modules/text_analysis/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 (
    create_concept_graph,
    visualize_concept_graph,
    identify_and_contextualize_entities,
    POS_COLORS,
    POS_TRANSLATIONS,
    ENTITY_LABELS
)

def compare_semantic_analysis(text1, text2, nlp, lang):
    doc1 = nlp(text1)
    doc2 = nlp(text2)

    # Identificar entidades y conceptos clave para ambos documentos
    entities1, key_concepts1 = identify_and_contextualize_entities(doc1, lang)
    entities2, key_concepts2 = identify_and_contextualize_entities(doc2, lang)

    # Crear grafos de conceptos para ambos documentos
    concepts1 = [concept for concept, _ in key_concepts1]
    concepts2 = [concept for concept, _ in key_concepts2]
    G1 = create_concept_graph(text1, concepts1)
    G2 = create_concept_graph(text2, concepts2)

    # Visualizar los grafos de conceptos
    fig1 = visualize_concept_graph(G1, lang)
    fig2 = visualize_concept_graph(G2, lang)

    # Añadir títulos específicos para cada documento
    fig1.suptitle("Documento 1: Relaciones Conceptuales", fontsize=16, fontweight='bold')
    fig2.suptitle("Documento 2: Relaciones Conceptuales", fontsize=16, fontweight='bold')

    return fig1, fig2, entities1, entities2, key_concepts1, key_concepts2

def perform_discourse_analysis(text1, text2, nlp, lang):
    graph1, graph2, entities1, entities2, key_concepts1, key_concepts2 = compare_semantic_analysis(text1, text2, nlp, lang)
    
    # Aquí puedes añadir más análisis de discurso si lo necesitas
    # Por ejemplo, podrías comparar las entidades y conceptos clave entre los dos textos

    return {
        'graph1': graph1,
        'graph2': graph2,
        'entities1': entities1,
        'entities2': entities2,
        'key_concepts1': key_concepts1,
        'key_concepts2': key_concepts2
    }