/***************************************************
  Consentium IoT - TinyML Data Acquisition Example
  -------------------------------------------------
  This example demonstrates the use of Consentium's TinyML library 
  for logging sensor data and preparing it for real-time inference 
  or processing. The library is designed to work seamlessly with 
  EdgeModelKit , a Python library for sensor data acquisition, 
  logging, and real-time model testing.

  Features:
  - Compatible with ESP32 and Raspberry Pi Pico W edge boards
  - Data acquisition from sensor (e.g., IMU) for logging purposes
  - Logging data values to serial for monitoring and debugging
  - Modular structure for easy integration of further processing or ML models
  - Seamless integration with EdgeModelKit for advanced workflows:
    - Fetch and log sensor data transmitted over serial in real time
    - Organize sensor data with class labels, timestamps, and counters
    - Test `.keras` models and convert them to TFLite formats for edge deployments

  Tutorials and Documentation:
  Visit us at: 
  - TinyML Library: https://docs.consentiumiot.com
  - EdgeModelKit: https://github.com/ConsentiumIoT/edgemodelkit

  For Support:
  Email: official@consentiumiot.com

  MIT license - Redistribution must include this header.
 ****************************************************/

#include <EdgeStream.h> 

EdgeStream stream;

void setup() {
  stream.begin();
}

void loop() {
  // Define the sensor name and sample sensor values (can be replaced with actual sensor data)
  const char* sensorName = "IMU";
  vector<double> sensorValues = {1, 2, 3};

  // Log the sensor data to the Serial Monitor
  stream.logData(sensorName, sensorValues);

  // Delay of 1 second before the next loop iteration
  delay(1000);
}
