# Usage Example for the Fall Prediction Dataset
# Please install dependencies before:
# pip install -r requirements.txt

# Import necessary libraries
import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM, Dropout
from sklearn.model_selection import train_test_split

# Load the dataset from Huggingface or a local file path
# Example for local loading; replace with Huggingface dataset call if applicable
real_data = pd.read_csv('dataset.csv.bz2', compression='bz2')

# Preview the dataset
print(real_data.head())

# Select relevant columns (replace these with actual column names from your dataset)
# Here we assume that the dataset contains sensor readings like gyroscope and accelerometer data
relevant_columns = ['gyro_x', 'gyro_y', 'gyro_z', 'acc_x', 'acc_y', 'acc_z', 'upright']
sensordata = real_data[relevant_columns]

# Split the data into features (X) and labels (y)
# 'fall_label' is assumed to be the column indicating whether a fall occurred
X = sensordata.drop(columns=['upright'])  # Replace 'fall_label' with the actual label column
y = sensordata['upright']

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Reshape data for LSTM input (assuming time-series data)
# Adjust the reshaping based on your dataset structure
X_train = X_train.values.reshape(X_train.shape[0], X_train.shape[1], 1)
X_test = X_test.values.reshape(X_test.shape[0], X_test.shape[1], 1)

# Define a simple LSTM model
model = Sequential()
model.add(LSTM(64, input_shape=(X_train.shape[1], 1)))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))

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

# Train the model
history = model.fit(X_train, y_train, epochs=10, batch_size=64, validation_data=(X_test, y_test))

# Evaluate the model on the test set
loss, accuracy = model.evaluate(X_test, y_test)
print(f"Test Accuracy: {accuracy * 100:.2f}%")

# You can save the model if needed
# model.save('fall_prediction_model.h5')