updated

app.py

@@ -3,14 +3,14 @@ import tensorflow as tf
 import cv2
 import numpy as np
 from huggingface_hub import from_pretrained_keras
-from lime import lime_image
-from skimage.segmentation import mark_boundaries
+#from lime import lime_image
+#from skimage.segmentation import mark_boundaries
 import matplotlib.pyplot as plt
 
 
 
 model = from_pretrained_keras('ErnestBeckham/BreastResViT')
-explainer = lime_image.LimeImageExplainer()
+#explainer = lime_image.LimeImageExplainer()
 
 hp = {}
 hp['class_names'] = ["breast_benign", "breast_malignant"]
@@ -64,7 +64,7 @@ def predict_single_image(image, model, hp):
     return class_name
 
 
-def xai_result(image):
+"""def xai_result(image):
     path = "lime_explanation.png"
     tem = cv2.resize(image, [512,512])
     gray_img = cv2.cvtColor(tem, cv2.COLOR_BGR2GRAY)
@@ -73,7 +73,7 @@ def xai_result(image):
     top_labels=1000, hide_color=0,  num_samples=1000)
     temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=5, hide_rest=True)
     plt.imshow(mark_boundaries(temp / 2 + 0.5, mask), interpolation='nearest')
-    plt.savefig(path)
+    plt.savefig(path)"""
     
 
 if __name__ == "__main__":