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app.py

@@ -11,9 +11,12 @@ img_size = 224
 # Reconstruct the base model (EfficientNetB0) architecture
 base_model = tf.keras.applications.EfficientNetB0(
     include_top=False,
-    weights="imagenet", # Load ImageNet weights for the base model first
+    weights=None, # IMPORTANT: Do not load imagenet weights here directly
     input_shape=(img_size, img_size, 3)
 )
+# Explicitly build the base model and then load its ImageNet weights from the local file
+base_model.build(input_shape=(None, img_size, img_size, 3))
+base_model.load_weights('efficientnetb0_notop.h5') # Load pre-downloaded ImageNet weights
 
 # Freeze base_model. The fine-tuned weights will be loaded later.
 base_model.trainable = False

efficientnetb0_notop.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0683edcf6151811fb3ddd6f098ac48df644d74592c4919a5acdee1c37554add2
+size 16705208