Update modules/studentact/student_activities_v2.py
Browse files
modules/studentact/student_activities_v2.py
CHANGED
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@@ -536,193 +536,113 @@ def display_semantic_activities(username: str, t: dict):
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###################################################################################################
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def
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"""
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Muestra
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"""
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return
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logger.info(f"Procesando {len(analyses)} análisis del discurso")
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for analysis in analyses:
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try:
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continue
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# Formatear fecha
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timestamp = datetime.fromisoformat(analysis['timestamp'].replace('Z', '+00:00'))
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formatted_date = timestamp.strftime("%d/%m/%Y %H:%M:%S")
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# Crear expander para cada análisis
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with st.expander(f"{t.get('analysis_date', 'Fecha')}: {formatted_date}", expanded=False):
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# Mostrar conceptos clave primero - esto da contexto al análisis
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if 'key_concepts1' in analysis and 'key_concepts2' in analysis:
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col1, col2 = st.columns(2)
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with col1:
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st.markdown(f"**{t.get('concepts_text_1', 'Conceptos Texto 1')}**")
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if analysis['key_concepts1']:
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df1 = pd.DataFrame(analysis['key_concepts1'], columns=['Concepto', 'Relevancia'])
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st.dataframe(df1, use_container_width=True)
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with col2:
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st.markdown(f"**{t.get('concepts_text_2', 'Conceptos Texto 2')}**")
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if analysis['key_concepts2']:
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df2 = pd.DataFrame(analysis['key_concepts2'], columns=['Concepto', 'Relevancia'])
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st.dataframe(df2, use_container_width=True)
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# Mostrar los gráficos - siguiendo el estilo de display_semantic_activities
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# Gráfico 1
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if analysis.get('graph1'):
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st.markdown("### " + t.get('graph1_title', 'Red de Conceptos - Texto 1'))
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try:
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image_data = analysis['graph1']
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# Procesar la imagen según su tipo
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if isinstance(image_data, bytes):
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image_bytes = image_data
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elif isinstance(image_data, str):
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# Intentar decodificar base64
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try:
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if image_data.startswith('data:image'):
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# Formato data URI
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import base64
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image_bytes = base64.b64decode(image_data.split(',')[1])
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else:
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# Base64 directo
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import base64
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image_bytes = base64.b64decode(image_data)
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except:
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logger.error("Error decodificando imagen 1")
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image_bytes = None
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else:
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# Si es otro tipo (como un objeto matplotlib figure)
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st.pyplot(image_data)
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image_bytes = None
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# Mostrar imagen si tenemos bytes
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if image_bytes:
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st.image(
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image_bytes,
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caption=t.get('graph1_caption', 'Red Semántica del Texto 1'),
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use_column_width=True
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)
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except Exception as e:
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logger.error(f"Error mostrando gráfico 1: {str(e)}")
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st.error(t.get('error_graph1', 'Error al cargar el gráfico del Texto 1'))
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# Gráfico 2
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if analysis.get('graph2'):
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st.markdown("### " + t.get('graph2_title', 'Red de Conceptos - Texto 2'))
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try:
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image_data = analysis['graph2']
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# Procesar la imagen según su tipo
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if isinstance(image_data, bytes):
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image_bytes = image_data
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elif isinstance(image_data, str):
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# Intentar decodificar base64
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try:
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if image_data.startswith('data:image'):
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# Formato data URI
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import base64
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image_bytes = base64.b64decode(image_data.split(',')[1])
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else:
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# Base64 directo
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import base64
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image_bytes = base64.b64decode(image_data)
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except:
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logger.error("Error decodificando imagen 2")
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image_bytes = None
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else:
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# Si es otro tipo (como un objeto matplotlib figure)
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st.pyplot(image_data)
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image_bytes = None
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# Mostrar imagen si tenemos bytes
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if image_bytes:
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st.image(
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image_bytes,
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caption=t.get('graph2_caption', 'Red Semántica del Texto 2'),
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use_column_width=True
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)
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except Exception as e:
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logger.error(f"Error mostrando gráfico 2: {str(e)}")
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st.error(t.get('error_graph2', 'Error al cargar el gráfico del Texto 2'))
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# Gráfico combinado - el más importante
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if analysis.get('combined_graph'):
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st.markdown("### " + t.get('combined_graph_title', 'Análisis Comparativo de Textos'))
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try:
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image_data = analysis['combined_graph']
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# Procesar la imagen según su tipo
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if isinstance(image_data, bytes):
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image_bytes = image_data
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elif isinstance(image_data, str):
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# Intentar decodificar base64
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try:
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if image_data.startswith('data:image'):
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# Formato data URI
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import base64
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image_bytes = base64.b64decode(image_data.split(',')[1])
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else:
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# Base64 directo
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import base64
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image_bytes = base64.b64decode(image_data)
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except:
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logger.error("Error decodificando imagen combinada")
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image_bytes = None
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else:
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# Si es otro tipo (como un objeto matplotlib figure)
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st.pyplot(image_data)
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image_bytes = None
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# Mostrar imagen si tenemos bytes
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if image_bytes:
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st.image(
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image_bytes,
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caption=t.get('combined_graph_caption', 'Visualización Comparativa de Redes Semánticas'),
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use_column_width=True
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)
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except Exception as e:
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logger.error(f"Error mostrando gráfico combinado: {str(e)}")
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st.error(t.get('error_combined_graph', 'Error al cargar el gráfico comparativo'))
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# Si no hay gráficos disponibles
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if all(analysis.get(k) is None for k in ['graph1', 'graph2', 'combined_graph']):
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st.info(t.get('no_graphs', 'No hay visualizaciones disponibles para este análisis'))
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# Si tenemos al menos los conceptos clave, mostrar un mensaje más descriptivo
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if 'key_concepts1' in analysis and 'key_concepts2' in analysis:
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st.markdown(t.get('concepts_only_message',
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"""
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**Solo se muestran los conceptos clave extraídos de los textos.**
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Las visualizaciones gráficas no están disponibles para este análisis.
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"""))
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except Exception as e:
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#################################################################################
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###################################################################################################
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def display_discourse_comparison(analysis: dict, t: dict):
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"""
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Muestra la comparación de análisis del discurso de manera minimalista pero efectiva.
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Incluye tanto las tablas de conceptos como los gráficos disponibles.
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"""
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st.subheader(t.get('comparison_results', 'Resultados de la comparación'))
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# 1. Mostrar conceptos en columnas
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col1, col2 = st.columns(2)
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with col1:
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st.markdown(f"**{t.get('concepts_text_1', 'Conceptos Texto 1')}**")
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if 'key_concepts1' in analysis and analysis['key_concepts1']:
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try:
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df1 = pd.DataFrame(analysis['key_concepts1'],
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columns=['Concepto', 'Relevancia'])
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st.dataframe(df1)
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except Exception as e:
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# En caso de error, mostrar los datos sin procesar
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st.write(analysis['key_concepts1'])
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else:
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st.info(t.get('no_concepts1', 'No hay conceptos disponibles'))
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with col2:
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st.markdown(f"**{t.get('concepts_text_2', 'Conceptos Texto 2')}**")
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if 'key_concepts2' in analysis and analysis['key_concepts2']:
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try:
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df2 = pd.DataFrame(analysis['key_concepts2'],
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columns=['Concepto', 'Relevancia'])
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st.dataframe(df2)
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except Exception as e:
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# En caso de error, mostrar los datos sin procesar
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st.write(analysis['key_concepts2'])
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else:
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st.info(t.get('no_concepts2', 'No hay conceptos disponibles'))
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# 2. Mostrar los gráficos si están disponibles
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st.markdown("---") # Separador
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# Verificar y mostrar gráficos
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has_graphs = False
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| 579 |
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# Gráfico 1
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| 581 |
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if 'graph1' in analysis and analysis['graph1']:
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| 582 |
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has_graphs = True
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| 583 |
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st.markdown(f"### {t.get('graph1_title', 'Gráfico Texto 1')}")
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| 584 |
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try:
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| 585 |
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# Intentar mostrar gráfico (asumiendo que es bytes o base64)
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| 586 |
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if isinstance(analysis['graph1'], bytes):
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| 587 |
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st.image(analysis['graph1'], use_column_width=True)
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| 588 |
+
elif isinstance(analysis['graph1'], str):
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| 589 |
+
import base64
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| 590 |
+
try:
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| 591 |
+
image_bytes = base64.b64decode(analysis['graph1'])
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| 592 |
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st.image(image_bytes, use_column_width=True)
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| 593 |
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except:
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| 594 |
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st.error(t.get('error_decoding', 'Error decodificando imagen'))
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| 595 |
+
else:
|
| 596 |
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# Intentar como objeto matplotlib
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| 597 |
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st.pyplot(analysis['graph1'])
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| 598 |
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except Exception as e:
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| 599 |
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st.error(t.get('error_graph1', 'Error mostrando gráfico 1'))
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| 600 |
+
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| 601 |
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# Gráfico 2
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| 602 |
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if 'graph2' in analysis and analysis['graph2']:
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| 603 |
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has_graphs = True
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| 604 |
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st.markdown(f"### {t.get('graph2_title', 'Gráfico Texto 2')}")
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| 605 |
+
try:
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| 606 |
+
# Intentar mostrar gráfico (asumiendo que es bytes o base64)
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| 607 |
+
if isinstance(analysis['graph2'], bytes):
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| 608 |
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st.image(analysis['graph2'], use_column_width=True)
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| 609 |
+
elif isinstance(analysis['graph2'], str):
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| 610 |
+
import base64
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| 611 |
+
try:
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| 612 |
+
image_bytes = base64.b64decode(analysis['graph2'])
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| 613 |
+
st.image(image_bytes, use_column_width=True)
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| 614 |
+
except:
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| 615 |
+
st.error(t.get('error_decoding', 'Error decodificando imagen'))
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| 616 |
+
else:
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| 617 |
+
# Intentar como objeto matplotlib
|
| 618 |
+
st.pyplot(analysis['graph2'])
|
| 619 |
+
except Exception as e:
|
| 620 |
+
st.error(t.get('error_graph2', 'Error mostrando gráfico 2'))
|
| 621 |
+
|
| 622 |
+
# Gráfico combinado
|
| 623 |
+
if 'combined_graph' in analysis and analysis['combined_graph']:
|
| 624 |
+
has_graphs = True
|
| 625 |
+
st.markdown(f"### {t.get('combined_graph_title', 'Gráfico Comparativo')}")
|
| 626 |
+
try:
|
| 627 |
+
# Intentar mostrar gráfico (asumiendo que es bytes o base64)
|
| 628 |
+
if isinstance(analysis['combined_graph'], bytes):
|
| 629 |
+
st.image(analysis['combined_graph'], use_column_width=True)
|
| 630 |
+
elif isinstance(analysis['combined_graph'], str):
|
| 631 |
+
import base64
|
| 632 |
+
try:
|
| 633 |
+
image_bytes = base64.b64decode(analysis['combined_graph'])
|
| 634 |
+
st.image(image_bytes, use_column_width=True)
|
| 635 |
+
except:
|
| 636 |
+
st.error(t.get('error_decoding', 'Error decodificando imagen'))
|
| 637 |
+
else:
|
| 638 |
+
# Intentar como objeto matplotlib
|
| 639 |
+
st.pyplot(analysis['combined_graph'])
|
| 640 |
+
except Exception as e:
|
| 641 |
+
st.error(t.get('error_combined_graph', 'Error mostrando gráfico comparativo'))
|
| 642 |
+
|
| 643 |
+
# Mensaje si no hay gráficos
|
| 644 |
+
if not has_graphs:
|
| 645 |
+
st.info(t.get('no_graphs_available', 'No hay visualizaciones disponibles para este análisis.'))
|
| 646 |
|
| 647 |
|
| 648 |
#################################################################################
|