Update modules/database/database.py

modules/database/database.py

@@ -13,7 +13,8 @@ from matplotlib.figure import Figure
 import bcrypt
 print(f"Bcrypt version: {bcrypt.__version__}")
 import uuid
-
+import plotly.graph_objects as go  # Para manejar el diagrama de Sankey
+import numpy as np  # Puede ser necesario para algunas operaciones
 logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger(__name__)
 
@@ -288,43 +289,35 @@ def store_semantic_result(username, text, analysis_result):
 
 def store_discourse_analysis_result(username, text1, text2, analysis_result):
     try:
-        # Crear una nueva figura combinada
-        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
-
-        # Añadir la primera imagen
-        ax1.imshow(analysis_result['graph1'].canvas.renderer.buffer_rgba())
-        ax1.set_title("Documento 1: Relaciones Conceptuales")
-        ax1.axis('off')
-
-        # Añadir la segunda imagen
-        ax2.imshow(analysis_result['graph2'].canvas.renderer.buffer_rgba())
-        ax2.set_title("Documento 2: Relaciones Conceptuales")
-        ax2.axis('off')
-
-        # Ajustar el diseño
-        plt.tight_layout()
-
-        # Convertir la figura combinada a una imagen base64
-        buf = io.BytesIO()
-        fig.savefig(buf, format='png')
-        buf.seek(0)
-        img_str = base64.b64encode(buf.getvalue()).decode('utf-8')
-
-        # Cerrar las figuras para liberar memoria
-        plt.close(fig)
-        plt.close(analysis_result['graph1'])
-        plt.close(analysis_result['graph2'])
+        # Convertir los grafos individuales a imágenes base64
+        buf1 = io.BytesIO()
+        analysis_result['graph1'].savefig(buf1, format='png')
+        buf1.seek(0)
+        img_str1 = base64.b64encode(buf1.getvalue()).decode('utf-8')
+
+        buf2 = io.BytesIO()
+        analysis_result['graph2'].savefig(buf2, format='png')
+        buf2.seek(0)
+        img_str2 = base64.b64encode(buf2.getvalue()).decode('utf-8')
+
+        # Convertir el diagrama de Sankey a imagen base64
+        buf_sankey = io.BytesIO()
+        analysis_result['sankey_diagram'].write_image(buf_sankey, format='png')
+        buf_sankey.seek(0)
+        img_str_sankey = base64.b64encode(buf_sankey.getvalue()).decode('utf-8')
 
         # Convertir los conceptos clave a listas de tuplas
-        key_concepts1 = [(concept, float(frequency)) for concept, frequency in analysis_result['table1'].values.tolist()]
-        key_concepts2 = [(concept, float(frequency)) for concept, frequency in analysis_result['table2'].values.tolist()]
+        key_concepts1 = [(concept, float(frequency)) for concept, frequency in analysis_result['key_concepts1']]
+        key_concepts2 = [(concept, float(frequency)) for concept, frequency in analysis_result['key_concepts2']]
 
         analysis_document = {
             'username': username,
             'timestamp': datetime.utcnow(),
             'text1': text1,
             'text2': text2,
-            'combined_graph': img_str,
+            'graph1': img_str1,
+            'graph2': img_str2,
+            'sankey_diagram': img_str_sankey,
             'key_concepts1': key_concepts1,
             'key_concepts2': key_concepts2,
             'analysis_type': 'discourse'