#!/usr/bin/env python3
"""
Importiert abgeschlossene Topics aus context-buffer/ als Engramme.
Ein Topic gilt als abgeschlossen, wenn es den Status 'done' oder 'completed' hat.
"""

from __future__ import annotations

import json
import sqlite3
import subprocess
import sys
from datetime import datetime, timezone
from pathlib import Path

BRAIN_DIR = Path("/root/.openclaw/workspace/second-brain")
WORKSPACE = Path("/root/.openclaw/workspace")
HANDLER = WORKSPACE / "context-buffer" / "handler.py"

def run():
    # Hole alle Topics mit status done/completed via handler
    try:
        result = subprocess.run(
            ["python3", str(HANDLER), "search", "--status", "done"],
            capture_output=True, text=True, timeout=30
        )
        if result.returncode != 0:
            raise Exception(f"Handler error: {result.stderr}")
        topics = json.loads(result.stdout)
    except Exception as e:
        print(json.dumps({"success": False, "error": str(e)}, indent=2, ensure_ascii=False))
        return

    # Alternative: auch 'completed' suchen
    try:
        result2 = subprocess.run(
            ["python3", str(HANDLER), "search", "--status", "completed"],
            capture_output=True, text=True, timeout=30
        )
        if result2.returncode == 0:
            topics_completed = json.loads(result2.stdout)
            topics.extend(topics_completed)
    except Exception:
        pass

    if not topics:
        print(json.dumps({"success": True, "imported": 0, "message": "No completed topics found"}, indent=2, ensure_ascii=False))
        return

    # Import in Second Brain
    DB_PATH = BRAIN_DIR / "data" / "brain.sqlite"
    conn = sqlite3.connect(str(DB_PATH))
    conn.row_factory = sqlite3.Row
    c = conn.cursor()

    sys.path.insert(0, str(BRAIN_DIR))
    from src.store import EngramStore
    from src.engram import Engram, Grounding

    store = EngramStore(str(DB_PATH))
    imported = 0

    for topic in topics:
        topic_id = topic.get("id")
        title = topic.get("title", "Untitled Topic")
        content = topic.get("content", "")
        if not content.strip():
            continue

        # Tags aus topic-type und status
        tags = ["context-buffer", topic.get("status", "unknown")]
        if topic.get("type"):
            tags.append(topic["type"])

        eg = Engram.create(
            content=content,
            source="context-buffer",
            tags=tags,
            grounding=Grounding.ASSUMPTION,
        )
        eg.metadata.update({
            "title": title,
            "context_buffer_id": topic_id,
            "imported_from": "context-buffer",
            "original_status": topic.get("status"),
        })
        store.save(eg)
        imported += 1

    conn.close()

    print(json.dumps({
        "success": True,
        "time": datetime.now(timezone.utc).isoformat(),
        "topics_found": len(topics),
        "imported": imported,
    }, indent=2, ensure_ascii=False))

if __name__ == "__main__":
    import sys
    sys.path.insert(0, str(BRAIN_DIR))
    run()