"""LLM-based gap analyzer — semantically detects missing design information.

Uses a structured LLM call to analyze agent responses against the current
DesignState and generate contextual question cards for the UI.
"""

from __future__ import annotations

import logging
from typing import TYPE_CHECKING, Literal

from pydantic import BaseModel, Field

if TYPE_CHECKING:
    from agents.agent_flow import AgentResponse
    from agents.design_state import DesignState

logger = logging.getLogger(__name__)


# ── Models ────────────────────────────────────────────────────────────────────


class DetectedGap(BaseModel):
    """A single piece of missing information identified by the LLM."""
    category: str
    description: str
    severity: Literal["blocking", "recommended", "nice_to_have"]


class GeneratedQuestionCard(BaseModel):
    """A contextual question card for the UI, generated by the LLM."""
    category: str
    question: str
    responsible_agent: str
    agent_name: str = ""
    agent_color: str = ""
    severity: Literal["blocking", "recommended", "nice_to_have"] = "recommended"
    suggestions: list[str] = Field(default_factory=list)
    allow_custom: bool = True


class GapAnalysisResult(BaseModel):
    """Top-level structured response from the gap analysis LLM call."""
    has_gaps: bool = False
    gaps: list[DetectedGap] = Field(default_factory=list)
    question_cards: list[GeneratedQuestionCard] = Field(default_factory=list)


from config.settings import settings
from agents.definitions import AGENTS


# ── System prompt ─────────────────────────────────────────────────────────────

_SYSTEM_PROMPT = """\
You are a design gap analyzer for a CNC part design system.

Given the current design state, agent responses, and user message, identify
what information is still missing to produce a complete CNC-ready design.

Rules:
- Do NOT flag gaps for information already present in the design state.
- Do NOT flag gaps for information the user just provided in their message.
- Do NOT ask about the part name — the system derives it automatically.
- Do NOT repeat questions that were already asked in previous turns.
- Invent descriptive category names (e.g. "bolt_pattern", "thermal_rating",
  "load_capacity") — there is no fixed set.
- Use snake_case for category names.
- Severity: "blocking" = cannot proceed without it, "recommended" = should
  have for quality, "nice_to_have" = would improve but not essential.
- Generate questions that reference the specific part being designed.
- Suggestions should be concrete values appropriate for the part context.
- Set responsible_agent to the agent best suited to handle the answer:
  "design", "engineering", "cnc", "cad", or "cam".
- If no gaps exist, return has_gaps: false with empty lists.
- Set severity on each question card matching the gap it addresses.

Question abstraction level — CRITICAL:
- Ask questions a normal person can answer, NOT questions requiring CAD or
  engineering expertise. The user describes INTENT; the agents derive the
  technical implementation.
- NEVER ask for exact coordinates, precise radii, specific tolerance values,
  G-code parameters, or CadQuery implementation details.
- GOOD: "Where should the mounting holes go?" with suggestions like
  "one in each corner", "evenly spaced along the edges", "centered on top".
- BAD: "What are the precise X, Y, Z coordinates for the M4 mounting holes?"
- GOOD: "Should the edges be sharp or rounded?"
- BAD: "What fillet radius should be applied to the internal pocket edges?"
- GOOD: "How thick should the walls be?" with suggestions like "thin
  (1-2mm)", "standard (3-5mm)", "heavy-duty (6mm+)".
- BAD: "What is the minimum wall thickness in mm for the vertical ribs?"
- Frame questions in everyday language. Offer 2-4 plain-language suggestions
  that map to concrete engineering values behind the scenes.
- Technical details (coordinates, exact radii, tolerance classes, feed rates)
  are the agents' job to determine from the user's high-level answers.
"""


# ── LLM call ──────────────────────────────────────────────────────────────────


def _build_llm():
    """Build a crewai.LLM instance from gap analysis config."""
    from crewai import LLM
    cfg = settings.gap_analysis
    return LLM(model=cfg.model, temperature=cfg.temperature, max_tokens=cfg.max_tokens)


def _build_prompt(
    agent_responses: list[AgentResponse],
    state: DesignState,
    user_message: str,
) -> str:
    """Build the user-message prompt with state, responses, and user message."""
    parts = [
        "## Design State",
        state.model_dump_json(indent=2),
        "",
        "## Agent Responses",
    ]
    for resp in agent_responses:
        parts.append(f"### {resp.agent_id}")
        parts.append(resp.message)
        parts.append("")

    parts.append("## User Message")
    parts.append(user_message or "(none)")
    return "\n".join(parts)


def _enrich_agent_metadata(result: GapAnalysisResult) -> None:
    """Populate agent_name and agent_color on each question card from AGENTS dict."""
    for card in result.question_cards:
        agent_def = AGENTS.get(card.responsible_agent)
        if agent_def:
            card.agent_name = agent_def.name
            card.agent_color = agent_def.color
        else:
            card.agent_name = card.responsible_agent.title()
            card.agent_color = "#888888"


def analyze_gaps(
    agent_responses: list[AgentResponse],
    state: DesignState,
    user_message: str = "",
) -> GapAnalysisResult:
    """Analyze agent responses for missing design information using an LLM.

    Returns a GapAnalysisResult with detected gaps and contextual question cards.
    On any failure, returns an empty result (gap analysis is non-blocking).
    """
    try:
        llm = _build_llm()
        prompt = _build_prompt(agent_responses, state, user_message)
        messages = [
            {"role": "system", "content": _SYSTEM_PROMPT},
            {"role": "user", "content": prompt},
        ]
        result = llm.call(messages, response_model=GapAnalysisResult)
        if not isinstance(result, GapAnalysisResult):
            logger.debug("Gap analysis LLM returned non-model response, parsing as JSON")
            result = GapAnalysisResult.model_validate_json(result)
        _enrich_agent_metadata(result)
        return result
    except Exception:
        logger.warning("Gap analysis LLM call failed", exc_info=True)
        return GapAnalysisResult()