Create app.py

app.py

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+import torch
+from transformers import AutoModelForImageSegmentation
+from PIL import Image
+from torchvision import transforms
+import gradio as gr
+
+# Load the model from Hugging Face
+birefnet = AutoModelForImageSegmentation.from_pretrained('zhengpeng7/BiRefNet_lite', trust_remote_code=True)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+birefnet.to(device)
+birefnet.eval()
+
+# Define the transform to preprocess the input image
+image_size = (1024, 1024)
+transform_image = transforms.Compose([
+    transforms.Resize(image_size),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+def extract_object(image):
+    input_images = transform_image(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        preds = birefnet(input_images)[-1].sigmoid().cpu()
+    pred = preds[0].squeeze()
+    pred_pil = transforms.ToPILImage()(pred)
+    mask = pred_pil.resize(image.size)
+    image_with_alpha = image.convert("RGBA")
+    image_with_alpha.putalpha(mask)
+    return image_with_alpha
+
+iface = gr.Interface(
+    fn=extract_object,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=gr.Image(type="pil", label="Segmented Image"),
+    title="BiRefNet Background Removal",
+    description="Upload an image and get the foreground object extracted."
+)
+
+if __name__ == "__main__":
+    iface.launch()