"""
Translation Service — Batch-translates vocabulary words into target languages.

Uses Ollama (local LLM) to translate EN/DE word pairs into TR, AR, UK, RU, PL.
Translations are cached in vocabulary_words.translations JSONB field.

All processing happens locally — no external API calls, GDPR-compliant.
"""

import json
import logging
import os
from typing import Any, Dict, List

import httpx

logger = logging.getLogger(__name__)

OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://host.docker.internal:11434")
TRANSLATION_MODEL = os.getenv("TRANSLATION_MODEL", "qwen3:30b-a3b")

LANGUAGE_NAMES = {
    "tr": "Turkish",
    "ar": "Arabic",
    "uk": "Ukrainian",
    "ru": "Russian",
    "pl": "Polish",
    "fr": "French",
    "es": "Spanish",
}


async def translate_words_batch(
    words: List[Dict[str, str]],
    target_language: str,
    batch_size: int = 30,
) -> List[Dict[str, str]]:
    """
    Translate a batch of EN/DE word pairs into a target language.

    Args:
        words: List of dicts with 'english' and 'german' keys
        target_language: ISO 639-1 code (tr, ar, uk, ru, pl)
        batch_size: Words per LLM request

    Returns:
        List of dicts with 'english', 'translation', 'example' keys
    """
    lang_name = LANGUAGE_NAMES.get(target_language, target_language)
    all_translations = []

    for i in range(0, len(words), batch_size):
        batch = words[i:i + batch_size]
        word_list = "\n".join(
            f"{j+1}. {w['english']} = {w.get('german', '')}"
            for j, w in enumerate(batch)
        )

        prompt = f"""Translate these English/German word pairs into {lang_name}.
For each word, provide the translation and a short example sentence in {lang_name}.

Words:
{word_list}

Reply ONLY with a JSON array, no explanation:
[
  {{"english": "word", "translation": "...", "example": "..."}},
  ...
]"""

        try:
            async with httpx.AsyncClient(timeout=120.0) as client:
                resp = await client.post(
                    f"{OLLAMA_BASE_URL}/api/generate",
                    json={
                        "model": TRANSLATION_MODEL,
                        "prompt": prompt,
                        "stream": False,
                        "options": {"temperature": 0.2, "num_predict": 4096},
                    },
                )
                resp.raise_for_status()
                response_text = resp.json().get("response", "")

                # Parse JSON from response
                import re
                match = re.search(r'\[[\s\S]*\]', response_text)
                if match:
                    batch_translations = json.loads(match.group())
                    all_translations.extend(batch_translations)
                    logger.info(
                        f"Translated batch {i//batch_size + 1}: "
                        f"{len(batch_translations)} words → {lang_name}"
                    )
                else:
                    logger.warning(f"No JSON array in LLM response for {lang_name}")

        except Exception as e:
            logger.error(f"Translation batch failed ({lang_name}): {e}")

    return all_translations


async def translate_and_store(
    word_ids: List[str],
    target_language: str,
) -> int:
    """
    Translate vocabulary words and store in the database.

    Fetches words from DB, translates via LLM, stores in translations JSONB.
    Skips words that already have a translation for the target language.

    Returns count of newly translated words.
    """
    from vocabulary_db import get_pool

    pool = await get_pool()
    async with pool.acquire() as conn:
        # Fetch words that need translation
        rows = await conn.fetch(
            """
            SELECT id, english, german, translations
            FROM vocabulary_words
            WHERE id = ANY($1::uuid[])
            """,
            [__import__('uuid').UUID(wid) for wid in word_ids],
        )

    words_to_translate = []
    word_map = {}
    for row in rows:
        translations = row["translations"] or {}
        if isinstance(translations, str):
            translations = json.loads(translations)
        if target_language not in translations:
            words_to_translate.append({
                "english": row["english"],
                "german": row["german"],
            })
            word_map[row["english"].lower()] = str(row["id"])

    if not words_to_translate:
        logger.info(f"All {len(rows)} words already translated to {target_language}")
        return 0

    # Translate
    results = await translate_words_batch(words_to_translate, target_language)

    # Store results
    updated = 0
    async with pool.acquire() as conn:
        for result in results:
            en = result.get("english", "").lower()
            word_id = word_map.get(en)
            if not word_id:
                continue

            translation = result.get("translation", "")
            example = result.get("example", "")
            if not translation:
                continue

            await conn.execute(
                """
                UPDATE vocabulary_words
                SET translations = translations || $1::jsonb
                WHERE id = $2
                """,
                json.dumps({target_language: {
                    "text": translation,
                    "example": example,
                }}),
                __import__('uuid').UUID(word_id),
            )
            updated += 1

    logger.info(f"Stored {updated} translations for {target_language}")
    return updated