Update app.py

app.py

@@ -1,35 +1,30 @@
 import streamlit as st
 import numpy as np
-import joblib
 import pandas as pd
+import joblib
 from tensorflow.keras.models import load_model
 
-# Load the trained autoencoder model
-try:
-    model = load_model("autoencoder_model.keras")
-except Exception as e:
-    st.error(f"Error loading model: {e}")
-    st.stop()
+# Cache the model so it's loaded only once per session
+@st.cache_resource
+def load_my_model():
+    return load_model("autoencoder_model.keras")
+
+# Cache the scaler as well
+@st.cache_resource
+def load_my_scaler():
+    return joblib.load("scaler1.pkl")
 
-# Load the scaler
-try:
-    with open("scaler.pkl", "rb") as file:
-        scaler = joblib.load(file)
-    # Check that the scaler has a transform method
-    if not hasattr(scaler, "transform"):
-        raise ValueError("Loaded scaler object does not have a 'transform' method. Please save a valid scaler.")
-except Exception as e:
-    st.error(f"Error loading scaler: {e}")
-    st.stop()
+model = load_my_model()
+scaler = load_my_scaler()
 
 # Set an anomaly threshold (adjust based on your model calibration)
-THRESHOLD = 0.7
+THRESHOLD = 0.1
 
 st.title("Maritime Anomaly Detection")
 st.markdown("### Input the following AIS features:")
 
 # Taking inputs from the user
-timestamp_str = st.text_input("# Timestamp (Format: DD/MM/YYYY HH:MM:SS)", value="27/02/2024 03:42:19")
+timestamp_str = st.text_input("# Timestamp (Format: DD/MM/YYYY HH:MM:SS)", "27/02/2024 03:42:19")
 mmsi = st.number_input("MMSI", value=0.0)
 latitude = st.number_input("Latitude", value=0.0)
 longitude = st.number_input("Longitude", value=0.0)
@@ -39,7 +34,7 @@ heading = st.number_input("Heading", value=0.0)
 
 if st.button("Run Detection"):
     try:
-        # Convert timestamp to seconds since epoch
+        # Convert the timestamp to seconds since epoch
         timestamp_sec = pd.to_datetime(timestamp_str, format="%d/%m/%Y %H:%M:%S").value / 1e9
     except Exception as e:
         st.error(f"Invalid Timestamp Format: {e}")
@@ -49,7 +44,7 @@ if st.button("Run Detection"):
     input_data = np.array([[timestamp_sec, mmsi, latitude, longitude, sog, cog, heading]])
     
     try:
-        # Scale the input data using the scaler's transform method
+        # Scale the input data
         input_scaled = scaler.transform(input_data)
         
         # Reconstruct the input using the autoencoder