Update app.py

app.py

@@ -6,7 +6,7 @@ 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()
@@ -44,7 +44,27 @@ def findCorrelation(dataset, target):
     
   return d, fig1, fig2, fig3, fig4
 
-examples = [["boston.csv", "MEDV"]]
+with gr.Blocks() as demo:
+    with gr.Row():
+      with gr.Column():
+        file = gr.File()
+      with gr.Column():
+        inp = gr.Textbox(placeholder="Enter the target feature name")
+    btn = gr.Button("Find Correlation")
+    with gr.Row():
+       labels = gr.Label(num_top_classes = 10)
+    with gr.Row():
+       fig1 = gr.Plot()
+    with gr.Row():
+       with gr.Column():
+         fig2 = gr.Plot()
+       with gr.Column():
+         fig3 = gr.Plot()
+    with gr.Row():
+       fig4 = gr.Plot()
+    with gr.Row():
+      gr.Examples(
+                examples = [["boston.csv", "MEDV"]], fn=findCorrelation, inputs=[file, inp], outputs=[labels, fig1, fig2, fig3, fig4])
+    btn.click( fn=findCorrelation, inputs=[file, inp], outputs=[labels, 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", examples = examples)
-demo.launch(debug=True)
+demo.launch()