app.py

import streamlit as st
import transformers
import pickle
import seaborn as sns
from pandas import DataFrame
from transformers import AutoTokenizer, AutoModelForSequenceClassification

st.markdown("# Hello, friend!")
st.markdown(" This magic application going to help you with understanding of science paper topic! Cool? Yeah! ")

try:
    model_name_global = "allenai/scibert_scivocab_uncased"
    tokenizer_ = AutoTokenizer.from_pretrained(model_name_global)
    with open('./models/scibert/decode_dict.pkl', 'rb') as f:
        decode_dict = pickle.load(f)
except ValueError:
    st.error("Load tokenizer or decode answer dict goes wrong! Pls contact author [email protected]")

with st.form(key="my_form"):
    st.markdown("### 🎈 Do you want a little magic?  ")
    st.markdown(" Write your article title and abstract to textboxes bellow and I'll gues topic of your paper!  ")
    ce, c2, c3 = st.columns([0.07, 7, 0.07])

    with c2:
        doc_title = st.text_area(
            "Paste your abstract title below (1 to 50 words)",
            height=210,
        )

        doc_abstract = st.text_area(
            "Paste your abstract text below (1 to 500 words)",
            height=410,
        )

        MAX_WORDS_TITLE, MAX_WORDS_ABSTRACT = 50, 500
        import re

        len_title = len(re.findall(r"\w+", doc_title))
        len_abstract = len(re.findall(r"\w+", doc_abstract))

        if len_title > MAX_WORDS_TITLE:
            st.warning(
                "⚠️ Your title contains "
                + str(len_title)
                + " words."
                + " Only the first 50 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
            )

            doc_title = doc_title[:MAX_WORDS_TITLE]

        if len_abstract > MAX_WORDS_ABSTRACT:
            st.warning(
                "⚠️ Your abstract contains "
                + str(len_abstract)
                + " words."
                + " Only the first 500 words will be reviewed. Stay tuned as increased allowance is coming! 😊"
            )

            doc_abstract = doc_abstract[:MAX_WORDS_ABSTRACT]

        submit_button = st.form_submit_button(label="✨ Let's play, try it!")

if not submit_button:
    st.stop()

if len_title < 1:
    st.error("Article without any words in title? Pls give me correct title!")
    st.stop()

if len_abstract < 1:
    st.error("Article without any words in abstract? Pls give me correct abstract!")
    st.stop()


#  allow_output_mutation=True
@st.cache(suppress_st_warning=True)
def load_model():
    st.write("Loading big model")
    return AutoModelForSequenceClassification.from_pretrained("models/scibert/")


def make_predict(tokens, decode_dict):

    model_ = load_model()
    outs = model_(tokens.input_ids)

    probs = outs["logits"].softmax(dim=-1).tolist()[0]
    topic_probs = {}
    for i, p in enumerate(probs):
        if p > 0.1:
            topic_probs[decode_dict[i]] = p
    return topic_probs


model_local = "models/scibert/"

title = doc_title
abstract = doc_abstract
try:
    tokens = tokenizer_(title + abstract, return_tensors="pt")
except ValueError:
    st.error("Word parsing into tokens went wrong! Is input valid? If yes, pls contact author [email protected]")

predicts = make_predict(tokens, decode_dict)

st.markdown("## 🎈 Yor article probably about:  ")
st.header("")

df = (
    DataFrame(predicts.items(), columns=["Topic", "Prob"])
        .sort_values(by="Prob", ascending=False)
        .reset_index(drop=True)
)

df.index += 1

# Add styling
cmGreen = sns.light_palette("green", as_cmap=True)
cmRed = sns.light_palette("red", as_cmap=True)
df = df.style.background_gradient(
    cmap=cmGreen,
    subset=[
        "Prob",
    ],
)

c1, c2, c3 = st.columns([1, 3, 1])

format_dictionary = {
    "Prob": "{:.1%}",
}

df = df.format(format_dictionary)

with c2:
    st.table(df)