| #!/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) | |