Create app.py

app.py

@@ -0,0 +1,83 @@
+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.set_float32_matmul_precision(["high", "highest"][0])
+
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+    "briaai/RMBG-2.0", trust_remote_code=True
+)
+birefnet.to("cpu")
+transform_image = transforms.Compose(
+    [
+        transforms.Resize((1024, 1024)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+output_folder = 'output_images'
+if not os.path.exists(output_folder):
+    os.makedirs(output_folder)
+
+def fn(image):
+    im = load_img(image, output_type="pil")
+    im = im.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
+
+@spaces.GPU
+def process(image):
+    image_size = image.size
+    input_images = transform_image(image).unsqueeze(0).to("cpu")
+
+    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.putalpha(mask)
+    return image
+
+def process_file(f):
+    name_path = f.rsplit(".",1)[0]+".png"
+    im = load_img(f, output_type="pil")
+    im = im.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")
+
+
+tab1 = gr.Interface(
+    fn, inputs=image, outputs=[slider1, gr.File(label="output png file")], api_name="image", description="⚠️ Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding."
+)
+
+tab2 = gr.Interface(
+    fn, inputs=text, outputs=[slider2, gr.File(label="output png file")], api_name="text", description="⚠️ Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding."
+)
+
+tab3 = gr.Interface(
+    process_file, inputs=image2, outputs=png_file, api_name="png", description="⚠️ Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding."
+)
+
+demo = gr.TabbedInterface(
+    [tab1, tab2], ["Using Image", "Usling URL"], title="✂️ RMBG Image Background Remover ✂️", theme="Yntec/HaleyCH_Theme_Orange"
+)
+
+if __name__ == "__main__":
+    demo.launch(show_error=True, debug=True)