diff --git a/gradio/preprocessing_utils.py b/gradio/preprocessing_utils.py
index 9708b3e603..50d90bc856 100644
--- a/gradio/preprocessing_utils.py
+++ b/gradio/preprocessing_utils.py
@@ -22,9 +22,7 @@ def encode_array_to_base64(image_array):
         PIL_image = Image.fromarray(skimage.img_as_ubyte(image_array))
         PIL_image.save(output_bytes, 'PNG')
         bytes_data = output_bytes.getvalue()
-
     base64_str = str(base64.b64encode(bytes_data), 'utf-8')
-
     return "data:image/png;base64," + base64_str
 
 
diff --git a/gradio/test.py b/gradio/test.py
index c9b785d9fc..38576aeb56 100644
--- a/gradio/test.py
+++ b/gradio/test.py
@@ -2,30 +2,20 @@ import tensorflow as tf
 import gradio
 
 
-#CREATE INTERFACE USING 'GOOD' MNIST MODEL
 (x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
-model = tf.keras.models.load_model('MNIST_9344.h5')
-input = gradio.inputs.Sketchpad(sample_inputs=x_train[:10])
-iface = gradio.Interface(inputs=input, outputs="label", model=model, model_type='keras')
-iface.launch(inline=False, share=False, inbrowser=True);
+x_train, x_test = x_train / 255.0, x_test / 255.0
 
+model = tf.keras.models.Sequential([
+  tf.keras.layers.Flatten(),
+  tf.keras.layers.Dense(512, activation=tf.nn.relu),
+  tf.keras.layers.Dropout(0.2),
+  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
+])
 
-#CREATE INTERFACE BY TRAINING MSNIST MODEL
-
-# (x_train, y_train),(x_test, y_test) = tf.keras.datasets.mnist.load_data()
-# x_train, x_test = x_train / 255.0, x_test / 255.0
-#
-# model = tf.keras.models.Sequential([
-#   tf.keras.layers.Flatten(),
-#   tf.keras.layers.Dense(512, activation=tf.nn.relu),
-#   tf.keras.layers.Dropout(0.2),
-#   tf.keras.layers.Dense(10, activation=tf.nn.softmax)
-# ])
-#
-# model.compile(optimizer='adam',
-#               loss='sparse_categorical_crossentropy',
-#               metrics=['accuracy'])
-# model.fit(x_train, y_train, epochs=1)
-# inp = gradio.inputs.Sketchpad(sample_inputs=x_train[:10])
-# iface = gradio.Interface(inputs=inp, outputs="label", model=model, model_type='keras')
-# iface.launch(inline=False, share=False, inbrowser=True);
\ No newline at end of file
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+model.fit(x_train, y_train, epochs=1)
+inp = gradio.inputs.Sketchpad(sample_inputs=x_train[:10])
+iface = gradio.Interface(inputs=inp, outputs="label", model=model, model_type='keras')
+iface.launch(inline=False, share=False, inbrowser=True);
\ No newline at end of file
diff --git a/test/test_networking.py b/test/test_networking.py
index 3922db5222..0c89210752 100644
--- a/test/test_networking.py
+++ b/test/test_networking.py
@@ -41,7 +41,7 @@ class TestSetSampleData(unittest.TestCase):
         config_file = os.path.join(temp_dir, 'static/config.json')
         with open(config_file) as json_file:
             data = json.load(json_file)
-            self.assertFalse(test_array == data["sample_inputs"])
+            self.assertTrue(test_array == data["sample_inputs"])
 
 # class TestCopyFiles(unittest.TestCase):
 #     def test_copy_files(self):