Update app.py

app.py

@@ -1,31 +1,30 @@
-# streamlit_app.py
+# ─── streamlit_app.py ────────────────────────────────────────────────────────
+
 import streamlit as st
+
+# ─── THIS MUST BE FIRST ───────────────────────────────────────────────────────
+st.set_page_config(page_title="Keystroke Dynamics Auth", layout="wide")
+
 import pandas as pd
 import numpy as np
 import joblib
 import tensorflow as tf
 
-# ─── Caching loaders so they only run once ───────────────────────────────────
-@st.cache(allow_output_mutation=True)
+# ─── Caching loaders so they only run only once per session ──────────────────
+@st.cache_resource
 def load_preprocessor(path="preprocessor.pkl"):
     return joblib.load(path)
 
-@st.cache(allow_output_mutation=True)
+@st.cache_resource
 def load_label_encoder(path="label_encoder.pkl"):
     return joblib.load(path)
 
-@st.cache(allow_output_mutation=True)
+@st.cache_resource
 def load_model(path="keystroke_dnn.h5"):
     return tf.keras.models.load_model(path)
 
 # ─── Prediction function ────────────────────────────────────────────────────
 def predict_subjects(df_raw):
-    """
-    Takes a DataFrame of raw keystroke features, drops any
-    'subject'/'sessionIndex'/'rep' columns, re-orders to the
-    exact list the preprocessor saw at train-time, scales,
-    runs the DNN, and returns predicted IDs + probabilities.
-    """
     preprocessor  = load_preprocessor()
     label_encoder = load_label_encoder()
     model         = load_model()
@@ -58,7 +57,6 @@ def predict_subjects(df_raw):
 
 # ─── Streamlit UI ──────────────────────────────────────────────────────────
 def main():
-    st.set_page_config(page_title="Keystroke Dynamics Auth", layout="wide")
     st.title("🔑 Keystroke Dynamics Authentication")
     st.markdown(
         "Upload a CSV of raw keystroke‐feature vectors (one row per sample). "