Update modules/studentact/current_situation_interface.py
Browse files
modules/studentact/current_situation_interface.py
CHANGED
@@ -158,6 +158,7 @@ def display_current_situation_interface(lang_code, nlp_models, t):
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###################################3333
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def display_results(metrics, text_type=None):
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"""
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Muestra los resultados del análisis: métricas verticalmente y gráfico radar.
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@@ -234,7 +235,91 @@ def display_results(metrics, text_type=None):
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except Exception as e:
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logger.error(f"Error mostrando resultados: {str(e)}")
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st.error("Error al mostrar los resultados")
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######################################
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def display_radar_chart(metrics_config, thresholds):
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###################################3333
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'''
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def display_results(metrics, text_type=None):
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"""
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Muestra los resultados del análisis: métricas verticalmente y gráfico radar.
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except Exception as e:
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logger.error(f"Error mostrando resultados: {str(e)}")
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st.error("Error al mostrar los resultados")
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'''
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def display_results(metrics, text_type=None, t=None):
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"""
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Muestra los resultados del análisis: métricas verticalmente y gráfico radar,
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junto con recomendaciones personalizadas en el idioma seleccionado.
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"""
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try:
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# Usar valor por defecto si no se especifica tipo
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text_type = text_type or 'student_essay'
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# Obtener el código de idioma actual
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lang_code = st.session_state.lang_code
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# Obtener traducciones si no se proporcionaron
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if t is None:
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t = get_translations(lang_code)
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# Obtener umbrales según el tipo de texto
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thresholds = TEXT_TYPES[text_type]['thresholds']
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# Crear dos columnas para las métricas y el gráfico
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metrics_col, graph_col = st.columns([1, 1.5])
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# Columna de métricas
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with metrics_col:
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metrics_config = [
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{
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'label': t.get('SITUATION_ANALYSIS', {}).get('vocabulary', "Vocabulario"),
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'key': 'vocabulary',
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'value': metrics['vocabulary']['normalized_score'],
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'help': t.get('SITUATION_ANALYSIS', {}).get('vocabulary_help', "Riqueza y variedad del vocabulario"),
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'thresholds': thresholds['vocabulary']
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},
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{
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'label': t.get('SITUATION_ANALYSIS', {}).get('structure', "Estructura"),
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'key': 'structure',
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'value': metrics['structure']['normalized_score'],
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'help': t.get('SITUATION_ANALYSIS', {}).get('structure_help', "Organización y complejidad de oraciones"),
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'thresholds': thresholds['structure']
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},
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{
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'label': t.get('SITUATION_ANALYSIS', {}).get('cohesion', "Cohesión"),
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'key': 'cohesion',
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'value': metrics['cohesion']['normalized_score'],
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'help': t.get('SITUATION_ANALYSIS', {}).get('cohesion_help', "Conexión entre ideas y párrafos"),
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'thresholds': thresholds['cohesion']
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},
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{
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'label': t.get('SITUATION_ANALYSIS', {}).get('clarity', "Claridad"),
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'key': 'clarity',
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'value': metrics['clarity']['normalized_score'],
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'help': t.get('SITUATION_ANALYSIS', {}).get('clarity_help', "Facilidad de comprensión"),
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'thresholds': thresholds['clarity']
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}
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]
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# Mostrar las métricas como barras verticales
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for metric in metrics_config:
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st.metric(
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label=f"{metric['label']} - {int(metric['value']*100)}%",
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value="",
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help=metric['help']
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)
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st.progress(metric['value'])
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st.markdown("---")
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# Columna del gráfico radar
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with graph_col:
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# Generar y mostrar el gráfico radar
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fig = create_radar_chart(metrics, t)
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st.pyplot(fig)
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# Generar recomendaciones basadas en métricas y tipo de texto
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recommendations = generate_recommendations(metrics, text_type, lang_code, t)
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# Mostrar sección de recomendaciones
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st.subheader(t.get('SITUATION_ANALYSIS', {}).get('recommendations_title', "Recomendaciones para mejorar"))
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# Mostrar recomendaciones en tarjetas con estilo
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display_recommendations(recommendations, t)
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except Exception as e:
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logger.error(f"Error displaying results: {str(e)}")
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st.error(t.get('SITUATION_ANALYSIS', {}).get('display_error', "Error al mostrar los resultados del análisis"))
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######################################
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def display_radar_chart(metrics_config, thresholds):
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