Update app.py

app.py

@@ -1,9 +1,9 @@
 # ============================================================
-# RFT-Ω FRAMEWORK — TOTAL-PROOF API (Gradio Stand-Alone Build)
+# RFT-Ω FRAMEWORK — TOTAL-PROOF API (Gradio Stable Build)
 # Author: Liam Grinstead | RFT Systems | All Rights Reserved
 # ============================================================
 
-import os, json, time, hashlib, zipfile, random
+import json, hashlib, random
 from datetime import datetime
 import numpy as np
 import gradio as gr
@@ -16,6 +16,7 @@ LEGAL_NOTICE = (
     "Research validation use only. No reverse-engineering without written consent."
 )
 
+# ------------------ System Profiles -------------------------
 PROFILES = {
     "AI / Neural": {"base": (0.86, 0.80), "w": (0.65, 0.35)},
     "SpaceX / Aerospace": {"base": (0.84, 0.79), "w": (0.60, 0.40)},
@@ -23,7 +24,8 @@ PROFILES = {
     "Extreme Perturbation": {"base": (0.82, 0.77), "w": (0.50, 0.50)},
 }
 
-def _rng(seed:int): return np.random.RandomState(seed)
+def _rng(seed:int): 
+    return np.random.RandomState(seed)
 
 def simulate_step(rng, profile, sigma, dist):
     base_q, base_z = PROFILES[profile]["base"]
@@ -36,56 +38,53 @@ def simulate_step(rng, profile, sigma, dist):
         zn = rng.normal(0, sigma*0.8)
     q = float(np.clip(base_q + wq*qn, 0.0, 0.99))
     z = float(np.clip(base_z + wz*zn, 0.0, 0.99))
-    variance = abs(qn)+abs(zn)
+    variance = abs(qn) + abs(zn)
     if variance > 0.15:
-        status="critical"
+        status = "critical"
     elif variance > 0.07:
-        status="perturbed"
+        status = "perturbed"
     else:
-        status="nominal"
-    return {"σ": round(sigma,6),"QΩ":q,"ζ_sync":z,"status":status}
+        status = "nominal"
+    return {"σ": round(sigma, 6), "QΩ": q, "ζ_sync": z, "status": status}
 
-# ------------------ Main Runner -----------------------------
+# ------------------ Simulation Runner -----------------------
 def run(profile, dist, sigma, seed, samples):
     rng = _rng(int(seed))
-    results = []
-    for _ in range(samples):
-        results.append(simulate_step(rng, profile, sigma, dist))
+    results = [simulate_step(rng, profile, sigma, dist) for _ in range(samples)]
     q_mean = np.mean([r["QΩ"] for r in results])
     z_mean = np.mean([r["ζ_sync"] for r in results])
-    summary = {
-        "QΩ_mean": round(float(q_mean),6),
-        "ζ_sync_mean": round(float(z_mean),6),
-        "samples": samples,
+    majority = max(["nominal","perturbed","critical"],
+                   key=lambda s: sum(1 for r in results if r["status"]==s))
+    return {
         "profile": profile,
-        "noise": sigma,
-        "dist": dist,
-        "status_majority": max(
-            ["nominal","perturbed","critical"],
-            key=lambda s: sum(1 for r in results if r["status"]==s)
-        ),
+        "noise_scale": sigma,
+        "distribution": dist,
+        "QΩ_mean": round(float(q_mean), 6),
+        "ζ_sync_mean": round(float(z_mean), 6),
+        "status_majority": majority,
+        "timestamp_utc": datetime.utcnow().isoformat() + "Z",
         "rft_notice": LEGAL_NOTICE
     }
-    return summary
 
-# ------------------ Gradio UI -------------------------------
+# ------------------ Gradio Interface ------------------------
 with gr.Blocks(title="RFT-Ω Total-Proof Kernel") as demo:
     gr.Markdown(f"### RFT-Ω Total-Proof Kernel ({RFT_VERSION})  \n"
                 f"DOI: [{RFT_DOI}]({RFT_DOI})  \n{LEGAL_NOTICE}")
-    
+
     with gr.Row():
         profile = gr.Dropdown(list(PROFILES.keys()), label="System Profile", value="AI / Neural")
         dist = gr.Radio(["gauss","uniform"], label="Noise Distribution", value="gauss")
+
     with gr.Row():
         sigma = gr.Slider(0.0, 0.3, value=0.05, step=0.01, label="Noise Scale (σ)")
-        seed = gr.Number(value=123, precision=0, label="Seed")
-        samples = gr.Slider(1, 20, value=5, step=1, label="Samples")
-    
+        seed = gr.Number(value=123, label="Seed (integer)")
+        samples = gr.Slider(1, 20, value=5, step=1, label="Samples per run")
+
     run_btn = gr.Button("Run Simulation")
-    output = gr.JSON(label="Run Summary")
+    output = gr.JSON(label="Simulation Results")
 
     run_btn.click(run, inputs=[profile, dist, sigma, seed, samples], outputs=[output])
 
 # ------------------ Launch -------------------------------
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True, debug=False)
+    demo.launch(server_name="0.0.0.0", server_port=7860)