Update app.py

app.py

@@ -4,7 +4,6 @@ from transformers import AutoModelForImageSegmentation
 from PIL import Image
 from torchvision import transforms
 import gradio as gr
-import os
 
 # Load the model from Hugging Face
 birefnet = AutoModelForImageSegmentation.from_pretrained('zhengpeng7/BiRefNet_lite', trust_remote_code=True)
@@ -20,9 +19,9 @@ transform_image = transforms.Compose([
     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
 ])
 
-def extract_objects(files):
-    # Open all images from the uploaded files
-    images = [Image.open(file.name).convert("RGB") for file in files]
+def extract_objects(filepaths):
+    # Open all images from the uploaded file paths
+    images = [Image.open(path).convert("RGB") for path in filepaths]
 
     start_time = time.time()
     inputs = []
@@ -64,7 +63,7 @@ def extract_objects(files):
         f"Total request time: {total_time:.2f} s\n"
         f"Inference time (batch): {inference_time:.2f} s\n"
         f"Pre/Post-processing time: {prep_post_time:.2f} s\n"
-        "Per-image post-processing times:\n" + 
+        "Per-image post-processing times:\n" +
         "\n".join([f"  Image {i+1}: {t:.2f} s" for i, t in enumerate(image_times)])
     )
 
@@ -72,7 +71,7 @@ def extract_objects(files):
 
 iface = gr.Interface(
     fn=extract_objects,
-    inputs=gr.Files(label="Upload Multiple Images", type="file", file_count="multiple"),
+    inputs=gr.Files(label="Upload Multiple Images", type="filepath", file_count="multiple"),
     outputs=[gr.Gallery(label="Processed Images"), gr.Textbox(label="Timing Info")],
     title="BiRefNet Bulk Background Removal",
     description="Upload multiple images and process them in one request. Timing information for the full request and per-image processing is provided."