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app.py

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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()
+
+  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")
+
+  # use the function regplot to make a scatterplot
+  sns.regplot(x=df["sepal length (cm)"], y=df["sepal width (cm)"])
+  plt.show()
+  return plt, plt
+
+demo = gr.Interface(fn=findCorrelation, inputs=[], outputs=[gr.Plot(), gr.Plot()], title="Find correlation")
+demo.launch()