Update app.py

app.py

@@ -2,31 +2,7 @@ from deepsparse import Pipeline
 import time
 import gradio as gr
 
-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)
-## What is DeepSparse
-DeepSparse is an inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application. Sparsification is a powerful technique for optimizing models for inference, reducing the compute needed with a limited accuracy tradeoff. DeepSparse is designed to take advantage of model sparsity, enabling you to deploy models with the flexibility and scalability of software on commodity CPUs with the best-in-class performance of hardware accelerators, enabling you to standardize operations and reduce infrastructure costs.
-Similar to Hugging Face, DeepSparse provides off-the-shelf pipelines for computer vision and NLP that wrap the model with proper pre- and post-processing to run performantly on CPUs by using sparse models.
-The text classification Pipeline, for example, wraps an NLP model with the proper preprocessing and postprocessing pipelines, such as tokenization.
-### Inference API Example
-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 {}"},)
-text = "The senate passed 3 laws today"
-inference = pipeline(sequences= text,labels=['politics', 'public health', 'Europe'],)
-print(inference)
-```
-## Use Case Description
-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.  
-When deploying a text classification model, decreasing the model’s latency and increasing its throughput is critical. This is why DeepSparse Pipelines have sparse text classification models. 
-[Want to train a sparse model on your data? Checkout the documentation on sparse transfer learning](https://docs.neuralmagic.com/use-cases/natural-language-processing/question-answering)
-'''
+
 task = "zero_shot_text_classification"
 sparse_classification_pipeline = Pipeline.create(
         task=task,
@@ -49,10 +25,7 @@ with gr.Blocks() as demo:
         with gr.Column():
             gr.Markdown(markdownn)
             
-        with gr.Column():
-            gr.Markdown("""
-            ### Text classification demo
-            """)
+       )
             text = gr.Text(label="Text")
             btn = gr.Button("Submit")