| 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}, | |
| # ) |