Update app.py

app.py

@@ -60,12 +60,12 @@ if len(mx.test_utils.list_gpus())==0:
 else:
     ctx = mx.gpu(0)
 # Load module
-mod = mx.mod.Module(symbol=sym, context=ctx, data_names=['data_0'], label_names=None)
-mod.bind(for_training=False, data_shapes=[('data_0', (1,3,224,224))],label_shapes=mod._label_shapes)
+mod = mx.mod.Module(symbol=sym, context=ctx, data_names=['conv1/7x7_s2_w_0'], label_names=None)
+mod.bind(for_training=False, data_shapes=[('conv1/7x7_s2_w_0', (1,3,224,224))],label_shapes=mod._label_shapes)
 mod.set_params(arg_params, aux_params, allow_missing=True, allow_extra=True)
 
-title="MobileNet"
-description="MobileNet improves the state-of-the-art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. MobileNet is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. Additionally, it removes non-linearities in the narrow layers in order to maintain representational power."
+title="GoogleNet"
+description="GoogLeNet is the name of a convolutional neural network for classification, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2014."
 
 examples=[['catonnx.jpg']]
 gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True)