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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() |