Updated lines 27-28 with: [notation] | Updated line 72 with: app title st.markdown("<h1 style='text-align: center; color: navy;'>Brain Tumor One Hot Encode TF Model</h1>", unsafe_allow_html=True)

app.py

@@ -21,8 +21,12 @@ inv_class_mappings = {v: k for k, v in class_mappings.items()}
 class_names = list(class_mappings.keys())
 
 # Load the true and predicted labels
-true_labels = np.load('true_labels.npy')
-predicted_labels = np.load('predicted_labels.npy')
+true_labels = np.load('true_labels.npy') # Model trained on Brain Tumor MRI Dataset
+predicted_labels = np.load('predicted_labels.npy') # Model foments predictions based upon the training/testing of the Brain Tumor MRI Dataset
+
+# Note0: The One Hot Encode Predictions are leveraging the TF CNN Model's training/testing of the Brain Tumor MRI Dataset
+# Note1: The One Hot Encode Predictions are deriving from another dataset -> Crystal Clean: Brain Tumors MRI Dataset
+
 
 # Function to load and preprocess an image
 def load_and_preprocess_image(image_path, image_shape=(168, 168)):
@@ -64,6 +68,9 @@ predictions = [model.predict(image) for image in images]
 # Determine the predicted labels
 predicted_labels = [inv_class_mappings[np.argmax(one_hot)] for one_hot in predictions]
 
+# Create Streamlit app title
+st.markdown("<h1 style='text-align: center; color: navy;'>Brain Tumor One Hot Encode TF Model</h1>", unsafe_allow_html=True)
+
 # Output the predictions
 st.write(f'Class Mappings: {class_mappings}')
 st.write("\nNormal Image Prediction:", np.round(predictions[0], 3)[0])