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# 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)) |