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import streamlit as st
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch
import numpy as np
MAPPING = {
'cs': 'Computer Science', 'stat': 'Statistics', 'math': 'Mathematics', 'q-bio': 'Quantitative Biology',
'physics': 'Physics', 'cmpl-lg': 'Computation and Language', 'eess': 'Electrical Engineering and Systems Science',
'quant-ph': 'Quantum Physics', 'cond-mat': 'Condensed Matter', 'astro-ph': 'Astrophysics', 'nlin': 'Nonlinear Sciences',
'q-fin': 'Quantitative Finance', ':)': 'Something else'
}
@st.cache_resource
def load_model():
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased')
model = DistilBertForSequenceClassification.from_pretrained('model/')
return tokenizer, model
tokenizer, model = load_model()
st.title('arXiv Article Classifier')
title = st.text_input('Title')
abstract = st.text_area('Abstract')
text = title + ' ' + abstract if abstract else title
if st.button('Predict'):
if not text.strip():
st.error('Please enter at least a title.')
else:
inputs = tokenizer(
text,
truncation=True,
padding=True,
max_length=512,
return_tensors='pt'
)
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.nn.functional.softmax(logits, dim=1).numpy()[0]
sorted_indices = np.argsort(-probs)
cumulative = 0
result = []
for idx in sorted_indices:
cumulative += probs[idx]
result.append((model.config.id2label[idx], probs[idx]))
if cumulative >= 0.95:
break
for tag, prob in result:
if tag in MAPPING:
st.write(f'{MAPPING[tag]}: {prob:.2%}')
else:
st.write(f'{tag}: {prob:.2%}') |