changed out examples for new morphs

app.py

@@ -49,20 +49,20 @@ model_path = hf_hub_download(repo_id="samfhy/morphmarket_model", filename="model
 checkpoint = torch.load(model_path, map_location=device)
 
 new_layers = nn.Sequential(
-nn.LazyLinear(2048),  
-nn.BatchNorm1d(2048),  
-nn.ReLU(),            
-nn.Dropout(0.5),      
-nn.LazyLinear(num_labels)
-)
+    nn.LazyLinear(2048),  
+    nn.BatchNorm1d(2048),  
+    nn.ReLU(),            
+    nn.Dropout(0.5),      
+    nn.LazyLinear(num_labels)
+    )
 
 IMAGE_SIZE = 480
 transform = v2.Compose([
-v2.ToImage(),
-v2.Resize((IMAGE_SIZE, IMAGE_SIZE)),
-v2.ToDtype(torch.float32),
-v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
+    v2.ToImage(),
+    v2.Resize((IMAGE_SIZE, IMAGE_SIZE)),
+    v2.ToDtype(torch.float32),
+    v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
 
 efficientnet = models.efficientnet_v2_l(weights='EfficientNet_V2_L_Weights.DEFAULT')
 efficientnet.classifier = new_layers
@@ -87,7 +87,7 @@ gr.Interface(fn=predict,
              inputs=[gr.Image(type="pil"),
                      gr.Slider(0, 1, value=0.5, label="Confidence", info="Show predictions that are above this confidence level")],
              outputs=gr.Label(),
-             examples=[["pastel_yb.png", 0.5], ["piebald.png", 0.5], ["leopard_fire.png", 0.5]],
+             examples=[["enchi_albino_clown.png", 0.5], ["mojave_ghi.png", 0.5], ["hypo_banana_pastel_enchi.png", 0.5]],
              title='Ball Python Morph Identifier',
              description="Upload or paste an image of your ball python to identify its morphs!"
              ).launch()