
import io
import torch 
import torch.nn as nn 
import torchvision.transforms as transforms 
import torch.nn.functional as F
from PIL import Image

class BasicBlock(nn.Module):
    expansion = 1
    

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()

        DROPOUT = 0.1

        self.conv1 = nn.Conv2d(
            in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.dropout = nn.Dropout(DROPOUT)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.dropout = nn.Dropout(DROPOUT)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes),
                nn.Dropout(DROPOUT)
            )

    def forward(self, x):
        out = F.relu(self.dropout(self.bn1(self.conv1(x))))
        out = self.dropout(self.bn2(self.conv2(out)))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return F.log_softmax(out, dim=-1)


def ResNet18():
    return ResNet(BasicBlock, [2, 2, 2, 2])

model = ResNet18()


PATH = "app/resnet18_cifar10_model.pth"
model.load_state_dict(torch.load(PATH,map_location=torch.device('cpu')))
model.eval()

def transform_image(image_bytes):
    transform = transforms.Compose([transforms.Resize((32,32)),
                                    transforms.ToTensor(),
                                    transforms.Normalize((0.491, 0.482, 0.446), (0.247, 0.243, 0.261))])

    image = Image.open(io.BytesIO(image_bytes))
    return transform(image).unsqueeze(0)



def get_prediction(image_tensor):
    classes = ['plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
    images = image_tensor.reshape(-1,3,32,32)
    output = model(images)
    pred = output.argmax(dim=1, keepdim=True)
    return {'pred value':pred.item(),'class name':classes[pred.item()]}