fixed app.py issues

app.py

@@ -40,7 +40,8 @@ st.write(""" The performance metric used is an estimation of the percentage of c
 with st.container():
     st.write(f"**Overall performance: {overall_performance}%**")
 # %%
-st.header("Fairness ratios")
+st.header("Bias ratios")
+st.write('Bias ratio is defined as the ratio of the highest performance to the lowest performance among reliable categories for a characteristic.')
 fairness_results = data_dict['Fairness results']
 
 characteristic_list = []
@@ -51,7 +52,7 @@ for key, val in fairness_results.items():
 
 ch_df = pd.DataFrame({
     'Characteristic': characteristic_list,
-    'Fairness ratio': fairness_ratio_list
+    'Bias ratio': fairness_ratio_list
 })
 st.dataframe(ch_df)
 
@@ -106,7 +107,7 @@ st.header("Performance, Fairness, Robustness")
 embedder_categories = data_dict['Embedder categories']
 
 option = st.selectbox(
-     'Select higher-level categorization:',
+     'Select higher-level categorization/characteristic:',
      list(embedder_categories.keys()))
 
 
@@ -176,7 +177,7 @@ fig_fair.update_layout(yaxis_title="Performance in %")
 st.plotly_chart(fig_fair, theme="streamlit", use_container_width=True)
 st.markdown("---")
 
-
+st.write('The following plots show the normalized average performance for each category of a characteristic, for each level of perturbation, starting with no perturbation. Each curve represents the normalized average performance on a category, by which we mean that we divide the average performance at every level of perturbation by the average performance without perturbation. ')
 
 t_result = data_dict['Performance Robustness']['Embedder wise results'][option]
 # Embedder categories
@@ -196,6 +197,8 @@ for item in global_perturber_families:
     temp_header = f'Perturber family: {family_name}'
     # st.markdown(f'##### {temp_header}')
     t_pert_fig = px.line(merged_df, x="Levels", y="normalized performance", color='category')
+    t_pert_fig.update_layout(yaxis_title="Normalized performance")
+
     # px.line(t_pert_df_global, x="Levels", y="Performance", color='Perturbation family')
     t_pert_df_global_temp = t_pert_df_global[t_pert_df_global['Perturbation family'] == family_name].copy(deep=True)
     t_pert_df_global_temp['category'] = 'Overall'
@@ -205,4 +208,4 @@ for item in global_perturber_families:
 
     st.write(f'The following plot illustrates the normalized performance of the model across different categories for perturbation family: {family_name}.')
     st.plotly_chart(t_pert_fig, theme="streamlit", use_container_width=True)
-    st.markdown("---")
+st.markdown("---")