import gradio as gr from transformers import pipeline import torch import numpy as np from PIL import Image import io def remove_background(input_image): try: # Initialize the pipeline segmentor = pipeline("image-segmentation", model="briaai/RMBG-1.4", device=-1) # CPU inference # Process the image result = segmentor(input_image) return result['output_image'] except Exception as e: raise gr.Error(f"Error processing image: {str(e)}") # Create the Gradio interface with custom styling css = """ .gradio-container { font-family: 'Segoe UI', sans-serif; background: linear-gradient(135deg, #1a1a1a 0%, #2d2d2d 100%); } .gr-image { border-radius: 12px; border: 2px solid rgba(255, 215, 0, 0.3); } .gr-button { background: linear-gradient(45deg, #FFD700, #FFA500); border: none; color: black; } .gr-button:hover { transform: translateY(-2px); box-shadow: 0 4px 12px rgba(255, 215, 0, 0.3); } """ with gr.Blocks(css=css) as demo: gr.HTML( """

AI Background Remover

Powered by RMBG V1.4 model from BRIA AI

""" ) with gr.Row(): with gr.Column(): input_image = gr.Image( label="Upload Image", type="pil", sources=["upload", "clipboard"] ) with gr.Column(): output_image = gr.Image( label="Result", type="pil" ) with gr.Row(): clear_btn = gr.Button("Clear") process_btn = gr.Button("Remove Background", variant="primary") # Event handlers process_btn.click( fn=remove_background, inputs=[input_image], outputs=[output_image] ) clear_btn.click( lambda: (None, None), outputs=[input_image, output_image] ) demo.launch()