Create app.py

app.py

@@ -0,0 +1,48 @@
+from deepsparse import Pipeline
+import time
+import gradio as gr
+
+
+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()