#!/usr/bin/env python3
"""Enricher split_sentences — parte texto en frases (regex puro, offline).

Wire protocol estandar (issue 0026):
  - stdin:  JSON con node_id, node_name, metadata, ops_db_path, app_dir,
            cache_dir, registry_root, params.
  - stderr: lineas `PROGRESS:<float> <stage>` para feedback de UI.
  - stdout: una linea JSON al final con resumen.
  - exit code 0 = ok, !=0 = error.

Lectura del texto (en orden de prioridad):
  1. `entities.notes`      (lo que el usuario escribe en el panel Note
                             via doble click — sitio canonico de texto
                             largo)
  2. node_name             (titulo del nodo, fallback minimo)

Si tras esto el texto es < min_length, falla con exit 2 y mensaje claro.

Grouping (issue 0035c, mismo patron que web_search):
  - Si len(sentences) >= GROUP_THRESHOLD y la BD soporta group_id:
      * Crea Group `type_ref='Group'` colgando del source con SENTENCE_OF.
      * Primeras GROUP_PREVIEW_K frases sueltas (group_id=NULL).
      * Resto con group_id apuntando al Group recien creado.
  - Si <threshold: todas sueltas, sin Group.
"""
from __future__ import annotations

import json
import os
import re
import sqlite3
import sys
import time
import uuid
from datetime import datetime, timezone


DEFAULT_GROUP_THRESHOLD = 50
GROUP_PREVIEW_K         = 10

# Split por delimitador de oracion (.!?) seguido de whitespace seguido de
# inicial de oracion en mayusculas (incluye acentos espanoles). Robusto
# para texto en espanol e ingles. Casos limite (abreviaturas como "Sr.",
# "Dr.") quedan como falsos negativos aceptables — el split es heuristico.
_SENT_SPLIT_RE = re.compile(r'(?<=[.!?])\s+(?=[A-ZÁÉÍÓÚÜÑ])')


def progress(p: float, stage: str = "") -> None:
    sys.stderr.write(f"PROGRESS:{p:.2f} {stage}\n")
    sys.stderr.flush()


def log(msg: str) -> None:
    sys.stderr.write(f"{msg}\n")
    sys.stderr.flush()


def now_iso() -> str:
    return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")


def now_ms() -> int:
    return int(time.time() * 1000)


def has_group_id_column(conn: sqlite3.Connection) -> bool:
    """Detecta si la columna `group_id` existe en `entities`.

    El schema actual la incluye (issue 0035a) pero las BDs viejas pueden
    no tenerla. Si no esta, insertamos sin esa columna.
    """
    try:
        cur = conn.execute("PRAGMA table_info(entities)")
        for row in cur:
            if row[1] == "group_id":
                return True
    except sqlite3.Error:
        pass
    return False


def read_text(ops_db_path: str, node_id: str, node_name: str) -> str:
    """Resuelve el texto a procesar.

    Prioridad:
      1. `entities.notes` del nodo (lo que el usuario escribe en el panel
         Note via doble click). Es el sitio canonico para texto largo.
      2. `node_name` (titulo del nodo) como fallback minimo.
    """
    notes = ""
    try:
        c = sqlite3.connect(ops_db_path)
        try:
            row = c.execute(
                "SELECT notes FROM entities WHERE id=?", (node_id,)
            ).fetchone()
            if row and isinstance(row[0], str):
                notes = row[0]
        finally:
            c.close()
    except sqlite3.Error:
        notes = ""
    if notes and notes.strip():
        return notes.strip()
    return (node_name or "").strip()


def split_into_sentences(text: str, min_length: int) -> list[str]:
    """Aplica el regex de split y filtra por longitud minima."""
    parts = _SENT_SPLIT_RE.split(text)
    out: list[str] = []
    for p in parts:
        s = p.strip()
        if len(s) < min_length:
            continue
        out.append(s)
    return out


def insert_sentence(conn: sqlite3.Connection, *, sentence: str, rank: int,
                     batch_id: str, group_id: str | None,
                     has_group_col: bool) -> str:
    """Inserta un nodo Sentence y devuelve su id. No deduplica — cada
    ejecucion crea entidades nuevas (las frases pueden repetirse entre
    ejecuciones distintas y el rank/batch las distingue).
    """
    ts = now_iso()
    new_id = f"Sentence_{now_ms()}_{rank}"
    name = sentence[:80] + ("..." if len(sentence) > 80 else "")
    meta = {
        "text":     sentence,
        "rank":     rank,
        "batch_id": batch_id,
    }
    meta_json = json.dumps(meta, ensure_ascii=False)
    if has_group_col:
        conn.execute(
            "INSERT INTO entities (id, name, type_ref, source, metadata, "
            " group_id, created_at, updated_at) "
            "VALUES (?, ?, 'Sentence', 'enricher:split_sentences', ?, ?, ?, ?)",
            (new_id, name, meta_json, group_id, ts, ts),
        )
    else:
        conn.execute(
            "INSERT INTO entities (id, name, type_ref, source, metadata, "
            " created_at, updated_at) "
            "VALUES (?, ?, 'Sentence', 'enricher:split_sentences', ?, ?, ?)",
            (new_id, name, meta_json, ts, ts),
        )
    return new_id


def insert_group_entity(conn: sqlite3.Connection, *, source_node_id: str,
                         source_node_name: str, count: int,
                         batch_id: str) -> str:
    ts = now_iso()
    new_id = f"Group_{now_ms()}_{abs(hash(source_node_id + batch_id)) % 100000}"
    name = f"split_sentences: {source_node_name} ({count})"
    meta = {
        "enricher":       "split_sentences",
        "count":          count,
        "batch_id":       batch_id,
        "source_node_id": source_node_id,
    }
    meta_json = json.dumps(meta, ensure_ascii=False)
    conn.execute(
        "INSERT INTO entities (id, name, type_ref, source, metadata, "
        " created_at, updated_at) "
        "VALUES (?, ?, 'Group', 'enricher:split_sentences', ?, ?, ?)",
        (new_id, name, meta_json, ts, ts),
    )
    return new_id


_REL_COUNTER = 0


def insert_relation(conn: sqlite3.Connection, from_id: str, to_id: str,
                    name: str) -> bool:
    global _REL_COUNTER
    cur = conn.execute(
        "SELECT 1 FROM relations WHERE from_entity=? AND to_entity=? "
        "AND name=? LIMIT 1",
        (from_id, to_id, name),
    )
    if cur.fetchone():
        return False
    ts = now_iso()
    _REL_COUNTER += 1
    rel_id = f"rel_{now_ms()}_{_REL_COUNTER}_{name.lower()}"
    conn.execute(
        "INSERT INTO relations (id, name, from_entity, to_entity, "
        " created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?)",
        (rel_id, name, from_id, to_id, ts, ts),
    )
    return True


def main() -> int:
    raw = sys.stdin.read()
    try:
        ctx = json.loads(raw)
    except Exception as e:
        log(f"stdin not valid JSON: {e}")
        return 2

    node_id     = ctx.get("node_id") or ""
    node_name   = (ctx.get("node_name") or "").strip()
    metadata    = ctx.get("metadata") or {}
    if isinstance(metadata, str):
        try:
            metadata = json.loads(metadata)
        except Exception:
            metadata = {}
    ops_db_path = ctx.get("ops_db_path") or ""
    params      = ctx.get("params") or {}
    max_sentences = int(params.get("max_sentences", 200))
    min_length    = int(params.get("min_length", 20))

    if not node_id or not ops_db_path:
        log("missing node_id / ops_db_path")
        return 2

    # Normalizar y resolver path como en web_search.
    ops_db_path = ops_db_path.replace("\\", "/")
    app_dir_raw = (ctx.get("app_dir") or "").replace("\\", "/")
    if not os.path.isabs(ops_db_path):
        if app_dir_raw and os.path.isdir(app_dir_raw):
            cand = os.path.normpath(os.path.join(app_dir_raw, ops_db_path))
            if os.path.exists(cand):
                ops_db_path = cand
        if not os.path.isabs(ops_db_path):
            ops_db_path = os.path.abspath(ops_db_path)

    if not os.path.exists(ops_db_path):
        log(f"ops_db_path no existe: {ops_db_path}")
        print(json.dumps({"error": "ops_db not found",
                          "ops_db_path": ops_db_path,
                          "entities_added": 0, "relations_added": 0}))
        return 7

    progress(0.10, "reading")
    text = read_text(ops_db_path, node_id, node_name)
    if len(text) < min_length:
        msg = (f"texto demasiado corto ({len(text)} chars < {min_length}). "
               f"Escribe el texto en el panel Note del nodo (doble click "
               f"para abrir) o pon un name mas largo")
        log(msg)
        print(json.dumps({"error": msg, "entities_added": 0,
                          "relations_added": 0}))
        return 2

    progress(0.30, "splitting")
    sentences = split_into_sentences(text, min_length)
    if max_sentences > 0:
        sentences = sentences[:max_sentences]

    if not sentences:
        msg = (f"sin frases tras split (texto de {len(text)} chars, "
               f"min_length={min_length})")
        log(msg)
        print(json.dumps({"error": msg, "entities_added": 0,
                          "relations_added": 0}))
        return 2

    progress(0.55, "writing")
    conn = sqlite3.connect(ops_db_path)
    conn.execute("PRAGMA foreign_keys=OFF")
    entities_added = 0
    relations_added = 0
    group_id: str | None = None
    batch_id = uuid.uuid4().hex
    try:
        has_group_col = has_group_id_column(conn)
        n_total = len(sentences)
        threshold = DEFAULT_GROUP_THRESHOLD

        if n_total >= threshold and has_group_col:
            group_id = insert_group_entity(
                conn,
                source_node_id=node_id,
                source_node_name=node_name or "(text)",
                count=n_total,
                batch_id=batch_id,
            )
            entities_added += 1
            if insert_relation(conn, group_id, node_id, "SENTENCE_OF"):
                relations_added += 1
            preview = sentences[:GROUP_PREVIEW_K]
            grouped = sentences[GROUP_PREVIEW_K:]
        else:
            preview = sentences
            grouped = []

        # Frases sueltas (preview).
        for i, s in enumerate(preview):
            sid = insert_sentence(
                conn, sentence=s, rank=i + 1, batch_id=batch_id,
                group_id=None, has_group_col=has_group_col,
            )
            entities_added += 1
            if insert_relation(conn, sid, node_id, "SENTENCE_OF"):
                relations_added += 1

        # Frases agrupadas — siguen colgando del source con SENTENCE_OF.
        for j, s in enumerate(grouped):
            rank = GROUP_PREVIEW_K + j + 1
            sid = insert_sentence(
                conn, sentence=s, rank=rank, batch_id=batch_id,
                group_id=group_id, has_group_col=has_group_col,
            )
            entities_added += 1
            if insert_relation(conn, sid, node_id, "SENTENCE_OF"):
                relations_added += 1

            if grouped and j % 25 == 0:
                progress(0.55 + 0.40 * (j / max(1, len(grouped))), "writing")

        conn.commit()
    finally:
        conn.close()

    progress(1.0, "done")
    print(json.dumps({
        "sentences":       len(sentences),
        "entities_added":  entities_added,
        "relations_added": relations_added,
        "batch_id":        batch_id,
        "group_id":        group_id or "",
        "grouped":         bool(group_id),
    }, ensure_ascii=False))
    return 0


if __name__ == "__main__":
    sys.exit(main())