Update app.py

app.py

@@ -45,16 +45,16 @@ def load_models():
 
     # Audio models
     audio_models = {}
-for p, repo, f in [
-    ('A', 'ericjedha/pilier_a', 'best_pilier_a_e29_f1_0.9005.pth'), 
-    ('B', 'ericjedha/pilier_b', 'best_pilier_b_f1_0.9103.pth')
-]:
-    path = hf_hub_download(repo_id=repo, filename=f)
-    m = timm.create_model("vit_small_patch16_224", num_classes=len(CATEGORIES), in_chans=3)
-    m.load_state_dict(torch.load(path, map_location=DEVICE)['model_state_dict'])
-    audio_models[p] = m.to(DEVICE).eval()
+    for p, repo, f in [
+        ('A', 'ericjedha/pilier_a', 'best_pilier_a_e29_f1_0.9005.pth'), 
+        ('B', 'ericjedha/pilier_b', 'best_pilier_b_f1_0.9103.pth')
+    ]:
+        path = hf_hub_download(repo_id=repo, filename=f)
+        m = timm.create_model("vit_small_patch16_224", num_classes=len(CATEGORIES), in_chans=3)
+        m.load_state_dict(torch.load(path, map_location=DEVICE)['model_state_dict'])
+        audio_models[p] = m.to(DEVICE).eval()
         
-    path_c = hf_hub_download(repo_id="ericjedha/pilier_c", filename="best_pilier_c_ast_v95_2_f1_0_9109.pth")
+    path_c = hf_hub_download(repo_id="ericjedha/pilier_c", filename="best_pilier_c_ast_v95_2_f1_0.9109.pth")
     model_c = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593", num_labels=len(CATEGORIES), ignore_mismatched_sizes=True)
     sd = torch.load(path_c, map_location=DEVICE)['model_state_dict']
     model_c.load_state_dict({k.replace('ast.', ''): v for k, v in sd.items()}, strict=False)