Create app.py

app.py

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+import gradio as gr
+from loadimg import load_img
+import spaces
+from transformers import AutoModelForImageSegmentation
+import torch
+from torchvision import transforms
+
+torch.set_float32_matmul_precision("high")
+
+# تحميل النموذج
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+    "ZhengPeng7/BiRefNet", trust_remote_code=True
+)
+birefnet.to("cuda")
+
+# تجهيز الصورة قبل الإدخال
+transform_image = transforms.Compose([
+    transforms.Resize((1024, 1024)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
+
+# المعالجة الأساسية
+@spaces.GPU
+def process(image):
+    image_size = image.size
+    input_images = transform_image(image).unsqueeze(0).to("cuda")
+    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
+
+# واجهة المستخدم
+def from_upload(image):
+    im = load_img(image, output_type="pil").convert("RGB")
+    origin = im.copy()
+    processed = process(im)
+    return (processed, origin)
+
+def from_url(url):
+    im = load_img(url, output_type="pil").convert("RGB")
+    origin = im.copy()
+    processed = process(im)
+    return (processed, origin)
+
+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
+
+# واجهات التبويبات
+tab1 = gr.Interface(from_upload, inputs=gr.Image(), outputs=[gr.Image(label="Processed"), gr.Image(label="Original")], title="Upload Image")
+tab2 = gr.Interface(from_url, inputs=gr.Textbox(label="Paste Image URL"), outputs=[gr.Image(label="Processed"), gr.Image(label="Original")], title="From URL")
+tab3 = gr.Interface(process_file, inputs=gr.Image(type="filepath"), outputs=gr.File(), title="Save Transparent PNG")
+
+demo = gr.TabbedInterface([tab1, tab2, tab3], ["Upload", "URL", "Save PNG"], title="Background Removal with BiRefNet")
+
+if __name__ == "__main__":
+    demo.launch(show_error=True)