"""Constrained, node-by-node L1 decision-tree generation (spec §4/§5/§6.1).

Each call produces ONE node given the problem, category, and full walked path.
Generation is constrained to safe/reversible L1 steps and biased to escalate
early. normalize_walked_path() turns a resolved walk into a valid tree object
for flywheel capture.
"""
import logging
from typing import Any, Optional

from app.core.ai_provider import get_ai_provider
from app.core.config import settings
from app.services.l1_category_service import HARD_FLOOR_TEXT_PATTERNS
from app.services.llm_utils import parse_llm_json

logger = logging.getLogger(__name__)

MAX_DEPTH = 12
VALID_NODE_TYPES = {"question", "instruction", "resolved", "escalate"}


class UnsafeNodeError(ValueError):
    """Raised when a generated node violates the hard floor or is malformed."""


SYSTEM_PROMPT = """\
You are an L1 helpdesk troubleshooting guide builder. Given a problem and the
steps already tried, produce the SINGLE next node of a yes/no decision tree.

HARD RULES:
- Only safe, reversible, observe-or-restart-class steps: checking status, toggling,
  restarting, reconnecting, re-entering credentials the USER already knows.
- NEVER produce steps that: edit the registry/system files/boot config; delete or
  format data/disks; change credentials/MFA/security/firewall/AV; run elevated or
  admin scripts; touch domain controllers/DNS/DHCP or production servers; or have
  billing/license impact. These are out of L1 scope.
- When you run out of safe in-scope steps, DO NOT GUESS. Emit an "escalate" node.

Return ONLY a JSON object for ONE node, one of:
{"node_type":"question","text":"<yes/no question>"}
{"node_type":"instruction","text":"<one safe reversible action>"}
{"node_type":"resolved","text":"<confirmation the issue is fixed>"}
{"node_type":"escalate","reason_category":"exhausted_safe_steps","text":"<why>"}
No prose, no markdown fences.
"""


def _strip_meta(walked_path: list[dict]) -> list[dict]:
    """Drop the hidden ``meta`` entry (category carrier) the intake endpoint seeds.

    The first walked_path entry on an ai_build session may be a
    ``{"node_type": "meta", "category": ...}`` marker used to persist the
    classified category; it is not a real walk step and must be excluded from
    both model context and tree normalization.
    """
    return [s for s in walked_path if s.get("node_type") != "meta"]


def _build_context(problem_text: str, category: str, walked_path: list[dict]) -> str:
    walked_path = _strip_meta(walked_path)
    lines = [f"PROBLEM: {problem_text}", f"CATEGORY: {category}", "STEPS SO FAR:"]
    if not walked_path:
        lines.append("(none yet — produce the first diagnostic question)")
    for i, step in enumerate(walked_path, 1):
        ans = step.get("answer")
        suffix = f" -> {ans}" if ans else ""
        lines.append(f"{i}. [{step.get('node_type','?')}] {step.get('text','')}{suffix}")
    return "\n".join(lines)


def validate_node(node: dict[str, Any]) -> dict[str, Any]:
    """Shape + hard-floor validation. Raises UnsafeNodeError on violation."""
    if not isinstance(node, dict) or node.get("node_type") not in VALID_NODE_TYPES:
        raise UnsafeNodeError(f"invalid node_type: {node!r}")
    text = (node.get("text") or "").lower()
    for pat in HARD_FLOOR_TEXT_PATTERNS:
        if pat in text:
            raise UnsafeNodeError(f"hard-floor pattern '{pat}' in node text")
    return node


def escalate_if_depth_exceeded(walked_path: list[dict]) -> Optional[dict[str, Any]]:
    if len(walked_path) >= MAX_DEPTH:
        return {
            "node_type": "escalate",
            "reason_category": "depth_cap",
            "text": "Reached the L1 troubleshooting depth limit — escalating to engineering.",
        }
    return None


async def generate_next_node(
    problem_text: str, category: str, walked_path: list[dict]
) -> dict[str, Any]:
    """Generate + validate the next node. Regenerate once on failure, then escalate."""
    capped = escalate_if_depth_exceeded(walked_path)
    if capped:
        return capped

    provider = get_ai_provider(settings.get_model_for_action("l1_realtime_build"))
    context = _build_context(problem_text, category, walked_path)

    for attempt in range(2):
        try:
            raw, _, _ = await provider.generate_json(
                system_prompt=SYSTEM_PROMPT,
                messages=[{"role": "user", "content": context}],
                max_tokens=1024,
            )
            node = parse_llm_json(raw)
            return validate_node(node)
        except Exception as e:
            logger.warning("ai_tree_builder node attempt %d failed: %s", attempt + 1, e)
            continue

    return {
        "node_type": "escalate",
        "reason_category": "generation_failed",
        "text": "Could not generate a safe next step — escalating to engineering.",
    }


def normalize_walked_path(walked_path: list[dict]) -> dict[str, Any]:
    """Turn a resolved walk into a valid troubleshooting tree (spec §6.1).

    Root = first node's id; question nodes' traversed branch points to the next
    node, the untraversed branch to a needs_review stub; terminal node ends it.
    Returns {id, nodes: {id: node}} — a dict with an id (passes the proposal
    approval guard).
    """
    walked_path = _strip_meta(walked_path)
    nodes: dict[str, Any] = {}
    if not walked_path:
        root_id = "root"
        nodes[root_id] = {"id": root_id, "node_type": "needs_review",
                          "text": "Empty walk — needs authoring."}
        return {"id": root_id, "nodes": nodes}

    stub_seq = 0
    for i, step in enumerate(walked_path):
        nid = step.get("id") or f"n{i+1}"
        ntype = step.get("node_type", "question")
        nxt = walked_path[i + 1].get("id", f"n{i+2}") if i + 1 < len(walked_path) else None
        node: dict[str, Any] = {"id": nid, "node_type": ntype, "text": step.get("text", "")}
        if step.get("reason_category"):
            node["reason_category"] = step["reason_category"]
        if ntype == "question":
            answer = (step.get("answer") or "").lower()
            stub_seq += 1
            stub_id = f"review-{stub_seq}"
            nodes[stub_id] = {"id": stub_id, "node_type": "needs_review",
                              "text": "Branch not explored during the originating call."}
            traversed_next = nxt
            if traversed_next is None:
                # Walk ended on this question (no terminal recorded) — stub the
                # branch the tech actually took so the tree has no dangling edge.
                stub_seq += 1
                traversed_next = f"review-{stub_seq}"
                nodes[traversed_next] = {"id": traversed_next, "node_type": "needs_review",
                                         "text": "Walk ended here before a terminal step was reached."}
            node["yes_next"] = traversed_next if answer == "yes" else stub_id
            node["no_next"] = traversed_next if answer == "no" else stub_id
        elif ntype == "instruction":
            node["next"] = nxt
        nodes[nid] = node

    return {"id": walked_path[0].get("id", "n1"), "nodes": nodes}