Remove custom handler

handler.py

@@ -1,71 +0,0 @@
-import os
-from typing import Dict, List, Any
-from unsloth import FastLanguageModel
-from unsloth.chat_templates import get_chat_template
-import torch
-from huggingface_hub import login
-import os
-
-class EndpointHandler:
-    def __init__(self, path=""):   
-        # access_token = os.environ["HUGGINGFACE_TOKEN"]
-        # login(token=access_token)
-        # Load the model and tokenizer
-        self.model, self.tokenizer = FastLanguageModel.from_pretrained(
-            model_name = path,  # Use the current directory path
-            max_seq_length = 2048,
-            dtype = None,
-            load_in_4bit = True,
-        )
-        FastLanguageModel.for_inference(self.model)
-
-        # Set up the chat template
-        self.tokenizer = get_chat_template(
-            self.tokenizer,
-            chat_template="llama-3",
-            mapping={"role": "from", "content": "value", "user": "human", "assistant": "gpt"}
-        )
-
-    def __call__(self, data: Dict[str, Any]) -> List[str]:
-        inputs = data.pop("inputs", data)
-        parameters = data.pop("parameters", {})
-
-        # Extract parameters or use defaults
-        max_tokens = parameters.get("max_new_tokens", 512)
-        temperature = parameters.get("temperature", 0.2)
-        top_p = parameters.get("top_p", 0.5)
-        system_message = parameters.get("system_message", "")
-
-        # Prepare messages
-        messages = [{"from": "human", "value": system_message}]
-        if isinstance(inputs, str):
-            messages.append({"from": "human", "value": inputs})
-        elif isinstance(inputs, list):
-            for msg in inputs:
-                role = "human" if msg["role"] == "user" else "gpt"
-                messages.append({"from": role, "value": msg["content"]})
-
-        # Tokenize input
-        tokenized_input = self.tokenizer.apply_chat_template(
-            messages,
-            tokenize=True,
-            add_generation_prompt=True,
-            return_tensors="pt"
-        ).to("cuda")
-
-        # Generate output
-        with torch.no_grad():
-            output = self.model.generate(
-                input_ids=tokenized_input,
-                max_new_tokens=max_tokens,
-                temperature=temperature,
-                top_p=top_p,
-                use_cache=True
-            )
-
-        # Decode and process the output
-        full_response = self.tokenizer.decode(output[0], skip_special_tokens=True)
-        response_lines = [line.strip() for line in full_response.split('\n') if line.strip()]
-        last_response = response_lines[-1] if response_lines else ""
-
-        return [last_response]