import os
import random

from disarming.parameters.hash_function import SeriesHash, SpecificityHash
from objects.mine_models.mine import Mine
from algorithms.learn.decision_tree.decision_tree import DecisionTree
from algorithms.learn.neural_network.neural_network import NNType, NeuralNetwork

SERIES_IMAGES_PATH = r"resources/data/neural_network/series/disarm"
SPECIFICITY_IMAGES_PATH = r"resources/data/neural_network/specificity/disarm"

class_to_series = \
    {SeriesHash[name].value[2]: SeriesHash[name].value[1] for name, _ in SeriesHash.__members__.items()}

class_to_specificity = \
    {SpecificityHash[name].value[2]: SpecificityHash[name].value[1] for name, _ in SpecificityHash.__members__.items()}


class DisarmingHandler:
    def __init__(self, mine: Mine):
        self.mine = mine
        self.mine_params = dict()

        self.series_img = None
        self.specificity_img = None

        self.recognized_series = None
        self.recognized_specificity = None

        self.correct_wire = None
        self.chosen_wire = None

        self._set_mine_params()
        self._set_correct_wire()

    def _set_mine_params(self):
        self.mine_params = self.mine.investigate()

    def _set_correct_wire(self):
        self.correct_wire = self.mine.wire

    def get_mine_params(self):
        return [self.mine_params["mine_type"], self.mine_params["weight"], self.mine_params["danger_cls"],
                self.mine_params["indicator"], self.mine_params["series"], self.mine_params["specificity"]]

    def pick_series_image(self):
        series_class = SeriesHash[self.mine_params["series"].upper().replace(" ", "_")].value[2]
        imgs_dir = os.path.join(SERIES_IMAGES_PATH, series_class)

        self.series_img = os.path.join(
            imgs_dir, random.choice([x for x in os.listdir(imgs_dir) if os.path.isfile(os.path.join(imgs_dir, x))]))
        return self.series_img

    def pick_specificity_image(self):
        specificity_class = SpecificityHash[self.mine_params["specificity"].upper().replace(" ", "_")].value[2]

        imgs_dir = os.path.join(SPECIFICITY_IMAGES_PATH, specificity_class)

        self.specificity_img = os.path.join(
            imgs_dir, random.choice([x for x in os.listdir(imgs_dir) if os.path.isfile(os.path.join(imgs_dir, x))]))

        return self.specificity_img

    def recognize_series(self):
        nn = NeuralNetwork(NNType.SERIES, load_from_file=True)
        answer, confidence = nn.get_answer(self.series_img)
        self.recognized_series = class_to_series[answer]

        return self.recognized_series, self.mine_params["series"] == self.recognized_series

    def recognize_specificity(self):
        nn = NeuralNetwork(NNType.SPECIFICITY, load_from_file=True)
        answer, confidence = nn.get_answer(self.specificity_img)
        self.recognized_specificity = class_to_specificity[answer]

        return self.recognized_specificity, self.mine_params["specificity"] == self.recognized_specificity

    def choose_wire(self):
        dt = DecisionTree(load_from_file=True)
        self.chosen_wire = dt.get_answer(self.mine_params)[0]

        return self.chosen_wire

    def defuse(self):
        return self.mine.disarm(self.chosen_wire)