Create handler.py

handler.py

@@ -0,0 +1,51 @@
+from typing import Dict, List, Any
+from setfit import SetFitModel
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model
+        self.model = SetFitModel.from_pretrained(path)
+        # ag_news id to label mapping
+        self.id2label = {0: "Absent", 1: "Present"}
+
+    # def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+    #     """
+    #      data args:
+    #           inputs (:obj: `str`)
+    #     Return:
+    #           A :obj:`list` | `dict`: will be serialized and returned
+    #     """
+    #     # get inputs
+    #     inputs = data.pop("inputs", data)
+    #     if isinstance(inputs, str):
+    #         inputs = [inputs]
+
+    #     # run normal prediction
+    #     scores = self.model.predict_proba(inputs)[0]
+
+    #     return [{"label": self.id2label[i], "score": score.item()} for i, score in enumerate(scores)]
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        data args:
+            inputs (:obj: `List[str]`) - List of strings
+        Return:
+            A :obj:`list` of dicts: each dict contains 'label' and 'score' for each input string
+        """
+        # get inputs
+        inputs = data.pop("inputs", data)
+        if not isinstance(inputs, list):
+            raise ValueError("Input must be a list of strings")
+
+        # run normal prediction
+        all_scores = self.model.predict_proba(inputs)  # This returns a list of score arrays
+
+        # Format the results for each input string
+        results = []
+        for scores in all_scores:
+            results.append([
+                {"label": self.id2label[i], "score": score.item()} for i, score in enumerate(scores)
+            ])
+
+        return results