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import streamlit as st
import torch
import numpy as np
from transformers import AutoTokenizer, AutoModel
import torch.nn as nn
from scipy.special import softmax
st.markdown("<img width=400px src='https://blog.predanie.ru/wp-content/uploads/2018/10/content_2-1.jpg'>", unsafe_allow_html=True)
labels_articles = {1: 'Computer Science', 2: 'Economics', 3: "Electrical Engineering And Systems Science",
4: "Mathematics", 5: "Physics", 6: "Quantitative Biology", 7: "Quantitative Finance",
8: "Statistics"
}
@st.cache(allow_output_mutation=True)
def models():
class Net(nn.Module):
def __init__(self):
super(Net,self).__init__()
self.layer = nn.Sequential(
nn.Linear(768, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 8),
)
def forward(self,x):
return self.layer(x)
model_second = Net()
model_second.load_state_dict(torch.load('model.txt'))
model_second.eval()
model_name = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model_first = AutoModel.from_pretrained(model_name)
return (model_first, model_second, tokenizer)
model_first, model_second, tokenizer = models()
title = st.text_area("Write the title of your article, please")
abstract = st.text_area("Write the abstract")
text = title + '. ' + abstract
tokens_info = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
out_first = model_first(**tokens_info).pooler_output
out_second = model_second(out_first).detach().numpy()
out_second = softmax(out_second)
indices = np.argsort(out_second)[0][::-1]
sum_prob = 0
for i in indices:
st.write(labels_articles[i+1])
sum_prob += out_second[0][i]
if sum_prob >= 0.95:
break |