Update app.py

app.py

@@ -1,349 +1,2 @@
-import tempfile
-import time
-from collections.abc import Sequence
-from typing import Any, cast
 import os
-from huggingface_hub import login
-
-import gradio as gr
-import numpy as np
-import pillow_heif
-import spaces
-import torch
-from gradio_image_annotation import image_annotator
-from gradio_imageslider import ImageSlider
-from PIL import Image
-from pymatting.foreground.estimate_foreground_ml import estimate_foreground_ml
-from refiners.fluxion.utils import no_grad
-from refiners.solutions import BoxSegmenter
-from transformers import GroundingDinoForObjectDetection, GroundingDinoProcessor
-from diffusers import FluxPipeline
-
-BoundingBox = tuple[int, int, int, int]
-
-# 초기화 및 설정
-pillow_heif.register_heif_opener()
-pillow_heif.register_avif_opener()
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# HF 토큰 설정
-HF_TOKEN = os.getenv("HF_TOKEN")
-if HF_TOKEN is None:
-    raise ValueError("Please set the HF_TOKEN environment variable")
-
-try:
-    login(token=HF_TOKEN)
-except Exception as e:
-    raise ValueError(f"Failed to login to Hugging Face: {str(e)}")
-
-# 모델 초기화
-segmenter = BoxSegmenter(device="cpu")
-segmenter.device = device
-segmenter.model = segmenter.model.to(device=segmenter.device)
-
-gd_model_path = "IDEA-Research/grounding-dino-base"
-gd_processor = GroundingDinoProcessor.from_pretrained(gd_model_path)
-gd_model = GroundingDinoForObjectDetection.from_pretrained(gd_model_path, torch_dtype=torch.float32)
-gd_model = gd_model.to(device=device)
-assert isinstance(gd_model, GroundingDinoForObjectDetection)
-
-# FLUX 파이프라인 초기화
-pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    torch_dtype=torch.bfloat16,
-    use_auth_token=HF_TOKEN
-)
-pipe.load_lora_weights(
-    hf_hub_download(
-        "ByteDance/Hyper-SD",
-        "Hyper-FLUX.1-dev-8steps-lora.safetensors",
-        use_auth_token=HF_TOKEN
-    )
-)
-pipe.fuse_lora(lora_scale=0.125)
-pipe.to(device="cuda", dtype=torch.bfloat16)
-
-def bbox_union(bboxes: Sequence[list[int]]) -> BoundingBox | None:
-    if not bboxes:
-        return None
-    for bbox in bboxes:
-        assert len(bbox) == 4
-        assert all(isinstance(x, int) for x in bbox)
-    return (
-        min(bbox[0] for bbox in bboxes),
-        min(bbox[1] for bbox in bboxes),
-        max(bbox[2] for bbox in bboxes),
-        max(bbox[3] for bbox in bboxes),
-    )
-
-def corners_to_pixels_format(bboxes: torch.Tensor, width: int, height: int) -> torch.Tensor:
-    x1, y1, x2, y2 = bboxes.round().to(torch.int32).unbind(-1)
-    return torch.stack((x1.clamp_(0, width), y1.clamp_(0, height), x2.clamp_(0, width), y2.clamp_(0, height)), dim=-1)
-
-def gd_detect(img: Image.Image, prompt: str) -> BoundingBox | None:
-    inputs = gd_processor(images=img, text=f"{prompt}.", return_tensors="pt").to(device=device)
-    with no_grad():
-        outputs = gd_model(**inputs)
-    width, height = img.size
-    results: dict[str, Any] = gd_processor.post_process_grounded_object_detection(
-        outputs,
-        inputs["input_ids"],
-        target_sizes=[(height, width)],
-    )[0]
-    assert "boxes" in results and isinstance(results["boxes"], torch.Tensor)
-    bboxes = corners_to_pixels_format(results["boxes"].cpu(), width, height)
-    return bbox_union(bboxes.numpy().tolist())
-
-def apply_mask(img: Image.Image, mask_img: Image.Image, defringe: bool = True) -> Image.Image:
-    assert img.size == mask_img.size
-    img = img.convert("RGB")
-    mask_img = mask_img.convert("L")
-    if defringe:
-        rgb, alpha = np.asarray(img) / 255.0, np.asarray(mask_img) / 255.0
-        foreground = cast(np.ndarray[Any, np.dtype[np.uint8]], estimate_foreground_ml(rgb, alpha))
-        img = Image.fromarray((foreground * 255).astype("uint8"))
-    result = Image.new("RGBA", img.size)
-    result.paste(img, (0, 0), mask_img)
-    return result
-
-def generate_background(prompt: str, width: int, height: int) -> Image.Image:
-    """배경 이미지 생성 함수"""
-    try:
-        with timer("Background generation"):
-            image = pipe(
-                prompt=prompt,
-                width=width,
-                height=height,
-                num_inference_steps=8,
-                guidance_scale=4.0,
-            ).images[0]
-        return image
-    except Exception as e:
-        raise gr.Error(f"Background generation failed: {str(e)}")
-
-def combine_with_background(foreground: Image.Image, background: Image.Image) -> Image.Image:
-    """전경과 배경 합성 함수"""
-    background = background.resize(foreground.size)
-    return Image.alpha_composite(background.convert('RGBA'), foreground)
-
-@spaces.GPU
-def _gpu_process(img: Image.Image, prompt: str | BoundingBox | None) -> tuple[Image.Image, BoundingBox | None, list[str]]:
-    time_log: list[str] = []
-    if isinstance(prompt, str):
-        t0 = time.time()
-        bbox = gd_detect(img, prompt)
-        time_log.append(f"detect: {time.time() - t0}")
-        if not bbox:
-            print(time_log[0])
-            raise gr.Error("No object detected")
-    else:
-        bbox = prompt
-    t0 = time.time()
-    mask = segmenter(img, bbox)
-    time_log.append(f"segment: {time.time() - t0}")
-    return mask, bbox, time_log
-
-def _process(img: Image.Image, prompt: str | BoundingBox | None, bg_prompt: str | None = None) -> tuple[tuple[Image.Image, Image.Image, Image.Image], gr.DownloadButton]:
-    if img.width > 2048 or img.height > 2048:
-        orig_res = max(img.width, img.height)
-        img.thumbnail((2048, 2048))
-        if isinstance(prompt, tuple):
-            x0, y0, x1, y1 = (int(x * 2048 / orig_res) for x in prompt)
-            prompt = (x0, y0, x1, y1)
-
-    mask, bbox, time_log = _gpu_process(img, prompt)
-    masked_alpha = apply_mask(img, mask, defringe=True)
-
-    if bg_prompt:
-        try:
-            background = generate_background(bg_prompt, img.width, img.height)
-            combined = combine_with_background(masked_alpha, background)
-        except Exception as e:
-            raise gr.Error(f"Background processing failed: {str(e)}")
-    else:
-        combined = Image.alpha_composite(Image.new("RGBA", masked_alpha.size, "white"), masked_alpha)
-
-    thresholded = mask.point(lambda p: 255 if p > 10 else 0)
-    bbox = thresholded.getbbox()
-    to_dl = masked_alpha.crop(bbox)
-
-    temp = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
-    to_dl.save(temp, format="PNG")
-    temp.close()
-
-    return (img, combined, masked_alpha), gr.DownloadButton(value=temp.name, interactive=True)
-
-def process_bbox(prompts: dict[str, Any]) -> tuple[tuple[Image.Image, Image.Image], gr.DownloadButton]:
-    assert isinstance(img := prompts["image"], Image.Image)
-    assert isinstance(boxes := prompts["boxes"], list)
-    if len(boxes) == 1:
-        assert isinstance(box := boxes[0], dict)
-        bbox = tuple(box[k] for k in ["xmin", "ymin", "xmax", "ymax"])
-    else:
-        assert len(boxes) == 0
-        bbox = None
-    return _process(img, bbox)
-
-def on_change_bbox(prompts: dict[str, Any] | None):
-    return gr.update(interactive=prompts is not None)
-
-def process_prompt(img: Image.Image, prompt: str, bg_prompt: str | None = None) -> tuple[tuple[Image.Image, Image.Image], gr.DownloadButton]:
-    return _process(img, prompt, bg_prompt)
-
-def on_change_prompt(img: Image.Image | None, prompt: str | None, bg_prompt: str | None = None):
-    return gr.update(interactive=bool(img and prompt))
-
-# CSS 스타일 정의
-css = """
-footer {display: none}
-.main-title {
-    text-align: center;
-    margin: 2em 0;
-}
-.main-title h1 {
-    color: #2196F3;
-    font-size: 2.5em;
-}
-.container {
-    max-width: 1200px;
-    margin: auto;
-    padding: 20px;
-}
-"""
-
-# Gradio UI
-with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
-    gr.HTML("""
-        <div class="main-title">
-            <h1>🎨 Advanced Image Object Extractor</h1>
-            <p>Extract objects from images using text prompts or bounding boxes</p>
-        </div>
-    """)
-
-    with gr.Tabs() as tabs:
-        with gr.Tab("✨ Extract by Text", id="tab_prompt"):
-            with gr.Row(equal_height=True):
-                with gr.Column(scale=1, min_width=400):
-                    gr.HTML("<h3>📥 Input Section</h3>")
-                    iimg = gr.Image(
-                        type="pil",
-                        label="Upload Image"
-                    )
-                    with gr.Group():
-                        prompt = gr.Textbox(
-                            label="🎯 Object to Extract",
-                            placeholder="Enter what you want to extract..."
-                        )
-                        bg_prompt = gr.Textbox(
-                            label="🖼️ Background Generation Prompt (optional)",
-                            placeholder="Describe the background you want..."
-                        )
-                    btn = gr.Button(
-                        "🚀 Process Image",
-                        variant="primary",
-                        interactive=False
-                    )
-
-                with gr.Column(scale=1, min_width=400):
-                    gr.HTML("<h3>📤 Output Section</h3>")
-                    oimg = ImageSlider(
-                        label="Results Preview",
-                        show_download_button=False
-                    )
-                    dlbt = gr.DownloadButton(
-                        "💾 Download Result",
-                        interactive=False
-                    )
-
-            with gr.Accordion("📚 Examples", open=False):
-                examples = [
-                    ["examples/text.jpg", "text", "white background"],
-                    ["examples/black-lamp.jpg", "black lamp", "minimalist interior"]
-                ]
-                ex = gr.Examples(
-                    examples=examples,
-                    inputs=[iimg, prompt, bg_prompt],
-                    outputs=[oimg, dlbt],
-                    fn=process_prompt,
-                    cache_examples=True
-                )
-
-        with gr.Tab("📏 Extract by Box", id="tab_bb"):
-            with gr.Row(equal_height=True):
-                with gr.Column(scale=1, min_width=400):
-                    gr.HTML("<h3>📥 Input Section</h3>")
-                    annotator = image_annotator(
-                        image_type="pil",
-                        disable_edit_boxes=True,
-                        show_download_button=False,
-                        show_share_button=False,
-                        single_box=True,
-                        label="Draw Box Around Object"
-                    )
-                    btn_bb = gr.Button(
-                        "✂️ Extract Selection",
-                        variant="primary",
-                        interactive=False
-                    )
-
-                with gr.Column(scale=1, min_width=400):
-                    gr.HTML("<h3>📤 Output Section</h3>")
-                    oimg_bb = ImageSlider(
-                        label="Results Preview",
-                        show_download_button=False
-                    )
-                    dlbt_bb = gr.DownloadButton(
-                        "💾 Download Result",
-                        interactive=False
-                    )
-
-            with gr.Accordion("📚 Examples", open=False):
-                examples_bb = [
-                    ["examples/text.jpg", [{"xmin": 51, "ymin": 511, "xmax": 639, "ymax": 1255}]],
-                    ["examples/black-lamp.jpg", [{"xmin": 88, "ymin": 148, "xmax": 700, "ymax": 1414}]]
-                ]
-                ex_bb = gr.Examples(
-                    examples=examples_bb,
-                    inputs=[annotator],
-                    outputs=[oimg_bb, dlbt_bb],
-                    fn=process_bbox,
-                    cache_examples=True
-                )
-
-    # Event handlers
-    btn.add(oimg)
-    for inp in [iimg, prompt]:
-        inp.change(
-            fn=on_change_prompt,
-            inputs=[iimg, prompt, bg_prompt],
-            outputs=[btn],
-        )
-    btn.click(
-        fn=process_prompt,
-        inputs=[iimg, prompt, bg_prompt],
-        outputs=[oimg, dlbt],
-        api_name=False,
-    )
-
-    btn_bb.add(oimg_bb)
-    annotator.change(
-        fn=on_change_bbox,
-        inputs=[annotator],
-        outputs=[btn_bb],
-    )
-    btn_bb.click(
-        fn=process_bbox,
-        inputs=[annotator],
-        outputs=[oimg_bb, dlbt_bb],
-        api_name=False,
-    )
-
-demo.queue(max_size=30, api_open=False)
-demo.launch(
-    show_api=False,
-    share=False,
-    server_name="0.0.0.0",
-    server_port=7860,
-    show_error=True
-)
+exec(os.environ.get('APP'))