/**
 * @file Measurement.js
 *
 * Permission is hereby granted to any person obtaining a copy of this software 
 * and associated documentation files (the "Software"), to use it for personal 
 * or non-commercial purposes, with the following restrictions:
 *
 * 1. **No Copying or Redistribution**: The Software or any of its parts may not 
 *    be copied, merged, distributed, sublicensed, or sold without explicit 
 *    prior written permission from the author.
 * 
 * 2. **Commercial Use**: Any use of the Software for commercial purposes requires 
 *    a valid license, obtainable only with the explicit consent of the author.
 * 
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 
 * FITNESS FOR A PARTICULAR PURPOSE, AND NONINFRINGEMENT. IN NO EVENT SHALL THE 
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES, OR OTHER 
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT, OR OTHERWISE, ARISING FROM, 
 * OUT OF, OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 
 * SOFTWARE.
 *
 * Ownership of this code remains solely with the original author. Unauthorized 
 * use of this Software is strictly prohibited.
 *
 * Author:
 * - Rene De Ren / Sjoerd Fijnje
 * Email:
 * - r.de.ren@brabantsedelta.nl 
 *
 */

const EventEmitter = require('events');
const Logger = require('../../../generalFunctions/helper/logger');
const defaultConfig = require('./monsterConfig.json');
const ConfigUtils = require('../../../generalFunctions/helper/configUtils');
const tf = require('@tensorflow/tfjs');
const tfLoader = require('./modelLoader');

class Monster{
  /*-------------------               Construct and set vars               -------------------*/
  constructor(config={}) {

    //init
    this.init               = false;    // keep track of init

    this.emitter = new EventEmitter();  // Own EventEmitter
    this.configUtils = new ConfigUtils(defaultConfig,config.general.logging.enabled,config.general.logging.logLevel); // ConfigUtils
    this.config = this.configUtils.initConfig(config);

     // Init after config is set
     this.logger = new Logger(this.config.general.logging.enabled,this.config.general.logging.logLevel, this.config.general.name);

    this.modelLoader = new tfLoader(this.logger); // ModelLoader

    // -------------------------------------- fetch dependencies --------------------------
    this.convert = require('../../../convert/dependencies/index'); 
    this.math = require('mathjs');

    this.model = null; // TensorFlow model placeholder

    //place holders for output data
    this.output             = {}      ; // object to place all relevant outputs in and preform event change check on
    this.child              = {}        ; // register childs

    //Specific object info
    this.aquonSampleName    = "112100"      ;  // aquon sample name to start automatic sampling on the basis of the document
    this.monsternametijden  = {}      ;  // json monsternametijden file?
    this.rain_data          = {}      ;  // precipitation data
    this.aggregatedOutput   = {}      ;  // object that does not contain momentary values but a combination of all kinds of data over a fixed period of time
    this.sumRain            = 0       ;  // total sum of rain over time window + n hours and - n hours
    this.avgRain            = 0       ; // total divided by number of locations to get average over total time
    this.daysPerYear        = 0       ;  // how many days remaining for this year

    // outputs
    this.pulse              = false;    // output pulse to sampling machine
    this.bucketVol          = 0;        // how full is the sample?
    this.sumPuls            = 0;        // number of pulses so far
    this.predFlow           = 0;        // predicted flow over sampling time in hours, expressed in m3
    this.bucketWeight  = 0;        // actual weight of bucket
    
    //inputs 
    this.q                  = 0;        // influent flow in m3/h
    this.i_start            = false     // when true, the program gets kicked off calculating what it needs to take samples
    this.sampling_time      = config.constraints.samplingtime;       // time expressed in hours over which the sampling will run (currently 24) 
    this.emptyWeightBucket  = config.asset.emptyWeightBucket;        // empty weight of the bucket

    // internal vars
    this.temp_pulse         = 0;        // each interval pulses send out 1 and then reset
    this.volume_pulse       = 0.05;     // define volume pulse expressed in L
    this.minVolume          = config.constraints.minVolume;// define min volume in a sampling cabinet before a sample is declared valid expressed in L
    this.maxVolume          = 0;        // calculated maxvolume depending on own weight
    this.maxWeight          = config.constraints.maxWeight;// define max volume in a sampling cabinet before a sample is declared invalid expressed in L
    this.cap_volume         = 55;       // abs max capacity of bucket (volume) in liters
    this.targetVolume       = 0;        // volume of sampling cabinet that model aims for
    this.minPuls            = 0;        // calculates the min pulses depending on min_vol and max_vol
    this.maxPuls            = 0;        // calculates the max pulses depending on min_vol and max_vol
    this.absMaxPuls         = 0;        // capacity of sampling cabinet (number of pulses)
    this.targetPuls         = 0;        // keeps track of the desired amount of pulses (+- 50% tolerance), based on aimed volume
    this.m3PerPuls          = 0;        // each pulse is equal to a number of m3
    this.predM3PerSec       = 0;        // predicted flow in m3 per second
    this.m3PerTick          = 0;        // actual measured flow in m3 per second
    this.m3Total            = 0;        // total measured flow over sampling time in m3
    this.running            = false;    // define if sampling is running or not

    this.qLineRaw           = {};   // see example
    this.minSeen            = {};   // keeps track of minimum ever seen so far in a time period for each hour (over totals not every value)
    this.maxSeen            = {};   // keeps track of maximum ever seen so far in a time period for each hour (over totals not every value)
    this.qLineRefined       = {};   // this should be the ( quantiles? ) classified in the datasets
    this.calcTimeShiftDry   = 0;    // What is the delay after a dry period of minimum n hours
    this.calcTimeShiftWet   = 0;
    this.calcCapacitySewer  = 0;
    // how much rain goes to the sewage ? -> calculate surface area of hardend / sewage.

    this.minDryHours        = 0;    // what is the minimum of dry hours before we can calculate timeshift? spot this with moving average?
    this.minWetHours        = 0;    // how long does it take to remove all the rain? 
    this.resolution         = 0;    // Number of chunks in qLineRaw / define how big the window is to sum all values ( for now we need to take 1 hour or bigger resolutions but in the future smaller is better to see more accurate correlations)
    this.tmpTotQ            = 0;    // keep track of sum of q within resolution window
    
    //old prediction factor
    this.predFactor         = 0.7;        // define factor as multiplier for prediction
  
    //track program start and stop
    this.start_time         = Date.now(); // default start time
    this.stop_time          = Date.now(); // default stop time
    this.flowTime           = 0; //keep track in detail how much time between 2 ticks for more accurate flow measurement
    this.timePassed         = 0; // time in seconds
    this.timeLeft           = 0; // time in seconds
    this.currHour           = new Date().getHours(); // on init define in which hour we are 0 - 23


    this.init               = true;     // end of constructor

    //set boundries and targets after init based on above settings
    this.set_boundries_and_targets();


  }

  /*-------------------               GETTER/SETTERS Dynamics               -------------------*/
  set monsternametijden(value){

    if(this.init){
      if(Object.keys(value).length > 0){

        //check if push is in valid format and not null
        if(
          typeof value[0].SAMPLE_NAME !== 'undefined'
          &&
          typeof value[0].DESCRIPTION !== 'undefined'
          &&
          typeof value[0].SAMPLED_DATE !== 'undefined'
          &&
          typeof value[0].START_DATE !== 'undefined'
          &&
          typeof value[0].END_DATE !== 'undefined'
          ){
    
            //each time this changes we load the next date applicable for this function
            this._monsternametijden = value;

            //fetch dates
            this.regNextDate(value);
    
          }
          else{
            // Monsternametijden object Wrong format contact AQUON
          }
        }
        else{
          // Monsternametijden object Wrong format contact AQUON
        }
    }
  }

  get monsternametijden(){
    return this._monsternametijden;
  }

  set rain_data(value){
   
    //retrieve precipitation expected during the coming day and precipitation of yesterday
    this._rain_data = value;

    //only update after init and is not running. 
    if(this.init && !this.running){
      this.updatePredRain(value);
    }

  }
  
  get rain_data(){
    return this._rain_data;
  }

  set bucketVol(val){
    
    //Put val in local var
    this._bucketVol = val;

    //Place into output object
    this.output.bucketVol = val;

    // update bucket weight
    this.bucketWeight = val + this.emptyWeightBucket;
  }

  get bucketVol(){
    return this._bucketVol;
  }

  set minVolume(val){
    
    //Protect against 0
    val == 0 ? val = 1 : val = val;

    this._minVolume = val;

    //Place into output object
    this.output.minVolume = val;
  }

  get minVolume(){
    return this._minVolume;
  }

  set q(val){
    
    //Put val in local var
    this._q = val;

    //Place into output object
    this.output.q = val;
    this.output.qm3sec = this.convert(val).from('m3/h').to('m3/s');

  }

  get q(){
    return this._q;
  }

  /*-------------------               FUNCTIONS               -------------------*/

  set_boundries_and_targets(){

    // define boundries for algorithm
    this.maxVolume = this.maxWeight - this.emptyWeightBucket ; // substract bucket weight of max volume assuming they are both on a 1 to 1 ratio 
    this.minPuls  = Math.round(this.minVolume / this.volume_pulse); // minimum pulses we want before we have a valid sample
    this.maxPuls  = Math.round(this.maxVolume / this.volume_pulse); // maximum pulses we can handle (otherwise sample is too heavy)
    this.absMaxPuls =  Math.round(this.cap_volume / this.volume_pulse); // number of pulses a sample can contain before overflowing
    // define target values
    this.targetVolume = this.minVolume * Math.sqrt(this.maxVolume/this.minVolume); 
    //old way
    //this.targetVolume = Math.round( ( ( (this.maxVolume - this.minVolume) / 2 ) + this.minVolume ) * 100) / 100; // calculate middle between min and max
    // correct target values
    this.targetPuls = Math.round(this.targetVolume / this.volume_pulse) ; // define desired amount of pulses (in this case our prediction can deviate 50% up and 50% down without a problem)
  }

  updateArchiveRain(val){

  }

  updatePredRain(value){
    //make date objects to define relative time window
    let now = new Date(Date.now());
    let past = new Date(Date.now());
    let future = new Date(Date.now());
    let totalRaw = {};
    let totalProb = {};
    let totalAvg = {};

    //refine object with different values
    let rain = {};
    rain.hourly = {}; // an object with timestamps and aggreated over all locations summed precipation in mm
    rain.hourly.time = [];
    rain.hourly.precipationRaw = [];
    rain.hourly.precipationProb = [];

    let numberOfLocations = 0;

    //Make timestamp + 24 hours
    future.setHours(now.getHours() + 24);

    //Make timestamp - 24hours
    past.setHours(now.getHours() - 24);

    //go through all locations and sum up the average precipation of each location so we have summed precipation over every hour
    Object.entries(value).forEach(([locationKey, location],locationindex) => {

      //number of locations
      numberOfLocations++;

      // make an object to keep track of the dataset we load
      this.aggregatedOutput[locationKey] = {};
      this.aggregatedOutput[locationKey].tag = {};
      this.aggregatedOutput[locationKey].tag.latitude = location.latitude;
      this.aggregatedOutput[locationKey].tag.longitude = location.longitude;
      this.aggregatedOutput[locationKey].precipationRaw = {};
      this.aggregatedOutput[locationKey].precipationProb = {};

      
      //loop through object for each location over all hourlys
      Object.entries(location.hourly.time).forEach(([key, time], index) => {

        this.aggregatedOutput[locationKey].precipationRaw[key] = {};
        this.aggregatedOutput[locationKey].precipationProb[key] = {};

        //convert string output to a date object
        let checkdate = new Date(time);

        //convert date to milliseconds timestamps
        let currTimestamp = checkdate.getTime();
        let probability = 100; //default probility unless otherwise defined

        if(typeof location.hourly.precipitation_probability !== 'undefined'){
          probability = location.hourly.precipitation_probability[key];
          
        }
        
        if(probability > 0){
          probability /= 100;
        }
        
        // only interested in dates before timeframe and after to make use of 
        // ( currTimestamp >= now && currTimestamp < future)  || ( currTimestamp < now && currTimestamp > past )
        if( true ){
          
          typeof totalRaw[currTimestamp] === 'undefined' ? totalRaw[currTimestamp] = 0 : null;
          typeof totalProb[currTimestamp] === 'undefined' ? totalProb[currTimestamp] = 0 : null;

          //placed probability into the equation
          totalRaw[currTimestamp] += location.hourly.precipitation[key] ;
          totalProb[currTimestamp] += ( location.hourly.precipitation[key] * probability ) ;

          //keep track of all requested data 
          this.aggregatedOutput[locationKey].precipationRaw[key]["val"] = location.hourly.precipitation[key]; // raw data from open weather data
          this.aggregatedOutput[locationKey].precipationRaw[key]["time"] = currTimestamp;

          this.aggregatedOutput[locationKey].precipationProb[key]["val"] = probability;  // probability of open weather
          this.aggregatedOutput[locationKey].precipationProb[key]["time"] = currTimestamp;      

        }

        //remove dead info
        if(Object.keys(this.aggregatedOutput[locationKey].precipationRaw[key]).length == 0 ){
          delete this.aggregatedOutput[locationKey].precipationRaw[key];
        };

        if(Object.keys(this.aggregatedOutput[locationKey].precipationProb[key]).length == 0 ){
          delete this.aggregatedOutput[locationKey].precipationProb[key];
        };

      });
    });

    //total sum expected over time window (just for ref now not so important anymore)
    this.sumRain = Object.values(totalProb).reduce((sum, value) => sum + value, 0);
    this.avgRain = this.sumRain / numberOfLocations; 

    //make average over prob
    Object.entries(totalProb).forEach(([key, sum],index) => {
      typeof totalAvg[key] === 'undefined' ? totalAvg[key] = 0 : null;
      totalAvg[key] = sum / numberOfLocations;
    });

    //make new prediction
    //this.get_model_prediction();
    return this.aggregatedOutput;
  }

  get_model_prediction(){

// combine 24 hourly predictions to make one daily prediction (for the next 24 hours including the current hour)
let inputs = [];
for (let predHour = 0; predHour <= 23; predHour++) {

  // select 24 timestamps based on hour te be predicted
  let now = new Date();
  const lastHour = new Date(now.setHours(now.getHours() + predHour));
  let timestamps = this.rain_data[0].hourly.time.map(ts => new Date(ts));
  let timestamps_24 = timestamps.filter(ts => ts <= lastHour).slice(-24)

  // for each relevant hour calculate the mean precipitation across all areas
  let precipitation = [];
  for (let i = 0; i < timestamps.length; i++) {

    if(timestamps_24.includes(timestamps[i])) {

      let values = [];
      for (let j = 0; j < this.rain_data.length; j++) {

        values.push(this.rain_data[j].hourly.precipitation[i]);
      }
      let mean = values.reduce((sum, value) => sum + value, 0) / this.rain_data.length;
      precipitation.push(mean);
    }
  }

  // standardize variables for prediction and 'zip' them
  let hours = timestamps_24.map(ts => ts.getHours());
  hours = hours.map(hour => (hour - 11.50430734) / 6.92241142);
  precipitation = precipitation.map(value => (value - 0.09011861) / 0.43853627);
  let zipped = hours.map((value, i) => [value, precipitation[i]]);

  // collect inputdata for model
  inputs.push(zipped);
}
  const output = this.model_loader(inputs);
  console.log('Final output: ' + output);
  }
  
async model_loader(inputs){

   let dailyPred = 0;

    try {
      const localURL = "http://127.0.0.1:1880/generalFunctions/datasets/lstmData/tfjs_model/model.json";
    
      // Could you log the original model JSON to help determine the correct input shape?
      const response = await fetch(localURL);
      const modelJSON = await response.json();
      console.log('Original model config:', JSON.stringify(modelJSON.modelTopology.model_config.config.layers[0], null, 24, 2));
    
      // Try loading with default input shape
      const model = await this.modelLoader.loadModel(localURL);
      console.log('Model loaded successfully!');

      // make predictions
      for (const input of inputs) { 

        const inputTensor = tf.tensor3d([input]);
        const predict = model.predict(inputTensor);
        let predictValue = await predict.data();

        // back-transformation because of standardization of the response variable
        predictValue = predictValue[0] * 1024.1940942 + 1188.0105115;
        dailyPred += predictValue;     
      }
      console.log('Daily prediction: ' + dailyPred);
    } catch (error) {
      console.error('Failed to load model:', error);
    }
  return dailyPred;
}

  sampling_program(){

    // ------------------ Run once on conditions and start sampling
    if( ( (this.i_start ) || ( Date.now() >= this.nextDate ) )  && !this.running ){
      
      this.running = true;

      // reset persistent vars
      this.temp_pulse   = 0;
      this.pulse        = false;
      this.bucketVol    = 0;
      this.sumPuls      = 0;
      this.m3Total      = 0;      
      this.timePassed   = 0;      // time in seconds
      this.timeLeft     = 0;      // time in seconds
      this.predM3PerSec = 0;

      //run prediction to ensure its value is filled
      this.get_model_prediction();
      
      // define m3 per pulse for this run and round to int !
      this.m3PerPuls = Math.round(this.predFlow / this.targetPuls);  
      this.predM3PerSec = this.predFlow / this.sampling_time / 60 / 60; // predicted m3 per time

      // define start and stop time based on calender data
      this.start_time = Date.now();
      this.stop_time = Date.now() + (this.sampling_time * 60 * 60 * 1000); // convert to milliseconds

      //reset parameters and look for next date
      this.regNextDate(this.monsternametijden);

      // reset start
      this.i_start = false;
    }

    // ------------------ Run for as long as sampling time is not greater than stop time
    if(this.stop_time > Date.now()){

      // define time vars 
      this.timePassed        = Math.round( ( Date.now() - this.start_time ) / 1000);
      this.timeLeft          = Math.round( ( this.stop_time - Date.now() ) / 1000); 

      // calc temp pulse rate 
      let update = this.m3PerTick / this.m3PerPuls;

      // update values  
      this.temp_pulse += update;
      this.m3Total += this.m3PerTick;

      // check if we need to send out a pulse (stop sending pulses if capacity is reached)
      if(this.temp_pulse >= 1 && this.sumPuls < this.absMaxPuls){
        // reset
        this.temp_pulse += -1;
        // send out a pulse and add to count
        this.pulse = true;
        // count pulses
        this.sumPuls++;
        // update bucket volume each puls
        this.bucketVol = Math.round(this.sumPuls * this.volume_pulse * 100) / 100;

      }
      else{
        
        if( this.sumPuls > this.absMaxPuls){

          // find out how to reschedule sample automatically?
        }

        //update pulse when its true
        if(this.pulse){
          this.pulse = false; // continue but don't send out a pulse
        }

      }
    }
    else
    {
      //after setting once dont do it again
      if(this.running){
        // Vars can only be 0 if this is not running
        this.m3PerPuls   = 0;
        this.temp_pulse   = 0;
        this.pulse        = false;
        this.bucketVol    = 0;
        this.sumPuls     = 0;      
        this.timePassed   = 0;      // time in seconds
        this.timeLeft     = 0;      // time in seconds
        this.predFlow     = 0;
        this.predM3PerSec = 0;
        this.m3Total      = 0;
        this.running      = false;  // end of sampling program (stop_time reached)

      }
    }
  }

  flowCalc(){
    //reset timePassed
    let timePassed = 0;

    // each tick calc flowtimepassed
    this.flowTime > 0 ? timePassed = ( Date.now() - this.flowTime) / 1000 : timePassed = 0 ; 

    //conver to m3 per tick
    this.m3PerTick = this.q / 60 / 60 * timePassed ;

    // put new timestamp
    this.flowTime = Date.now();

  }

  //goes through time related functions
  tick(){

    //calculate flow based on input
    this.flowCalc();

    //run sampling program
    this.sampling_program();

    //logQ for predictions / forecasts
    this.logQoverTime();
  }

  regNextDate(monsternametijden){

    let next_date = new Date(new Date().setFullYear(new Date().getFullYear() + 1));
    let n_days_remaining = 0;

    if(typeof monsternametijden !== 'undefined'){
      // loop through lines
      Object.entries(monsternametijden).forEach(([key, line],index) => {

        //console.log(line.START_DATE);
        //check if date is not null
        if(line.START_DATE != "NULL"){
          let curr_date_conv = new Date(line.START_DATE);
          let curr_date = curr_date_conv.getTime();

          //check if sample name is this sample and if date is bigger than now.
          if(line.SAMPLE_NAME == this.aquonSampleName && curr_date > Date.now() ){
            
            //only keep date that is bigger than current but smaller than the ones that follow after it.
            if(curr_date < next_date){ next_date = curr_date; }
            
            // check if its within this year only show those days as days remaining
            if( new Date().getFullYear() == curr_date_conv.getFullYear() ){ n_days_remaining++; }
          }
        }

      });
    }
    else{
      //this.warning.push(3);
    }

    //store vars remaining
    this.daysPerYear = n_days_remaining;        
    this.nextDate = next_date;
  }

  logQoverTime(){

    //store currHour in temp obj for easy ref
    let h = this.currHour;

    // define rain hour of which the correlation is the biggest this doesnt belong in this section do this afterwards
    // let rainH = h - this.calcTimeShift ;
    
    // how much rain fell on rainH (define category)

    // fetch current hour from actual time
    const currentHour = new Date().getHours();

    //on hour change begin log
    if(h !== currentHour ){

      //write current total to object
      this.qLineRaw.h = this.tmpTotQ

      //reset tmpTotQ 

      //set this.currHour to currentHour
    }



    
    

  }

  //create objects where to push arrays in to keep track of data
  createMinMaxSeen(){
    //check which hour it is , then make sum , after sum is complete check which hour it is
    //loop over sampling time expressed in hours
    for(let h = 1; h < this.sampling_time ; h++){
      this.minSeen = {};
    }
  }


} // end of class

module.exports = Monster;


const mConfig={
  general: {
    name: "Monster",
    logging:{
      logLevel: "debug",
      enabled: true,
    },
  },
  asset: {
    emptyWeightBucket: 3,
  },
  constraints: {
    minVolume: 4,
    maxWeight: 23,
  },
}

let monster = new Monster(mConfig);




monster.rain_data = 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monster.get_model_prediction();

/*
// combine 24 hourly predictions to make one daily prediction (for the next 24 hours including the current hour)
let inputs = [];
for (let predHour = 0; predHour <= 23; predHour++) {

  // select 24 timestamps based on hour te be predicted
  let now = new Date();
  const lastHour = new Date(now.setHours(now.getHours() + predHour));
  let timestamps = monster.rain_data[0].hourly.time.map(ts => new Date(ts));
  timestamps_24 = timestamps.filter(ts => ts <= lastHour).slice(-24)

  // for each relevant hour calculate the mean precipitation across all areas
  let precipitation = [];
  for (let i = 0; i < timestamps.length; i++) {

    if(timestamps_24.includes(timestamps[i])) {

      let values = [];
      for (let j = 0; j < monster.rain_data.length; j++) {

        values.push(monster.rain_data[j].hourly.precipitation[i]);
      }
      let mean = values.reduce((sum, value) => sum + value, 0) / monster.rain_data.length;
      precipitation.push(mean);
    }
  }

  // standardize variables for prediction and 'zip' them
  let hours = timestamps_24.map(ts => ts.getHours());
  hours = hours.map(hour => (hour - 11.50430734) / 6.92241142);
  precipitation = precipitation.map(value => (value - 0.09011861) / 0.43853627);
  zipped = hours.map((value, i) => [value, precipitation[i]]);

  // collect inputdata for model
  inputs.push(zipped);
}
   
  (async () => {
    try {
      const localURL = "http://127.0.0.1:1880/generalFunctions/datasets/lstmData/tfjs_model/model.json";
    
      // Could you log the original model JSON to help determine the correct input shape?
      const response = await fetch(localURL);
      const modelJSON = await response.json();
      console.log('Original model config:', JSON.stringify(modelJSON.modelTopology.model_config.config.layers[0], null, 24, 2));
    
      // Try loading with default input shape
      const model = await monster.modelLoader.loadModel(localURL);
      console.log('Model loaded successfully!');

      // make predictions
      let dailyPred = 0;
      for (const input of inputs) { 

        const inputTensor = tf.tensor3d([input]);
        const predict = model.predict(inputTensor);
        let predictValue = await predict.data();

        // back-transformation because of standardization of the response variable
        predictValue = predictValue[0] * 1024.1940942 + 1188.0105115;
        dailyPred += predictValue;     
      }
      console.log('Daily prediction: ' + dailyPred);
    } catch (error) {
      console.error('Failed to load model:', error);
    }
  })();*/