Update app.py

app.py

@@ -8,12 +8,12 @@ import networkx as nx
 def generate_potentiality_matrix(sequence_length):
     """
     Visualizes the "Prime Potentiality" by aligning the integer stream to Mod 10.
-    1, 3, 7, 9 are the 'Active Lanes' (Rivers) where Primes can exist.
+    1, 3, 7, 9 are the 'Active Lanes' (Rivers).
     """
-    # 1. Create the Integer Stream (Deep depth to show the 'River' effect)
+    # 1. Create the Integer Stream
     integers = np.arange(sequence_length)
     
-    # 2. Mod 10 Alignment (The "Natural" Width of the decimal system)
+    # 2. Mod 10 Alignment (The "Natural" Width)
     width = 10 
     rows = int(np.ceil(sequence_length / width))
     
@@ -22,21 +22,18 @@ def generate_potentiality_matrix(sequence_length):
     padded_ints = np.pad(integers, (0, padded_len - len(integers)), mode='constant')
     
     # 3. Calculate "Potentiality" (The Heat)
-    # 0.0 = Ground State (Even/5s - Composite)
-    # 0.2 = Potential Lane (Odd Composite in 1,3,7,9)
-    # 1.0 = Kinetic Signal (Actual Prime)
     matrix_values = np.zeros(padded_len)
     
     for i, val in enumerate(padded_ints):
-        val_int = int(val)
-        if sympy.isprime(val_int) and val_int > 5:
+        val = int(val)
+        if sympy.isprime(val) and val > 5:
             matrix_values[i] = 1.0  # Kinetic Signal (Prime)
-        elif val_int % 2 == 0 or val_int % 5 == 0:
+        elif val % 2 == 0 or val % 5 == 0:
             matrix_values[i] = 0.0  # Ground State (Composite)
         else:
             matrix_values[i] = 0.2  # Potential Energy (Odd Composite in Prime Lane)
 
-    # Reshape to Mod 10 Grid (Rows x 10 Columns)
+    # Reshape to Mod 10 Grid
     grid = matrix_values.reshape(rows, width)
     
     # 4. Visualization
@@ -53,11 +50,11 @@ def generate_potentiality_matrix(sequence_length):
     ))
     
     fig.update_layout(
-        title=f"Prime Potentiality Rivers (Mod 10 Alignment)",
+        title=f"Prime Potentiality Rivers (Mod 10)",
         xaxis_title="Modulus 10 Residue (Last Digit)",
         yaxis_title="Sequence Depth (Time)",
         template="plotly_dark",
-        yaxis=dict(autorange="reversed"), # Stream flows down like the matrix screenshots
+        yaxis=dict(autorange="reversed"), # Stream flows down
         height=800
     )
     return fig
@@ -71,7 +68,6 @@ def visualize_prime_matroska(shells, show_feed):
     fig = go.Figure()
     
     # 1. Draw the "Prime Vectors" (Spokes)
-    # These are the rails the data travels on based on the Mod 10 index
     max_radius = shells * 10
     for i in range(10):
         angle = (2 * np.pi * i) / 10
@@ -80,7 +76,6 @@ def visualize_prime_matroska(shells, show_feed):
         color = "#00ffea" if is_prime_lane else "#333"
         width = 2 if is_prime_lane else 1
         
-        # Draw the vector spoke
         fig.add_trace(go.Scatter(
             x=[0, max_radius * np.cos(angle)],
             y=[0, max_radius * np.sin(angle)],
@@ -91,7 +86,6 @@ def visualize_prime_matroska(shells, show_feed):
         ))
 
     # 2. Draw the Concentric Shells (Domains)
-    # These represent the Nested Matroska Layers
     for layer in range(1, shells + 1):
         radius = layer * 10
         t = np.linspace(0, 2*np.pi, 100)
@@ -106,17 +100,15 @@ def visualize_prime_matroska(shells, show_feed):
             showlegend=False
         ))
         
-        # 3. Simulate "Active Atoms" (The Feed)
-        # Data atoms only inhabit the Prime Potentiality Vectors (1, 3, 7, 9)
+        # 3. Simulate "Data Atoms" residing in the shell
         if show_feed:
             for p_mod in [1, 3, 7, 9]:
                 p_angle = (2 * np.pi * p_mod) / 10
-                # Add 'Data' markers at the intersection of Shells and Prime Vectors
                 fig.add_trace(go.Scatter(
                     x=[radius * np.cos(p_angle)], 
                     y=[radius * np.sin(p_angle)],
                     mode='markers',
-                    marker=dict(size=6, color='#ff0055'), # Red = Hot Data Packet
+                    marker=dict(size=6, color='#ff0055'), # Red = Hot Data
                     showlegend=False
                 ))
 
@@ -132,11 +124,11 @@ def visualize_prime_matroska(shells, show_feed):
 
 # --- THE INTERFACE ---
 with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
-    gr.Markdown("# LOGOS: Prime-Indexed Topology & Potentiality")
+    gr.Markdown("# LOGOS: Prime-Indexed Topology")
     gr.Markdown("Interactive architectural validation for **Mod 10 Potentiality Rivers** and the **Radial Matroska Network**.")
     
     with gr.Tab("Prime Potentiality (Mod 10)"):
-        gr.Markdown("Visualizing the 'Vertical Rivers' of prime potential (1, 3, 7, 9) vs the Composite banks (0, 2, 4, 5, 6, 8).")
+        gr.Markdown("The vertical 'Rivers' of prime potential (1, 3, 7, 9) vs the Composite banks.")
         seq_len = gr.Slider(100, 10000, value=2500, label="Stream Depth")
         matrix_plot = gr.Plot(label="Potentiality Matrix")
         btn_matrix = gr.Button("Generate Matrix")
@@ -152,6 +144,6 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
         btn_net = gr.Button("Build Topology")
         btn_net.click(visualize_prime_matroska, inputs=[shells_slider, feed_toggle], outputs=matroska_plot)
 
-# --- LAUNCH COMMAND ---
-# Disable SSR (Server Side Rendering) to prevent asyncio/file descriptor crashes in HF Spaces
-demo.launch(ssr_mode=False)
+# --- CRITICAL FIX: SSR DISABLED ---
+if __name__ == "__main__":
+    demo.launch(ssr_mode=False)