Update app.py

app.py

@@ -2,17 +2,10 @@ import gradio as gr
 
 from ultralytics import YOLO
 model = YOLO('./best.pt')  # load your custom trained model
-
 import torch
-from ultralyticsplus import render_result
+#from ultralyticsplus import render_result
+from render import custom_render_result
 
-torch.hub.download_url_to_file(
-    'https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Ftexashafts.com%2Fwp-content%2Fuploads%2F2016%2F04%2Fconstruction-worker.jpg',
-    'one.jpg')
-torch.hub.download_url_to_file(
-    'https://www.pearsonkoutcherlaw.com/wp-content/uploads/2020/06/Construction-Workers.jpg', 'two.jpg')
-torch.hub.download_url_to_file(
-    'https://nssgroup.com/wp-content/uploads/2019/02/Building-maintenance-blog.jpg', 'three.jpg')
 
 
 def yoloV8_func(image: gr.Image = None,
@@ -44,7 +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 = custom_render_result(model=model, image=image, result=results[0])
     return render
 
 
@@ -57,7 +50,7 @@ inputs = [
 
 outputs = gr.Image(type="filepath", label="Output Image")
 
-title = "YOLOv8 101: Custom Object Detection on Construction Workers"
+title = "YOLOv8 101: Custom Object Detection on meter"
 
 examples = [['img1.jpg', 640, 0.5, 0.7],
             ['img2.jpg', 800, 0.5, 0.6],
@@ -73,4 +66,5 @@ yolo_app = gr.Interface(
 )
 
 # Launch the Gradio interface in debug mode with queue enabled
-yolo_app.launch(debug=True, share=True).queue()
+yolo_app.launch(debug=True).queue()
+