Upload app.py

app.py

@@ -53,7 +53,7 @@ inputs = [
                             ' 5th-6th', ' Assoc-voc', ' 9th', ' 12th', ' 1st-4th', ' Preschool'],
                    type='value', label='education', value=' Bachelors'),
           gr.Slider(minimum=1, maximum=16, step=1, label='education_num', value=10),
-          gr.Radio(choices=[' Never-married', ' Married-civ-spouse', ' Widowed', ' Divorced', ' Separated', ' Married-spouse-absent', ' Married-AF-spouse'],
+          gr.Radio(choices=['', ' Married-civ-spouse', ' Widowed', ' Divorced', ' Separated', ' Married-spouse-absent', ' Married-AF-spouse'],
                    type='value', label='marital_status', value=' Married-civ-spouse'),
           gr.Radio(choices=[' Machine-op-inspct', ' Farming-fishing', ' Protective-serv', ' ?', ' Other-service', ' Prof-specialty', ' Craft-repair',
                             ' Adm-clerical', ' Exec-managerial', ' Tech-support', ' Sales', ' Priv-house-serv', ' Transport-moving', ' Handlers-cleaners', ' Armed-Forces'],
@@ -81,11 +81,11 @@ 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, ' Private', ' Assoc-voc', 11.0, ' Divorced', ' Tech-support', ' Not-in-family', ' White', ' Female', 0.0, 0.0, 40.0, ' United-States'],
-            [60.0, ' Private', ' 12th', 8.0, ' Married-civ-spouse', ' Handlers-cleaners', ' Husband', ' White', ' Male', 0.0, 0.0, 40.0, ' United-States'],
-            [42.0, ' Private',' Masters', 14.0, ' Married-civ-spouse', ' Prof-specialty', ' Husband', ' Asian-Pac-Islander', ' Male', 0.0, 0.0, 40.0, ' Taiwan',],
-            [31.0, ' Local-gov',' Bachelors', 13.0, ' Married-civ-spouse', ' Craft-repair', ' Husband', ' White', ' Male', 0.0, 0.0, 51.0, ' United-States'],
-            [30.0, ' Private', ' Masters', 14.0, ' Never-married', ' Prof-specialty', ' Not-in-family', ' Asian-Pac-Islander', ' Male', 0.0, 0.0, 45.0, ' Iran']]
+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']]
 
 gr.Interface(infer, inputs, output, examples= examples, allow_flagging='never',
              title=title, description=description, article=article, live=False).launch(enable_queue=True, debug=True, inbrowser=True)