Update modules/studentact/current_situation_interface.py

modules/studentact/current_situation_interface.py

@@ -94,161 +94,92 @@ ANALYSIS_DIMENSION_MAPPING = {
 
 def display_current_situation_interface(lang_code, nlp_models, t):
     """
-    Interfaz con columnas de texto y resultados distribuidas equitativamente.
+    Interfaz simplificada con gráfico de radar para visualizar métricas.
     """
     # Inicializar estados si no existen
-    if 'text_base' not in st.session_state:
-        st.session_state.text_base = ""
-    
-    if 'text_iteration' not in st.session_state:
-        st.session_state.text_iteration = ""
-    
-    if 'show_base_results' not in st.session_state:
-        st.session_state.show_base_results = False
-    
-    if 'show_iteration_results' not in st.session_state:
-        st.session_state.show_iteration_results = False
-    
-    if 'base_doc' not in st.session_state:
-        st.session_state.base_doc = None
-    
-    if 'iteration_doc' not in st.session_state:
-        st.session_state.iteration_doc = None
-    
-    if 'base_metrics' not in st.session_state:
-        st.session_state.base_metrics = None
-    
-    if 'iteration_metrics' not in st.session_state:
-        st.session_state.iteration_metrics = None
-
-    try:
-        with st.spinner(t.get('processing_base', "Analizando línea base...")):
-        doc = nlp_models[lang_code](text_base)
-        metrics = analyze_text_dimensions(doc)
-        
-        storage_success = store_current_situation_result(
-            username=st.session_state.username,
-            text=text_base,
-            metrics=metrics,
-            feedback=None,
-            analysis_type='base'
-        )
+    if 'text_input' not in st.session_state:
+        st.session_state.text_input = ""
+    if 'text_area' not in st.session_state:
+        st.session_state.text_area = ""
+    if 'show_results' not in st.session_state:
+        st.session_state.show_results = False
+    if 'current_doc' not in st.session_state:
+        st.session_state.current_doc = None
+    if 'current_metrics' not in st.session_state:
+        st.session_state.current_metrics = None
         
-        # Container principal con cuatro columnas
+    try:
+        # Container principal para las columnas
         with st.container():
-            # Crear las cuatro columnas con distribución equitativa
-            text_base_col, metrics_base_col, text_iter_col, metrics_iter_col = st.columns([3,2,3,2], gap="small")
-    
-            # COLUMNA 1: Texto Base
+            # Crear cuatro columnas con distribución equitativa
+            text_base_col, results_base_col, text_iter_col, results_iter_col = st.columns(4)
+            
+            # Columna 1: Texto Base
             with text_base_col:
-                text_base = st.text_area(
-                    t.get('input_prompt_base', "Escribe o pega el texto base aquí:"),
+                st.markdown("### Texto Base")
+                text_input = st.text_area(
+                    "Escribe o pega tu texto aquí:",
                     height=400,
                     key="text_area_base",
-                    value=st.session_state.text_base,
-                    help="Este texto servirá como línea base para la comparación"
+                    value=st.session_state.text_input,
+                    help="Este texto servirá como línea base"
                 )
-    
-                # Manejar cambios en el texto base
-                if text_base != st.session_state.text_base:
-                    st.session_state.text_base = text_base
-                    st.session_state.show_base_results = False
-    
-                if st.button(
-                    t.get('analyze_base_button', "Establecer Línea Base"),
+                
+                baseline_button = st.button(
+                    "Establecer Línea Base",
                     type="primary",
-                    disabled=not text_base.strip(),
-                    use_container_width=True,
-                ):
-                    try:
-                        with st.spinner(t.get('processing_base', "Analizando línea base...")):
-                            doc = nlp_models[lang_code](text_base)
-                            metrics = analyze_text_dimensions(doc)
-                            
-                            storage_success = store_current_situation_result(
-                                username=st.session_state.username,
-                                text=text_base,
-                                metrics=metrics,
-                                feedback=None,
-                                analysis_type='base'
-                            )
-                            
-                            if not storage_success:
-                                logger.warning("No se pudo guardar el análisis base en la base de datos")
-                            
-                            st.session_state.base_doc = doc
-                            st.session_state.base_metrics = metrics
-                            st.session_state.show_base_results = True
-                    except Exception as e:
-                        logger.error(f"Error en análisis base: {str(e)}")
-                        st.error(t.get('analysis_error', "Error al analizar el texto base"))
-    
-            # COLUMNA 2: Resultados Base
-            with metrics_base_col:
-                if st.session_state.show_base_results and st.session_state.base_metrics is not None:
-                    st.markdown("### Métricas Base")
-                    display_results(
-                        metrics=st.session_state.base_metrics,
-                        text_type=st.session_state.get('current_text_type', 'student_essay')
-                    )
-    
-            # COLUMNA 3: Texto Iteración
+                    disabled=not text_input.strip(),
+                    use_container_width=True
+                )
+
+            # Columna 2: Resultados Base
+            with results_base_col:
+                if baseline_button and text_input:
+                    with st.spinner("Analizando línea base..."):
+                        doc = nlp_models[lang_code](text_input)
+                        metrics = analyze_text_dimensions(doc)
+                        display_results(metrics, 'student_essay')
+                        st.session_state.baseline_metrics = metrics
+
+            # Columna 3: Texto Iteración
             with text_iter_col:
-                text_iter = st.text_area(
-                    t.get('input_prompt_iter', "Escribe la nueva versión del texto:"),
+                st.markdown("### Texto Iterativo")
+                iteration_text = st.text_area(
+                    "Nueva versión del texto:",
                     height=400,
                     key="text_area_iter",
-                    value=st.session_state.text_iteration,
-                    help="Este texto será comparado con la línea base",
-                    disabled=not st.session_state.show_base_results
+                    help="Escribe la versión mejorada de tu texto"
                 )
-    
-                # Manejar cambios en el texto de iteración
-                if text_iter != st.session_state.text_iteration:
-                    st.session_state.text_iteration = text_iter
-                    st.session_state.show_iteration_results = False
-    
-                if st.button(
-                    t.get('analyze_iter_button', "Analizar Iteración"),
+                
+                iterate_button = st.button(
+                    "Analizar Iteración",
                     type="primary",
-                    disabled=not text_iter.strip() or not st.session_state.show_base_results,
-                    use_container_width=True,
-                ):
-                    try:
-                        with st.spinner(t.get('processing_iter', "Analizando iteración...")):
-                            doc = nlp_models[lang_code](text_iter)
-                            metrics = analyze_text_dimensions(doc)
-                            
-                            storage_success = store_current_situation_result(
-                                username=st.session_state.username,
-                                text=text_iter,
-                                metrics=metrics,
-                                feedback=None,
-                                analysis_type='iteration'
-                            )
-                            
-                            if not storage_success:
-                                logger.warning("No se pudo guardar el análisis de iteración en la base de datos")
-                            
-                            st.session_state.iteration_doc = doc
-                            st.session_state.iteration_metrics = metrics
-                            st.session_state.show_iteration_results = True
-                    except Exception as e:
-                        logger.error(f"Error en análisis de iteración: {str(e)}")
-                        st.error(t.get('analysis_error', "Error al analizar la iteración"))
-    
-            # COLUMNA 4: Resultados Iteración
-            with metrics_iter_col:
-                if st.session_state.show_iteration_results and st.session_state.iteration_metrics is not None:
-                    st.markdown("### Métricas Iteración")
-                    display_results(
-                        metrics=st.session_state.iteration_metrics,
-                        text_type=st.session_state.get('current_text_type', 'student_essay')
+                    disabled=not iteration_text.strip() or not st.session_state.baseline_metrics,
+                    use_container_width=True
+                )
+
+            # Columna 4: Resultados Iteración
+            with results_iter_col:
+                if iterate_button and iteration_text:
+                    with st.spinner("Analizando iteración..."):
+                        doc = nlp_models[lang_code](iteration_text)
+                        metrics = analyze_text_dimensions(doc)
+                        display_results(metrics, 'student_essay')
+
+        # Container separado para el gráfico radar
+        with st.container():
+            with st.expander("Ver Comparación Visual", expanded=False):
+                if 'baseline_metrics' in st.session_state and 'metrics' in locals():
+                    display_radar_chart(
+                        metrics_config=prepare_metrics_config(metrics),
+                        thresholds=TEXT_TYPES['student_essay']['thresholds'],
+                        baseline_metrics=st.session_state.baseline_metrics
                     )
-    
+
     except Exception as e:
         logger.error(f"Error en interfaz principal: {str(e)}")
         st.error("Ocurrió un error al cargar la interfaz")
+
        
 ###################################