Update modules/ui/ui.py

modules/ui/ui.py

@@ -892,19 +892,27 @@ def display_discourse_analysis_interface(nlp_models, lang_code):
             text_content2 = uploaded_file2.getvalue().decode('utf-8')
 
             # Realizar el análisis
-            graph1, graph2 = perform_discourse_analysis(text_content1, text_content2, nlp_models[lang_code], lang_code)
+            analysis_result = perform_discourse_analysis(text_content1, text_content2, nlp_models[lang_code], lang_code)
 
             # Mostrar los gráficos de comparación
             st.subheader(t['comparison'])
             col1, col2 = st.columns(2)
             with col1:
-                st.pyplot(graph1)
+                st.pyplot(analysis_result['graph1'])
             with col2:
-                st.pyplot(graph2)
+                st.pyplot(analysis_result['graph2'])
+
+            # Mostrar conceptos clave
+            st.subheader("Conceptos Clave - Documento 1")
+            for concept, frequency in analysis_result['key_concepts1']:
+                st.write(f"{concept}: {frequency:.2f}")
+
+            st.subheader("Conceptos Clave - Documento 2")
+            for concept, frequency in analysis_result['key_concepts2']:
+                st.write(f"{concept}: {frequency:.2f}")
 
             # Guardar el resultado del análisis
-            #if store_discourse_analysis_result(st.session_state.username, text_content1 + "\n\n" + text_content2, graph1, graph2):
-            if store_discourse_analysis_result(st.session_state.username, text_content1, text_content2, graph1, graph2):    
+            if store_discourse_analysis_result(st.session_state.username, text_content1, text_content2, analysis_result['graph1'], analysis_result['graph2']):
                 st.success(t['success_message'])
             else:
                 st.error(t['error_message'])