Update app.py

app.py

@@ -2,44 +2,43 @@ 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",
+    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_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]}
+    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)
-            
-       )
-            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],)
-    
+            text = gr.Textbox(placeholder="Enter text here...", label="Text", lines=5, width=500)
+            btn = gr.Button("Submit", style="info", size="lg", block=True)
+
+        with gr.Column():
+            gr.Markdown("### Text Classification Demo")
+            sparse_answers = gr.Label(label="Sparse Model Answers", num_top_classes=3, style="info")
+            sparse_duration = gr.Number(label="Sparse Latency (ms)", style="success", size="lg")
+
     btn.click(
         run_pipeline,
         inputs=[text],
-        outputs=[sparse_answers,sparse_duration],
+        outputs=[sparse_answers, sparse_duration],
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()