Register Kiku AI redirect URI for OAuth

app.py

@@ -1,158 +1,161 @@
-"""The dedicated ZeroGPU transcription API for Score Studio."""
+"""The dedicated ZeroGPU transcription API for Score Studio."""
 import logging
 import os
 from pathlib import Path
-import shutil
-import subprocess
-import sys
-import tempfile
-import time
-
-import gradio as gr
-import spaces
+import shutil
+import subprocess
+import sys
+import tempfile
+import time
+
+import gradio as gr
+import spaces
 import torch
 from transformers import AutoModel
-
-MODEL_ID = "m-a-p/SheetSage2"
-REVISION = "eab522a8168e8b8b8c4856bf8609cd86198f01fe"
-ROOT = Path(tempfile.gettempdir()) / "audio-to-score"
-ROOT.mkdir(exist_ok=True)
-logging.basicConfig(level=logging.INFO)
-
-# ZeroGPU's CUDA emulation lets the model be placed during startup; the actual
-# 48 GB GPU is attached only while the decorated conversion function runs.
-MODEL = AutoModel.from_pretrained(
-    MODEL_ID, revision=REVISION, trust_remote_code=True,
-    torch_dtype=torch.bfloat16,
-).eval().to("cuda")
-
-# The upstream renderer is fully offline but needs its matching headless browser.
-subprocess.run([sys.executable, "-m", "playwright", "install", "--only-shell", "chromium"], check=True)
-
-
+
+MODEL_ID = "m-a-p/SheetSage2"
+REVISION = "eab522a8168e8b8b8c4856bf8609cd86198f01fe"
+ROOT = Path(tempfile.gettempdir()) / "audio-to-score"
+ROOT.mkdir(exist_ok=True)
+logging.basicConfig(level=logging.INFO)
+
+# ZeroGPU's CUDA emulation lets the model be placed during startup; the actual
+# 48 GB GPU is attached only while the decorated conversion function runs.
+MODEL = AutoModel.from_pretrained(
+    MODEL_ID, revision=REVISION, trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+).eval().to("cuda")
+
+# The upstream renderer is fully offline but needs its matching headless browser.
+subprocess.run([sys.executable, "-m", "playwright", "install", "--only-shell", "chromium"], check=True)
+
+
 def gpu_seconds(audio, start, duration, melody_only, piano, progress=None, **kwargs):
-    # Short excerpts should fit visitor quotas instead of reserving five minutes.
-    return max(45, min(300, int(float(duration) * 0.4) + 30))
-
-
-@spaces.GPU(duration=gpu_seconds)
+    # Short excerpts should fit visitor quotas instead of reserving five minutes.
+    return max(45, min(300, int(float(duration) * 0.4) + 30))
+
+
+@spaces.GPU(duration=gpu_seconds)
 def transcribe(audio, start, duration, melody_only, piano, progress=gr.Progress()):
-    if not audio:
-        raise gr.Error("Upload an audio file first.")
-    if start < 0 or not 5 <= duration <= 600:
-        raise gr.Error("Choose a start of 0 or later and a duration from 5 to 600 seconds.")
-    # Clear the previous run immediately so failed requests cannot show stale scores.
-    yield "Preparing your audio…", [], None, None, [], ""
-    # Keep the current result until Gradio has copied it to its managed cache.
-    # Remove abandoned job directories from earlier runs after one day.
-    cutoff = time.time() - 86400
-    for old in ROOT.glob("score-*"):
-        if old.is_dir() and not old.is_symlink() and old.stat().st_mtime < cutoff:
-            shutil.rmtree(old, ignore_errors=True)
-    job = Path(tempfile.mkdtemp(prefix="score-", dir=ROOT))
-    clip = job / "input.wav"
-    output = job / "results"
-    output.mkdir()
-    try:
-        progress(0.03, desc="Preparing audio excerpt")
-        completed = subprocess.run(
-            ["ffmpeg", "-nostdin", "-v", "error", "-ss", str(start),
-             "-i", str(audio), "-t", str(duration), "-ac", "1", "-ar", "24000",
-             "-y", str(clip)], capture_output=True, timeout=120,
-        )
-        if completed.returncode or not clip.exists() or clip.stat().st_size < 2048:
-            raise gr.Error("Could not read this excerpt. Check the file and start time.")
-        progress(0.06, desc="Loading SheetSage2 — first run takes longer")
-        model = MODEL
-        started = time.monotonic()
-
-        def update(event):
-            stage = event.get("stage", "working")
-            labels = {"audio": "Reading audio", "encoding": "Listening to the music",
-                      "decoding": "Writing notes and chords", "notation": "Preparing notation",
-                      "complete": "Rendering printable score"}
-            detail = labels.get(stage, "Processing music")
-            if "window" in event:
-                detail += f" · section {event['window']}/{event['windows']}"
-            if "tokens" in event:
-                detail += f" · {event['tokens']} symbols"
-            progress(None, desc=detail)
-
-        notice = ""
-        try:
-            with torch.inference_mode():
-                result = model.transcribe(str(clip), output_dir=str(output),
-                    melody_only=bool(melody_only), render_audio=bool(piano),
-                    render_score="pdf,png", dtype="bf16", progress=update)
-        except Exception as exc:
-            # Upstream preserves valid MIDI/ABC when notation or rendering fails.
-            result = getattr(exc, "result", None)
-            if result is None or not any(output.glob("*.mid")):
-                raise
-            logging.exception("Partial transcription")
-            notice = " Some score rendering failed; available transcription files are included."
-        progress(0.97, desc="Preparing downloads")
-        files = sorted(p for p in output.iterdir() if p.is_file() and p.suffix != ".partial")
-        if not files:
-            raise RuntimeError("The model returned no output files.")
-        archive = shutil.make_archive(str(job / "score-downloads"), "zip", output)
-        pngs = [str(p) for p in sorted(output.glob("score_*.png"))]
-        pdf = output / "score.pdf"
-        wavs = sorted(output.glob("*.wav"))
-        abc_path = output / "score.abc"
-        abc = abc_path.read_text(encoding="utf-8") if abc_path.exists() else ""
-        elapsed = round(time.monotonic() - started)
-        warnings = result.get("warnings", [])
-        status = f"Finished in {elapsed // 60}m {elapsed % 60}s. Download your files below.{notice}"
-        if warnings:
-            status += " The model reported warnings; review result.json in the download."
-        yield status, pngs, str(pdf) if pdf.exists() else None, str(wavs[0]) if wavs else None, [archive] + [str(p) for p in files], abc
-    except gr.Error:
-        raise
-    except Exception:
-        logging.exception("Transcription failed")
+    if not audio:
+        raise gr.Error("Upload an audio file first.")
+    if start < 0 or not 5 <= duration <= 600:
+        raise gr.Error("Choose a start of 0 or later and a duration from 5 to 600 seconds.")
+    # Clear the previous run immediately so failed requests cannot show stale scores.
+    yield "Preparing your audio…", [], None, None, [], ""
+    # Keep the current result until Gradio has copied it to its managed cache.
+    # Remove abandoned job directories from earlier runs after one day.
+    cutoff = time.time() - 86400
+    for old in ROOT.glob("score-*"):
+        if old.is_dir() and not old.is_symlink() and old.stat().st_mtime < cutoff:
+            shutil.rmtree(old, ignore_errors=True)
+    job = Path(tempfile.mkdtemp(prefix="score-", dir=ROOT))
+    clip = job / "input.wav"
+    output = job / "results"
+    output.mkdir()
+    try:
+        progress(0.03, desc="Preparing audio excerpt")
+        completed = subprocess.run(
+            ["ffmpeg", "-nostdin", "-v", "error", "-ss", str(start),
+             "-i", str(audio), "-t", str(duration), "-ac", "1", "-ar", "24000",
+             "-y", str(clip)], capture_output=True, timeout=120,
+        )
+        if completed.returncode or not clip.exists() or clip.stat().st_size < 2048:
+            raise gr.Error("Could not read this excerpt. Check the file and start time.")
+        progress(0.06, desc="Loading SheetSage2 — first run takes longer")
+        model = MODEL
+        started = time.monotonic()
+
+        def update(event):
+            stage = event.get("stage", "working")
+            labels = {"audio": "Reading audio", "encoding": "Listening to the music",
+                      "decoding": "Writing notes and chords", "notation": "Preparing notation",
+                      "complete": "Rendering printable score"}
+            detail = labels.get(stage, "Processing music")
+            if "window" in event:
+                detail += f" · section {event['window']}/{event['windows']}"
+            if "tokens" in event:
+                detail += f" · {event['tokens']} symbols"
+            progress(None, desc=detail)
+
+        notice = ""
+        try:
+            with torch.inference_mode():
+                result = model.transcribe(str(clip), output_dir=str(output),
+                    melody_only=bool(melody_only), render_audio=bool(piano),
+                    render_score="pdf,png", dtype="bf16", progress=update)
+        except Exception as exc:
+            # Upstream preserves valid MIDI/ABC when notation or rendering fails.
+            result = getattr(exc, "result", None)
+            if result is None or not any(output.glob("*.mid")):
+                raise
+            logging.exception("Partial transcription")
+            notice = " Some score rendering failed; available transcription files are included."
+        progress(0.97, desc="Preparing downloads")
+        files = sorted(p for p in output.iterdir() if p.is_file() and p.suffix != ".partial")
+        if not files:
+            raise RuntimeError("The model returned no output files.")
+        archive = shutil.make_archive(str(job / "score-downloads"), "zip", output)
+        pngs = [str(p) for p in sorted(output.glob("score_*.png"))]
+        pdf = output / "score.pdf"
+        wavs = sorted(output.glob("*.wav"))
+        abc_path = output / "score.abc"
+        abc = abc_path.read_text(encoding="utf-8") if abc_path.exists() else ""
+        elapsed = round(time.monotonic() - started)
+        warnings = result.get("warnings", [])
+        status = f"Finished in {elapsed // 60}m {elapsed % 60}s. Download your files below.{notice}"
+        if warnings:
+            status += " The model reported warnings; review result.json in the download."
+        yield status, pngs, str(pdf) if pdf.exists() else None, str(wavs[0]) if wavs else None, [archive] + [str(p) for p in files], abc
+    except gr.Error:
+        raise
+    except Exception:
+        logging.exception("Transcription failed")
         raise gr.Error("Conversion failed. Try a shorter excerpt. Details are available in the Space logs.")
 
 
 with gr.Blocks(title="Score Studio API", delete_cache=(3600, 86400)) as demo:
-    gr.Markdown("# Score Studio API\nThe ZeroGPU transcription engine for Score Studio. "
-                "Use the API below to turn audio into editable ABC, MIDI, PDF and piano audio.")
-    audio = gr.Audio(type="filepath", label="Audio", sources=["upload"])
-    start = gr.Number(value=0, label="Start (seconds)", minimum=0)
-    duration = gr.Number(value=30, label="Duration (seconds)", minimum=5, maximum=600)
-    melody = gr.Checkbox(value=False, label="Melody only")
-    piano = gr.Checkbox(value=True, label="Render piano preview")
-    run = gr.Button("Transcribe")
-    status = gr.Textbox(label="Status")
-    gallery = gr.Gallery(label="Score pages")
-    pdf = gr.File(label="PDF")
-    preview = gr.Audio(label="Piano preview")
-    downloads = gr.File(label="Downloads", file_count="multiple")
-    abc = gr.Code(label="ABC notation", language=None)
+    gr.Markdown("# Score Studio API\nThe ZeroGPU transcription engine for Score Studio. "
+                "Use the API below to turn audio into editable ABC, MIDI, PDF and piano audio.")
+    audio = gr.Audio(type="filepath", label="Audio", sources=["upload"])
+    start = gr.Number(value=0, label="Start (seconds)", minimum=0)
+    duration = gr.Number(value=30, label="Duration (seconds)", minimum=5, maximum=600)
+    melody = gr.Checkbox(value=False, label="Melody only")
+    piano = gr.Checkbox(value=True, label="Render piano preview")
+    run = gr.Button("Transcribe")
+    status = gr.Textbox(label="Status")
+    gallery = gr.Gallery(label="Score pages")
+    pdf = gr.File(label="PDF")
+    preview = gr.Audio(label="Piano preview")
+    downloads = gr.File(label="Downloads", file_count="multiple")
+    abc = gr.Code(label="ABC notation", language=None)
     run.click(transcribe, [audio, start, duration, melody, piano],
-              [status, gallery, pdf, preview, downloads, abc],
+              [status, gallery, pdf, preview, downloads, abc],
               api_name="transcribe", concurrency_limit=1)
-    gr.Markdown("Powered by [SheetSage2](https://huggingface.co/m-a-p/SheetSage2), "
-                "CC BY-NC 4.0. Temporary files expire after 24 hours.")
-
+    gr.Markdown("Powered by [SheetSage2](https://huggingface.co/m-a-p/SheetSage2), "
+                "CC BY-NC 4.0. Temporary files expire after 24 hours.")
+
 def oauth_metadata():
-    return {
-        "client_id": "https://reubencf-score-studio-api.hf.space/.well-known/oauth-cimd",
-        "client_name": "Score Studio",
-        "client_uri": "https://huggingface.co/spaces/Reubencf/Score-Studio-API",
-        "redirect_uris": [
-            "http://localhost/auth/callback", "http://127.0.0.1/auth/callback",
-            "https://reubencf-score-studio.hf.space/auth/callback",
-        ],
-        "token_endpoint_auth_method": "none",
-        "grant_types": ["authorization_code"],
-        "response_types": ["code"],
-        "scope": "openid profile",
-    }
-
+    return {
+        "client_id": "https://reubencf-score-studio-api.hf.space/.well-known/oauth-cimd",
+        "client_name": "Kiku AI",
+        "client_uri": "https://huggingface.co/spaces/Reubencf/Score-Studio-API",
+        # Every origin that signs users in must be listed here, so the rebranded
+        # app and the original Score Studio deployment are both kept valid.
+        "redirect_uris": [
+            "http://localhost/auth/callback", "http://127.0.0.1/auth/callback",
+            "https://reubencf-kiku-ai.hf.space/auth/callback",
+            "https://reubencf-score-studio.hf.space/auth/callback",
+        ],
+        "token_endpoint_auth_method": "none",
+        "grant_types": ["authorization_code"],
+        "response_types": ["code"],
+        "scope": "openid profile",
+    }
+
 def health():
-    return {"status": "ready", "model": MODEL_ID, "revision": REVISION}
-
+    return {"status": "ready", "model": MODEL_ID, "revision": REVISION}
+
 if __name__ == "__main__":
     # The native Gradio launch hook registers the GPU worker with Spaces.
     app, _, _ = demo.queue(max_size=5).launch(