Update app.py

app.py

@@ -1,9 +1,9 @@
 import gradio as gr
 import sahi
 import torch
-from ultralyticsplus import YOLO, render_model_output
+from ultralyticsplus import YOLO
 
-# Images
+# Download images
 sahi.utils.file.download_from_url(
     "https://raw.githubusercontent.com/kadirnar/dethub/main/data/images/highway.jpg",
     "highway.jpg",
@@ -17,7 +17,6 @@ sahi.utils.file.download_from_url(
     "zidane.jpg",
 )
 
-
 model_names = [
     "yolov8n-seg.pt",
     "yolov8s-seg.pt",
@@ -46,7 +45,7 @@ def yolov8_inference(
         conf_threshold: Confidence threshold
         iou_threshold: IOU threshold
     Returns:
-        Rendered image
+        Bounding box coordinates in xyxy format
     """
     global model
     global current_model_name
@@ -56,14 +55,14 @@ def yolov8_inference(
     model.overrides["conf"] = conf_threshold
     model.overrides["iou"] = iou_threshold
     results = model.predict(image, imgsz=image_size, return_outputs=True)
-    renders = []
-    for image_results in model.predict(image, imgsz=image_size, return_outputs=True):
-        render = render_model_output(
-            model=model, image=image, model_output=image_results
-        )
-        renders.append(render)
+    
+    boxes = []
+    for result in results:
+        # Extract bounding boxes (xyxy format)
+        for box in result.boxes.xyxy:
+            boxes.append(box.tolist())
 
-    return renders[0]
+    return boxes
 
 
 inputs = [
@@ -80,8 +79,8 @@ inputs = [
     gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
 ]
 
-outputs = gr.Image(type="filepath", label="Output Image")
-title = "Ultralytics YOLOv8 Segmentation Demo"
+outputs = gr.JSON(label="Bounding Boxes (xyxy format)")
+title = "YOLOv8 Bounding Box Extraction Demo"
 
 examples = [
     ["zidane.jpg", "yolov8m-seg.pt", 640, 0.6, 0.45],