Update modules/semantic/semantic_process.py

modules/semantic/semantic_process.py

@@ -1,108 +1,109 @@
-# modules/semantic/semantic_process.py
-import streamlit as st
-import matplotlib.pyplot as plt
-import io
-import base64
-import logging
-
-from ..text_analysis.semantic_analysis import (
-    perform_semantic_analysis,
-    identify_key_concepts,
-    create_concept_graph,
-    visualize_concept_graph
-)
-from ..database.semantic_mongo_db import store_student_semantic_result
-
-logger = logging.getLogger(__name__)
-
-def process_semantic_input(text, lang_code, nlp_models, t):
-    """
-    Procesa el texto ingresado para realizar el análisis semántico.
-    """
-    try:
-        logger.info(f"Iniciando análisis semántico para texto de {len(text)} caracteres")
-        
-        # Realizar el análisis semántico
-        nlp = nlp_models[lang_code]
-        analysis_result = perform_semantic_analysis(text, nlp, lang_code)
-        
-        if not analysis_result['success']:
-            return {
-                'success': False,
-                'message': analysis_result['error'],
-                'analysis': None
-            }
-            
-        logger.info("Análisis semántico completado. Guardando resultados...")
-        
-        # Intentar guardar en la base de datos
-        try:
-            store_result = store_student_semantic_result(
-                st.session_state.username,
-                text,
-                analysis_result
-            )
-            if not store_result:
-                logger.warning("No se pudo guardar el análisis en la base de datos")
-        except Exception as db_error:
-            logger.error(f"Error al guardar en base de datos: {str(db_error)}")
-        
-        # Devolver el resultado incluso si falla el guardado
-        return {
-            'success': True,
-            'message': t.get('success_message', 'Analysis completed successfully'),
-            'analysis': {
-                'key_concepts': analysis_result['key_concepts'],
-                'concept_graph': analysis_result['concept_graph']
-            }
-        }
-        
-    except Exception as e:
-        logger.error(f"Error en process_semantic_input: {str(e)}")
-        return {
-            'success': False,
-            'message': str(e),
-            'analysis': None
-        }
-
-def format_semantic_results(analysis_result, t):
-    """
-    Formatea los resultados del análisis para su visualización.
-    """
-    try:
-        if not analysis_result['success']:
-            return {
-                'formatted_text': analysis_result['message'],
-                'visualizations': None
-            }
-            
-        formatted_sections = []
-        analysis = analysis_result['analysis']
-        
-        # Formatear conceptos clave
-        if 'key_concepts' in analysis:
-            concepts_section = [f"### {t.get('key_concepts', 'Key Concepts')}"]
-            concepts_section.extend([
-                f"- {concept}: {frequency:.2f}"
-                for concept, frequency in analysis['key_concepts']
-            ])
-            formatted_sections.append('\n'.join(concepts_section))
-        
-        return {
-            'formatted_text': '\n\n'.join(formatted_sections),
-            'visualizations': {
-                'concept_graph': analysis.get('concept_graph')
-            }
-        }
-        
-    except Exception as e:
-        logger.error(f"Error en format_semantic_results: {str(e)}")
-        return {
-            'formatted_text': str(e),
-            'visualizations': None
-        }
-
-__all__ = [
-    'process_semantic_input',
-    'format_semantic_results'
+# modules/semantic/semantic_process.py
+import streamlit as st
+import matplotlib.pyplot as plt
+import io
+import base64
+import logging
+
+from ..text_analysis.semantic_analysis import (
+    perform_semantic_analysis,
+    identify_key_concepts,
+    create_concept_graph,
+    visualize_concept_graph
+)
+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")
+        
+        # Realizar el análisis semántico
+        nlp = nlp_models[lang_code]
+        analysis_result = (text_content, nlp_models[lang_code], lang_code, semantic_t)
+        return format_semantic_results(result, lang_code, semantic_t)
+        
+        if not analysis_result['success']:
+            return {
+                'success': False,
+                'message': analysis_result['error'],
+                'analysis': None
+            }
+            
+        logger.info("Análisis semántico completado. Guardando resultados...")
+        
+        # Intentar guardar en la base de datos
+        try:
+            store_result = store_student_semantic_result(
+                st.session_state.username,
+                text,
+                analysis_result
+            )
+            if not store_result:
+                logger.warning("No se pudo guardar el análisis en la base de datos")
+        except Exception as db_error:
+            logger.error(f"Error al guardar en base de datos: {str(db_error)}")
+        
+        # Devolver el resultado incluso si falla el guardado
+        return {
+            'success': True,
+            'message': t.get('success_message', 'Analysis completed successfully'),
+            'analysis': {
+                'key_concepts': analysis_result['key_concepts'],
+                'concept_graph': analysis_result['concept_graph']
+            }
+        }
+        
+    except Exception as e:
+        logger.error(f"Error en process_semantic_input: {str(e)}")
+        return {
+            'success': False,
+            'message': str(e),
+            'analysis': None
+        }
+
+def format_semantic_results(analysis_result, t):
+    """
+    Formatea los resultados del análisis para su visualización.
+    """
+    try:
+        if not analysis_result['success']:
+            return {
+                'formatted_text': analysis_result['message'],
+                'visualizations': None
+            }
+            
+        formatted_sections = []
+        analysis = analysis_result['analysis']
+        
+        # Formatear conceptos clave
+        if 'key_concepts' in analysis:
+            concepts_section = [f"### {t.get('key_concepts', 'Key Concepts')}"]
+            concepts_section.extend([
+                f"- {concept}: {frequency:.2f}"
+                for concept, frequency in analysis['key_concepts']
+            ])
+            formatted_sections.append('\n'.join(concepts_section))
+        
+        return {
+            'formatted_text': '\n\n'.join(formatted_sections),
+            'visualizations': {
+                'concept_graph': analysis.get('concept_graph')
+            }
+        }
+        
+    except Exception as e:
+        logger.error(f"Error en format_semantic_results: {str(e)}")
+        return {
+            'formatted_text': str(e),
+            'visualizations': None
+        }
+
+__all__ = [
+    'process_semantic_input',
+    'format_semantic_results'
 ]