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| from transformers import pipeline | |
| class TextSummarizer: | |
| def __init__(self): | |
| print("Loading summarization model...") | |
| self.summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| print("Summarization model loaded successfully.") | |
| def summarize_text(self, text, max_length=150, min_length=40): | |
| # Check if the input text is too short for summarization | |
| if len(text.split()) < 30: | |
| print("Input text too short to summarize. Skipping summarization.") | |
| return "Transcript too short to summarize." | |
| print("Summarizing text...") | |
| summary = self.summarizer(text, max_length=max_length, min_length=min_length, do_sample=False) | |
| summarized_text = summary[0]['summary_text'] | |
| print("Text summarized successfully.") | |
| return summarized_text | |