app.py

import streamlit as st
import torch
import requests
import time
import numpy as np
import os
from Models.toxic1 import toxicity_page
from Models.strim_nlp import classic_ml_page
from Models.lstm import lstm_model_page
from Models.bert_strim import bert_model_page
import base64
import pandas as pd

background_image = 'Data/chad_806facbe78804299a9eeeab5fb0a387b_3.png'
st.markdown(
    f"""
    <style>
    .reportview-container {{
        background: url(data:image/jpeg;base64,{base64.b64encode(open(background_image, "rb").read()).decode()});
        background-size: cover;
    }}
    </style>
    """, unsafe_allow_html=True
)
def app_description_page():
    st.title("Welcome to My App!")
    st.markdown("<h3 style='font-size: 18px;'>This is a Streamlit application where you can explore four different models.</h3>", unsafe_allow_html=True)
    st.markdown("<h3 style='font-size: 18px;'>About the project:</h3>", unsafe_allow_html=True)
    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)
    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)
    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("<h3 style='font-size: 18px;'>Models:</h3>", unsafe_allow_html=True)
    st.markdown("<h3 style='font-size: 18px;'>1. CatBoostClassifier trained on TF-IDF </h3>", unsafe_allow_html=True)
    st.markdown("<h3 style='font-size: 18px;'>2. LSTM with BahdanauAttention </h3>", unsafe_allow_html=True)
    st.markdown("<h3 style='font-size: 18px;'>3. Rubert-tiny2 </h3>", unsafe_allow_html=True)
    st.markdown("<h3 style='font-size: 18px;'>4. Rubert-tiny-toxicity </h3>", unsafe_allow_html=True)
    st.dataframe(df)
    st.image('Data/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()