modules/summarizer.py

# modules/summarizer.py

from transformers import pipeline
from config.settings import SUMMARIZER_MODEL

# Load summarization pipeline
summarizer_pipeline = pipeline("summarization", model=SUMMARIZER_MODEL)

def summarize_texts(texts: list, max_length: int = 60, min_length: int = 20) -> list:
    """
    Generate summaries for a list of texts.
    Returns a list of dictionaries with original text and summary.
    """
    results = []
    for t in texts:
        if t.strip():
            try:
                summary = summarizer_pipeline(
                    t,
                    max_length=max_length,
                    min_length=min_length,
                    do_sample=False
                )[0]["summary_text"]

                results.append({
                    "text": t,
                    "summary": summary
                })
            except Exception as e:
                results.append({
                    "text": t,
                    "summary": f"ERROR: {str(e)}"
                })
    return results