Test spacy

handler.py

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+from typing import  Dict, List, Any
+
+import spacy
+import os
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load the optimized model
+        os.system("python -m spacy download en_core_web_sm")
+        self.pipeline = spacy.load("en_core_web_sm")
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
+                - "label": A string representing what the label/class is. There can be multiple labels.
+                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
+        """
+        inputs = data.pop("inputs", data)
+
+        doc = self.pipeline(inputs)
+        res = []
+        for token in doc:
+            res.append({"token": token.text, "pos": token.pos_, "dep": token.dep_})
+
+        return res

requirements.txt

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+spacy