remove debug logging

app.py

@@ -59,19 +59,13 @@ def detect_brain_tumor_owlv2(image, seg_input, debug: bool = True):
     Returns:
         tuple: (numpy array of image, list of (label, (x1, y1, x2, y2)) tuples)
     """
-    if debug:
-        logger.debug(f"Image received, shape: {image.shape}")
 
     # Step 2: Detect brain tumor using owl_v2
     prompt = "detect brain tumor"
     detections = owl_v2(prompt, image)
-    if debug:
-        logger.debug(f"Raw detections: {detections}")
 
     # Step 3: Overlay bounding boxes on the image
     image_with_bboxes = overlay_bounding_boxes(image, detections)
-    if debug:
-        logger.debug("Bounding boxes overlaid on the image")
 
     # Prepare annotations for AnnotatedImage output
     annotations = []
@@ -85,9 +79,6 @@ def detect_brain_tumor_owlv2(image, seg_input, debug: bool = True):
         x1, y1, x2, y2 = int(x1*width), int(y1*height), int(x2*width), int(y2*height)
         annotations.append(((x1, y1, x2, y2), f"{label} {score:.2f}"))
 
-    if debug:
-        logger.debug(f"Annotations: {annotations}")
-
     # Convert image to numpy array if it's not already
     if isinstance(image_with_bboxes, Image.Image):
         image_with_bboxes = np.array(image_with_bboxes)
@@ -107,19 +98,13 @@ def detect_brain_tumor_dino(image, seg_input, debug: bool = True):
     Returns:
         tuple: (numpy array of image, list of (label, (x1, y1, x2, y2)) tuples)
     """
-    if debug:
-        logger.debug(f"Image received, shape: {image.shape}")
 
     # Step 2: Detect brain tumor using grounding_dino
     prompt = "detect brain tumor"
     detections = grounding_dino(prompt, image)
-    if debug:
-        logger.debug(f"Raw detections: {detections}")
 
     # Step 3: Overlay bounding boxes on the image
     image_with_bboxes = overlay_bounding_boxes(image, detections)
-    if debug:
-        logger.debug("Bounding boxes overlaid on the image")
 
     # Prepare annotations for AnnotatedImage output
     annotations = []
@@ -133,9 +118,6 @@ def detect_brain_tumor_dino(image, seg_input, debug: bool = True):
         x1, y1, x2, y2 = int(x1*width), int(y1*height), int(x2*width), int(y2*height)
         annotations.append(((x1, y1, x2, y2), f"{label} {score:.2f}"))
 
-    if debug:
-        logger.debug(f"Annotations: {annotations}")
-
     # Convert image to numpy array if it's not already
     if isinstance(image_with_bboxes, Image.Image):
         image_with_bboxes = np.array(image_with_bboxes)
@@ -155,19 +137,13 @@ def detect_brain_tumor_florence2(image, seg_input, debug: bool = True):
     Returns:
         tuple: (numpy array of image, list of (label, (x1, y1, x2, y2)) tuples)
     """
-    if debug:
-        logger.debug(f"Image received, shape: {image.shape}")
 
     # Step 2: Detect brain tumor using florencev2 - NO PROMPT
     prompt = "detect brain tumor"
     detections = florencev2_object_detection(prompt)
-    if debug:
-        logger.debug(f"Raw detections: {detections}")
 
     # Step 3: Overlay bounding boxes on the image
     image_with_bboxes = overlay_bounding_boxes(image, detections)
-    if debug:
-        logger.debug("Bounding boxes overlaid on the image")
 
     # Prepare annotations for AnnotatedImage output
     annotations = []
@@ -181,9 +157,6 @@ def detect_brain_tumor_florence2(image, seg_input, debug: bool = True):
         x1, y1, x2, y2 = int(x1*width), int(y1*height), int(x2*width), int(y2*height)
         annotations.append(((x1, y1, x2, y2), f"{label} {score:.2f}"))
 
-    if debug:
-        logger.debug(f"Annotations: {annotations}")
-
     # Convert image to numpy array if it's not already
     if isinstance(image_with_bboxes, Image.Image):
         image_with_bboxes = np.array(image_with_bboxes)