Update app.py

app.py

@@ -1,17 +1,18 @@
 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])
+# Remove this line as it is GPU specific
+# torch.set_float32_matmul_precision(["high", "highest"][0])
 
 birefnet = AutoModelForImageSegmentation.from_pretrained(
     "ZhengPeng7/BiRefNet", trust_remote_code=True
 )
-birefnet.to("cuda")
+birefnet.to("cpu")  # Change to CPU
+
 transform_image = transforms.Compose(
     [
         transforms.Resize((1024, 1024)),
@@ -20,15 +21,14 @@ transform_image = transforms.Compose(
     ]
 )
 
-
-@spaces.GPU
 def fn(image):
     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("cpu")  # Change to CPU
+    
     # Prediction
     with torch.no_grad():
         preds = birefnet(input_images)[-1].sigmoid().cpu()
@@ -38,13 +38,11 @@ def fn(image):
     image.putalpha(mask)
     return (image, origin)
 
-
 slider1 = ImageSlider(label="birefnet", type="pil")
 slider2 = ImageSlider(label="birefnet", type="pil")
 image = gr.Image(label="Upload an image")
 text = gr.Textbox(label="Paste an image URL")
 
-
 chameleon = load_img("chameleon.jpg", output_type="pil")
 
 url = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"
@@ -54,10 +52,9 @@ tab1 = gr.Interface(
 
 tab2 = gr.Interface(fn, inputs=text, outputs=slider2, examples=[url], api_name="text")
 
-
 demo = gr.TabbedInterface(
     [tab1, tab2], ["image", "text"], title="birefnet for background removal"
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()