Update modules/studentact/student_activities_v2.py
Browse files
modules/studentact/student_activities_v2.py
CHANGED
@@ -536,160 +536,6 @@ def display_semantic_activities(username: str, t: dict):
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###################################################################################################
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def display_discourse_activities(username: str, t: dict):
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"""
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Solución simplificada para mostrar análisis de discurso con máxima tolerancia a errores
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"""
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try:
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# Importar directamente la colección
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from ..database.mongo_db import get_collection
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# Registrar información de inicio
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logger.info(f"Intentando obtener análisis de discurso para {username}")
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# Obtener colección directamente
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collection = get_collection('student_discourse_analysis')
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if not collection:
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st.info(t.get('no_discourse_analyses', 'No hay análisis comparados de textos registrados'))
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return
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# Consulta simplificada: solo por username
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results = list(collection.find({"username": username}).sort("timestamp", -1))
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logger.info(f"Recuperados {len(results)} documentos para {username}")
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if not results:
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st.info(t.get('no_discourse_analyses', 'No hay análisis comparados de textos registrados'))
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return
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# Mostrar cada resultado
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for doc in results:
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try:
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# Extraer y formatear fecha con manejo de errores
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timestamp_str = doc.get('timestamp', 'Fecha desconocida')
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try:
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if isinstance(timestamp_str, str):
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dt = datetime.fromisoformat(timestamp_str.replace('Z', '+00:00'))
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formatted_date = dt.strftime("%d/%m/%Y %H:%M:%S")
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else:
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formatted_date = str(timestamp_str)
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except:
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formatted_date = str(timestamp_str)
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# Crear expander
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expander_label = f"Análisis: {formatted_date}"
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with st.expander(expander_label):
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# Mostrar texto si existe
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if 'text1' in doc and doc['text1']:
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st.text_area("Texto analizado",
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value=doc['text1'],
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height=100,
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disabled=True)
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# Mostrar conceptos clave si existen
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has_concepts = False
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# Intentar mostrar conceptos clave en dos columnas
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if 'key_concepts1' in doc or 'key_concepts2' in doc:
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has_concepts = True
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col1, col2 = st.columns(2)
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# Columna 1
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with col1:
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st.markdown("**Conceptos clave (Texto 1)**")
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if 'key_concepts1' in doc and doc['key_concepts1']:
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try:
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# Mostrar simple como texto para evitar errores de formato
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concepts = doc['key_concepts1']
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if isinstance(concepts, list):
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concept_text = ""
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for c in concepts:
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if isinstance(c, list) and len(c) >= 2:
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concept_text += f"• {c[0]}: {c[1]}\n"
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else:
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concept_text += f"• {c}\n"
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st.text(concept_text)
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else:
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st.text(str(concepts))
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except:
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st.text("Error mostrando conceptos")
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else:
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st.text("Sin conceptos disponibles")
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# Columna 2
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with col2:
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st.markdown("**Conceptos clave (Texto 2)**")
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if 'key_concepts2' in doc and doc['key_concepts2']:
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try:
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# Mostrar simple como texto para evitar errores de formato
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concepts = doc['key_concepts2']
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if isinstance(concepts, list):
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concept_text = ""
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for c in concepts:
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if isinstance(c, list) and len(c) >= 2:
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concept_text += f"• {c[0]}: {c[1]}\n"
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else:
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concept_text += f"• {c}\n"
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st.text(concept_text)
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else:
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st.text(str(concepts))
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except:
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st.text("Error mostrando conceptos")
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else:
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st.text("Sin conceptos disponibles")
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# Buscar visualizaciones
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st.markdown("---")
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st.markdown("**Visualizaciones**")
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# Variable para verificar si se mostró alguna visualización
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shown_images = 0
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# Mostrar todas las visualizaciones posibles
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for field_name in ['graph1', 'graph2', 'combined_graph']:
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if field_name in doc and doc[field_name]:
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try:
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data = doc[field_name]
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# Si es bytes, mostrar directamente
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if isinstance(data, bytes):
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st.image(data, caption=field_name, use_column_width=True)
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shown_images += 1
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# Si es string, intentar como base64
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elif isinstance(data, str):
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try:
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import base64
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image_bytes = base64.b64decode(data)
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st.image(image_bytes, caption=field_name, use_column_width=True)
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shown_images += 1
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except:
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# Si falla la decodificación, solo registrarlo
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logger.error(f"No se pudo decodificar {field_name}")
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except Exception as img_err:
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logger.error(f"Error mostrando {field_name}: {str(img_err)}")
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# Mensaje si no hay visualizaciones
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if shown_images == 0:
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st.info("No hay visualizaciones disponibles para este análisis")
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# Mostrar los datos crudos para depuración (opcional)
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with st.expander("Datos completos del análisis", expanded=False):
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# Filtrar campos binarios y muy grandes
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filtered_doc = {k: v for k, v in doc.items()
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if not isinstance(v, bytes)
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and not (isinstance(v, str) and len(str(v)) > 500)}
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st.json(filtered_doc)
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except Exception as doc_err:
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logger.error(f"Error procesando documento: {str(doc_err)}")
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st.error(f"Error procesando análisis: {str(doc_err)}")
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except Exception as e:
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logger.error(f"Error general: {str(e)}")
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st.error(f"Error recuperando análisis: {str(e)}")
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# Mostrar el error completo para depuración
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st.exception(e)
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#################################################################################
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def display_discourse_comparison(analysis: dict, t: dict):
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###################################################################################################
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#################################################################################
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def display_discourse_comparison(analysis: dict, t: dict):
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