"""
Pipeline feedback — feeds execution results back into the policy engine
so it can learn which nodes / actions succeed vs fail.

Currently the PolicyEngine doesn't have a `update_reputation` method
(see backend/policy/engine.py for the simplified evaluate() interface).
This module is kept as a stub for future learning-loop work.
"""

from backend.policy.engine import PolicyEngine

# Singleton policy engine (db=None — no learning yet)
policy = PolicyEngine(db=None)


def report_execution(node, action, success, duration, temp_before, temp_after):
    """Feed execution results back to the policy engine.

    In the future this should update per-node reputation scores so the
    scheduler can prefer nodes that historically succeed and avoid nodes
    that overheat. For now it's a no-op logging shim.
    """
    thermal_spike = (temp_after - temp_before) > 0.15

    # Future: policy.update_reputation(node, success, duration, thermal_spike)
    # For now, just return the assessment
    return {
        "node": node,
        "action": action,
        "success": success,
        "duration": duration,
        "thermal_spike": thermal_spike,
        "temp_delta": temp_after - temp_before,
    }