app.py

import gradio as gr
import pandas as pd
from sklearn import datasets
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder

def findCorrelation(dataset, target):

  df = pd.read_csv(dataset.name)
  
  non_numeric_cols = df.select_dtypes('object').columns.tolist()
  
  if target in non_numeric_cols:
    label_encoder = LabelEncoder()
    df[non_numeric_col] = label_encoder.fit_transform(df[target])
  
  d = df.corr()[target].to_dict()
  d.pop(target)
  
  keys = sorted(d.items(), key=lambda x: x[0], reverse=True) 
    
  fig1 = plt.figure()
  hm = sns.heatmap(df.corr(), annot = True)
  hm.set(title = "Correlation matrix of dataset\n")
    
  try:
    fig2 = plt.figure()
    sns.regplot(x=df[keys[0][0]], y=df[target])
  except:
    fig2 = plt.figure()
  
  try:
    fig3 = plt.figure()
    sns.regplot(x=df[keys[1][0]], y=df[target])
  except:
    fig3 = plt.figure()

  try:
    fig4 = plt.figure()
    sns.regplot(x=df[keys[2][0]], y=df[target])
  except:
    fig4 = plt.figure()
    
  return d, fig1, fig2, fig3, fig4

with gr.Blocks() as demo:
    with gr.Row():
      with gr.Column():
        file = gr.File()
      with gr.Column():
        inp = gr.Textbox(placeholder="Enter the target feature name")
    btn = gr.Button("Find Correlation")
    with gr.Row():
       labels = gr.Label(num_top_classes = 10)
    with gr.Row():
       fig1 = gr.Plot()
    with gr.Row():
       with gr.Column():
         fig2 = gr.Plot()
       with gr.Column():
         fig3 = gr.Plot()
    with gr.Row():
       fig4 = gr.Plot()
    with gr.Row():
      gr.Examples(
                examples = [["boston.csv", "MEDV"]], fn=findCorrelation, inputs=[file, inp], outputs=[labels, fig1, fig2, fig3, fig4])
    btn.click( fn=findCorrelation, inputs=[file, inp], outputs=[labels, fig1, fig2, fig3, fig4])

demo.launch()