updated app.py

app.py

@@ -7,25 +7,34 @@ from skimage.segmentation import mark_boundaries
 import matplotlib.pyplot as plt
 from tensorflow.keras.models import load_model
 from grad_cam import GradCam
+from vit import CNN_ViT
 
 hp = {}
-hp['image_size'] = 512
+hp['image_size'] = 256
 hp['num_channels'] = 3
-hp['patch_size'] = 64
+hp['patch_size'] = 32
 hp['num_patches'] = (hp['image_size']**2) // (hp["patch_size"]**2)
 hp["flat_patches_shape"] = (hp["num_patches"], hp['patch_size']*hp['patch_size']*hp["num_channels"])
 hp['batch_size'] = 32
 hp['lr'] = 1e-4
 hp["num_epochs"] = 30
 hp['num_classes'] = 2
-hp["num_layers"] = 12
-hp["hidden_dim"] = 512
-hp["mlp_dim"] = 3072
-hp['num_heads'] = 12
+hp["num_layers"] = 6
+hp["hidden_dim"] = 256
+hp["mlp_dim"] = 256
+hp['num_heads'] = 6
 hp['dropout_rate'] = 0.1
 hp['class_names'] = ["breast_benign", "breast_malignant"]
 
-model = load_model("model/resnet_for_breast_cancer-v1.h5")
+#model = load_model("model/resnet_for_breast_cancer-v1.h5")
+model = CNN_ViT(hp)
+
+model.compile(loss='binary_crossentropy',
+              optimizer = tf.keras.optimizers.Adam(hp['lr'], clipvalue=1.0),
+              metrics=['acc']
+             )
+model.load_weights("model/Breast-ResViT.keras")
+
 print("Model initiated")
 explainer = lime_image.LimeImageExplainer()
 
@@ -40,16 +49,38 @@ def main():
 
     if uploaded_file is not None:
         # Convert the uploaded file to OpenCV format
-        image = convert_to_opencv(uploaded_file)
-        gradCam = GradCam(model, image, last_conv_layer_name='conv5_block3_3_conv')
+        image, gray_img = convert_to_opencv(uploaded_file)
+        gray_img = cv2.resize(gray_img, [256,256])
+        #gradCam = GradCam(model, image, last_conv_layer_name='conv5_block3_3_conv')
         
         # Display the uploaded image
         st.image(image, channels="BGR", caption="Uploaded Image", use_column_width=True)
 
         # Display the image shape
         image_class = predict_single_image(image, model, hp)
-        gradCam.save_and_display_gradcam()
+        #gradCam.save_and_display_gradcam()
         st.write(f"Image Class: {image_class}")
+        explanation = explainer.explain_instance(
+            gray_img.astype('double'),
+            model.predict,
+            top_labels=4,
+            hide_color=0,
+            num_samples=500
+        )
+        temp, mask = explanation.get_image_and_mask(
+            explanation.top_labels[0],
+            positive_only=True,
+            num_features=4,
+            hide_rest=True
+        )
+
+        temp = (temp / 2 + 0.5)
+        xai = mark_boundaries(temp.clip(0, 1), mask)
+
+        # Save and display LIME explanation
+        lime_explanation_path = 'lime_explanation.png'
+        cv2.imwrite(lime_explanation_path, (xai * 255).astype(np.uint8))
+        st.image(lime_explanation_path, caption="LIME Explanation", use_column_width=True)
         
 
 def convert_to_opencv(uploaded_file):
@@ -57,11 +88,12 @@ def convert_to_opencv(uploaded_file):
     image_bytes = uploaded_file.read()
     np_arr = np.frombuffer(image_bytes, np.uint8)
     image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
-    return image
+    gray_img = cv2.imdecode(np_arr, cv2.IMREAD_GRAYSCALE)
+    return image, gray_img
 
 def process_image_as_batch(image):
     #resize the image
-    image = cv2.resize(image, [512, 512])
+    image = cv2.resize(image, [256, 256])
     #scale the image
     image = image / 255.0
     #change the data type of image