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from math import e
import streamlit as st
from PIL import Image
st.title("NLP project")
description_show_options = ['main','film_review','toxic_messages','над проектом работали']
description_show = st.sidebar.radio("Description", description_show_options)
if description_show == 'над проектом работали':
st.title(" над проектом работали")
col1, col2, col3 = st.columns(3)
with col1:
romaimage = Image.open("images/roma.png")
st.image(romaimage, caption="Рома | custom attention enjoyer | DevOps", use_column_width=True, )
with col2:
leraimage = Image.open("images/Lera.png")
st.image(leraimage, caption="Лера | GPT bender | Data Scientist", use_column_width=True)
with col3:
olyaimage = Image.open("images/baur.jpg")
st.image(olyaimage, caption="Бауржан | TF/IDF master | Frontender", use_column_width=True)
elif description_show == 'GPT':
st.title("GPT")
elif description_show == 'main':
st.title("main")
elif description_show == 'film_review':
st.title("film_review")
st.write("------------")
st.write("BERT embedding + LSTM + roman attention")
text = """Weighted F1-score: 0.70\n
Classification Report:
precision recall f1-score support
Bad 0.67 0.81 0.74 960
Neutral 0.65 0.50 0.56 922
Good 0.82 0.82 0.82 896
-----
accuracy 0.71 2778
macro avg 0.71 0.71 0.71 2778
weighted avg 0.71 0.71 0.71 2778"""
st.markdown(text)
png = Image.open("images/film_lstm.png")
st.image(png, use_column_width=True)
st.write("------------")
st.write("tf-idf + Logreg")
png = Image.open("images/film_tfidf.jpg")
st.image(png, use_column_width=True)
png = Image.open("images/tf_idf_cm.jpg")
st.image(png, use_column_width=True)
st.write("------------")
st.write("Bert embedding + LogReg")
png = Image.open("images/film_bert.jpg")
st.image(png, use_column_width=True)
elif description_show == 'toxic_messages':
st.title("toxic_messages")
png = Image.open("images/toxic.png")
st.image(png, use_column_width=True)
elif description_show == 'toxic_messages':
st.title("toxic_messages")
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