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ResNet152_DenseNet201_best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cf755020938d8ae9bb3e61ab2acf960977a78474b6c3a76148bf8e701d5acb44
+size 323570735

app.py

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+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from PIL import Image
+import gradio as gr
+
+# --------- Load Model ----------
+MODEL_PATH = "ResNet152_DenseNet201_best.pt"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load your trained model
+model = torch.load(MODEL_PATH, map_location=device)
+model.eval()
+
+# --------- Define Preprocessing ----------
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # match training size
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], 
+                         [0.229, 0.224, 0.225])  # standard ImageNet normalization
+])
+
+# Class labels (adjust if yours are different)
+class_names = ["No Alzheimer’s", "Very Mild", "Mild", "Moderate"]
+
+# --------- Prediction Function ----------
+def predict(image):
+    image = transform(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        outputs = model(image)
+        _, predicted = torch.max(outputs, 1)
+    return class_names[predicted.item()]
+
+# --------- Gradio Interface ----------
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload MRI Scan"),
+    outputs=gr.Label(num_top_classes=4, label="Predicted Alzheimer’s Stage"),
+    title="Alzheimer’s MRI Classifier",
+    description="Upload an MRI brain scan to classify into one of four stages of Alzheimer's disease."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+torch
+torchvision
+gradio
+Pillow