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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():
iris = datasets.load_iris()
df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
df["target"] = iris.target
correlation = df["sepal length (cm)"].corr(df["petal length (cm)"])
corr = df.corr()
fig1 = plt.figure()
hm = sns.heatmap(df.corr(), annot = True)
hm.set(xlabel='\nIRIS Flower Details', ylabel='IRIS Flower Details\t', title = "Correlation matrix of IRIS data\n")
fig2 = plt.figure()
# use the function regplot to make a scatterplot
sns.regplot(x=df["sepal length (cm)"], y=df["sepal width (cm)"])
plt.show()
return fig1, fig2, plt
demo = gr.Interface(fn=findCorrelation, inputs=[], outputs=[gr.Plot(), gr.Plot(), gr.Plot()], title="Find correlation")
demo.launch()