import numpy as np
import pandas as pd
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 dataset (ensure 'energy_data.csv' is in the /data folder)
data = pd.read_csv('data/energy_data.csv')  # Replace with your actual filename if different
energy_data = data['energy_generation'].values.reshape(-1, 1)

# Normalize data
scaler = MinMaxScaler(feature_range=(0, 1))
energy_data = scaler.fit_transform(energy_data)

# Prepare data 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, y = np.array(X), np.array(y)
X = X.reshape((X.shape[0], X.shape[1], 1))

# Define 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 and train
model.compile(optimizer=Adam(), loss='mean_squared_error')
model.fit(X, y, epochs=10, batch_size=32)

# Save trained model
model.save('models/lstm_energy_model.h5')
print("✅ LSTM model trained and saved to models/lstm_energy_model.h5")