Update imagePreprocess.py

imagePreprocess.py

@@ -8,17 +8,28 @@ from tensorflow.keras.applications.resnet50 import preprocess_input
 import torch
 import clip
 
-BASE_DIR = "MAS-AI-0000/Authentica/tree/main"
+BASE_DIR = "MAS-AI-0000/Authentica"
 MODELS_DIR = os.path.join(BASE_DIR, "Lib/Models/Image")
 
+# ==== CONFIG ====
+REPO_ID = "MAS-AI-0000/Authentica"
+CLIP_MODEL_FILENAME = "Lib/Models/Image/clip_model.keras"
+CNN_MODEL_FILENAME = "Lib/Models/Image/cnn_model.keras"
+RESNET_MODEL_FILENAME = "Lib/Models/Image/resnet_model.keras"
+
+# ==== Load assets ====
+clip_model_path = hf_hub_download(repo_id=REPO_ID, filename=CLIP_MODEL_FILENAME)
+cnn_model_path = hf_hub_download(repo_id=REPO_ID, filename=CNN_MODEL_FILENAME)
+resnet_model_path = hf_hub_download(repo_id=REPO_ID, filename=RESNET_MODEL_FILENAME)
+
 # Load models and preprocessing once at module level
 clip_mod, clip_pre = clip.load("ViT-B/32", jit=False)
 clip_mod.eval()
 for p in clip_mod.parameters():
     p.requires_grad = False
-mlp_model= tf.keras.models.load_model(os.path.join(MODELS_DIR, "clip_model.keras"))
-cnn_model = tf.keras.models.load_model(os.path.join(MODELS_DIR, "cnn_model.keras"))
-resnet_model = tf.keras.models.load_model(os.path.join(MODELS_DIR, "resnet_model.keras"))
+mlp_model= tf.keras.models.load_model(clip_model_path)
+cnn_model = tf.keras.models.load_model(cnn_model_path)
+resnet_model = tf.keras.models.load_model(resnet_model_path)
 
 
 def center_crop(image: Image.Image, crop_size=512) -> Image.Image | str: