Update app.py

app.py

@@ -9,6 +9,7 @@ from math import sqrt
 from scipy import stats as st
 from matplotlib import pyplot as plt
 
+from sklearn.utils import resample
 from sklearn.calibration import CalibratedClassifierCV
 
 import shap
@@ -186,12 +187,29 @@ output_y4 = (
 
 
 #Define predict for y1.
+def bootstrap_ci(data, model, alpha=0.05):
+    """Compute bootstrap confidence intervals"""
+    bootstrapped_probs = [model.predict_proba(resample(data))[0][1] for _ in range(1000)]
+    lower = np.percentile(bootstrapped_probs, 100*(alpha/2.0))
+    upper = np.percentile(bootstrapped_probs, 100*(1-alpha/2.0))
+    return lower, upper
+
 def y1_predict(*args):
     df1 = pd.DataFrame([args], columns=x1.columns)
     pos_pred = y1_calib_model.predict_proba(df1)
     prob = pos_pred[0][1]
-    prob = 1-prob
-    output = output_y1.format(prob * 100)
+    prob = 1 - prob
+    lower, upper = bootstrap_ci(df1, y1_calib_model)
+    output = (
+        f"""
+        <br/>
+        <center>The probability of 6-month survival:</center>
+        <br/>
+        <center><h1>{prob:.2f}%</h1></center>
+        <br/>
+        <center>95% Confidence Interval: [{lower:.2f}%, {upper:.2f}%]</center>
+        """
+    )
     return output
 
 #Define predict for y2.