Upload app.py

app.py

@@ -81,8 +81,8 @@ description = 'Using Transformer to predict whether the income will be larger th
 
 article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. Based on this <a href=\"https://keras.io/examples/structured_data/tabtransformer/\">keras example</a> by <a href=\"https://www.linkedin.com/in/khalid-salama-24403144\">Khalid Salama.</a> HuggingFace Model <a href=\"https://huggingface.co/keras-io/tab_transformer\">here</a> "
 
-examples = [[39.0, '  State-gov', ' Assoc-voc', 11.0, ' Divorced', ' Tech-support', ' Not-in-family', ' White', ' Female', 50000.0, 0.0, 40.0, ' Puerto-Rico'],
-            [65.0, '  Self-emp-inc', ' 12th', 8.0, ' Married-civ-spouse', ' Handlers-cleaners', ' Husband', ' Black', ' Male', 41000.0, 0.0, 55.0, ' United-States'],
+examples = [[39.0, ' State-gov', ' Assoc-voc', 11.0, ' Divorced', ' Tech-support', ' Not-in-family', ' White', ' Female', 50000.0, 0.0, 40.0, ' Puerto-Rico'],
+            [65.0, ' Self-emp-inc', ' 12th', 8.0, ' Married-civ-spouse', ' Handlers-cleaners', ' Husband', ' Black', ' Male', 41000.0, 0.0, 55.0, ' United-States'],
             [42.0, ' Private',' Masters', 14.0, ' Married-civ-spouse', ' Prof-specialty', ' Husband', ' Asian-Pac-Islander', ' Male', 35000.0, 0.0, 40.0, ' Taiwan',],
             [25.0, ' Local-gov',' Bachelors', 13.0, ' Never-married', ' Craft-repair', ' Unmarried', ' White', ' Male', 75000.0, 0.0, 51.0, ' England'],
             [57.0, ' Private', ' Masters', 14.0, ' Never-married', ' Prof-specialty', ' Not-in-family', ' Asian-Pac-Islander', ' Male', 150000.0, 0.0, 45.0, ' Iran']]