from transformers import BartTokenizer
import torch
import streamlit as st
import pickle

class Summarizer:
    def __init__(self):
        self.model = None
        self.tokenizer = None

    def load_model(self):
        try:
            self.tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
            with open('bart_ami_finetuned.pkl', 'rb') as f:
                self.model = pickle.load(f)
            return self.model
        except Exception as e:
            st.error(f"Error loading summarization model: {str(e)}")
            return None

    def process(self, text: str, max_length: int = 130, min_length: int = 30):
        try:
            inputs = self.tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
            summary_ids = self.model.generate(inputs["input_ids"], max_length=max_length, min_length=min_length)
            return self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        except Exception as e:
            st.error(f"Error in summarization: {str(e)}")
            return None