Update app.py

app.py

@@ -310,62 +310,10 @@ elif selected_menu == "Explainable AI":
     # Define the Streamlit app
     st.title('SHAP Explanation for Image Classification')
 
-    # Define the base directory containing the subfolders
-    dataset_folder = 'Validation Set'
-
-    images = []
-
-    # Iterate over files in the folder
-    for image_file in os.listdir(dataset_folder):
-        image_path = os.path.join(dataset_folder, image_file)
-        if os.path.isfile(image_path):
-            image = cv2.imread(image_path)
-            if image is not None:
-                image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-                image = cv2.resize(image, (48, 48))
-                images.append(image)
-
-    # Convert list to numpy array
-    images = np.array(images)
-
-    # Normalize pixel values and expand dimension for grayscale
-    images = images / 255.0
-    images = np.expand_dims(images, -1)
-
-    val_images = images
-
-    # Function to generate SHAP explanations
-    def generate_shap_explanations(model, images, index):
-        def f(x):
-            tmp = x.copy()
-            return model(tmp)
-
-        masker_blur = shap.maskers.Image("blur(32,32)", images[0].shape)
-
-        # Resize images to the expected shape
-        resized_images = np.array([cv2.resize(img, (48, 48)) for img in images])
-        resized_images = np.expand_dims(resized_images, axis=-1)  # Add channel dimension
-
-        explainer = shap.Explainer(f, masker_blur, output_names=list(range(3)))
-        shap_values = explainer(resized_images[index:index+1], max_evals=5000, batch_size=50)  # Use slicing to ensure single image
-        return shap_values
-
-    # Choose a valid index within the bounds of your dataset
-    index = st.slider('Choose an index', 0, len(val_images)-1, 0)
-
-    # Generate SHAP explanations when a button is clicked
-    if st.button('Generate SHAP Explanations'):
-        # Generate SHAP explanations
-        shap_values_ = generate_shap_explanations(model, val_images, index)
-
-        # Display the SHAP values
-        if shap_values_ is not None:
-            plt.figure(figsize=(10, 5))
-            shap.image_plot(shap_values_[0], val_images[index], show=False)
-            st.set_option('deprecation.showPyplotGlobalUse', False)
-            st.pyplot()
-        else:
-            st.write("Error: SHAP values are None")
+    st.write(" ")
+    st.write(" ")
+
+    st.image("Screenshot 2024-05-03 at 3.41.00 PM.png")
 
 elif selected_menu == "MLFlow":
     st.title("MLFlow")