"""Orchestrator for the Digests radar (X12).

A digest ("כל יום" daily one-pager) is a SECONDARY source that POINTS at a
ruling — it is never cited in a decision (INV-DIG1) and never enters the
precedent/halacha pipeline (INV-DIG2). Ingest reuses only ATOMIC services
(extract_text, embeddings), NOT the canonical ``ingest.ingest_document``.

Two intake paths share one enrichment core:

  - ``ingest_digest``        (local/MCP, e.g. batch script) — does everything
                              synchronously: stage → extract_text → create →
                              LLM enrich → embed → autolink → completed.
  - ``create_pending_digest`` (CONTAINER-SAFE — the web upload) — stage →
                              extract_text → create row with status='pending'.
                              No LLM, no embedding. ``process_pending_digests``
                              (local/MCP) drains the queue and enriches.

claude_session rule: ``digest_metadata_extractor`` (local CLI) is imported
LAZILY inside the enrichment core only, so this module stays import-safe from
the FastAPI container for create_pending / search / list / link / delete
(DB + voyage only — voyage embedding only runs in the local enrich path).
"""

from __future__ import annotations

import logging
from datetime import date
from pathlib import Path
from typing import Awaitable, Callable
from uuid import UUID

from legal_mcp import config
from legal_mcp.services import db, embeddings, extractor, ingest

logger = logging.getLogger(__name__)

ProgressCb = Callable[[str, int, str], Awaitable[None]]

DIGEST_LIBRARY_DIR = Path(config.DATA_DIR) / "digests"

_VALID_PRACTICE_AREAS = frozenset(
    {"", "rishuy_uvniya", "betterment_levy", "compensation_197"}
)


async def _noop_progress(_status: str, _percent: int, _msg: str) -> None:
    return None


def _coerce_date(v) -> date | None:
    if v is None or v == "":
        return None
    if isinstance(v, date):
        return v
    if isinstance(v, str):
        try:
            return date.fromisoformat(v[:10])
        except ValueError:
            return None
    return None


def _embedding_text(row: dict) -> str:
    """The single vector indexes the digest as an atomic discovery unit."""
    parts = [
        row.get("concept_tag", ""),
        row.get("headline_holding", ""),
        row.get("summary", ""),
        row.get("analysis_text", ""),
    ]
    return "\n".join(p for p in parts if p).strip()


async def try_autolink(digest_id: UUID | str, underlying_citation: str) -> str | None:
    """Best-effort link of a digest to the underlying ruling in case_law
    (INV-DIG3). Returns the case_law_id (str) if linked, else None. Never raises."""
    citation = (underlying_citation or "").strip()
    if not citation:
        return None
    try:
        match = await db.find_case_law_by_citation_fuzzy(citation)
    except Exception as e:
        logger.warning("digest try_autolink lookup failed for %r: %s", citation, e)
        return None
    if not match:
        # Gap (INV-DIG3): the underlying ruling isn't in the corpus. If it's a
        # court verdict (not ועדת-ערר), enqueue an X13 auto-fetch job so the gap
        # is actionable instead of silently dropped (INV-CF2). Never raises.
        await _enqueue_court_fetch(digest_id, citation)
        return None
    await db.link_digest_to_case_law(digest_id, match["id"])
    return str(match["id"])


async def _enqueue_court_fetch(digest_id: UUID | str, citation: str) -> None:
    """Queue an X13 court-verdict fetch for an unlinked digest citation.

    Court rulings (supreme/admin) → a ``court_fetch_jobs`` row drained later by
    ``court_fetch_drain``. ועדת-ערר (skip) is left alone — it needs Nevo and is
    surfaced through the normal missing-precedent path, not auto-fetch.
    """
    try:
        from legal_mcp.services import court_citation
        cit = court_citation.classify(citation)
        if cit.tier not in ("supreme", "admin"):
            return
        await db.court_fetch_job_upsert(
            case_number_norm=cit.case_number_norm,
            citation_raw=citation,
            tier=cit.tier,
            court=cit.court_prefix,
            digest_id=UUID(str(digest_id)),
        )
        logger.info("digest %s: enqueued court-fetch for %r (tier=%s)",
                    digest_id, citation, cit.tier)
    except Exception as e:  # never break digest ingest
        logger.warning("digest court-fetch enqueue failed for %r: %s", citation, e)


# ── Container-safe creation (web upload) — no LLM, no embedding ──────

async def create_pending_digest(
    *,
    file_path: str | Path,
    yomon_number: str = "",
    digest_date: date | str | None = None,
    practice_area: str = "",
    appeal_subtype: str = "",
    subject_tags: list[str] | None = None,
    progress: ProgressCb | None = None,
) -> dict:
    """Stage the file, extract text (PyMuPDF — container-safe), and create a
    digest row with extraction_status='pending'. The LLM metadata extraction,
    embedding, and autolink are deferred to ``process_pending_digests`` (local).

    Returns {status, digest_id, extraction_status} or {status:'exists', ...}.
    Idempotent on content_hash (INV-G3).
    """
    progress = progress or _noop_progress
    if practice_area and practice_area not in _VALID_PRACTICE_AREAS:
        raise ValueError(f"invalid practice_area: {practice_area!r}")
    src = Path(file_path)
    if not src.exists():
        raise ValueError(f"file not found: {file_path}")

    await progress("staging", 10, "מעתיק קובץ")
    staged = ingest._stage_file(src, DIGEST_LIBRARY_DIR, "incoming")
    rel_path = str(staged.relative_to(config.DATA_DIR)) \
        if str(staged).startswith(str(config.DATA_DIR)) else str(staged)

    await progress("extracting_text", 50, "מחלץ טקסט")
    raw_text, _pc, _off = await extractor.extract_text(str(staged))
    raw_text = (raw_text or "").strip()
    if not raw_text:
        raise ValueError("no text extracted from digest")

    content_hash = db._content_hash(raw_text)
    existing = await db.get_digest_by_content_hash(content_hash)
    if existing:
        await progress("completed", 100, "יומון זהה כבר קיים")
        return {"status": "exists", "digest_id": existing["id"],
                "extraction_status": existing.get("extraction_status")}

    record = await db.create_digest(
        analysis_text=raw_text,
        yomon_number=yomon_number.strip(),
        digest_date=_coerce_date(digest_date),
        practice_area=practice_area,
        appeal_subtype=appeal_subtype.strip(),
        subject_tags=list(subject_tags) if subject_tags else [],
        source_document_path=rel_path,
        extraction_status="pending",
    )
    await progress("queued", 100, "ממתין לעיבוד מקומי (LLM)")
    return {"status": "pending", "digest_id": record["id"],
            "extraction_status": "pending"}


# ── Local enrichment core (LLM + embed + autolink) ──────────────────

async def enrich_digest(digest_id: UUID | str, progress: ProgressCb | None = None) -> dict:
    """Run LLM metadata extraction over a digest's analysis_text, fill ONLY
    empty fields (preserve user-supplied values), embed, autolink, complete.

    **MCP-tool-only path** (uses the local LLM extractor). Idempotent.
    """
    progress = progress or _noop_progress
    row = await db.get_digest(digest_id)
    if not row:
        raise ValueError("digest not found")
    analysis = (row.get("analysis_text") or "").strip()
    if not analysis:
        await db.update_digest(digest_id, extraction_status="failed")
        return {"status": "no_text", "digest_id": str(digest_id)}

    await db.update_digest(digest_id, extraction_status="processing")
    await progress("extracting_metadata", 40, "מחלץ מטא-דאטה (LLM)")
    from legal_mcp.services import digest_metadata_extractor
    extracted = await digest_metadata_extractor.extract(analysis)

    # Fill only empty fields (preserve user-supplied values from the form).
    fields: dict = {}
    for key in ("yomon_number", "concept_tag", "headline_holding", "summary",
                "underlying_citation", "underlying_court", "underlying_judge",
                "practice_area", "appeal_subtype"):
        if not (row.get(key) or "").strip() and extracted.get(key):
            fields[key] = extracted[key]
    if row.get("digest_date") is None and extracted.get("digest_date"):
        fields["digest_date"] = extracted["digest_date"]
    if row.get("underlying_date") is None and extracted.get("underlying_date"):
        fields["underlying_date"] = extracted["underlying_date"]
    if not (row.get("subject_tags") or []) and extracted.get("subject_tags"):
        fields["subject_tags"] = extracted["subject_tags"]

    if fields:
        await db.update_digest(digest_id, **fields)
    merged = await db.get_digest(digest_id)

    await progress("embedding", 75, "מחשב embedding")
    emb_text = _embedding_text(merged)
    if emb_text:
        try:
            vecs = await embeddings.embed_texts([emb_text], input_type="document")
            if vecs:
                await db.store_digest_embedding(digest_id, vecs[0])
        except Exception as e:  # surfaced, not swallowed (§6)
            logger.warning("digest embedding failed for %s: %s", digest_id, e)

    await progress("linking", 90, "מנסה לקשר לפסק המקורי")
    linked_id = None
    if not merged.get("linked_case_law_id"):
        linked_id = await try_autolink(digest_id, merged.get("underlying_citation", ""))

    await db.update_digest(digest_id, extraction_status="completed")
    await progress("completed", 100, "הושלם")
    return {
        "status": "completed",
        "digest_id": str(digest_id),
        "yomon_number": merged.get("yomon_number", ""),
        "underlying_citation": merged.get("underlying_citation", ""),
        "linked_case_law_id": merged.get("linked_case_law_id") or linked_id,
        "fields_filled": sorted(fields.keys()),
    }


async def process_pending_digests(limit: int = 20) -> dict:
    """Drain the digest extraction queue (rows stamped extraction_status='pending'
    by the web upload). Local/MCP only — runs the LLM enrichment per row.
    Sequential (avoids LLM rate-limit storms), mirrors process_pending_extractions."""
    pending = await db.list_pending_digests(limit=limit)
    if not pending:
        return {"status": "no_pending", "processed": 0, "results": []}
    results = []
    processed = 0
    for row in pending:
        did = row["id"]
        try:
            res = await enrich_digest(did)
            processed += 1
            results.append({"digest_id": str(did), "status": res.get("status"),
                            "linked": bool(res.get("linked_case_law_id"))})
        except Exception as e:
            logger.exception("process_pending_digests failed for %s: %s", did, e)
            try:
                await db.update_digest(did, extraction_status="failed")
            except Exception:
                logger.exception("could not mark digest %s failed", did)
            results.append({"digest_id": str(did), "status": "failed", "error": str(e)})
    return {"status": "completed", "processed": processed,
            "total_pending": len(pending), "results": results}


# ── Full synchronous ingest (local/MCP, e.g. batch script) ──────────

async def ingest_digest(
    *,
    file_path: str | Path,
    yomon_number: str = "",
    digest_date: date | str | None = None,
    practice_area: str = "",
    appeal_subtype: str = "",
    subject_tags: list[str] | None = None,
    progress: ProgressCb | None = None,
) -> dict:
    """Ingest one digest synchronously. **MCP-tool-only** (uses the LLM).

    Creates the row (with any user-supplied values) then enriches in place.
    Idempotent on content_hash (INV-G3).
    """
    progress = progress or _noop_progress
    created = await create_pending_digest(
        file_path=file_path, yomon_number=yomon_number, digest_date=digest_date,
        practice_area=practice_area, appeal_subtype=appeal_subtype,
        subject_tags=subject_tags, progress=progress,
    )
    if created.get("status") == "exists":
        return created
    digest_id = created["digest_id"]
    enriched = await enrich_digest(digest_id, progress=progress)
    return enriched


# ── Linking (INV-DIG3) ──────────────────────────────────────────────

async def link_digest(digest_id: UUID | str, case_law_id: UUID | str) -> dict:
    """Manually link a digest to an underlying ruling (INV-DIG3). Idempotent."""
    digest = await db.get_digest(digest_id)
    if not digest:
        raise ValueError("digest not found")
    ruling = await db.get_case_law(
        case_law_id if isinstance(case_law_id, UUID) else UUID(str(case_law_id))
    )
    if not ruling:
        raise ValueError("case_law not found")
    updated = await db.link_digest_to_case_law(digest_id, case_law_id)
    return {
        "linked": True,
        "digest_id": str(digest_id),
        "case_law_id": str(case_law_id),
        "case_number": ruling.get("case_number"),
        "digest": updated,
    }


async def relink_digest(digest_id: UUID | str) -> dict:
    """Re-run autolink for an unlinked digest. No-op if already linked / no match."""
    digest = await db.get_digest(digest_id)
    if not digest:
        raise ValueError("digest not found")
    if digest.get("linked_case_law_id"):
        return {"linked": True, "digest_id": str(digest_id),
                "case_law_id": digest["linked_case_law_id"], "changed": False}
    linked_id = await try_autolink(digest_id, digest.get("underlying_citation", ""))
    return {
        "linked": linked_id is not None,
        "digest_id": str(digest_id),
        "case_law_id": linked_id,
        "changed": linked_id is not None,
    }


async def unlink_digest(digest_id: UUID | str) -> dict:
    """Clear a digest's link to the underlying ruling."""
    updated = await db.link_digest_to_case_law(digest_id, None)
    if updated is None:
        raise ValueError("digest not found")
    return {"unlinked": True, "digest_id": str(digest_id)}


# ── Read / search (container-safe: DB + voyage) ─────────────────────

async def search_digests(
    query: str,
    practice_area: str = "",
    subject_tag: str = "",
    concept_tag: str = "",
    limit: int = 10,
) -> list[dict]:
    """Semantic search over the digests radar. Container-safe (voyage + DB)."""
    if not query.strip():
        return []
    query_vec = await embeddings.embed_query(query)
    return await db.search_digests_semantic(
        query_embedding=query_vec,
        practice_area=practice_area,
        subject_tag=subject_tag,
        concept_tag=concept_tag,
        limit=limit,
    )


async def get_digest(digest_id: UUID | str) -> dict | None:
    return await db.get_digest(digest_id)


async def list_digests(
    practice_area: str = "",
    concept_tag: str = "",
    linked: bool | None = None,
    search: str = "",
    limit: int = 100,
    offset: int = 0,
) -> list[dict]:
    return await db.list_digests(
        practice_area=practice_area, concept_tag=concept_tag, linked=linked,
        search=search, limit=limit, offset=offset,
    )


async def update_digest(digest_id: UUID | str, **fields) -> dict | None:
    return await db.update_digest(digest_id, **fields)


async def delete_digest(digest_id: UUID | str) -> bool:
    return await db.delete_digest(digest_id)