app.py

import gradio as gr
import pandas as pd 
from realtabformer import REaLTabFormer
from scipy.io import arff
import os

rtf_model = REaLTabFormer(
    model_type="tabular",
    epochs=25, # Default is 200
    gradient_accumulation_steps=4)


def generate_data(file, num_samples):
    if '.arff' in file.name:
        data = arff.loadarff(open(file.name,'rt'))
        df = pd.DataFrame(data[0])
    elif '.csv' in file.name:
        df = pd.read_csv(file.name)
    rtf_model.fit(df, num_bootstrap=10) # Default is 500
    # Generate synthetic data
    samples = rtf_model.sample(n_samples=num_samples)

    return samples, samples.to_csv('samples.csv')

def generate_relational_data(parent_file, child_file, join_on):
    parent_df = pd.read_csv(parent_file.name)
    child_df = pd.read_csv(child_file.name)

    #Make sure join_on column exists in both
    assert ((join_on in parent_df.columns) and
        (join_on in child_df.columns))

    rtf_model.fit(parent_df.drop(join_on, axis=1), num_bootstrap=100)

    pdir = Path("rtf_parent/")
    rtf_model.save(pdir)

    # # Get the most recently saved parent model,
    # # or a specify some other saved model.
    # parent_model_path = pdir / "idXXX"
    parent_model_path = sorted([
        p for p in pdir.glob("id*") if p.is_dir()],
        key=os.path.getmtime)[-1]

    child_model = REaLTabFormer(
    model_type="relational",
    parent_realtabformer_path=parent_model_path,
    epochs = 25,
    output_max_length=None,
    train_size=0.8)

    child_model.fit(
    df=child_df,
    in_df=parent_df,
    join_on=join_on,
    num_bootstrap=10)

    # Generate parent samples.
    parent_samples = rtf_model.sample(5)

    # Create the unique ids based on the index.
    parent_samples.index.name = join_on
    parent_samples = parent_samples.reset_index()

    # Generate the relational observations.
    child_samples = child_model.sample(
        input_unique_ids=parent_samples[join_on],
        input_df=parent_samples.drop(join_on, axis=1),
        gen_batch=5)

    return parent_samples, child_samples, gr.update(visible = True), parent_samples.to_csv('parent_samples.csv'), child_samples.to_csv('child_samples.csv')
    

with gr.Blocks() as demo:
    gr.Markdown("""
                ## REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers
            """)
    gr.HTML('''
     <p style="margin-bottom: 10px; font-size: 94%">
                This is an unofficial demo for REaLTabFormer, an approach that can be used to generate synthetic data from single tabular data using GPT. The demo is based on the <a href='https://github.com/avsolatorio/REaLTabFormer' style='text-decoration: underline;' target='_blank'> Github </a> implementation provided by the authors.
              </p>
              ''')
    gr.HTML('''
    <p align="center"><img src="https://github.com/avsolatorio/RealTabFormer/raw/main/img/REalTabFormer_Final_EQ.png" style="width:40%"/></p>
    ''')
    
    with gr.Column():
        
        with gr.Tab("Upload Data as File: Tabular Data"):
            data_input_u = gr.File(label = 'Upload Data File (Currently supports CSV and ARFF)', file_types=[".csv", ".arff"])
            num_samples = gr.Slider(label="Number of Samples", minimum=5, maximum=100, value=5, step=10)
            generate_data_btn = gr.Button('Generate Synthetic Data')

        with gr.Tab("Upload Data as File: Relational Data"):
            data_input_parent = gr.File(label = 'Upload Data File for Parent Dataset', file_types=[ ".csv"])
            data_input_child = gr.File(label = 'Upload Data File for Child Dataset', file_types=[ ".csv"])
            join_on = gr.Textbox(label = 'Column name to join on')
            
            generate_data_btn_relational = gr.Button('Generate Synthetic Data')

        with gr.Row():
            #data_sample = gr.Dataframe(label = "Original Data")
            data_output = gr.Dataframe(label = "Synthetic Data")
            data_output_file = gr.File(label = "Synthetic Data File")
        with gr.Row(visible = False) as child_sample:
            data_output_child = gr.Dataframe(label = "Synthetic Data for Child Dataset")
            data_output_file_child = gr.File(label = "Synthetic Data File for Child Dataset")
    
    
    generate_data_btn.click(generate_data, inputs = [data_input_u,num_samples], outputs = [data_output, data_output_file])
    generate_data_btn_relational.click(generate_relational_data, inputs = [data_input_parent,data_input_child,join_on], outputs = [data_output, data_output_child, child_sample, data_output_file, data_output_file_child])
    examples = gr.Examples(examples=[['diabetes.arff',5], ["titanic.csv", 15]],inputs = [data_input_u,num_samples], outputs = [data_output,data_output_file], cache_examples = True, fn = generate_data)

    
demo.launch()