Update my_pages/multiverse.py

my_pages/multiverse.py

@@ -1,7 +1,7 @@
 # pages/multiverse.py
 import streamlit as st
 import plotly.graph_objects as go
-from utils import add_navigation, add_instruction_text
+from utils import add_navigation, add_instruction_text, add_red_text
 
 import random
 import pandas as pd
@@ -116,8 +116,8 @@ def render():
 
     add_instruction_text(
         """
-        Explore the multiverse of models visually.<br> 
-        Make a choice, and the corresponding model will be trained for you.
+        Visually explore the multiverse of AI models to judge loan applications.<br> 
+        Make a choice, and scroll down to see the properties of the trained model.
         """
     )
 
@@ -260,17 +260,15 @@ def render():
     placeholder.write("Training your model.")
     model.fit(X_train, y_train)
     placeholder.empty()
-    
-    acc = model.score(X_test, y_test)
-    
-    acc_men = model.score(X_test[group_test==1], y_test[group_test==1])
-    acc_women = model.score(X_test[group_test==0], y_test[group_test==0])
-    
-    disp = abs(acc_men - acc_women)
-    
-    st.subheader("📊 Model Performance Metrics")
-    st.markdown(f"""
-    Your model was tested on a separate test dataset, and you achieved the following overall model accuracy as well as disparity in accuracy across men and women in the dataset.
-    """)
-    st.metric(label="Model Accuracy", value=f"{acc * 100:.1f}%")
-    st.metric(label="Gender Disparity in Accuracy", value=f"{disp * 100:.1f}%")
+
+    y_pred = model.predict(X_test)
+
+    uniq_perc = 10.5
+    add_red_text(f"""
+        Your chosen model rejected {uniq_perc}% loans that would have been accepted by more than 90% of the other models.<br><br>
+        You might want a higher value here, as you will not be competiting for the same loan applicants with others, and will give a chance to unique applicants. 
+        In other words, you are concerned about homogenization and are trying to avoid it.<br><br>
+        On the other hand, you might instead want a lower value here, as you will be taking lower risks and giving loans to only applicants who are highly likely to repay.
+        Moreover, you do not have to face a justification crisis, where you cannot explain why certain applicants were rejected when they were accepted by everyone else.
+        In other words, you are concerned about arbitrariness and are trying to avoid it.<br><br>
+    """)