add inference endpoints handler.py

handler.py

@@ -0,0 +1,35 @@
+from typing import Dict, List, Any
+from transformers import pipeline
+
+import torch.nn.functional as F
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+def average_pool(last_hidden_states: Tensor,
+                 attention_mask: Tensor) -> Tensor:
+    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
+    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.pipeline = pipeline("feature-extraction", model=path)
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModel.from_pretrained(path)
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `List[str]`)
+      Return:
+            A :obj:`List[List[int]]`: will be serialized and returned
+        """
+        # get inputs
+        inputs = data.pop("inputs",data)
+
+        batch_dict = self.tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors='pt')
+
+        outputs = self.model(**batch_dict)
+
+        embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+
+        return embeddings