import torch
import cv2
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.optim as optim
from PIL import Image


transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 71 * 71, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 4)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(x.size(0), 16 * 71 * 71)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


def train():
    net = Net()
    trainset = torchvision.datasets.ImageFolder(
        root='./resources/zbior_uczacy', transform=transform)
    trainloader = torch.utils.data.DataLoader(
        trainset, batch_size=2, shuffle=True, num_workers=2)

    classes = ('glass', 'metal', 'paper', 'plastic')
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

    for epoch in range(10):
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            inputs, labels = data
            optimizer.zero_grad()
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
            if i:
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss))
                running_loss = 0.0

    print('Finished Training')
    PATH = './wytrenowaned.pth'
    torch.save(net.state_dict(), PATH)


def predict(img_path):
    net = Net()
    PATH = './wytrenowaned.pth'
    img = Image.open(img_path)
    pil_to_tensor = transforms.ToTensor()(img).unsqueeze_(0)
    classes = ('glass', 'metal', 'paper', 'plastic')
    net.load_state_dict(torch.load(PATH))
    net.eval()
    outputs = net(pil_to_tensor)
    return classes[torch.max(outputs, 1)[1]]