Create app.py

app.py

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+import os
+import gradio as gr
+from gradio_imageslider import ImageSlider
+from loadimg import load_img
+import spaces
+from transformers import AutoModelForImageSegmentation
+import torch
+from torchvision import transforms
+
+torch.backends.mkldnn.enabled = True  # Enable CPU optimizations
+
+# Load model on CPU
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+    "briaai/RMBG-2.0", trust_remote_code=True
+)
+birefnet.to("cpu")
+
+# Reduce image size for efficiency
+transform_image = transforms.Compose(
+    [
+        transforms.Resize((512, 512)),  # Reduced from 1024x1024
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+output_folder = 'output_images'
+os.makedirs(output_folder, exist_ok=True)
+
+def fn(image):
+    im = load_img(image, output_type="pil").convert("RGB")
+    origin = im.copy()
+    image = process(im)    
+    image_path = os.path.join(output_folder, "no_bg_image.png")
+    image.save(image_path)
+    return (image, origin), image_path
+
+def process(image):
+    image_size = image.size
+    input_images = transform_image(image).unsqueeze(0).to("cpu")
+    
+    with torch.inference_mode():  # Efficient inference mode
+        preds = birefnet(input_images)[-1].sigmoid()
+    
+    pred = preds[0].squeeze()
+    pred_pil = transforms.ToPILImage()(pred)
+    mask = pred_pil.resize(image_size)
+    image.putalpha(mask)
+    return image
+  
+def process_file(f):
+    name_path = f.rsplit(".", 1)[0] + ".png"
+    im = load_img(f, output_type="pil").convert("RGB")
+    transparent = process(im)
+    transparent.save(name_path)
+    return name_path
+
+slider1 = ImageSlider(label="RMBG-2.0", type="pil")
+slider2 = ImageSlider(label="RMBG-2.0", type="pil")
+image = gr.Image(label="Upload an image")
+image2 = gr.Image(label="Upload an image", type="filepath")
+text = gr.Textbox(label="Paste an image URL")
+png_file = gr.File(label="output png file")
+
+chameleon = load_img("giraffe.jpg", output_type="pil")
+url = "http://farm9.staticflickr.com/8488/8228323072_76eeddfea3_z.jpg"
+
+tab1 = gr.Interface(
+    fn, inputs=image, outputs=[slider1, gr.File(label="output png file")], examples=[chameleon], api_name="image"
+)
+tab2 = gr.Interface(fn, inputs=text, outputs=[slider2, gr.File(label="output png file")], examples=[url], api_name="text")
+tab3 = gr.Interface(process_file, inputs=image2, outputs=png_file, examples=["giraffe.jpg"], api_name="png")
+
+demo = gr.TabbedInterface(
+    [tab1, tab2], ["input image", "input url"], title=(
+    "RMBG-2.0 for background removal <br>"
+    "<span style='font-size:16px; font-weight:300;'>"
+    "Background removal model developed by "
+    "<a href='https://bria.ai' target='_blank'>BRIA.AI</a>, trained on a carefully selected dataset,<br> "
+    "and is available as an open-source model for non-commercial use.</span><br>"
+    "<span style='font-size:16px; font-weight:500;'> For testing upload your image and wait.<br>"
+    "<a href='https://go.bria.ai/3ZCBTLH' target='_blank'>Commercial use license</a> | "
+    "<a href='https://huggingface.co/briaai/RMBG-2.0' target='_blank'>Model card</a> | "
+    "<a href='https://blog.bria.ai/brias-new-state-of-the-art-remove-background-2.0-outperforms-the-competition' target='_blank'>Blog</a>"
+    "</span>")
+)
+
+if __name__ == "__main__":
+    demo.launch(show_api=True,show_error=True)
+