Create handler.py

handler.py

@@ -0,0 +1,27 @@
+from typing import Dict, Any, List
+import torch
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        try:
+            self.model = T5ForConditionalGeneration.from_pretrained(path).to(self.device)
+            self.tokenizer = T5Tokenizer.from_pretrained(path)
+        except Exception as e:
+            print(f"Error loading model or tokenizer from path {path}: {e}")
+            # Handle error (e.g., exit or set model/tokenizer to None)
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        inputs = data.get("inputs", "")
+        if not inputs:
+            return [{"error": "No inputs provided"}]
+
+        tokenized_input = self.tokenizer(inputs, return_tensors="pt", truncation=True, max_length=512, padding="max_length")
+        tokenized_input = tokenized_input.to(self.device)  # Move input tensors to the same device as model
+
+        summary_ids = self.model.generate(**tokenized_input, max_length=400, do_sample=True, top_p=0.8)
+
+        summary_text = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+        return [{"summary": summary_text}]