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import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
from safetensors.torch import load_file as safe_load
target_to_ind = {'cs': 0, 'econ': 1, 'eess': 2, 'math': 3, 'phys': 4, 'q-bio': 5, 'q-fin': 6, 'stat': 7}
target_to_label = {'cs': 'Computer Science', 'econ': 'Economics', 'eess': 'Electrical Engineering and Systems Science', 'math': 'Mathematics', 'phys': 'Physics',
'q-bio': 'Quantitative Biology', 'q-fin': 'Quantitative Finance', 'stat': 'Statistics'}
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
model, tokenizer = load_model_and_tokenizer()
def get_predict(title: str, abstract: str) -> (str, float, dict):
text = [title + tokenizer.sep_token + abstract[:128]]
tokens_info = tokenizer(
text,
padding=True,
truncation=True,
return_tensors="pt",
)
with torch.no_grad():
out = model(**tokens_info)
probs = torch.nn.functional.softmax(out.logits, dim=-1).tolist()[0]
return list(sorted([(p, ind_to_target[i]) for i, p in enumerate(probs)]))[::-1]
title = st.text_area("Title ", "", height=100)
abstract = st.text_area("Abstract ", "", height=150)
mode = st.radio("Mode: ", ("Best prediction", "Top 95%"))
if st.button("Get prediction", key="manual"):
if len(title) == 0:
st.error("Please, provide paper's title")
else:
with st.spinner("Be patient, I'm doing my best"):
predict = get_predict(title, abstract)
tags = []
threshold = 0 if status == "Best prediction" else 0.95
sum_p = 0
for p, tag in predict:
sum_p += p
tags.append(target_to_label[tag])
if sum_p >= threshold:
break
st.succes(f"{'\n'.join(tags)}")
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