Create handler.py

handler.py

@@ -0,0 +1,31 @@
+from typing import Dict, List, Any
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/msmarco-MiniLM-L-6-v3')
+        self.model = AutoModel.from_pretrained('sentence-transformers/msmarco-MiniLM-L-6-v3')
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `str` | `PIL.Image` | `np.array`)
+            kwargs
+      Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+        encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+        # Compute token embeddings
+        with torch.no_grad():
+            model_output = self.model(**encoded_input)
+
+        # Perform pooling. In this case, max pooling.
+        sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+        return model_output