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| from __future__ import annotations | |
| import os | |
| 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 | |
| _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 _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() | |
| if _tok: | |
| _conn = Client(_BACKEND_SPACE, hf_token=_tok) | |
| else: | |
| _conn = _backend_client() | |
| try: | |
| _raw = _conn.predict( | |
| 'facebook/melodyflow-t24-30secs', | |
| text, | |
| 'midpoint', | |
| steps, | |
| target_flowstep, | |
| regularize, | |
| regularization_strength, | |
| duration, | |
| handle_file(melody), | |
| api_name="/predict", | |
| ) | |
| except Exception as _exc: # surface a token-aware hint, never the token | |
| 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) | |