from governor import thermal_cap
from cache import make_build_key, cache_hit


# ----------------------------
# analyze cluster health
# ----------------------------
def cluster_heat(nodes):
    if not nodes:
        return 0.5

    total = 0
    count = 0

    for n in nodes:
        agent = n.get("agent") or {}
        load = agent.get("load", "1 1 1")

        try:
            total += float(load.split()[0])
        except:
            total += 1.0

        count += 1

    return total / max(count, 1)


# ----------------------------
# thermal risk scoring
# ----------------------------
def risk_level(node):
    agent = node.get("agent") or {}

    cap = thermal_cap(agent)

    if cap < 0.3:
        return "HIGH_RISK"
    elif cap < 0.6:
        return "MODERATE"
    return "SAFE"


# ----------------------------
# predict build cost
# ----------------------------
def estimate_build_cost(nodes):
    cost = 0.0

    for n in nodes:
        agent = n.get("agent") or {}
        load = agent.get("load", "1 1 1")

        try:
            cost += float(load.split()[0])
        except:
            cost += 1.0

    return cost / max(len(nodes), 1)


# ----------------------------
# recommendation engine
# ----------------------------
def recommend(project, snapshot, nodes):
    recommendations = {
        "build_now": True,
        "risk": "SAFE",
        "cache_likely": False,
        "preferred_nodes": [],
        "reason": []
    }

    # ----------------------------
    # cache prediction
    # ----------------------------
    key_sample = make_build_key(
        snapshot["hash"],
        list(project["targets"].keys())[0],
        project.get("build_env", {})
    )

    if cache_hit(key_sample):
        recommendations["cache_likely"] = True
        recommendations["reason"].append("Cache hit likely for current snapshot")

    # ----------------------------
    # cluster load analysis
    # ----------------------------
    heat = cluster_heat(nodes)

    if heat > 3.0:
        recommendations["build_now"] = False
        recommendations["reason"].append("Cluster overloaded - recommend delay")

    # ----------------------------
    # node filtering
    # ----------------------------
    safe_nodes = []

    for n in nodes:
        if risk_level(n) == "SAFE":
            safe_nodes.append(n["name"])

    recommendations["preferred_nodes"] = safe_nodes

    # ----------------------------
    # final risk classification
    # ----------------------------
    if heat > 4.0:
        recommendations["risk"] = "HIGH_RISK"
    elif heat > 2.5:
        recommendations["risk"] = "MODERATE"

    return recommendations