.

handler.py

@@ -0,0 +1,39 @@
+from typing import Dict, List, Any
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+
+class EndpointHandler:
+    def __init__(self,
+                 path=""
+                 ):
+        # Preload all the elements you are going to need at inference.
+        # pseudo:
+        self.model = AutoModelForSequenceClassification.from_pretrained(path)
+        self.tokenizer = AutoTokenizer.from_pretrained("roberta-large", padding_side='right')
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+
+        input_dict = data.pop("inputs", data)
+
+        self.model.eval()
+        input = self.tokenizer(input_dict['answer'],
+                               input_dict['source'],
+                               truncation=True,
+                               max_length=None,
+                               return_tensors="pt"
+                               )
+        # input.to(device)
+        # with torch.no_grad():
+        #     output = model(**input)
+        output = model(**input)
+
+        prediction = output.logits.argmax(dim=-1)
+
+        #smax = nn.Softmax(dim=1)
+        #score = smax(output.logits)
+
+        return [{
+                 "label": prediction.item(),
+                 #"score": score[0][0].item()
+                }
+                ]