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handler.py

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+from typing import Dict, Any
+from deepsparse import Pipeline
+from time import perf_counter
+
+class EndpointHandler:
+
+    def __init__(self, path=""):
+
+        self.pipeline = Pipeline.create(task="text-classification", model_path=path)
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        """
+        Args:
+            data (:obj:): prediction input text
+        """
+        inputs = data.pop("inputs", data)
+
+        start = perf_counter()
+        prediction = self.pipeline(inputs)
+        end = perf_counter()
+        latency = end - start
+
+        return {
+            "labels": prediction.labels, 
+            "scores": prediction.scores,
+            "latency (secs.)": latency
+        }

requirements.txt

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+deepsparse>=1.2.0