Create handler.py

handler.py

@@ -0,0 +1,27 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(path)
+        self.model.eval()
+
+    def __call__(self, data):
+        inputs = data.get("inputs", "")
+        parameters = data.get("parameters", {})
+        
+        if not inputs:
+            return [{"error": "Missing 'inputs' in payload"}]
+
+        input_ids = self.tokenizer.encode(inputs, return_tensors="pt")
+        outputs = self.model.generate(
+            input_ids,
+            max_new_tokens=parameters.get("max_new_tokens", 128),
+            do_sample=True,
+            temperature=parameters.get("temperature", 0.7),
+            top_p=parameters.get("top_p", 0.9),
+        )
+
+        result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return [{"generated_text": result}]  # ✅ Must return a list