Update app.py

app.py

@@ -5,7 +5,7 @@ import gradio as gr
 markdownn = '''
 # Text Classification Pipeline with 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 pipelines are similar to Hugging Face pipelines but are faster because they have been pruned and quantized. Here is a sample code for a question-answering pipeline: 
+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")
@@ -13,27 +13,31 @@ inference = pipeline(text)
 print(inference)
 ```
 '''
-task = "text-classification"
+task = "zero_shot_text_classification"
 dense_classification_pipeline = Pipeline.create(
         task=task,
-        model_path="zoo:nlp/text_classification/bert-base_cased/pytorch/huggingface/mnli/base-none",
+        model_path="zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none",
+     model_scheme="mnli",
+    model_config={"hypothesis_template": "This text is related to {}"},
     )
 
 sparse_classification_pipeline = Pipeline.create(
         task=task,
-        model_path="zoo:nlp/text_classification/bert-base_cased/pytorch/huggingface/mnli/pruned90_quant-none",
+        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 {}"},
     )
 def run_pipeline(text):
     dense_start = time.perf_counter()
     
-    dense_output = dense_classification_pipeline([text])
+    dense_output = dense_classification_pipeline(sequences= text,labels=['politics', 'public health', 'Europe'],)
     dense_result = dict(dense_output)
     dense_end = time.perf_counter()
     dense_duration = (dense_end - dense_start) * 1000.0
 
     sparse_start = time.perf_counter()
     
-    sparse_output = sparse_classification_pipeline([text])
+    sparse_output = sparse_classification_pipeline(sequences= text,labels=['politics', 'public health', 'Europe'],)
     sparse_result = dict(sparse_output)
     sparse_end = time.perf_counter()
     sparse_duration = (sparse_end - sparse_start) * 1000.0
@@ -66,8 +70,8 @@ with gr.Blocks() as demo:
    
     gr.Examples(
         [
-            ["DeepSparse is sparsity-aware inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application"],
-            ["SparseML is a Library for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models"],
+            ["Fun for adults and children"],
+            ["Fun for only children"],
             ["Gradio is an open-source Python package that allows you to quickly create easy-to-use, customizable UI components for your ML model, any API, or even an arbitrary Python function using a few lines of code. You can integrate the Gradio GUI directly into your Jupyter notebook or share it as a link with anyone."],
         ],
         inputs=[text],