--- a
+++ b/influx.py
@@ -0,0 +1,43 @@
+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!")