app.py

import gradio as gr
import os
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

def dataset_change(dataset):   
    df = pd.read_csv(dataset.name)
    features = df.columns
    features_object_list = [feature for feature in features]
    describe = df.describe(include='all')
    print(describe)
    return describe.reset_index(), gr.Dropdown.update(choices = features_object_list), gr.Dropdown.update(choices = features_object_list)

def feature_select(dataset, feature, hue = None):
    df = pd.read_csv(dataset.name)
    non_numeric_cols = df.select_dtypes('object').columns.tolist()
    
    if feature in non_numeric_cols:
      kde = False
      plot2 = plt.figure()
      if hue:
        sns.countplot(x = feature, data = df, palette='rainbow', hue = hue)
      else:
        sns.countplot(x = feature, data = df, palette='rainbow')
    else:
      kde = True
      plot2 = plt.figure()
      if hue:
        sns.boxplot(x = feature, data = df, hue = hue)
      else:
        sns.boxplot(x = feature, data = df )

    plot1 = plt.figure()
    if hue:
      sns.histplot(data = df, x = feature, kde = kde, hue = hue, multiple="stack")
    else:
      sns.histplot(data = df, x = feature, kde = kde)
   
    return plot1, plot2

css = """
footer {display:none !important}
.overflow-x-scroll {
    overflow-x: scroll !important;
    height: 15rem !important;
    overflow-y: scroll !important;
}

.max-h-\[30rem\] {max-height: 18rem !important;}

.hover\:bg-orange-50:hover {
    --tw-bg-opacity: 1 !important;
    background-color: rgb(229,225,255) !important;
}

.output-markdown h2{
    z-index: 14;
    align-self: flex-start;
    min-width: 0px;
    order: 5;
    min-height: 0px;
    height: max-content;
    flex-grow: 0;
    flex-shrink: 0;
    width: calc(100% - 0px);
    margin: 5px 0px;
    white-space: pre-wrap;
    overflow: visible;
    word-break: break-word;
    font-size: 18px !important;
    font-weight: 500 !important;
    color: rgb(9, 23, 71) !important;
    line-height: 1 !important;
    border-radius: 0px !important;
    opacity: 1 !important;
}

.gr-button-lg {
    z-index: 14;
    width: 113px !important;
    height: 30px !important;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important; 
    background: none rgb(17, 20, 45) !important;
    border: none !important;
    text-align: center !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 6px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: none !important;
}
.gr-button-lg:hover{
    z-index: 14;
    width: 113px !important;
    height: 30px !important;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important; 
    background: none rgb(66, 133, 244) !important;
    border: none !important;
    text-align: center !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 6px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
}
"""

with gr.Blocks(title="Describe Dataset | Data Science Dojo", css = css) as demo:
    gr.Markdown("""## Input Dataset""")
    with gr.Row():
        dataset = gr.File()
    gr.Markdown("""## Dataset Description""")
    with gr.Row():
        dataframe = gr.Dataframe()
    gr.Markdown("""## Select the feature to visualize""")
    with gr.Row():
      with gr.Column():
        features = gr.Dropdown(label="Select feature to visualize")
      with gr.Column():
        hue = gr.Dropdown(label="Select hue")
    with gr.Row():
      btn = gr.Button("Visualize")

    gr.Markdown("""## Visualization""")
    with gr.Row():
        plot1 = gr.Plot()
    with gr.Row():
        plot2 = gr.Plot()
    
    gr.Examples(
          examples=[["boston.csv"]],
          fn = dataset_change,
          inputs = dataset, 
          outputs = [dataframe, features, hue],
          cache_examples=True
          )

    dataset.change(fn=dataset_change, 
        inputs = dataset, 
        outputs = [dataframe, features, hue]
        )
    
    btn.click(fn=feature_select, 
        inputs=[dataset, features, hue],
        outputs=[plot1, plot2]
        )
    
demo.launch(debug=True)