Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+import nibabel as nib
+from model import My3DModel
+
+model = My3DModel()
+model.load_state_dict(torch.load("model.pt", map_location="cpu"))
+model.eval()
+
+def predict_from_mri(file_obj):
+    img = nib.load(file_obj.name)
+    data = img.get_fdata().astype(np.float32)
+    data = np.expand_dims(data, axis=0)  # (1, D, H, W)
+    tensor = torch.tensor(data).unsqueeze(0)  # (1, 1, D, H, W)
+
+    with torch.no_grad():
+        output = model(tensor)
+        pred_class = torch.argmax(output, dim=1).item()
+    
+    return ["CN", "MCI", "AD"][pred_class]
+
+iface = gr.Interface(fn=predict_from_mri,
+                     inputs=gr.File(label="MRI .nii dosyası"),
+                     outputs="text",
+                     title="3D MRI Alzheimer Teşhis Modeli")
+iface.launch()