Update app.py

app.py

@@ -3,10 +3,6 @@ import pickle
 import pandas as pd
 from sklearn.preprocessing import StandardScaler
 from sklearn.model_selection import train_test_split
-from sklearn import __version__ as sklearn_version
-
-# Check scikit-learn version
-print(f"scikit-learn version: {sklearn_version}")
 
 # Load the data
 heart = pd.read_csv('heart.dat', header=None, sep=' ', names=['age', 'sex', 'cp', 'trestbps', 'chol',
@@ -69,22 +65,22 @@ def make_prediction(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang,
 
 # Create the Gradio interface
 inputs = [
-    gr.inputs.Number(label='age'), 
-    gr.inputs.Radio(choices=[0,1], label='sex'),
-    gr.inputs.Dropdown(choices=[1,2,3,4], label='chest pain type'),
-    gr.inputs.Number(label='resting blood pressure'),
-    gr.inputs.Number(label='serum cholestoral'),
-    gr.inputs.Radio(choices=[0,1], label='fasting blood sugar'),
-    gr.inputs.Radio(choices=[0,1,2], label='resting electrocardiographic'),
-    gr.inputs.Number(label='maximum heart rate'),
-    gr.inputs.Radio(choices=[0,1], label='exercise induced angina'),
-    gr.inputs.Number(label='oldpeak'),
-    gr.inputs.Dropdown(choices=[1,2,3], label='slope ST'),
-    gr.inputs.Dropdown(choices=[0,1,2,3], label='major vessels'),
-    gr.inputs.Dropdown(choices=[3,6,7], label='thal'),
-    gr.inputs.Dropdown(choices=['Tree', 'QDA', 'MLP', 'Log', 'LDA', 'For', 'SVM'], label='Select the model')
+    gr.Number(label='age'),
+    gr.Radio(choices=[0,1], label='sex'),
+    gr.Dropdown(choices=[1,2,3,4], label='chest pain type'),
+    gr.Number(label='resting blood pressure'),
+    gr.Number(label='serum cholestoral'),
+    gr.Radio(choices=[0,1], label='fasting blood sugar'),
+    gr.Radio(choices=[0,1,2], label='resting electrocardiographic'),
+    gr.Number(label='maximum heart rate'),
+    gr.Radio(choices=[0,1], label='exercise induced angina'),
+    gr.Number(label='oldpeak'),
+    gr.Dropdown(choices=[1,2,3], label='slope ST'),
+    gr.Dropdown(choices=[0,1,2,3], label='major vessels'),
+    gr.Dropdown(choices=[3,6,7], label='thal'),
+    gr.Dropdown(choices=['Tree', 'QDA', 'MLP', 'Log', 'LDA', 'For', 'SVM'], label='Select the model')
 ]
 
-outputs = gr.outputs.Label(label='Predicted class probabilities')
+outputs = gr.Label(label='Predicted class probabilities')
 
 gr.Interface(fn=make_prediction, inputs=inputs, outputs=outputs).launch()