Update app.py

app.py

@@ -1,105 +1,114 @@
 import os
+import torch
 import gradio as gr
 from gradio_imageslider import ImageSlider
-from loadimg import load_img
-import spaces
+# Ensure loadimg.py is in your directory. 
+# If not, we use a fallback to PIL.
+try:
+    from loadimg import load_img
+except ImportError:
+    def load_img(path, output_type="pil"):
+        from PIL import Image
+        import requests
+        from io import BytesIO
+        if path.startswith('http'):
+            response = requests.get(path)
+            img = Image.open(BytesIO(response.content))
+        else:
+            img = Image.open(path)
+        return img
+
 from transformers import AutoModelForImageSegmentation
-import torch
 from torchvision import transforms
 
-torch.set_float32_matmul_precision(["high", "highest"][0])
-
-birefnet = AutoModelForImageSegmentation.from_pretrained(
-    "briaai/RMBG-2.0", trust_remote_code=True
-)
-birefnet.to("cuda")
-transform_image = transforms.Compose(
-    [
-        transforms.Resize((1024, 1024)),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
-    ]
+# --- Hardware Setup ---
+# Force CPU if CUDA is not available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Current Hardware: {device.upper()}")
+
+# --- Model Initialization ---
+# RMBG-2.0 is heavy; we use trust_remote_code=True for the BiRefNet architecture
+print("Loading model... this may take a minute on CPU.")
+model = AutoModelForImageSegmentation.from_pretrained(
+    "briaai/RMBG-2.0", 
+    trust_remote_code=True
 )
+model.to(device)
+model.eval()
 
-output_folder = 'output_images'
-if not os.path.exists(output_folder):
-    os.makedirs(output_folder)
+# Standard ImageNet normalization used by BiRefNet
+preprocess = transforms.Compose([
+    transforms.Resize((1024, 1024)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+])
 
-def fn(image):
-    im = load_img(image, output_type="pil")
-    im = im.convert("RGB")
-    origin = im.copy()
-    image = process(im)    
-    image_path = os.path.join(output_folder, "no_bg_image.png")
-    image.save(image_path)
-    return (image, origin), image_path
-
-@spaces.GPU
-def process(image):
-    image_size = image.size
-    input_images = transform_image(image).unsqueeze(0).to("cuda")
-    # Prediction
+output_folder = 'output_images'
+os.makedirs(output_folder, exist_ok=True)
+
+def remove_background(image_input):
+    if image_input is None:
+        return None, None
+    
+    # Handle both filepath (str) and PIL Image inputs
+    if isinstance(image_input, str):
+        orig_img = load_img(image_input, output_type="pil").convert("RGB")
+    else:
+        orig_img = image_input.convert("RGB")
+    
+    w, h = orig_img.size
+    
+    # Preprocess
+    input_tensor = preprocess(orig_img).unsqueeze(0).to(device)
+    
+    # Inference
     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
-  
-def process_file(f):
-    name_path = f.rsplit(".",1)[0]+".png"
-    im = load_img(f, output_type="pil")
-    im = im.convert("RGB")
-    transparent = process(im)
-    transparent.save(name_path)
-    return name_path
-
-slider1 = ImageSlider(label="RMBG-2.0", type="pil")
-slider2 = ImageSlider(label="RMBG-2.0", type="pil")
-image = gr.Image(label="Upload an image")
-image2 = gr.Image(label="Upload an image",type="filepath")
-text = gr.Textbox(label="Paste an image URL")
-png_file = gr.File(label="output png file")
-
-
-chameleon = load_img("giraffe.jpg", output_type="pil")
-
-url = "http://farm9.staticflickr.com/8488/8228323072_76eeddfea3_z.jpg"
-
-tab1 = gr.Interface(
-    fn, inputs=image, outputs=[slider1, gr.File(label="output png file")], examples=[chameleon], api_name="image"
-)
-
-tab2 = gr.Interface(fn, inputs=text, outputs=[slider2, gr.File(label="output png file")], examples=[url], api_name="text")
-tab3 = gr.Interface(process_file, inputs=image2, outputs=png_file, examples=["giraffe.jpg"], api_name="png")
-
-
-demo = gr.TabbedInterface(
-    [tab1, tab2], ["input image", "input url"], title = (
-    "RMBG-2.0 for background removal <br>"
-    "<span style='font-size:16px; font-weight:300;'>"
-    "Background removal model developed by "
-    "<a href='https://bria.ai' target='_blank'>BRIA.AI</a>, trained on a carefully selected dataset,<br> "
-    "and is available as an open-source model for non-commercial use.</span><br>"
-    "<span style='font-size:16px; font-weight:500;'> For testing upload your image and wait.<br>"
-    "<a href='https://huggingface.co/briaai/RMBG-2.0' target='_blank'>Model card</a> | "
-    "<a href='https://blog.bria.ai/brias-new-state-of-the-art-remove-background-2.0-outperforms-the-competition' target='_blank'>Blog</a>"
-    "</span><br>"
-    "<span style='font-size:16px; font-weight:300;'>"
-    "API Endpoint available on: "
-    "<a href='https://docs.bria.ai/image-editing/v2-endpoints/background-remove' target='_blank'>Bria.ai</a>, "
-    "<a href='https://fal.ai/models/fal-ai/bria/background/remove' target='_blank'>fal.ai</a><br>"
-    "ComfyUI node is available here: "
-    "<a href='https://github.com/Bria-AI/ComfyUI-BRIA-API' target='_blank'>ComfyUI Node</a><br>"
-    "Purchase weigths for commercial use: "
-    "<a href='https://share-eu1.hsforms.com/2sj9FVZTGSFmFRibDLhr_ZAf4e04' target='_blank'>here</a>"
-    "</span>"
-)
-
-
-
-)
+        # BiRefNet returns a list of preds; we take the last one
+        result = model(input_tensor)[-1].sigmoid().cpu()
+    
+    # Post-process Mask
+    mask = transforms.ToPILImage()(result[0].squeeze())
+    mask = mask.resize((w, h))
+    
+    # Create Final Transparent Image
+    no_bg_img = orig_img.copy()
+    no_bg_img.putalpha(mask)
+    
+    save_path = os.path.join(output_folder, "result.png")
+    no_bg_img.save(save_path)
+    
+    return (no_bg_img, orig_img), save_path
+
+# --- UI Setup ---
+with gr.Blocks(title="RMBG 2.0 CPU") as demo:
+    gr.Markdown("# RMBG-2.0 Background Remover")
+    gr.Markdown("Optimized for CPU/GPU deployment.")
+    
+    with gr.Tab("Image Upload"):
+        with gr.Row():
+            in_img = gr.Image(label="Upload Image", type="pil")
+            out_slider = ImageSlider(label="Comparison", type="pil")
+        
+        out_file = gr.File(label="Download PNG")
+        submit_btn = gr.Button("Remove Background", variant="primary")
+        
+        submit_btn.click(
+            fn=remove_background, 
+            inputs=in_img, 
+            outputs=[out_slider, out_file]
+        )
+
+    with gr.Tab("URL / Batch"):
+        url_input = gr.Textbox(label="Paste Image URL")
+        url_slider = ImageSlider(label="Comparison", type="pil")
+        url_file = gr.File(label="Download PNG")
+        url_btn = gr.Button("Process URL")
+        
+        url_btn.click(
+            fn=remove_background,
+            inputs=url_input,
+            outputs=[url_slider, url_file]
+        )
 
 if __name__ == "__main__":
-    demo.launch(show_error=True)
+    demo.launch()