app.py

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

# Define the model architecture
class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(64 * 64 * 64, 512)
        self.fc2 = nn.Linear(512, 2)
        self.dropout = nn.Dropout(0.5)


    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 64 * 64 * 64)
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x

# Load the saved model
model_path = 'modelv2.pth'
model = SimpleCNN()
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.eval()

# Define transformations for images
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.ToTensor()
])

# Define the prediction function
def predict(image):
    image = Image.fromarray(image)
    image = transform(image).unsqueeze(0)
    with torch.no_grad():
        outputs = model(image)
        probabilities = torch.softmax(outputs, dim=1)
        prob_benign = probabilities[0][0].item()
        prob_malignant = probabilities[0][1].item()
    return {'Benign': prob_benign, 'Malignant': prob_malignant}






# Create Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image( label="Upload an Image"),
    outputs=gr.Label(num_top_classes=2, label="Predictions")
)

# Run the interface
if __name__ == "__main__":
    iface.launch()