app.py

import math
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

import gradio as gr
from huggingface_hub import from_pretrained_keras

model = from_pretrained_keras('keras-io/tab_transformer')

def get_dataset_from_pandas(data):
    for col in data.columns:
        if data[col].dtype == 'float64':
            data[col] = data[col].astype('float32')
        elif col == 'age':
            data[col] = data[col].astype('float32')
    ds = tf.data.Dataset.from_tensors(dict(data.drop(columns = [i for i in ['income_bracket','fnlwgt'] if i in data.columns])))
    return ds


def infer(age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):

    data = pd.DataFrame({
        'age': age,
        'workclass': workclass,
        'education': education,
        'education_num': education_num,
        'marital_status': marital_status,
        'occupation': occupation,
        'relationship':relationship,
        'race': race,
        'gender': gender,
        'capital_gain': capital_gain,
        'capital_loss': capital_loss,
        'hours_per_week':hours_per_week,
        'native_country': native_country,
    }, index=[0])
    validation_dataset = get_dataset_from_pandas(data)
    # validation_dataset = get_dataset_from_csv(test_data_file, 1)
    pred = model.predict(validation_dataset)

    return f"{round(pred.flatten()[0]*100, 2)}%"

# get the inputs
inputs = [
          gr.Slider(minimum=16, maximum=120, step=1, label='age', value=30),
          gr.Radio(choices=[' Private', ' Local-gov', ' ?', ' Self-emp-not-inc',' Federal-gov', ' State-gov', ' Self-emp-inc', ' Without-pay', ' Never-worked'],
                    label='workclass', type='value',value=' Private'),
          gr.Radio(choices=[' 11th', ' HS-grad', ' Assoc-acdm', ' Some-college', ' 10th', ' Prof-school', ' 7th-8th', ' Bachelors', ' Masters', ' Doctorate',
                            ' 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'],
                   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'],
                   type='value', label='occupation', value=' Tech-support'),
          gr.Radio(choices=[' Own-child', ' Husband', ' Not-in-family', ' Unmarried', ' Wife', ' Other-relative'],
                   type='value', label='relationship', value=' Wife'),
          gr.Radio(choices=[' Black', ' White', ' Asian-Pac-Islander', ' Other', ' Amer-Indian-Eskimo'],
                   type='value', label='race', value=' Other'),
          gr.Radio(choices=[' Male', ' Female'], type='value', label='gender', value=' Female'),
          gr.Slider(minimum=0, maximum=500000, step=1, label='capital_gain', value=80000),
          gr.Slider(minimum=0, maximum=50000, step=1, label='capital_loss', value=1000),
          gr.Slider(minimum=1, maximum=168, step=1, label='hours_per_week', value=40),
          gr.Radio(choices=[' United-States', ' ?', ' Peru', ' Guatemala', ' Mexico', ' Dominican-Republic', ' Ireland', ' Germany', ' Philippines', ' Thailand', ' Haiti',
                            ' El-Salvador', ' Puerto-Rico', ' Vietnam', ' South', ' Columbia', ' Japan', ' India', ' Cambodia', ' Poland', ' Laos', ' England', ' Cuba', ' Taiwan',
                            ' Italy', ' Canada', ' Portugal', ' China', ' Nicaragua', ' Honduras', ' Iran', ' Scotland', ' Jamaica', ' Ecuador', ' Yugoslavia', ' Hungary',
                            ' Hong', ' Greece', ' Trinadad&Tobago', ' Outlying-US(Guam-USVI-etc)', ' France'],
                    type='value', label='native_country', value=' Vietnam'),
          ]

# the app outputs two segmented images
output = gr.Textbox(label='Probability of income larger than 50,000 USD per year:')
# it's good practice to pass examples, description and a title to guide users
title = 'Tab Transformer for Structured data'
description = 'Using Transformer to predict whether the income will be larger than 50,000 USD given the input features.'

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']]

gr.Interface(infer, inputs, output, examples= examples, allow_flagging='never',
             title=title, description=description, article=article, live=False).launch(enable_queue=True, debug=False)