Update app.py

app.py

@@ -6,19 +6,21 @@ from PIL import Image
 # Load the model
 model = tf.keras.models.load_model("vgg19_binary_nonbinary.h5")
 
-# Preprocess the image
+# Define the preprocessing function
 def preprocess_image(image):
-    image = image.resize((224, 224))
-    image = np.array(image) / 255.0
-    image = np.expand_dims(image, axis=0)
+    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'.")
 
+# File uploader
 uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 if uploaded_file is not None:
+    # Display the uploaded image
     image = Image.open(uploaded_file)
     st.image(image, caption="Uploaded Image", use_column_width=True)
     st.write("Classifying...")
@@ -29,5 +31,6 @@ if uploaded_file is not None:
     class_names = ["binary", "non-binary"]
     confidence = {class_names[i]: float(predictions[0][i]) for i in range(2)}
 
+    # Display the prediction
     st.write("Prediction:")
     st.write(confidence)