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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") | |