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()
    
    plot1 = plt.figure()
    if hue:
      sns.histplot(data = df, x = df[feature], kde = True, hue = hue)
    else:
      sns.histplot(data = df, x = df[feature], kde = True)
    
    if feature in non_numeric_cols:
      plot2 = plt.figure()
      if hue:
        sns.countplot(x = df[feature], data = df, palette='rainbow', hue = hue)
      else:
        sns.countplot(x = df[feature], data = df, palette='rainbow')
    else:
      plot2 = plt.figure()
      if hue:
        sns.boxplot(x = df[feature], hue = hue)
      else:
        sns.boxplot(x = df[feature])

    return plot1, plot2


with gr.Blocks() as demo:
    gr.Markdown("""### Input Dataset""")
    with gr.Row():
        dataset = gr.File()
    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("""### Visualizations""")
    with gr.Row():
        plot1 = gr.Plot()
    with gr.Row():
        plot2 = gr.Plot()
    
    gr.Examples(
          examples=[],
          fn = dataset_change,
          inputs = dataset, 
          outputs = [dataframe, features, hue]
          )

    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)