Create handler.py

handler.py

@@ -0,0 +1,36 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+
+class EndpointHandler:
+    def __init__(self, path=""):        
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModelForSequenceClassification.from_pretrained(path, num_labels=3)
+        def tokenize(text, topic):
+            return self.tokenizer(
+                topic,
+                text,
+                max_length=384, #512
+                truncation="only_second",
+                return_offsets_mapping=False,
+                padding="max_length",
+                return_tensors='pt'
+            )
+        self.tokenize = tokenize
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+         data args:
+              topic (:obj: `str`)
+              text (:obj: `str`)
+        Return:
+              A :obj:`list` | `dict`: will be serialized and returned
+        """
+        topic = data.pop("topic", data)
+        text = data.pop("text", data)
+        tokenized_inputs = self.tokenize(text, topic)
+        
+        output = self.model(**tokenized_inputs)
+        prediction = torch.argmax(output.logits, dim=-1).item()
+        label = self.model.config.id2label[prediction]
+        return label