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Update modules/studentact/current_situation_interface.py
Browse files
modules/studentact/current_situation_interface.py
CHANGED
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@@ -33,6 +33,37 @@ plt.rcParams['axes.spines.right'] = False
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logger = logging.getLogger(__name__)
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####################################
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def display_current_situation_interface(lang_code, nlp_models, t):
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"""
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Interfaz simplificada con gráfico de radar para visualizar métricas.
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@@ -48,6 +79,17 @@ def display_current_situation_interface(lang_code, nlp_models, t):
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st.session_state.current_metrics = None
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# st.markdown("## Análisis Inicial de Escritura")
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try:
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# Container principal con dos columnas
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@@ -117,16 +159,40 @@ def display_results(metrics):
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# Crear dos columnas para métricas y gráfico
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metrics_col, graph_col = st.columns([1, 1.5])
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metrics_config = [
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]
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# Mostrar métricas verticalmente
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with metrics_col:
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# Contenedor con bordes para las métricas
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st.markdown("""
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<style>
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.metric-container {
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@@ -139,52 +205,70 @@ def display_results(metrics):
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</style>
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""", unsafe_allow_html=True)
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for
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with st.container():
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st.metric(
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label,
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f"{value:.2f}",
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"Meta:
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delta_color=
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help=
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)
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st.markdown("<div style='margin-bottom: 1rem;'></div>", unsafe_allow_html=True)
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# Gráfico radar en la columna derecha
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with graph_col:
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# Preparar datos para el gráfico
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categories = [m[
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values_user = [m[
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# Crear y configurar gráfico
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fig = plt.figure(figsize=(8, 8))
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ax = fig.add_subplot(111, projection='polar')
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# Configurar
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angles = [n / float(len(categories)) * 2 * np.pi for n in range(len(categories))]
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angles += angles[:1]
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values_user += values_user[:1]
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# Configurar ejes
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ax.set_xticks(angles[:-1])
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ax.set_xticklabels(categories, fontsize=10)
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circle_ticks = np.arange(0, 1.1, 0.2)
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ax.set_yticks(circle_ticks)
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ax.set_yticklabels([f'{tick:.1f}' for tick in circle_ticks], fontsize=8)
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ax.set_ylim(0, 1)
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# Dibujar
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ax.plot(angles,
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ax.
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ax.plot(angles, values_user, '#3498db', linewidth=2, label='Tu escritura')
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ax.fill(angles, values_user, '#3498db', alpha=0.2)
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-
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# Ajustar leyenda
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ax.legend(
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loc='upper right',
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bbox_to_anchor=(0.1, 0.1),
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fontsize=10,
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frameon=True,
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facecolor='white',
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logger = logging.getLogger(__name__)
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####################################
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TEXT_TYPES = {
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'academic_article': {
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'name': 'Artículo Académico',
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'thresholds': {
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'vocabulary': {'min': 0.70, 'target': 0.85},
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'structure': {'min': 0.75, 'target': 0.90},
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'cohesion': {'min': 0.65, 'target': 0.80},
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'clarity': {'min': 0.70, 'target': 0.85}
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}
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},
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'student_essay': {
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'name': 'Trabajo Universitario',
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'thresholds': {
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'vocabulary': {'min': 0.60, 'target': 0.75},
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'structure': {'min': 0.65, 'target': 0.80},
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'cohesion': {'min': 0.55, 'target': 0.70},
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'clarity': {'min': 0.60, 'target': 0.75}
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}
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},
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'general_communication': {
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'name': 'Comunicación General',
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'thresholds': {
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'vocabulary': {'min': 0.50, 'target': 0.65},
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'structure': {'min': 0.55, 'target': 0.70},
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'cohesion': {'min': 0.45, 'target': 0.60},
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'clarity': {'min': 0.50, 'target': 0.65}
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}
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}
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}
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####################################
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def display_current_situation_interface(lang_code, nlp_models, t):
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"""
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Interfaz simplificada con gráfico de radar para visualizar métricas.
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st.session_state.current_metrics = None
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# st.markdown("## Análisis Inicial de Escritura")
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# Añadir selector de tipo de texto
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text_type = st.selectbox(
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"Tipo de texto a analizar:",
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options=list(TEXT_TYPES.keys()),
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format_func=lambda x: TEXT_TYPES[x]['name'],
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help="Selecciona el tipo de texto para ajustar los criterios de evaluación"
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)
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# Guardar el tipo seleccionado en session_state
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st.session_state.current_text_type = text_type
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try:
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# Container principal con dos columnas
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# Crear dos columnas para métricas y gráfico
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metrics_col, graph_col = st.columns([1, 1.5])
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# Configuración de métricas con sus umbrales
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metrics_config = [
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{
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'label': "Vocabulario",
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'key': 'vocabulary',
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'value': metrics['vocabulary']['normalized_score'],
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'help': "Riqueza y variedad del vocabulario",
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'thresholds': {'min': 0.60, 'target': 0.75}
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},
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{
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'label': "Estructura",
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'key': 'structure',
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'value': metrics['structure']['normalized_score'],
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'help': "Organización y complejidad de oraciones",
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'thresholds': {'min': 0.65, 'target': 0.80}
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},
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{
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'label': "Cohesión",
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'key': 'cohesion',
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'value': metrics['cohesion']['normalized_score'],
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'help': "Conexión y fluidez entre ideas",
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'thresholds': {'min': 0.55, 'target': 0.70}
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},
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{
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'label': "Claridad",
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'key': 'clarity',
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'value': metrics['clarity']['normalized_score'],
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'help': "Facilidad de comprensión del texto",
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'thresholds': {'min': 0.60, 'target': 0.75}
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}
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]
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# Mostrar métricas verticalmente
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with metrics_col:
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st.markdown("""
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<style>
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.metric-container {
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</style>
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""", unsafe_allow_html=True)
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for metric in metrics_config:
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with st.container():
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# Determinar estado basado en umbrales
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value = metric['value']
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if value < metric['thresholds']['min']:
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status = "⚠️ Por mejorar"
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color = "inverse"
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elif value < metric['thresholds']['target']:
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status = "📈 Aceptable"
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color = "off"
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else:
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status = "✅ Óptimo"
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color = "normal"
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st.metric(
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metric['label'],
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f"{value:.2f}",
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f"{status} (Meta: {metric['thresholds']['target']:.2f})",
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delta_color=color,
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help=metric['help']
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)
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st.markdown("<div style='margin-bottom: 1rem;'></div>", unsafe_allow_html=True)
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# Gráfico radar en la columna derecha
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with graph_col:
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# Preparar datos para el gráfico
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categories = [m['label'] for m in metrics_config]
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values_user = [m['value'] for m in metrics_config]
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min_values = [m['thresholds']['min'] for m in metrics_config]
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target_values = [m['thresholds']['target'] for m in metrics_config]
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# Crear y configurar gráfico
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fig = plt.figure(figsize=(8, 8))
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ax = fig.add_subplot(111, projection='polar')
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# Configurar radar
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angles = [n / float(len(categories)) * 2 * np.pi for n in range(len(categories))]
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angles += angles[:1]
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values_user += values_user[:1]
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min_values += min_values[:1]
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target_values += target_values[:1]
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# Configurar ejes
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ax.set_xticks(angles[:-1])
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ax.set_xticklabels(categories, fontsize=10)
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circle_ticks = np.arange(0, 1.1, 0.2)
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ax.set_yticks(circle_ticks)
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ax.set_yticklabels([f'{tick:.1f}' for tick in circle_ticks], fontsize=8)
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ax.set_ylim(0, 1)
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# Dibujar áreas de umbrales
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ax.plot(angles, min_values, '#e74c3c', linestyle='--', linewidth=1, label='Mínimo', alpha=0.5)
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ax.plot(angles, target_values, '#2ecc71', linestyle='--', linewidth=1, label='Meta', alpha=0.5)
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ax.fill_between(angles, target_values, [1]*len(angles), color='#2ecc71', alpha=0.1)
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ax.fill_between(angles, [0]*len(angles), min_values, color='#e74c3c', alpha=0.1)
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# Dibujar valores del usuario
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ax.plot(angles, values_user, '#3498db', linewidth=2, label='Tu escritura')
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ax.fill(angles, values_user, '#3498db', alpha=0.2)
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# Ajustar leyenda
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ax.legend(
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loc='upper right',
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bbox_to_anchor=(0.1, 0.1),
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fontsize=10,
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frameon=True,
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facecolor='white',
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