Update modules/semantic/semantic_interface.py

modules/semantic/semantic_interface.py

@@ -25,39 +25,49 @@ from ..utils.widget_utils import generate_unique_key
 from ..database.semantic_mongo_db import store_student_semantic_result
 from ..database.semantic_export import export_user_interactions
 
+
 def display_semantic_interface(lang_code, nlp_models, semantic_t):
     """
     Interfaz para el análisis semántico
-    Args:
-        lang_code: Código del idioma actual
-        nlp_models: Modelos de spaCy cargados
-        semantic_t: Diccionario de traducciones semánticas
     """
     try:
-        # Inicializar el estado si no existe
-        if 'semantic_analysis_counter' not in st.session_state:
-            st.session_state.semantic_analysis_counter = 0
+        # Mantener página en semántico
+        st.session_state.page = 'semantic'
 
-        # Opción para cargar archivo con key única
-        uploaded_file = st.file_uploader(
-            semantic_t.get('file_uploader', 'Upload a text file for analysis'),
-            type=['txt'],
-            key=f"semantic_file_uploader_{st.session_state.semantic_analysis_counter}"
-        )
+        # Inicializar estados específicos para semántico
+        if 'semantic_state' not in st.session_state:
+            st.session_state.semantic_state = {
+                'counter': 0,
+                'has_file': False,
+                'file_content': None,
+                'result': None
+            }
 
-        # Botón de análisis con key única
-        col1, col2, col3 = st.columns([2,1,2])
-        with col2:
-            analyze_button = st.button(
-                semantic_t.get('analyze_button', 'Analyze text'),
-                key=f"semantic_analyze_button_{st.session_state.semantic_analysis_counter}",
-                use_container_width=True
+        # Contenedor principal
+        with st.container():
+            # Área de carga de archivo
+            uploaded_file = st.file_uploader(
+                semantic_t.get('file_uploader', 'Upload a text file for analysis'),
+                type=['txt'],
+                key=f"semantic_uploader_{st.session_state.semantic_state['counter']}"
             )
 
+            # Botones en una fila
+            col1, col2, col3 = st.columns([2,1,2])
+            with col2:
+                analyze_button = st.button(
+                    semantic_t.get('analyze_button', 'Analyze text'),
+                    key=f"semantic_analyze_{st.session_state.semantic_state['counter']}",
+                    use_container_width=True,
+                    disabled=not uploaded_file
+                )
+
+        # Procesamiento
         if analyze_button and uploaded_file is not None:
             try:
                 with st.spinner(semantic_t.get('processing', 'Processing...')):
                     text_content = uploaded_file.getvalue().decode('utf-8')
+                    st.session_state.semantic_state['file_content'] = text_content
                     
                     analysis_result = process_semantic_input(
                         text_content, 
@@ -67,17 +77,15 @@ def display_semantic_interface(lang_code, nlp_models, semantic_t):
                     )
                     
                     if analysis_result['success']:
-                        st.session_state.semantic_result = analysis_result
-                        st.session_state.semantic_analysis_counter += 1
+                        st.session_state.semantic_state['result'] = analysis_result
+                        st.session_state.semantic_state['counter'] += 1
                         
-                        # Guardar en la base de datos
                         if store_student_semantic_result(
                             st.session_state.username,
                             text_content,
                             analysis_result['analysis']
                         ):
                             st.success(semantic_t.get('success_message', 'Analysis saved successfully'))
-                            # Mostrar resultados
                             display_semantic_results(
                                 analysis_result,
                                 lang_code,
@@ -94,9 +102,9 @@ def display_semantic_interface(lang_code, nlp_models, semantic_t):
             st.warning(semantic_t.get('warning_message', 'Please upload a file first'))
         
         # Mostrar resultados previos
-        elif 'semantic_result' in st.session_state and st.session_state.semantic_result is not None:
+        elif st.session_state.semantic_state['result'] is not None:
             display_semantic_results(
-                st.session_state.semantic_result,
+                st.session_state.semantic_state['result'],
                 lang_code,
                 semantic_t
             )
@@ -106,6 +114,7 @@ def display_semantic_interface(lang_code, nlp_models, semantic_t):
     except Exception as e:
         logger.error(f"Error general en interfaz semántica: {str(e)}")
         st.error("Se produjo un error. Por favor, intente de nuevo.")
+        
 
 def display_semantic_results(result, lang_code, semantic_t):
     """