Create discourse_analysis.py

modules/discourse_analysis.py

@@ -0,0 +1,37 @@
+import streamlit as st
+import spacy
+import networkx as nx
+import matplotlib.pyplot as plt
+from collections import defaultdict
+from .semantic_analysis import visualize_semantic_relations
+
+def compare_semantic_analysis(text1, text2, nlp, lang):
+    doc1 = nlp(text1)
+    doc2 = nlp(text2)
+    
+    fig1 = visualize_semantic_relations(doc1, lang)
+    fig2 = visualize_semantic_relations(doc2, lang)
+    
+    # Create a new figure with two subplots side by side
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(72, 27))
+    
+    # Get the contents of fig1 and fig2
+    fig1.axes[0].get_children()
+    for child in fig1.axes[0].get_children():
+        ax1.add_artist(child.copy())
+    ax1.set_title("Documento 1: Relaciones Semánticas Relevantes", fontsize=24, fontweight='bold')
+    ax1.axis('off')
+    
+    fig2.axes[0].get_children()
+    for child in fig2.axes[0].get_children():
+        ax2.add_artist(child.copy())
+    ax2.set_title("Documento 2: Relaciones Semánticas Relevantes", fontsize=24, fontweight='bold')
+    ax2.axis('off')
+    
+    plt.tight_layout()
+    
+    return fig
+
+def perform_discourse_analysis(text1, text2, nlp, lang):
+    comparison_graph = compare_semantic_analysis(text1, text2, nlp, lang)
+    return comparison_graph