from transformers import Pipeline
from transformers.pipelines import PIPELINE_REGISTRY
import floret
from huggingface_hub import hf_hub_download



class Pipeline_One(Pipeline):
    # def __init__(self, model_path: str):
    #     """
    #     Initialize the Floret language detection pipeline
        
    #     Args:
    #         model_path (str): Path to the .bin model file
    #     """
    #     super().__init__()
    #     self.model = floret.FastText.load_model(model_path)
    
    # def __init__(self, model_name="floret_model.bin", repo_id="Maslionok/pipeline1", revision="main", **kwargs):
    #     """
    #     Initialize the Floret language detection pipeline.
    #     Args:
    #         model_name (str): The name of the Floret model file.
    #         repo_id (str): The Hugging Face repository ID.
    #         revision (str): The branch/revision to download from.
    #     """
    #     super().__init__(**kwargs) 


    #     model_path = hf_hub_download(repo_id=repo_id, filename=model_name, revision=revision)


    #     self.model = floret.load_model(model_path)

    # def _sanitize_parameters(self, **kwargs):
    #     # Add any additional parameter handling if necessary
    #     return {}, {}, {}
    
    def _sanitize_parameters(self, **kwargs):
        print("000000000")
        preprocess_kwargs = {}
        if "text" in kwargs:
            preprocess_kwargs["text"] = kwargs["text"]
        return preprocess_kwargs, {}, {}

    def preprocess(self, text, **kwargs):
        print("this is preprocessing:")
        print(text)
        return text

    def _forward(self, inputs):
        model_output = self.model.predict(**inputs, k=1)

        return model_output

    def postprocess(self, outputs, **kwargs):
        return outputs
    


# PIPELINE_REGISTRY.register_pipeline(
#     task="language-detection",
#     pipeline_class=Pipeline_One,
#     default={"model": None},
# )