Create app.py

app.py

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+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,
+        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):
+    sparse_start = time.perf_counter()
+    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
+    dict_r = {sparse_result['labels'][0]:sparse_result['scores'][0],sparse_result['labels'][1]:sparse_result['scores'][1], sparse_result['labels'][2]:sparse_result['scores'][2]}
+    return dict_r, sparse_duration
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown(markdownn)
+            
+        with gr.Column():
+            gr.Markdown("""
+            ### Text classification demo
+            """)
+            text = gr.Text(label="Text")
+            btn = gr.Button("Submit")
+            
+            sparse_answers = gr.Label(label="Sparse model answers",
+                                    num_top_classes=3
+                                     )
+            sparse_duration = gr.Number(label="Sparse Latency (ms):")
+            gr.Examples([["The senate passed 3 laws today"],["Who are you voting for in 2020?"],["Public health is very important"]],inputs=[text],)
+    
+    btn.click(
+        run_pipeline,
+        inputs=[text],
+        outputs=[sparse_answers,sparse_duration],
+    )
+
+if __name__ == "__main__":
+    demo.launch()