import math

def post_processing(processing_output):
    prediction       = processing_output["prediction"]
    shape_reasoncode = processing_output["shape_reasoncode"]

    # grade mapping: [0.00,0.01]→M1, (0.01,0.02]→M2, … cap at M14
    if prediction < 0:
        grade = "M14"
    else:
        m = math.ceil(prediction / 0.01)
        m = max(m, 1)
        m = min(m, 14)
        grade = f"M{m}"

    # if prediction ≤ 0.04, not declined
    if prediction <= 0.04:
        return {
            "grade":             grade,
            "reason_description": None
        }

    conditions = {
        'evtg04':   lambda x: x < 700,
        'eads66':   lambda x: x < 700,
        's004s':    lambda x: x < 12,
        'mt34s':    lambda x: x > 95,
        'ct320':    lambda x: x <= 3,
        'us21s':    lambda x: x <= 3,
        'utlmag02': lambda x: x > 300,
        'trv01':    lambda x: x > 3,
        'us34s':    lambda x: x > 90
    }

    reason_map = {
        'evtg04':   "System Generated",
        'eads66':   "System Generated",
        's004s':    "Length of time on file is too short",
        'mt34s':    "Too high open mortgage credit utilization recently",
        'ct320':    "Insufficient payment activity",
        'us21s':    "Length of time since most recent installment account has been established is too short",
        'utlmag02': "Too high revolving credit utilization over the last 24 months",
        'trv01':    "Recency of a balance overlimit on a bankcard account",
        'us34s':    "Too high open unsecured installment credit utilization recently"
    }

    for item in shape_reasoncode:
        feat = item["feature"]
        val  = item["value"]
        cond = conditions.get(feat)
        if cond and cond(val):
            return {
                "grade":             grade,
                "reason_description": reason_map[feat]
            }

    return {
        "grade":             grade,
        "reason_description": None
    }