Update app.py

app.py

@@ -18,7 +18,6 @@ The DeepSparse library provides an `image_classification` pipeline that you can
 The pipeline enables you to choose a sparsified model from the [SparseZoo](https://sparsezoo.neuralmagic.com/). 
 SparseZoo contains models that have been pruned and quantized. They are, therefore, smaller than the original models. This makes them easier to deploy, especially on edge devices. These small classification models have the same accuracy as the original models but are faster and have a higher throughput.
 Here is sample code for an Image Classification pipeline with the ResNet model.
-
 ```python
 from deepsparse import Pipeline
 pipeline = Pipeline.create(task="image_classification",model_path = "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none")
@@ -26,7 +25,6 @@ input_image = "my_image.png" # path to input image
 inference = pipeline(input_image)
 print(inference)
 ```
-
 ## Use Case Description
 Image classification is applicable in scenarios where you are interested in only classifying an object in an image without localizing it. 
 For example, you can build an image classification model to classify products in a store.