from __future__ import annotations

from typing import List, Optional

from pydantic import BaseModel, Field

from app.agents.cerebras_client import CerebrasClient
from app.schemas.graph import FormulaContent, FormulaStep, HTML5VisualPayload

_MAX_CHUNK_CHARS = 3000  # mirrors ModalityRouter._MAX_CHUNK_CHARS / BrainAgent.extract_curriculum cap

_DEFAULT_DECLINE_REASON = "This concept doesn't have an explicit formula in the source — better explored in chat."


class FormulaGrounding(BaseModel):
    renderable: bool
    anchor: str = ""
    main_latex: str = ""
    steps: List[FormulaStep] = Field(default_factory=list)
    decline_reason: str = ""


class FormulaEngine:
    """Two-stage grounded formula extraction: extract (with a self-checked verbatim
    anchor) then render into an HTML5VisualPayload. Mirrors the D3TemplateRouter/
    D3DataExtractor split and the old TutorAgent visual-grounding pipeline."""

    def __init__(self, client: Optional[CerebrasClient] = None) -> None:
        self._client = client or CerebrasClient()

    def generate(self, concept: str, chunks: List[dict], familiarity: str) -> HTML5VisualPayload:
        from app.agents.tutor_agent import TutorAgent

        grounding = self._extract(concept, chunks, familiarity)

        if not grounding.renderable:
            reason = grounding.decline_reason or _DEFAULT_DECLINE_REASON
            return HTML5VisualPayload(
                html_code=TutorAgent._decline_html(concept, reason),
                animation_type="2d_text",
                explanation=reason,
            )

        return HTML5VisualPayload(
            html_code="",
            animation_type="formula",
            formula=FormulaContent(main_latex=grounding.main_latex, steps=grounding.steps),
            explanation=(f"From the source: {grounding.anchor}" if grounding.anchor else ""),
            source="paper" if grounding.anchor else "model_knowledge",
        )

    def _extract(self, concept: str, chunks: List[dict], familiarity: str) -> FormulaGrounding:
        chunk_text = "\n\n".join(f"[Source: {c.get('source', '?')}]: {c['text']}" for c in chunks)[:_MAX_CHUNK_CHARS]
        messages = [
            {"role": "system", "content": (
                "You extract a grounded formula/equation for a concept from the SOURCE MATERIAL only. "
                "If the source contains an explicit formula/equation for this concept, copy a VERBATIM "
                "30-80 character substring of the source's actual equation/formula line as `anchor` — "
                "it must match the source text literally, not a paraphrase. Fill `main_latex` and `steps` "
                "using ONLY numbers, coefficients, and notation actually present in the source — never "
                "invent or estimate values. If no explicit formula/equation exists in the source, set "
                "renderable=false and give a short, calm, student-facing `decline_reason` (e.g. "
                f"\"'{concept}' doesn't have an explicit formula in the source — better explored in "
                "chat.\").\n\n"
                f"SOURCE MATERIAL:\n{chunk_text}"
            )},
            {"role": "user", "content": f"Concept: '{concept}' (level: {familiarity}). Extract the formula or decline."},
        ]
        grounding = self._client.structured_complete(messages, FormulaGrounding, reasoning_effort="medium")

        if grounding.anchor and grounding.anchor.strip() not in chunk_text:
            grounding = grounding.model_copy(update={"anchor": ""})

        return grounding