k', max_k)
+        if max_k is None:
-        kmeans_init = getattr(modules.globals, 'kmeans_init', 'k-means++')
+            max_k = getattr(modules.globals, 'max_cluster_k', 10)
+        if kmeans_init is None:
+            kmeans_init = getattr(modules.globals, 'kmeans_init', 'k-means++')
         
         # Try silhouette method first
         best_k = 2  # Start with minimum viable cluster count

modules/globals.py

@@ -68,6 +68,14 @@ max_cluster_k = DEFAULT_SETTINGS['max_cluster_k']
 kmeans_init = DEFAULT_SETTINGS['kmeans_init']
 nsfw_threshold = DEFAULT_SETTINGS['nsfw_threshold']
 
+def init() -> None:
+    """
+    Initialize the globals module and load settings
+    Should be called explicitly by the application during startup
+    """
+    load_settings()
+    logger.info("Globals module initialized")
+
 def load_settings() -> None:
     """
     Load user settings from config file
@@ -114,5 +122,5 @@ def save_settings() -> None:
     except Exception as e:
         logger.error(f"Error saving settings: {str(e)}")
 
-# Load settings at module import time
+# Don't load settings at import time to avoid side effects
-load_settings()
+# Will be called explicitly by the application's initialization

modules/predicter.py

@@ -41,12 +41,13 @@ def get_nsfw_model():
         _model = load_nsfw_model()
     return _model
 
-def predict_frame(target_frame: Frame) -> bool:
+def predict_frame(target_frame: Frame, threshold=None) -> bool:
     """
     Predict if a frame contains NSFW content
     
     Args:
         target_frame: Frame to analyze as numpy array
+        threshold: NSFW probability threshold (default: from globals)
         
     Returns:
         True if NSFW content detected, False otherwise
@@ -56,8 +57,9 @@ def predict_frame(target_frame: Frame) -> bool:
             logger.warning("Cannot predict on None frame")
             return False
             
-        # Get threshold from globals
+        # Get threshold from globals if not explicitly provided
-        threshold = getattr(modules.globals, 'nsfw_threshold', 0.85)
+        if threshold is None:
+            threshold = getattr(modules.globals, 'nsfw_threshold', 0.85)
         
         # Convert the frame to RGB if needed
         expected_format = 'RGB' if modules.globals.color_correction else 'BGR'
@@ -86,35 +88,39 @@ def predict_frame(target_frame: Frame) -> bool:
         logger.error(f"Error during NSFW prediction: {str(e)}")
         return False
 
-def predict_image(target_path: str) -> bool:
+def predict_image(target_path: str, threshold=None) -> bool:
     """
     Predict if an image file contains NSFW content
     
     Args:
         target_path: Path to image file
+        threshold: NSFW probability threshold (default: from globals)
         
     Returns:
         True if NSFW content detected, False otherwise
     """
     try:
-        threshold = getattr(modules.globals, 'nsfw_threshold', 0.85)
+        if threshold is None:
+            threshold = getattr(modules.globals, 'nsfw_threshold', 0.85)
         return opennsfw2.predict_image(target_path) > threshold
     except Exception as e:
         logger.error(f"Error predicting NSFW for image {target_path}: {str(e)}")
         return False
 
-def predict_video(target_path: str) -> bool:
+def predict_video(target_path: str, threshold=None) -> bool:
     """
     Predict if a video file contains NSFW content
     
     Args:
         target_path: Path to video file
+        threshold: NSFW probability threshold (default: from globals)
         
     Returns:
         True if NSFW content detected, False otherwise
     """
     try:
-        threshold = getattr(modules.globals, 'nsfw_threshold', 0.85)
+        if threshold is None:
+            threshold = getattr(modules.globals, 'nsfw_threshold', 0.85)
         _, probabilities = opennsfw2.predict_video_frames(
             video_path=target_path, 
             frame_interval=100