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import streamlit as st |
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import torch |
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import requests |
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import time |
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import numpy as np |
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import os |
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from toxic1 import toxicity_page |
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from strim_nlp import classic_ml_page |
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from lstm import lstm_model_page |
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from bert_strim import bert_model_page |
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import pandas as pd |
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st.markdown( |
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""" |
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<style> |
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/* Основные цвета */ |
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:root { |
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--primaryColor: #00ffcc; /* Изумрудный */ |
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--backgroundColor: #e6fff2; /* Светло-изумрудный фон */ |
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--secondaryBackgroundColor: #80ffea; /* Средний оттенок изумрудного */ |
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--textColor: #303030; /* Темно-серый для чёткости текста на светлом фоне */ |
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--font: sans-serif; /* Шрифт без засечек для чистого и современного вида */ |
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} |
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/* Кастомные стили CSS для боковой панели */ |
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.css-1aumxhk { |
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background-color: #007a66; /* Слегка отличающийся фон для боковой панели */ |
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border-right: 2px solid #ff6666; /* Добавляем границу с основным цветом */ |
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} |
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/* Кастомные стили для кнопок */ |
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.stButton>button { |
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color: #FAFAFA; |
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border: 2px solid #ff6666; |
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border-radius: 20px; /* Закругленные углы */ |
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padding: 10px 24px; |
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font-size: 16px; |
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font-weight: bold; |
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background-color: transparent; |
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transition: all 0.3s; /* Плавное изменение стилей */ |
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} |
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.stButton>button:hover { |
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background-color: #ff6666; |
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color: #2E2E2E; |
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} |
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</style> |
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""", |
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unsafe_allow_html=True |
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) |
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def app_description_page(): |
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st.title("Welcome to My App!") |
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st.markdown("<h3 style='font-size: 18px;'>This is a Streamlit application where you can explore four different models.</h3>", unsafe_allow_html=True) |
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st.markdown("<h3 style='font-size: 18px;'>About the project:</h3>", unsafe_allow_html=True) |
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st.markdown("<h3 style='font-size: 18px;'>The task is to train 3 different models on a dataset that contains reviews about the clinic.</h3>", unsafe_allow_html=True) |
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st.markdown("<h3 style='font-size: 18px;'>You can write text and the model will classify it as “Negative” or “Positive”</h3>", unsafe_allow_html=True) |
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data = { |
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"Model": ["CatBoostClassifier", "LSTM", "Rubert-tiny2", "Rubert-tiny-toxicity"], |
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"F1 metric": [0.87, 0.94, 0.90, 0.84] |
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} |
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df = pd.DataFrame(data) |
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st.markdown("<h3 style='font-size: 18px;'>Models:</h3>", unsafe_allow_html=True) |
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st.markdown("<h3 style='font-size: 18px;'>1. CatBoostClassifier trained on TF-IDF </h3>", unsafe_allow_html=True) |
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st.markdown("<h3 style='font-size: 18px;'>2. LSTM with BahdanauAttention </h3>", unsafe_allow_html=True) |
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st.markdown("<h3 style='font-size: 18px;'>3. Rubert-tiny2 </h3>", unsafe_allow_html=True) |
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st.markdown("<h3 style='font-size: 18px;'>4. Rubert-tiny-toxicity </h3>", unsafe_allow_html=True) |
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st.dataframe(df) |
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st.image('20182704132259.jpg', use_column_width=True) |
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def model_selection_page(): |
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st.sidebar.title("Model Selection") |
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selected_model = st.sidebar.radio("Select a model", ("Classic ML", "LSTM", "BERT")) |
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if selected_model == "Classic ML": |
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classic_ml_page() |
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st.write("You selected Classic ML.") |
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elif selected_model == "LSTM": |
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lstm_model_page() |
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st.write("You selected LSTM.") |
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elif selected_model == "BERT": |
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bert_model_page() |
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st.write("You selected BERT.") |
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def main(): |
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page = st.sidebar.radio("Go to", ("App Description", "Model Selection", "Toxicity Model")) |
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if page == "App Description": |
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app_description_page() |
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elif page == "Model Selection": |
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model_selection_page() |
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elif page == "Toxicity Model": |
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toxicity_page() |
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if __name__ == "__main__": |
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main() |
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