Delete handler.py

handler.py

@@ -1,49 +0,0 @@
-from typing import Any, Dict, List
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-
-MAX_INPUT_LENGTH = 256   
-MAX_OUTPUT_LENGTH = 128 
-
-class EndpointHandler:
-    def __init__(self, model_dir: str = "", **kwargs: Any) -> None:
-        self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
-        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_dir)
-        self.model.eval()
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.model.to(self.device)
-
-    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
-        inputs = data.get("inputs")
-        if not inputs:
-            raise ValueError("No 'inputs' found in the request data.")
-
-        if isinstance(inputs, str):
-            inputs = [inputs]
-
-        tokenized_inputs = self.tokenizer(
-            inputs,
-            max_length=MAX_INPUT_LENGTH,
-            padding=True,
-            truncation=True,
-            return_tensors="pt"
-        ).to(self.device)
-
-        try:
-            with torch.no_grad():
-                outputs = self.model.generate(
-                    tokenized_inputs["input_ids"],
-                    attention_mask=tokenized_inputs["attention_mask"],
-                    max_length=MAX_OUTPUT_LENGTH,
-                    num_beams=4,  # Slightly faster
-                    no_repeat_ngram_size=3,
-                    early_stopping=True,
-                    do_sample=False,
-                    pad_token_id=self.tokenizer.pad_token_id
-                )
-            decoded_outputs = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
-            results = [{"generated_text": text} for text in decoded_outputs]
-            return results
-        except Exception as e:
-            # Log error and return a message
-            return [{"generated_text": f"Error: {str(e)}"}]