app.py

import torch
import torch.nn as nn
from torchvision import transforms as T
import os
import gradio as gr

#################################
# Define problem parameters
#################################

class config:

    img_size = 224

    pn_mean = [0.4752, 0.4752, 0.4752]  # Pneumonia dataset mean
    pn_std = [0.2234, 0.2234, 0.2234]   # Pneumonia dataset std

    class_names = ["Normal", "Pneumonia"]


device = torch.device('cpu')

print(f"device: {device}")

#######################################
# Define image transformation pipeline
#######################################

class Gray2RGB:
    def __call__(self, image):
        if image.shape[0] == 3:
            return image
        else:
            return image.repeat(3, 1, 1) # Repeat the single channel across 3 channels to convert to RGB

test_transform_custom = T.Compose([
                            T.Resize(size=(config.img_size, config.img_size)),
                            T.ToTensor(),
                            Gray2RGB(),
                            T.Normalize(config.pn_mean, config.pn_std),
                        ])


#################################
# Define model architecture
#################################

class ConvolutionalNetwork(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv1 = nn.Sequential(
                        nn.Conv2d(3, 8, 3, stride=1, padding=1),
                        nn.ReLU(inplace=True),
                        nn.BatchNorm2d(8),
                        nn.MaxPool2d(2,2))

        self.conv2 = nn.Sequential(
                        nn.Conv2d(8, 16, 3, stride=1, padding=1),
                        nn.ReLU(inplace=True),
                        nn.BatchNorm2d(16),
                        nn.MaxPool2d(2,2))

        self.conv3 = nn.Sequential(
                        nn.Conv2d(16, 32, 3, stride=1, padding=1),
                        nn.ReLU(inplace=True),
                        nn.BatchNorm2d(32),
                        nn.MaxPool2d(2,2))

        self.conv4 = nn.Sequential(
                        nn.Conv2d(32, 64, 3, stride=1, padding=1),
                        nn.ReLU(inplace=True),
                        nn.BatchNorm2d(64),
                        nn.MaxPool2d(2,2))

        self.conv5 = nn.Sequential(
                        nn.Conv2d(64, 128, 3, stride=1, padding=1),
                        nn.ReLU(inplace=True),
                        nn.BatchNorm2d(128),
                        nn.MaxPool2d(2,2))

        self.fc = nn.Sequential(
                    nn.Linear(128*7*7, 512),
                    nn.ReLU(inplace=True),
                    nn.BatchNorm1d(512),
                    nn.Dropout(0.5),
                    nn.Linear(512, 2))

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        x = x.view(x.shape[0], -1)
        x = self.fc(x)
        return x


cnn_model = ConvolutionalNetwork()

cnn_model.to(device)

status = cnn_model.load_state_dict(torch.load('pneumonia_cnn_model.pt', map_location=device, weights_only=True))
print(f"Status: {status}")

#################################
# Define the prediction fucntion
#################################

def predict(image):
    """Transforms and performs a prediction on an image and returns the prediction dictionary."""
    
    image = test_transform_custom(image).unsqueeze(0)
    
    cnn_model.eval()
    with torch.no_grad():
        pred_probs = torch.softmax(cnn_model(image), dim=1)
    
    # Create a prediction probability dictionary for each prediction class
    pred_dict = {config.class_names[i]: float(pred_probs[0][i]) for i in range(len(config.class_names))}
    
    # Return the prediction dictionary
    return pred_dict

##########################
# Create the Gradio demo
##########################

title = "Pneumonia Detection"

description = """This is a pneumonia detection model that uses a custom convolutional neural network to predict whether an image contains pneumonia or not. \
GitHub project can be accessed [here](https://github.com/mma666/Pneumonia-Detection-Computer-Vision).
"""

# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]

# Create the Gradio demo
demo = gr.Interface(fn=predict,
                    inputs=[gr.Image(label="Upload image", type="pil", height=320, width=320)],
                    outputs=[gr.Label(num_top_classes=2, label="Predictions")],
                    examples=example_list, 
                    title=title,
                    description=description,
                    cache_examples=False)

demo.launch()