diff --git a/main.py b/main.py
index ea40cfb..fc209ce 100644
--- a/main.py
+++ b/main.py
@@ -4,6 +4,7 @@ import threading
 import subprocess
 from flask import Flask, request, render_template, jsonify, redirect, url_for
 from timelapse import process_faces, ProcessConfig, validate_immich_connection
+import logging
 
 app = Flask(__name__)
 
@@ -24,6 +25,13 @@ processing_thread = None
 # Global flag to signal cancellation
 cancel_requested = False
 
+class ProgressRouteFilter(logging.Filter):
+    def filter(self, record):
+        # Filter out logs containing the progress route
+        return "/progress" not in record.getMessage()
+
+log = logging.getLogger('werkzeug')
+log.addFilter(ProgressRouteFilter())
 
 def update_progress(current, total):
     """
@@ -215,13 +223,8 @@ def index():
             resize_size = int(request.form.get("resize_size", 512))
             face_resolution_threshold = int(request.form.get("face_resolution_threshold", 128))
             pose_threshold = float(request.form.get("pose_threshold", 25))
-
-            # Parse left_eye_pos tuple from form
-            left_eye_x = float(request.form.get("left_eye_x", 0.35))
-            left_eye_y = float(request.form.get("left_eye_y", 0.45))
-            left_eye_pos = (left_eye_x, left_eye_y)
-
             max_workers = int(request.form.get("max_workers", 1))
+            left_eye_pos = (0.4, 0.4)
 
             # Date ranges are optional
             date_from = request.form.get("date_from") or None
diff --git a/timelapse.py b/timelapse.py
index 147a131..54c2ef9 100644
--- a/timelapse.py
+++ b/timelapse.py
@@ -50,7 +50,7 @@ class ProcessConfig:
     min_face_width: int = 128
     min_face_height: int = 128
     pose_threshold: float = 25
-    left_eye_pos: tuple = (0.35, 0.45)
+    left_eye_pos: tuple = (0.4, 0.4)
     landmark_model_path: str = "shape_predictor_68_face_landmarks.dat"
 
 
@@ -275,17 +275,14 @@ def align_face(image, desired_face_width, desired_face_height, left_eye_pos, pos
     img_width, img_height = image.size
     face_width = img_width / (1 + 2 * padding_percent)
     face_height = img_height / (1 + 2 * padding_percent)
-    
-    # Calculate the face rectangle coordinates
     x1 = int((img_width - face_width) / 2)
     x2 = int(x1 + face_width)
     y1 = int((img_height - face_height) / 2)
     y2 = int(y1 + face_height)
 
+    # Resize the image for landmark detection
     optimal_size = 256  # optimal image resolution for landmark detection (between 200-400px)
     scale_factor = optimal_size / face_width
-    
-    # Resize the image for landmark detection
     resized_width = int(img_width * scale_factor)
     resized_height = int(img_height * scale_factor)
     resized_gray = cv2.resize(gray, (resized_width, resized_height))