for_gradio.py

import gradio as gr
from PIL import Image
import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation

# Setup constants
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Define image transformation pipeline
transform_image = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# Load the model ONCE globally
try:
    torch.set_float32_matmul_precision("high")
    model = AutoModelForImageSegmentation.from_pretrained(
        "ZhengPeng7/BiRefNet_lite",
        trust_remote_code=True
    ).to(DEVICE)
    print("Model loaded successfully.")
except Exception as e:
    print(f"Error loading model: {str(e)}")
    model = None

def process_image(image):
    """Process a single image and remove its background"""
    image = image.convert("RGB")
    original_size = image.size
    input_tensor = transform_image(image).unsqueeze(0).to(DEVICE)

    with torch.no_grad():
        preds = model(input_tensor)[-1].sigmoid().cpu()
        pred = preds[0].squeeze()
        mask = transforms.ToPILImage()(pred).resize(original_size)

        result = image.copy()
        result.putalpha(mask)

    return result

def predict(image):
    """Gradio interface function"""
    if model is None:
        raise gr.Error("Model not loaded. Check server logs.")
    if image is None:
        return None, None  # Return None for both image and file

    try:
        result_image = process_image(image)
        file_path = "processed_image.png"
        result_image.save(file_path, "PNG")
        return result_image, file_path
    
    except Exception as e:
        raise gr.Error(f"Error processing image: {e}")

# Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Image(type="pil", label="Processed Image"),
        gr.File(label="Download Processed Image")
    ],
    examples=[['example.jpeg']],
    title="Background Removal App",
    description="Upload an image to remove its background and download the processed image as a PNG."
)

interface.launch()