Update utils_gdmk.py

utils_gdmk.py

@@ -104,7 +104,7 @@ def inicializar_grafo():
     # print("✅ Graph Edges:", list(G.edges))
 
 def visualizar_grafo():
-    plt.figure(figsize=(10, 6))
+    plt.figure(figsize=(12, 8))  # Aumenta el tamaño de la imagen para mejorar la visibilidad
 
     if len(G.nodes) == 0:
         print("⚠️ Warning: The graph is empty! Check if data was loaded correctly.")
@@ -114,26 +114,28 @@ def visualizar_grafo():
         return "graph.png"
 
     centrality = nx.betweenness_centrality(G)
-    pos = nx.kamada_kawai_layout(G)
+    pos = nx.kamada_kawai_layout(G, scale=2.0)  # 🔄 Expandir el grafo
     node_colors = []
     node_sizes = []
-    
+
     for node in G.nodes():
-        if node in students:  # Si es un estudiante
-            node_colors.append('lightblue')  # 🔵 Color para nombres
+        if node in students:
+            node_colors.append('lightblue')  # 🔵 Color para estudiantes
         else:
             node_colors.append('lightgreen')  # 🟢 Color para conceptos
-        
-        node_sizes.append(2000 + 8000 * centrality.get(node, 0)) 
+        node_sizes.append(800 + 5000 * centrality.get(node, 0))  # 🔽 Reduce la escala
+
     nx.draw(
         G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
-        node_size=node_sizes, font_size=10
+        node_size=node_sizes, font_size=8  # 🔄 Reduce font_size para más espacio
     )
+
     edge_labels = nx.get_edge_attributes(G, 'label')
-    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=8)
-    plt.title("Red de Aportes - Optimizada por Betweenness")
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=7)
+    plt.title("Red de Aportes - Optimizada y Expandida")
     plt.savefig("graph.png")
     plt.close()
+
     return "graph.png"
 
 def guardar_en_airtable(nombre, conceptos):