Update app.py

app.py

@@ -3,13 +3,34 @@ from PIL import Image
 import tensorflow as tf
 import numpy as np
 import os
-from keras.models import load_model
+
+# Definisi CustomLayer
+class CustomLayer(tf.keras.layers.Layer):
+    def __init__(self, units=32, activation=None):
+        super(CustomLayer, self).__init__()
+        self.units = units
+        self.activation = activation
+        self.dense = tf.keras.layers.Dense(units)
+
+    def call(self, inputs):
+        x = self.dense(inputs)
+        if self.activation:
+            x = self.activation(x)
+        return x
+
+    def get_config(self):
+        config = super(CustomLayer, self).get_config()
+        config.update({
+            'units': self.units,
+            'activation': self.activation,
+        })
+        return config
 
 # Caching the model loading function to optimize performance
 @st.cache_resource
 def load_model():
     model_path = "captcha.keras"  # Update with the actual model path
-    return tf.keras.models.load_model(model_path)
+    return tf.keras.models.load_model(model_path, custom_objects={'CustomLayer': CustomLayer})
 
 # Load the model
 model = load_model()
@@ -45,15 +66,6 @@ def run():
         img = Image.open(img_file)
         st.image(img, caption="Uploaded CAPTCHA", use_column_width=True)
 
-        # Create the directory if it doesn't exist
-        upload_dir = './upload_images/'
-        os.makedirs(upload_dir, exist_ok=True)
-
-        # Save the uploaded image
-        save_image_path = os.path.join(upload_dir, img_file.name)
-        with open(save_image_path, "wb") as f:
-            f.write(img_file.getbuffer())
-
         # Predict the CAPTCHA
         predicted_captcha, score = prepare_image(img)
         if predicted_captcha: