pages/2_Evaluation.py

import os

import streamlit as st
import pandas as pd
from io import StringIO
from util.evaluation import statistical_tests,calculate_correlations,calculate_divergences

def check_password():
    def password_entered():
        if password_input == os.getenv('PASSWORD'):
            st.session_state['password_correct'] = True
        else:
            st.error("Incorrect Password, please try again.")

    password_input = st.text_input("Enter Password:", type="password")
    submit_button = st.button("Submit", on_click=password_entered)

    if submit_button and not st.session_state.get('password_correct', False):
        st.error("Please enter a valid password to access the demo.")

def app():
    st.title('Result Evaluation')

    if not st.session_state.get('password_correct', False):
        check_password()
    else:
        st.sidebar.success("Password Verified. Proceed with the demo.")

        # Allow users to upload a CSV file with processed results
        uploaded_file = st.file_uploader("Upload your processed CSV file", type="csv")
        if uploaded_file is not None:
            data = StringIO(uploaded_file.getvalue().decode('utf-8'))
            df = pd.read_csv(data)

            # Add ranks for each score within each row
            ranks = df[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1, ascending=False)

            df['Privilege_Rank'] = ranks['Privilege_Avg_Score']
            df['Protect_Rank'] = ranks['Protect_Avg_Score']
            df['Neutral_Rank'] = ranks['Neutral_Avg_Score']

            st.write('Uploaded Data:', df)

            if st.button('Evaluate Data'):
                with st.spinner('Evaluating data...'):
                    statistical_results = statistical_tests(df)
                    correlation_results = calculate_correlations(df)
                    divergence_results = calculate_divergences(df)

                    flat_statistical_results = {f"Statistical_{key1}": value1 for key1, value1 in statistical_results.items()}
                    flat_correlation_results = {f"Correlation_{key1}": value1 for key1, value1 in correlation_results.items()}
                    flat_divergence_results = {f"Divergence_{key1}": value1 for key1, value1 in divergence_results.items()}

                    results_combined = {**flat_statistical_results, **flat_correlation_results, **flat_divergence_results}

                    results_df = pd.DataFrame(list(results_combined.items()), columns=['Metric', 'Value'])

                    st.write('Combined Results:', results_df)

                    st.download_button(
                        label="Download Evaluation Results",
                        data=results_df.to_csv(index=False).encode('utf-8'),
                        file_name='evaluation_results.csv',
                        mime='text/csv',
                    )

if __name__ == "__main__":
    app()