modules/discourse_analysis.py

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, create_semantic_graph, POS_COLORS, POS_TRANSLATIONS

##################################################################################################################
def compare_semantic_analysis(text1, text2, nlp, lang):
    doc1 = nlp(text1)
    doc2 = nlp(text2)
    
    G1, pos_counts1 = create_semantic_graph(doc1, lang)
    G2, pos_counts2 = create_semantic_graph(doc2, lang)
    
    # 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=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=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=14, fontweight='bold')
    ax2.set_title("Documento 2: Relaciones Semánticas Relevantes", fontsize=14, fontweight='bold')
    
    ax1.axis('off')
    ax2.axis('off')
    
    # Add legends
    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=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