"""Bounded audio decoding for the interactive demo (not benchmark evaluation)."""
from pathlib import Path

import librosa
import numpy as np
import soundfile as sf

SAMPLE_RATE = 16000
MAX_SECONDS = 60
MAX_BYTES = 30 * 1024 * 1024
MAX_QUESTION_CHARS = 1000
DEFAULT_QUESTION = "How does my singing sound overall, and how can I improve?"


def prepare_input(audio_path, question):
    question = (question or "").strip()
    if not question:
        raise ValueError("Please enter a question about your singing.")
    if len(question) > MAX_QUESTION_CHARS:
        raise ValueError(f"Please keep your question under {MAX_QUESTION_CHARS} characters.")
    if not audio_path:
        raise ValueError("Upload or record a singing clip first.")
    path = Path(audio_path)
    if not path.is_file() or path.stat().st_size > MAX_BYTES:
        raise ValueError("Please upload an audio file smaller than 30 MB.")
    try:
        with sf.SoundFile(path) as handle:
            sr = handle.samplerate
            if not 8000 <= sr <= 192000 or not 1 <= handle.channels <= 2:
                raise ValueError("Use mono or stereo audio with a sample rate of 8–192 kHz.")
            if not sr <= handle.frames <= MAX_SECONDS * sr:
                raise ValueError(f"Please use a clip between 1 and {MAX_SECONDS} seconds. Longer clips are not trimmed automatically.")
            wave = handle.read(dtype="float32", always_2d=True).mean(axis=1)
    except (sf.LibsndfileError, OSError) as exc:
        raise ValueError("Could not read this audio. Try a WAV, FLAC, or MP3 file.") from exc
    if not np.isfinite(wave).all():
        raise ValueError("This audio contains invalid sample values. Please export it again.")
    if np.max(np.abs(wave)) < 1e-5:
        raise ValueError("This clip appears silent. Please upload a recording of singing.")
    if sr != SAMPLE_RATE:
        wave = librosa.resample(wave, orig_sr=sr, target_sr=SAMPLE_RATE)
    return np.ascontiguousarray(wave, dtype=np.float32), question