Update app.py

app.py

@@ -64,7 +64,6 @@ def get_predictions(y_prediction_encoded):
   return predicted_label_indices
 
 def predict(image):
-
     # Steps to get prediction
     sample_image_resized = resize_image(image)
     y_pred = ensemble_predict(sample_image_resized)
@@ -74,14 +73,19 @@ def predict(image):
     colors = ['cyan', 'yellow', 'magenta', 'green', 'blue', 'black', 'white']
     # Create a ListedColormap
     cmap = ListedColormap(colors)
-    # Create colorbar and set ticks and ticklabels
-    cbar = plt.colorbar(ticks=np.arange(1, 8))
-    cbar.set_ticklabels(['Urban', 'Agriculture', 'Range Land', 'Forest', 'Water', 'Barren', 'Unknown'])
 
-
-    # Create a figure without saving it to a file
+    # Create a figure
     fig, ax = plt.subplots()
-    cax = ax.imshow(y_pred, cmap=cmap, vmin=1, vmax=7)
+
+    # Display the image
+    ax.imshow(sample_image_resized)
+
+    # Display the predictions using the specified colormap
+    cax = ax.imshow(y_pred, cmap=cmap, vmin=1, vmax=7, alpha=0.5)
+
+    # Create colorbar and set ticks and ticklabels
+    cbar = plt.colorbar(cax, ticks=np.arange(1, 8))
+    cbar.set_ticklabels(['Urban', 'Agriculture', 'Range Land', 'Forest', 'Water', 'Barren', 'Unknown'])
 
     # Convert the figure to a PIL Image
     image_buffer = io.BytesIO()