import modules
import streamlit as st
from streamlit_extras.let_it_rain import rain

# Options
DISCLAIMER = "*Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam urna sem, bibendum efficitur pellentesque a, sollicitudin pharetra urna. Nam vel lectus vitae elit luctus feugiat a a purus. Aenean mollis quis ipsum sed ornare. Nunc sit amet ultricies tellus. Vivamus vulputate sem id molestie viverra. Etiam egestas lobortis enim, sit amet lobortis ligula sollicitudin vel. Nunc eget ipsum sollicitudin, convallis.*"

# Cleaning parameters
drop_missing = None,
remove_duplicates = None,

# Anonymizing parameters
anonymize_data = None

# Page Config
st.set_page_config(layout="wide")

# Default Sidebar
with st.sidebar:
    st.header("2hack2furious anonymiser",)
    with st.container() as upload:
        file = st.file_uploader(f"Upload dataset:", type=modules.SUPPORTED_TYPES)
        df, (filename, extension), result = modules.load_file(file)

# Main
if df is None:
    rain("🤠")
else:
    st.dataframe(df)

    df = modules.data_cleaner(df, drop_missing, remove_duplicates)
    st.dataframe(df)

    download_file = modules.create_file(df, extension)
    with st.sidebar:
        with st.container() as cleaning_options:
            st.markdown("Data cleaning options:")
            drop_missing = st.checkbox("Drop Missing", value=True)
            remove_duplicates = st.checkbox("Remove Duplicates", value=True)

        with st.container() as anonymizing_options:
            st.markdown("Anonymizing options:")
            anonymize_data = st.checkbox("Anonymize data", value=True)

        if df is not None:
            st.download_button("Download cleaned data", download_file, file_name=filename)
        
        st.markdown(
            f"""
            Disclaimer:  
            {DISCLAIMER}
            
            Created by team #2hack2furious for the hackthethreat2023
            """
            )