Update app.py

app.py

@@ -6,15 +6,19 @@ markdownn = '''
 # Text Classification Pipeline with DeepSparse
 Text Classification involves assigning a label to a given text. For example, sentiment analysis is an example of a text classification use case. 
 ![Text Classification Pipeline with DeepSparse](https://huggingface.co/spaces/neuralmagic/nlp-text-classification/resolve/main/text-classification.png)
-![Text Classification Pipeline with DeepSparse](https://huggingface.co/spaces/neuralmagic/nlp-text-classification/resolve/main/text-classification.png)
+## What is DeepSparse
 DeepSparse is sparsity-aware inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application. DeepSparse provides sparsified pipelines for computer vision and NLP. 
-The text classification Pipeline, for example, wraps an NLP model with the proper preprocessing and postprocessing pipelines, such as tokenization. Here is sample code for a text classification pipeline: 
+The text classification Pipeline, for example, wraps an NLP model with the proper preprocessing and postprocessing pipelines, such as tokenization.
+### Inference
+Here is sample code for a text classification pipeline: 
 ```
 from deepsparse import Pipeline
 pipeline = Pipeline.create(task="zero_shot_text_classification", model_path="zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/pruned80_quant-none-vnni",model_scheme="mnli",model_config={"hypothesis_template": "This text is related to {}"},)
 inference = pipeline(sequences= text,labels=['politics', 'public health', 'Europe'],)
 print(inference)
 ```
+## Resources 
+[Classify Even Longer Customer Reviews Using Sparsity with DeepSparse](https://neuralmagic.com/blog/accelerate-customer-review-classification-with-sparse-transformers/)
 '''
 task = "zero_shot_text_classification"
 dense_classification_pipeline = Pipeline.create(