Add handler.py to support inference endpoints

handler.py

@@ -0,0 +1,33 @@
+from typing import Any, Dict, List
+
+from span_marker import SpanMarkerModel
+
+
+class EndpointHandler:
+    def __init__(self, model_id: str) -> None:
+        self.model = SpanMarkerModel.from_pretrained(model_id)
+        # Try to place it on CUDA, do nothing if it fails
+        self.model.try_cuda()
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        Args:
+            data (Dict[str, Any]):
+                a dictionary with the "inputs" key corresponding to a string containing some text
+        Return:
+            A List[Dict[str, Any]]:. The object returned should be like [{"entity_group": "XXX", "word": "some word", "start": 3, "end": 6, "score": 0.82}] containing :
+                - "entity_group": A string representing what the entity is.
+                - "word": A rubstring of the original string that was detected as an entity.
+                - "start": the offset within `input` leading to `answer`. context[start:stop] == word
+                - "end": the ending offset within `input` leading to `answer`. context[start:stop] === word
+                - "score": A score between 0 and 1 describing how confident the model is for this entity.
+        """
+        return [
+            {
+                "entity_group": entity["label"],
+                "word": entity["span"],
+                "start": entity["char_start_index"],
+                "end": entity["char_end_index"],
+                "score": entity["score"],
+            }
+            for entity in self.model.predict(data["inputs"])