app.py

import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification

target_to_ind = {'cs': 0, 'econ': 1, 'eess': 2, 'math': 3, 'phys': 4, 'q-bio': 5, 'q-fin': 6, 'stat': 7}

ind_to_target = {ind: target for target, ind in target_to_ind.items()}

@st.cache_resource
def load_model_and_tokenizer():
    model_name = 'distilbert/distilbert-base-cased'
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=len(target_to_ind))
    
    state_dict = safe_load("model.safetensors")
    model.load_state_dict(state_dict)
    
    return model, tokenizer


def get_predict(title: str, abstract: str) -> (str, float, dict):
    tokenized_text = tokenizer(title + tokenizer.sep_token + abstact[:128], padding="max_length", truncation=True)

    with torch.no_grad():
        outputs = model(tokenized_text)
        probs = torch.nn.functional.softmax(out.logits, dim=-1)

        return list(sorted([(p, ind_to_target[i]) for i, p in enumerate(probs)], reversed=True))


title = st.text_area("Title ", "", height=100)
abstract = st.text_area("Abstract ", "", height=150)

if st.button("Классифицировать", key="manual"):
    if len(title_text) == 0:
        st.error("Please, provide paper's title")
    else:
        with st.spinner("Be patient, I'm doing my best"):
            predict = get_predict(title, abstract)
        st.success(f"Предсказанный тэг: **{predict[0][1]}**")
        
        
model, tokenizer = load_model_and_tokenizer()