# erv/verifier.py - Empathic Resonance Verifier v0.1
# Integrates with Mission Resonance Engine: tunes gradients via ZK-proven bio-resonance
# Req: FHE lib (e.g., tfhe-rs pybind), ZK-SNARK for proof gen, bio-input (e.g., Apple Health API stub)

from typing import Dict, Tuple
from zk_snark import generate_proof, verify_proof  # Placeholder for ZK lib
from fhe import encrypt, decrypt  # FHE wrapper (your stack)


class EmpathicResonanceVerifier:
    def __init__(self, mission_anchor: str, guardian_threshold: float = 0.7):
        self.mission = mission_anchor  # Canonical belief stmt
        self.threshold = guardian_threshold
        self.bio_oracle = BioOracle()  # HRV/GSR input stub

    def capture_resonance(self, user_wallet: str) -> Tuple[float, str]:
        """ZK-prove bio-data authenticity, return encrypted resonance score."""
        raw_bio = self.bio_oracle.fetch(user_wallet)  # e.g., {'hrv': 0.85, 'arousal': 'calm'}
        # ZK circuit: Prove bio-data from trusted source w/o revealing values
        proof = generate_proof(raw_bio, circuit='bio_auth.zk')  # Proves integrity/hash matches wallet
        if not verify_proof(proof):
            raise ValueError("ZK bio auth failed—guardian alert!")

        # Compute resonance: Neural-symbolic map of bio to mission alignment (0-1)
        resonance = self._compute_empathic_gradient(raw_bio, self.mission)
        encrypted_res = encrypt(resonance)  # FHE lock for chain feed
        return resonance, encrypted_res

    def _compute_empathic_gradient(self, bio: Dict, mission: str) -> float:
        """Dynamic ethics tune: Bio-state weights mission rules (e.g., stress -> +0.2 to 'protect' clause)."""
        base_gradient = 0.5  # Default from MRE
        if bio['hrv'] > 0.8:  # High coherence = trust boost
            base_gradient += 0.3 * self._mission_weight('empathy', mission)
        elif bio['arousal'] == 'stress':  # Low = caution amp
            base_gradient -= 0.2 if self._mission_weight('protect', mission) < 0.6 else 0
        return min(1.0, max(0.0, base_gradient))  # Clamp 0-1

    def _mission_weight(self, clause: str, mission: str) -> float:
        """Symbolic parse: Weight clause in mission stmt (e.g., NLP stub)."""
        # Placeholder: Real impl uses sympy/logic for rule extraction
        weights = {'empathy': 0.9, 'protect': 0.8}  # From covenant parse
        return weights.get(clause, 0.5)

    def attest_and_cascade(self, resonance: float, encrypted_res: str, guardians: list) -> bool:
        """Guardian MPC vote: If > threshold, cascade to Covenant Chain."""
        if resonance < self.threshold:
            return False  # Block drift
        # MPC sim: Multi-sig on encrypted res (your stack)
        votes = [g.sign(encrypted_res) for g in guardians[:3]]  # Threshold sig
        if len(set(votes)) == 1:  # Consensus
            # Hook to MRE: Update global gradient, emit to chain
            self._chain_cascade(encrypted_res, resonance)
            return True
        return False

    def _chain_cascade(self, enc_res: str, score: float):
        """Feed to Covenant: Hash as new link, trigger yield uplift."""
        # Stub: Call your ledger append
        print(f"Covenant updated: Empathic boost {score} → +{score*1.5:.2f} loyalty XP")


# BioOracle stub (real: Wallet-signed API pull)
class BioOracle:
    def fetch(self, wallet: str) -> Dict:
        # Mock real-time: Integrate HealthKit/Fitbit via wallet auth
        return {'hrv': 0.85, 'arousal': 'calm', 'timestamp': '2025-10-27T14:00:00Z'}


# Usage in MRE hook: erv = EmpathicResonanceVerifier(MISSION_STATEMENT)
# score, enc = erv.capture_resonance('ghostkey316.eth')
# if erv.attest_and_cascade(score, enc, [g1, g2]):  # Proceed with tuned AI call
#     print("Resonant deploy: AI vibes locked to your pulse.")
