Update app.py

app.py

@@ -1,64 +1,101 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from loadimg import load_img
+import spaces
+from transformers import AutoModelForImageSegmentation
+import torch
+from torchvision import transforms
+from typing import Union, Tuple
+from PIL import Image
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+torch.set_float32_matmul_precision(["high", "highest"][0])
 
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+    "ZhengPeng7/BiRefNet", trust_remote_code=True
+)
+birefnet.to("cuda")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+transform_image = transforms.Compose(
+    [
+        transforms.Resize((1024, 1024)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def fn(image: Union[Image.Image, str]) -> Tuple[Image.Image, Image.Image]:
+    """
+    Remove the background from an image and return both the transparent version and the original.
+    This function performs background removal using a BiRefNet segmentation model. It is intended for use
+    with image input (either uploaded or from a URL). The function returns a transparent PNG version of the image
+    with the background removed, along with the original RGB version for comparison.
+    Args:
+        image (PIL.Image or str): The input image, either as a PIL object or a filepath/URL string.
+    Returns:
+        tuple:
+            - processed_image (PIL.Image): The input image with the background removed and transparency applied.
+            - origin (PIL.Image): The original RGB image, unchanged.
+    """
+    im = load_img(image, output_type="pil")
+    im = im.convert("RGB")
+    origin = im.copy()
+    processed_image = process(im)
+    return (processed_image, origin)
 
-    messages.append({"role": "user", "content": message})
+@spaces.GPU
+def process(image: Image.Image) -> Image.Image:
+    """
+    Apply BiRefNet-based image segmentation to remove the background.
+    This function preprocesses the input image, runs it through a BiRefNet segmentation model to obtain a mask,
+    and applies the mask as an alpha (transparency) channel to the original image.
+    Args:
+        image (PIL.Image): The input RGB image.
+    Returns:
+        PIL.Image: The image with the background removed, using the segmentation mask as transparency.
+    """
+    image_size = image.size
+    input_images = transform_image(image).unsqueeze(0).to("cuda")
+    # Prediction
+    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
 
-    response = ""
+def process_file(f: str) -> str:
+    """
+    Load an image file from disk, remove the background, and save the output as a transparent PNG.
+    Args:
+        f (str): Filepath of the image to process.
+    Returns:
+        str: Path to the saved PNG image with background removed.
+    """
+    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
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+slider1 = gr.ImageSlider(label="Processed Image", type="pil", format="png")
+slider2 = gr.ImageSlider(label="Processed Image from URL", type="pil", format="png")
+image_upload = gr.Image(label="Upload an image")
+image_file_upload = gr.Image(label="Upload an image", type="filepath")
+url_input = gr.Textbox(label="Paste an image URL")
+output_file = gr.File(label="Output PNG File")
 
-        response += token
-        yield response
+# Example images
+chameleon = load_img("butterfly.jpg", output_type="pil")
+url_example = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"
 
+tab1 = gr.Interface(fn, inputs=image_upload, outputs=slider1, examples=[chameleon], api_name="image")
+tab2 = gr.Interface(fn, inputs=url_input, outputs=slider2, examples=[url_example], api_name="text")
+tab3 = gr.Interface(process_file, inputs=image_file_upload, outputs=output_file, examples=["butterfly.jpg"], api_name="png")
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+demo = gr.TabbedInterface(
+    [tab1, tab2, tab3], ["Image Upload", "URL Input", "File Output"], title="Background Removal Tool"
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(show_error=True, mcp_server=True)