models/summarizer.py

import torch
from transformers import BartForConditionalGeneration, BartTokenizer

class TextSummarizer:
    def __init__(self):
        print("Initializing Text Summarizer...")
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"Using device: {self.device}")
        
        # Load model and tokenizer
        self.model_name = "facebook/bart-large-cnn"
        self.tokenizer = BartTokenizer.from_pretrained(self.model_name)
        self.model = BartForConditionalGeneration.from_pretrained(self.model_name).to(self.device)
        print(f"Loaded {self.model_name} model and moved to {self.device}")

    def summarize(self, text, max_length=130, min_length=30):
        try:
            # Tokenize the input text
            inputs = self.tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
            inputs = inputs.to(self.device)

            # Generate summary
            summary_ids = self.model.generate(
                inputs["input_ids"],
                max_length=max_length,
                min_length=min_length,
                num_beams=4,
                length_penalty=2.0,
                early_stopping=True
            )

            # Decode the generated summary
            summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
            return summary

        except Exception as e:
            return f"Error generating summary: {str(e)}"