🌘w🌖

app.py

@@ -6,26 +6,30 @@ from transformers import AutoModelForImageSegmentation
 import torch
 from torchvision import transforms
 from PIL import Image
-torch.set_float32_matmul_precision(['high', 'highest'][0])
 
-birefnet = AutoModelForImageSegmentation.from_pretrained('ZhengPeng7/BiRefNet', trust_remote_code=True)
-birefnet.to("cuda")
-transform_image = transforms.Compose([
-    transforms.Resize((1024, 1024)),
-    transforms.ToTensor(),
-    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-])
+torch.set_float32_matmul_precision(["high", "highest"][0])
 
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+    "ZhengPeng7/BiRefNet", trust_remote_code=True
+)
+birefnet.to("cuda")
+transform_image = transforms.Compose(
+    [
+        transforms.Resize((1024, 1024)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
 
 
 @spaces.GPU
 def fn(image):
-    im = load_img(image,output_type="pil")
-    im = im.convert('RGB') 
+    im = load_img(image, output_type="pil")
+    im = im.convert("RGB")
     image_size = im.size
     origin = im.copy()
     image = load_img(im)
-    input_images = transform_image(image).unsqueeze(0).to('cuda')
+    input_images = transform_image(image).unsqueeze(0).to("cuda")
     # Prediction
     with torch.no_grad():
         preds = birefnet(input_images)[-1].sigmoid().cpu()
@@ -33,7 +37,8 @@ def fn(image):
     pred_pil = transforms.ToPILImage()(pred)
     mask = pred_pil.resize(image_size)
     image.putalpha(mask)
-    return (image , origin)
+    return (image, origin)
+
 
 slider1 = ImageSlider(label="birefnet", type="pil")
 slider2 = ImageSlider(label="birefnet", type="pil")
@@ -41,11 +46,19 @@ image = gr.Image(label="Upload an image")
 text = gr.Textbox(label="Paste an image URL")
 
 chameleon = Image.open("chameleon.jpg")
+cool = Image.open("cool kid.jpg")
+
 url = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"
-tab1 = gr.Interface(fn,inputs= image, outputs= slider1,examples=[chameleon], api_name="image")
-tab2 = gr.Interface(fn,inputs= text, outputs= slider2,examples=[url], api_name="text")
+tab1 = gr.Interface(
+    fn, inputs=image, outputs=slider1, examples=[[chameleon], [cool]], api_name="image"
+)
+
+tab2 = gr.Interface(fn, inputs=text, outputs=slider2, examples=[url], api_name="text")
+
 
-demo = gr.TabbedInterface([tab1,tab2],["image","text"],title="birefnet with image slider")
+demo = gr.TabbedInterface(
+    [tab1, tab2], ["image", "text"], title="birefnet for background removal"
+)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()