Update app.py

app.py

@@ -5,10 +5,10 @@ import numpy as np
 from tensorflow.image import resize
 
 # Load the image classification model
-image_classification_model = tf.keras.models.load_model("mobilenetfashion.h5")
+image_classification_model = tf.keras.models.load_model("mobilenetfashion_v2.h5")
 
 # Load pre-trained GPT-2 model and tokenizer
-gpt2_model_name = "diamantrsd/copywriting-otomatis-v1"
+gpt2_model_name = "diamantrsd/copywriting-otomatis"
 gpt2_model = GPT2LMHeadModel.from_pretrained(gpt2_model_name)
 gpt2_tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
 
@@ -35,13 +35,9 @@ def classify_and_generate_text(image, keywords=""):
 
 def map_class_label_to_category(class_label):
     # Map the class label to a category (replace with your own mapping)
-    categories = ['Blouse', 'Blazer', 'Kemeja', 'Bomber', 'Anorak', 
-                  'Tee', 'Tank', 'Top', 'Sweater', 'Flannel', 'Hoodie', 
-                  'Cardigan', 'Jaket', 'Henley', 'Poncho', 'Jersey', 'Turtleneck', 'Parka', 
-                  'Peacoat', 'Halter', 'Skirt', 'Shorts', 'Jeans', 'Joggers', 'Sweatpants', 'Jeggings', 
-                  'Cutoffs', 'Sweatshorts', 'Leggings', 'Culottes', 'Chinos', 'Culottes', 'Sarung', 'Gauchos', 'Jodhpurs', 
-                  'Capris', 'Dress', 'Romper', 'Coat', 'Kimono', 'Jumpsuit', 
-                  'Robe', 'Caftan', 'Kaftan', 'Coverup', 'Onesie']
+    categories = [ 'Backpack','Celana Panjang','Celana Pendek','Dompet',
+        'Dress','Kacamata','Kaos', 'Kaos Kaki','Kemeja', 'Outerwear','Sandal', 'Sepatu',
+        'Sepatu Flat','Tas','Topi']
     return categories[np.argmax(class_label, axis=-1)[0]]
 
 def generate_text_with_gpt2(product_category, keywords):