Update modules/discourse_analysis.py

modules/discourse_analysis.py

@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 from collections import defaultdict
 from .semantic_analysis import visualize_semantic_relations, create_semantic_graph, POS_COLORS, POS_TRANSLATIONS
 
+##################################################################################################################
 def compare_semantic_analysis(text1, text2, nlp, lang):
     doc1 = nlp(text1)
     doc2 = nlp(text2)
@@ -12,26 +13,26 @@ def compare_semantic_analysis(text1, text2, nlp, lang):
     G1, pos_counts1 = create_semantic_graph(doc1, lang)
     G2, pos_counts2 = create_semantic_graph(doc2, lang)
     
-    # Create two separate figures
-    fig1, ax1 = plt.subplots(figsize=(36, 27))
-    fig2, ax2 = plt.subplots(figsize=(36, 27))
+    # Create two separate figures with a smaller size
+    fig1, ax1 = plt.subplots(figsize=(18, 13))
+    fig2, ax2 = plt.subplots(figsize=(18, 13))
     
     # Draw the first graph
     pos1 = nx.spring_layout(G1, k=0.7, iterations=50)
     nx.draw(G1, pos1, ax=ax1, node_color=[POS_COLORS.get(G1.nodes[node]['pos'], '#CCCCCC') for node in G1.nodes()],
-            with_labels=True, node_size=8000, font_size=16, font_weight='bold',
-            arrows=True, arrowsize=30, width=3, edge_color='gray')
-    nx.draw_networkx_edge_labels(G1, pos1, edge_labels=nx.get_edge_attributes(G1, 'label'), font_size=14, ax=ax1)
+            with_labels=True, node_size=4000, font_size=10, font_weight='bold',
+            arrows=True, arrowsize=20, width=2, edge_color='gray')
+    nx.draw_networkx_edge_labels(G1, pos1, edge_labels=nx.get_edge_attributes(G1, 'label'), font_size=8, ax=ax1)
     
     # Draw the second graph
     pos2 = nx.spring_layout(G2, k=0.7, iterations=50)
     nx.draw(G2, pos2, ax=ax2, node_color=[POS_COLORS.get(G2.nodes[node]['pos'], '#CCCCCC') for node in G2.nodes()],
-            with_labels=True, node_size=8000, font_size=16, font_weight='bold',
-            arrows=True, arrowsize=30, width=3, edge_color='gray')
-    nx.draw_networkx_edge_labels(G2, pos2, edge_labels=nx.get_edge_attributes(G2, 'label'), font_size=14, ax=ax2)
+            with_labels=True, node_size=4000, font_size=10, font_weight='bold',
+            arrows=True, arrowsize=20, width=2, edge_color='gray')
+    nx.draw_networkx_edge_labels(G2, pos2, edge_labels=nx.get_edge_attributes(G2, 'label'), font_size=8, ax=ax2)
     
-    ax1.set_title("Documento 1: Relaciones Semánticas Relevantes", fontsize=28, fontweight='bold')
-    ax2.set_title("Documento 2: Relaciones Semánticas Relevantes", fontsize=28, fontweight='bold')
+    ax1.set_title("Documento 1: Relaciones Semánticas Relevantes", fontsize=14, fontweight='bold')
+    ax2.set_title("Documento 2: Relaciones Semánticas Relevantes", fontsize=14, fontweight='bold')
     
     ax1.axis('off')
     ax2.axis('off')
@@ -40,13 +41,14 @@ def compare_semantic_analysis(text1, text2, nlp, lang):
     legend_elements = [plt.Rectangle((0,0),1,1,fc=POS_COLORS.get(pos, '#CCCCCC'), edgecolor='none', 
                        label=f"{POS_TRANSLATIONS[lang].get(pos, pos)}")
                        for pos in ['NOUN', 'VERB']]
-    ax1.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(0, 1), fontsize=16)
-    ax2.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(0, 1), fontsize=16)
+    ax1.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(0, 1), fontsize=8)
+    ax2.legend(handles=legend_elements, loc='upper left', bbox_to_anchor=(0, 1), fontsize=8)
     
     plt.tight_layout()
     
     return fig1, fig2
 
+##################################################################################################################
 def perform_discourse_analysis(text1, text2, nlp, lang):
     graph1, graph2 = compare_semantic_analysis(text1, text2, nlp, lang)
     return graph1, graph2