import gradio as gr
import torch
from torchvision.transforms import transforms
import numpy as np
from typing import Optional
import torch.nn as nn

class BasicBlock(nn.Module):
    """ResNet Basic Block.

    Parameters
    ----------
    in_channels : int
        Number of input channels
    out_channels : int
        Number of output channels
    stride : int, optional
        Convolution stride size, by default 1
    identity_downsample : Optional[torch.nn.Module], optional
        Downsampling layer, by default None
    """

    def __init__(self,
                in_channels: int,
                out_channels: int,
                stride: int = 1,
                identity_downsample: Optional[torch.nn.Module] = None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels,
                              out_channels,
                              kernel_size = 3,
                              stride = stride,
                              padding = 1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(out_channels,
                              out_channels,
                              kernel_size = 3,
                              stride = 1,
                              padding = 1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.identity_downsample = identity_downsample

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Apply forward computation."""
        identity = x
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)

        # Apply an operation to the identity output.
        # Useful to reduce the layer size and match from conv2 output
        if self.identity_downsample is not None:
            identity = self.identity_downsample(identity)
        x += identity
        x = self.relu(x)
        return x

class ResNet18(nn.Module):
    """Construct ResNet-18 Model.

    Parameters
    ----------
    input_channels : int
        Number of input channels
    num_classes : int
        Number of class outputs
    """

    def __init__(self, input_channels, num_classes):

        super(ResNet18, self).__init__()
        self.conv1 = nn.Conv2d(input_channels,
                               64, kernel_size = 7,
                              stride = 2, padding=3)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size = 3,
                                   stride = 2,
                                   padding = 1)

        self.layer1 = self._make_layer(64, 64, stride = 1)
        self.layer2 = self._make_layer(64, 128, stride = 2)
        self.layer3 = self._make_layer(128, 256, stride = 2)
        self.layer4 = self._make_layer(256, 512, stride = 2)

        # Last layers
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def identity_downsample(self, in_channels: int, out_channels: int) -> nn.Module:
        """Downsampling block to reduce the feature sizes."""
        return nn.Sequential(
             nn.Conv2d(in_channels,
                       out_channels,
                       kernel_size = 3,
                       stride = 2,
                       padding = 1),
            nn.BatchNorm2d(out_channels)
        )

    def _make_layer(self, in_channels: int, out_channels: int, stride: int) -> nn.Module:
        """Create sequential basic block."""
        identity_downsample = None

        # Add downsampling function
        if stride != 1:
            identity_downsample = self.identity_downsample(in_channels, out_channels)

        return nn.Sequential(
                    BasicBlock(in_channels, out_channels, identity_downsample=identity_downsample, stride=stride),
                    BasicBlock(out_channels, out_channels)
                    )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.shape[0], -1)
        x = self.fc(x)
        return x

model = ResNet18(1, 5)

checkpoint = torch.load('acc=0.94.ckpt', map_location=torch.device('cpu'))

# The state dict will contains net.layer_name
# Our model doesn't contains `net.` so we have to rename it
state_dict = checkpoint['state_dict']
for key in list(state_dict.keys()):
    if 'net.' in key:
        state_dict[key.replace('net.', '')] = state_dict[key]
        del state_dict[key]

model.load_state_dict(state_dict)
model.eval()

class_names = ['abdominal', 'adult', 'others', 'pediatric', 'spine']
class_names.sort()

transformation_pipeline = transforms.Compose([
    transforms.ToPILImage(),
    transforms.Grayscale(num_output_channels=1),
    transforms.CenterCrop((384, 384)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.50807575], std=[0.20823])
])


def preprocess_image(image: np.ndarray):
    """Preprocess the input image.

    Note that the input image is in RGB mode.

    Parameters
    ----------
    image: np.ndarray
        Input image from callback.
    """

    image = transformation_pipeline(image)
    image = torch.unsqueeze(image, 0)

    return image


def image_classifier(inp):
    """Image Classifier Function.

    Parameters
    ----------
    inp: Optional[np.ndarray] = None
        Input image from callback

    Returns
    -------
    Dict
        A dictionary class names and its probability
    """

    # If input not valid, return dummy data or raise error
    if inp is None:
        return {'cat': 0.3, 'dog': 0.7}

    # preprocess
    image = preprocess_image(inp)
    image = image.to(dtype=torch.float32)

    # inference
    result = model(image)

    # postprocess
    result = torch.nn.functional.softmax(result, dim=1) # apply softmax
    result = result[0].detach().numpy().tolist() # take the first batch
    labeled_result = {name:score for name, score in zip(class_names, result)}

    return labeled_result

demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
demo.launch()