Deploy HARP wrapper via model agent

.harp/manifest.json

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+{
+  "backend_space": "facebook/MelodyFlow",
+  "deploy_mode": "remote-backend",
+  "entry": "app.py",
+  "framework": "gradio_client",
+  "generated": true,
+  "io": {
+    "inputs": [
+      "textbox",
+      "slider",
+      "slider",
+      "checkbox",
+      "slider",
+      "slider",
+      "audio"
+    ],
+    "outputs": [
+      "audio",
+      "audio",
+      "audio"
+    ]
+  },
+  "repo_id": "facebook/MelodyFlow",
+  "source": "recipe",
+  "space_layout": "huggingface-gradio",
+  "task": "custom"
+}

README.md

@@ -1,13 +1,19 @@
 ---
-title: Melodyflow
-emoji: ⚡
-colorFrom: purple
-colorTo: red
+title: "Melodyflow"
+colorFrom: indigo
+colorTo: gray
 sdk: gradio
-sdk_version: 6.20.0
-python_version: '3.13'
+sdk_version: 5.28.0
 app_file: app.py
 pinned: false
+license: "other"
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Melodyflow
+
+TODO: describe this model.
+
+- Inputs: textbox, slider, slider, checkbox, slider, slider, audio
+- Outputs: audio, audio, audio
+
+Generated by the HARP model agent from a recipe.

app.py

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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)

requirements.txt

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+git+https://github.com/TEAMuP-dev/pyharp.git@v0.3.0
+gradio>=4.0
+gradio_client