model.py

from transformers import T5Tokenizer, T5ForConditionalGeneration

def summarize_text(text: str,
                   model_name: str = "t5-base",
                   max_length: int = 150,
                   min_length: int = 40,
                   num_beams: int = 4) -> str:
    """
    Summarizes the given text using a T5 model.
    
    Parameters:
      - text: The long input text to be summarized.
      - model_name: The pre-trained T5 model to use (e.g., "t5-base", "t5-small", etc.)
      - max_length: The maximum length (in tokens) of the generated summary.
      - min_length: The minimum length (in tokens) of the generated summary.
      - num_beams: The number of beams for beam search (affects summary quality).
      
    Returns:
      - The summarized text (str)
    """
    # Load tokenizer and model (using new tokenizer behavior)
    tokenizer = T5Tokenizer.from_pretrained(model_name, legacy=False)
    model = T5ForConditionalGeneration.from_pretrained(model_name)
    
    # Add the summarization prompt (T5 uses prompt-based approach)
    input_text = "summarize: " + text.strip()
    
    # Tokenize the input text
    input_ids = tokenizer.encode(input_text,
                                 return_tensors="pt",
                                 max_length=512,
                                 truncation=True)
    
    # Generate summary using the model's generate method
    summary_ids = model.generate(input_ids,
                                 max_length=max_length,
                                 min_length=min_length,
                                 num_beams=num_beams,
                                 early_stopping=True)
    
    # Decode the generated tokens back into text
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

if __name__ == "__main__":
    # Example English text to summarize
    long_text = (
        "In recent years, the global economy has faced various challenges. Trade tensions, "
        "inflationary pressures, and rapid technological advancements have contributed to "
        "significant changes in market dynamics. Experts believe that these factors will continue "
        "to influence economic trends, while governments around the world are exploring policies "
        "to stabilize the economy. Meanwhile, the rise of the digital economy and the transition "
        "to green energy are emerging as key drivers of future economic growth."
    )
    
    # Execute the summarization
    summary_result = summarize_text(long_text)
    print("Summary:")
    print(summary_result)