Update app.py

app.py

@@ -4,25 +4,33 @@ 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)
+def findCorrelation(dataset, target):
+  df = pd.read_csv(dataset)
 
-  df["target"] = iris.target
+  df["target"] = target
   
-  correlation = df["sepal length (cm)"].corr(df["petal length (cm)"])
-  corr = df.corr()
+  d = df.corr()['target'].to_dict()
+  labels = sorted(d.items(), key=lambda x: x[1], reverse=True)
+  
+  labels.pop("target")
   
   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")
+  hm.set(title = "Correlation matrix of dataset\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()
+  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 fig1, fig2, plt
+  return labels, fig1, fig2, fig3, fig4
 
-demo = gr.Interface(fn=findCorrelation, inputs=[], outputs=[gr.Plot(), gr.Plot(), gr.Plot()], title="Find correlation")
+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()