app.py

import gradio as gr
import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image


# Load the model
loaded_model = models.densenet121()

num_features = loaded_model.classifier.in_features
loaded_model.classifier = nn.Linear(num_features, 5)
loaded_model.load_state_dict(torch.load('derma_diseases_detection_best.pt',map_location=torch.device('cpu')))
loaded_model.eval()

# Define the image preprocessing function
def preprocess_image(image):

    image = Image.fromarray(image)
    # Transform the image using the same transformations as during training
    transform = transforms.Compose([
        transforms.Resize([224, 224]),
        transforms.ToTensor(),
        #transforms.Normalize(mean=[0.5523, 0.5288, 0.5106], std=[0.1012, 0.0820, 0.0509])
    ])
    image = transform(image)
    image = image.unsqueeze(0)  # Add batch dimension
    return image

# Define the prediction function
def predict_skin_disease(image):
    # Preprocess the input image
    preprocessed_image = preprocess_image(image)

    # Make prediction
    with torch.no_grad():
        output = loaded_model(preprocessed_image)
        _, predicted_class = torch.max(output, 1)

    # Map the predicted class index to the corresponding class label
    class_label =  ['No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative']
    class_label = class_label[predicted_class.item()]


    return class_label

# Create a Gradio interface
iface = gr.Interface(fn=predict_skin_disease, inputs="image", outputs="text", live=True)

# Launch the Gradio interface
iface.launch()