Update app.py

app.py

@@ -8,8 +8,13 @@ DeepSparse is sparsity-aware inference runtime offering GPU-class performance on
 The text classification Pipeline, for example, wraps an NLP model with the proper preprocessing and postprocessing pipelines, such as tokenization. Here is a sample code for a question-answering pipeline: 
 ```
 from deepsparse import Pipeline
-pipeline = pipeline.create(task="text-classification", model_path="zoo:nlp/text_classification/bert-base_cased/pytorch/huggingface/mnli/pruned90_quant-none")
-inference = pipeline(text)
+pipeline = Pipeline.create(
+        task=task,
+        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)
 ```
 '''