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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)
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