import uuid
from datetime import datetime, timezone

from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException
from pydantic import BaseModel
from sqlalchemy.ext.asyncio import AsyncSession
import anthropic
import deepl

from ..auth import verify_token
from ..database import get_db, AsyncSessionLocal
from ..models import Job
from ..config import settings

router = APIRouter(prefix="/generate", tags=["generation"])

SUPPORTED_LANGUAGES: dict[str, str] = {
    "en": "English",
    "fr": "French",
    "es": "Spanish",
    "it": "Italian",
    "de": "German",
}
SUPPORTED_LEVELS = {"A1", "A2", "B1", "B2", "C1", "C2"}

# Maps our language codes to DeepL source/target language codes
DEEPL_SOURCE_LANG: dict[str, str] = {
    "en": "EN",
    "fr": "FR",
    "es": "ES",
    "it": "IT",
    "de": "DE",
}
# DeepL target codes (English needs a regional variant)
DEEPL_TARGET_LANG: dict[str, str] = {
    "en": "EN-US",
    "fr": "FR",
    "es": "ES",
    "it": "IT",
    "de": "DE",
}


class GenerationRequest(BaseModel):
    target_language: str
    complexity_level: str
    input_texts: list[str]
    topic: str | None = None
    source_language: str = "en"


class GenerationResponse(BaseModel):
    job_id: str


async def _run_generation(job_id: uuid.UUID, request: GenerationRequest) -> None:
    async with AsyncSessionLocal() as db:
        job = await db.get(Job, job_id)
        job.status = "processing"
        job.started_at = datetime.now(timezone.utc)
        await db.commit()

        try:
            from_language = SUPPORTED_LANGUAGES[request.source_language]
            language_name = SUPPORTED_LANGUAGES[request.target_language]

            # Build a short summary of the input to store (not the full text)
            topic_part = f"Topic: {request.topic}. " if request.topic else ""
            combined_preview = " ".join(request.input_texts)[:300]
            input_summary = (
                f"{topic_part}Based on {len(request.input_texts)} input text(s): "
                f"{combined_preview}..."
            )

            source_material = "\n\n".join(request.input_texts[:3])
            topic_line = f"\nTopic focus: {request.topic}" if request.topic else ""

            prompt = (
                f"You are a language learning content creator. "
                f"Using the input provided, you generate engaging realistic text in {language_name} "
                f"at {request.complexity_level} proficiency level (CEFR scale).\n\n"
                f"The text should:\n"
                f"- Be appropriate for a {request.complexity_level} learner\n"
                f"- Maintain a similar tone to the input text.  Where appropriate, use idioms\n"
                f"- Feel natural and authentic, like content a native speaker would read\n"
                f"- Be formatted in markdown with paragraphs and line breaks\n"
                f"- Be 200–400 words long\n"
                f"- Be inspired by the following source material "
                f"(but written originally in {language_name}):\n\n"
                f"{source_material}"
                f"{topic_line}\n\n"
                f"Respond with ONLY the generated text in {language_name}, "
                f"no explanations or translations.\n"
                f"The 'Topic focus' should be a comma-separated list of up to three topics, in {language_name}."
            )

            client = anthropic.Anthropic(api_key=settings.anthropic_api_key)

            message = client.messages.create(
                model="claude-sonnet-4-6",
                max_tokens=1024,
                messages=[{"role": "user", "content": prompt}],
            )
            generated_text = message.content[0].text

            # TODO: Come back to this when DeepL unblock my account for being "high risk"
            # Translate generated text back into the learner's source language via DeepL
            # translator = deepl.Translator(settings.deepl_api_key)
            # translation = translator.translate_text(
            #    generated_text,
            #    source_lang=DEEPL_SOURCE_LANG[request.target_language],
            #    target_lang=DEEPL_TARGET_LANG[request.source_language],
            #)

            translate_prompt = (
                f"You are a helpful assistant that translates text. Translate just the previous summary "
                f"content in {language_name} text you generated based on the input I gave you.  Translate "
                f"it back into {from_language}.\n"
                f"- Keep the translation as close as possible to the original meaning and tone\n"
                f"- Send through only the translated text, no explanations or notes\n"
            )

            translate_message = client.messages.create(
                model="claude-sonnet-4-6",
                max_tokens=1024,
                messages=[
                    { "role": "user", "content": prompt },
                    { "role": "assistant", "content": message.content },
                    { "role": "user", "content": translate_prompt }
                ],
            )

            job.status = "succeeded"
            job.generated_text = generated_text
            job.translated_text = translate_message.content[0].text
            job.input_summary = input_summary[:500]
            job.completed_at = datetime.now(timezone.utc)

        except Exception as exc:
            job.status = "failed"
            job.error_message = str(exc)
            job.completed_at = datetime.now(timezone.utc)

        await db.commit()


@router.post("", response_model=GenerationResponse, status_code=202)
async def create_generation_job(
    request: GenerationRequest,
    background_tasks: BackgroundTasks,
    db: AsyncSession = Depends(get_db),
    _: dict = Depends(verify_token),
) -> GenerationResponse:
    if request.target_language not in SUPPORTED_LANGUAGES:
        raise HTTPException(
            status_code=400,
            detail=f"Unsupported language '{request.target_language}'. "
            f"Supported: {list(SUPPORTED_LANGUAGES)}",
        )
    if request.complexity_level not in SUPPORTED_LEVELS:
        raise HTTPException(
            status_code=400,
            detail=f"Unsupported level '{request.complexity_level}'. "
            f"Supported: {sorted(SUPPORTED_LEVELS)}",
        )

    job = Job(
        source_language=request.source_language,
        target_language=request.target_language,
        complexity_level=request.complexity_level,
    )
    db.add(job)
    await db.commit()
    await db.refresh(job)

    background_tasks.add_task(_run_generation, job.id, request)

    return GenerationResponse(job_id=str(job.id))