App and Models

app.py

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+from shiny import App, render, ui
+from shiny.ui import output_image, input_file, file_input, panel_main, panel_sidebar, layout_sidebar
+import matplotlib.pyplot as plt
+import numpy as np
+import torch  # Assuming the model is a PyTorch model
+from PIL import Image
+import io
+
+# Load your pre-trained model
+model = torch.load('path/to/your/model.pth')
+model.eval()
+
+def predict(image_file):
+    # Open image
+    image = Image.open(image_file)
+    # Process image as per model requirements (resize, normalize, etc.)
+    # Convert to tensor, e.g., if model expects tensor input
+    image_tensor = torch.Tensor(np.array(image) / 255).unsqueeze(0)  # adjust processing as needed
+    with torch.no_grad():
+        output = model(image_tensor)
+    # Post-process output, e.g., binarize, segment
+    segmentation = (output > 0.5).squeeze().numpy()  # adjust as needed
+    return segmentation
+
+def server(input, output, session):
+    @output
+    @render.image
+    def segmented_image():
+        # Check if a file has been uploaded
+        if not input.image_file:
+            return None
+
+        # Predict segmentation
+        uploaded_file = input.image_file()[0]
+        segmentation = predict(uploaded_file)
+
+        # Create figure to show segmentation
+        plt.imshow(segmentation, cmap='gray')
+        plt.axis('off')
+
+        # Save to buffer and return
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png')
+        buf.seek(0)
+        return buf
+
+app_ui = ui.page_fluid(
+    layout_sidebar(
+        panel_sidebar(
+            file_input("image_file", "Upload an image:", multiple=False),
+        ),
+        panel_main(
+            output_image("segmented_image"),
+        ),
+    )
+)
+
+app = App(app_ui, server)

models/model_checkpoint_trained_on_train.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:95f8d4de38c23599b69a8a99b60b5b2c4cf1671847ca2cdfe8760a9a8ec4d302
+size 375066348

requirements.txt

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+shiny
+torch
+numpy
+pillow
+matplotlib