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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
from sklearn.preprocessing import LabelEncoder
def findCorrelation(dataset, target):
df = pd.read_csv(dataset.name)
non_numeric_cols = df.select_dtypes('object').columns.tolist()
if target in non_numeric_cols:
label_encoder = LabelEncoder()
df[non_numeric_col] = label_encoder.fit_transform(df[target])
d = df.corr()[target].to_dict()
d.pop(target)
keys = sorted(d.items(), key=lambda x: x[0], reverse=True)
fig1 = plt.figure()
hm = sns.heatmap(df.corr(), annot = True)
hm.set(title = "Correlation matrix of dataset\n")
fig2 = plt.figure()
sns.regplot(x=df[keys[0][0]], y=df[target])
fig3 = plt.figure()
sns.regplot(x=df[keys[1][0]], y=df[target])
fig4 = plt.figure()
sns.regplot(x=df[keys[2][0]], y=df[target])
return d, fig1, fig2, fig3, fig4
demo = gr.Interface(fn=findCorrelation, inputs=[gr.File(), 'text'], outputs=[gr.Label(num_top_classes = 10), gr.Plot(), gr.Plot(), gr.Plot(), gr.Plot()], title="Find correlation")
demo.launch(debug=True) |