Update handler.py

handler.py

@@ -1,20 +1,26 @@
-from typing import  Dict, List, Any
-from optimum.onnxruntime import ORTModelForSequenceClassification
-from transformers import AutoTokenizer, pipeline
-
+from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification, pipeline
 
 class EndpointHandler():
     def __init__(self, path=""):
         # load the optimized model
-        model = ORTModelForSequenceClassification.from_pretrained(path)
-        tokenizer = AutoTokenizer.from_pretrained(path, do_lower_case=True)
+        model = DistilBertForSequenceClassification.from_pretrained(path)
+        tokenizer = DistilBertTokenizerFast.from_pretrained(path, do_lower_case=True)
 
         self.pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
 
 
-    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+    def __call__(self, data):
         inputs = data.pop("inputs", data)
 
-        prediction = self.pipeline(inputs, padding=True, truncation=True, max_length=253)
+        def iterator():
+            for i in inputs:
+                yield i
+
+        labels = []
+        for out in pipeline(iterator(), padding=True, truncation=True, max_length=253):
+            labels.append(int(out["label"][-1]))
 
-        return prediction
+        return {
+            "pairs": inputs,
+            "evaluations": labels
+        }