|
import re |
|
|
|
import seaborn as sns |
|
import streamlit as st |
|
|
|
from demo.utils import load_model, process_text |
|
|
|
st.set_page_config( |
|
page_title="BERT Keyword Extractor", |
|
page_icon="🎈", |
|
) |
|
|
|
|
|
def _max_width_(): |
|
max_width_str = "max-width: 1400px;" |
|
st.markdown( |
|
f""" |
|
<style> |
|
.reportview-container .main .block-container{{ |
|
{max_width_str} |
|
}} |
|
</style> |
|
""", |
|
unsafe_allow_html=True, |
|
) |
|
|
|
|
|
st.header("🔑 Automated Essay Evaluator") |
|
|
|
with st.expander("ℹ️ - About this app", expanded=True): |
|
st.write( |
|
""" |
|
- This application demonstrates how automated essay evaluation works: given as an input text with max. \ |
|
length of 512, it scores it (from 1.0 to 4.0) for different criteria: cohesion, syntax, vocabulary, \ |
|
phraseology, grammar and conventions. |
|
- This solution is based on fine-tuned deberta-v3-large model. |
|
""" |
|
) |
|
|
|
st.markdown("") |
|
|
|
st.markdown("") |
|
st.markdown("## 📌 **Paste document**", unsafe_allow_html=True) |
|
with st.form(key="my_form"): |
|
_, c2, _ = st.columns([0.07, 5, 0.07]) |
|
|
|
with c2: |
|
doc = st.text_area( |
|
"Paste your text below (max 500 words)", |
|
height=510, |
|
) |
|
|
|
MAX_WORDS = 500 |
|
|
|
res = len(re.findall(r"\w+", doc)) |
|
doc = doc[:MAX_WORDS] |
|
|
|
submit_button = st.form_submit_button(label="✨ Assess my text!") |
|
|
|
if not submit_button: |
|
st.stop() |
|
|
|
st.markdown("## 🎈 **Check results**") |
|
|
|
st.header("") |
|
|
|
cs, c1, c2, c3, cLast = st.columns([2, 1.5, 1.5, 1.5, 2]) |
|
|
|
st.header("") |
|
|
|
model = load_model() |
|
df = process_text(doc, model) |
|
|
|
df.index += 1 |
|
|
|
|
|
cmGreen = sns.light_palette("green", as_cmap=True) |
|
cmRed = sns.light_palette("red", as_cmap=True) |
|
df = df.style.background_gradient( |
|
cmap=cmGreen, |
|
subset=[ |
|
"Grade", |
|
], |
|
) |
|
|
|
|
|
format_dictionary = { |
|
"Relevancy": "{:.1%}", |
|
} |
|
|
|
df = df.format(format_dictionary) |
|
|
|
st.table(df) |
|
|
