Update app.py

app.py

@@ -61,7 +61,6 @@ summary = summary.round(2)
 
 slight_summary = summary[['mean', 'std', '25%', '50%', '75%', 'max']]
 
-st.write("### Summary Table")
 st.dataframe(slight_summary, use_container_width=True)
 
 
@@ -107,7 +106,7 @@ df_out = df.copy()
 df_out["complexity_score"] = cs
 df_out["complexity_rank"] = df_out["complexity_score"].rank(method="min", ascending=False).astype(int)
 
-st.write("### Table With Complexity Score")
+st.write("The following table represents the ontologies with complexity score and their ranking based on this score.")
 st.dataframe(df_out, use_container_width=True)
 
 
@@ -161,24 +160,28 @@ st.pyplot(fig)
 # =====================
 # CORRELATIONS
 # =====================
-st.subheader("📡 Domain-Wise Metric Correlations")
+st.subheader("📡 Domain-Wise Correlations")
 
 domains = sorted(df["Domain"].unique())
 n_domains = len(domains)
 
-fig, axes = plt.subplots(2, int(np.ceil(n_domains / 2)), figsize=(18, 8))
+n_rows = 2
+n_cols = math.ceil(n_domains / n_rows)
+
+fig, axes = plt.subplots(n_rows, int(np.ceil(n_domains / 2)), figsize=(n_cols * 3.5, n_rows * 3.5))
 axes = axes.flatten()
 
 for i, dom in enumerate(domains):
     sub = df[df["Domain"] == dom][metrics]
     corr = sub.corr()
 
-    sns.heatmap(corr, cmap="coolwarm", square=True, cbar=False,
+    sns.heatmap(corr, cmap="coolwarm", square=True, cbar=False, linewidths=0.2,
                 xticklabels=False, yticklabels=False, ax=axes[i])
-    axes[i].set_title(dom)
+    axes[i].set_title(dom, fontsize=13)
 
 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)
+st.pyplot(fig)