"""
hint_node.py — Generates an adaptive, escalating hint for the user.

Improvements over v1:
  - Uses llm.with_structured_output() for guaranteed schema compliance
  - 4-level escalation system (Conceptual → Approach → Pseudocode → Code)
  - Personalizes based on UserProfile weak topics
  - Injects misconception library for topic-targeted hints
  - Increments turn_count for loop control
"""

from agent.models import AgentState, HintOutput
from agent.llm_factory import get_llm
from agent.prompts import HINT_PROMPT
from agent.knowledge import get_misconceptions
from agent.memory import load_profile, top_weak_topics

_llm = get_llm()
_structured_llm = _llm.with_structured_output(HintOutput, method="function_calling")

_MAX_HINT_LEVEL = 4


def generate_hint(state: AgentState) -> dict:
    """
    Generates a hint based on the gap, strictness, hint level, and user profile.
    Increments current_hint_level and turn_count.
    Populates explain-why-wrong fields in final_response if present.
    """
    gap = state["identified_gap"]
    topic = state.get("problem_topic", "Unknown")
    hint_level = min(state.get("current_hint_level", 1), _MAX_HINT_LEVEL)
    session_id = state.get("session_id", "anonymous")

    # ── Determine effective strictness (personalize for weak topics) ─────────
    strictness = state["strictness"]
    try:
        profile = load_profile(session_id)
        weak = top_weak_topics(profile, n=3)
        if topic.strip().lower() in weak:
            # Promote to Lenient if user consistently struggles with this topic
            if strictness == "Strict":
                strictness = "Moderate"
            elif strictness == "Moderate":
                strictness = "Lenient"
        weak_topics_str = ", ".join(weak) if weak else "none yet"
    except Exception:
        weak_topics_str = "none yet"

    misconceptions = "; ".join(get_misconceptions(topic))

    # ── Build prompt and invoke structured LLM ───────────────────────────────
    try:
        result: HintOutput = _structured_llm.invoke(
            HINT_PROMPT.format_messages(
                strictness=strictness,
                topic=topic,
                gap=gap,
                hint_level=hint_level,
                weak_topics=weak_topics_str,
                misconceptions=misconceptions,
            )
        )
        hint_text = result.hint
        hint_type = result.type
    except Exception as e:
        print(f"[hint_node] Structured output error: {e}")
        hint_text = f"LLM Parsing Error: {str(e)}"
        hint_type = "Error"

    # ── Score: decreases with gap magnitude ──────────────────────────────────
    score = max(0, 100 - (state["gap_magnitude"] * 10))

    # ── Build final_response — include explain-why-wrong when available ──────
    final_response: dict = {
        "hint":      hint_text,
        "type":      hint_type,
        "score":     score,
        "hint_level": hint_level,
    }
    if state.get("mistake"):
        final_response["mistake"]          = state["mistake"]
        final_response["why_wrong"]        = state.get("why_wrong")
        final_response["correct_thinking"] = state.get("correct_thinking")

    return {
        "final_response":     final_response,
        "current_hint_level": min(hint_level + 1, _MAX_HINT_LEVEL),
        "turn_count":         state.get("turn_count", 0) + 1,
    }