Update modules/database/database.py
Browse files- modules/database/database.py +24 -13
modules/database/database.py
CHANGED
@@ -256,20 +256,26 @@ def store_semantic_result(username, text, analysis_result):
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if analysis_collection is None:
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logger.error("La conexión a MongoDB no está inicializada")
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return False
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try:
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buf = io.BytesIO()
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analysis_result['relations_graph'].savefig(buf, format='png')
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buf.seek(0)
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img_str = base64.b64encode(buf.getvalue()).decode('utf-8')
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analysis_document = {
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'username': username,
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'timestamp': datetime.utcnow(),
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'text': text,
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'
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'
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'network_diagram': img_str, # Cambiado de 'relations_graph' a 'network_diagram'
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'analysis_type': 'semantic'
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}
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result = analysis_collection.insert_one(analysis_document)
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logger.info(f"Análisis semántico guardado con ID: {result.inserted_id} para el usuario: {username}")
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logger.info(f"Longitud de la imagen guardada: {len(img_str)}")
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@@ -280,19 +286,19 @@ def store_semantic_result(username, text, analysis_result):
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###############################################################################################################
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-
def store_discourse_analysis_result(username, text1, text2,
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try:
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# Crear una nueva figura combinada
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
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# Añadir la primera imagen
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ax1.imshow(graph1.
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ax1.set_title("Documento
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ax1.axis('off')
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# Añadir la segunda imagen
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ax2.imshow(graph2.
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ax2.set_title("Documento
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ax2.axis('off')
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# Ajustar el diseño
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@@ -306,8 +312,12 @@ def store_discourse_analysis_result(username, text1, text2, graph1, graph2):
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# Cerrar las figuras para liberar memoria
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plt.close(fig)
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plt.close(graph1
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plt.close(graph2
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analysis_document = {
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'username': username,
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@@ -315,11 +325,12 @@ def store_discourse_analysis_result(username, text1, text2, graph1, graph2):
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'text1': text1,
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'text2': text2,
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'combined_graph': img_str,
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'analysis_type': 'discourse'
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}
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result = analysis_collection.insert_one(analysis_document)
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logger.info(f"Análisis discursivo guardado con ID: {result.inserted_id} para el usuario: {username}")
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return True
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except Exception as e:
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if analysis_collection is None:
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logger.error("La conexión a MongoDB no está inicializada")
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return False
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+
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try:
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+
# Convertir el gráfico a imagen base64
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buf = io.BytesIO()
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analysis_result['relations_graph'].savefig(buf, format='png')
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buf.seek(0)
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img_str = base64.b64encode(buf.getvalue()).decode('utf-8')
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+
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# Convertir los conceptos clave a una lista de tuplas
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key_concepts = [(concept, float(frequency)) for concept, frequency in analysis_result['key_concepts']]
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analysis_document = {
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'username': username,
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'timestamp': datetime.utcnow(),
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'text': text,
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'key_concepts': key_concepts,
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'network_diagram': img_str,
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'analysis_type': 'semantic'
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}
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result = analysis_collection.insert_one(analysis_document)
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logger.info(f"Análisis semántico guardado con ID: {result.inserted_id} para el usuario: {username}")
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logger.info(f"Longitud de la imagen guardada: {len(img_str)}")
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###############################################################################################################
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def store_discourse_analysis_result(username, text1, text2, analysis_result):
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try:
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# Crear una nueva figura combinada
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
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# Añadir la primera imagen
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ax1.imshow(analysis_result['graph1'].canvas.renderer.buffer_rgba())
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ax1.set_title("Documento 1: Relaciones Conceptuales")
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ax1.axis('off')
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# Añadir la segunda imagen
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ax2.imshow(analysis_result['graph2'].canvas.renderer.buffer_rgba())
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ax2.set_title("Documento 2: Relaciones Conceptuales")
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ax2.axis('off')
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# Ajustar el diseño
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# Cerrar las figuras para liberar memoria
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plt.close(fig)
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plt.close(analysis_result['graph1'])
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plt.close(analysis_result['graph2'])
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# Convertir los conceptos clave a listas de tuplas
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key_concepts1 = [(concept, float(frequency)) for concept, frequency in analysis_result['table1'].values.tolist()]
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key_concepts2 = [(concept, float(frequency)) for concept, frequency in analysis_result['table2'].values.tolist()]
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analysis_document = {
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'username': username,
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'text1': text1,
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'text2': text2,
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'combined_graph': img_str,
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'key_concepts1': key_concepts1,
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'key_concepts2': key_concepts2,
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'analysis_type': 'discourse'
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}
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result = analysis_collection.insert_one(analysis_document)
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logger.info(f"Análisis discursivo guardado con ID: {result.inserted_id} para el usuario: {username}")
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return True
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except Exception as e:
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