Publish CardioSafe Gradio demo

app.py

@@ -48,28 +48,6 @@ import gradio as gr
 import pandas as pd
 import torch
 
-
-def _ensure_torch_scatter() -> None:
-    """MolGpKa loads `torch_scatter` at module import. The matching wheel
-    URL is torch-version-specific (PyG hosts them at data.pyg.org), so we
-    install it at boot rather than in requirements.txt."""
-    try:
-        import torch_scatter  # noqa: F401
-        return
-    except ImportError:
-        pass
-    torch_ver = torch.__version__.split("+")[0]
-    wheel_index = f"https://data.pyg.org/whl/torch-{torch_ver}+cpu.html"
-    print(f"  installing torch_scatter for torch=={torch_ver} from {wheel_index}")
-    subprocess.run(
-        [sys.executable, "-m", "pip", "install", "--quiet",
-         "torch_scatter", "-f", wheel_index],
-        check=True,
-    )
-
-
-_ensure_torch_scatter()
-
 from inference.ensemble import load_ensemble, load_l1000_encoder, predict  # noqa: E402
 from inference.featurize import featurize_batch  # noqa: E402
 from model.chemberta_encoder import ChemBERTaEncoder  # noqa: E402

requirements.txt

@@ -1,6 +1,8 @@
 gradio>=5.0
-torch>=2.6
-torch-geometric>=2.6
+torch==2.6.0
+--find-links https://data.pyg.org/whl/torch-2.6.0+cpu.html
+torch-scatter
+torch-geometric>=2.5
 transformers>=4.51
 rdkit>=2025.9.6
 numpy>=2.0