diff --git a/app.py b/app.py
index 87b31ea..e464729 100644
--- a/app.py
+++ b/app.py
@@ -12,52 +12,52 @@ base_path = '/app/data/'
 print(str(root_api_url))
 
 #try:
-    while True:
-        r = requests.get('{}/anonymize'.format(root_api_url))
-        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-        response =  r.json()
+while True:
+    r = requests.get('{}/anonymize'.format(root_api_url))
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    response =  r.json()
 
-        for detection in response:
-            img_path = "{}{}".format(base_path, detection['source_image_path'])
+    for detection in response:
+        img_path = "{}{}".format(base_path, detection['source_image_path'])
 
-            #Skip Non existing files
-            if (not os.path.exists(img_path)):
-                continue
+        #Skip Non existing files
+        if (not os.path.exists(img_path)):
+            continue
 
-            img = cv2.imread(img_path)
-            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-            faces, rejects, weights = face_cascade.detectMultiScale3(gray, 1.1, 4, outputRejectLevels = 1)
+        img = cv2.imread(img_path)
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        faces, rejects, weights = face_cascade.detectMultiScale3(gray, 1.1, 4, outputRejectLevels = 1)
 
-            #Count faces  is null dont anonimize file
-            if (len(faces) < 0):
-                continue
-            
-            i = 0
-            for (x, y, w, h) in faces:
-                confidence = float(weights[i])
-                if (confidence > treashold):
-                    face_roi = img[y:y+h, x:x+w]
-                    blurred_face = cv2.GaussianBlur(face_roi, (99, 99), 50)
-                    img[y:y+h, x:x+w] = blurred_face
-
-                    print(weights[i])
-                    text = "{}".format(weights[i])
-                    #cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
-                    #cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
-
-                i = i +1
-            
-            extension = img_path[img_path.index(".")+1:]
-            new_img_path = img_path.replace((".{}".format(extension)), ('_anonym.{}'.format(extension)))
-            cv2.imwrite(new_img_path, img)
-
-            data = {
-                "id": detection['id'],
-                "source_image_path": new_img_path.replace(base_path,"")
-            }
-            response = requests.post('{}/anonymize'.format(root_api_url), json=data)
+        #Count faces  is null dont anonimize file
+        if (len(faces) < 0):
+            continue
         
-        time.sleep(900)
+        i = 0
+        for (x, y, w, h) in faces:
+            confidence = float(weights[i])
+            if (confidence > treashold):
+                face_roi = img[y:y+h, x:x+w]
+                blurred_face = cv2.GaussianBlur(face_roi, (99, 99), 50)
+                img[y:y+h, x:x+w] = blurred_face
+
+                print(weights[i])
+                text = "{}".format(weights[i])
+                #cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
+                #cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
+
+            i = i +1
+        
+        extension = img_path[img_path.index(".")+1:]
+        new_img_path = img_path.replace((".{}".format(extension)), ('_anonym.{}'.format(extension)))
+        cv2.imwrite(new_img_path, img)
+
+        data = {
+            "id": detection['id'],
+            "source_image_path": new_img_path.replace(base_path,"")
+        }
+        response = requests.post('{}/anonymize'.format(root_api_url), json=data)
+    
+    time.sleep(900)
 
 # except Exception as e:
 #     print("An exception occurred")