Update app.py

app.py

@@ -1,29 +1,18 @@
 import gradio as gr
 import tensorflow as tf
 import numpy as np
-from PIL import Image
 
 # Load the trained model
 model = tf.keras.models.load_model("denis_mnist_cnn_model.h5")
 
 # Preprocessing function for images
 def preprocess_image(image):
-    # Convert PIL image to a tensor
-    image = tf.convert_to_tensor(image)
-
     # Resize the image to 28x28 as expected by the model
     image = tf.image.resize(image, (28, 28))  # Resize to 28x28
-
-    # If the image is RGB, convert it to grayscale
-    if image.shape[-1] == 3:
-        image = tf.image.rgb_to_grayscale(image)  # Convert RGB to grayscale
     
-    # Normalize pixel values to [0, 1]
-    image = image / 255.0
+    # Convert image to float32 and normalize pixel values to [0, 1]
+    image = tf.cast(image, tf.float32) / 255.0
     
-    # Convert grayscale to RGB (3 channels)
-    image = tf.image.grayscale_to_rgb(image)  # Convert grayscale to RGB (3 channels)
-
     # Add batch dimension (model expects batch of images)
     image = tf.expand_dims(image, axis=0)