import time
import pygame
import sys
from random import seed
from random import random
from astar import RoutePlanning, astar
from world import set_world, World, Agent
from genetic_algorithm import *
from decision_tree import *
from neural_network2 import *
from neural_network_vers2 import *
start_point = (0, 0)
position = list(start_point)
window = pygame.display.set_mode((800, 560))
cell_size = 40
final_point = (1, 0)
world_map_file = set_world('worldmap.txt')
pygame.init()
world = World(world_map_file, cell_size)
sprites = pygame.sprite.Group()
agent = Agent(world.get_world_cell_size(), world)
sprites.add(agent)
window.fill((0, 0, 0))
roads = []
houses = [
    (5,1)],[(11,2)],[(6,6)],[(12,6)],[(3,9)],[(7,9)],[(17,9)],[(6,12)
]



for w in range(0, len(world_map_file[0])):
    for h in range(0, len(world_map_file)):
        if int(world_map_file[h][w]) != 0:
            roads.append((w, h))

def move(actions):
    for x in actions:
        if x == "Go right":
            while agent.get_directions()[0] != "E":
                agent.turn_right()
            agent.move()

        if x == "Go left":
            while agent.get_directions()[0] != "W":
                agent.turn_left()
            agent.move()

        if x == "Go down":
            while agent.get_directions()[0] != "S":
                agent.turn_left()
            agent.move()

        if x == "Go up":
            while agent.get_directions()[0] != "N":
                agent.turn_right()
            agent.move()
        world.draw(window)
        sprites.update()
        sprites.draw(window)
        time.sleep(0.2)
        pygame.display.update()

    while agent.get_directions()[0] != "E":
        agent.turn_right()
        world.draw(window)
        sprites.update()
        sprites.draw(window)
        time.sleep(0.1)
        pygame.display.update()
    if final_point[0] == 19 and final_point[1] == 8:
         agent.change_image2()
    for i in range(0, 8):
        if (houses[i][0] == (final_point[0],final_point[1])):
            print("----------drzewo decyzyjne---------------")
            t = buildTree()
            seed(1)
            trash = [[random.random(),random.random(),random.random(),random.random(),random.random(),random.random(),random.random(),random.random()],[random.random(),random.random(),random.random(),random.random(),random.random(),random.random(),random.random(),random.random()]]
            print(trash)  # prints data array
            typeOfTrashes = predictTypeOfTrash(t, trash)  # prints recognised trash
            print(typeOfTrashes)
            print("-----------sieci neuronowe--------------")
            b()
            agent.change_image()
        world.draw(window)
        sprites.update()
        sprites.draw(window)
        time.sleep(0.1)
        pygame.display.update()


while True:
    world.draw(window)
    sprites.update()
    sprites.draw(window)

    for event in pygame.event.get():
        if event.type == pygame.QUIT:
            pygame.quit()
            sys.exit()
        if event.type == pygame.KEYDOWN:
            if event.key == pygame.K_w:
                agent.move()

            if event.key == pygame.K_a:
                agent.turn_left()

            if event.key == pygame.K_d:
                agent.turn_right()
            elif event.type == pygame.KEYDOWN:
                if event.key == pygame.K_SPACE or event.key == pygame.K_UP:
                    actions = k((agent.get_second(), agent.get_first()))
                    move(actions)
            if event.key == pygame.K_KP_ENTER:
                for i in range(0, 8):
                    if (houses[i][0] == (agent.get_first(), agent.get_second())):
                        predict()

        elif event.type == pygame.MOUSEBUTTONDOWN:
            start_point = (agent.get_first(), agent.get_second())
            pos_x = int(pygame.mouse.get_pos()[1] / cell_size)
            pos_y = int(pygame.mouse.get_pos()[0] / cell_size)
            if int(world_map_file[pos_x][pos_y]) != 0:
                final_point = (pos_y, pos_x)
                route = RoutePlanning(start_point, final_point, roads)
                actions = astar(route).reconstruct_path()
                move(actions)




    pygame.display.update()