"""Hugging Face Inference Endpoints custom handler for Breeze TTS 2.

POST JSON:
  {"inputs": "Hello from Breeze.", "parameters": {"instruction": "A warm narrator."}}
  {"input": "...", "instructions": "...", "voice": "..."}  # OpenAI-ish aliases

Returns WAV bytes as base64 plus sampling_rate so the toolkit and suite
wake path can both consume it.
"""
from __future__ import annotations

from typing import Any

from generate import sample_rate, synthesize


class EndpointHandler:
    def __init__(self, path: str = "") -> None:
        if path:
            import os

            os.environ.setdefault("BREEZE_MODEL_PATH", path)
        # Load at init so HF health is not a cold first request.
        sample_rate()

    def __call__(self, data: dict[str, Any]) -> dict[str, Any]:
        payload = dict(data or {})
        parameters = payload.pop("parameters", None) or {}
        if not isinstance(parameters, dict):
            parameters = {}
        text = (
            payload.pop("inputs", None)
            or payload.pop("input", None)
            or payload.pop("text", None)
            or parameters.get("text")
            or ""
        )
        instruction = (
            payload.pop("instructions", None)
            or payload.pop("instruction", None)
            or payload.pop("voice", None)
            or parameters.get("instruction")
            or parameters.get("instructions")
            or parameters.get("voice")
        )
        cfg = payload.pop("cfg_scale", parameters.get("cfg_scale"))
        seed = payload.pop("seed", parameters.get("seed", 42))
        wav = synthesize(
            str(text),
            instruction=str(instruction) if instruction else None,
            cfg_scale=float(cfg) if cfg is not None else None,
            seed=int(seed or 42),
        )
        import base64

        return {
            "audio": base64.b64encode(wav).decode("ascii"),
            "sampling_rate": sample_rate(),
            "content_type": "audio/wav",
        }