diff --git a/secure_pixelation/detect_humans.py b/secure_pixelation/detect_humans.py
index 02a3c7e..16d9378 100644
--- a/secure_pixelation/detect_humans.py
+++ b/secure_pixelation/detect_humans.py
@@ -255,86 +255,6 @@ def detect_human_parts(human: dict, face_padding: int = 20):
             print(clean_points)
 
 
-            
-
-        """
-        for idx in face_indices:
-            x, y, conf = keypoints[idx]
-            name = keypoint_names[idx]
-            if conf > 0.3:
-                face_points.append((x, y))
-
-                point = (int(x), int(y))
-                name = keypoint_names[idx]
-                cv2.circle(image, point, 4, (0, 255, 0), -1)
-                cv2.putText(image, name, (point[0] + 5, point[1] + 5),
-                cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
-        """
-
-
-        """
-        nose, left_eye, right_eye, left_ear, right_ear = face_points
-        print(face_points)
-
-        # Calculate pairwise distances
-        nose_to_left_eye = euclidean_distance(nose, left_eye)
-        nose_to_right_eye = euclidean_distance(nose, right_eye)
-        eyes_distance = euclidean_distance(left_eye, right_eye)
-        left_ear_to_nose = euclidean_distance(left_ear, nose)
-        right_ear_to_nose = euclidean_distance(right_ear, nose)
-
-        # Relative distances
-        eye_to_eye_ratio = eyes_distance / (left_ear_to_nose + right_ear_to_nose)  # Eyes vs. nose-to-ears
-        nose_to_eye_ratio = (nose_to_left_eye + nose_to_right_eye) / (left_ear_to_nose + right_ear_to_nose)  # Nose-to-eye vs. ear-to-nose
-        ear_to_nose_ratio = (left_ear_to_nose + right_ear_to_nose) / 2  # Ear-to-nose proportionality
-
-        # Validate using relative distances
-        if not (EYE_RATIO_THRESHOLD < eye_to_eye_ratio < 0.5):  # Arbitrary ratio threshold
-            print("⚠️ Rejected due to unrealistic eye-to-eye ratio:", eye_to_eye_ratio)
-            has_valid_face = False
-
-        if not (NOSE_EYE_RATIO_THRESHOLD < nose_to_eye_ratio < 0.4):  # Arbitrary ratio threshold
-            print("⚠️ Rejected due to unrealistic nose-to-eye ratio:", nose_to_eye_ratio)
-            has_valid_face = False
-
-        if not (0.5 < ear_to_nose_ratio < EAR_NOSE_RATIO_THRESHOLD):
-            print("⚠️ Rejected due to unrealistic ear-to-nose ratio:", ear_to_nose_ratio)
-            has_valid_face = False
-
-
-        # If all checks pass, calculate the bounding box
-        xs, ys, _ = zip(*face_points)
-        x_min, x_max = int(min(xs)), int(max(xs))
-        y_min, y_max = int(min(ys)), int(max(ys))
-
-        x_min = max(x_min - face_padding, 0)
-        y_min = max(y_min - face_padding, 0)
-        x_max = min(x_max + face_padding, image.shape[1])
-        y_max = min(y_max + face_padding, image.shape[0])
-
-        # Compute box size
-        box_w = x_max - x_min
-        box_h = y_max - y_min
-
-        if has_valid_face:
-            cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
-        else:
-            cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (255, 0, 0), 2)
-            
-
-
-
-        for i, (x, y, conf) in enumerate(keypoints):
-            point = (int(x), int(y))
-            name = keypoint_names[i]
-            # cv2.circle(image, point, 4, (0, 255, 0), -1)
-            # cv2.putText(image, name, (point[0] + 5, point[1] - 5),
-            # cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
-
-        # cv2.circle(image, head, 5, (255, 0, 0), -1)   # Head in blue
-        # cv2.circle(image, foot, 5, (0, 0, 255), -1)   # Foot in red
-        """ 
-
     cv2.imwrite(detected, image)