v3 / modules /database /discourse_mongo_db.py
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# modules/database/discourse_mongo_db.py
# Importaciones estándar
import io
import base64
from datetime import datetime, timezone
import logging
# Importaciones de terceros
import matplotlib.pyplot as plt
from .mongo_db import (
get_collection,
insert_document,
find_documents,
update_document,
delete_document
)
# Configuración del logger
logger = logging.getLogger(__name__)
COLLECTION_NAME = 'student_discourse_analysis'
########################################################################
def store_student_discourse_result(username, text1, text2, analysis_result):
"""
Guarda el resultado del análisis de discurso comparativo en MongoDB.
"""
try:
# Los gráficos ya vienen en bytes, solo necesitamos codificar a base64
graph1_data = None
graph2_data = None
combined_graph_data = None
if 'graph1' in analysis_result and analysis_result['graph1'] is not None:
try:
graph1_data = base64.b64encode(analysis_result['graph1']).decode('utf-8')
except Exception as e:
logger.error(f"Error al codificar gráfico 1: {str(e)}")
if 'graph2' in analysis_result and analysis_result['graph2'] is not None:
try:
graph2_data = base64.b64encode(analysis_result['graph2']).decode('utf-8')
except Exception as e:
logger.error(f"Error al codificar gráfico 2: {str(e)}")
if 'combined_graph' in analysis_result and analysis_result['combined_graph'] is not None:
try:
combined_graph_data = base64.b64encode(analysis_result['combined_graph']).decode('utf-8')
except Exception as e:
logger.error(f"Error al codificar gráfico combinado: {str(e)}")
# Crear documento para MongoDB
analysis_document = {
'username': username,
'timestamp': datetime.now(timezone.utc).isoformat(),
'text1': text1,
'text2': text2,
'analysis_type': 'discourse',
'key_concepts1': analysis_result.get('key_concepts1', []),
'key_concepts2': analysis_result.get('key_concepts2', []),
'graph1': graph1_data,
'graph2': graph2_data,
'combined_graph': combined_graph_data
}
# Insertar en MongoDB
result = insert_document(COLLECTION_NAME, analysis_document)
if result:
logger.info(f"Análisis del discurso guardado con ID: {result} para el usuario: {username}")
return True
logger.error("No se pudo insertar el documento en MongoDB")
return False
except Exception as e:
logger.error(f"Error al guardar el análisis del discurso: {str(e)}")
return False
#################################################################################
def get_student_discourse_analysis(username, limit=10):
"""
Recupera los análisis del discurso de un estudiante.
"""
try:
# Obtener la colección
collection = get_collection(COLLECTION_NAME)
if collection is None: # Cambiado de if not collection a if collection is None
logger.error("No se pudo obtener la colección discourse")
return []
# Consulta
query = {
"username": username,
"analysis_type": "discourse"
}
# Campos a recuperar
projection = {
"timestamp": 1,
"combined_graph": 1,
"_id": 1
}
# Ejecutar consulta
try:
cursor = collection.find(query, projection).sort("timestamp", -1)
if limit:
cursor = cursor.limit(limit)
# Convertir cursor a lista
results = list(cursor)
logger.info(f"Recuperados {len(results)} análisis del discurso para {username}")
return results
except Exception as db_error:
logger.error(f"Error en la consulta a MongoDB: {str(db_error)}")
return []
except Exception as e:
logger.error(f"Error recuperando análisis del discurso: {str(e)}")
return []
#####################################################################################
def get_student_discourse_data(username):
"""
Obtiene un resumen de los análisis del discurso de un estudiante.
"""
try:
analyses = get_student_discourse_analysis(username, limit=None)
formatted_analyses = []
for analysis in analyses:
formatted_analysis = {
'timestamp': analysis['timestamp'],
'text1': analysis.get('text1', ''),
'text2': analysis.get('text2', ''),
'key_concepts1': analysis.get('key_concepts1', []),
'key_concepts2': analysis.get('key_concepts2', [])
}
formatted_analyses.append(formatted_analysis)
return {'entries': formatted_analyses}
except Exception as e:
logger.error(f"Error al obtener datos del discurso: {str(e)}")
return {'entries': []}
###########################################################################
def update_student_discourse_analysis(analysis_id, update_data):
"""
Actualiza un análisis del discurso existente.
"""
try:
query = {"_id": analysis_id}
update = {"$set": update_data}
return update_document(COLLECTION_NAME, query, update)
except Exception as e:
logger.error(f"Error al actualizar análisis del discurso: {str(e)}")
return False
###########################################################################
def delete_student_discourse_analysis(analysis_id):
"""
Elimina un análisis del discurso.
"""
try:
query = {"_id": analysis_id}
return delete_document(COLLECTION_NAME, query)
except Exception as e:
logger.error(f"Error al eliminar análisis del discurso: {str(e)}")
return False