# ============================================================
# RFT-Ω FRAMEWORK — TOTAL-PROOF API (Gradio Stand-Alone Build)
# Author: Liam Grinstead | RFT Systems | All Rights Reserved
# ============================================================

import os, json, time, hashlib, zipfile, random
from datetime import datetime
import numpy as np
import gradio as gr

# ------------------ About / Legal ---------------------------
RFT_VERSION = "v4.0-total-proof-stable"
RFT_DOI = "https://doi.org/10.5281/zenodo.17466722"
LEGAL_NOTICE = (
    "All Rights Reserved — RFT-IPURL v1.0 (UK / Berne). "
    "Research validation use only. No reverse-engineering without written consent."
)

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)},
    "Energy / RHES": {"base": (0.83, 0.78), "w": (0.55, 0.45)},
    "Extreme Perturbation": {"base": (0.82, 0.77), "w": (0.50, 0.50)},
}

def _rng(seed:int): return np.random.RandomState(seed)

def simulate_step(rng, profile, sigma, dist):
    base_q, base_z = PROFILES[profile]["base"]
    wq, wz = PROFILES[profile]["w"]
    if dist == "uniform":
        qn = rng.uniform(-sigma, sigma)
        zn = rng.uniform(-sigma*0.8, sigma*0.8)
    else:
        qn = rng.normal(0, sigma)
        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)
    if variance > 0.15:
        status="critical"
    elif variance > 0.07:
        status="perturbed"
    else:
        status="nominal"
    return {"σ": round(sigma,6),"QΩ":q,"ζ_sync":z,"status":status}

# ------------------ Main 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))
    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,
        "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)
        ),
        "rft_notice": LEGAL_NOTICE
    }
    return summary

# ------------------ Gradio UI -------------------------------
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")
    
    run_btn = gr.Button("Run Simulation")
    output = gr.JSON(label="Run Summary")

    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)