Update modules/semantic/semantic_process.py

modules/semantic/semantic_process.py

@@ -1,27 +1,108 @@
-
+#modules/semantic/semantic_process.py
 import streamlit as st
-
-
 from ..text_analysis.semantic_analysis import (
     perform_semantic_analysis,
     fig_to_bytes,
-    fig_to_html
-    identify_key_concepts, 
-    create_concept_graph, 
-    visualize_concept_graph, 
-    create_entity_graph, 
-    visualize_entity_graph, 
-    create_topic_grap, 
+    fig_to_html,
+    identify_key_concepts,
+    create_concept_graph,
+    visualize_concept_graph,
+    create_entity_graph,
+    visualize_entity_graph,
+    create_topic_graph,
     visualize_topic_graph,
-    generate_summary, 
-    extract_entities, 
+    generate_summary,
+    extract_entities,
     analyze_sentiment,
-    extract_topics    
+    extract_topics
 )
 
-from ..database.morphosintax_mongo_db import store_student_morphosyntax_result
+from ..database.semantic_mongo_db import store_student_semantic_result
 
 import logging
 logger = logging.getLogger(__name__)
 
+def process_semantic_input(text, lang_code, nlp_models, t):
+    """
+    Procesa el texto ingresado para realizar el análisis semántico.
+    
+    Args:
+        text: Texto a analizar
+        lang_code: Código del idioma
+        nlp_models: Diccionario de modelos spaCy
+        t: Diccionario de traducciones
+    
+    Returns:
+        dict: Resultados del análisis
+    """
+    try:
+        # Realizar el análisis semántico
+        doc = nlp_models[lang_code](text)
+        
+        # Obtener el análisis completo
+        analysis = perform_semantic_analysis(text, nlp_models[lang_code], lang_code)
+        
+        # Guardar el análisis en la base de datos
+        store_student_semantic_result(
+            st.session_state.username,
+            text,
+            analysis
+        )
+        
+        return {
+            'analysis': analysis,
+            'success': True,
+            'message': t.get('success_message', 'Analysis completed successfully')
+        }
+        
+    except Exception as e:
+        logger.error(f"Error en el análisis semántico: {str(e)}")
+        return {
+            'analysis': None,
+            'success': False,
+            'message': t.get('error_message', f'Error in analysis: {str(e)}')
+        }
+
+def format_semantic_results(analysis_result, t):
+    """
+    Formatea los resultados del análisis para su visualización.
+    
+    Args:
+        analysis_result: Resultado del análisis semántico
+        t: Diccionario de traducciones
+    
+    Returns:
+        dict: Resultados formateados para visualización
+    """
+    if not analysis_result['success']:
+        return {
+            'formatted_text': analysis_result['message'],
+            'visualizations': None
+        }
+    
+    # Formatear los resultados
+    formatted_sections = []
+    
+    # Formatear conceptos clave
+    if 'key_concepts' in analysis_result['analysis']:
+        concepts_section = [f"### {t.get('key_concepts', 'Key Concepts')}"]
+        concepts_section.extend([
+            f"- {concept}: {frequency:.2f}"
+            for concept, frequency in analysis_result['analysis']['key_concepts']
+        ])
+        formatted_sections.append('\n'.join(concepts_section))
+    
+    return {
+        'formatted_text': '\n\n'.join(formatted_sections),
+        'visualizations': {
+            'concept_graph': analysis_result['analysis'].get('concept_graph'),
+            'entity_graph': analysis_result['analysis'].get('entity_graph')
+        }
+    }
+
+# Re-exportar funciones necesarias
+__all__ = [
+    'process_semantic_input',
+    'format_semantic_results'
+]