Delete handler.py

handler.py

@@ -1,31 +0,0 @@
-from typing import Dict, List, Any
-from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
-import torch
-
-
-
-class EndpointHandler:
-    def __init__(self, path=""):
-        # load model and processor from path
-        guider_config = AutoConfig.from_pretrained(path)
-        self.model =  AutoModelForSequenceClassification.from_pretrained(path, config=guider_config)
-        self.tokenizer = AutoTokenizer.from_pretrained(path)
-
-    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
-        """
-        Args:
-            data (:dict:):
-                The payload with the text prompt.
-        """
-        # process input
-        gen_outputs_no_input_decoded = data.pop("gen_outputs_no_input_decoded", data)
-
-        # Guiding the model with his ranking,
-        guider_inputs = self.tokenizer([gen_output_no_input_decoded for gen_output_no_input_decoded in gen_outputs_no_input_decoded],
-                                        return_tensors='pt', padding=True, truncation=True)
-
-        guider_outputs = self.model(**guider_inputs)
-
-        # the slicing at the end [:,x]: x=0 for negative, x=1 for neutral, x=2 for positive
-        guider_predictions = torch.nn.functional.softmax(guider_outputs.logits, dim=-1)[:, 0].tolist()
-        return {"guider_predictions": guider_predictions}