Update app.py

app.py

@@ -65,8 +65,8 @@ mod.bind(for_training=False, data_shapes=[('data', (1,3,224,224))],
          label_shapes=mod._label_shapes)
 mod.set_params(arg_params, aux_params, allow_missing=True, allow_extra=True)
 
-title="SqueezeNet"
-description="SqueezeNet is a small CNN which achieves AlexNet level accuracy on ImageNet with 50x fewer parameters. SqueezeNet requires less communication across servers during distributed training, less bandwidth to export a new model from the cloud to an autonomous car and more feasible to deploy on FPGAs and other hardware with limited memory."
+title="VGG"
+description="VGG models perform image classification - they take images as input and classify the major object in the image into a set of pre-defined classes. They are trained on ImageNet dataset which contains images from 1000 classes. VGG models provide very high accuracies but at the cost of increased model sizes. They are ideal for cases when high accuracy of classification is essential and there are limited constraints on model sizes."
 
 examples=[['catonnx.jpg']]
 gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True)