language-learning-app/api/app/routers/generation.py

import uuid
from functools import partial

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

from ..auth import verify_token
from ..database import get_db, AsyncSessionLocal
from ..models import Job
from ..storage import upload_audio
from ..services import llm, tts, job_repo
from ..services.tts import VOICE_BY_LANGUAGE
from .. import worker

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"}


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)
        await job_repo.mark_processing(db, job)

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

            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}."
            )

            generated_text = await llm.generate_text(prompt)

            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"
            )

            translated_text = await llm.translate_text(prompt, generated_text, translate_prompt)

            # Save LLM results before attempting TTS so they're preserved on failure
            await job_repo.save_llm_results(
                db, job, generated_text, translated_text, input_summary[:500]
            )

            voice = VOICE_BY_LANGUAGE.get(request.target_language, "Kore")
            wav_bytes = await tts.generate_audio(generated_text, voice)
            audio_key = f"audio/{job_id}.wav"
            upload_audio(audio_key, wav_bytes)

            await job_repo.mark_succeeded(db, job, audio_key)

        except Exception as exc:
            await job_repo.mark_failed(db, job, str(exc))


@router.post("", response_model=GenerationResponse, status_code=202)
async def create_generation_job(
    request: GenerationRequest,
    db: AsyncSession = Depends(get_db),
    token_data: 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(
        user_id=uuid.UUID(token_data["sub"]),
        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)

    await worker.enqueue(partial(_run_generation, job.id, request))

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