Upload handler.py

handler.py

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+from typing import Dict, List, Any
+
+import torch
+from transformers import pipeline, XLMRobertaTokenizerFast, XLMRobertaForSequenceClassification
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # load the optimized model
+        model = XLMRobertaForSequenceClassification.from_pretrained(path)
+        tokenizer = XLMRobertaTokenizerFast.from_pretrained(path)
+        model.eval()
+        # create inference pipeline
+        self.pipline = pipeline("text-classification", tokenizer=tokenizer, model=model, device=self.device)
+
+    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.pipline(inputs, **parameters)
+        else:
+            prediction = self.pipline(inputs)
+        # postprocess the prediction
+        return prediction