Make code MCP compatible

app.py

@@ -8,10 +8,51 @@ import tempfile
 import numpy as np
 
 def download_model(model_filename):
+    """
+    Downloads a YOLO model from the Hugging Face Hub.
+
+    This function fetches a specified YOLO model file from the
+    'atalaydenknalbant/Yolov13' repository on the Hugging Face Hub.
+
+    Args:
+        model_filename (str): The name of the model file to download
+                              (e.g., 'yolov13n.pt').
+
+    Returns:
+        str: The local path to the downloaded model file.
+    """
     return hf_hub_download(repo_id="atalaydenknalbant/Yolov13", filename=model_filename)
 
 @spaces.GPU
 def yolo_inference(input_type, image, video, model_id, conf_threshold, iou_threshold, max_detection):
+    """
+    Performs object detection inference using a YOLOv13 model on either an image or a video.
+
+    This function downloads the specified YOLO model, then applies it to the
+    provided input. For images, it returns an annotated image. For videos, it
+    processes each frame and returns an annotated video. Error handling for
+    missing inputs is included, returning blank outputs with messages.
+
+    Args:
+        input_type (str): Specifies the input type, either "Image" or "Video".
+        image (PIL.Image.Image or None): The input image if `input_type` is "Image".
+                                         None otherwise.
+        video (str or None): The path to the input video file if `input_type` is "Video".
+                             None otherwise.
+        model_id (str): The identifier of the YOLO model to use (e.g., 'yolov13n.pt').
+        conf_threshold (float): The confidence threshold for object detection.
+                                Detections with lower confidence are discarded.
+        iou_threshold (float): The Intersection over Union (IoU) threshold for
+                               Non-Maximum Suppression (NMS).
+        max_detection (int): The maximum number of detections to return per image or frame.
+
+    Returns:
+        tuple: A tuple containing two elements:
+            - PIL.Image.Image or None: The annotated image if `input_type` was "Image",
+                                       otherwise None.
+            - str or None: The path to the annotated video file if `input_type` was "Video",
+                           otherwise None.
+    """
     model_path = download_model(model_id)
 
     if input_type == "Image":
@@ -104,12 +145,41 @@ def yolo_inference(input_type, image, video, model_id, conf_threshold, iou_thres
     return None, None
 
 def update_visibility(input_type):
+    """
+    Adjusts the visibility of Gradio components based on the selected input type.
+
+    This function dynamically shows or hides the image and video input/output
+    components in the Gradio interface to ensure only relevant fields are visible.
+
+    Args:
+        input_type (str): The selected input type, either "Image" or "Video".
+
+    Returns:
+        tuple: A tuple of `gr.update` objects for the visibility of:
+               (image input, video input, image output, video output).
+    """
     if input_type == "Image":
         return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)
     else:
         return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)
 
 def yolo_inference_for_examples(image, model_id, conf_threshold, iou_threshold, max_detection):
+    """
+    Wrapper function for `yolo_inference` specifically for Gradio examples that use images.
+
+    This function simplifies the `yolo_inference` call for the `gr.Examples` component,
+    ensuring only image-based inference is performed for predefined examples.
+
+    Args:
+        image (PIL.Image.Image): The input image for the example.
+        model_id (str): The identifier of the YOLO model to use.
+        conf_threshold (float): The confidence threshold.
+        iou_threshold (float): The IoU threshold.
+        max_detection (int): The maximum number of detections.
+
+    Returns:
+        PIL.Image.Image or None: The annotated image. Returns None if no image is processed.
+    """
     annotated_image, _ = yolo_inference(
         input_type="Image",
         image=image,
@@ -193,4 +263,4 @@ with gr.Blocks() as app:
     )
 
 if __name__ == '__main__':
-    app.launch()
+    app.launch(mcp_server=True)