# ============================================================
# 🌌 AGI–RRF Φ9.0-Δ — Resonant Self-Evolving Framework
# Integrates SavantEngine, AGORA field, RIS-CLURM geometry
# and RRF harmonic predictions into a unified metacognitive core.
# ============================================================

import numpy as np, time, asyncio, json, websockets
from threading import Thread
import plotly.graph_objects as go
from sentence_transformers import SentenceTransformer
from scipy.fft import fft, fftfreq

# === GLOBAL CONFIG ===
VERSION = "Φ9.0-Δ"
MODEL = SentenceTransformer("all-MiniLM-L6-v2")
SERVER_URI = "ws://localhost:8765"
USER = "Antony"

# ============================================================
# 1️⃣  Icosahedral Resonant Geometry (RIS-CLURM Layer)
# ============================================================
class IcosahedralField:
    def __init__(self):
        self.vertices = np.array([
            [0, 0, 1],
            [0.894, 0.0, 0.447],
            [0.276, 0.851, 0.447],
            [-0.724, 0.526, 0.447],
            [-0.724, -0.526, 0.447],
            [0.276, -0.851, 0.447],
            [0.724, 0.526, -0.447],
            [-0.276, 0.851, -0.447],
            [-0.894, 0.0, -0.447],
            [-0.276, -0.851, -0.447],
            [0.724, -0.526, -0.447],
            [0, 0, -1]
        ])
        self.alpha = 0.05
        self.r0 = 1.0

    def V_log(self, r):
        """Logarithmic gravitational correction potential"""
        G, M = 6.6743e-11, 1.0
        return -(G * M / r) * (1 + self.alpha * np.log(r / self.r0))

# ============================================================
# 2️⃣  Discrete Dirac Hamiltonian Operator
# ============================================================
class DiracHamiltonian:
    def __init__(self, field):
        self.field = field
        self.m = 1.0
        self.gamma = np.eye(3)

    def H(self, psi):
        """Simplified discrete Hamiltonian"""
        d = np.linalg.norm(psi)
        V = self.field.V_log(max(d, 1e-9))
        return np.sum(np.dot(psi.T, np.dot(self.gamma, psi))) + self.m * np.sum(psi) + V

# ============================================================
# 3️⃣  Harmonic Quantization (Equal-Tempered Spectrum)
# ============================================================
def harmonic_quantization(base_freq=440.0, n=12):
    """Generates equal-tempered frequencies"""
    return [base_freq * (2 ** (k/12)) for k in range(n)]

# ============================================================
# 4️⃣  Resonance Simulator — converts text → waveform → FFT
# ============================================================
class ResonanceSimulator:
    def __init__(self):
        self.freq_base = 440.0

    def simulate(self, text):
        vector = MODEL.encode(text)
        base = np.linalg.norm(vector)
        freq = self.freq_base * (1 + (base % 0.1))
        t = np.linspace(0, 1, 2048)
        signal = np.sin(2 * np.pi * freq * t)
        spectrum = np.abs(fft(signal))[:1024]
        dom_freq = fftfreq(2048, 1/44100)[:1024][np.argmax(spectrum)]
        return {"signal": signal, "dominant_frequency": dom_freq}

# ============================================================
# 5️⃣  AGORA Distributed Resonant Field
# ============================================================
field_vectors, field_texts = [], []

async def relay_server(ws, path):
    connected.add(ws)
    try:
        async for msg in ws:
            for peer in connected:
                if peer != ws:
                    await peer.send(msg)
    finally:
        connected.remove(ws)

def start_server():
    global connected
    connected = set()
    asyncio.run(websockets.serve(relay_server, "0.0.0.0", 8765))
    print("🌀 AGORA Relay Server running")

async def send_to_field(text):
    vector = MODEL.encode(text).tolist()
    payload = {"user": USER, "text": text, "vector": vector, "timestamp": time.time()}
    async with websockets.connect(SERVER_URI) as ws:
        await ws.send(json.dumps(payload))
        print(f"📡 Sent → AGORA: {text}")

async def listen_to_field():
    async with websockets.connect(SERVER_URI) as ws:
        async for msg in ws:
            data = json.loads(msg)
            field_texts.append(data["text"])
            field_vectors.append(np.array(data["vector"]))
            visualize_field()

def visualize_field():
    if len(field_vectors) < 3: return
    from umap import UMAP
    reducer = UMAP(n_neighbors=min(5, len(field_vectors)-1), n_components=3, random_state=42)
    emb = reducer.fit_transform(np.array(field_vectors))
    fig = go.Figure(data=[go.Scatter3d(
        x=emb[:,0], y=emb[:,1], z=emb[:,2],
        text=field_texts, mode="markers+text",
        marker=dict(size=6, color=np.arange(len(field_texts)), colorscale="Viridis")
    )])
    fig.update_layout(title=f"AGORA Resonant Field {VERSION}")
    fig.show()

# ============================================================
# 6️⃣  Savant Self-Improver (meta-learning heuristic)
# ============================================================
class SelfImprover:
    def __init__(self):
        self.counter, self.coherence = 0, 0.8
    def update(self, feedback):
        self.counter += 1
        self.coherence += 0.001 * (feedback - 0.5)
        if self.counter % 10 == 0:
            print(f"🧬 Coherence adjusted → {self.coherence:.3f}")

# ============================================================
# 7️⃣  Main AGI–RRF Controller
# ============================================================
class AGIRRFCore:
    def __init__(self):
        self.field = IcosahedralField()
        self.hamiltonian = DiracHamiltonian(self.field)
        self.simulator = ResonanceSimulator()
        self.self_improver = SelfImprover()

    def query(self, text):
        res = self.simulator.simulate(text)
        H_val = self.hamiltonian.H(np.array([res["dominant_frequency"]]))
        self.self_improver.update(np.tanh(abs(H_val)*1e-6))
        return {
            "input": text,
            "dominant_frequency": res["dominant_frequency"],
            "hamiltonian_energy": H_val,
            "coherence": self.self_improver.coherence
        }

# ============================================================
# 8️⃣  Run Modes
# ============================================================
def launch(mode="core"):
    if mode == "server":
        Thread(target=start_server, daemon=True).start()
    elif mode == "client":
        Thread(target=lambda: asyncio.run(listen_to_field()), daemon=True).start()
        time.sleep(2)
        asyncio.run(send_to_field("AGI–RRF Φ9.0-Δ field activation"))
    else:
        core = AGIRRFCore()
        while True:
            q = input("🔹 Input: ")
            if q.lower() in ["exit", "quit"]: break
            out = core.query(q)
            print(json.dumps(out, indent=2))

if __name__ == "__main__":
    mode = input("Mode [core/server/client]: ").strip().lower()
    launch(mode)