added custom handler

handler.py

@@ -0,0 +1,31 @@
+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}

requirements.txt

@@ -0,0 +1,3 @@
+accelerate
+bitsandbytes
+transformers