Create handler.py

handler.py

@@ -0,0 +1,29 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import DataCollatorWithPadding
+from torch.nn.functional import softmax
+
+import torch
+from typing import Any, Dict, List
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModelForSequenceClassification.from_pretrained(path)
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        batch_of_strings = data["inputs"]
+
+        tokens = self.tokenizer(
+            batch_of_strings, padding=True, truncation=True, return_tensors="pt"
+        )
+
+        # Calculate the loss
+        with torch.no_grad():
+            outputs = self.model(**tokens)
+
+        probabilities = softmax(outputs.logits, dim=1)
+
+        return {
+            "predictions": [pred[0] for pred in probabilities.tolist()],
+        }