remove handler.py

handler.py

@@ -1,43 +0,0 @@
-import torch
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, BitsAndBytesConfig
-from typing import Any
-
-class EndpointHandler():
-    def __init__(self, path=""):
-        self.model = AutoModelForSeq2SeqLM.from_pretrained(f"{path}/4-bit", device_map="auto")
-        self.tokenizer = AutoTokenizer.from_pretrained(path)
-
-    def __call__(self, data: dict[str, Any]) -> dict[str, Any]:
-        inputs = data.get("inputs")
-        parameters = data.get("parameters")
-
-        if inputs is None:
-            raise ValueError(f"'inputs' is missing from the request body")
-
-        if not isinstance(inputs, str):
-            raise ValueError(f"Expected 'inputs' to be a str, but found {type(inputs)}")
-
-        if parameters is not None and not isinstance(parameters, dict):
-            raise ValueError(f"Expected 'parameters' to be a dict, but found {type(parameters)}")
-
-        # Truncate the tokens to 1024 to prevent errors with BART and long text.
-        tokens = self.tokenizer(
-            inputs,
-            max_length=1024,
-            truncation=True,
-            return_tensors="pt",
-            return_attention_mask=False,
-        )
-
-        # Ensure the input_ids and the model are both on the GPU to prevent errors.
-        input_ids = tokens.input_ids.to("cuda")
-
-        # Gradient calculation is not needed for inference.
-        with torch.no_grad():
-            if parameters is None:
-                output = self.model.generate(input_ids)
-            else:
-                output = self.model.generate(input_ids, **parameters)
-
-        generated_text = self.tokenizer.decode(output[0], skip_special_tokens=True)
-        return {"generated_text": generated_text}