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

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=100) # Default is 500
    # 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], ["titanic.csv", 15]],inputs = [data_input_u,num_samples], outputs = [data_output], cache_examples = True, fn = generate_data)

    
demo.launch()