Create app.py

app.py

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+import torch
+import numpy as np
+import pydicom
+import gradio as gr
+from torchvision import transforms
+from PIL import Image
+
+# Load your PyTorch model
+model = torch.load('your_model.pth')  # Replace with your model path
+model.eval()
+
+# Define a function to preprocess the DICOM
+def preprocess_dicom(dicom_path):
+    # Load DICOM file
+    dicom = pydicom.dcmread(dicom_path)
+    image = dicom.pixel_array  # Extract image data
+    # Normalize to [0, 1] and convert to PIL Image for transforms
+    image = (image - np.min(image)) / (np.max(image) - np.min(image))
+    image = Image.fromarray((image * 255).astype(np.uint8))
+    # Apply transforms
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),  # Resize to model's input size
+        transforms.ToTensor(),
+    ])
+    return transform(image).unsqueeze(0)  # Add batch dimension
+
+# Prediction function
+def predict_dicom(dicom_file):
+    # Preprocess
+    input_tensor = preprocess_dicom(dicom_file.name)
+    # Inference
+    with torch.no_grad():
+        output = model(input_tensor)
+    # Convert output tensor to image (dummy example, replace as needed)
+    output_image = output.squeeze().numpy()
+    output_image = (output_image - np.min(output_image)) / (np.max(output_image) - np.min(output_image)) * 255
+    output_image = Image.fromarray(output_image.astype(np.uint8))
+    return output_image
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=predict_dicom,
+    inputs=gr.inputs.File(label="Upload DICOM File"),
+    outputs="image",
+    title="DICOM Image Prediction"
+)
+
+# Launch the Gradio app
+interface.launch()