# -*- coding: utf-8 -*-
  import time, json
  
  import dateutil
  import numpy as np
  import pandas as pd
  
  import Utils
  
  try:
      import cPickle as pickle
  except:
      import pickle
  import PredictionKorvesis as pdm
  import paho.mqtt.client as paho
  import ReportTimeDB

  import ReportSyslog

  import logging
  import logging.config
  import yaml
  import os
  import urllib3
  
  urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
  
  MYDIR = os.path.dirname(os.path.realpath(__file__))
  LOGGING_CONF_FILE = os.path.join(MYDIR, "logging.yml")
  
  
  class OnlinePrediction:
      def __init__(self):
          self.data_dates = []
          self.data_values = []
          self.read_log_conf(LOGGING_CONF_FILE)
          self.logger = logging.getLogger("mltd-online")
          self.logger.info("Online MLTD is running")
  
      def read_log_conf(self, yaml_file):
          with open(yaml_file) as f:
              logging.config.dictConfig(yaml.safe_load(f))
  
      def on_connect(self, client, userdata, flags, rc):
          if rc == 0:
              self.logger.debug("Connected to broker")
  
              self.Connected = True  # Signal connection
          else:
              self.logger.error("Connection failed")
  
      def on_message(self, client, userdata, message):
          """
          {
              "asset_id": "string",
              "timestamp": "2020-02-27T13:40:18.224Z",
              "event_alarm": [
                  {
                      "event_alarm_id": "string",
                      "event_alarm_char": "string",
                      "name": "string",
                      "source_ip": "string",
                      "source_port": 0,
                      "destination_ip": "string",
                      "destination_port": 0,
                      "priority": 0,

                      "confidence": 0

                  }

              ]

          }
          :param client:
          :param userdata:
          :param message:
          :return:
          """
          self.logger.debug(
              "Event received: " + str(json.loads(message.payload.decode("UTF-8")))
          )
          json.loads(message.payload)
          data_dates = []
          data_values = []
          measDict = json.loads(message.payload.decode("UTF-8"))
  
          if measDict["asset_id"] == self.asset_id:
              for event in range(len(measDict["event_alarm"])):
                  data_dates.append(measDict["timestamp"])
                  # datetime.datetime.fromtimestamp(measDict["timestamp"]).strftime(
                  #     "%Y-%m-%dT%H:%M:%SZ"
                  # )
                  # )
                  event_alarm_id = measDict["event_alarm"][event]["event_alarm_id"]
                  data_values.append(event_alarm_id)
  
          self.do_the_monitoring(data_dates, data_values)
  
      def do_the_monitoring(self, data_dates=[], data_values=[]):
          predictions = []
  
          data_values = self.data_values + data_values
          data_dates = self.data_dates + data_dates
  
          if len(data_dates) > 0:
              first_date_str = data_dates[0]
              last_date_str = data_dates[-1]
              first_date = dateutil.parser.parse(first_date_str)
              last_date = dateutil.parser.parse(last_date_str)
              duration = (last_date - first_date).total_seconds()
              if duration >= self.ts_seconds:
                  self.data_values = []
                  self.data_dates = []
                  self.logger.info(f"Events Received: {len(data_values)}"
                                   f" - Duration: {round(duration,2)} secs")
                  self.logger.info("Prediction triggered")
                  predictions = self.predict(data_dates, data_values)
              else:
                  self.data_values = data_values
                  self.data_dates = data_dates
  
          if max(predictions) > self.sigmoid_threshold:

              timeframe = Utils.sigmoid_mins(
                  max(predictions),
                  self.rf_s,
                  Utils.convert_hours_to_mins(Utils.strtime_to_hours(self.rf_midpoint)),
                  self.hours_before,
              )
              self.logger.info(f"A prominent security incident is predicted"
                               f" - Risk level: {round(max(predictions),2)}"
                               f" - Expected timeframe: {round(timeframe,2)} secs")

  
              ReportSyslog.report(
                  self.asset_id, max(predictions) * 100, timeframe
              )
              time_db_client = ReportTimeDB.connect(self.time_db_host, self.time_db_port,
                  self.time_db_database, self.time_db_username, self.time_db_password,
                  self.time_db_ssl, )

              ReportTimeDB.report(
                  time_db_client, self.asset_id, max(predictions) * 100, timeframe
              )
              self.logger.info(f"The incident was reportered on TimescaleDB")
          else:
              self.logger.info(
                  f"The predicted risk {round(max(predictions),2)} is "
                  f"below the alarm threshold {round(self.sigmoid_threshold,2)}"
              )
  
      def predict(self, data_dates=[], data_values=[]):
          dataset = pd.DataFrame({"Timestamps": data_dates, "Event_id": data_values})
          predictions = pdm.predict(
              self.regr, dataset, self.time_segments, self.feature_importance.index
          )
          self.logger.debug(f"Risk predictions: {predictions}")
          return predictions
  
      def form_dataset(self, dates_list, events_list, feature_importance):
          # Create a Pandas dataframe with all the non zero event ids
          # TODO handle differently the zero event ids based on some policy
          loc = 0
          dataset = pd.DataFrame(columns=["Timestamps", "Event_id"])
  
          if len(events_list) != abs(sum(events_list)):
              for i in range(len(events_list)):
                  if i < len(dates_list) and feature_importance.index.contains(
                      events_list[i]
                  ):
                      dataset.loc[loc] = pd.Series(
                          {"Timestamps": dates_list[i], "Event_id": events_list[i]}
                      )
                      loc += 1
  
          if not dataset.empty:
              # dropping ALL duplicate values
              dataset.drop_duplicates(subset="Timestamps", keep="first", inplace=True)
              dataset.set_index(
                  pd.to_datetime(dataset["Timestamps"]), drop=False, inplace=True
              )
              self.logger.debug(f"Formed dataest: {dataset}")
          return dataset
  
      def load_data(self, filename):
          infile = open(filename, "rb")
          pat_length = pickle.load(infile)
          weak_bins_mapping = pickle.load(infile)
          mp = pickle.load(infile)
          train_dataset_values = np.array(pickle.load(infile))
          regr = pickle.load(infile)
          feature_importance = pickle.load(infile)
          artificial_events_generation = pickle.load(infile)
          infile.close()
          return (
              pat_length,
              weak_bins_mapping,
              mp,
              train_dataset_values,
              regr,
              feature_importance,
              artificial_events_generation,
          )
  
      def start_online_prediction_MQTT(
          self,
          trainID,
          broker_address,
          port,
          mqtt_topic,
          prediction_threshold,
          report_time_db_host,
          report_time_db_port,
          report_time_db_username,
          report_time_db_password,
          report_time_db_database,
          report_time_db_table,
          report_time_db_ssl,
          report_asset_id,
      ):
          self.sigmoid_threshold = prediction_threshold
          self.time_db_host = report_time_db_host
          self.time_db_port = report_time_db_port
          self.time_db_username = report_time_db_username
          self.time_db_password = report_time_db_password
          self.time_db_database = report_time_db_database
          self.time_db_table = report_time_db_table
          self.time_db_ssl = report_time_db_ssl
          self.asset_id = report_asset_id
          sql_conn = Utils.create_sqlite_connection("pdm.sqlite")
          self.time_segments = Utils.select_model_attribute(
              sql_conn, trainID, "time_segments"
          )
          self.rf_s = Utils.select_model_attribute(sql_conn, trainID, "rf_s")
          self.rf_midpoint = Utils.select_model_attribute(
              sql_conn, trainID, "rf_midpoint"
          )
          self.hours_before = Utils.select_model_attribute(
              sql_conn, trainID, "hours_before"
          )
          ts_hours = Utils.strtime_to_hours(self.time_segments)
          self.ts_seconds = ts_hours * 3600
          # load the data from pickle (binary) files - should consider to move to a database solution(?)
          (
              self.pat_length,
              self.weak_bins_mapping,
              self.mp,
              self.dataset_values,
              self.regr,
              self.feature_importance,
              self.artificial_events_generation,
          ) = self.load_data("train_" + str(trainID) + ".dat")
  
          self.Connected = False
          client = paho.Client(
              "Prediction_client" + str(time.time())
          )  # create new instance
          client.on_connect = self.on_connect  # attach function to callback
          client.on_message = self.on_message  # attach function to callback
  
          client.connect(broker_address, port=port)  # connect to broker
  
          client.loop_start()  # start the loop
  
          while self.Connected != True:  # Wait for connection
              time.sleep(0.1)
  
          client.subscribe(mqtt_topic)
  
          try:
              while True:
                  time.sleep(1)
          except KeyboardInterrupt:
              self.logger.debug("exiting")
              client.disconnect()
              client.loop_stop()
  
  
  if __name__ == "__main__":
      op = OnlinePrediction()
      op.start_online_prediction_MQTT(
          10,
          "localhost",
          1884,
          "auth/incidents",
          0.1,
          "83.212.116.5",
          5432,
          "postgres",
          "xs?Z7HsY",
          "kea",
          "mltd",
          False,
          "server",
      )