import streamlit as st
import torch
import requests
import time
import numpy as np
import os
from toxic1 import toxicity_page
from strim_nlp import classic_ml_page
from lstm import lstm_model_page
from bert_strim import bert_model_page
import pandas as pd
def app_description_page():
st.title("Welcome to My App!")
st.markdown("This is a Streamlit application where you can explore four different models.
", unsafe_allow_html=True)
st.markdown("About the project:
", unsafe_allow_html=True)
st.markdown("The task is to train 3 different models on a dataset that contains reviews about the clinic.
", unsafe_allow_html=True)
st.markdown("You can write text and the model will classify it as “Negative” or “Positive”
", unsafe_allow_html=True)
data = {
"Model": ["CatBoostClassifier", "LSTM", "Rubert-tiny2", "Rubert-tiny-toxicity"],
"F1 metric": [0.87, 0.94, 0.90, 0.84]
}
df = pd.DataFrame(data)
st.markdown("Models:
", unsafe_allow_html=True)
st.markdown("1. CatBoostClassifier trained on TF-IDF
", unsafe_allow_html=True)
st.markdown("2. LSTM with BahdanauAttention
", unsafe_allow_html=True)
st.markdown("3. Rubert-tiny2
", unsafe_allow_html=True)
st.markdown("4. Rubert-tiny-toxicity
", unsafe_allow_html=True)
st.dataframe(df)
st.image('20182704132259.jpg', use_column_width=True)
def model_selection_page():
st.sidebar.title("Model Selection")
selected_model = st.sidebar.radio("Select a model", ("Classic ML", "LSTM", "BERT"))
if selected_model == "Classic ML":
classic_ml_page()
st.write("You selected Classic ML.")
elif selected_model == "LSTM":
lstm_model_page()
st.write("You selected LSTM.")
elif selected_model == "BERT":
bert_model_page()
st.write("You selected BERT.")
def main():
page = st.sidebar.radio("Go to", ("App Description", "Model Selection", "Toxicity Model"))
if page == "App Description":
app_description_page()
elif page == "Model Selection":
model_selection_page()
elif page == "Toxicity Model":
toxicity_page()
if __name__ == "__main__":
main()