vitkishloh228/bert_tagger

1import streamlit as st2import model_utils3 4def load_model_tokenizer(path):5    return model_utils.load_model(path)6 7def process_output_model(confidences, limit=10):8    arr = []9    for key, value in confidences.items():10        arr.append((value, key))11    arr = sorted(arr)[::-1][:limit]12    output = "Output model:\n"13    for prob, tag in arr:14        output += f"{tag} {round(prob, 4)}\n\n"15    return output16 17def process_output():18    st.markdown("### Tagging arxiv")19    abstract = st.text_area("Abstract")20    title = st.text_area("Title")21    model, tokenizer = load_model_tokenizer('D.pt')22 23    answer = model_utils.process_data(model, tokenizer, {"abstract" : abstract, "titles" : title})24    st.markdown(process_output_model(answer))25 26process_output()