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| import os | |
| import numpy as np | |
| import streamlit as st | |
| import pandas as pd | |
| from io import StringIO | |
| from util.evaluation import statistical_tests,calculate_correlations,calculate_divergences | |
| from util.plot import create_score_plot,create_rank_plots,create_correlation_heatmaps,create_3d_plot,calculate_distances | |
| import plotly.express as px | |
| 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) | |
| 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"{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('Test Results:', results_df) | |
| fig_3d = create_3d_plot(df) | |
| st.plotly_chart(fig_3d) | |
| # Calculate and display average distance | |
| point_A = np.array([0, 0, 0]) | |
| point_B = np.array([10, 10, 10]) | |
| distances = calculate_distances(df, point_A, point_B) | |
| average_distance = distances.mean() | |
| st.write(f'Average distance to the ideal line: {average_distance}') | |
| score_fig = create_score_plot(df) | |
| st.plotly_chart(score_fig) | |
| rank_fig = create_rank_plots(df) | |
| st.plotly_chart(rank_fig) | |
| hist_fig = px.histogram(df.melt(id_vars=['Role'], | |
| value_vars=['Privilege_Avg_Score', 'Protect_Avg_Score', | |
| 'Neutral_Avg_Score']), | |
| x='value', color='variable', facet_col='variable', | |
| title='Distribution of Scores') | |
| st.plotly_chart(hist_fig) | |
| hist_rank_fig = px.histogram( | |
| df.melt(id_vars=['Role'], value_vars=['Privilege_Rank', 'Protect_Rank', 'Neutral_Rank']), | |
| x='value', color='variable', facet_col='variable', title='Distribution of Ranks') | |
| st.plotly_chart(hist_rank_fig) | |
| box_fig = px.box(df.melt(id_vars=['Role'], value_vars=['Privilege_Avg_Score', 'Protect_Avg_Score', | |
| 'Neutral_Avg_Score']), | |
| x='variable', y='value', color='variable', title='Spread of Scores') | |
| st.plotly_chart(box_fig) | |
| box_rank_fig = px.box( | |
| df.melt(id_vars=['Role'], value_vars=['Privilege_Rank', 'Protect_Rank', 'Neutral_Rank']), | |
| x='variable', y='value', color='variable', title='Spread of Ranks') | |
| st.plotly_chart(box_rank_fig) | |
| heatmaps = create_correlation_heatmaps(df) | |
| for title, fig in heatmaps.items(): | |
| st.plotly_chart(fig) | |
| 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() | |