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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 |