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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 _sanitize_parameters(self, **kwargs):
# Add any additional parameter handling if necessary
return {}, {}, {}
def preprocess(self, text, **kwargs):
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},
) |