Update app.py

app.py

@@ -3,11 +3,18 @@ import numpy as np
 import cv2
 import tensorflow as tf
 from PIL import Image
-from sklearn.preprocessing import LabelEncoder  # Assuming you have this imported as well
+from sklearn.preprocessing import LabelEncoder
+
+# Load your pre-trained model (Make sure this matches the version used during training)
+model = tf.keras.models.load_model('dementia_cnn_model.h5')
+
+# Example class labels (update this list with your actual class labels)
+class_labels = ['Non Demented', 'Very mild Dementia', 'Mild Dementia', 'Moderate Dementia']
+label_encoder = LabelEncoder()
+label_encoder.fit(class_labels)  # Fit the label encoder with your class labels
 
 # Function to load and preprocess the uploaded image
 def load_and_preprocess_image(uploaded_file):
-    # Convert uploaded file to a format suitable for OpenCV
     img = Image.open(uploaded_file)
     img = img.convert("RGB")  # Convert to RGB if it's in another format
     img = np.array(img)  # Convert to NumPy array
@@ -26,9 +33,6 @@ def predict_image(img):
 def get_class_label(predicted_class_index):
     return label_encoder.inverse_transform([predicted_class_index])[0]  # Get class label
 
-# Load your pre-trained model (Make sure this matches the version used during training)
-model = tf.keras.models.load_model('dementia_cnn_model.h5')
-
 # Streamlit App UI
 st.title("Dementia Detection using CNN")
 st.write("Upload a brain scan (JPG format), and the model will predict its class.")
@@ -38,7 +42,7 @@ uploaded_file = st.file_uploader("Choose a JPG image...", type="jpg")
 
 if uploaded_file is not None:
     st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
-    st.write("Detecting...")
+    st.write("Classifying...")
 
     # Load and preprocess the image
     processed_image = load_and_preprocess_image(uploaded_file)