Update README.md

README.md

@@ -58,29 +58,29 @@ pip install tensorflow pillow matplotlib numpy
 
 To load and use the model for predictions:
 
-'''python
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-'''
-# Load the model
-model = tf.keras.models.load_model("path_to_model.h5")
-
-# Prepare an image for prediction
-def prepare_image(image_path):
-img = Image.open(image_path).convert("RGB")
-img = img.resize((224, 224))
-img_array = tf.keras.preprocessing.image.img_to_array(img)
-img_array = np.expand_dims(img_array, axis=0)
-return img_array
-
-# Prediction
-image_path = "path_to_image.jpg"
-img_array = prepare_image(image_path)
-predictions = model.predict(img_array)
-predicted_class = np.argmax(predictions[0])
-
-print(f"Predicted Class: {predicted_class}")
+    python
+    import tensorflow as tf
+    from PIL import Image
+    import numpy as np
+
+    # Load the model
+    model = tf.keras.models.load_model("path_to_model.h5")
+
+    # Prepare an image for prediction
+    def prepare_image(image_path):
+        img = Image.open(image_path).convert("RGB")
+        img = img.resize((224, 224))
+        img_array = tf.keras.preprocessing.image.img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        return img_array
+
+    # Prediction
+    image_path = "path_to_image.jpg"
+    img_array = prepare_image(image_path)
+    predictions = model.predict(img_array)
+    predicted_class = np.argmax(predictions[0])
+
+    print(f"Predicted Class: {predicted_class}")
 
 
 ### Grad-CAM Visualization
@@ -89,13 +89,13 @@ The integrated *Grad-CAM* functionality allows interpretation of the model's pre
 
 Grad-CAM example usage:
 
-python
-# Example usage of the make_gradcam_heatmap function
-heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name="last_conv_layer_name")
+    python
+    # Example usage of the make_gradcam_heatmap function
+    heatmap = make_gradcam_heatmap(img_array, model, last_conv_layer_name="last_conv_layer_name")
 
-# Superimpose the heatmap on the original image
-superimposed_img = superimpose_heatmap(Image.open(image_path), heatmap)
-superimposed_img.show()
+    # Superimpose the heatmap on the original image
+    superimposed_img = superimpose_heatmap(Image.open(image_path), heatmap)
+    superimposed_img.show()
 
 
 ## Evaluation