Update app.py

app.py

@@ -2,7 +2,25 @@ import gradio as gr
 import pydicom
 import numpy as np
 import matplotlib.pyplot as plt
-from monai.transforms import Compose, LoadImage, ScaleIntensity, ToTensor
+import torch
+from monai.networks.nets import UNet
+from monai.transforms import Compose, ScaleIntensity, ToTensor
+
+# 1. Define a simple MONAI model (2D UNet)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = UNet(
+    dimensions=2,
+    in_channels=1,
+    out_channels=1,
+    channels=(16, 32, 64, 128, 256),
+    strides=(2, 2, 2, 2),
+    num_res_units=2,
+).to(device)
+model.eval()  # Set model to evaluation mode
+
+# 2. Dummy weights (for demo only)
+# In real use, load pre-trained weights:
+# model.load_state_dict(torch.load("your_model.pth", map_location=device))
 
 def interpret_dicom(files):
     slices = []
@@ -11,19 +29,37 @@ def interpret_dicom(files):
         slices.append(ds.pixel_array)
     slices = np.array(slices)
     mid_slice = slices[len(slices)//2]
-    plt.imshow(mid_slice, cmap='gray')
-    plt.axis('off')
-    plt.title('Middle Slice')
+
+    # Preprocess for MONAI model
+    transform = Compose([ScaleIntensity(), ToTensor()])
+    input_tensor = transform(mid_slice.astype(np.float32))
+    input_tensor = input_tensor.unsqueeze(0).to(device)  # Add batch dimension
+
+    # 3. Run through MONAI model (dummy output for now)
+    with torch.no_grad():
+        output = model(input_tensor)
+        output_np = output.cpu().numpy()[0, 0]
+
+    # 4. Show original and model output side by side
+    fig, axs = plt.subplots(1, 2, figsize=(8, 4))
+    axs[0].imshow(mid_slice, cmap='gray')
+    axs[0].set_title('Original')
+    axs[0].axis('off')
+    axs[1].imshow(output_np, cmap='hot')
+    axs[1].set_title('Model Output')
+    axs[1].axis('off')
+    plt.tight_layout()
     plt.savefig('output.png')
     plt.close()
-    return 'output.png', "Interpretation: (placeholder)"
+
+    return 'output.png', "Interpretation: Model output shown (demo weights)."
 
 iface = gr.Interface(
     fn=interpret_dicom,
     inputs=gr.File(file_count="multiple", label="Upload DICOM files"),
-    outputs=[gr.Image(type="filepath", label="Middle Slice"), gr.Textbox(label="Interpretation")],
-    title="DICOM Radiology Interpreter",
-    description="Upload your DICOM files (e.g., CT scan slices). The app will show the middle slice and provide an interpretation."
+    outputs=[gr.Image(type="filepath", label="Result"), gr.Textbox(label="Interpretation")],
+    title="DICOM Radiology Interpreter with MONAI",
+    description="Upload your DICOM files (e.g., CT scan slices). The app will show the middle slice and a MONAI model output."
 )
 
 if __name__ == "__main__":