model.py

from transformers import pipeline
from transformers import ( TokenClassificationPipeline, AutoModelForTokenClassification, AutoTokenizer)
from transformers.pipelines import AggregationStrategy
import numpy as np


# ================================= summarize code =================================
def summerize(text):
    summarizer = pipeline("summarization", model="Falconsai/text_summarization")

    text_len = len(text.split(' '))
    max_length = int((text_len * 80)/100)
    min_length = int((text_len * 30)/100)

    return{"output": summarizer(text, max_length=max_length, min_length=min_length, do_sample=True)}


# =================================s keywords code =================================


# Define keyphrase extraction pipeline
# class KeyphraseExtractionPipeline(TokenClassificationPipeline):
#     def __init__(self, model, *args, **kwargs):
#         super().__init__(
#             model=AutoModelForTokenClassification.from_pretrained(model),
#             tokenizer=AutoTokenizer.from_pretrained(model),
#             *args,
#             **kwargs
#         )

#     def postprocess(self, all_outputs):
#         results = super().postprocess(
#             all_outputs=all_outputs,
#             aggregatsion_strategy=AggregationStrategy.FIRST,
#         )
#         return np.unique([result.get("word").strip() for result in results])

# # Load pipeline
# model_name = "ml6team/keyphrase-extraction-distilbert-inspec"
# extractor = KeyphraseExtractionPipeline(model=model_name)

# keyphrases = extractor(text)

# print(keyphrases)