aiair-server/controllers/PredictController.py

from flask import request, jsonify
import numpy as np
import os
import pandas as pd
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
from xgboost import XGBRegressor
from prophet import Prophet
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression 
from keras.models import model_from_json

import warnings
warnings.filterwarnings('ignore')

RANDOM_SEED = 42
np.random.seed(RANDOM_SEED)

script_dir = os.path.dirname(os.path.abspath(__file__))
server_dir = os.path.dirname(os.path.dirname(script_dir))

p_gb = {'n_estimators': 500, 'max_depth': 10, 'min_samples_split': 2,'learning_rate': 0.09, 'loss': 'squared_error', 'random_state': RANDOM_SEED}
p_xgb = {'n_estimators': 700, 'max_depth': 12, 'learning_rate': 0.09, 'random_state': RANDOM_SEED}
p_rf = {'n_estimators': 1000, 'max_depth': 10, 'random_state': RANDOM_SEED}
p_knn = {'n_neighbors': 3}

class PredictController:
  #-------------------Prophet-LSTM-------------------
  def predictTempProphetLSTM():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataTemp']

        # push data to array
        tempTime = []
        for i in objectFormat['time']:
          tempTime.append(i)

        tempData = []
        for i in objectFormat['value']:
          tempData.append(i)

        arrayData = np.array(tempData)
        arrayTime = np.array(tempTime)
        datetimeTemp = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeTemp, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset.reset_index(inplace=True)

        scaler = MinMaxScaler()
        scaled_temp = scaler.fit_transform(dataset[['y']])
        sequence_length = 12
        if len(scaled_temp) < sequence_length:
            padded_temp = np.pad(scaled_temp, ((sequence_length - len(scaled_temp), 0), (0, 0)), mode='constant')
        else:
            padded_temp = scaled_temp[-sequence_length:]
        input_data = padded_temp.reshape((1, 1, sequence_length))
        
        # Load model LSTM
        temp_lstm_json = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/test-lstm.json')
        temp_lstm_weight = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/test_lstm_weight.h5')
        with open(temp_lstm_json, 'r') as json_file:
            loaded_model_json_lstm = json_file.read()
        
        loaded_model_lstm = model_from_json(loaded_model_json_lstm)
        loaded_model_lstm.load_weights(temp_lstm_weight)

        # Load model BPNN (json and h5)
        temp_bpnn_json = os.path.join(server_dir, 'aiair-server/datasets/models/prophet-lstm/temp-bpnn-model.json')
        temp_bpnn_weight = os.path.join(server_dir, 'aiair-server/datasets/models/prophet-lstm/temp-bpnn-model.h5')
        with open(temp_bpnn_json, 'r') as json_file:
            loaded_model_json_bpnn = json_file.read()

        loaded_model_bpnn = model_from_json(loaded_model_json_bpnn)
        loaded_model_bpnn.load_weights(temp_bpnn_weight)

        if os.path.exists(temp_lstm_weight) and os.path.exists(temp_bpnn_weight):
          #-----------lstm-----------
          print("--------model loaded lstm---------")
          predictions = loaded_model_lstm.predict(input_data)
          predictions_inv = scaler.inverse_transform(predictions)[0]
          arrayForecast = np.array(predictions_inv)
          arrayForecast = np.around(arrayForecast, decimals=4)
          lstmForecast = arrayForecast

          #-----------prophet-----------
          print("--------model loaded prophet---------")
          dataset['ds'] = dataset['ds'].dt.tz_localize(None)

          model_prophet = Prophet()
          model_prophet.fit(dataset)

          future = model_prophet.make_future_dataframe(periods=12, freq='5T')
          prophetForecast = model_prophet.predict(future)
          prophetForecast = prophetForecast.tail(12)

          # get only ds and yhat
          prophetForecast = prophetForecast[['ds', 'yhat']]
          prophetForecast = prophetForecast.set_index('ds')
          prophetForecast.reset_index(inplace=True)

          #-----------bpnn-----------
          dataset_bpnn = dataset.copy().tail(12)
          dataset_bpnn['ds'] = pd.to_datetime(dataset_bpnn['ds'])
          dataset_bpnn['hour'] = dataset_bpnn['ds'].dt.hour
          dataset_bpnn['minute'] = dataset_bpnn['ds'].dt.minute
          dataset_bpnn['day_of_week'] = dataset_bpnn['ds'].dt.dayofweek
          
          # drop ds and y column
          dataset_bpnn = dataset_bpnn.drop(['ds', 'y'], axis=1)

          #add lstm forecast to dataset
          dataset_bpnn['lstm'] = lstmForecast
          
          # add prophet forecast to dataset
          dataset_bpnn['prophet'] = prophetForecast['yhat'].values
          
          print("--------model loaded bpnn---------")
          predictions = loaded_model_bpnn.predict(dataset_bpnn)
        
          # convert 2D to 1D array
          predictions = predictions.flatten()

          # round up to 2 decimal
          arrayForecast = np.around(predictions, decimals=4)

          # convert to list
          listForecast = arrayForecast.tolist()

          # convert to json
          objectFormat['forecast'] = listForecast

          return jsonify(objectFormat)

        else:
          print(f"File not found: {temp_lstm_weight} or {temp_bpnn_weight}")
      except Exception as e:
        print(e)
  
  #-------------------Prophet-------------------
  def predictTempProphet():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataTemp']

        # push data to array
        tempTime = []
        for i in objectFormat['time']:
          tempTime.append(i)

        tempData = []
        for i in objectFormat['value']:
          tempData.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(tempData)
        arrayTime = np.array(tempTime)
        datetimeTemp = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeTemp, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset.reset_index(inplace=True)

        model_prophet = Prophet()
        model_prophet.fit(dataset)

        # Make a future dataframe for 1 hours later (5 minutes each)
        future = model_prophet.make_future_dataframe(periods=12, freq='5T')
        forecast = model_prophet.predict(future)

        # get last 12 rows
        forecast = forecast.tail(12)

        # get only ds and yhat
        forecast = forecast[['ds', 'yhat']]
        forecast = forecast.set_index('ds')
        forecast.reset_index(inplace=True)

        # convert to numpy array
        arrayForecast = np.array(forecast['yhat'])

        # round up to 2 decimal
        arrayForecast = np.around(arrayForecast, decimals=2)

        # combine array
        # arrayForecast = np.concatenate((arrayData, arrayForecast), axis=0)
        # print(arrayForecast)

        # convert to list
        listForecast = arrayForecast.tolist()

        # convert to json
        objectFormat['forecast'] = listForecast
      
      except Exception as e:
        print(e)
      
    return jsonify(objectFormat)
  
  def predictHumiProphet():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataHumi']

        humiTime = []
        for i in objectFormat['time']:
          humiTime.append(i)

        humiData = []
        for i in objectFormat['value']:
          humiData.append(i)

        arrayData = np.array(humiData)
        arrayTime = np.array(humiTime)
        datetimeHumi = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeHumi, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset.reset_index(inplace=True)

        model_prophet = Prophet()
        model_prophet.fit(dataset)

        future = model_prophet.make_future_dataframe(periods=12, freq='5T')
        forecast = model_prophet.predict(future)
        forecast = forecast.tail(12)

        forecast = forecast[['ds', 'yhat']]
        forecast = forecast.set_index('ds')
        forecast.reset_index(inplace=True)

        arrayForecast = np.array(forecast['yhat'])
        arrayForecast = np.around(arrayForecast, decimals=2)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
      except Exception as e:
        print(e)
    return jsonify(objectFormat)
  
  def predictCO2Prophet():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO2']

        co2Time = []
        for i in objectFormat['time']:
          co2Time.append(i)

        co2Data = []
        for i in objectFormat['value']:
          co2Data.append(i)

        arrayData = np.array(co2Data)
        arrayTime = np.array(co2Time)
        datetimeCO2 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO2, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset.reset_index(inplace=True)

        model_prophet = Prophet()
        model_prophet.fit(dataset)

        future = model_prophet.make_future_dataframe(periods=12, freq='5T')
        forecast = model_prophet.predict(future)
        forecast = forecast.tail(12)

        forecast = forecast[['ds', 'yhat']]
        forecast = forecast.set_index('ds')
        forecast.reset_index(inplace=True)

        arrayForecast = np.array(forecast['yhat'])
        arrayForecast = np.around(arrayForecast, decimals=2)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
      except Exception as e:
        print(e)
    return jsonify(objectFormat)
  
  def predictCOProphet():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO']

        coTime = []
        for i in objectFormat['time']:
          coTime.append(i)

        coData = []
        for i in objectFormat['value']:
          coData.append(i)

        arrayData = np.array(coData)
        arrayTime = np.array(coTime)
        datetimeCO = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset.reset_index(inplace=True)

        model_prophet = Prophet()
        model_prophet.fit(dataset)

        future = model_prophet.make_future_dataframe(periods=12, freq='5T')
        forecast = model_prophet.predict(future)
        forecast = forecast.tail(12)

        forecast = forecast[['ds', 'yhat']]
        forecast = forecast.set_index('ds')
        forecast.reset_index(inplace=True)

        arrayForecast = np.array(forecast['yhat'])
        arrayForecast = np.around(arrayForecast, decimals=2)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
      except Exception as e:
        print(e)
    return jsonify(objectFormat)
  
  def predictUVProphet():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataUV']

        uvTime = []
        for i in objectFormat['time']:
          uvTime.append(i)

        uvData = []
        for i in objectFormat['value']:
          uvData.append(i)

        arrayData = np.array(uvData)
        arrayTime = np.array(uvTime)
        datetimeUV = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeUV, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset.reset_index(inplace=True)

        model_prophet = Prophet()
        model_prophet.fit(dataset)

        future = model_prophet.make_future_dataframe(periods=12, freq='5T')
        forecast = model_prophet.predict(future)
        forecast = forecast.tail(12)

        forecast = forecast[['ds', 'yhat']]
        forecast = forecast.set_index('ds')
        forecast.reset_index(inplace=True)

        arrayForecast = np.array(forecast['yhat'])
        arrayForecast = np.around(arrayForecast, decimals=2)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
      except Exception as e:
        print(e)
    return jsonify(objectFormat)
  
  def predictPM25Prophet():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataPM25']

        pm25Time = []
        for i in objectFormat['time']:
          pm25Time.append(i)

        pm25Data = []
        for i in objectFormat['value']:
          pm25Data.append(i)

        arrayData = np.array(pm25Data)
        arrayTime = np.array(pm25Time)
        datetimePM25 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimePM25, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset.reset_index(inplace=True)

        model_prophet = Prophet()
        model_prophet.fit(dataset)

        future = model_prophet.make_future_dataframe(periods=12, freq='5T')
        forecast = model_prophet.predict(future)
        forecast = forecast.tail(12)

        forecast = forecast[['ds', 'yhat']]
        forecast = forecast.set_index('ds')
        forecast.reset_index(inplace=True)

        arrayForecast = np.array(forecast['yhat'])
        arrayForecast = np.around(arrayForecast, decimals=2)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
      except Exception as e:
        print(e)
    return jsonify(objectFormat)
  
  #-------------------LSTM-------------------
  def predictTempLSTM():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataTemp']

        # push data to array
        tempTime = []
        for i in objectFormat['time']:
          tempTime.append(i)

        tempData = []
        for i in objectFormat['value']:
          tempData.append(i)

        arrayData = np.array(tempData)
        arrayTime = np.array(tempTime)
        datetimeTemp = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeTemp, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        
        dataset.reset_index(inplace=True)

        # Scale the data to be between 0 and 1
        scaler = MinMaxScaler()
        scaled_temp = scaler.fit_transform(dataset[['y']])

        # Ensure the sequence length matches the model's input (100 time steps)
        sequence_length = 12

        # Pad or truncate the sequence to match the model's input sequence length
        if len(scaled_temp) < sequence_length:
            padded_temp = np.pad(scaled_temp, ((sequence_length - len(scaled_temp), 0), (0, 0)), mode='constant')
        else:
            padded_temp = scaled_temp[-sequence_length:]

        # Reshape the data to be suitable for LSTM (samples, time steps, features)
        input_data = padded_temp.reshape((1, 1, sequence_length))
        
        # Load model architecture from JSON file
        temp_lstm_json = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/test-lstm.json')
        temp_lstm_weight = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/test_lstm_weight.h5')
        with open(temp_lstm_json, 'r') as json_file:
            loaded_model_json = json_file.read()
        
        # Load model json
        loaded_model = model_from_json(loaded_model_json)

        # Load model weights
        loaded_model.load_weights(temp_lstm_weight)

        if os.path.exists(temp_lstm_weight) and os.path.exists(temp_lstm_json):
          print("--------model loaded---------")
          predictions = loaded_model.predict(input_data)

          # # Inverse transform the predictions to get original scale
          predictions_inv = scaler.inverse_transform(predictions)[0]

          # get data from predictions
          arrayForecast = np.array(predictions_inv)

          # round up to 2 decimal
          arrayForecast = np.around(arrayForecast, decimals=4)

          # convert to list
          listForecast = arrayForecast.tolist()

          # convert to json
          objectFormat['forecast'] = listForecast

        else:
          print(f"File not found: {temp_lstm_weight}")
      except Exception as e:
        print(e)
      
    return jsonify(objectFormat)

  def predictHumiLSTM():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataHumi']

        # push data to array
        humiTime = []
        for i in objectFormat['time']:
          humiTime.append(i)

        humiData = []
        for i in objectFormat['value']:
          humiData.append(i)

        arrayData = np.array(humiData)
        arrayTime = np.array(humiTime)
        datetimeHumi = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeHumi, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset.reset_index(inplace=True)

        scaler = MinMaxScaler()
        scaled_humi = scaler.fit_transform(dataset[['y']])

        sequence_length = 100
        if len(scaled_humi) < sequence_length:
            padded_humi = np.pad(scaled_humi, ((sequence_length - len(scaled_humi), 0), (0, 0)), mode='constant')
        else:
            padded_humi = scaled_humi[-sequence_length:]
        input_data = padded_humi.reshape((1, 1, sequence_length))
        
        humi_lstm_json = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/humi-lstm.json')
        humi_lstm_weight = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/humi_lstm_weight.h5')
        with open(humi_lstm_json, 'r') as json_file:
            loaded_model_json = json_file.read()
        
        loaded_model = model_from_json(loaded_model_json)
        loaded_model.load_weights(humi_lstm_weight)

        if os.path.exists(humi_lstm_weight):
          predictions = loaded_model.predict(input_data)
          predictions_inv = scaler.inverse_transform(predictions)[0]
          arrayForecast = np.array(predictions_inv)
          arrayForecast = np.around(arrayForecast, decimals=4)
          listForecast = arrayForecast.tolist()
          objectFormat['forecast'] = listForecast
        else:
          print(f"File not found: {humi_lstm_weight}")
      except Exception as e:
        print(e)
    return jsonify(objectFormat)
  
  def predictCO2LSTM():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO2']

        # push data to array
        co2Time = []
        for i in objectFormat['time']:
          co2Time.append(i)

        co2Data = []
        for i in objectFormat['value']:
          co2Data.append(i)

        arrayData = np.array(co2Data)
        arrayTime = np.array(co2Time)
        datetimeCO2 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO2, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset.reset_index(inplace=True)

        scaler = MinMaxScaler()
        scaled_co2 = scaler.fit_transform(dataset[['y']])

        sequence_length = 100
        if len(scaled_co2) < sequence_length:
            padded_co2 = np.pad(scaled_co2, ((sequence_length - len(scaled_co2), 0), (0, 0)), mode='constant')
        else:
            padded_co2 = scaled_co2[-sequence_length:]
        input_data = padded_co2.reshape((1, 1, sequence_length))
        
        co2_lstm_json = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/co2-lstm.json')
        co2_lstm_weight = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/co2_lstm_weight.h5')
        with open(co2_lstm_json, 'r') as json_file:
            loaded_model_json = json_file.read()
        
        loaded_model = model_from_json(loaded_model_json)
        loaded_model.load_weights(co2_lstm_weight)

        if os.path.exists(co2_lstm_weight):
          predictions = loaded_model.predict(input_data)
          predictions_inv = scaler.inverse_transform(predictions)[0]
          arrayForecast = np.array(predictions_inv)
          arrayForecast = np.around(arrayForecast, decimals=4)
          listForecast = arrayForecast.tolist()
          objectFormat['forecast'] = listForecast
        else:
          print(f"File not found: {co2_lstm_weight}")
      except Exception as e:
        print(e)
    return jsonify(objectFormat)
  
  def predictCOLSTM():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO']

        # push data to array
        coTime = []
        for i in objectFormat['time']:
          coTime.append(i)

        coData = []
        for i in objectFormat['value']:
          coData.append(i)

        arrayData = np.array(coData)
        arrayTime = np.array(coTime)
        datetimeCO = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset.reset_index(inplace=True)

        scaler = MinMaxScaler()
        scaled_co = scaler.fit_transform(dataset[['y']])

        sequence_length = 100
        if len(scaled_co) < sequence_length:
            padded_co = np.pad(scaled_co, ((sequence_length - len(scaled_co), 0), (0, 0)), mode='constant')
        else:
            padded_co = scaled_co[-sequence_length:]
        input_data = padded_co.reshape((1, 1, sequence_length))
        
        co_lstm_json = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/co-lstm.json')
        co_lstm_weight = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/co_lstm_weight.h5')
        with open(co_lstm_json, 'r') as json_file:
            loaded_model_json = json_file.read()
        
        loaded_model = model_from_json(loaded_model_json)
        loaded_model.load_weights(co_lstm_weight)

        if os.path.exists(co_lstm_weight):
          predictions = loaded_model.predict(input_data)
          predictions_inv = scaler.inverse_transform(predictions)[0]
          arrayForecast = np.array(predictions_inv)
          arrayForecast = np.around(arrayForecast, decimals=4)
          listForecast = arrayForecast.tolist()
          objectFormat['forecast'] = listForecast
        else:
          print(f"File not found: {co_lstm_weight}")
      except Exception as e:
        print(e)
    return jsonify(objectFormat)
  
  def predictUVLSTM():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataUV']

        # push data to array
        uvTime = []
        for i in objectFormat['time']:
          uvTime.append(i)

        uvData = []
        for i in objectFormat['value']:
          uvData.append(i)

        arrayData = np.array(uvData)
        arrayTime = np.array(uvTime)
        datetimeUV = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeUV, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset.reset_index(inplace=True)

        scaler = MinMaxScaler()
        scaled_uv = scaler.fit_transform(dataset[['y']])

        sequence_length = 100
        if len(scaled_uv) < sequence_length:
            padded_uv = np.pad(scaled_uv, ((sequence_length - len(scaled_uv), 0), (0, 0)), mode='constant')
        else:
            padded_uv = scaled_uv[-sequence_length:]
        input_data = padded_uv.reshape((1, 1, sequence_length))
        
        uv_lstm_json = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/uv-lstm.json')
        uv_lstm_weight = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/uv_lstm_weight.h5')
        with open(uv_lstm_json, 'r') as json_file:
            loaded_model_json = json_file.read()
        
        loaded_model = model_from_json(loaded_model_json)
        loaded_model.load_weights(uv_lstm_weight)

        if os.path.exists(uv_lstm_weight):
          predictions = loaded_model.predict(input_data)
          predictions_inv = scaler.inverse_transform(predictions)[0]
          arrayForecast = np.array(predictions_inv)
          arrayForecast = np.around(arrayForecast, decimals=4)
          listForecast = arrayForecast.tolist()
          objectFormat['forecast'] = listForecast
        else:
          print(f"File not found: {uv_lstm_weight}")
      except Exception as e:
        print(e)
    return jsonify(objectFormat)
  
  def predictPM25LSTM():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataPM25']

        # push data to array
        pm25Time = []
        for i in objectFormat['time']:
          pm25Time.append(i)

        pm25Data = []
        for i in objectFormat['value']:
          pm25Data.append(i)

        arrayData = np.array(pm25Data)
        arrayTime = np.array(pm25Time)
        datetimePM25 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimePM25, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset.reset_index(inplace=True)

        scaler = MinMaxScaler()
        scaled_pm25 = scaler.fit_transform(dataset[['y']])

        sequence_length = 100
        if len(scaled_pm25) < sequence_length:
            padded_pm25 = np.pad(scaled_pm25, ((sequence_length - len(scaled_pm25), 0), (0, 0)), mode='constant')
        else:
            padded_pm25 = scaled_pm25[-sequence_length:]
        input_data = padded_pm25.reshape((1, 1, sequence_length))
        
        pm25_lstm_json = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/pm25-lstm.json')
        pm25_lstm_weight = os.path.join(server_dir, 'aiair-server/datasets/models/lstm/pm25_lstm_weight.h5')
        with open(pm25_lstm_json, 'r') as json_file:
            loaded_model_json = json_file.read()
        
        loaded_model = model_from_json(loaded_model_json)
        loaded_model.load_weights(pm25_lstm_weight)

        if os.path.exists(pm25_lstm_weight):
          predictions = loaded_model.predict(input_data)
          predictions_inv = scaler.inverse_transform(predictions)[0]
          arrayForecast = np.array(predictions_inv)
          arrayForecast = np.absolute(arrayForecast)
          arrayForecast = np.around(arrayForecast, decimals=4)
          listForecast = arrayForecast.tolist()
          objectFormat['forecast'] = listForecast
        else:
          print(f"File not found: {pm25_lstm_weight}")
      except Exception as e:
        print(e)
    return jsonify(objectFormat)
  
  #-------------------LR-------------------
  def predictLRTemp():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataTemp']

        tempData = []
        for i in objectFormat['value']:
          tempData.append(i)

        tempTime = []
        for i in objectFormat['time']:
          tempTime.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(tempData)
        arrayTime = np.array(tempTime)
        datetimeTemp = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeTemp, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_lr = LinearRegression()
        model_lr.fit(X, y)

        # get the last timestamp in the dataset
        last_timestamp = dataset.index[-1]

        # Generate timestamps for the next hour with 5-minute intervals
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')

        # Reshape timestamps to be used as features for prediction
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})

        next_hour_features.set_index('date', inplace=True)

        next_hour_features['time'] = np.arange(len(next_hour_features))

        # Use the trained model to predict vehicle count for the next hour
        predicted_counts = model_lr.predict(next_hour_features)

        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=8)

        # convert to list
        listForecast = arrayForecast.tolist()

        # convert to json
        objectFormat['forecast'] = listForecast

        # input_datetime_str = str(dataset['ds'].max())
        # old_date = pd.to_datetime(input_datetime_str)
        
        # # #get current date
        # current_date = pd.Timestamp.now()
        # time_differences = (current_date - old_date).total_seconds()

        # model_path = os.path.join(server_dir, 'server/datasets/models/linear_regression/model_lr_temp.pkl')
        # if os.path.exists(model_path):
        #     loaded_model = load(model_path)
            
        #     # Predicting 12 values
        #     predictions = []
        #     for _ in range(12):
        #         prediction = loaded_model.predict([[time_differences]])
        #         predictions.append(prediction[0])
        #         time_differences += 300  # Assuming hourly predictions
            
        #     arrayForecast = np.around(predictions, decimals=8)

        #     # convert to list
        #     listForecast = arrayForecast.tolist()

        #     # convert to json
        #     objectFormat['forecast'] = listForecast
        # else:
        #     print(f"File not found: {model_path}")

        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictLRHumi():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataHumi']

        humiData = []
        for i in objectFormat['value']:
          humiData.append(i)

        humiTime = []
        for i in objectFormat['time']:
          humiTime.append(i)

        arrayData = np.array(humiData)
        arrayTime = np.array(humiTime)
        datetimeHumi = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeHumi, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_lr = LinearRegression()
        model_lr.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_lr.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=8)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictLRCO2():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO2']

        co2Data = []
        for i in objectFormat['value']:
          co2Data.append(i)

        co2Time = []
        for i in objectFormat['time']:
          co2Time.append(i)

        arrayData = np.array(co2Data)
        arrayTime = np.array(co2Time)
        datetimeCO2 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO2, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_lr = LinearRegression()
        model_lr.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_lr.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=8)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictLRCO():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO']

        coData = []
        for i in objectFormat['value']:
          coData.append(i)

        coTime = []
        for i in objectFormat['time']:
          coTime.append(i)

        arrayData = np.array(coData)
        arrayTime = np.array(coTime)
        datetimeCO = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_lr = LinearRegression()
        model_lr.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_lr.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=8)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictLRUV():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataUV']

        uvData = []
        for i in objectFormat['value']:
          uvData.append(i)

        uvTime = []
        for i in objectFormat['time']:
          uvTime.append(i)

        arrayData = np.array(uvData)
        arrayTime = np.array(uvTime)
        datetimeUV = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeUV, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_lr = LinearRegression()
        model_lr.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_lr.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=8)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictLRPM25():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataPM25']

        pm25Data = []
        for i in objectFormat['value']:
          pm25Data.append(i)

        pm25Time = []
        for i in objectFormat['time']:
          pm25Time.append(i)

        arrayData = np.array(pm25Data)
        arrayTime = np.array(pm25Time)
        datetimePM25 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimePM25, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_lr = LinearRegression()
        model_lr.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_lr.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=8)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  #-------------------GB-------------------
  def predictGBTemp():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataTemp']

        tempData = []
        for i in objectFormat['value']:
          tempData.append(i)

        tempTime = []
        for i in objectFormat['time']:
          tempTime.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(tempData)
        arrayTime = np.array(tempTime)
        datetimeTemp = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeTemp, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = GradientBoostingRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)

        # model_path = os.path.join(server_dir, 'server/datasets/models/gradient_boost/model_gb_temp.pkl')
        # if os.path.exists(model_path):
        #     loaded_model = load(model_path)
        #     print(loaded_model)
            
        #     # Predicting 12 values
        #     predictions = []
        #     for _ in range(12):
        #         prediction = loaded_model.predict([[time_differences]])
        #         predictions.append(prediction[0])
        #         time_differences += 300  # Assuming hourly predictions
            
        #     # round up to 2 decimal
        #     arrayForecast = np.around(predictions, decimals=8)

        #     # convert to list
        #     listForecast = arrayForecast.tolist()

        #     # convert to json
        #     objectFormat['forecast'] = listForecast
        # else:
        #     print(f"File not found: {model_path}")

        # return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictGBHumi():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataHumi']

        humiData = []
        for i in objectFormat['value']:
          humiData.append(i)

        humiTime = []
        for i in objectFormat['time']:
          humiTime.append(i)

        arrayData = np.array(humiData)
        arrayTime = np.array(humiTime)
        datetimeHumi = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeHumi, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = GradientBoostingRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictGBCO2():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO2']

        co2Data = []
        for i in objectFormat['value']:
          co2Data.append(i)

        co2Time = []
        for i in objectFormat['time']:
          co2Time.append(i)

        arrayData = np.array(co2Data)
        arrayTime = np.array(co2Time)
        datetimeCO2 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO2, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = GradientBoostingRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)  
  
  def predictGBCO():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO']

        coData = []
        for i in objectFormat['value']:
          coData.append(i)

        coTime = []
        for i in objectFormat['time']:
          coTime.append(i)

        arrayData = np.array(coData)
        arrayTime = np.array(coTime)
        datetimeCO = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = GradientBoostingRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e) 
  
  def predictGBUV():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataUV']

        uvData = []
        for i in objectFormat['value']:
          uvData.append(i)

        uvTime = []
        for i in objectFormat['time']:
          uvTime.append(i)

        arrayData = np.array(uvData)
        arrayTime = np.array(uvTime)
        datetimeUV = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeUV, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = GradientBoostingRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictGBPM25():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataPM25']

        pm25Data = []
        for i in objectFormat['value']:
          pm25Data.append(i)

        pm25Time = []
        for i in objectFormat['time']:
          pm25Time.append(i)

        arrayData = np.array(pm25Data)
        arrayTime = np.array(pm25Time)
        datetimePM25 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimePM25, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = GradientBoostingRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  #-------------------XGB-------------------
  def predictXGBTemp():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataTemp']

        tempData = []
        for i in objectFormat['value']:
          tempData.append(i)

        tempTime = []
        for i in objectFormat['time']:
          tempTime.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(tempData)
        arrayTime = np.array(tempTime)
        datetimeTemp = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeTemp, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = XGBRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictXGBHumi():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataHumi']

        humiData = []
        for i in objectFormat['value']:
          humiData.append(i)

        humiTime = []
        for i in objectFormat['time']:
          humiTime.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(humiData)
        arrayTime = np.array(humiTime)
        datetimeHumi = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeHumi, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = XGBRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictXGBCO2():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO2']

        co2Data = []
        for i in objectFormat['value']:
          co2Data.append(i)

        co2Time = []
        for i in objectFormat['time']:
          co2Time.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(co2Data)
        arrayTime = np.array(co2Time)
        datetimeCO2 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO2, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = XGBRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictXGBCO():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO']

        coData = []
        for i in objectFormat['value']:
          coData.append(i)

        coTime = []
        for i in objectFormat['time']:
          coTime.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(coData)
        arrayTime = np.array(coTime)
        datetimeCO = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = XGBRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictXGBPM25():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataUV']

        uvData = []
        for i in objectFormat['value']:
          uvData.append(i)

        uvTime = []
        for i in objectFormat['time']:
          uvTime.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(uvData)
        arrayTime = np.array(uvTime)
        datetimeUV = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeUV, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = XGBRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictXGBUV():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataPM25']

        pm25Data = []
        for i in objectFormat['value']:
          pm25Data.append(i)

        pm25Time = []
        for i in objectFormat['time']:
          pm25Time.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(pm25Data)
        arrayTime = np.array(pm25Time)
        datetimePM25 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimePM25, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_gb = XGBRegressor(**p_gb)
        model_gb.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_gb.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  #-------------------RF-------------------
  def predictRFTemp():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataTemp']

        tempData = []
        for i in objectFormat['value']:
          tempData.append(i)

        tempTime = []
        for i in objectFormat['time']:
          tempTime.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(tempData)
        arrayTime = np.array(tempTime)
        datetimeTemp = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeTemp, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_rf = RandomForestRegressor(**p_rf)
        model_rf.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_rf.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictRFHumi():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataHumi']

        humiData = []
        for i in objectFormat['value']:
          humiData.append(i)

        humiTime = []
        for i in objectFormat['time']:
          humiTime.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(humiData)
        arrayTime = np.array(humiTime)
        datetimeHumi = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeHumi, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_rf = RandomForestRegressor(**p_rf)
        model_rf.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_rf.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictRFCO2():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO2']

        co2Data = []
        for i in objectFormat['value']:
          co2Data.append(i)

        co2Time = []
        for i in objectFormat['time']:
          co2Time.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(co2Data)
        arrayTime = np.array(co2Time)
        datetimeCO2 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO2, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_rf = RandomForestRegressor(**p_rf)
        model_rf.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_rf.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictRFCO():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataCO']

        coData = []
        for i in objectFormat['value']:
          coData.append(i)

        coTime = []
        for i in objectFormat['time']:
          coTime.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(coData)
        arrayTime = np.array(coTime)
        datetimeCO = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeCO, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_rf = RandomForestRegressor(**p_rf)
        model_rf.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_rf.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictRFUV():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataUV']

        uvData = []
        for i in objectFormat['value']:
          uvData.append(i)

        uvTime = []
        for i in objectFormat['time']:
          uvTime.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(uvData)
        arrayTime = np.array(uvTime)
        datetimeUV = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimeUV, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_rf = RandomForestRegressor(**p_rf)
        model_rf.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_rf.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)

  def predictRFPM25():
    if request.method == 'POST':
      try:
        data = request.json
        objectFormat = data['dataPM25']

        pm25Data = []
        for i in objectFormat['value']:
          pm25Data.append(i)

        pm25Time = []
        for i in objectFormat['time']:
          pm25Time.append(i)

        # convert to numpy array and pandas dataframe
        arrayData = np.array(pm25Data)
        arrayTime = np.array(pm25Time)
        datetimePM25 = pd.to_datetime(arrayTime)

        dataset = pd.DataFrame({'ds': datetimePM25, 'y': arrayData})
        dataset = dataset.set_index('ds')
        dataset = dataset.resample('5T').ffill()
        dataset = dataset.dropna()
        dataset = dataset.iloc[1:]
        dataset['time'] = np.arange(len(dataset))

        X = dataset[['time']]
        y = dataset['y']

        model_rf = RandomForestRegressor(**p_rf)
        model_rf.fit(X, y)

        last_timestamp = dataset.index[-1]
        next_hour_timestamps = pd.date_range(last_timestamp, periods=12, freq='5T')
        next_hour_features = pd.DataFrame({'date': next_hour_timestamps})
        next_hour_features.set_index('date', inplace=True)
        next_hour_features['time'] = np.arange(len(next_hour_features))

        predicted_counts = model_rf.predict(next_hour_features)
        predictions = []
        for i, count in enumerate(predicted_counts):
          predictions.append(count)

        arrayForecast = np.around(predictions, decimals=10)
        listForecast = arrayForecast.tolist()
        objectFormat['forecast'] = listForecast
        return jsonify(objectFormat)
      except Exception as e:
          print(e)