Update app.py

app.py

@@ -9,6 +9,7 @@ warnings.filterwarnings("ignore")
 
 import gc
 import numpy as np
+import numpy.core.multiarray  # explicit import for safe_globals
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -200,14 +201,32 @@ model = HybridAIDetector(
     num_features=NUM_FEATURES, feat_embed_dim=128, num_classes=2,
 )
 
-# Load trained hybrid head weights
-ckpt = torch.load(HYBRID_HEAD_PATH, map_location="cpu")
+# ── FIX: PyTorch 2.6 changed weights_only default to True.
+#    Our checkpoint stores numpy arrays (scaler_mean / scaler_std), so we must
+#    either allowlist the numpy globals or load with weights_only=False.
+#    We use the safe-globals approach (preferred over weights_only=False).
+# ─────────────────────────────────────────────────────────────────────────────
+_numpy_safe_globals = [
+    numpy.core.multiarray._reconstruct,
+    np.ndarray,
+    np.dtype,
+]
+# numpy 2.x moved internals to numpy._core; allowlist both spellings defensively
+try:
+    import numpy._core.multiarray as _nca
+    _numpy_safe_globals.append(_nca._reconstruct)
+except Exception:
+    pass
+
+with torch.serialization.safe_globals(_numpy_safe_globals):
+    ckpt = torch.load(HYBRID_HEAD_PATH, map_location="cpu", weights_only=True)
+
 model.feat_mlp.load_state_dict(ckpt["feat_mlp"])
 model.head.load_state_dict(ckpt["head"])
 
 # Load scaler params
-SCALER_MEAN = ckpt["scaler_mean"].astype(np.float32)
-SCALER_STD  = ckpt["scaler_std"].astype(np.float32)
+SCALER_MEAN = np.array(ckpt["scaler_mean"], dtype=np.float32)
+SCALER_STD  = np.array(ckpt["scaler_std"],  dtype=np.float32)
 
 model.to(DEVICE)
 model.eval()
@@ -264,9 +283,9 @@ def get_tta_tensors(img: Image.Image) -> list:
     flipped = transforms.functional.hflip(img)
     hf_img  = high_freq_residual(img)
     return [
-        ("original",   base_transforms(img).unsqueeze(0).to(DEVICE)),
-        ("hflip",      base_transforms(flipped).unsqueeze(0).to(DEVICE)),
-        ("hf_residual",base_transforms(hf_img).unsqueeze(0).to(DEVICE)),
+        ("original",    base_transforms(img).unsqueeze(0).to(DEVICE)),
+        ("hflip",       base_transforms(flipped).unsqueeze(0).to(DEVICE)),
+        ("hf_residual", base_transforms(hf_img).unsqueeze(0).to(DEVICE)),
     ]
 
 
@@ -475,9 +494,9 @@ def predict(image: Image.Image) -> dict:
     final_probs = sum(w * p for w, p in zip(weights, all_probs))
 
     return {
-        "ai_prob":    float(final_probs[0]),
-        "real_prob":  float(final_probs[1]),
-        "raw_scores": raw_scores,
+        "ai_prob":     float(final_probs[0]),
+        "real_prob":   float(final_probs[1]),
+        "raw_scores":  raw_scores,
         "tta_weights": weights.tolist(),
     }
 
@@ -572,7 +591,6 @@ custom_css = """
     --shadow:      0 4px 6px -1px rgba(0,0,0,.10), 0 2px 4px -1px rgba(0,0,0,.06);
 }
 .gradio-container { max-width: 1400px !important; margin: 0 auto !important; padding: 1.5rem !important; }
-
 #header-banner {
     background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
     border-radius: 14px; padding: 2rem 2.5rem; text-align: center;
@@ -580,9 +598,7 @@ custom_css = """
 }
 #header-banner h1 { font-size: 2.2rem; font-weight: 700; margin: 0 0 .5rem; color: white; }
 #header-banner p  { font-size: 1rem; opacity: .92; margin: 0 auto; max-width: 760px; color: white; }
-
 #main-row { gap: 1.25rem !important; align-items: stretch !important; }
-
 #input-panel, #output-panel {
     background: var(--c-bg) !important; border: 1px solid var(--c-border) !important;
     border-radius: 14px !important; padding: 1.5rem !important;
@@ -598,7 +614,6 @@ custom_css = """
     transition: border-color .25s ease !important;
 }
 #image-upload:hover { border-color: var(--c-primary) !important; }
-
 #btn-analyze {
     background: linear-gradient(135deg, var(--c-primary) 0%, var(--c-primary-d) 100%) !important;
     color: white !important; border: none !important; border-radius: 8px !important;
@@ -614,7 +629,6 @@ custom_css = """
     transition: border-color .2s ease, color .2s ease !important;
 }
 #btn-clear:hover { border-color: var(--c-danger) !important; color: var(--c-danger) !important; }
-
 #output-md .markdown { overflow-y: auto !important; max-height: 680px !important; padding-right: .5rem !important; }
 #output-md h2 { font-size: 1.2rem !important; font-weight: 700 !important; color: #1d4ed8 !important;
     margin: 1.1rem 0 .5rem !important; padding-bottom: .3rem !important; border-bottom: 1px solid var(--c-border) !important; }
@@ -630,7 +644,6 @@ custom_css = """
 #output-md hr { border: none !important; border-top: 1.5px solid var(--c-border) !important; margin: .9rem 0 !important; }
 #output-md blockquote { border-left: 3px solid var(--c-primary) !important; margin: .6rem 0 !important;
     padding: .3rem .7rem !important; background: #eff6ff !important; border-radius: 0 6px 6px 0 !important; }
-
 #footer { text-align: center; margin-top: 1.5rem; padding: 1.25rem; background: var(--c-bg);
     border-radius: 12px; border: 1px solid var(--c-border); color: var(--c-text-muted); font-size: .875rem; }
 """