Update app.py

app.py

@@ -24,9 +24,9 @@ def resize_image(image, input_shape=(224, 224, 3)):
 
     return image_resized
 
-def load_model():
+def load_model_file(filename):
     model_dir = snapshot_download(REPO_ID)
-    # saved_model_dir = os.path.join(download_dir, "saved_model")
+    saved_model_dir = os.path.join(download_dir, filename)
     unet_model = load_model(model_dir)
     return unet_model
 
@@ -41,9 +41,9 @@ def ensemble_predict(X_array):
 
     X_array = np.expand_dims(X_array, axis=0)
 
-    unet_model = load_model('REPO_ID/train_2024-02-14 11-20-17/base_u_net.0098-acc-0.75-val_acc-0.74-loss-0.79.h5')
-    vgg16_model = load_model('REPO_ID/vgg16_u_net.0092-acc-0.74-val_acc-0.74-loss-0.82.h5')
-    resnet50_model = load_model('REPO_ID/resnet50_u_net.0095-acc-0.79-val_acc-0.76-loss-0.72.h5')
+    unet_model = load_model_file('base_u_net.0098-acc-0.75-val_acc-0.74-loss-0.79.h5')
+    vgg16_model = load_model_file('vgg16_u_net.0092-acc-0.74-val_acc-0.74-loss-0.82.h5')
+    resnet50_model = load_model_file('resnet50_u_net.0095-acc-0.79-val_acc-0.76-loss-0.72.h5')
 
     pred_y_unet = unet_model.predict(X_array)
     pred_y_vgg16 = vgg16_model.predict(X_array)