42

handler copy.py

@@ -0,0 +1,51 @@
+from typing import  Dict, List, Any
+# import torch
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+
+
+# from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import AutoModel
+# from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+from transformers import pipeline, AutoTokenizer
+
+# checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
+checkpoint = "distilbert-base-uncased"
+
+class EndpointHandler():
+    
+    def __init__(self, path=""):
+        # load the optimized model
+        # model = ORTModelForSequenceClassification.from_pretrained(path)
+        # model = AutoModel.from_pretrained(checkpoint)
+        # model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
+
+        # tokenizer = AutoTokenizer.from_pretrained(path)
+        # tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=checkpoint)
+        model = DistilBertForSequenceClassification.from_pretrained(checkpoint)
+        tokenizer = DistilBertTokenizer.from_pretrained(checkpoint)
+
+        # create inference pipeline
+        self.pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+
+    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)
+        parameters = data.pop("parameters", None)
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            prediction = self.pipeline(inputs, **parameters)
+        else:
+            prediction = self.pipeline(inputs)
+        # postprocess the prediction
+        return prediction

handler.py

@@ -1,51 +1,9 @@
 from typing import  Dict, List, Any
-# import torch
-from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
-
-
-# from optimum.onnxruntime import ORTModelForSequenceClassification
-from transformers import AutoModel
-# from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-from transformers import pipeline, AutoTokenizer
-
-# checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
-checkpoint = "distilbert-base-uncased"
 
 class EndpointHandler():
-    
     def __init__(self, path=""):
-        # load the optimized model
-        # model = ORTModelForSequenceClassification.from_pretrained(path)
-        # model = AutoModel.from_pretrained(checkpoint)
-        # model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
-
-        # tokenizer = AutoTokenizer.from_pretrained(path)
-        # tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=checkpoint)
-        model = DistilBertForSequenceClassification.from_pretrained(checkpoint)
-        tokenizer = DistilBertTokenizer.from_pretrained(checkpoint)
-
-        # create inference pipeline
-        self.pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
+        pass
 
 
     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)
-        parameters = data.pop("parameters", None)
-
-        # pass inputs with all kwargs in data
-        if parameters is not None:
-            prediction = self.pipeline(inputs, **parameters)
-        else:
-            prediction = self.pipeline(inputs)
-        # postprocess the prediction
-        return prediction
+        return 42