attempt fix keras3 compatibility

app.py

@@ -7,21 +7,20 @@ def softmax(vector):
     e = np.exp(vector)
     return e / e.sum()
 
-def image_to_output (input_img):
-    gr_img = []
-    gr_img.append(input_img)
-    img2 = tf.image.resize(tf.cast(gr_img, tf.float32)/255. , [224, 224])
 
-    # print(img2)
-
-    x_test = np.asarray(img2)
+def image_to_output(input_img):
+    # Preprocess the image as before
+    img2 = tf.image.resize(tf.cast([input_img], tf.float32)/255., [224, 224])
+    
+    # Run inference
+    output = infer(tf.constant(img2))
 
-    prediction = model2(x_test,batch_size=1).flatten()
+    print(output)
+    
+    # Process the output
+    prediction = output['output_0'].numpy().flatten()
     prediction = softmax(prediction)
-
     confidences = {labels[i]: float(prediction[i]) for i in range(102)}
-    # confidences = {labels[i]:float(top[i]) for i in range(num_predictions)}
-
     return confidences
 
 # Download the model checkpoint
@@ -47,8 +46,11 @@ for item in [
         f.write(params.content)
     
 
-# Load the model
-model2=keras.layers.TFSMLayer(pretrained_repo)
+# Load the SavedModel
+loaded = tf.saved_model.load(pretrained_repo)
+
+# Get the inference function
+infer = loaded.signatures["serving_default"]
 
 # Read the labels
 with open('flower_names.txt') as f: