Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import json
+import sys
+
+sys.path.insert(0, ".")
+
+# Models are loaded at startup, not per-request.
+# Cold start time on CPU Basic: 3–8 min. This cost is paid once per Space
+# wake-up, not on every extraction call.
+from feature_extractor_cpu import extract_mir_features, ENG_DIMS
+from clap_embedder import get_clap_embedding
+
+print("[startup] All models loaded. Space ready.")
+
+
+def extract(audio_file_path: str) -> str:
+    """
+    Receives a local file path from Gradio (gr.File type='filepath').
+    Returns JSON with keys: l3, clap, eng_raw, true_peak_dbfs.
+    Any exception propagates to gradio_client as a job error — no silent failures.
+    """
+    if audio_file_path is None:
+        raise ValueError("No audio file received.")
+
+    l3, eng_raw, true_peak = extract_mir_features(audio_file_path)
+    clap = get_clap_embedding(audio_file_path)
+
+    if len(eng_raw) != len(ENG_DIMS):
+        raise ValueError(
+            f"eng_raw length mismatch: got {len(eng_raw)}, expected {len(ENG_DIMS)}"
+        )
+
+    return json.dumps({
+        "l3":             l3.tolist(),
+        "clap":           clap.tolist(),
+        "eng_raw":        eng_raw.tolist(),
+        "true_peak_dbfs": float(true_peak),
+    })
+
+
+# demo.queue() is REQUIRED — not optional.
+# Without it, Gradio's async event loop kills any function that runs longer
+# than a few seconds. CPU demucs with shifts=5 runs for 33–136 min per track.
+# queue() enables proper long-running background job execution.
+demo = gr.Interface(
+    fn=extract,
+    inputs=gr.File(label="Audio file (.mp3 or .wav)", type="filepath"),
+    outputs=gr.Textbox(label="Feature JSON"),
+    title="Playlisting Feature Extractor (CPU Basic)",
+)
+demo.queue(max_size=50)
+
+if __name__ == "__main__":
+    demo.launch(show_error=True)