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import streamlit as st
from pandas import DataFrame
import seaborn as sns
from model import ArxivClassifierModelsPipeline
st.markdown("# Hello, friend!")
st.markdown(" This magic application going to help you with understanding of science paper topic! Cool? Yeah! ")
model = ArxivClassifierModelsPipeline()
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, 5, 0.07])
with c2:
doc_title = st.text_area(
"Paste your paper's title below (max 100 words)",
height=210,
)
doc_abstract = st.text_area(
"Paste your paper's abstract text below (max 100500 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()
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]")
preds_topic, preds_maintopic = model.make_predict(title + abstract)
st.markdown("## 🎈 Yor article probably about: ")
st.header("")
df = (
DataFrame(preds_topic.items(), columns=["Topic", "Probability"])
.sort_values(by="Probability", 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=[
"Probability",
],
)
c1, c2, c3 = st.columns([1, 3, 1])
format_dictionary = {
"Probability": "{:.1%}",
}
df = df.format(format_dictionary)
with c2:
st.markdown("#### We suppose your research about: ")
st.markdown(f"### {preds_maintopic}! ")
st.markdown(f"Wow, we're impressed, are you addicted to {preds_maintopic.lower()}?! Coool! ")
st.markdown("##### More detailed, it's about topic: ")
st.table(df)
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