from answers import HighestWeightedScoreSelection, MultipleAnswerSelection
import logging, sys
import configParser, subprocess
from DecisionMaker import DecisionMaker
from AgentManager import AgentManager

def dialogue():

    # Check config.xml to verify workmode
    defaultAgentsMode = configParser.getDefaultAgentsMode()


    # Lucene initialization
    list_args = ["java", "LuceneWrapper", "0", configParser.getCorpusPath(), "", configParser.getLanguage(), configParser.getIndexPath(), configParser.getHitsPerQuery(), configParser.getDbPath()]
    sp1 = subprocess.Popen(list_args,shell=False)

    exitCode = sp1.wait()


    # If "multi", call the multiagent SSS framework; else, call the "classic" version of SSS
    if(defaultAgentsMode == 'multi'):
        multiAgentAnswerMode()
    else:
        classicDialogueMode()



def classicDialogueMode():

    """
    Classic mode for SSS: only calls the Evaluators inside SSS's source, doesn't take external agents into account
    """


    #Initialize AnswerSelection object
    highestWeightedScoreSelection = HighestWeightedScoreSelection()


    #SSS classic workloop: receive user query, determine answer through the AnswerSelection object, print the answer for the user
    while True:
        query = ""

        while (query == ""):
            query = input("Say something:\n")

        if query == "exit":
            break;

        logging.basicConfig(filename='log.txt', filemode='w', format='%(message)s', level=logging.INFO)
        logging.info("Query: " + query)


        answer = highestWeightedScoreSelection.provideAnswer(query)


        print("Question:", query)
        print("Answer:", answer)




def multiAgentAnswerMode():

    """
    Initialize main modules:
    MultipleAnswerSelection will give us the answers from the classic SSS's agents
    AgentManager will generate our external agents and retrieve their answers
    DecisionMaker will receive both the answers from MultipleAnswerSelection and AgentManager, and will decide the best answer to give to the user
    """

    multipleAnswerSelection = MultipleAnswerSelection()
    agentManager = AgentManager()
    decisionMaker = DecisionMaker(configParser.getDecisionMethod())


    # SSS workloop
    while True:
        query = ""

        while (query == ""):
            query = input("Say something:\n")

        if query == "exit":
            break;

        logging.basicConfig(filename='log.txt', filemode='w', format='%(message)s', level=logging.INFO)
        logging.info("Query: " + query)


        defaultAgentsAnswers = multipleAnswerSelection.provideAnswer(query)
        externalAgentsAnswers = agentManager.generateAgentsAnswers(query)
        # Both defaultAgentsAnswers and externalAgentsAnswers are dictionaries in the format {'agent1': 'answer1', 'agent2': 'answer2'}


        # Calling the DecisionMaker after having all of the answers stored in the above dictionaries
        answer = decisionMaker.decideBestAnswer(defaultAgentsAnswers,externalAgentsAnswers)


        print("Question:", query)
        print("Final Answer:", answer)







if __name__ == "__main__":
    dialogue()


#TODO mode evaluation
#TODO mode learning