Update app.py

app.py

@@ -5,7 +5,7 @@ from PIL import Image
 torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/06/15/01/11/soccer-1457988_1280.jpg', 'soccer.jpg')
 torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/11/21/14/31/vw-bus-1845719_1280.jpg', 'bus.jpg')
 # Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5')  # or yolov3-spp, yolov3-tiny, custom
+model = torch.hub.load('ultralytics/yolov5', 'custom', 'customModel/model.pt')
 def yolo(im, size=1920):
     g = (size / max(im.size))  # gain
     im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
@@ -14,7 +14,7 @@ def yolo(im, size=1920):
     return Image.fromarray(results.imgs[0])
 inputs = gr.inputs.Image(type='pil', label="Original Image")
 outputs = gr.outputs.Image(type="pil", label="Output Image")
-title = "YOLOv3"
+title = "Custom YOLOv5"
 description = "YOLOv3 Gradio demo for object detection. Upload an image or click an example image to use."
 article = "<p style='text-align: center'>YOLOv3 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov3' target='_blank'>Source code</a> |<a href='https://apps.apple.com/app/id1452689527' target='_blank'>iOS App</a></p>"
 examples = [['soccer.jpg'], ['bus.jpg']]