Update app.py

app.py

@@ -4,6 +4,8 @@ import networkx as nx
 import matplotlib.pyplot as plt
 import pandas as pd
 from sklearn.metrics import log_loss
+from io import BytesIO
+from PIL import Image
 
 def sigmoid(z):
     return 1.0/(1.0+ np.exp(-z))
@@ -77,10 +79,13 @@ def visualize_neural(a):
   labels = nx.get_edge_attributes(G, 'weight')
   nx.draw_networkx_edge_labels(G, pos, edge_labels=labels,font_size=3,label_pos=0.8)
   plt.title('Neural Network Graph')
-  plt.savefig(buffer, format='png')
-  plt.close()
+  buffer = BytesIO()
+  plt.savefig(buffer, format='png')  # Save the plot to the buffer in PNG format
   buffer.seek(0)
   image = Image.open(buffer)
+  plt.close()  # Close the plot to prevent displaying it
+  
+  # Convert the PIL image to a numpy array
   image_array = np.array(image)
   if output<0.5:
       return image_array,"Non palindrom"