app.py

import gradio as gr
import torch
from torchvision.transforms import transforms
import numpy as np
from typing import Optional
import torch.nn as nn
import os
import shutil
from utils import page_utils

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(3, 7)

checkpoint = torch.load('ham10000.ckpt', map_location=torch.device('cpu'))

# The state dict will contain net.layer_name
# Our model doesn't contain `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()

# Updated class names
class_names = {
    'akk': 'Actinic Keratosis',
    'bcc': 'Basal Cell Carcinoma',
    'bkl': 'Benign Keratosis',
    'df': 'Dermatofibroma',
    'mel': 'Melanoma',
    'nv': 'Melanocytic Nevi',
    'vasc': 'Vascular Lesion'
}

examples_dir = "sample"

transformation_pipeline = transforms.Compose([
    transforms.ToPILImage(),
    transforms.Grayscale(num_output_channels=3),
    transforms.CenterCrop((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

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 an error
    if inp is None:
        return {
            'Actinic Keratosis': 0.0,
            'Basal Cell Carcinoma': 0.0,
            'Benign Keratosis': 0.0,
            'Dermatofibroma': 0.0,
            'Melanoma': 0.0,
            'Melanocytic Nevi': 0.0,
            'Vascular Lesion': 0.0
        }
    # 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 = {class_names[name]: score for name, score in zip(class_names, result)}

    return labeled_result

# gradio code block for input and output
with gr.Blocks() as app:
    gr.Markdown("# Skin Cancer Classification")

with open('index.html', encoding="utf-8") as f:
    description = f.read()


# gradio code block for input and output
with gr.Blocks(theme=gr.themes.Default(primary_hue=page_utils.KALBE_THEME_COLOR, secondary_hue=page_utils.KALBE_THEME_COLOR).set(
        button_primary_background_fill="*primary_600",
        button_primary_background_fill_hover="*primary_500",
        button_primary_text_color="white",
    )) as app:
    with gr.Column():
        gr.HTML(description)

    with gr.Row():
        with gr.Column():
            inp_img = gr.Image()
            with gr.Row():
                clear_btn = gr.Button(value="Clear")
                process_btn = gr.Button(value="Process", variant="primary")
        with gr.Column():
            out_txt = gr.Label(label="Probabilities", num_top_classes=3)

    process_btn.click(image_classifier, inputs=inp_img, outputs=out_txt)
    clear_btn.click(lambda: (
        gr.update(value=None),
        gr.update(value=None)
    ),
        inputs=None,
        outputs=[inp_img, out_txt])

    gr.Markdown("## Image Examples")
    gr.Examples(
        examples=[os.path.join(examples_dir, "nv.jpeg"),
                  os.path.join(examples_dir, "bcc.jpeg"),
                  os.path.join(examples_dir, "bkl_1.jpeg"),
                  os.path.join(examples_dir, "akk.jpeg"),
                  os.path.join(examples_dir, "mel-_3_.jpeg"),
                  ],
        inputs=inp_img,
        outputs=out_txt,
        fn=image_classifier,
        cache_examples=False,
    )
    gr.Markdown(line_breaks=True, value='Author: M HAIKAL FEBRIAN P (haikalphona23@gmail.com) <div class="row"><a href="https://github.com/HAikalfebrianp96?tab=repositories"><img alt="GitHub" src="https://img.shields.io/badge/haikal%20phona-000000?logo=github"> </div>')

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