--- a
+++ b/aggmap/aggmodel/xAI/gradcam.py
@@ -0,0 +1,37 @@
+# grad-cam example codes: https://keras.io/examples/vision/grad_cam/#the-gradcam-algorithm
+
+
+
+def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):
+    # First, we create a model that maps the input image to the activations
+    # of the last conv layer as well as the output predictions
+    grad_model = tf.keras.models.Model(
+        [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
+    )
+
+    # Then, we compute the gradient of the top predicted class for our input image
+    # with respect to the activations of the last conv layer
+    with tf.GradientTape() as tape:
+        last_conv_layer_output, preds = grad_model(img_array)
+        if pred_index is None:
+            pred_index = tf.argmax(preds[0])
+        class_channel = preds[:, pred_index]
+
+    # This is the gradient of the output neuron (top predicted or chosen)
+    # with regard to the output feature map of the last conv layer
+    grads = tape.gradient(class_channel, last_conv_layer_output)
+
+    # This is a vector where each entry is the mean intensity of the gradient
+    # over a specific feature map channel
+    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
+
+    # We multiply each channel in the feature map array
+    # by "how important this channel is" with regard to the top predicted class
+    # then sum all the channels to obtain the heatmap class activation
+    last_conv_layer_output = last_conv_layer_output[0]
+    heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
+    heatmap = tf.squeeze(heatmap)
+
+    # For visualization purpose, we will also normalize the heatmap between 0 & 1
+    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
+    return heatmap.numpy()
\ No newline at end of file