diff --git a/app/config.py b/app/config.py
index 31062f3..d8a4d5d 100644
--- a/app/config.py
+++ b/app/config.py
@@ -5,6 +5,7 @@
 
 class Config(BaseSettings):
     streamlit_title: Optional[str] = "VideoLabelMagic"
+    debug: Optional[bool] = False
     models_directory: Optional[str] = "models/"
     output_directory: Optional[str] = "outputs/"
     object_class_directory: Optional[str] = "object_class/"
diff --git a/app/extractor.py b/app/extractor.py
index b6d1551..25b0002 100644
--- a/app/extractor.py
+++ b/app/extractor.py
@@ -27,7 +27,7 @@ def __init__(self, config, video_path, frame_rate, output_dir, model_path, class
 
         # Only initialize SahiUtils if SAHI is enabled
         if sahi_config:
-            self.sahi_utils = SahiUtils(os.path.join('models', model_path), **sahi_config)
+            self.sahi_utils = SahiUtils(self.config.debug, os.path.join('models', model_path), **sahi_config)
         else:
             self.sahi_utils = None
 
diff --git a/example.env b/example.env
index ce763d6..b7fc799 100644
--- a/example.env
+++ b/example.env
@@ -1,5 +1,6 @@
 # Streamlit Configuration
 STREAMLIT_TITLE=VideoLabelMagic
+DEBUG=True
 
 # Directories
 MODELS_DIRECTORY=models/
diff --git a/utils/sahi_utils.py b/utils/sahi_utils.py
index d05f6e1..675c23e 100644
--- a/utils/sahi_utils.py
+++ b/utils/sahi_utils.py
@@ -1,16 +1,25 @@
+import cv2
+import uuid
+import time
+import os
+from sahi import AutoDetectionModel
 from sahi.predict import get_sliced_prediction
 from sahi.utils.cv import read_image_as_pil
-from sahi import AutoDetectionModel
 import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
 
 
 class SahiUtils:
-    def __init__(self, model_path, model_type='yolov8', device='cpu', slice_size=(256, 256), overlap_ratio=(0.2, 0.2)):
+    def __init__(self, debug, model_path, model_type='yolov8', device='cpu', slice_size=(256, 256),
+                 overlap_ratio=(0.2, 0.2)):
+        self.debug = debug
         self.device = device  # Can be 'cpu' or 'cuda:0' for GPU
         self.model_type = model_type
         self.model = self.load_model(model_path)
         self.slice_size = slice_size
         self.overlap_ratio = overlap_ratio
+        self.debug_annotated_directory = str(uuid.uuid4())
 
     def load_model(self, model_path):
         """Loads a detection model based on the specified type and path."""
@@ -22,6 +31,23 @@ def load_model(self, model_path):
         )
         return detection_model
 
+    def show_image(self, image, title="Image"):
+        """Displays a NumPy image using matplotlib."""
+        # Convert BGR to RGB for correct color
+        plt.imshow(image if len(image.shape) == 2 else cv2.cvtColor(
+            image,
+            cv2.COLOR_BGR2RGB
+        ))
+        plt.title(title)
+        plt.axis('off')  # Hide axes
+        plt.show()
+
+    def show_annotated_image(self, image_path):
+        img = Image.open(image_path)
+        plt.imshow(img)
+        plt.axis('off')
+        plt.show()
+
     def perform_sliced_inference(self, image):
         """Performs object detection on an image using sliced prediction."""
         pil_image = read_image_as_pil(image)
@@ -34,6 +60,24 @@ def perform_sliced_inference(self, image):
             overlap_width_ratio=self.overlap_ratio[1],
             verbose=False
         )
+        if self.debug:
+            random_value = str(uuid.uuid4())
+            # Start exporting the image
+            results.export_visuals(export_dir=f"temp/{self.debug_annotated_directory}/", file_name=random_value)
+
+            # Wait until the file is created
+            file_path = f"temp/{self.debug_annotated_directory}/{random_value}.png"
+            timeout = 10  # Set a timeout limit of 10 seconds or more if necessary
+            start_time = time.time()
+
+            while not os.path.exists(file_path):
+                if time.time() - start_time > timeout:
+                    raise TimeoutError(f"File creation exceeded {timeout} seconds.")
+                time.sleep(0.1)  # Wait for 100 milliseconds before checking again
+
+            # Once the file exists, display it
+            self.show_annotated_image(file_path)
+
         return self.format_predictions(results)
 
     def format_predictions(self, prediction_result):