Update app.py

app.py

@@ -32,13 +32,6 @@ When deploying a text classification model, decreasing the model’s latency and
 
 '''
 task = "zero_shot_text_classification"
-dense_classification_pipeline = Pipeline.create(
-        task=task,
-        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/distilbert-none/pytorch/huggingface/mnli/pruned80_quant-none-vnni",
@@ -46,21 +39,13 @@ sparse_classification_pipeline = Pipeline.create(
     model_config={"hypothesis_template": "This text is related to {}"},
     )
 def run_pipeline(text):
-    dense_start = time.perf_counter()
-    
-    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(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
     
-    return sparse_result, sparse_duration, dense_result, dense_duration
+    return sparse_result, sparse_duration
 
 
 with gr.Blocks() as demo:
@@ -85,7 +70,7 @@ with gr.Blocks() as demo:
     btn.click(
         run_pipeline,
         inputs=[text],
-        outputs=[sparse_answers,sparse_duration,dense_answers,dense_duration],
+        outputs=[sparse_answers,sparse_duration],
     )
 
 if __name__ == "__main__":