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#!/usr/bin/env python3
"""
Minimal OneFormer Demo for HuggingFace Spaces
"""
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import gradio as gr
import torch
import numpy as np
from PIL import Image
# Force CPU
device = torch.device("cpu")
def process_image(image):
"""Simple image processing function"""
if image is None:
return None
# For now, just return the image with a message
# Replace this with actual OneFormer inference
return image
# Create simple interface
iface = gr.Interface(
fn=process_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Image(type="numpy"),
title="OneFormer Demo",
description="OneFormer: Universal Image Segmentation (CPU Mode)",
)
if __name__ == "__main__":
print(f"PyTorch version: {torch.__version__}")
print(f"Running on: CPU")
iface.launch(server_name="0.0.0.0", server_port=7860)
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