Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+import numpy as np
+
+# Use a pipeline as a high-level helper
+pipe = pipeline("mask-generation", model="lightmedsam")
+
+def predict(image_path):
+    # Perform image segmentation
+    predictions = pipe(image_path)
+
+    # Access the segmented mask or any other relevant information in predictions
+    segmented_mask = predictions["segmentation_mask"]
+
+    # Convert the segmentation mask to an RGB image
+    segmented_image = colorize_mask(segmented_mask)
+
+    return segmented_image
+
+def colorize_mask(mask):
+    # Assuming `mask` is a single-channel segmentation mask (grayscale)
+    # You may need to adjust this function based on the specifics of your model's output
+
+    # Convert single-channel mask to 3-channel (RGB) mask
+    colored_mask = np.zeros((*mask.shape, 3), dtype=np.uint8)
+    colored_mask[:, :, 0] = mask
+    colored_mask[:, :, 1] = mask
+    colored_mask[:, :, 2] = mask
+
+    return colored_mask
+
+gr.Interface(
+    predict,
+    inputs=gr.Image(label="Upload medical image", type="filepath"),
+    outputs=gr.Image(label="Segmented image"),
+    title="Segmented medical image",
+    allow_flagging="manual"
+).launch()