Update my_pages/ica.py

my_pages/ica.py

@@ -76,9 +76,9 @@ 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", 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.text(*vertices[0], "Intentional", ha="center", va="bottom", color="green", weight="heavy")
+    ax.text(*vertices[1], "Conventional", ha="right", va="top", color="green", weight="heavy")
+    ax.text(*vertices[2], "Arbitrary", ha="left", va="top", color="green", weight="heavy")
     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")
@@ -92,7 +92,7 @@ def render():
     locations = [
         (0.9, 0.1, "Random Seeds", "Random Seeds are highly arbitrary, without any convention or intentionality.", "left", "bottom"),
         (0.35, 0.06, "Neural networks for Tabular Data", "Using neural networks of some arbitrary size (hidden layers) for a setting where \
-                                                        they are not needed is highly conventional, a bit arbitrary, and has very low intentionality.", "right", "bottom"),
+                                                        they are not needed is highly conventional, a bit arbitrary, and has very low intentionality.", "left", "bottom"),
         (0.4, 0.5, "Pre-trained LLM for a Complex Task", "Using a high performing LLM for a complex task is intentional, however, it also has \
                                                            conventionality to it, as a specialized model could have worked, depending on context.\
                                                            No arbitrariness.", "right", "bottom"),
@@ -112,7 +112,7 @@ def render():
         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=ha, va=va, color="red", zorder=6)
+            ax.text(x, y + 0.03, label, ha=ha, va=va, color="red", zorder=6, weight="heavy")
             explanations.append((label, labeltext))
         else:
             ax.scatter(x, y, c="red", s=50, zorder=6, alpha=0.3)