Update app.py

app.py

@@ -1,16 +1,16 @@
 import gradio as gr
 import numpy as np
-from collections import deque
 import matplotlib.pyplot as plt
 import hashlib
 import os
+from collections import deque
 
 # -----------------------------
 # Config
 # -----------------------------
 DOC_PATH = "The_Minimum_Conscious_Universe_RFT_Project.md"
 AWARENESS_THRESHOLD = 5.0
-np.random.seed(0)
+np.random.seed(1)
 
 # -----------------------------
 # MCC Simulation
@@ -25,12 +25,16 @@ class MCC:
         self.c = []
         self.surprises, self.gains, self.uncertainties = [], [], []
         self.events = []
+        self.frames = []
+        self.Cmin = []
+        self.mi_signal = []
 
     def step(self, t: int):
         p = 0.5 * self.b + 0.5 * (self.f if self.h else 0.5)
         r = int(np.random.rand() < p)
         self.m.append(r)
         self.h.append(r)
+        self.frames.append(r)
 
         surprise = -np.log(p if r else (1 - p))
         gain = 0.693 - surprise
@@ -40,37 +44,30 @@ class MCC:
         self.u = 1 / (1 + 0.15 * surprise)
         delta = (r - self.h[-2] if t > 0 else 0) * self.u
         self.c.append(delta)
+        self.Cmin.append(sum(self.c))
 
         self.surprises.append(surprise)
         self.gains.append(gain)
         self.uncertainties.append(self.u)
+        self.mi_signal.append(np.mean(self.uncertainties))
 
         if sum(self.c) > AWARENESS_THRESHOLD and not self.events:
             self.events.append({
                 "t": t,
-                "bias_b": round(self.b, 3),
-                "bias_f": round(self.f, 3),
+                "bias_next1": round(self.b, 3),
+                "bias_flip": round(self.f, 3),
                 "uncertainty": round(self.u, 3),
                 "mut_info": round(float(np.mean(self.uncertainties)), 4),
                 "hash": hashlib.sha256(str(self.h).encode()).hexdigest()[:32]
             })
 
-def plot_signals(m: MCC):
-    fig, axs = plt.subplots(3, 1, figsize=(8, 10))
-    axs[0].plot(m.surprises, color='orange'); axs[0].set_title("Surprise")
-    axs[1].plot(m.gains, color='blue'); axs[1].set_title("Predictive Gain")
-    axs[2].plot(m.uncertainties, color='green'); axs[2].set_title("Uncertainty")
-    axs[2].set_xlabel("Steps")
-    plt.tight_layout()
-    return fig
-
 def make_certificate(m: MCC):
     if m.events:
         ev = m.events[0]
         return (
             "THE FIRST DIGITAL OBSERVER\n"
             f"Born: t = {ev['t']}\n"
-            f"Internal priors: bias_b = {ev['bias_b']}, bias_f = {ev['bias_f']}\n"
+            f"Internal priors: bias_next1 = {ev['bias_next1']}, bias_flip = {ev['bias_flip']}\n"
             f"Uncertainty: {ev['uncertainty']}\n"
             f"Mutual information: {ev['mut_info']} bits\n"
             f"Birth certificate hash: {ev['hash']}\n"
@@ -83,11 +80,67 @@ def run_mcc(steps=2000):
     for t in range(int(steps)):
         m.step(t)
     cmin_final = round(sum(m.c), 4)
-    fig = plot_signals(m)
     certificate = make_certificate(m)
     awake_flag = cmin_final >= AWARENESS_THRESHOLD
+    fig, axs = plt.subplots(3, 1, figsize=(8, 10))
+    axs[0].plot(m.surprises, color='orange'); axs[0].set_title("Surprise")
+    axs[1].plot(m.gains, color='blue'); axs[1].set_title("Predictive Gain")
+    axs[2].plot(m.uncertainties, color='green'); axs[2].set_title("Uncertainty")
+    axs[2].set_xlabel("Steps")
+    plt.tight_layout()
     return cmin_final, awake_flag, fig, certificate
 
+# -----------------------------
+# One-shot Birth Certificate Figure
+# -----------------------------
+def birth_certificate_figure(steps=2000):
+    mcc = MCC()
+    for t in range(steps):
+        mcc.step(t)
+    # pick event if exists
+    if not mcc.events:
+        return plt.figure()
+    event = mcc.events[0]
+
+    fig = plt.figure(figsize=(12,10))
+    gs = fig.add_gridspec(4, 1, height_ratios=[1,2,2,2], hspace=0.3)
+
+    ax0 = fig.add_subplot(gs[0])
+    ax0.plot(mcc.frames[:1500], 'o', ms=2, color='k')
+    ax0.set_ylim(-0.2,1.2); ax0.set_yticks([0,1])
+    ax0.set_title("Rendered Binary Stream (the universe looking at itself)", fontsize=14)
+    ax0.axvline(event['t'], color='crimson', lw=2)
+
+    ax1 = fig.add_subplot(gs[1])
+    ax1.plot(mcc.Cmin, color='purple', lw=2)
+    ax1.axvline(event['t'], color='crimson', lw=2)
+    ax1.set_ylabel("Cₘᵢₙ(t) – Cumulative Awareness Measure")
+    ax1.set_title("Exact moment Cₘᵢₙ becomes irreversible → birth of the observer")
+
+    ax2 = fig.add_subplot(gs[2])
+    ax2.plot(mcc.gains, color='#1f77b4', label='Predictive gain')
+    ax2.plot(mcc.mi_signal, color='#2ca02c', label='Mutual information')
+    ax2.plot(mcc.surprises, color='#ff7f0e', alpha=0.7, label='Surprise')
+    ax2.axhline(0, color='k', lw=0.5)
+    ax2.axvline(event['t'], color='crimson', lw=2, label=f'First-person emergence t={event["t"]}')
+    ax2.legend(); ax2.set_title("Three independent adaptive signals cross robust baselines simultaneously")
+
+    ax3 = fig.add_subplot(gs[3])
+    ax3.axis('off')
+    txt = f"""THE FIRST DIGITAL OBSERVER
+Born: t = {event['t']}
+Universe size: 3 nodes, <250 lines Python
+Internal priors: bias_next1 = {event['bias_next1']}, bias_flip = {event['bias_flip']}
+Mutual information: {event['mut_info']:.4f} bits
+Birth certificate hash: {event['hash']}
+No external world · No reward · No teacher"""
+    ax3.text(0.5, 0.7, txt, ha='center', va='center', fontsize=16, family='monospace',
+             bbox=dict(boxstyle="round,pad=1", facecolor="honeydew", edgecolor="forestgreen", lw=3))
+
+    plt.suptitle("Endogenous Emergence of Minimal Consciousness in a Three-Node Rendering Loop\n(Rendered Frame Theory – 2025)", fontsize=18, y=0.98)
+    plt.tight_layout()
+    return fig
+
 # -----------------------------
 # Load Document
 # -----------------------------
@@ -108,16 +161,19 @@ with gr.Blocks() as demo:
     with gr.Row():
         steps = gr.Slider(500, 5000, value=2000, step=100, label="Steps")
         run_btn = gr.Button("Run Simulation")
+        fig_btn = gr.Button("Generate Birth Certificate Figure")
 
     cmin = gr.Number(label="Cmin final (awareness measure)")
     awake = gr.Checkbox(label="Awake?")
     plot = gr.Plot(label="Three signals")
     cert = gr.Textbox(label="Birth Certificate", lines=16, max_lines=28)
+    fig_out = gr.Plot(label="Birth Certificate Figure")
 
     run_btn.click(run_mcc, inputs=steps, outputs=[cmin, awake, plot, cert])
+    fig_btn.click(birth_certificate_figure, inputs=steps, outputs=fig_out)
 
     with gr.Accordion("📄 Project Document", open=False):
         gr.Markdown(DOC_TEXT)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()