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import streamlit as st
import torch
import transformers
st.markdown("### Articles classificator.")
# st.markdown("<img width=200px src='https://rozetked.me/images/uploads/dwoilp3BVjlE.jpg'>", unsafe_allow_html=True)
@st.cache
def LoadModel():
return torch.load('model.pt'), AutoTokenizer.from_pretrained('bert-base-uncased')()
model, tokenizer = LoadModel()
def process(title, summary):
text = title + summary
model.eval()
lines = [text]
X = tokenizer(lines, padding=True, truncation=True, return_tensors="pt")
out = model(X)
probs = torch.exp(out[0])
return probs
title = st.text_area("Title")
summary = st.text_area("Summary")
st.markdown(f"{process(title, summary)}") |