Create handler.py

handler.py

@@ -0,0 +1,60 @@
+from typing import Dict, List, Any
+import json
+import torch
+from transformers import BertTokenizerFast, BertForTokenClassification
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Load the tokenizer and model
+        self.tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
+        self.model = BertForTokenClassification.from_pretrained(path)
+        self.model.eval()
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.model.to(self.device)
+        
+        # ID to label mapping
+        self.id2label = {
+            0: 'O',
+            1: 'B-STEREO',
+            2: 'I-STEREO',
+            3: 'B-GEN',
+            4: 'I-GEN',
+            5: 'B-UNFAIR',
+            6: 'I-UNFAIR'
+        }
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        Args:
+            data (Dict[str, Any]): A dictionary containing the input text under 'inputs'.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries with token labels.
+        """
+        # Extract the input sentence
+        sentence = data.get("inputs", "")
+        if not sentence:
+            return [{"error": "Input 'inputs' is required."}]
+
+        # Tokenize the input sentence
+        inputs = self.tokenizer(sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
+        input_ids = inputs['input_ids'].to(self.device)
+        attention_mask = inputs['attention_mask'].to(self.device)
+
+        # Run inference
+        with torch.no_grad():
+            outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)
+            logits = outputs.logits
+            probabilities = torch.sigmoid(logits)
+            predicted_labels = (probabilities > 0.5).int()
+
+        # Prepare the result
+        result = []
+        tokens = self.tokenizer.convert_ids_to_tokens(input_ids[0])
+        for i, token in enumerate(tokens):
+            if token not in self.tokenizer.all_special_tokens:
+                label_indices = (predicted_labels[0][i] == 1).nonzero(as_tuple=False).squeeze(-1)
+                labels = [self.id2label[idx.item()] for idx in label_indices] if label_indices.numel() > 0 else ['O']
+                result.append({"token": token, "labels": labels})
+
+        return result