from __future__ import annotations

import json
from pathlib import Path
from typing import Any

POLICY_CANDIDATE = Path('/home/openclaw/.openclaw/workspace/data/policy_candidate.json')


def load_policy_candidate(path: Path = POLICY_CANDIDATE) -> dict[str, Any]:
    try:
        return json.loads(path.read_text(encoding='utf-8'))
    except Exception:
        return {'plans': {}, 'families': {}, 'generated_at': ''}



def plan_prior(policy: dict[str, Any], chosen_plan: str) -> dict[str, Any]:
    row = ((policy or {}).get('plans') or {}).get(chosen_plan or '', {}) or {}
    return {
        'mode': row.get('mode', 'observe'),
        'beta_mean': float(row.get('beta_mean', 0.5) or 0.5),
        'count': int(row.get('count', 0) or 0),
        'alpha': float(row.get('alpha', 1.0) or 1.0),
        'beta': float(row.get('beta', 1.0) or 1.0),
    }



def family_priors(policy: dict[str, Any], families: list[str]) -> list[dict[str, Any]]:
    out = []
    bucket = (policy or {}).get('families') or {}
    for fam in families or []:
        row = bucket.get(fam, {}) or {}
        out.append({
            'family': fam,
            'mode': row.get('mode', 'observe'),
            'beta_mean': float(row.get('beta_mean', 0.5) or 0.5),
            'count': int(row.get('count', 0) or 0),
            'alpha': float(row.get('alpha', 1.0) or 1.0),
            'beta': float(row.get('beta', 1.0) or 1.0),
        })
    return out



def apply_bandit_bias(*, base_decision: str, base_reason: str, chosen_plan: str, families: list[str], uncertainty: dict[str, Any], policy: dict[str, Any]) -> dict[str, Any]:
    plan = plan_prior(policy, chosen_plan)
    fams = family_priors(policy, families)
    decision = base_decision
    reason = base_reason

    if plan['mode'] == 'prefer' and plan['beta_mean'] >= 0.65:
        decision = 'run_plan'
        reason = f'policy_prefers_plan:{chosen_plan}'
    elif plan['mode'] == 'avoid' and uncertainty.get('level') in {'medium', 'high'}:
        if 'ambiguous_access' in families:
            decision = 'ask_clarification'
            reason = f'policy_avoids_plan:{chosen_plan}'
        elif base_decision == 'run_plan':
            decision = 'answer_direct'
            reason = f'policy_deescalates_plan:{chosen_plan}'

    return {
        'decision': decision,
        'reason': reason,
        'plan_prior': plan,
        'family_priors': fams,
    }