Update app.py

app.py

@@ -6,13 +6,16 @@ from PIL import Image
 # Load the model
 model = tf.keras.models.load_model("vgg19_binary_nonbinary.h5")
 
-# Define the preprocessing function
 def preprocess_image(image):
+    # Convert RGBA to RGB if the image has an alpha channel
+    if image.mode == "RGBA":
+        image = image.convert("RGB")
+    # Resize and normalize the image
     image = image.resize((224, 224))  # Resize to match model input size
     image = np.array(image) / 255.0   # Normalize pixel values
     image = np.expand_dims(image, axis=0)  # Add batch dimension
     return image
-
+    
 # Streamlit app
 st.title("Binary vs Non-Binary Image Classification")
 st.write("Upload an image to classify it as 'binary' or 'non-binary'.")