Update app.py

app.py

@@ -1,7 +1,8 @@
 import streamlit as st, base64
 import pandas as pd, seaborn as sns
 import os, matplotlib.pyplot as plt
-import pickle, numpy as np
+import pickle, numpy as np, xgboost as xgb
+from keras.models import load_model
 from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
 
@@ -246,7 +247,8 @@ def main():
                 # XGBoost
             if st.sidebar.checkbox("XGBoost"):
                 st.subheader(":orange[XGBoost] :sunglasses:")
-                xg = pickle.load(open("scoring_xg.pkl", "rb"))
+                xg = xgb.XGBClassifier()
+                xg = load_model("xg.json")
                 pred = xg.predict(X)
                 pred_proba = xg.predict_proba(X)
                 st.subheader(':green[Prediction]')
@@ -273,7 +275,7 @@ def main():
                 # ANN
             if st.sidebar.checkbox("Neural Network"):
                 st.subheader(":orange[Neural Network] :sunglasses:")
-                ann = pickle.load(open("scoring_ann.pkl", "rb"))
+                ann = load_model("ann.h5")
                 pred = ann.predict(X)
                 pred_proba = ann.predict_proba(X)
                 st.subheader(':green[Prediction]')