app.py

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

model = torch.load("squeezenet.pth")
model.eval()

transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])

def classify_brain_tumor(image):
    image = transform(image).unsqueeze(0)
    with torch.no_grad():
        output = model(image)
        _, predicted = torch.max(output, 1)
    return "Tumor" if predicted.item() == 1 else "No Tumor"

interface = gr.Interface(
    fn=classify_brain_tumor,
    inputs=gr.inputs.Image(type="pil"),
    outputs="text",
    title="Brain Tumor Classification",
    description="Upload an MRI image to classify if it has a tumor or not. The Model is SqueezeNet."
)

interface.launch()