# src/summarizer.py — Text summarization using Hugging Face XSum model

from transformers import pipeline

# Load the summarization pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-xsum")

def summarize(text, max_length=150, min_length=30):
    """Summarize the input text using a pre-trained model."""
    summary = summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)
    return summary[0]['summary_text']

if __name__ == "__main__":
    sample_text = (
        "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. "
        "It is named after the engineer Gustave Eiffel, whose company designed and built the tower."
        "Constructed from 1887 to 1889 as the entrance to the 1889 World's Fair, it was initially "
        "criticized by some of France's leading artists and intellectuals for its design, but it "
        "has become a global cultural icon of France and one of the most recognizable structures in the world."
    )
    print("Original Text:\n", sample_text)
    print("\nSummary:\n", summarize(sample_text))