diff --git a/coder/image.py b/coder/image.py
index 9f832ee..50114d1 100644
--- a/coder/image.py
+++ b/coder/image.py
@@ -3,27 +3,29 @@ from PIL import Image
 import matplotlib.pyplot as plt
 import cv2
 
-img = cv2.cvtColor(cv2.imread('test.jpg'), cv2.COLOR_BGR2GRAY)
-img = cv2.GaussianBlur(img, (15, 15), 0) # poprawia jakosc
-img = cv2.resize(img, (8, 8), interpolation=cv2.INTER_AREA)
 
-print(type(img))
-print(img.shape)
-print(img)
-plt.imshow(img ,cmap='binary')
-plt.show()
+def image():
+    img = cv2.cvtColor(cv2.imread('test.jpg'), cv2.COLOR_BGR2GRAY)
+    img = cv2.GaussianBlur(img, (15, 15), 0)  # poprawia jakosc
+    img = cv2.resize(img, (8, 8), interpolation=cv2.INTER_AREA)
 
-data = []
+    print(type(img))
+    print(img.shape)
+    print(img)
+    plt.imshow(img, cmap='binary')
+    plt.show()
 
-rows, cols = img.shape
-for i in range(rows):
-    for j in range(cols):
-        k = img[i, j]
-        if k > 200:
-            k = 0 # brak czarnego
-        else:
-            k = 1
+    data = []
 
-        data.append(k)
+    rows, cols = img.shape
+    for i in range(rows):
+        for j in range(cols):
+            k = img[i, j]
+            if k > 200:
+                k = 0  # brak czarnego
+            else:
+                k = 1
 
-print(data)
\ No newline at end of file
+            data.append(k)
+
+    print(data)
diff --git a/coder/train.py b/coder/train.py
index f659b20..4c8abd1 100644
--- a/coder/train.py
+++ b/coder/train.py
@@ -6,22 +6,24 @@ from sklearn.neural_network import MLPClassifier
 from sklearn.metrics import accuracy_score
 from PIL import Image
 
-#recznie napisane cyfry
-digits = datasets.load_digits()
 
-y = digits.target
-x = digits.images.reshape((len(digits.images), -1))
+def train():
+    # recznie napisane cyfry
+    digits = datasets.load_digits()
 
-x_train = x[:1000000]
-y_train = y[:1000000]
-x_test = x[1000:]
-y_test = y[1000:]
+    y = digits.target
+    x = digits.images.reshape((len(digits.images), -1))
 
-mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
-                    solver='sgd', tol=1e-4, random_state=1,
-                    learning_rate_init=.1, verbose=True)
+    x_train = x[:1000000]
+    y_train = y[:1000000]
+    x_test = x[1000:]
+    y_test = y[1000:]
 
-mlp.fit(x_train, y_train)
+    mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
+                        solver='sgd', tol=1e-4, random_state=1,
+                        learning_rate_init=.1, verbose=True)
 
-predictions = mlp.predict(x_test)
-print(accuracy_score(y_test, predictions))
+    mlp.fit(x_train, y_train)
+
+    predictions = mlp.predict(x_test)
+    print(accuracy_score(y_test, predictions))