#include <LiquidCrystal_I2C.h>
#include <WiFi.h>
#include "ThingSpeak.h"
#include <Wire.h>
#include <DFRobot_ADS1115.h>

LiquidCrystal_I2C lcd(0x27, 20, 4);
DFRobot_ADS1115 ads(&Wire);

const char* ssid = "Lab Aplikom";   // your network SSID (name)
const char* password = "maubuatapa";

WiFiClient  client;

unsigned long myChannelNumber = 1;
const char * myWriteAPIKey = "FAGO9WN3R6Q06DEN";

// Timer variables
unsigned long lastTime = 0;
unsigned long timerDelay = 30000;
unsigned long lastTimeIndikatorON = 0;
unsigned long timerDelayIndON = 700;
unsigned long lastTimeIndikatorOFF = 0;
unsigned long timerDelayIndOFF = 500;

int16_t Raw;
int16_t adc0, adc1, adc2, adc3;
float volts0, volts1, volts2, volts3;
int16_t mVsensor1, mVsensor2, mVsensor3;
String kondisiBuah;

char rawIinput[40];
uint16_t sensorDataArray[3] = {};
int counter = 0;

String inString = "";
String dataSensor1; //data String
String dataSensor2; //data String
String dataSensor3; //data String

// Neural Network Constants
const float x1_xoffset[3] = {204, 347, 498};
const float x1_gain[3] = {0.000769822940723634, 0.000485083676934271, 0.000507872016251904};
const float x1_ymin = -1;

// Layer 1
const float b1[3] = {-0.87085459544090348949, -1.8304478896182221348, -2.0406971720356703592};
const float IW1_1[3][3] = {
  {3.2587232865308464191, 0.99038146993084930081, 1.5118115008354817697},
  {3.3304866802176937846, 0.98307291099736404671, 4.2364332413093377383},
  {-1.3090364286156148665, -0.58868635165202010118, 1.2396102658128185237}
};

// Layer 2
const float b2 = 1.1547200452001507021;
const float LW2_1[3] = {-5.0122745796862417933, -10.063040696826149656, 1.126059049957864211};

// Sigmoid Positive Transfer Function
float logsig_apply(float n) {
  return 1.0 / (1.0 + exp(-n));
}

// Sigmoid Symmetric Transfer Function
float tansig_apply(float n) {
  return 2.0 / (1.0 + exp(-2 * n)) - 1;
}

// Map Minimum and Maximum Input Processing Function
void mapminmax_apply(float x[], float y[], int size) {
  for (int i = 0; i < size; i++) {
    y[i] = (x[i] - x1_xoffset[i]) * x1_gain[i] + x1_ymin;
  }
}

// Neural Network Simulation Function
void myNeuralNetworkFunction(float x1[], float y1[], int Q) {
  float xp1[3];
  mapminmax_apply(x1, xp1, 3);

  float a1[3];
  for (int i = 0; i < 3; i++) {
    a1[i] = tansig_apply(b1[i] + IW1_1[i][0] * xp1[0] + IW1_1[i][1] * xp1[1] + IW1_1[i][2] * xp1[2]);
  }

  float a2 = logsig_apply(b2 + LW2_1[0] * a1[0] + LW2_1[1] * a1[1] + LW2_1[2] * a1[2]);

  y1[0] = a2;
}

void setup() {
  Serial.begin(115200);
  lcd.init();
  lcd.backlight();
  lcd.setCursor(0, 0);
  lcd.print("Deteksi Buah Segar &");
  lcd.setCursor(0, 1);
  lcd.print(" Busuk berbasis JST ");
  lcd.setCursor(0, 2);
  lcd.print("TGS2600,2602&TGS822 ");
  lcd.setCursor(0, 3);
  lcd.print("    =============   ");
  ads.setAddr_ADS1115(ADS1115_IIC_ADDRESS0);   // 0x48
  ads.setGain(eGAIN_TWOTHIRDS);   // 2/3x gain
  ads.setMode(eMODE_SINGLE);       // single-shot mode
  ads.setRate(eRATE_128);          // 128SPS (default)
  ads.setOSMode(eOSMODE_SINGLE);   // Set to start a single-conversion
  ads.init();
  lcd.clear();
  delay(100);
}

void loop() {
  float y1[1];
  mVsensor1 = ads.readVoltage(0);
  mVsensor2 = ads.readVoltage(1);
  mVsensor3 = ads.readVoltage(2);
  float x1[3] = {mVsensor1, mVsensor2, mVsensor3};
  myNeuralNetworkFunction(x1, y1, 3);
  
  lcd.setCursor(0, 0);
  lcd.print("Deteksi Buah Sgr&Bsk");
  lcd.setCursor(0, 1);
  lcd.print("S1:");lcd.print(mVsensor1);lcd.print(" ");
  lcd.setCursor(0, 2);
  lcd.print("S2:");lcd.print(mVsensor2);lcd.print(" ");
  lcd.setCursor(0, 3);
  lcd.print("S3:");lcd.print(mVsensor3);lcd.print(" ");
  Serial.print(mVsensor1);Serial.print(",");Serial.print(mVsensor2);Serial.print(",");Serial.println(mVsensor3);
  if(y1[0]<0.5){
    kondisiBuah = "Busuk";
  }
  if(y1[0]>0.8){
    kondisiBuah = "Segar";
  }
  lcd.setCursor(8, 1);
  lcd.print(y1[0],9);
  lcd.setCursor(8, 2);
  lcd.print("Buah: ");lcd.print(kondisiBuah);
  lcd.setCursor(8, 3);
  lcd.print("Ch.Update OK");
  delay(1000);
}