Update my_pages/ica.py

my_pages/ica.py

@@ -78,9 +78,9 @@ def render():
     fig, ax = plt.subplots()
     ax.plot(*np.append(vertices, [vertices[0]], axis=0).T)
     # ax.scatter(vertices[:,0], vertices[:,1], c=["blue", "green", "red"], s=100)
-    ax.text(*vertices[0], "Intentional", ha="center", va="bottom")
-    ax.text(*vertices[1], "Conventional", ha="right", va="top")
-    ax.text(*vertices[2], "Arbitrary", ha="left", va="top")
+    ax.text(*vertices[0], "Intentional", ha="center", va="bottom", color="green")
+    ax.text(*vertices[1], "Conventional", ha="right", va="top", color="green")
+    ax.text(*vertices[2], "Arbitrary", ha="left", va="top", color="green")
     ax.scatter(point[0], point[1], c="white", s=10000)
     ax.scatter(point[0], point[1], c="orange", s=10000, zorder=5, alpha=0.3)
     ax.set_aspect("equal")
@@ -92,33 +92,26 @@ def render():
     # --- Dummy points scattered inside triangle ---
     # (x, y, text)
     locations = [
-        (0.3, 0.2, "Example A"),
-        (0.7, 0.25, "Example B"),
-        (0.5, 0.6, "Example C"),
-        (0.2, 0.4, "Example D"),
-        (0.8, 0.5, "Example E"),
+        (0.9, 0.1, r"$\mathbf{Random\ Seeds}$\nHighly arbitrary, low conventional, low intentional: random", "left", "bottom"),
+        (0.7, 0.25, "Example B", "left", "bottom"),
+        (0.5, 0.6, "Example C", "left", "bottom"),
+        (0.2, 0.4, "Example D", "left", "bottom"),
+        (0.8, 0.5, "Example E", "left", "bottom"),
     ]
 
     torch_radius = 0.177  # how far the "torch" illuminates
 
     # Illuminate nearby points
-    for (x, y, label) in locations:
+    for (x, y, label, ha, va) in locations:
         dist = np.linalg.norm([x - point[0], y - point[1]])
         if dist <= torch_radius:
             ax.scatter(x, y, c="red", s=50, zorder=6)
-            ax.text(x, y + 0.03, label, ha="center", va="bottom", color="red", zorder=6)
+            ax.text(x, y + 0.03, label, ha=ha, va=va, color="red", zorder=6)
+        else:
+            ax.scatter(x, y, c="red", s=50, zorder=6, alpha=0.3)
     
     st.pyplot(fig)
 
-    # Display interpretation
-    closest = max(w.items(), key=lambda x: x[1])[0]
-    if closest == "Arbitrary":
-        st.info("Example: Random seeds — a truly arbitrary decision.")
-    elif closest == "Intentional":
-        st.info("Example: Ethical constraints — a fully intentional choice.")
-    elif closest == "Conventional":
-        st.info("Example: Industry standard preprocessing — a conventional decision.")
-
     st.markdown("---")
     col1, col2, col3, col4 = st.columns([2, 1, 1, 1])
     with col3: