Add application file

app.py

@@ -2,15 +2,14 @@ import gradio as gr
 import torch
 from ultralyticsplus import YOLO, render_result
 
-
 torch.hub.download_url_to_file(
-    'https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg', 
+    'https://cdn.theatlantic.com/thumbor/xoh2WVVSx4F2uboG9xbT5BDprtM=/0x0:4939x2778/960x540/media/img/mt/2023/11/LON68717_copy/original.jpg', 
     'one.jpg')
 torch.hub.download_url_to_file(
-    'https://www.state.gov/wp-content/uploads/2022/01/shutterstock_248799484-scaled.jpg', 
+    'https://i.ytimg.com/vi/lZQX2mmLo2s/maxresdefault.jpg', 
     'two.jpg')
 torch.hub.download_url_to_file(
-    'https://cdn.theatlantic.com/thumbor/xoh2WVVSx4F2uboG9xbT5BDprtM=/0x0:4939x2778/960x540/media/img/mt/2023/11/LON68717_copy/original.jpg', 
+    'https://assets.bwbx.io/images/users/iqjWHBFdfxIU/ioQgA.854d7s/v1/-1x-1.jpg', 
     'three.jpg')
 
 
@@ -38,7 +37,7 @@ def yoloV8_func(image: gr.Image = None,
     print("Probability:", box.conf)
 
     # Render the output image with bounding boxes around detected objects
-    render = render_result(model=model, image=image, result=results[0])
+    render = render_result(model=model, image=image, result=results[0], rect_th = 4, text_th = 4)
     return render
 
 
@@ -58,9 +57,9 @@ outputs = gr.Image(type="filepath", label="Output Image")
 title = "YOLOv8 101: Custom Object Detection on Objects in Big Cities"
 
 
-examples = [['one.jpg', 640, 0.5, 0.7],
-            ['two.jpg', 800, 0.5, 0.6],
-            ['three.jpg', 900, 0.5, 0.8]]
+examples = [['one.jpg', 900, 0.5, 0.8],
+            ['two.jpg', 1152, 0.05, 0.05],
+            ['three.jpg', 1024, 0.25, 0.25]]
 
 yolo_app = gr.Interface(
     fn=yoloV8_func,