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Update modules/studentact/current_situation_interface.py
Browse files
modules/studentact/current_situation_interface.py
CHANGED
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@@ -90,7 +90,6 @@ ANALYSIS_DIMENSION_MAPPING = {
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}
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}
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##############################################################################
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# FUNCIÓN PRINCIPAL
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##############################################################################
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@@ -98,16 +97,13 @@ def display_current_situation_interface(lang_code, nlp_models, t):
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"""
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TAB:
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- Expander con radio para tipo de texto
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Contenedor-1 (2 "filas" con borde):
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- Fila 1: Métricas base
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- Fila 2: Métricas iteración
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-
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Contenedor-2 (2 columnas):
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- Col1: Texto base
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- Col2: Texto iteración
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-
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Al final, Recomendaciones en una sola línea.
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"""
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# --- Inicializar session_state ---
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if 'base_text' not in st.session_state:
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@@ -123,10 +119,10 @@ def display_current_situation_interface(lang_code, nlp_models, t):
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if 'show_iter' not in st.session_state:
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st.session_state.show_iter = False
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# Creamos un
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tabs = st.tabs(["Análisis de Texto"])
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with tabs[0]:
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# 1
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with st.expander("Selecciona el tipo de texto", expanded=True):
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text_type = st.radio(
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"¿Qué tipo de texto quieres analizar?",
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@@ -142,32 +138,39 @@ def display_current_situation_interface(lang_code, nlp_models, t):
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# CONTENEDOR-1: Métricas (2 filas con borde)
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# ---------------------------------------------------------------------
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with st.container():
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# Fila 1: Métricas base
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""
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display_metrics_in_one_row(st.session_state.base_metrics, text_type)
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# Fila 2: Métricas de iteración
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<
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display_metrics_in_one_row(st.session_state.iter_metrics, text_type)
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# ---------------------------------------------------------------------
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# CONTENEDOR-2: 2 columnas (texto base | texto iteración)
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@@ -187,6 +190,7 @@ def display_current_situation_interface(lang_code, nlp_models, t):
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with st.spinner("Analizando texto base..."):
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doc = nlp_models[lang_code](text_base)
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metrics = analyze_text_dimensions(doc)
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st.session_state.base_text = text_base
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st.session_state.base_metrics = metrics
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st.session_state.show_base = True
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@@ -211,32 +215,27 @@ def display_current_situation_interface(lang_code, nlp_models, t):
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st.session_state.show_iter = True
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# ---------------------------------------------------------------------
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# Recomendaciones al final (una sola
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# ---------------------------------------------------------------------
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# Solo mostramos si tenemos iteración
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if st.session_state.show_iter:
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-
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with st.container():
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st.markdown("**Recomendaciones**")
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# Si las quieres en una sola línea, podrías hacer algo como:
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# rec_col = st.columns(1)[0] # Solo 1 columna
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# con rec_col: ...
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# Pero en general, un for con st.write estará apilado verticalmente.
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# Ejemplo de iterar dimensiones
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any_reco = False
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for dimension, values in st.session_state.iter_metrics.items():
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score = values['normalized_score']
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target = TEXT_TYPES[text_type]['thresholds'][dimension]['target']
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if score < target:
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if
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st.info("¡No hay recomendaciones! Todas las métricas superan la meta.")
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#Funciones de visualización ##################################
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##############################################################################
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# Función para mostrar métricas en una sola fila
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thresholds = TEXT_TYPES[text_type]['thresholds']
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dimensions = ["vocabulary", "structure", "cohesion", "clarity"]
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-
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cols = [col1, col2, col3, col4]
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for dim, col in zip(dimensions, cols):
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@@ -278,6 +278,27 @@ def display_metrics_in_one_row(metrics, text_type):
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####################################################################
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def display_metrics_analysis(metrics, text_type=None):
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}
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}
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##############################################################################
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# FUNCIÓN PRINCIPAL
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##############################################################################
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"""
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TAB:
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- Expander con radio para tipo de texto
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Contenedor-1 (2 filas con borde):
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- Fila 1: Métricas base
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- Fila 2: Métricas iteración
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Contenedor-2 (2 columnas):
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- Col1: Texto base
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- Col2: Texto iteración
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Al final, Recomendaciones en un expander (una sola fila).
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"""
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# --- Inicializar session_state ---
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if 'base_text' not in st.session_state:
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if 'show_iter' not in st.session_state:
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st.session_state.show_iter = False
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# Creamos un tab
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tabs = st.tabs(["Análisis de Texto"])
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with tabs[0]:
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# [1] Expander con radio para seleccionar tipo de texto
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with st.expander("Selecciona el tipo de texto", expanded=True):
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text_type = st.radio(
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"¿Qué tipo de texto quieres analizar?",
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# CONTENEDOR-1: Métricas (2 filas con borde)
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# ---------------------------------------------------------------------
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with st.container():
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# Fila 1: Métricas de la línea base
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st.markdown(
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"""
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<div style="border:1px solid black; padding:10px; margin-bottom:10px;">
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<p style="font-weight:bold;">Métricas de la línea base</p>
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""",
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unsafe_allow_html=True
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)
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# Si se presionó "Analizar Base", se muestran los valores
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# De lo contrario, mostramos la maqueta vacía
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if st.session_state.show_base and st.session_state.base_metrics:
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display_metrics_in_one_row(st.session_state.base_metrics, text_type)
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else:
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display_empty_metrics_row()
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st.markdown("</div>", unsafe_allow_html=True)
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# Fila 2: Métricas de iteración
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st.markdown(
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"""
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<div style="border:1px solid black; padding:10px; margin-bottom:10px;">
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<p style="font-weight:bold;">Métricas de la iteración</p>
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""",
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unsafe_allow_html=True
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)
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if st.session_state.show_iter and st.session_state.iter_metrics:
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display_metrics_in_one_row(st.session_state.iter_metrics, text_type)
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else:
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display_empty_metrics_row()
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st.markdown("</div>", unsafe_allow_html=True)
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# ---------------------------------------------------------------------
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# CONTENEDOR-2: 2 columnas (texto base | texto iteración)
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with st.spinner("Analizando texto base..."):
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doc = nlp_models[lang_code](text_base)
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metrics = analyze_text_dimensions(doc)
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st.session_state.base_text = text_base
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st.session_state.base_metrics = metrics
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st.session_state.show_base = True
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st.session_state.show_iter = True
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# ---------------------------------------------------------------------
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# Recomendaciones al final en un expander (una sola “fila”)
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# ---------------------------------------------------------------------
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# Solo mostramos si tenemos iteración
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if st.session_state.show_iter:
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with st.expander("Recomendaciones", expanded=False):
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reco_list = []
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for dimension, values in st.session_state.iter_metrics.items():
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score = values['normalized_score']
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target = TEXT_TYPES[text_type]['thresholds'][dimension]['target']
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if score < target:
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suggestions = get_dimension_suggestions(dimension) # Ejemplo
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reco_list.extend(suggestions)
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if reco_list:
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# Todas en una sola línea, separadas por " | "
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st.write(" | ".join(reco_list))
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else:
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st.info("¡No hay recomendaciones! Todas las métricas superan la meta.")
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#Funciones de visualización ##################################
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##############################################################################
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# Función para mostrar métricas en una sola fila
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thresholds = TEXT_TYPES[text_type]['thresholds']
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dimensions = ["vocabulary", "structure", "cohesion", "clarity"]
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# Forzamos ancho uniforme de columnas con: st.columns([1,1,1,1])
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col1, col2, col3, col4 = st.columns([1,1,1,1])
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cols = [col1, col2, col3, col4]
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for dim, col in zip(dimensions, cols):
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)
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# -------------------------------------------------------------------------
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# Función que muestra una fila de 4 columnas “vacías”
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# -------------------------------------------------------------------------
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def display_empty_metrics_row():
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"""
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Muestra una fila de 4 columnas vacías (Vocabulario, Estructura, Cohesión, Claridad).
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Cada columna se dibuja con st.metric en blanco (“-”).
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"""
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empty_cols = st.columns([1,1,1,1])
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labels = ["Vocabulario", "Estructura", "Cohesión", "Claridad"]
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for col, lbl in zip(empty_cols, labels):
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with col:
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col.metric(
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label=lbl,
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value="-",
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delta="",
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border=True
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)
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####################################################################
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def display_metrics_analysis(metrics, text_type=None):
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