# agdr_medical_swarm.py — v0.6 Medical Swarm Sovereign
# (full production skeleton — drop into any TensorRT-LLM or NemoClaw deployment)

from dataclasses import dataclass
import hashlib, struct, time
from pathlib import Path

@dataclass
class MedicalAgDR:
    adr_id: str
    ppp: dict
    human_delta: str
    blake3_hash: str
    previous_hash: str

class MedicalSwarmSpine:
    def __init__(self, shard_id: int, federal_root: bool = False):
        self.shard_id = shard_id
        self.federal_root = federal_root
        self._prev = b'\x00'*32
        self.count = 0

    def capture(self, agent_id: str, patient_pseudo: str, consent_ver: str,
                risk_tier: str, reasoning: str) -> MedicalAgDR:
        t0 = time.perf_counter_ns()
        ppp = {
            "provenance": hashlib.blake2b(f"nemotron-med:{agent_id}".encode(), digest_size=32).hexdigest(),
            "place": f"cuda:0:shard_{self.shard_id}:CA_PHIPA",
            "purpose": "town_weave_2026_v1",
            "patient_pseudonym_hash": hashlib.blake2b(patient_pseudo.encode()).hexdigest()[:32],
            "consent_version": consent_ver,
            "clinical_risk_tier": risk_tier
        }
        partial = struct.pack("!24s32s", f"adr_med_{self.count}".encode()[:24], bytes.fromhex(ppp["provenance"]))
        current = hashlib.blake2b(partial + self._prev).digest()
        latency = (time.perf_counter_ns() - t0) / 1000

        rec = MedicalAgDR(
            adr_id=f"adr_med_{self.count}",
            ppp=ppp,
            human_delta="foi-physician-override-001",
            blake3_hash="0x" + current.hex(),
            previous_hash="0x" + self._prev.hex()
        )
        self._prev = current
        self.count += 1
        return rec

# Swarm coordinator example (one per region)
spines = [MedicalSwarmSpine(i) for i in range(1024)]
# In production: each spine runs on its own GPU node + CUDA batch kernel
