Delete handler.py

handler.py

@@ -1,44 +0,0 @@
-from typing import Dict, Any
-import logging
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftConfig, PeftModel
-import torch.cuda
-
-
-LOGGER = logging.getLogger(__name__)
-logging.basicConfig(level=logging.INFO)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-class EndpointHandler():
-    def __init__(self, path=""):
-        config = PeftConfig.from_pretrained(path)
-        model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map='auto',trust_remote_code=True)
-        self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
-        # Load the Lora model
-        self.model = PeftModel.from_pretrained(model, path)
-
-    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
-        """
-        Args:
-            data (Dict): The payload with the text prompt and generation parameters.
-        """
-        LOGGER.info(f"Received data: {data}")
-        # Get inputs
-        prompt = data.pop("inputs", None)
-        parameters = data.pop("parameters", None)
-        if prompt is None:
-            raise ValueError("Missing prompt.")
-        # Preprocess
-        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(device)
-        # Forward
-        LOGGER.info(f"Start generation.")
-        if parameters is not None:
-            output = self.model.generate(input_ids=input_ids, **parameters)
-        else:
-            output = self.model.generate(input_ids=input_ids)
-        # Postprocess
-        prediction = self.tokenizer.decode(output[0])
-        LOGGER.info(f"Generated text: {prediction}")
-        return {"generated_text": prediction}