Update app.py

app.py

@@ -6,15 +6,16 @@ from transformers import AutoModelForImageSegmentation
 import torch
 from torchvision import transforms
 from PIL import Image
-import io
 import os
 import tempfile
 
 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)),
@@ -31,22 +32,21 @@ def fn(image):
     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(im).unsqueeze(0).to("cuda")
     # 예측
     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, origin
+    im.putalpha(mask)
+    return im, origin
 
 def save_image(image):
     if image is None:
         return None
     with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
-        image.save(temp_file, format="PNG")
+        image.save(temp_file.name, format="PNG")
     return temp_file.name
 
 def process_and_download(input_image):
@@ -57,24 +57,25 @@ def process_and_download(input_image):
     original_path = save_image(original)
     return [result_path, original_path], result_path
 
+# 예제 이미지를 직접 PIL 객체로 로드
+example_image1 = Image.open("example_images/example1.png")
+example_image2 = Image.open("example_images/example2.png")
+example_image3 = Image.open("example_images/example3.png")
+
+# 인터페이스 컴포넌트 정의
 image = gr.Image(label="이미지 업로드")
 slider = ImageSlider(label="배경 제거 결과", type="filepath")
 png_output = gr.File(label="PNG 다운로드")
 
-examples = [
-    os.path.join(os.path.dirname(__file__), "예제1.png"),
-    os.path.join(os.path.dirname(__file__), "예제2.png"),
-    os.path.join(os.path.dirname(__file__), "예제3.png")
-]
-
+# Gradio 인터페이스 구성
 demo = gr.Interface(
     process_and_download,
     inputs=image,
     outputs=[slider, png_output],
-    examples=examples,
+    examples=[example_image1, example_image2, example_image3],
     title="배경 제거",
     description="이미지를 업로드하면 BiRefNet 모델을 사용하여 배경을 제거합니다. 결과를 PNG 파일로 다운로드할 수 있습니다."
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()