import io
import os
import torch
from contextlib import asynccontextmanager
from typing import Optional

import edge_tts
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import Response
from pydantic import BaseModel, field_validator
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel

# ─── Config ───────────────────────────────────────────────────────────────────
ADAPTER          = os.getenv("ADAPTER", "robsonrtp/ngananlp-v2")
BASE_MODEL       = "facebook/nllb-200-distilled-1.3B"
DEVICE           = "cuda" if torch.cuda.is_available() else "cpu"
HF_TOKEN         = os.getenv("HF_TOKEN", None)

# Zulu neural voice — Bantu language, sounds natural for Angolan Bantu content
DEFAULT_TTS_VOICE = os.getenv("TTS_VOICE", "zu-ZA-ThandoNeural")

TTS_VOICE_MAP: dict[str, str] = {
    "por_Latn": "pt-PT-RaquelNeural",
}

LANGUAGES = {
    "por_Latn": "Português",
    "lin_Latn": "Lingala",
    "umb_Latn": "Umbundu",
    "kmb_Latn": "Kimbundu",
    "cjk_Latn": "Tchokwe",
    "lue_Latn": "Luvale",
}

# Direcções com baixa confiança (X → lue_Latn)
LOW_CONFIDENCE_TARGETS = {"lue_Latn"}

# ─── Model loading ────────────────────────────────────────────────────────────
tokenizer = None
model     = None

def load_model():
    global tokenizer, model
    print(f"Loading tokenizer from {ADAPTER}...", flush=True)
    tokenizer = AutoTokenizer.from_pretrained(ADAPTER, token=HF_TOKEN)

    print(f"Loading base model {BASE_MODEL}...", flush=True)
    base = AutoModelForSeq2SeqLM.from_pretrained(
        BASE_MODEL,
        torch_dtype=torch.bfloat16,
        low_cpu_mem_usage=True,
    )
    base.resize_token_embeddings(len(tokenizer))
    base = base.to(DEVICE)

    print(f"Loading LoRA adapter from {ADAPTER}...", flush=True)
    model = PeftModel.from_pretrained(base, ADAPTER, token=HF_TOKEN).eval()
    print("Model ready.", flush=True)

@asynccontextmanager
async def lifespan(app: FastAPI):
    load_model()
    yield

# ─── App ──────────────────────────────────────────────────────────────────────
app = FastAPI(
    title="NganaNLP v2",
    description="Multilingual translation API for Portuguese and Angolan Bantu languages.\n\nExample: POST /translate `{\"text\": \"Como tomar boas decisões sobre educação?\", \"src_lang\": \"por_Latn\", \"tgt_lang\": \"kmb_Latn\"}`",
    version="2.0.0",
    lifespan=lifespan,
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

# ─── Schemas ──────────────────────────────────────────────────────────────────
class TranslateRequest(BaseModel):
    text: str
    src_lang: str
    tgt_lang: str
    num_beams: Optional[int] = 4

    @field_validator("src_lang", "tgt_lang")
    @classmethod
    def validate_lang(cls, v):
        if v not in LANGUAGES:
            raise ValueError(f"Unsupported language '{v}'. Use one of: {list(LANGUAGES)}")
        return v

    @field_validator("num_beams")
    @classmethod
    def validate_beams(cls, v):
        if not (1 <= v <= 8):
            raise ValueError("num_beams must be between 1 and 8")
        return v

class SpeakRequest(BaseModel):
    text: str
    lang: Optional[str] = None   # language code (e.g. por_Latn) to auto-select voice
    voice: Optional[str] = None  # explicit override, takes precedence over lang

class TranslateResponse(BaseModel):
    translation: str
    src_lang: str
    tgt_lang: str
    low_confidence: bool

# ─── Endpoints ────────────────────────────────────────────────────────────────
@app.get("/")
def root():
    return {
        "name": "NganaNLP v2",
        "description": "Multilingual translation API for Portuguese and Angolan Bantu languages",
        "message": "A language unheard is a thought untranslated. We build bridges.",
        "languages": LANGUAGES,
        "endpoints": {
            "translate": "POST /translate",
            "speak": "POST /speak",
            "languages": "GET /languages",
            "docs": "GET /docs",
            "health": "GET /health",
        },
    }

@app.get("/health")
def health():
    return {"status": "ok", "device": DEVICE, "model": ADAPTER}

@app.get("/languages")
def languages():
    return {
        "languages": [
            {"code": code, "name": name, "unreliable_as_target": code in LOW_CONFIDENCE_TARGETS}
            for code, name in LANGUAGES.items()
        ]
    }

@app.post("/translate", response_model=TranslateResponse)
def translate(req: TranslateRequest):
    if req.src_lang == req.tgt_lang:
        raise HTTPException(status_code=400, detail="src_lang and tgt_lang must be different")

    if model is None:
        raise HTTPException(status_code=503, detail="Model not loaded yet")

    tokenizer.src_lang = req.src_lang
    inputs = tokenizer(
        req.text,
        return_tensors="pt",
        padding=True,
        truncation=True,
        max_length=192,
    ).to(DEVICE)

    forced_bos_token_id = tokenizer.convert_tokens_to_ids(req.tgt_lang)

    with torch.no_grad():
        output = model.generate(
            **inputs,
            forced_bos_token_id=forced_bos_token_id,
            max_length=256,
            num_beams=req.num_beams,
            no_repeat_ngram_size=3,
            repetition_penalty=1.2,
        )

    translation = tokenizer.batch_decode(output, skip_special_tokens=True)[0]

    return TranslateResponse(
        translation=translation,
        src_lang=req.src_lang,
        tgt_lang=req.tgt_lang,
        low_confidence=req.tgt_lang in LOW_CONFIDENCE_TARGETS,
    )


# ─── TTS ──────────────────────────────────────────────────────────────────────
@app.post(
    "/speak",
    summary="Text-to-speech via edge-tts (Microsoft Neural TTS)",
    responses={200: {"content": {"audio/mpeg": {}}}},
)
async def speak(req: SpeakRequest):
    """Convert text to speech and return an MP3 audio file."""
    if not req.text.strip():
        raise HTTPException(status_code=400, detail="text must not be empty")

    voice = req.voice or TTS_VOICE_MAP.get(req.lang or "", DEFAULT_TTS_VOICE)
    buf = io.BytesIO()

    try:
        communicate = edge_tts.Communicate(req.text, voice)
        async for chunk in communicate.stream():
            if chunk["type"] == "audio":
                buf.write(chunk["data"])
    except Exception as e:
        raise HTTPException(status_code=502, detail=f"TTS error: {e}")

    audio_bytes = buf.getvalue()
    if not audio_bytes:
        raise HTTPException(status_code=502, detail="TTS returned no audio")

    return Response(
        content=audio_bytes,
        media_type="audio/mpeg",
        headers={"Content-Disposition": "inline; filename=speech.mp3"},
    )