Update app.py

app.py

@@ -19,8 +19,7 @@ class_weights = torch.load("class_weights_period.pt")
 checkpoint_path = 'epoch=22-step=213621.ckpt'
 vae_model = VAE.load_from_checkpoint(checkpoint_path,image_channels=1,z_dim=12, lr =0.0001, use_classification_loss=True, num_classes=num_classes,
             loss_type="weighted", class_weights=class_weights, device = device)
-model.load_state_dict(torch.load('epoch=22-step=213621.ckpt'))
-model.eval()
+vae_model.eval()
 
 # Load your dataframe encoding
 df_encodings = pd.read_csv('df_vae_encoding_April16_all.csv')
@@ -37,10 +36,10 @@ def generate_image(period1, period2, interpolation_value):
     
     i = interpolation_value
     new_tablet = (1-i) * image1 + i * image2
-    new_tab_long = model.fc3(new_tablet).unsqueeze(0)
+    new_tab_long = vae_model.fc3(new_tablet).unsqueeze(0)
     
     with torch.no_grad():
-        generated_image = model.decoder(new_tab_long)
+        generated_image = vae_model.decoder(new_tab_long)
     generated_image = generated_image[0][0].detach().cpu().numpy()
     generated_image = (generated_image * 255).astype(np.uint8)
     pil_img = PILImage.fromarray(generated_image)