Create app.py

app.py

@@ -1,67 +1,67 @@
-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}")
+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}")