Update app.py

app.py

@@ -119,8 +119,8 @@ def unload_and_reload_models():
     new_birefnet = AutoModelForImageSegmentation.from_pretrained(
         "zhengpeng7/BiRefNet", trust_remote_code=True, cache_dir=CACHE_DIR
     )
-    # new_birefnet.to(device)
-    # new_birefnet.eval()
+    new_birefnet.to(device)
+    new_birefnet.eval()
     # new_u2net = U2NETP(3, 1)
     # new_u2net.load_state_dict(torch.load(os.path.join(CACHE_DIR, "u2netp.pth"), map_location="cpu"))
     # new_u2net.to(device)
@@ -455,13 +455,10 @@ def predict(
             except Exception:
                 raise ValueError("Invalid base64 image data")
 
-    # Preprocessing: reduce brightness to 0.5 and enhance sharpness.
+    # Apply brightness and sharpness enhancement.
     if isinstance(image, np.ndarray):
         pil_image = Image.fromarray(image)
-        # Reduce brightness.
-        dark_image = ImageEnhance.Brightness(pil_image).enhance(0.5)
-        # Enhance sharpness.
-        enhanced_image = ImageEnhance.Sharpness(dark_image).enhance(1)
+        enhanced_image = ImageEnhance.Sharpness(Bright).enhance(0.5)
         image = np.array(enhanced_image)
 
     # ---------------------
@@ -682,13 +679,6 @@ def predict(
             cv2.LINE_AA
         )
 
-    # Restore brightness for display purposes:
-    # Since we reduced brightness by 0.5 during preprocessing,
-    # we apply an enhancement factor of 2.0 here to bring it back.
-    display_img = Image.fromarray(output_img)
-    display_img = ImageEnhance.Brightness(display_img).enhance(2.0)
-    output_img = np.array(display_img)
-
     outlines_color = cv2.cvtColor(new_outlines, cv2.COLOR_BGR2RGB)
     print("Total prediction time: {:.2f} seconds".format(time.time() - overall_start))