handler.py

import torch
from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline

# get dtype
dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16


class EndpointHandler:
    def __init__(self, path=""):
        # load the model
        self.tokenizer = LlamaTokenizer.from_pretrained(path)
        model = LlamaForCausalLM.from_pretrained(path, load_in_4bit=True, device_map=0, torch_dtype=torch.float16)
        # create inference pipeline
        self.pipeline = pipeline("text-generation", model=model, tokenizer=self.tokenizer)

    # def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
    #     inputs = data.pop("inputs", data)
    #     parameters = data.pop("parameters", None)

    #     # pass inputs with all kwargs in data
    #     if parameters is not None:
    #         prediction = self.pipeline(inputs, **parameters)
    #     else:
    #         prediction = self.pipeline(inputs)
    #     # postprocess the prediction
    #     return prediction
    
    def __call__(self, message: str):
        
        sequences = self.pipeline(
            message,
            do_sample=True,
            top_k=10,
            num_return_sequences=1,
            eos_token_id=self.tokenizer.eos_token_id,
            max_length=2048,
        )
        
        
        generated_text = sequences[0]['generated_text']
        response = generated_text[len(message):]  # Remove the prompt from the output

        print("Chatbot:", response.strip())
        
        response.strip()