File size: 1,374 Bytes
9b2efc6 b1426fb 08d05f4 1ae5349 b1426fb 08d05f4 1ae5349 9b2efc6 08d05f4 8998fb8 08d05f4 b1426fb 9b2efc6 08d05f4 1ae5349 9b2efc6 08d05f4 9b2efc6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 |
from transformers import BartTokenizer, BartForConditionalGeneration
import torch
import streamlit as st
class Summarizer:
def __init__(self):
self.model = None
self.tokenizer = None
def load_model(self):
try:
self.tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
self.model = torch.load('bart_ami_finetuned.pkl')
self.model.to(torch.device('cuda' if torch.cuda.is_available() else 'cpu'))
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 = 150, min_length: int = 40):
try:
inputs = self.tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
inputs = {key: value.to(self.model.device) for key, value in inputs.items()}
summary_ids = self.model.generate(
inputs["input_ids"],
max_length=max_length,
min_length=min_length,
num_beams=4,
length_penalty=2.0
)
summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return summary
except Exception as e:
st.error(f"Error in summarization: {str(e)}")
return None
|