Add handler.py for Inference Endpoints

handler.py

@@ -0,0 +1,52 @@
+from typing import Dict, Any
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+class EndpointHandler:
+    def __init__(self, path: str = ""):
+        """Initialize model and tokenizer."""
+        self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+        
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            trust_remote_code=True
+        )
+        self.model.eval()
+        self.device = next(self.model.parameters()).device
+        print(f"✅ Model loaded on {self.device}")
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """Handle inference request."""
+        inputs = data.get("inputs", data.get("input", ""))
+        params = data.get("parameters", {})
+        
+        # Tokenize
+        encoded = self.tokenizer(
+            inputs,
+            return_tensors="pt",
+            truncation=True,
+            max_length=2048
+        ).to(self.device)
+        
+        # Generate
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **encoded,
+                max_new_tokens=params.get("max_new_tokens", 256),
+                temperature=params.get("temperature", 0.7),
+                top_p=params.get("top_p", 0.9),
+                do_sample=params.get("do_sample", True),
+                repetition_penalty=params.get("repetition_penalty", 1.1),
+                pad_token_id=self.tokenizer.pad_token_id,
+                eos_token_id=self.tokenizer.eos_token_id,
+            )
+        
+        # Decode (remove input tokens)
+        generated = outputs[0][encoded["input_ids"].shape[1]:]
+        text = self.tokenizer.decode(generated, skip_special_tokens=True)
+        
+        return {"generated_text": text}