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app.py

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+import streamlit as st
+import numpy as np
+import joblib
+import pandas as pd
+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()
+
+# 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()
+
+# Set an anomaly threshold (adjust based on your model calibration)
+THRESHOLD = 0.7
+
+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")
+mmsi = st.number_input("MMSI", value=0.0)
+latitude = st.number_input("Latitude", value=0.0)
+longitude = st.number_input("Longitude", value=0.0)
+sog = st.number_input("SOG", value=0.0)
+cog = st.number_input("COG", value=0.0)
+heading = st.number_input("Heading", value=0.0)
+
+if st.button("Run Detection"):
+    try:
+        # Convert 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}")
+        st.stop()
+    
+    # Create an array from the inputs
+    input_data = np.array([[timestamp_sec, mmsi, latitude, longitude, sog, cog, heading]])
+    
+    try:
+        # Scale the input data using the scaler's transform method
+        input_scaled = scaler.transform(input_data)
+        
+        # Reconstruct the input using the autoencoder
+        reconstructed = model.predict(input_scaled)
+    
+        # Calculate the reconstruction error (Mean Squared Error)
+        reconstruction_error = np.mean(np.square(input_scaled - reconstructed))
+    
+        # Display the result based on the threshold
+        if reconstruction_error > THRESHOLD:
+            st.error(f"Anomaly detected, Reconstruction Error = {reconstruction_error:.4f}")
+        else:
+            st.success(f"No anomaly detected, Reconstruction Error = {reconstruction_error:.4f}")
+    except Exception as e:
+        st.error(f"Error during detection: {e}")

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f9db2af34080e55f61f2a8b4e9a458bd419a8197f456c0c626ce7fec2e26c5f
+size 1151