Example from a Hugging Face pipeline

import typing as t

 from pipeline import Pipeline, Variable, pipeline_function, pipeline_model


# Example pipline from Hugging Face pipeline abstraction


 PIPELINE_NAME = "hf_roberta_text_classifier"


 @pipeline_model
 class model:
     def __init__(self):
         self.pipe = None

     @pipeline_function(run_once=True, on_startup=True)
     def load(self) -> bool:
         from transformers import pipeline

         self.pipe = pipeline(model="roberta-large-mnli")
         return True

     @pipeline_function
     def predict(self, input: list) -> list:
         return self.pipe(input)


 with Pipeline(PIPELINE_NAME) as pipeline:
     input = Variable(list, is_input=True)

     pipeline.add_variables(
         input,
     )

     model = model()
     model.load()

     output = model.predict(
         input,
     )

     pipeline.output(output)

 hf_pipeline = Pipeline.get_pipeline(PIPELINE_NAME)

 # example run

 input = ["I love Halloween"]

 # run locally
 [output] = hf_pipeline.run(input)

 print(output)