Update app.py

app.py

@@ -4,9 +4,6 @@ from sahi.prediction import ObjectPrediction
 from sahi.utils.cv import visualize_object_predictions, read_image
 from ultralyticsplus import YOLO, render_result
 
-# Images
-torch.hub.download_url_to_file('https://huggingface.co/spaces/foduucom/object_detection/samples/1.jpeg', '1.jpeg')
-torch.hub.download_url_to_file('https://huggingface.co/spaces/foduucom/object_detection/samples/2.jpg', '2.JPG')
 
 def yolov8_inference(
     image: gr.inputs.Image = None,
@@ -30,7 +27,8 @@ def yolov8_inference(
     model.overrides['conf'] = conf_threshold
     model.overrides['iou']= iou_threshold
     model.overrides['agnostic_nms'] = False  # NMS class-agnostic
-    model.overrides['max_det'] = 1000 
+    # Correct line of code
+    model.overrides['max_det'] = 999
     image = read_image(image)
     results = model.predict(image)
     render = render_result(model=model, image=image, result=results[0])
@@ -39,9 +37,8 @@ def yolov8_inference(
         
 
 inputs = [
-    gr.inputs.Image(type="filepath", label="Input Image"),
-    gr.inputs.Dropdown(["foduucom/object_detection"], 
-                       default="foduucom/object_detection", label="Model"),
+# Images
+    gr.Examples(['samples/1.jpeg', 'samples/2.JPG'],),
     gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
@@ -62,4 +59,4 @@ demo_app = gr.Interface(
     cache_examples=True,
     theme='huggingface',
 )
-demo_app.launch(debug=True, enable_queue=True)
+demo_app.launch(debug=True, enable_queue=True)