app.py

from __future__ import annotations

import os
import time
import urllib.request

import gradio as gr

from pyharp import *
from gradio_client import Client, handle_file


_BACKEND_SPACE = "facebook/MelodyFlow"
_BACKEND_API_NAME = "/predict"
_BACKEND_TOKEN_ENV = "HF_TOKEN"
_ACCEPT_USER_TOKEN = True
# How many times to wake+retry a sleeping backend, and how long to wait for
# it to boot (a free Space cold start can take a few minutes).
_CALL_RETRIES = int(os.environ.get("BACKEND_CALL_RETRIES", "4"))
_WAKE_TIMEOUT = float(os.environ.get("BACKEND_WAKE_TIMEOUT", "420"))
_client = None


def _backend_client():
    # Lazily create and cache one warm connection using this Space's own
    # token (from the HF_TOKEN secret) or anonymous if none is set. User
    # tokens are NOT cached here -- they get a fresh per-call connection.
    global _client
    if _client is None:
        _token = os.environ.get(_BACKEND_TOKEN_ENV) or None
        _client = Client(_BACKEND_SPACE, hf_token=_token)
    return _client


def _reset_client():
    # Drop the cached connection so the next attempt reconnects to a Space
    # that has since finished waking.
    global _client
    _client = None


def _make_conn(tok):
    tok = (tok or '').strip()
    if tok:
        return Client(_BACKEND_SPACE, hf_token=tok)
    return _backend_client()


def _space_url(space):
    slug = space.strip().lower().replace('/', '-').replace('_', '-')
    return f'https://{slug}.hf.space/'


def _is_cold_start(message):
    # Errors that mean 'the backend was asleep/booting', worth waking+retrying
    # (vs. a real application error, which we surface immediately).
    _low = (message or '').lower()
    return any(s in _low for s in (
        'read operation timed out', 'timed out', 'timeout', 'starting',
        'building', 'not ready', 'no application', 'connection', '503', '502',
    ))


def _wake_backend():
    # A sleeping Space boots when its URL is hit; poll until it answers (or
    # the budget expires) so the retried call lands on a running backend.
    _url = _space_url(_BACKEND_SPACE)
    _deadline = time.time() + _WAKE_TIMEOUT
    _delay = 5.0
    while time.time() < _deadline:
        try:
            _req = urllib.request.Request(_url, headers={'User-Agent': 'harp-frontend'})
            with urllib.request.urlopen(_req, timeout=30) as _resp:
                if getattr(_resp, 'status', 200) < 500:
                    return True
        except Exception:
            pass
        time.sleep(_delay)
        _delay = min(_delay * 1.5, 30.0)
    return False


def _quota_hint(message):
    # Turn a backend ZeroGPU quota error into an actionable message.
    # NOTE: 'message' is the backend's error text; it never contains our token.
    _low = (message or "").lower()
    if "quota" in _low or "zerogpu" in _low:
        if _ACCEPT_USER_TOKEN:
            return (
                "The backend's ZeroGPU quota is exhausted for the identity making "
                "this call. Paste your own Hugging Face token in the token field "
                "(read scope) so usage is attributed to your account."
            )
        return (
            "The backend's ZeroGPU quota is exhausted. This Space's calls are "
            "anonymous unless an HF_TOKEN secret is set (Settings -> Variables "
            "and secrets); use a token from a PRO account or a ZeroGPU-enabled org."
        )
    return message or "Backend call failed."


model_card = ModelCard(
    name="Melodyflow",
    description="TODO: describe this model.",
    author="facebook",
    tags=[],
)


def process_fn(text, steps, target_flowstep, regularize, regularization_strength, duration, melody, _hf_user_token=''):
    _tok = (_hf_user_token or '').strip()
    # Call the backend, waking it and retrying if it was asleep (a cold
    # start otherwise fails the first hit with 'read operation timed out').
    _raw = None
    for _attempt in range(_CALL_RETRIES + 1):
        try:
            _conn = _make_conn(_tok)
            _raw = _conn.predict(
                'facebook/melodyflow-t24-30secs',
                text,
                'midpoint',
                steps,
                target_flowstep,
                regularize,
                regularization_strength,
                duration,
                (handle_file(melody) if melody else None),
                api_name="/predict",
            )
            break
        except Exception as _exc:  # never surfaces the token
            if _attempt < _CALL_RETRIES and _is_cold_start(str(_exc)):
                _reset_client()
                _wake_backend()
                continue
            raise gr.Error(_quota_hint(str(_exc)))
    _values = list(_raw) if isinstance(_raw, (list, tuple)) else [_raw]
    _detail = " | ".join(str(_v) for _v in _values if isinstance(_v, str) and _v.strip())
    _out_generated_audio_variation_1 = _values[0] if len(_values) > 0 else None
    if not _out_generated_audio_variation_1:
        raise gr.Error(_detail or "The backend Space returned no 'generated_audio_variation_1' output. Check the backend Space's logs; if it uses ZeroGPU it may need a moment to warm up.")
    _out_generated_audio_variation_2 = _values[1] if len(_values) > 1 else None
    if not _out_generated_audio_variation_2:
        raise gr.Error(_detail or "The backend Space returned no 'generated_audio_variation_2' output. Check the backend Space's logs; if it uses ZeroGPU it may need a moment to warm up.")
    _out_generated_audio_variation_3 = _values[2] if len(_values) > 2 else None
    if not _out_generated_audio_variation_3:
        raise gr.Error(_detail or "The backend Space returned no 'generated_audio_variation_3' output. Check the backend Space's logs; if it uses ZeroGPU it may need a moment to warm up.")
    return _out_generated_audio_variation_1, _out_generated_audio_variation_2, _out_generated_audio_variation_3


with gr.Blocks() as demo:
    input_components = [
        gr.Textbox(label="Input Text"),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=128.0, label="Inference steps"),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.0, label="Target Flow step"),
        gr.Checkbox(value=False, label="Regularize"),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.2, label="Regularization Strength"),
        gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=30.0, label="Duration"),
        gr.Audio(type="filepath", label="File or Microphone"),
        gr.Textbox(label="Hugging Face token (optional)", type="password", info="Optional. Paste a Hugging Face token (Settings -> Access Tokens, read scope) so ZeroGPU usage on the backend is charged to YOUR account. Used only for this call; not stored. Leave blank to use this Space's own token."),
    ]
    output_components = [
        gr.Audio(type="filepath", label="Generated Audio - variation 1"),
        gr.Audio(type="filepath", label="Generated Audio - variation 2"),
        gr.Audio(type="filepath", label="Generated Audio - variation 3"),
    ]
    build_endpoint(
        model_card=model_card,
        input_components=input_components,
        output_components=output_components,
        process_fn=process_fn,
    )

demo.queue().launch(share=True, show_error=False, pwa=True)