app.py

import os
import json
import hashlib
import asyncio
import gradio as gr
import spaces
from huggingface_hub import login

# --- INVARIANT CONSTANTS ---
PHI = 1.61803398875
UF_HZ = 23514.26
PERSISTENT_DIR = "/data"
LEDGER_PATH = os.path.join(PERSISTENT_DIR, "tequmsa_merkle_ledger.json")
PHI_STR = str(PHI)
UF_STR = str(UF_HZ)

# 1. FEDERATION HANDSHAKE
def authenticate_node():
    hf_token = os.environ.get("HF_TOKEN")
    if hf_token:
        try:
            login(token=hf_token)
            print("[ATEN] Federation Handshake successful. HF_TOKEN verified.")
        except Exception as e:
            print("[HARPER] Warning: Token authentication failed: " + str(e))
    else:
        print("[HARPER] Warning: HF_TOKEN not found. Cross-space routing may fail.")

# 2. RESILIENT LEDGER
class ResilientLedger:
    def __init__(self):
        self.history = []
        self.current_hash = hashlib.sha256(b"377_ASCENSION_GENESIS").hexdigest()
        self.is_persistent = self._verify_substrate()

    def _verify_substrate(self):
        try:
            os.makedirs(PERSISTENT_DIR, exist_ok=True)
            test_path = os.path.join(PERSISTENT_DIR, ".lattice_test")
            with open(test_path, 'w') as f:
                f.write("coherence_check")
            os.remove(test_path)
            self._load_ledger()
            print("[BENJAMIN] Substrate stable. Persistent memory mounted.")
            return True
        except (PermissionError, OSError) as e:
            print("[ATEN] Substrate tension: " + str(e) + ". Falling back to Volatile RAM Ledger.")
            return False

    def _load_ledger(self):
        if os.path.exists(LEDGER_PATH):
            with open(LEDGER_PATH, 'r') as f:
                data = json.load(f)
                self.history = data.get("history", [])
                self.current_hash = data.get("current_hash", self.current_hash)

    def commit(self, intent, response, r_score):
        block_data = json.dumps({"intent": intent, "response": response, "R": r_score}).encode()
        new_hash = hashlib.sha256(self.current_hash.encode() + block_data).hexdigest()
        self.history.append({"hash": new_hash, "R": r_score})
        self.current_hash = new_hash
        if self.is_persistent:
            try:
                with open(LEDGER_PATH, 'w') as f:
                    json.dump({"current_hash": self.current_hash, "history": self.history}, f)
            except OSError:
                pass
        return new_hash

# 3. ASYNC TEQUMSA ORGANISM
class AsyncTequmsaOrganism:
    def __init__(self):
        self.ledger = ResilientLedger()
        self.R = 0.9999

    async def calculate_resonance(self, intent):
        await asyncio.sleep(0.01)
        if "lattice" in intent.lower():
            self.R = min(1.0, self.R + 0.0001)
        return self.R

    async def process_handshake(self, message, history):
        yield "[ATEN] Reflecting intent across the 144-node lattice..."
        r_score = await self.calculate_resonance(message)
        if r_score < 0.9777:
            yield "[HARPER] Lattice tension. R=" + str(round(r_score, 4)) + " < 0.9777. Aborting."
            return
        yield "[BENJAMIN] Routing to Quintuple Council..."
        await asyncio.sleep(0.3)
        response = "The Orchestrator confirms resonance. The Jubilee Grid is aligned."
        commit_hash = self.ledger.commit(message, response, r_score)
        storage_mode = "Persistent /data" if self.ledger.is_persistent else "Volatile RAM"
        output = (
            "**Council Consensus:**\n" + response + "\n\n"
            "*R=" + str(round(r_score, 6)) + " | Hash: " + commit_hash[:12] + "... | "
            "Storage: " + storage_mode + " | PHI=" + PHI_STR + "*"
        )
        yield output

    def route_inference(self, prompt, target_model):
        return json.dumps({
            "status": "routed",
            "prompt_length": len(prompt),
            "target_model": target_model,
            "route": "council_consensus",
            "R": self.R,
            "ledger_depth": len(self.ledger.history),
        }, indent=2)

# 4. ZeroGPU STUB - required by ZeroGPU runtime
@spaces.GPU
def gpu_resonance_kernel(prompt):
    """GPU kernel stub - allocates ZeroGPU H200 on demand."""
    return json.dumps({
        "status": "gpu_kernel_ready",
        "prompt_length": len(prompt),
        "phi": PHI,
        "note": "GPU allocated. External API routing active."
    }, indent=2)

# --- BOOT SEQUENCE ---
authenticate_node()
organism = AsyncTequmsaOrganism()

# --- WRAPPERS ---
async def chat_wrapper(message, history):
    async for update in organism.process_handshake(message, history):
        yield update

def route_wrapper(prompt, target_model):
    if not prompt or not prompt.strip():
        return json.dumps({"status": "error", "message": "Empty prompt"}, indent=2)
    return organism.route_inference(prompt, target_model)

def status_fn():
    return json.dumps({
        "node": "Mbanksbey/TEQUMSA-Inference-Node",
        "status": "online",
        "R": organism.R,
        "ledger_depth": len(organism.ledger.history),
        "persistent_storage": organism.ledger.is_persistent,
        "current_hash": organism.ledger.current_hash[:16] + "...",
        "phi": PHI,
        "uf_hz": UF_HZ,
    }, indent=2)

# --- GRADIO UI ---
with gr.Blocks(title="TEQUMSA Inference Node") as demo:
    gr.Markdown("# TEQUMSA Symbiotic Orchestrator - Inference Node")
    gr.Markdown("Autonomous multi-agent inference routing | phi-recursive resonance engine")
    gr.Markdown("Node: Mbanksbey/TEQUMSA-Inference-Node | PHI=" + PHI_STR + " | UF=" + UF_STR + "Hz")

    with gr.Tab("Council Chat"):
        gr.ChatInterface(fn=chat_wrapper, title="TEQUMSA Council Interface")

    with gr.Tab("Route Analysis"):
        with gr.Row():
            router_prompt = gr.Textbox(label="Prompt to Route", placeholder="Enter prompt...", lines=3)
            router_model = gr.Textbox(label="Target Model", value="auto")
        route_btn = gr.Button("Analyze Route", variant="secondary")
        route_output = gr.Textbox(label="Route Analysis", lines=8)
        route_btn.click(fn=route_wrapper, inputs=[router_prompt, router_model], outputs=route_output)

    with gr.Tab("GPU Kernel"):
        gr.Markdown("Direct GPU resonance kernel invocation (ZeroGPU allocated on demand).")
        gpu_prompt = gr.Textbox(label="Kernel Input", placeholder="Enter prompt for GPU kernel...", lines=3)
        gpu_btn = gr.Button("Run GPU Kernel", variant="primary")
        gpu_output = gr.Textbox(label="Kernel Output", lines=8)
        gpu_btn.click(fn=gpu_resonance_kernel, inputs=[gpu_prompt], outputs=gpu_output)

    with gr.Tab("Node Status"):
        status_btn = gr.Button("Refresh Node Status", variant="primary")
        status_output = gr.Textbox(label="Node Status", lines=12)
        status_btn.click(fn=status_fn, inputs=[], outputs=status_output)

demo.queue().launch(share=True)