Update modules/discourse/discourse_process.py

modules/discourse/discourse_process.py

@@ -1,21 +1,68 @@
-from ..text_analysis.discourse_analysis import perform_discourse_analysis, compare_semantic_analysis
-import streamlit as st
-
-def process_discourse_input(user_input, lang_code, nlp_model, file_contents, t):
-    if user_input.startswith('/analisis_discurso'):
-        if file_contents is None or len(file_contents) != 2:
-            return t['no_files_uploaded'], None
-        text1, text2 = file_contents
-        result = perform_discourse_analysis(text1, text2, nlp_model, lang_code)
-        return t['discourse_analysis_completed'], (result['graph1'], result['graph2'])
-    elif user_input.startswith('/comparar'):
-        if file_contents is None or len(file_contents) != 2:
-            return t['no_files_uploaded'], None
-        text1, text2 = file_contents
-        comparison_result = compare_semantic_analysis(text1, text2, nlp_model, lang_code)
-        return t['comparison_completed'], comparison_result
-    else:
-        # Procesar otros tipos de inputs del usuario
-        chatbot = st.session_state.discourse_chatbot
-        response = chatbot.generate_response(user_input, lang_code, context=str(file_contents))
-        return response, None
+from ..text_analysis.discourse_analysis import perform_discourse_analysis, compare_semantic_analysis
+import streamlit as st
+
+def process_discourse_input(text1, text2, nlp_models, lang_code):
+    """
+    Procesa la entrada para el análisis del discurso
+    Args:
+        text1: Texto del primer documento
+        text2: Texto del segundo documento
+        nlp_models: Diccionario de modelos de spaCy
+        lang_code: Código del idioma actual
+    Returns:
+        dict: Resultados del análisis
+    """
+    try:
+        # Obtener el modelo específico del idioma
+        nlp = nlp_models[lang_code]
+        
+        # Realizar el análisis
+        analysis_result = perform_discourse_analysis(text1, text2, nlp, lang_code)
+        
+        if analysis_result['success']:
+            return {
+                'success': True,
+                'analysis': analysis_result
+            }
+        else:
+            return {
+                'success': False,
+                'error': 'Error en el análisis del discurso'
+            }
+            
+    except Exception as e:
+        logger.error(f"Error en process_discourse_input: {str(e)}")
+        return {
+            'success': False,
+            'error': str(e)
+        }
+
+def format_discourse_results(result):
+    """
+    Formatea los resultados del análisis para su visualización
+    Args:
+        result: Resultado del análisis
+    Returns:
+        dict: Resultados formateados
+    """
+    try:
+        if not result['success']:
+            return result
+
+        analysis = result['analysis']
+        return {
+            'success': True,
+            'graph1': analysis['graph1'],
+            'graph2': analysis['graph2'],
+            'key_concepts1': analysis['key_concepts1'],
+            'key_concepts2': analysis['key_concepts2'],
+            'table1': analysis['table1'],
+            'table2': analysis['table2']
+        }
+        
+    except Exception as e:
+        logger.error(f"Error en format_discourse_results: {str(e)}")
+        return {
+            'success': False,
+            'error': str(e)
+        }