# -*- coding: utf-8 -*-
"""
Document-level summary storage and context prefix helpers.

This module is deliberately lazy: importing it does not require provider keys or
database/network availability. LLM/provider errors are handled inside
generate_doc_summary with a fallback summary.
"""

from __future__ import annotations

from datetime import datetime, timezone
import json
import logging
from pathlib import Path
from typing import Any

from pydantic import BaseModel, Field

from pluto.utils import extract_json_from_response


logger = logging.getLogger("pluto")
SUMMARY_FILENAME = ".doc_summaries.json"


class DocSummary(BaseModel):
    doc_id: str
    title: str = ""
    domain: str = ""
    key_claims: list[str] = Field(default_factory=list)
    structure: list[str] = Field(default_factory=list)
    open_questions: list[str] = Field(default_factory=list)
    created_at: str


def generate_doc_summary(doc_id: str, corpus_dir: str | Path) -> DocSummary:
    """Generate and persist a document summary, falling back on failure."""
    corpus_path = Path(corpus_dir)
    doc_text = _read_document_text(doc_id, corpus_path)
    created_at = _utc_now()

    try:
        raw = _call_summary_llm(doc_id=doc_id, doc_text=doc_text)
        summary = _parse_summary(doc_id=doc_id, raw=raw, created_at=created_at)
    except Exception as exc:
        logger.warning("Failed to generate document summary for %s: %s", doc_id, exc)
        summary = _fallback_summary(doc_id=doc_id, created_at=created_at)

    summaries = load_doc_summaries(corpus_path)
    summaries[doc_id] = summary
    save_doc_summaries(corpus_path, summaries)
    return summary


def load_doc_summary(doc_id: str, corpus_dir: str | Path) -> DocSummary | None:
    """Load one stored document summary if present."""
    return load_doc_summaries(corpus_dir).get(doc_id)


def load_doc_summaries(corpus_dir: str | Path) -> dict[str, DocSummary]:
    """Load all document summaries from disk."""
    path = _summary_path(corpus_dir)
    if not path.exists():
        return {}
    try:
        raw = path.read_text(encoding="utf-8")
        data = json.loads(raw)
        return {
            str(doc_id): DocSummary(**summary_data)
            for doc_id, summary_data in data.items()
            if isinstance(summary_data, dict)
        }
    except Exception as exc:
        logger.warning("Failed to load document summaries from %s: %s", path, exc)
        return {}


def save_doc_summaries(corpus_dir: str | Path, summaries: dict[str, DocSummary]) -> None:
    """Persist all document summaries as JSON."""
    path = _summary_path(corpus_dir)
    path.parent.mkdir(parents=True, exist_ok=True)
    data = {doc_id: summary.model_dump() for doc_id, summary in summaries.items()}
    path.write_text(json.dumps(data, ensure_ascii=False, indent=1), encoding="utf-8")


def apply_doc_summary_context(chunk_text: str, doc_id: str, corpus_dir: str | Path) -> str:
    """Prepend stored document context to a chunk, if available."""
    summary = load_doc_summary(doc_id, corpus_dir)
    if not summary:
        logger.warning("No document summary found for %s", doc_id)
        return chunk_text

    key_claims = "; ".join(summary.key_claims)
    prefix = (
        f"[Document context: {summary.title} | Domain: {summary.domain} | "
        f"Key claims: {key_claims}]"
    )
    return f"{prefix}\n\n{chunk_text}"


def _call_summary_llm(doc_id: str, doc_text: str) -> str:
    """Call the configured quick model for summary JSON."""
    from pluto.dispatcher import dispatch
    from pluto.modes import get_mode

    get_mode("MODE_QUICK")
    prompt = f"""Summarize this document as JSON only.

Schema:
{{
  "title": "short title",
  "domain": "subject/domain",
  "key_claims": ["claim1", "claim2"],
  "structure": ["intro", "methodology", "results", "conclusion"],
  "open_questions": ["question1"]
}}

Document id: {doc_id}

Document text:
---
{doc_text[:14000]}
---
"""
    return dispatch("MODE_QUICK", prompt)


def _parse_summary(doc_id: str, raw: str, created_at: str) -> DocSummary:
    data = json.loads(extract_json_from_response(raw))
    return DocSummary(
        doc_id=doc_id,
        title=str(data.get("title", "")),
        domain=str(data.get("domain", "")),
        key_claims=_string_list(data.get("key_claims")),
        structure=_string_list(data.get("structure")),
        open_questions=_string_list(data.get("open_questions")),
        created_at=created_at,
    )


def _fallback_summary(doc_id: str, created_at: str) -> DocSummary:
    return DocSummary(
        doc_id=doc_id,
        title=doc_id,
        domain="",
        key_claims=[],
        structure=[],
        open_questions=[],
        created_at=created_at,
    )


def _read_document_text(doc_id: str, corpus_dir: Path) -> str:
    for ext in (".md", ".txt"):
        path = corpus_dir / f"{doc_id}{ext}"
        if path.exists():
            return path.read_text(encoding="utf-8", errors="replace")
    return ""


def _summary_path(corpus_dir: str | Path) -> Path:
    return Path(corpus_dir) / SUMMARY_FILENAME


def _string_list(value: Any) -> list[str]:
    if not isinstance(value, list):
        return []
    return [str(item) for item in value if str(item).strip()]


def _utc_now() -> str:
    return datetime.now(timezone.utc).isoformat()