Update app.py

app.py

@@ -17,6 +17,14 @@ pipeline = Pipeline.create(task="zero_shot_text_classification", model_path="zoo
 inference = pipeline(sequences= text,labels=['politics', 'public health', 'Europe'],)
 print(inference)
 ```
+## Use case example
+Customer review classification is a great example of text classification in action. 
+
+The ability to quickly classify sentiment from customers is an added advantage for any business. 
+Therefore, whichever solution you deploy for classifying the customer reviews should deliver results in the shortest time possible. 
+By being fast the solution will process more volume, hence cheaper computational resources are utilized.  
+Deploying a deep learning model to tackle this problem is one solution.
+For this solution, decreasing the model’s latency and increasing its throughput is critical. This is why DeepSparse Pipelines have sparse text classification models. 
 ## Resources 
 [Classify Even Longer Customer Reviews Using Sparsity with DeepSparse](https://neuralmagic.com/blog/accelerate-customer-review-classification-with-sparse-transformers/)
 '''