diff --git a/simple_xception.keras b/simple_xception.keras
new file mode 100644
index 0000000000000000000000000000000000000000..2b195483fcd5c6109c4cf679d228a3af0e6663ca
Binary files /dev/null and b/simple_xception.keras differ
diff --git a/test.py b/test.py
index 4fe02cfb8eff44f8a11f8c449f488d82d246dfa7..1579836d4977f35dab5f0eb94b2b3fa6a39c9a31 100644
--- a/test.py
+++ b/test.py
@@ -1,8 +1,12 @@
 import keras
 import matplotlib.pyplot as plt
 import numpy as np
+import os
+import random
 
-# Load just enough to get class_names
+#os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+# Get class names from directory structure
 temp_ds = keras.utils.image_dataset_from_directory(
     "Combined_Dataset",
     labels="inferred",
@@ -11,33 +15,52 @@ temp_ds = keras.utils.image_dataset_from_directory(
     batch_size=1,
     shuffle=False
 )
-
-# Load class names
 class_names = temp_ds.class_names
 
 # Load model
-model = keras.models.load_model("save_at_6.keras")
+model = keras.models.load_model("simple_xception.keras")
 
-# Load and show image
-img = keras.utils.load_img(
-    "Combined_Dataset/Dracaufeu/0f0537c0761b48be754706eb260cf3634f71238a7cb6961dd39b9914857c6283.jpg",
-    target_size=(256, 256)
-)
-plt.imshow(img)
-plt.axis("off")
+# Base path
+base_path = "Combined_Dataset"
+
+# Prepare 2x2 plot
+plt.figure(figsize=(10, 10))
+
+for i in range(4):
+    # Pick random class and image
+    random_class = random.choice(class_names)
+    class_folder = os.path.join(base_path, random_class)
+    random_image = random.choice([
+        f for f in os.listdir(class_folder)
+        if f.lower().endswith(('.png', '.jpg', '.jpeg'))
+    ])
+    img_path = os.path.join(class_folder, random_image)
 
+    # Load and preprocess
+    img = keras.utils.load_img(img_path, target_size=(256, 256))
+    img_array = keras.utils.img_to_array(img)
+    img_array = keras.ops.expand_dims(img_array, 0)
 
-# Preprocess image
-img_array = keras.utils.img_to_array(img)
-img_array = keras.ops.expand_dims(img_array, 0)
+    # Predict
+    predictions = model.predict(img_array, verbose=0)
+    probabilities = keras.ops.softmax(predictions[0])
+    predicted_class_index = np.argmax(probabilities)
+    predicted_label = class_names[predicted_class_index]
+    confidence = 100 * probabilities[predicted_class_index]
 
-# Predict
-predictions = model.predict(img_array)
-probabilities = keras.ops.softmax(predictions[0])
-predicted_class_index = np.argmax(probabilities)
+    # Compare with actual
+    is_correct = predicted_label == random_class
 
-# Output result
-print(f"Predicted Pokémon: {class_names[predicted_class_index]}")
-print(f"Confidence: {100 * probabilities[predicted_class_index]:.2f}%")
+    # Plot
+    ax = plt.subplot(2, 2, i + 1)
+    plt.imshow(img)
+    plt.axis("off")
+    plt.title(
+        f"Pred: {predicted_label}\n"
+        f"True: {random_class}\n"
+        f"{'Yes' if is_correct else 'No'} | {confidence:.1f}%",
+        fontsize=10
+    )
 
+plt.tight_layout()
 plt.show()