Create app.py

app.py

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+import torch
+import torch.nn as nn
+from torchvision import transforms
+from PIL import Image
+import gradio as gr
+
+model.load_state_dict(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()