diff --git a/src/HowBadIsMyBatch.ipynb b/src/HowBadIsMyBatch.ipynb
index 7fcf4468aed..2f8d0669c40 100644
--- a/src/HowBadIsMyBatch.ipynb
+++ b/src/HowBadIsMyBatch.ipynb
@@ -254,7 +254,6 @@
     "    vocabulary=char_to_num.get_vocabulary(),\n",
     "    mask_token=None, invert=True)\n",
     "\n",
-    "\n",
     "def encode_single_sample(img_path):\n",
     "    # 1. Read image\n",
     "    img = tf.io.read_file(img_path)\n",
@@ -268,11 +267,13 @@
     "    # dimension to correspond to the width of the image.\n",
     "    img = tf.transpose(img, perm=[1, 0, 2])\n",
     "    # 7. Return a dict as our model is expecting two inputs\n",
+    "    return asSingleSampleBatch(img)\n",
+    "\n",
+    "def asSingleSampleBatch(img):\n",
     "    array = keras.utils.img_to_array(img)\n",
     "    array = np.expand_dims(array, axis=0)\n",
     "    return array\n",
     "\n",
-    "\n",
     "def decode_batch_predictions(pred):\n",
     "    input_len = np.ones(pred.shape[0]) * pred.shape[1]\n",
     "    # Use greedy search. For complex tasks, you can use beam search\n",
@@ -284,7 +285,6 @@
     "        output_text.append(res)\n",
     "    return output_text\n",
     "\n",
-    "\n",
     "def load_model():\n",
     "    _model = keras.models.load_model('model')\n",
     "    model = keras.models.Model(\n",
@@ -292,13 +292,11 @@
     "        _model.get_layer(name=\"dense2\").output)\n",
     "    return model\n",
     "\n",
-    "\n",
     "def getTextInCaptchaImage(captchaImageFile):\n",
     "    batchImages = encode_single_sample(captchaImageFile)\n",
     "    preds = model.predict(batchImages)\n",
     "    return decode_batch_predictions(preds)[0]\n",
     "\n",
-    "\n",
     "print(\"loading model...\")\n",
     "model = load_model()\n",
     "model.summary()"