Update handler.py

handler.py

@@ -0,0 +1,33 @@
+from typing import  Dict, List, Any
+from transformers import pipeline, AutoTokenizer
+import torch 
+import os
+
+os.system("python -m torch.utils.collect_env")
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load the optimized model
+        self.pipeline = pipeline("text-classification", model=path)
+
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
+                - "label": A string representing what the label/class is. There can be multiple labels.
+                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
+        """
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            prediction = self.pipeline(inputs, **parameters)
+        else:
+            prediction = self.pipeline(inputs)
+        # postprocess the prediction
+        return prediction