Update modules/semantic/semantic_interface.py

modules/semantic/semantic_interface.py

@@ -141,71 +141,65 @@ def display_semantic_interface(lang_code, nlp_models, semantic_t):
 #######################################
 def display_semantic_results(semantic_result, lang_code, semantic_t):
     """
-    Muestra los resultados del análisis semántico en tabs
+    Muestra los resultados del análisis semántico de conceptos clave.
+    
     Args:
         semantic_result: Diccionario con los resultados del análisis
         lang_code: Código del idioma actual
         semantic_t: Diccionario de traducciones semánticas
     """
-    # Verificar resultado usando el nombre correcto de la variable
+    # Verificar resultado
     if semantic_result is None or not semantic_result['success']:
         st.warning(semantic_t.get('no_results', 'No results available'))
         return
-        
-    # Usar semantic_result en lugar de result
+
     analysis = semantic_result['analysis']
-    
-    # Crear tabs para los resultados
-    tab1, tab2 = st.tabs([
-        semantic_t.get('concepts_tab', 'Key Concepts Analysis'),
-        semantic_t.get('entities_tab', 'Entities Analysis')
-    ])
-    
-    # Tab 1: Conceptos Clave
-    with tab1:
-        col1, col2 = st.columns(2)
-        
-        # Columna 1: Lista de conceptos
-        with col1:
-            st.subheader(semantic_t.get('key_concepts', 'Key Concepts'))
-            if 'key_concepts' in analysis:
-                concept_text = "\n".join([
-                    f"• {concept} ({frequency:.2f})" 
-                    for concept, frequency in analysis['key_concepts']
-                ])
-                st.markdown(concept_text)
-            else:
-                st.info(semantic_t.get('no_concepts', 'No key concepts found'))
-        
-        # Columna 2: Gráfico de conceptos
-        with col2:
-            st.subheader(semantic_t.get('concept_graph', 'Concepts Graph'))
-            if 'concept_graph' in analysis:
-                st.image(analysis['concept_graph'])
-            else:
-                st.info(semantic_t.get('no_graph', 'No concept graph available'))
-    
-    # Tab 2: Entidades
-    with tab2:
-        col1, col2 = st.columns(2)
-        
-        # Columna 1: Lista de entidades
-        with col1:
-            st.subheader(semantic_t.get('identified_entities', 'Identified Entities'))
-            if 'entities' in analysis:
-                for entity_type, entities in analysis['entities'].items():
-                    st.markdown(f"**{entity_type}**")
-                    st.markdown("• " + "\n• ".join(entities))
-            else:
-                st.info(semantic_t.get('no_entities', 'No entities found'))
+
+    # Crear contenedor para los resultados
+    col1, col2 = st.columns(2)
+
+    # Columna 1: Lista de conceptos clave
+    with col1:
+        st.subheader(semantic_t.get('key_concepts', 'Key Concepts'))
+        if 'key_concepts' in analysis and analysis['key_concepts']:
+            # Crear tabla de conceptos
+            df = pd.DataFrame(
+                analysis['key_concepts'], 
+                columns=[
+                    semantic_t.get('concept', 'Concept'),
+                    semantic_t.get('frequency', 'Frequency')
+                ]
+            )
+            st.dataframe(
+                df,
+                hide_index=True,
+                column_config={
+                    semantic_t.get('frequency', 'Frequency'): st.column_config.NumberColumn(
+                        format="%.2f"
+                    )
+                }
+            )
+        else:
+            st.info(semantic_t.get('no_concepts', 'No key concepts found'))
+
+    # Columna 2: Gráfico de conceptos
+    with col2:
+        st.subheader(semantic_t.get('concept_graph', 'Concepts Graph'))
+        if 'concept_graph' in analysis and analysis['concept_graph'] is not None:
+            st.image(analysis['concept_graph'])
+        else:
+            st.info(semantic_t.get('no_graph', 'No concept graph available'))
+
+    # Agregar explicación del análisis
+    with st.expander(semantic_t.get('analysis_explanation', 'About this analysis')):
+        st.markdown(semantic_t.get('concepts_explanation', """
+        This analysis shows the most important concepts in your text and their relationships:
+        - **Key Concepts**: Shows the main concepts and their frequency in the text
+        - **Concept Graph**: Visualizes how these concepts are related to each other
         
-        # Columna 2: Gráfico de entidades
-        with col2:
-            st.subheader(semantic_t.get('entity_graph', 'Entities Graph'))
-            if 'entity_graph' in analysis:
-                st.image(analysis['entity_graph'])
-            else:
-                st.info(semantic_t.get('no_entity_graph', 'No entity graph available'))
+        The larger nodes in the graph represent concepts that appear more frequently or have
+        more connections to other concepts.
+        """)
 
 '''
     # Botón de exportación al final