Update app.py

app.py

@@ -131,7 +131,7 @@ ax1.set_title(f"Top {top_n_val} Ontologies by Complexity Score")
 # PANEL 2 — PROCESSING TIME VS COMPLEXITY
 ax2 = plt.subplot2grid((3, 2), (1, 0))
 ax2.scatter(df_out["complexity_score"], df_out["Processing Time (s)"],
-            alpha=0.4, s=60, edgecolor="black")
+            alpha=0.4, s=60, edgecolor="black", linewidth=0.5, color="#DD8452")
 
 x = df_out["complexity_score"].values
 y = df_out["Processing Time (s)"].values
@@ -139,21 +139,25 @@ y = df_out["Processing Time (s)"].values
 coeffs = np.polyfit(x, y, 4)
 poly = np.poly1d(coeffs)
 xs = np.linspace(x.min(), x.max(), 300)
-ax2.plot(xs, poly(xs))
+ax2.plot(xs, poly(xs), color="#DD8452", linewidth=1.5)
 
 ax2.set_title("Processing Time vs Complexity")
+ax2.set_xlabel("Complexity Score", fontsize=12)
+ax2.set_ylabel("Processing Time (s)", fontsize=12)
 
 # PANEL 3 — DISTRIBUTION
 ax3 = plt.subplot2grid((3, 2), (1, 1))
-ax3.hist(df_out["complexity_score"], bins=20, edgecolor="black", alpha=0.8, density=True)
+ax3.hist(df_out["complexity_score"], bins=20, color="#55A868", edgecolor="black", alpha=0.8, density=True)
 
 values = df_out["complexity_score"].dropna().values
 kde = gaussian_kde(values)
 xx = np.linspace(values.min(), values.max(), 1000)
-ax3.plot(xx, kde(xx), linewidth=1.5)
+ax3.plot(xx, kde(xx), linewidth=1.5, color="green")
 
 ax3.set_title("Distribution of Complexity Scores")
-plt.tight_layout()
+ax3.set_xlabel("Complexity Score", fontsize=12)
+ax3.set_ylabel("Density", fontsize=12)
+plt.tight_layout(rect=[0, 0, 1, 0.97])
 
 st.pyplot(fig)
 
@@ -184,5 +188,4 @@ for j in range(i + 1, len(axes)):
     axes[j].axis("off")
 
 plt.tight_layout(rect=[0, 0, 1, 1])
-plt.tight_layout()
 st.pyplot(fig)