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import streamlit as st
import torch
import numpy as np
from transformers import TrainingArguments, \
Trainer, AutoTokenizer, DataCollatorWithPadding, \
AutoModelForSequenceClassification
categories = ['Biology', 'Computer science', 'Economics', 'Electrics', 'Finance',
'Math', 'Physics', 'Statistics']
labels = [i for i in range(len(categories))]
def print_probs(logits):
probs = torch.nn.functional.softmax(logits, dim=0).numpy()*100
ans = list(zip(probs,labels))
ans.sort(reverse=True)
sum = 0
i = 0
while sum <= 95:
prob, idx = ans[i]
text = categories[idx] + ": "+ str(np.round(prob,1)) + "%"
st.write(text)
sum+=prob
i+=1
@st.cache
def make_prediction(text):
tokenized_text = tokenizer(text, return_tensors='pt')
with torch.no_grad():
pred_logits = model(**tokenized_text).logits
st.write("Category probability:")
print_probs(pred_logits[0])
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=8)
model_name = "trained_model2"
model_path = model_name + '.zip'
model.load_state_dict(
torch.load(
model_path,
map_location=torch.device("cpu")
)
)
# MAIN
st.markdown("# Arxiv.org category classifier")
st.markdown(" ")
st.markdown("<img src='https://centroderecursosmarista.org/wp-content/uploads/2013/05/arvix.jpg'>", unsafe_allow_html=True)
st.markdown(" ")
st.markdown("### Article Title")
text = st.text_area("Введите название статьи", height=50)
st.markdown("### Article Abstract")
text = st.text_area("Введите описание статьи", height=400)
make_prediction(text)
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