app.py

import gradio as gr
from PIL import Image
import matplotlib.pyplot as plt
import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation

model = AutoModelForImageSegmentation.from_pretrained('briaai/RMBG-2.0', trust_remote_code=True)
torch.set_float32_matmul_precision(['high', 'highest'][0])
if torch.cuda.is_available():
    model = model.to('cuda')
model.eval()

def remove_background(input_image, holiday, message):
    image_size = (1024, 1024)
    # Transform the input image
    transform_image = transforms.Compose([
        transforms.Resize(image_size),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    
    # Process the image
    input_tensor = transform_image(input_image).unsqueeze(0)
    if torch.cuda.is_available():
        input_tensor = input_tensor.to('cuda')
    
    # Generate prediction
    with torch.no_grad():
        preds = model(input_tensor)[-1].sigmoid().cpu()
    pred = preds[0].squeeze()
    pred_pil = transforms.ToPILImage()(pred)
    mask = pred_pil.resize(input_image.size)
    
    # Create image without background
    result_image = input_image.copy()
    result_image.putalpha(mask)
    
    # Create image with only background
    only_background_image = input_image.copy()
    inverted_mask = Image.eval(mask, lambda x: 255 - x)  # Invert the mask
    only_background_image.putalpha(inverted_mask)
    
    first_output_image = result_image
    second_output_image = only_background_image
    third_output_image = result_image
    
    return first_output_image, second_output_image, third_output_image

# Replace the demo interface
demo = gr.Interface(
    fn=remove_background,
    inputs=[
        gr.Image(type="pil"),
        gr.Text(label="Holiday (e.g. Christmas, New Year's, etc.)"),
        gr.Text(label="Optional Message", placeholder="Enter your holiday message here...")
    ],
    outputs=[
        gr.Image(type="pil", label="First Output"),
        gr.Image(type="pil", label="Second Output"),
        gr.Image(type="pil", label="Third Output")
    ],
    title="Holiday Card Generator",
    description="Upload an image to generate a holiday card"
)

demo.launch()