Update modules/semantic/semantic_process.py

modules/semantic/semantic_process.py

@@ -15,12 +15,13 @@ from ..database.semantic_mongo_db import store_student_semantic_result
 
 logger = logging.getLogger(__name__)
 
+#############################################################################
 def process_semantic_input(text_content, lang_code, nlp_models, semantic_t):
     """
     Procesa el texto ingresado para realizar el análisis semántico.
     """
     try:
-        logger.info(f"Iniciando análisis semántico para texto de {len(text)} caracteres")
+        logger.info(f"Iniciando análisis semántico para texto de {len(text_content)} caracteres")
         
         # Realizar el análisis semántico
         nlp = nlp_models[lang_code]
@@ -44,7 +45,7 @@ def process_semantic_input(text_content, lang_code, nlp_models, semantic_t):
         try:
             store_result = store_student_semantic_result(
                 st.session_state.username,
-                text,
+                text_content,  # Cambiado de text a text_content
                 analysis_result
             )
             if not store_result:
@@ -55,7 +56,7 @@ def process_semantic_input(text_content, lang_code, nlp_models, semantic_t):
         # Devolver el resultado incluso si falla el guardado
         return {
             'success': True,
-            'message': t.get('success_message', 'Analysis completed successfully'),
+            'message': semantic_t.get('success_message', 'Analysis completed successfully'),  # Cambiado de t a semantic_t
             'analysis': {
                 'key_concepts': analysis_result['key_concepts'],
                 'concept_graph': analysis_result['concept_graph']
@@ -69,6 +70,7 @@ def process_semantic_input(text_content, lang_code, nlp_models, semantic_t):
             'message': str(e),
             'analysis': None
         }
+
 ########################################################################################
 def format_semantic_results(analysis_result, t):
     """