diff --git a/model_utilitary_functions/model_UTILS.py b/model_utilitary_functions/model_UTILS.py
index e49f8cff..420f8b4c 100644
--- a/model_utilitary_functions/model_UTILS.py
+++ b/model_utilitary_functions/model_UTILS.py
@@ -5,6 +5,25 @@
 from urllib.request import Request
 
 async def image_slicing(image_bytes: bytes, result_json: dict) -> list:
+    """
+    This function takes the image bytes and the result_json from the model and
+    returns a list of cropped images.
+    The result_json is expected to be in the following format:
+    {
+        "boxes": [
+            {
+                "box": {
+                    "topX": 0.0,
+                    "topY": 0.0,
+                    "bottomX": 0.0,
+                    "bottomY": 0.0
+                },
+                "label": "string",
+                "score": 0.0
+            }
+        ],
+    }
+    """
     boxes = result_json[0]['boxes']
     image_io_byte = io.BytesIO(base64.b64decode(image_bytes))
     image_io_byte.seek(0)
@@ -30,6 +49,14 @@ async def image_slicing(image_bytes: bytes, result_json: dict) -> list:
     return cropped_images
 
 async def swin_result_parser(img_box:dict, results: dict) -> list:
+    """
+    Args:
+        img_box (dict): The image box containing the bounding boxes and labels.
+        results (dict): The results from the model containing the detected seeds.
+
+    Returns:
+        list: The updated image box with modified labels and scores.
+    """
     for i, result in enumerate(results):
         img_box[0]['boxes'][i]['label'] = result[0].get('label')
         img_box[0]['boxes'][i]['score'] = result[0].get('score')
@@ -43,17 +70,17 @@ async def seed_detector_header(api_key: str) -> dict:
         "azureml-model-deployment": "seed-detector-1",
     }
 
-async def swin_header(api_key: str) -> dict:
-    return {
-        "Content-Type": "application/json",
-        "Authorization": ("Bearer " + api_key),
-    }
-
 # Eventually the goals would be to have a request factory that would return
 # a request for the specified models such as the following:
 async def request_factory(img_bytes: str | bytes, endpoint_url: str, api_key: str) -> Request:
     """
-    Return a request for calling AzureML AI model
+    Args:
+        img_bytes (str | bytes): The image data as either a string or bytes.
+        endpoint_url (str): The URL of the AI model endpoint.
+        api_key (str): The API key for accessing the AI model.
+
+    Returns:
+        Request: The request object for calling the AI model.
     """
 
     model_name = endpoint_url.split("/")[2].split(".")[0]