app.py

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()