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app.py

@@ -13,22 +13,21 @@ def _prepare_predictor_dir() -> str:
         repo_id=MODEL_REPO_ID,
         filename=ZIP_FILENAME,
         repo_type="model",
-        local_dir=os.getenv("HF_HOME", str(pathlib.Path.home() / ".cache" / "huggingface")),
-        local_dir_use_symlinks=False,
+        # do NOT pass local_dir_use_symlinks (deprecated)
+        # let HF pick the cache dir automatically
     )
-    # Extract to a writable temp dir
     workdir = tempfile.mkdtemp(prefix="ag_predictor_")
     with zipfile.ZipFile(local_zip, "r") as zf:
         zf.extractall(workdir)
-    # Some archives place files under a single top-level folder; handle both cases
     entries = list(pathlib.Path(workdir).iterdir())
     if len(entries) == 1 and entries[0].is_dir():
         return str(entries[0])
     return workdir
 
 PREDICTOR_DIR = _prepare_predictor_dir()
-# allow cross-version loads; the model is old (<=0.3.1), AG handles migration
-PREDICTOR = ag.TabularPredictor.load(PREDICTOR_DIR, require_py_version_match=False)
+
+# Match the version the predictor was saved with (1.4.0) -> we'll enforce version match
+PREDICTOR = ag.TabularPredictor.load(PREDICTOR_DIR, require_py_version_match=False, require_version_match=True)
 
 FEATURE_COLS = ["phone_hours","computer_hours","device_count","sleep_quality","sleep_time","sleep_hours"]
 LABEL_MAP = {0: "No (does not use phone before bed)", 1: "Yes (uses phone before bed)"}
@@ -73,6 +72,5 @@ with gr.Blocks() as demo:
     for c in comps:
         c.change(fn=do_predict, inputs=comps, outputs=out)
 
-# Spaces looks for a top-level variable named 'demo'
 if __name__ == "__main__":
     demo.launch()