Update models/energy_anomaly_detection.py

models/energy_anomaly_detection.py

@@ -1,42 +1,41 @@
-import numpy as np
+from tensorflow.keras.models import load_model
 import pandas as pd
+import numpy as np
 from sklearn.preprocessing import MinMaxScaler
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import LSTM, Dense
-from tensorflow.keras.optimizers import Adam
-
-# Load energy generation data (replace with your dataset)
-data = pd.read_csv('energy_data.csv')  # Replace with the path to your energy data
-energy_data = data['energy_generation'].values.reshape(-1, 1)
-
-# Normalize the data
-scaler = MinMaxScaler(feature_range=(0, 1))
-energy_data = scaler.fit_transform(energy_data)
-
-# Prepare the dataset for LSTM
-X = []
-y = []
-for i in range(60, len(energy_data)):
-    X.append(energy_data[i-60:i, 0])
-    y.append(energy_data[i, 0])
-
-X = np.array(X)
-y = np.array(y)
-
-# Reshape X for LSTM input
-X = X.reshape((X.shape[0], X.shape[1], 1))
-
-# Define the LSTM model
-model = Sequential()
-model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
-model.add(LSTM(units=50, return_sequences=False))
-model.add(Dense(units=1))
-
-# Compile the model
-model.compile(optimizer=Adam(), loss='mean_squared_error')
-
-# Train the model
-model.fit(X, y, epochs=10, batch_size=32)
 
-# Save the model to a file
-model.save('lstm_energy_model.h5')  # Save the trained model as .h5 file
+# Correct path to your saved LSTM model
+model = load_model("models/lstm_energy_model.h5")  # Adjust the path to the actual model location
+
+def detect_energy_anomalies(file_path):
+    """
+    Detect anomalies in energy generation data (time-series) using LSTM.
+    Args:
+    - file_path (str): Path to the CSV/JSON file containing energy generation data
+
+    Returns:
+    - anomaly (str): Energy anomaly detection result
+    """
+    # Load energy generation data (assumes CSV format with timestamp and generation columns)
+    data = pd.read_csv(file_path)
+    energy_data = data['energy_generation'].values.reshape(-1, 1)
+
+    # Normalize data for LSTM
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    energy_data = scaler.fit_transform(energy_data)
+
+    # Prepare data for LSTM
+    X = []
+    for i in range(60, len(energy_data)):
+        X.append(energy_data[i-60:i, 0])
+    X = np.array(X)
+    X = X.reshape((X.shape[0], X.shape[1], 1))
+
+    # Predict with LSTM model
+    prediction = model.predict(X)
+    last_predicted_value = prediction[-1][0]  # Get last prediction
+    
+    # Define threshold for anomaly (e.g., 10% deviation from the normal value)
+    threshold = 0.1
+    anomaly = "Anomaly Detected" if abs(last_predicted_value - energy_data[-1][0]) > threshold else "No Anomaly"
+
+    return anomaly