Update app.py

app.py

@@ -35,13 +35,14 @@ def image_preprocess(image, target_size, gt_boxes=None):
         
 input_size = 416
 
+os.system("wget https://github.com/AK391/models/raw/main/vision/object_detection_segmentation/yolov4/model/yolov4.onnx")
 
 # Start from ORT 1.10, ORT requires explicitly setting the providers parameter if you want to use execution providers
 # other than the default CPU provider (as opposed to the previous behavior of providers getting set/registered by default
 # based on the build flags) when instantiating InferenceSession.
 # For example, if NVIDIA GPU is available and ORT Python package is built with CUDA, then call API as following:
 # rt.InferenceSession(path/to/model, providers=['CUDAExecutionProvider'])
-sess = rt.InferenceSession("model.onnx")
+sess = rt.InferenceSession("yolov4.onnx")
 
 outputs = sess.get_outputs()