"""Synchronous section summarizer for Celery workers.

Groups chunks under major headings (H1/H2) and produces high-quality,
attributed summaries using the local Ollama model.

This is intentionally slow and high-quality — the author explicitly accepts
longer ingestion times in exchange for excellent "paper overview" answers.
"""

from __future__ import annotations

import hashlib
import re
from typing import Optional
from uuid import UUID

from sqlalchemy import text
from sqlalchemy.orm import Session

from app.llm.resolver import resolve_llm_sync
from app.core.logging import get_logger
from app.llm.client import chat_sync
from app.llm.ollama_client import hash_prompt

logger = get_logger(__name__)


# ─────────────────────────────────────────────────────────────────────────────
# High-quality scientific paper summarization prompt (v1)
#
# Tuned for research papers. Produces structured, citable, useful output.
# Not "exactly two paragraphs" — real papers need real structure.
# ─────────────────────────────────────────────────────────────────────────────

SECTION_SUMMARY_PROMPT_V1 = """You are an expert research assistant helping a scientist deeply understand their own paper.

You will receive one major section of a scientific paper (including any subsections, paragraphs, figure/table captions, and equations under that heading).

Your job is to produce an **excellent, dense, faithful summary** of ONLY this section.

Requirements:
- Write 2-4 tight paragraphs + a short bullet list of the most important specific claims, methods, results, or contributions in this section.
- Preserve technical precision. Do not generalize away important details (model names, metrics, specific techniques, datasets, theorems, etc.).
- If the section contains key results or numbers, include the actual values.
- Explicitly note any limitations, open questions, or forward references mentioned in the section.
- Use the exact technical terminology from the paper.
- Do NOT add information that is not present in the provided text.
- Do NOT write a generic "this section discusses X". Be concrete.

Output format (use Markdown):

### Section Summary: <exact heading>

<2-4 high-density paragraphs>

**Key points:**
- bullet 1 (specific, with numbers/names where relevant)
- bullet 2
- ...

If the section is very short or contains only a single idea, a shorter summary is acceptable.

Here is the section content:

--- BEGIN SECTION ---
{section_text}
--- END SECTION ---

Now write the summary for this section only.
""".strip()


PAPER_OVERVIEW_PROMPT_V1 = """You are an expert research assistant helping a scientist deeply understand their own paper.

You will receive a list of high-quality summaries of every major section (H1 and important H2) of a scientific paper, in reading order.

Your job is to synthesize a **single excellent executive overview** of the entire paper (what the author would put in a really good "Broader Impact / Conclusion" or a very strong abstract).

Focus on:
- The core research problem and why it matters
- The key technical approach / contribution (be specific)
- The most important results and evidence
- How the pieces fit together
- Any surprising or particularly strong claims

Write 4-7 dense paragraphs. Use the section summaries as your only source of truth. Cite the major section names inline when they ground a claim (e.g. "In the Experiments section...").

Output format:

### Paper Overview: <Paper Title or Filename>

<4-7 high-density paragraphs>

**Core contribution:** one-sentence version

**Notable results:** bullets

**Open questions / limitations noted by authors:** bullets (if any)

Here are the section summaries:

--- BEGIN SECTION SUMMARIES ---
{section_summaries_text}
--- END SECTION SUMMARIES ---

Now write the integrated paper-level executive overview.
""".strip()


def get_paper_overview_prompt() -> str:
    """Return the current paper-level overview prompt (for hashing / debugging)."""
    return PAPER_OVERVIEW_PROMPT_V1


def _make_section_id(heading_path: list[str], level: int) -> str:
    """Create a stable section identifier."""
    if not heading_path:
        return f"level{level}-root"
    slug = re.sub(r"[^a-z0-9]+", "-", " ".join(heading_path).lower()).strip("-")
    return f"h{level}-{slug[:60]}"


def _collect_text_under_heading(
    chunks: list[dict],
    start_idx: int,
    current_level: int,
) -> tuple[str, int, list[str]]:
    """
    Collect rich text from chunks belonging to this heading and its sub-headings
    until we hit the next heading of same or higher level.

    Returns (collected_text, end_idx, source_chunk_ids_as_str)
    """
    parts: list[str] = []
    source_ids: list[str] = []
    i = start_idx

    current_heading_path = chunks[start_idx].get("heading_path") or []

    while i < len(chunks):
        ch = chunks[i]
        hp = ch.get("heading_path") or []

        # Stop when we see a heading at the same or higher level that is not a child
        if ch.get("chunk_type") == "heading":
            ch_level = len(hp) if hp else 0
            if ch_level <= current_level and i != start_idx:
                break

        # Add the content
        md = ch.get("markdown") or ch.get("plain_text") or ""
        if md.strip():
            parts.append(md.strip())

        if ch.get("id"):
            source_ids.append(str(ch["id"]))

        i += 1

    text = "\n\n".join(parts)
    return text, i, source_ids


def group_chunks_into_sections(chunks: list[dict]) -> list[dict]:
    """
    Group the flat chunk list into major sections (H1 and H2).

    Returns list of:
    {
        "section_id": "...",
        "level": 1 or 2,
        "heading_path": [...],
        "text": "full text under this section (including sub-headings)",
        "source_chunk_ids": [uuid strings],
        "sequence_start": int,
        "sequence_end": int,
    }
    """
    sections: list[dict] = []
    i = 0
    n = len(chunks)

    while i < n:
        ch = chunks[i]

        if ch.get("chunk_type") != "heading":
            i += 1
            continue

        hp = ch.get("heading_path") or []
        level = len(hp)

        # We only care about H1 and H2 for now (level 1 and 2)
        if level not in (1, 2):
            i += 1
            continue

        heading_text = hp[-1] if hp else "Untitled"

        section_text, end_idx, source_ids = _collect_text_under_heading(chunks, i, level)

        if not section_text.strip():
            i = end_idx
            continue

        seq_start = ch.get("sequence_id")
        # Find the last sequence id in the collected range
        seq_end: int | None = seq_start
        for j in range(i, end_idx):
            s = chunks[j].get("sequence_id")
            if s is not None:
                if seq_end is None:
                    seq_end = s
                else:
                    seq_end = max(seq_end, s)

        sections.append({
            "section_id": _make_section_id(hp, level),
            "level": level,
            "heading_path": list(hp),
            "heading_text": heading_text,
            "text": section_text,
            "source_chunk_ids": source_ids,
            "sequence_start": seq_start,
            "sequence_end": seq_end,
        })

        i = end_idx

    return sections


def _fetch_all_chunks_for_doc(session: Session, document_id: UUID) -> list[dict]:
    """Fetch every chunk for a document in physical order (for grouping)."""
    result = session.execute(
        text("""
            SELECT id, sequence_id, chunk_type, heading_path, markdown, plain_text
            FROM chunks
            WHERE document_id = :doc_id
            ORDER BY sequence_id ASC
        """),
        {"doc_id": str(document_id)},
    )
    return [dict(r) for r in result.mappings().all()]


def _store_section_summary(
    session: Session,
    *,
    document_id: UUID,
    section: dict,
    summary_md: str,
    summary_plain: str,
    model: str,
    prompt_hash: str,
) -> None:
    """Idempotent insert of one section summary."""
    source_uuids = [UUID(cid) for cid in section["source_chunk_ids"]]

    session.execute(
        text("""
            INSERT INTO section_summaries (
                document_id, section_id, level, heading_path,
                sequence_start, sequence_end,
                summary_markdown, summary_plain,
                source_chunk_ids,
                model, prompt_hash
            )
            VALUES (
                :document_id, :section_id, :level, :heading_path,
                :sequence_start, :sequence_end,
                :summary_markdown, :summary_plain,
                :source_chunk_ids,
                :model, :prompt_hash
            )
            ON CONFLICT (document_id, section_id, model) DO UPDATE SET
                summary_markdown = EXCLUDED.summary_markdown,
                summary_plain = EXCLUDED.summary_plain,
                source_chunk_ids = EXCLUDED.source_chunk_ids,
                heading_path = EXCLUDED.heading_path,
                sequence_start = EXCLUDED.sequence_start,
                sequence_end = EXCLUDED.sequence_end,
                prompt_hash = EXCLUDED.prompt_hash,
                created_at = NOW()
        """),
        {
            "document_id": str(document_id),
            "section_id": section["section_id"],
            "level": section["level"],
            "heading_path": section["heading_path"],
            "sequence_start": section.get("sequence_start"),
            "sequence_end": section.get("sequence_end"),
            "summary_markdown": summary_md,
            "summary_plain": summary_plain,
            "source_chunk_ids": source_uuids,
            "model": model,
            "prompt_hash": prompt_hash,
        },
    )


def generate_and_store_section_summaries_sync(
    session: Session,
    document_id: UUID,
    *,
    model: Optional[str] = None,
    force: bool = False,
) -> dict:
    """
    Main entry point. Groups chunks → calls LLM per major section → stores results.

    Also produces one top-level paper overview (level 0).

    Returns stats for logging / status.
    """
    # Resolved upfront because the model name keys the idempotency check and
    # the stored summary rows.
    model = model or resolve_llm_sync().chat_model
    prompt_hash = hash_prompt(SECTION_SUMMARY_PROMPT_V1 + PAPER_OVERVIEW_PROMPT_V1)

    logger.info(f"[summarizer] Starting high-quality section summarization for {document_id} using {model}")

    # Idempotency: if we already have summaries for this model + prompt_hash, skip unless forced
    if not force:
        existing = session.execute(
            text("""
                SELECT COUNT(*) as n FROM section_summaries
                WHERE document_id = :doc_id AND model = :model AND prompt_hash = :ph
            """),
            {"doc_id": str(document_id), "model": model, "ph": prompt_hash},
        ).mappings().first()
        if existing and existing["n"] > 0:
            logger.info(f"[summarizer] Summaries already exist for {document_id} with current prompt/model. Skipping.")
            return {"skipped": True, "reason": "already_exists", "count": int(existing["n"])}

    # Clean old summaries for this document + model (so we don't accumulate junk on re-runs)
    session.execute(
        text("DELETE FROM section_summaries WHERE document_id = :doc_id AND model = :model"),
        {"doc_id": str(document_id), "model": model},
    )

    chunks = _fetch_all_chunks_for_doc(session, document_id)
    if not chunks:
        logger.warning(f"[summarizer] No chunks found for {document_id} — nothing to summarize")
        return {"skipped": True, "reason": "no_chunks"}

    sections = group_chunks_into_sections(chunks)
    logger.info(f"[summarizer] Grouped into {len(sections)} major sections (H1/H2)")

    summaries_created = 0

    for sec in sections:
        section_text = sec["text"][:12000]  # Safety cap — very long sections get truncated

        messages = [
            {"role": "system", "content": SECTION_SUMMARY_PROMPT_V1},
            {"role": "user", "content": section_text},
        ]

        try:
            result = chat_sync(messages, model=model, temperature=0.25)
            content = (result.get("content") or "").strip()
        except Exception as e:
            logger.exception(f"[summarizer] LLM call failed for section {sec['section_id']}: {e}")
            # Store a placeholder so we don't keep retrying forever on bad sections
            content = f"[Summarization failed for this section: {e}]"

        if not content or content.startswith("[Summarization failed"):
            plain = content
        else:
            # Create a plain version by stripping markdown roughly
            plain = re.sub(r"#{1,6}\s+", "", content)
            plain = re.sub(r"\*\*(.+?)\*\*", r"\1", plain)
            plain = re.sub(r"\*(.+?)\*", r"\1", plain)
            plain = re.sub(r"\n{3,}", "\n\n", plain).strip()

        _store_section_summary(
            session,
            document_id=document_id,
            section=sec,
            summary_md=content,
            summary_plain=plain,
            model=model,
            prompt_hash=prompt_hash,
        )
        summaries_created += 1
        session.commit()  # Commit per section so partial progress survives crashes

        logger.info(f"[summarizer] Stored summary for {sec['heading_text'][:60]} (level {sec['level']})")

    # ── Paper-level executive overview (level 0) ─────────────────────────────
    if summaries_created > 0:
        # Fetch the section summaries we just created (in order)
        section_rows = session.execute(
            text("""
                SELECT heading_path, summary_markdown
                FROM section_summaries
                WHERE document_id = :doc_id AND model = :model AND level IN (1,2)
                ORDER BY sequence_start ASC NULLS LAST, created_at ASC
            """),
            {"doc_id": str(document_id), "model": model},
        ).mappings().all()

        combined = []
        for row in section_rows:
            hp = row.get("heading_path") or []
            title = " > ".join(hp) if hp else "Section"
            combined.append(f"## {title}\n\n{row['summary_markdown']}")

        overview_text = "\n\n---\n\n".join(combined)[:25000]

        overview_messages = [
            {"role": "system", "content": PAPER_OVERVIEW_PROMPT_V1},
            {"role": "user", "content": overview_text},
        ]

        try:
            ov_result = chat_sync(overview_messages, model=model, temperature=0.2)
            ov_content = (ov_result.get("content") or "").strip()
        except Exception as e:
            logger.exception(f"[summarizer] Paper overview LLM call failed: {e}")
            ov_content = f"[Paper-level overview generation failed: {e}]"

        ov_plain = re.sub(r"#{1,6}\s+", "", ov_content)
        ov_plain = re.sub(r"\n{3,}", "\n\n", ov_plain).strip()

        # Store the paper overview as level 0 with a stable section_id
        session.execute(
            text("""
                INSERT INTO section_summaries (
                    document_id, section_id, level, heading_path,
                    sequence_start, sequence_end,
                    summary_markdown, summary_plain,
                    source_chunk_ids,
                    model, prompt_hash
                )
                VALUES (
                    :document_id, 'paper_overview', 0, ARRAY[]::TEXT[],
                    NULL, NULL,
                    :summary_markdown, :summary_plain,
                    ARRAY[]::UUID[],
                    :model, :prompt_hash
                )
                ON CONFLICT (document_id, section_id, model) DO UPDATE SET
                    summary_markdown = EXCLUDED.summary_markdown,
                    summary_plain = EXCLUDED.summary_plain,
                    prompt_hash = EXCLUDED.prompt_hash,
                    created_at = NOW()
            """),
            {
                "document_id": str(document_id),
                "summary_markdown": ov_content,
                "summary_plain": ov_plain,
                "model": model,
                "prompt_hash": prompt_hash,
            },
        )
        session.commit()
        summaries_created += 1
        logger.info(f"[summarizer] Stored paper-level executive overview for {document_id}")

    logger.info(f"[summarizer] Finished. Created {summaries_created} summaries for document {document_id}")
    return {
        "created": summaries_created,
        "sections": len(sections),
        "model": model,
        "prompt_hash": prompt_hash,
    }