app.py

import streamlit as st
from PIL import Image
st.title("NLP project")

description_show_options = ['main','film_review','toxic_messages','GPT','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.jpg")
        st.image(romaimage, caption="Рома | cosplayNet enjoyer | DevOps", use_column_width=True)
    with col2:
        leraimage = Image.open("images/Lera.png")
        st.image(leraimage, caption="Лера | UNet bender | Data Scientist", use_column_width=True) 
    with col3:
        olyaimage = Image.open("images/olya.jpg")
        st.image(olyaimage, caption="Бауржан | streamlit 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")


#     Weighted F1-score: 0.7069352925929284
# 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
    
elif description_show == 'toxic_messages':
    st.title("toxic_messages")