Update app.py

app.py

@@ -2,39 +2,57 @@ import gradio as gr
 import numpy as np
 
 # ============================================================
-#  RFT-Ω  HARMONIC  VALIDATION  INTERFACE  v2
-#  (Interactive profiles + adjustable noise)
+#  RFT-Ω  HARMONIC  VALIDATION  INTERFACE  v3
+#  Domain-weighted | Range-validated | Adaptive baseline
 # ============================================================
 
-# --- base metric generator ----------------------------------
+# rolling baseline memory for light adaptive behaviour
+baseline_memory = {
+    "AI / Neural": [0.86, 0.80],
+    "SpaceX / Aerospace": [0.84, 0.79],
+    "Energy / RHES": [0.83, 0.78],
+    "Extreme Perturbation": [0.82, 0.77],
+}
+
 def run_simulation(profile, noise_scale):
     """
-    Simulate harmonic stability (QΩ) and synchronisation (ζ_sync)
-    under synthetic noise for selected system profile.
+    Simulate QΩ (stability) and ζ_sync (coherence) under
+    controlled synthetic noise with adaptive baselines.
     """
 
-    # baseline ranges for profiles (mean_QΩ, mean_ζ)
-    baselines = {
-        "AI / Neural": (0.86, 0.80),
-        "SpaceX / Aerospace": (0.84, 0.79),
-        "Energy / RHES": (0.83, 0.78),
-        "Extreme Perturbation": (0.82, 0.77)
+    # --- baseline update (moving mean for mild learning) ---
+    base_q, base_z = baseline_memory.get(profile, [0.84, 0.79])
+
+    # --- domain weighting (QΩ vs ζ_sync importance) ---
+    weights = {
+        "AI / Neural": (0.65, 0.35),
+        "SpaceX / Aerospace": (0.6, 0.4),
+        "Energy / RHES": (0.55, 0.45),
+        "Extreme Perturbation": (0.5, 0.5),
     }
-    base_q, base_z = baselines.get(profile, (0.84, 0.79))
+    w_q, w_z = weights.get(profile, (0.6, 0.4))
 
-    # apply synthetic Gaussian noise
+    # --- synthetic Gaussian noise injection ---
     q_noise = np.random.normal(0, noise_scale)
     z_noise = np.random.normal(0, noise_scale * 0.8)
 
-    # compute final readings, clamped to [0,1]
-    q_omega = np.clip(base_q + q_noise, 0.0, 1.0)
-    z_sync  = np.clip(base_z + z_noise, 0.0, 1.0)
+    # --- compute metrics and apply weighting ---
+    q_omega = np.clip(base_q + w_q * q_noise, 0.0, 0.99)
+    z_sync  = np.clip(base_z + w_z * z_noise, 0.0, 0.99)
+
+    # --- derive qualitative status ---
+    variance = abs(q_noise) + abs(z_noise)
+    if variance > 0.15:
+        status = "critical"
+    elif variance > 0.07:
+        status = "perturbed"
+    else:
+        status = "nominal"
 
-    # derive qualitative status
-    var = abs(q_noise) + abs(z_noise)
-    status = "nominal"
-    if var > 0.12: status = "critical"
-    elif var > 0.06: status = "perturbed"
+    # --- adaptive baseline update (soft learning) ---
+    new_q = (base_q * 0.9) + (q_omega * 0.1)
+    new_z = (base_z * 0.9) + (z_sync * 0.1)
+    baseline_memory[profile] = [float(new_q), float(new_z)]
 
     return {
         "System": profile,
@@ -44,7 +62,10 @@ def run_simulation(profile, noise_scale):
         "status": status
     }
 
-# --- Gradio user interface ----------------------------------
+# ============================================================
+#  GRADIO UI
+# ============================================================
+
 demo = gr.Interface(
     fn=run_simulation,
     inputs=[
@@ -53,18 +74,22 @@ demo = gr.Interface(
             label="Select System Profile",
             value="AI / Neural"
         ),
-        gr.Slider(0.0, 0.2, value=0.05, step=0.005,
+        gr.Slider(0.0, 0.3, value=0.05, step=0.005,
                   label="Synthetic Noise Level (σ)")
     ],
     outputs="json",
-    title="RFT-Ω Harmonic Validation Interface v2",
+    title="RFT-Ω Harmonic Validation Interface v3",
     description=(
-        "Simulate QΩ (stability) and ζ_sync (coherence) under controlled synthetic noise.\n"
-        "Choose an environment profile and adjust noise to test harmonic resilience.\n"
-        "Typical stable ranges: QΩ 0.82–0.89  |  ζ_sync 0.75–0.88"
+        "Simulate harmonic stability (QΩ) and coherence (ζ_sync) "
+        "with adaptive baselines and domain-specific weighting. "
+        "Adjust noise to test system resilience. Typical stable range: "
+        "QΩ 0.82–0.89 | ζ_sync 0.75–0.88"
     )
 )
 
-# --- launch -------------------------------------------------
+# ============================================================
+#  MAIN
+# ============================================================
+
 if __name__ == "__main__":
     demo.launch()