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

file_path = "/mnt/data/PPG_Dataset.csv"

df = pd.read_csv(file_path)

# Select only PPG-related fields (HR, SpO2) as features

X = df[['Heart_Rate', 'SpO2']].values  # Only HR & SpO2

y = df['Label'].values  # Binary classification (0 = Normal, 1 = Abnormal)

# Normalize features

scaler = StandardScaler()

X = scaler.fit_transform(X)

# Reshape X to fit LSTM input shape (samples, timesteps=1, features)

X = X.reshape(X.shape[0], 1, X.shape[1])  # (samples, 1 time step, 2 features)

# Split data into training and testing sets (80% train, 20% test)

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

# Define the LSTM model

model = Sequential([

    LSTM(64, input_shape=(1, X.shape[2])),  # Single timestep

    Dropout(0.2),

    Dense(32, activation='relu'),

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

])

# Compile the 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_no_timesteps.h5")

print("✅ Model training complete without time steps!")