diff --git a/3D_app/gng2.py b/3D_app/gng2.py
index 875b01c..d2c4a33 100644
--- a/3D_app/gng2.py
+++ b/3D_app/gng2.py
@@ -1,5 +1,5 @@
 import numpy as np
-import plotly.graph_objects as plt
+from progress.bar import IncrementalBar
 
 class Neuron:
     def __init__(self, position):
@@ -31,6 +31,7 @@ class GrowingNeuralGas:
         self.edges.append(Edge(self.nodes[0], self.nodes[1]))
 
     def fit(self, X, num_iterations=1000):
+        bar = IncrementalBar("Training", max=num_iterations)
         for iteration in range(num_iterations):
             # Step 1: Select a random input sample
             x = X[np.random.randint(len(X))]
@@ -78,6 +79,9 @@ class GrowingNeuralGas:
             # Step 10: Decrease all errors
             for node in self.nodes:
                 node.error *= self.delta
+                
+            bar.next()
+        bar.finish()
     
     def find_edge(self, node1, node2):
         for edge in self.edges:
diff --git a/3D_app/pages/gng.py b/3D_app/pages/gng.py
index 060ca75..698ef74 100644
--- a/3D_app/pages/gng.py
+++ b/3D_app/pages/gng.py
@@ -1,6 +1,7 @@
 import dash
 import plotly.graph_objects as go
-from dash import html, dcc
+from dash import html, dcc, callback, Input, Output
+import dash_bootstrap_components as dbc
 from gng2 import GrowingNeuralGas
 from sklearn import datasets as sk
 
@@ -10,34 +11,47 @@ dash.register_page(__name__, path="/gng", title="GNG", name="GNG")
 X, _ = sk.make_moons(n_samples=200, noise=0.1)
 
 # Create and fit the GNG model
-gng = GrowingNeuralGas(input_dim=2)
-gng.fit(X, num_iterations=2000)
 
-fig = go.Figure()
-for edge in gng.edges:
-    fig.add_trace(
-        go.Scatter(
-            x=[edge.nodes[0].position[0], edge.nodes[1].position[0]],
-            y=[edge.nodes[0].position[1], edge.nodes[1].position[1]],
-            mode="lines",
-            line=dict(width=2, color="black"),
+layout = html.Div([dbc.Button("Generate GNG", id="generate-gng"), dcc.Graph(id="gng-graph")])
+
+@callback(Output("gng-graph", "figure"), [Input("generate-gng", "n_clicks")])
+def generate_gng(n_clicks):
+    if n_clicks is None:
+        return go.Figure().update_layout(
+            showlegend=False,
+            margin=dict(l=0, r=0, t=0, b=0),
+            xaxis=dict(visible=False),
+            yaxis=dict(visible=False),
         )
-    )
-for node in gng.nodes:
-    fig.add_trace(
-        go.Scatter(
-            x=[node.position[0]],
-            y=[node.position[1]],
-            mode="markers",
-            marker=dict(size=10, color="red"),
+    
+    gng = GrowingNeuralGas(input_dim=2)
+    gng.fit(X, num_iterations=20000)
+
+    fig = go.Figure()
+    for edge in gng.edges:
+        fig.add_trace(
+            go.Scatter(
+                x=[edge.nodes[0].position[0], edge.nodes[1].position[0]],
+                y=[edge.nodes[0].position[1], edge.nodes[1].position[1]],
+                mode="lines",
+                line=dict(width=2, color="white"),
+            )
         )
+    for node in gng.nodes:
+        fig.add_trace(
+            go.Scatter(
+                x=[node.position[0]],
+                y=[node.position[1]],
+                mode="markers",
+                marker=dict(size=10, color="red"),
+            )
+        )
+        
+    fig.update_layout(
+        showlegend=False,
+        margin=dict(l=0, r=0, t=0, b=0),
+        xaxis=dict(visible=False),
+        yaxis=dict(visible=False),
     )
     
-fig.update_layout(
-    showlegend=False,
-    margin=dict(l=0, r=0, t=0, b=0),
-    xaxis=dict(visible=False),
-    yaxis=dict(visible=False),
-)
-
-layout = html.Div(dcc.Graph(figure=fig))
+    return fig