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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 with trust_remote_code=True
segmentor = pipeline(
"image-segmentation",
model="briaai/RMBG-1.4",
trust_remote_code=True,
device="cpu"
)
# Process the image and get result
result = segmentor(input_image, return_mask=True)
# Convert result to RGBA
if isinstance(result, Image.Image):
# Create transparent background
output = Image.new('RGBA', result.size, (0, 0, 0, 0))
output.paste(input_image, mask=result)
else:
output = result['output_image']
return output
except Exception as e:
raise gr.Error(f"Error processing image: {str(e)}")
# Custom theme and styling
theme = gr.themes.Soft(
primary_hue="gold",
secondary_hue="orange",
).set(
body_background_fill="linear-gradient(135deg, #1a1a1a 0%, #2d2d2d 100%)",
body_text_color="#ffffff",
button_primary_background_fill="linear-gradient(45deg, #FFD700, #FFA500)",
button_primary_text_color="#000000",
border_color_primary="#FFD700"
)
css = """
.gradio-container {
max-width: 1200px !important;
margin: 0 auto !important;
padding: 20px !important;
}
.image-container {
border-radius: 15px !important;
border: 2px solid rgba(255, 215, 0, 0.3) !important;
padding: 10px !important;
background: rgba(255, 255, 255, 0.1) !important;
transition: transform 0.3s ease !important;
}
.image-container:hover {
transform: translateY(-5px) !important;
}
.gr-button {
min-width: 200px !important;
height: 45px !important;
font-size: 16px !important;
margin: 10px !important;
transition: all 0.3s ease !important;
}
.gr-button:hover {
transform: translateY(-2px) !important;
box-shadow: 0 5px 15px rgba(255, 215, 0, 0.3) !important;
}
.footer {
text-align: center;
margin-top: 20px;
color: #666;
}
"""
# Create Gradio interface
with gr.Blocks(theme=theme, css=css) as demo:
gr.HTML(
"""
<div style="text-align: center; margin-bottom: 2rem;">
<h1 style="font-size: 3rem; margin-bottom: 1rem; background: linear-gradient(45deg, #FFD700, #FFA500); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">
AI Background Remover Pro
</h1>
<p style="color: #cccccc; font-size: 1.2rem;">
Remove backgrounds instantly using advanced AI technology
</p>
</div>
"""
)
with gr.Row():
with gr.Column():
input_image = gr.Image(
label="Upload Your Image",
type="pil",
elem_classes="image-container"
)
with gr.Row():
clear_btn = gr.Button("Clear", variant="secondary")
process_btn = gr.Button("Remove Background", variant="primary")
download_btn = gr.Button("Download", variant="primary", visible=False)
with gr.Column():
output_image = gr.Image(
label="Result",
type="pil",
elem_classes="image-container"
)
# Status message
status_msg = gr.Textbox(
label="Status",
placeholder="Ready to process your image...",
interactive=False
)
# Event handlers
def process_and_update(image):
if image is None:
return None, "Please upload an image first", gr.Button.update(visible=False)
try:
result = remove_background(image)
return (
result,
"✨ Background removed successfully!",
gr.Button.update(visible=True)
)
except Exception as e:
return None, f"❌ Error: {str(e)}", gr.Button.update(visible=False)
process_btn.click(
fn=process_and_update,
inputs=[input_image],
outputs=[output_image, status_msg, download_btn],
)
clear_btn.click(
fn=lambda: (None, None, "Ready to process your image...", gr.Button.update(visible=False)),
outputs=[input_image, output_image, status_msg, download_btn],
)
gr.HTML(
"""
<div class="footer">
<p>Powered by BRIA AI's RMBG V1.4 Model</p>
</div>
"""
)
# Launch the app
demo.launch()
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