Update app.py

app.py

@@ -6,12 +6,10 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import accuracy_score
 import gradio as gr
 
-# Example dataset (replace with actual dataset)
 # Load dataset
-#df = pd.read_csv('heart.csv')
-
 df = pd.read_csv("hf://datasets/buio/heart-disease/heart.csv")
 
+# Define features and target
 X = df.drop('target', axis=1)
 y = df['target']
 
@@ -40,10 +38,7 @@ def predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, e
 
     # Make prediction
     prediction = model.predict(input_data)
-    if prediction[0] == 1:
-        return "The person has heart disease."
-    else:
-        return "The person does not have heart disease."
+    return "The person has heart disease." if prediction[0] == 1 else "The person does not have heart disease."
 
 # Gradio integration
 def gradio_predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):