app.py

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

def findCorrelation(dataset, target):
 
 print(dataset.name)
 print("\n")
 
 print(target)
 print(type(target))
 print(str(target))
 print("\n")
 
 df = pd.read_csv(dataset.name)
 print(df)
 print("\n")
 
 d = df.corr()["coma"].to_dict()
 print(d)
 labels = sorted(d.items(), key=lambda x: x[1], reverse=True)
 
 del labels.[target]
 
 fig1 = plt.figure()
 hm = sns.heatmap(df.corr(), annot = True)
 hm.set(title = "Correlation matrix of dataset\n")
 
 fig2 = plt.figure()
 # use the function regplot to make a scatterplot
 sns.regplot(x=labels.keys()[0], y=df[target])
 
 fig3 = plt.figure()
 # use the function regplot to make a scatterplot
 sns.regplot(x=labels.keys()[1], y=df[target])

 fig4 = plt.figure()
 # use the function regplot to make a scatterplot
 sns.regplot(x=labels.keys()[2], y=df[target])
 
 return labels, fig1, fig2, fig3, fig4

demo = gr.Interface(fn=findCorrelation, inputs=[gr.File(), 'text'], outputs=[gr.Label(), gr.Plot(), gr.Plot(), gr.Plot(), gr.Plot()], title="Find correlation")
demo.launch(debug=True)