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| import gradio as gr | |
| import pandas as pd | |
| from realtabformer import REaLTabFormer | |
| from scipy.io import arff | |
| rtf_model = REaLTabFormer( | |
| model_type="tabular", | |
| epochs=50, | |
| 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) | |
| # Generate synthetic data | |
| samples = rtf_model.sample(n_samples=num_samples) | |
| return samples | |
| css = """ | |
| .gradio-container { | |
| font-family: 'IBM Plex Sans', sans-serif; | |
| } | |
| .gr-button { | |
| color: white; | |
| border-color: black; | |
| background: black; | |
| } | |
| input[type='range'] { | |
| accent-color: black; | |
| } | |
| .dark input[type='range'] { | |
| accent-color: #dfdfdf; | |
| } | |
| .container { | |
| max-width: 430px; | |
| margin: auto; | |
| padding-top: 1.5rem; | |
| } | |
| #gallery { | |
| min-height: 22rem; | |
| margin-bottom: 15px; | |
| margin-left: auto; | |
| margin-right: auto; | |
| border-bottom-right-radius: .5rem !important; | |
| border-bottom-left-radius: .5rem !important; | |
| } | |
| #gallery>div>.h-full { | |
| min-height: 20rem; | |
| } | |
| .details:hover { | |
| text-decoration: underline; | |
| } | |
| .gr-button { | |
| white-space: nowrap; | |
| } | |
| .gr-button:focus { | |
| border-color: rgb(147 197 253 / var(--tw-border-opacity)); | |
| outline: none; | |
| box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); | |
| --tw-border-opacity: 1; | |
| --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); | |
| --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); | |
| --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); | |
| --tw-ring-opacity: .5; | |
| } | |
| #advanced-btn { | |
| font-size: .7rem !important; | |
| line-height: 19px; | |
| margin-top: 12px; | |
| margin-bottom: 12px; | |
| padding: 2px 8px; | |
| border-radius: 14px !important; | |
| } | |
| #advanced-options { | |
| display: none; | |
| margin-bottom: 20px; | |
| } | |
| .footer { | |
| margin-bottom: 45px; | |
| margin-top: 35px; | |
| text-align: center; | |
| border-bottom: 1px solid #e5e5e5; | |
| } | |
| .footer>p { | |
| font-size: .8rem; | |
| display: inline-block; | |
| padding: 0 10px; | |
| transform: translateY(10px); | |
| background: white; | |
| } | |
| .dark .footer { | |
| border-color: #303030; | |
| } | |
| .dark .footer>p { | |
| background: #0b0f19; | |
| } | |
| """ | |
| with gr.Blocks(css = css) 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 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> | |
| ''') | |
| with gr.Column(): | |
| #gr.Markdown(""" ### Record audio """) | |
| # with gr.Tab("Record Audio"): | |
| # audio_input_r = gr.Audio(label = 'Record Audio Input',source="microphone",type="filepath") | |
| # transcribe_audio_r = gr.Button('Transcribe') | |
| with gr.Tab("Upload Data as File"): | |
| data_input_u = gr.File(label = 'Upload Data File', file_types=["text", ".json", ".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.Row(): | |
| #data_sample = gr.Dataframe(label = "Original Data") | |
| data_output = gr.Dataframe(label = "Synthetic Data") | |
| generate_data_btn.click(generate_data, inputs = [data_input_u,num_samples], outputs = [data_output]) | |
| examples = gr.Examples(examples=[['diabetes.arff',5]],inputs = [data_input_u,num_samples], outputs = [data_output], cache_examples = True, fn = generate_data) | |
| demo.launch() |