Update handler.py

handler.py

@@ -9,19 +9,43 @@ class EndpointHandler:
         # 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: `str`)
+        data args:
+            inputs (:obj: `List[str]`) - List of strings
         Return:
-              A :obj:`list` | `dict`: will be serialized and returned
+            A :obj:`list` of dicts: each dict contains 'label' and 'score' for each input string
         """
         # get inputs
         inputs = data.pop("inputs", data)
-        if isinstance(inputs, str):
-            inputs = [inputs]
+        if not isinstance(inputs, list):
+            raise ValueError("Input must be a list of strings")
 
         # run normal prediction
-        scores = self.model.predict_proba(inputs)[0]
+        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 [{"label": self.id2label[i], "score": score.item()} for i, score in enumerate(scores)]
+        return results