Update app.py

app.py

@@ -1,11 +1,14 @@
 import gradio as gr
 
+from ultralytics import YOLO
+model = YOLO('./best.pt')  # load your custom trained model
 
 import torch
-from ultralyticsplus import YOLO, render_result
+from ultralyticsplus import 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')
+    '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(
@@ -25,8 +28,8 @@ def yoloV8_func(image: gr.Image = None,
         iou_threshold (float, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
     """
     # Load the YOLOv8 model from the 'best.pt' checkpoint
-    model_path = "./best.pt"
-    model = YOLO(model_path)
+    model_path = "yolov8n.pt"
+    # model = torch.hub.load('ultralytics/yolov8', 'custom', path='/content/best.pt', force_reload=True, trust_repo=True)
 
     # Perform object detection on the input image using the YOLOv8 model
     results = model.predict(image,
@@ -43,6 +46,8 @@ def yoloV8_func(image: gr.Image = None,
     # Render the output image with bounding boxes around detected objects
     render = render_result(model=model, image=image, result=results[0])
     return render
+
+
 inputs = [
     gr.Image(type="filepath", label="Input Image"),
     gr.Slider(minimum=320, maximum=1280, step=32, label="Image Size", value=640),
@@ -50,15 +55,13 @@ inputs = [
     gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="IOU Threshold"),
 ]
 
-
-
 outputs = gr.Image(type="filepath", label="Output Image")
 
 title = "YOLOv8 101: Custom Object Detection on Construction Workers"
 
-examples = [['one.jpg', 640, 0.5, 0.7],
-            ['two.jpg', 800, 0.5, 0.6],
-            ['three.jpg', 900, 0.5, 0.8]]
+examples = [['img1.jpg', 640, 0.5, 0.7],
+            ['img2.jpg', 800, 0.5, 0.6],
+            ['img3.jpg', 900, 0.5, 0.8]]
 
 yolo_app = gr.Interface(
     fn=yoloV8_func,
@@ -70,4 +73,4 @@ yolo_app = gr.Interface(
 )
 
 # Launch the Gradio interface in debug mode with queue enabled
-yolo_app.launch(debug=True).queue()
+yolo_app.launch(debug=True, share=True).queue()