add custom handler

handler.py

@@ -0,0 +1,27 @@
+from typing import Dict, List, Any
+
+import torch
+from transformers import BertModel, BertTokenizerFast
+
+
+class EndpointHandler():
+    def __init__(self, path_to_model: str):
+        # Preload all the elements you are going to need at inference.
+        # pseudo:
+        self.tokenizer = BertTokenizerFast.from_pretrained(path_to_model)
+        self.model = BertModel.from_pretrained(path_to_model)
+        self.model = self.model.eval()
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        This method is called whenever a request is made to the endpoint.
+        :param data: { inputs [str]: list of strings to be encoded }
+        :return: A :obj:`list` | `dict`: will be serialized and returned
+        """
+
+        inputs = self.tokenizer(data['inputs'], return_tensors = "pt", padding = True)
+
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+
+        return outputs.pooler_output