Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForImageSegmentation, AutoTokenizer
+import torch
+from PIL import Image
+
+# Load the model
+model = AutoModelForImageSegmentation.from_pretrained("path/to/gelan-c-seg.pt")
+
+# Load the tokenizer (if needed)
+tokenizer = AutoTokenizer.from_pretrained("path/to/tokenizer")
+
+# Define the prediction function
+def predict_segmentation(image):
+    # Convert image to PyTorch tensor
+    image = torch.tensor(image).permute(2, 0, 1).unsqueeze(0).float()
+    # Perform inference
+    output = model(image)
+    # Process the output as needed (e.g., post-processing for segmentation masks)
+    # (Replace this with your actual processing code)
+    segmentation_mask = output.logits.argmax(dim=1).squeeze().detach().numpy()
+    return segmentation_mask
+
+# Create a Gradio interface
+inputs = gr.inputs.Image(shape=(224, 224))
+outputs = gr.outputs.Image(type="numpy", label="Segmentation Mask")
+title = "Image Segmentation Demo"
+description = "Upload an image and get the segmentation mask."
+examples = [["example.jpg"]]  # Add example images here if needed
+interface = gr.Interface(fn=predict_segmentation, inputs=inputs, outputs=outputs, title=title, description=description, examples=examples)
+
+# Run the interface
+if __name__ == "__main__":
+    interface.launch()