Update app.py

app.py

@@ -25,10 +25,7 @@ model_path = 'shufflenet-v2-10.onnx'
 model = onnx.load(model_path)
 session = ort.InferenceSession(model.SerializeToString())
 
-def get_image(path):
-    with Image.open(path) as img:
-        img = np.array(img.convert('RGB'))
-    return img
+
     
 preprocess = transforms.Compose([
         transforms.Resize(256),
@@ -40,8 +37,7 @@ preprocess = transforms.Compose([
 
     
 
-def predict(path):
-    img = get_image(path)
+def predict(img):
     input_tensor = preprocess(img)
     img = input_tensor.unsqueeze(0)
     ort_inputs = {session.get_inputs()[0].name: img.cpu().detach().numpy()}
@@ -58,4 +54,4 @@ title="ShuffleNet-v2"
 description="ShuffleNet is a deep convolutional network for image classification. ShuffleNetV2 is an improved architecture that is the state-of-the-art in terms of speed and accuracy tradeoff used for image classification."
 
 examples=[['kitten.jpg']]
-gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True,debug=True)
+gr.Interface(predict,gr.inputs.Image(type='pil'),"label",title=title,description=description,examples=examples).launch(enable_queue=True,debug=True)