app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
from transformers import pipeline


with open('labels.txt') as f:
    LABEL2STR = f.readline().split()


@st.cache(allow_output_mutation=True)
def load_model():
    tokenizer = AutoTokenizer.from_pretrained("kirillbogatiy/model_topics")
    model = AutoModelForSequenceClassification.from_pretrained("kirillbogatiy/model_topics")
    pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
    return pipe
    
    
def pretty_output(predictions, thr=0.95):
    cumulative_score = 0
    st.write('Possible topics:')
    for label, data in enumerate(sorted(predictions[0], key=lambda item: item['score'], reverse=True)):
        score = data['score']
        cumulative_score += score
        st.write('{}: {} %'.format(LABEL2STR[label], round(100 * score, 2)))
        if cumulative_score >= thr:
            return
      


if __name__ == '__main__':
    title = st.text_input('Input a title here:') 
    abstract = st.text_input('Input an abstract here:')
    pipe = load_model()
    if title:
        predictions = pipe('Title: {}\n\nAbstract: {}'.format(title, abstract))
        pretty_output(predictions)