from typing import  Dict, List, Any
from ctransformers import AutoModelForCausalLM


class EndpointHandler():
    def __init__(self, path=""):
        model_id = "djomo/MISTRALllux1000-7b-v5-GGUF"
        model_file="mistralllux1000-7b-v5.gguf.q5_k_m.bin"
        config = {'context_length' : 3048,'max_new_tokens': 856, 'repetition_penalty': 1.1,'temperature': 0.1, 'stream': True}
        llm = AutoModelForCausalLM.from_pretrained(
                                           model_id,
                                           model_file=model_file,
                                           model_type="mistral",
                                           gpu_layers=130,#50 #110
                                           **config
                                           )
        self.pipeline = llm


    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        """
        Args:
            data (:obj:):
                includes the input data and the parameters for the inference.
        Return:
            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
                - "label": A string representing what the label/class is. There can be multiple labels.
                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
        """
        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, stream=False)
        else:
            prediction = self.pipeline(inputs, stream=False)
        # postprocess the prediction
        return prediction