Update modules/text_analysis/discourse_analysis.py

modules/text_analysis/discourse_analysis.py

@@ -1,72 +1,67 @@
-import streamlit as st
-import spacy
-import networkx as nx
-import matplotlib.pyplot as plt
-import pandas as pd
-import numpy as np
-from .semantic_analysis import (
-    create_concept_graph,
-    visualize_concept_graph,
-    identify_key_concepts,
-    POS_COLORS,
-    POS_TRANSLATIONS,
-    ENTITY_LABELS
-)
-
-def compare_semantic_analysis(text1, text2, nlp, lang):
-    doc1 = nlp(text1)
-    doc2 = nlp(text2)
-
-    # Identificar conceptos clave para ambos documentos
-    key_concepts1 = identify_key_concepts(doc1)
-    key_concepts2 = identify_key_concepts(doc2)
-
-    # Crear grafos de conceptos para ambos documentos
-    G1 = create_concept_graph(doc1, key_concepts1)
-    G2 = create_concept_graph(doc2, key_concepts2)
-
-    # Visualizar los grafos de conceptos
-    fig1 = visualize_concept_graph(G1, lang)
-    fig2 = visualize_concept_graph(G2, lang)
-
-    # Remover los títulos superpuestos
-    fig1.suptitle("")
-    fig2.suptitle("")
-
-    return fig1, fig2, key_concepts1, key_concepts2
-
-def create_concept_table(key_concepts):
-    df = pd.DataFrame(key_concepts, columns=['Concepto', 'Frecuencia'])
-    df['Frecuencia'] = df['Frecuencia'].round(2)
-    return df
-
-def perform_discourse_analysis(text1, text2, nlp, lang):
-    graph1, graph2, key_concepts1, key_concepts2 = compare_semantic_analysis(text1, text2, nlp, lang)
-
-    # Crear tablas de conceptos clave
-    table1 = create_concept_table(key_concepts1)
-    table2 = create_concept_table(key_concepts2)
-
-    return {
-        'graph1': graph1,
-        'graph2': graph2,
-        'key_concepts1': key_concepts1,
-        'key_concepts2': key_concepts2
-    }
-
-def display_discourse_analysis_results(analysis_result, lang_code):
-    t = get_translations(lang_code)
-
-    col1, col2 = st.columns(2)
-
-    with col1:
-        with st.expander(t['doc1_title'], expanded=True):
-            st.pyplot(analysis_result['graph1'])
-            st.subheader(t['key_concepts'])
-            st.table(analysis_result['table1'])
-
-    with col2:
-        with st.expander(t['doc2_title'], expanded=True):
-            st.pyplot(analysis_result['graph2'])
-            st.subheader(t['key_concepts'])
-            st.table(analysis_result['table2'])
+import streamlit as st
+import spacy
+import networkx as nx
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+from .semantic_analysis import (
+    create_concept_graph,
+    visualize_concept_graph,
+    identify_key_concepts,
+    POS_COLORS,
+    POS_TRANSLATIONS,
+    ENTITY_LABELS
+)
+
+def compare_semantic_analysis(text1, text2, nlp, lang):
+    """
+    Realiza el análisis semántico comparativo entre dos textos
+    """
+    doc1 = nlp(text1)
+    doc2 = nlp(text2)
+
+    # Identificar conceptos clave para ambos documentos
+    key_concepts1 = identify_key_concepts(doc1)
+    key_concepts2 = identify_key_concepts(doc2)
+
+    # Crear grafos de conceptos para ambos documentos
+    G1 = create_concept_graph(doc1, key_concepts1)
+    G2 = create_concept_graph(doc2, key_concepts2)
+
+    # Visualizar los grafos de conceptos
+    fig1 = visualize_concept_graph(G1, lang)
+    fig2 = visualize_concept_graph(G2, lang)
+
+    # Remover los títulos superpuestos
+    fig1.suptitle("")
+    fig2.suptitle("")
+
+    return fig1, fig2, key_concepts1, key_concepts2
+
+def create_concept_table(key_concepts):
+    """
+    Crea una tabla de conceptos clave con sus frecuencias
+    """
+    df = pd.DataFrame(key_concepts, columns=['Concepto', 'Frecuencia'])
+    df['Frecuencia'] = df['Frecuencia'].round(2)
+    return df
+
+def perform_discourse_analysis(text1, text2, nlp, lang):
+    """
+    Realiza el análisis completo del discurso
+    """
+    graph1, graph2, key_concepts1, key_concepts2 = compare_semantic_analysis(text1, text2, nlp, lang)
+    
+    # Crear tablas de conceptos clave
+    table1 = create_concept_table(key_concepts1)
+    table2 = create_concept_table(key_concepts2)
+
+    return {
+        'graph1': graph1,
+        'graph2': graph2,
+        'key_concepts1': key_concepts1,
+        'key_concepts2': key_concepts2,
+        'table1': table1,
+        'table2': table2,
+        'success': True
+    }