rsk

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

def summarize_text(text, model_name):
    # Initialize tokenizer and model
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

    # Encode the text and generate a summary
    tokens_input = tokenizer.encode("summarize: " + text, return_tensors='pt', max_length=512, truncation=True)
    ids = model.generate(tokens_input, min_length=80, max_length=120)