import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import datasets, transforms, utils
from torchvision.transforms import Compose, Lambda, ToTensor
import matplotlib.pyplot as plt
from NN.model import *
from PIL import Image
import pygame
from area.constants import GREY

device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')

#data transform to tensors:
data_transformer = transforms.Compose([
    transforms.Resize((100, 100)), 
    transforms.ToTensor(),  
    transforms.Normalize((0.5, 0.5, 0.5 ), (0.5, 0.5, 0.5))  
    ])


#loading data:
train_set = datasets.ImageFolder(root='resources/train', transform=data_transformer)
test_set = datasets.ImageFolder(root='resources/test', transform=data_transformer)


#to mozna nawet przerzucic do funkcji train:
# train_loader = DataLoader(train_set, batch_size=64, shuffle=True)
#test_loader = DataLoader(test_set, batch_size=32, shuffle=True)


#function for training model
def train(model, dataset, iter=100, batch_size=64):
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
    criterion = nn.NLLLoss()
    train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
    model.train()

    for epoch in range(iter):
        for inputs, labels in train_loader:
            optimizer.zero_grad()
            output = model(inputs.to(device))
            loss = criterion(output, labels.to(device))
            loss.backward()
            optimizer.step()
        if epoch % 10 == 0:
                print('epoch: %3d loss: %.4f' % (epoch, loss))

#function for getting accuracy
def accuracy(model, dataset):
    model.eval()
    with torch.no_grad():
        correct = sum([
            (model(inputs.to(device)).argmax(dim=1) == labels.to(device)).sum()
            for inputs, labels in DataLoader(dataset, batch_size=64, shuffle=True)
        ])

    return correct.float() / len(dataset)


# model = Conv_Neural_Network_Model()
# model.to(device)

#loading the already saved model:
# model.load_state_dict(torch.load('CNN_model.pth'))
# model.eval()

# #training the model:
# train(model, train_set)
# print(f"Accuracy of the network is: {100*accuracy(model, test_set)}%")
# torch.save(model.state_dict(), 'CNN_model.pth')



def load_model():
    model = Conv_Neural_Network_Model()
    model.load_state_dict(torch.load('CNN_model.pth', map_location=torch.device('cpu')))
    model.eval()
    return model


def load_image(image_path):
    testImage = Image.open(image_path).convert('RGB')
    testImage = data_transformer(testImage)
    testImage = testImage.unsqueeze(0)
    return testImage

#display the image for prediction next to the field
def display_image(screen, image_path, position):
    image = pygame.image.load(image_path)
    image = pygame.transform.scale(image, (250, 250))
    screen.blit(image, position)

#display result of the guessed image (text under the image)
def display_result(screen, position, predicted_class):
    font = pygame.font.Font(None, 30)
    displayed_text = font.render("The predicted image is: "+str(predicted_class), 1, (255,255,255))
    screen.blit(displayed_text, position)

def clear_text_area(win, x, y, width, height, color=GREY):
    pygame.draw.rect(win, color, (x, y, width, height))
    pygame.display.update()

def guess_image(model, image_tensor):
    with torch.no_grad():
        testOutput = model(image_tensor)
        _, predicted = torch.max(testOutput, 1)
        predicted_class = train_set.classes[predicted.item()]
    return predicted_class



#TEST - loading the image and getting results:
# testImage_path = 'resources/images/plant_photos/1c76aa4d-11f4-47d1-8bdd-2cb78deeeccf.jpg'
# testImage = Image.open(testImage_path)
# testImage = data_transformer(testImage)
# testImage = testImage.unsqueeze(0)
# testImage = testImage.to(device)

# model.load_state_dict(torch.load('CNN_model.pth'))
# model.to(device)
# model.eval()

# testOutput = model(testImage)
# _, predicted = torch.max(testOutput, 1)
# predicted_class = train_set.classes[predicted.item()]
# print(f'The predicted class is: {predicted_class}')