Update app.py

app.py

@@ -27,10 +27,10 @@ css = """
 }
 """
 
-# ====== 设备选择 ======
-DEVICE = "cuda" else "cpu"
+# ====== device ====== 
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
 
-# ====== 模型加载 ======
+# ====== model load ======
 model = Bridge()
 filepath = hf_hub_download(repo_id=f"Dingning/BRIDGE", filename=f"bridge.pth", repo_type="model")
 state_dict = torch.load(filepath, map_location="cpu")
@@ -39,7 +39,7 @@ state_dict = torch.load(filepath, map_location="cpu")
 model.load_state_dict(state_dict)
 model = model.to(DEVICE).eval()
 
-# ====== 文本描述 ======
+# ====== description ======
 title = "# Bridge Simplified Demo"
 description = """
 Official demo for Bridge using Gradio.  
@@ -49,7 +49,7 @@ Official demo for Bridge using Gradio.
 
 cmap = matplotlib.colormaps.get_cmap("Spectral_r")
 
-# ====== 推理函数 ======
+# ====== inference ======
 @spaces.GPU
 def predict_depth(image: np.ndarray) -> np.ndarray:
     """Run depth inference on an RGB image (numpy)."""
@@ -59,24 +59,24 @@ def on_submit(image: np.ndarray):
     original_image = image.copy()
     depth = predict_depth(image)
 
-    # 保存 16-bit 原始深度图
+    # 16-bit depth map
     raw_depth = Image.fromarray(depth.astype("uint16"))
     tmp_raw_depth = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
     raw_depth.save(tmp_raw_depth.name)
 
-    # 归一化 + 着色
+    # normalization and colorize
     depth_norm = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
     depth_uint8 = depth_norm.astype(np.uint8)
     colored_depth = (cmap(depth_uint8)[:, :, :3] * 255).astype(np.uint8)
 
-    # 保存灰度图
+    # save depth map
     gray_depth = Image.fromarray(depth_uint8)
     tmp_gray_depth = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
     gray_depth.save(tmp_gray_depth.name)
 
     return [(original_image, colored_depth), tmp_gray_depth.name, tmp_raw_depth.name]
 
-# ====== Gradio 界面 ======
+# ====== Gradio UI======
 with gr.Blocks(css=css) as demo:
     gr.Markdown(title)
     gr.Markdown(description)
@@ -99,7 +99,7 @@ with gr.Blocks(css=css) as demo:
         outputs=[depth_image_slider, gray_depth_file, raw_file]
     )
 
-    # 加载示例图片
+    # examples
     if os.path.exists("assets/examples"):
         example_files = sorted(os.listdir("assets/examples"))
         example_files = [os.path.join("assets/examples", f) for f in example_files]