app.py

from magenta_rt import system, audio as au
import numpy as np
from fastapi import FastAPI, UploadFile, File, Form
import tempfile, io, base64, math, threading
from fastapi.middleware.cors import CORSMiddleware

# loudness utils
try:
    import pyloudnorm as pyln
    _HAS_LOUDNORM = True
except Exception:
    _HAS_LOUDNORM = False

def _measure_lufs(wav: au.Waveform) -> float:
    # pyloudnorm expects float32/float64, shape (n,) or (n, ch)
    meter = pyln.Meter(wav.sample_rate)  # defaults to BS.1770-4
    return float(meter.integrated_loudness(wav.samples))

def _rms(x: np.ndarray) -> float:
    if x.size == 0: return 0.0
    return float(np.sqrt(np.mean(x**2)))

def match_loudness_to_reference(
    ref: au.Waveform,
    target: au.Waveform,
    method: str = "auto",   # "auto"|"lufs"|"rms"|"none"
    headroom_db: float = 1.0
) -> tuple[au.Waveform, dict]:
    """
    Scales `target` to match `ref` loudness. Returns (adjusted_wave, stats).
    """
    stats = {"method": method, "applied_gain_db": 0.0}

    if method == "none":
        return target, stats

    if method == "auto":
        method = "lufs" if _HAS_LOUDNORM else "rms"

    if method == "lufs" and _HAS_LOUDNORM:
        L_ref = _measure_lufs(ref)
        L_tgt = _measure_lufs(target)
        delta_db = L_ref - L_tgt
        gain = 10.0 ** (delta_db / 20.0)
        y = target.samples.astype(np.float32) * gain
        stats.update({"ref_lufs": L_ref, "tgt_lufs_before": L_tgt, "applied_gain_db": delta_db})
    else:
        # RMS fallback
        ra = _rms(ref.samples)
        rb = _rms(target.samples)
        if rb <= 1e-12:
            return target, stats
        gain = ra / rb
        y = target.samples.astype(np.float32) * gain
        stats.update({"ref_rms": ra, "tgt_rms_before": rb, "applied_gain_db": 20*np.log10(max(gain,1e-12))})

    # simple peak “limiter” to keep headroom
    limit = 10 ** (-headroom_db / 20.0)   # e.g., -1 dBFS
    peak = float(np.max(np.abs(y))) if y.size else 0.0
    if peak > limit:
        y *= (limit / peak)
        stats["post_peak_limited"] = True
    else:
        stats["post_peak_limited"] = False

    target.samples = y.astype(np.float32)
    return target, stats

# ----------------------------
# Crossfade stitch (your good path)
# ----------------------------
def stitch_generated(chunks, sr, xfade_s):
    if not chunks:
        raise ValueError("no chunks")
    xfade_n = int(round(xfade_s * sr))
    if xfade_n <= 0:
        return au.Waveform(np.concatenate([c.samples for c in chunks], axis=0), sr)

    t = np.linspace(0, np.pi/2, xfade_n, endpoint=False, dtype=np.float32)
    eq_in, eq_out = np.sin(t)[:, None], np.cos(t)[:, None]

    first = chunks[0].samples
    if first.shape[0] < xfade_n:
        raise ValueError("chunk shorter than crossfade prefix")
    out = first[xfade_n:].copy()  # drop model pre-roll

    for i in range(1, len(chunks)):
        cur = chunks[i].samples
        if cur.shape[0] < xfade_n:
            continue
        head, tail = cur[:xfade_n], cur[xfade_n:]
        mixed = out[-xfade_n:] * eq_out + head * eq_in
        out = np.concatenate([out[:-xfade_n], mixed, tail], axis=0)

    return au.Waveform(out, sr)

# ----------------------------
# Bar-aligned token context
# ----------------------------
def make_bar_aligned_context(tokens, bpm, fps=25, ctx_frames=250, beats_per_bar=4):
    frames_per_bar_f = (beats_per_bar * 60.0 / bpm) * fps
    frames_per_bar = int(round(frames_per_bar_f))
    if abs(frames_per_bar - frames_per_bar_f) > 1e-3:
        reps = int(np.ceil(ctx_frames / len(tokens)))
        return np.tile(tokens, (reps, 1))[-ctx_frames:]
    reps = int(np.ceil(ctx_frames / len(tokens)))
    tiled = np.tile(tokens, (reps, 1))
    end = (len(tiled) // frames_per_bar) * frames_per_bar
    if end < ctx_frames:
        return tiled[-ctx_frames:]
    start = end - ctx_frames
    return tiled[start:end]

def hard_trim_seconds(wav: au.Waveform, seconds: float) -> au.Waveform:
    n = int(round(seconds * wav.sample_rate))
    return au.Waveform(wav.samples[:n], wav.sample_rate)

def apply_micro_fades(wav: au.Waveform, ms: int = 5) -> None:
    n = int(wav.sample_rate * ms / 1000.0)
    if n > 0 and wav.samples.shape[0] > 2*n:
        env = np.linspace(0.0, 1.0, n, dtype=np.float32)[:, None]
        wav.samples[:n]  *= env
        wav.samples[-n:] *= env[::-1]

# ----------------------------
# Main generation (single combined style vector)
# ----------------------------
def generate_loop_continuation_with_mrt(
    mrt,
    input_wav_path: str,
    bpm: float,
    extra_styles=None,
    style_weights=None,
    bars: int = 8,
    beats_per_bar: int = 4,
    loop_weight: float = 1.0,           # NEW
    loudness_mode: str = "auto",        # "auto"|"lufs"|"rms"|"none"
    loudness_headroom_db: float = 1.0,  # for the peak guard
):
    # Load loop & encode
    loop = au.Waveform.from_file(input_wav_path).resample(mrt.sample_rate).as_stereo()
    tokens_full = mrt.codec.encode(loop).astype(np.int32)
    tokens = tokens_full[:, :mrt.config.decoder_codec_rvq_depth]

    # Context
    context_tokens = make_bar_aligned_context(
        tokens,
        bpm=bpm,
        fps=int(mrt.codec.frame_rate),
        ctx_frames=mrt.config.context_length_frames,
        beats_per_bar=beats_per_bar,
    )
    state = mrt.init_state()
    state.context_tokens = context_tokens

    # ---------- STYLE: weighted avg into ONE vector ----------
    # Base embed from loop with adjustable loop_weight
    embeds = []
    weights = []

    # loop embedding
    loop_embed = mrt.embed_style(loop)
    embeds.append(loop_embed)
    weights.append(float(loop_weight))  # <--- use requested loop weight

    # extra styles
    if extra_styles:
        for i, s in enumerate(extra_styles):
            if s.strip():
                embeds.append(mrt.embed_style(s.strip()))
                w = style_weights[i] if (style_weights and i < len(style_weights)) else 1.0
                weights.append(float(w))

    # Prevent all-zero weights; normalize
    wsum = float(sum(weights))
    if wsum <= 0.0:
        # fallback: rely on loop to avoid NaNs
        weights = [1.0] + [0.0] * (len(weights) - 1)
        wsum = 1.0

    weights = [w / wsum for w in weights]

    # weighted sum -> single style vector (match dtype)
    combined_style = np.sum([w * e for w, e in zip(weights, embeds)], axis=0).astype(loop_embed.dtype)

    # Chunks to cover exact bars
    seconds_per_bar = beats_per_bar * (60.0 / bpm)
    total_secs = bars * seconds_per_bar
    chunk_secs = mrt.config.chunk_length_frames * mrt.config.frame_length_samples / mrt.sample_rate  # ~2.0
    steps = int(math.ceil(total_secs / chunk_secs)) + 1  # pad then trim

    # Generate
    chunks = []
    for _ in range(steps):
        wav, state = mrt.generate_chunk(state=state, style=combined_style)  # ONE style vector
        chunks.append(wav)

    # Stitch -> trim -> polish
    out = stitch_generated(chunks, mrt.sample_rate, mrt.config.crossfade_length).as_stereo()
    out = hard_trim_seconds(out, total_secs).peak_normalize(0.95)
    apply_micro_fades(out, 5)
    # Loudness match to the *input loop* so the return level feels consistent
    out, loud_stats = match_loudness_to_reference(
        ref=loop, target=out,
        method=loudness_mode,
        headroom_db=loudness_headroom_db,
    )
    return out, loud_stats

# ----------------------------
# FastAPI app with lazy, thread-safe model init
# ----------------------------
app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],   # or lock to your domain(s)
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

_MRT = None
_MRT_LOCK = threading.Lock()

def get_mrt():
    global _MRT
    if _MRT is None:
        with _MRT_LOCK:
            if _MRT is None:
                _MRT = system.MagentaRT(tag="base", guidance_weight=1.0, device="gpu", lazy=False)
    return _MRT

@app.post("/generate")
def generate(
    loop_audio: UploadFile = File(...),
    bpm: float = Form(...),
    bars: int = Form(8),
    beats_per_bar: int = Form(4),
    styles: str = Form("acid house"),
    style_weights: str = Form(""),
    loop_weight: float = Form(1.0),               # NEW
    loudness_mode: str = Form("auto"),            # NEW
    loudness_headroom_db: float = Form(1.0),      # NEW
):
    # Read file
    data = loop_audio.file.read()
    if not data:
        return {"error": "Empty file"}
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
        tmp.write(data)
        tmp_path = tmp.name

    # Parse styles + weights
    extra_styles = [s for s in (styles.split(",") if styles else []) if s.strip()]
    weights = [float(x) for x in style_weights.split(",")] if style_weights else None

    mrt = get_mrt()  # warm once, in this worker thread
    mrt = get_mrt()
    wav, loud_stats = generate_loop_continuation_with_mrt(
        mrt,
        input_wav_path=tmp_path,
        bpm=bpm,
        extra_styles=extra_styles,
        style_weights=weights,
        bars=bars,
        beats_per_bar=beats_per_bar,
        loop_weight=loop_weight,
        loudness_mode=loudness_mode,
        loudness_headroom_db=loudness_headroom_db,
    )

    # Return base64 WAV + minimal metadata
    buf = io.BytesIO()
    # add format="WAV" when writing to a file-like object
    wav.write(buf, subtype="FLOAT", format="WAV")
    buf.seek(0)
    audio_b64 = base64.b64encode(buf.read()).decode("utf-8")

    return {
        "audio_base64": audio_b64,
        "metadata": {
            "bpm": int(round(bpm)),
            "bars": int(bars),
            "beats_per_bar": int(beats_per_bar),
            "styles": extra_styles,
            "style_weights": weights,
            "loop_weight": loop_weight,
            "loudness": loud_stats,                       # NEW
            "sample_rate": mrt.sample_rate,
            "channels": mrt.num_channels,
            "crossfade_seconds": mrt.config.crossfade_length,
        },
    }

@app.get("/health")
def health():
    return {"ok": True}