import numpy as np

import pandas as pd

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import LSTM, Dense, Dropout

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

# Load dataset (replace with actual dataset path)

file_path = "PPG_Dataset.csv"  

df = pd.read_csv(file_path)

# Select only Heart Rate (HR) & SpO₂ as features

X = df[['Heart_Rate', 'SpO2']].values  

y = df['Label'].values  # Label: 0 = Normal, 1 = Abnormal

# Normalize input features

scaler = StandardScaler()

X = scaler.fit_transform(X)

# Reshape for LSTM input (samples, timesteps, features)

X = X.reshape(X.shape[0], 1, 2)  # 1 timestep, 2 features (HR & SpO₂)

# Split data into training (80%) and testing (20%)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define LSTM model

model = Sequential([

    LSTM(64, return_sequences=True, input_shape=(1, 2)),  

    Dropout(0.2),

    LSTM(32),

    Dropout(0.2),

    Dense(16, activation='relu'),

    Dense(1, activation='sigmoid')  # Binary classification output

])

# Compile model

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model

model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test))

# Save the trained model

model.save("heart_monitor_lstm.h5")

np.save("scaler.npy", scaler.mean_)  # Save scaler for real-time data

print("✅ Model training complete!")