Update train.py

train.py

@@ -1,14 +1,10 @@
 import os
 import tensorflow as tf
+from huggingface_hub import push_to_hub_keras
 from tensorflow.keras.preprocessing.image import ImageDataGenerator
 from tensorflow.keras.applications import VGG16
 from tensorflow.keras.layers import Flatten, Dense
-# from huggingface_hub import push_to_hub_keras
-from huggingface_hub import hf_hub_url, push_to_hub_keras
 
-# from huggingface_hub import upload_file
-from huggingface_hub import HfApi
-api = HfApi()
 
 # Environment variable for Hugging Face token
 sac = os.getenv('accesstoken')
@@ -94,20 +90,18 @@ print('Test accuracy:', test_acc)
 # Save model weights to local directory
 # model.save_pretrained("kerascatanddog")
 # model.savesave_format='tf'
-model.save('cat_dog_classifier2.h5',save_format='h5')
-new_model = tf.keras.models.load_model('cat_dog_classifier2.h5')
+# model.save('cat_dog_classifier2.h5',save_format='h5')
+# new_model = tf.keras.models.load_model('cat_dog_classifier2.h5')
 
 # Show the model architecture
-new_model.summary()
+model.summary()
 
 # Upload the model to your Hugging Face space repository
 push_to_hub_keras(
-    new_model, # model,  # Point to the SavedModel directory
+    model, # model,  # Point to the SavedModel directory
     repo_id="okeowo1014/kerascatanddog",
     commit_message="cats and dog image classifier with transfer learning",
     tags=["image-classifier", "data-augmentation", "class-weights"],
     include_optimizer=True,
     token=sac,
 )
-# # Upload the SavedModel directory to Hugging Face Hub
-# api.upload_folder(folder_path=export_dir, repo_id="okeowo1014/catsanddogs", token=sac)