Create app.py

RFT Adaptive Computing Kernel (v1.0)
This interface simulates compute stability and harmonic coherence across CPU, GPU, and TPU environments under the Rendered Frame Theory (RFT) model.

Each run dynamically calculates:
• QΩ (stability) and ζ_sync (coherence) — showing how balanced a workload remains under system noise.
• rate_items_per_sec — estimated computational throughput adjusted for randomised perturbations.
• SHA-512 proof log — a unique hash verifying run authenticity.

Use this panel to benchmark adaptive self-stabilisation across workloads (“matrix,” “transformer,” “mixed”) and hardware classes. All metrics are sealed under RFT-IPURL v1.0 (UK/Berne) for research validation only.

app.py

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+import gradio as gr
+import numpy as np
+import time, json, hashlib
+from datetime import datetime
+
+# =============================================================
+#  Rendered Frame Theory — Adaptive Computing Kernel (v1.0)
+# =============================================================
+
+def rft_kernel(profile, workload, cycles, seed):
+    np.random.seed(seed)
+    t0 = time.time()
+
+    # Simulated compute rates (items/sec × noise)
+    base_speed = {"CPU": 0.45, "GPU": 0.83, "TPU": 0.78}[profile]
+    noise = np.random.uniform(-0.05, 0.05)
+    rate = base_speed * (1 + noise)
+
+    # Harmonic metrics
+    QΩ = round(0.8 + np.random.uniform(-0.05, 0.05), 3)
+    ζ_sync = round(0.78 + np.random.uniform(-0.05, 0.05), 3)
+    status = "nominal" if ζ_sync > 0.76 else "perturbed"
+
+    # Hash-log for proof of run
+    log = {
+        "profile": profile,
+        "workload": workload,
+        "cycles": cycles,
+        "rate_items_per_sec": round(rate * 1e9, 2),
+        "QΩ": QΩ,
+        "ζ_sync": ζ_sync,
+        "status": status,
+        "timestamp_utc": datetime.utcnow().isoformat() + "Z"
+    }
+    log["sha512"] = hashlib.sha512(json.dumps(log).encode()).hexdigest()
+    time.sleep(0.5)
+    return json.dumps(log, indent=2)
+
+# =============================================================
+#  Interface
+# =============================================================
+
+iface = gr.Interface(
+    fn=rft_kernel,
+    inputs=[
+        gr.Radio(["CPU","GPU","TPU"], label="Compute Profile"),
+        gr.Radio(["matrix","transformer","mixed"], label="Workload Type"),
+        gr.Slider(1,10,step=1,value=3,label="Cycles"),
+        gr.Number(value=123, label="Seed")
+    ],
+    outputs=gr.JSON(label="Simulation Log"),
+    title="🧠 Rendered Frame Theory — Adaptive Computing Kernel",
+    description=(
+        "Simulates harmonic-stable computation under the RFT model.\n"
+        "Returns QΩ, ζ_sync, and items/sec metrics with SHA-512-sealed logs."
+    )
+)
+
+iface.launch()