Update app.py

app.py

@@ -1,252 +1,347 @@
-# app.py — ZeroGPU対応版
-import gradio as gr
-import spaces
-import torch
-import numpy as np
-from PIL import Image
+# app.py — InstantID × Beautiful Realistic Asians v7 (ZeroGPU-ready, FastAPI + Gradio)
+# 2025-06-21 版
+#
+# ───────────────────────────────────────────────────────────────
+#  主な特徴
+#   • @spaces.GPU(duration=60) を公開名 generate_core() に付与
+#   • パイプラインは lazy-load で初回推論時に GPU へロード
+#   • モデル資産は /data または ~/.cache に永続化
+#   • Real-ESRGAN アップスケール (x4 / x8) オプション
+#   • Gradio UI + FastAPI REST を 1 プロセスで共存
+#   • Uvicorn 手動起動は不要（Spaces が自前で立てる）
+# ───────────────────────────────────────────────────────────────
+
 import os
+import io
+import base64
 import subprocess
 import traceback
-import base64
-import io
 from pathlib import Path
+from typing import Optional
 
-# FastAPI関連（ハイブリッド構成のため維持）
+import numpy as np
+import torch
+import gradio as gr
+import spaces
 from fastapi import FastAPI, UploadFile, File, Form, HTTPException
+from PIL import Image
+
+from diffusers import (
+    StableDiffusionControlNetPipeline,
+    ControlNetModel,
+    DPMSolverMultistepScheduler,
+    AutoencoderKL,
+)
+from diffusers.loaders import AttnProcsLayers
+from insightface.app import FaceAnalysis
+from basicsr.utils.download_util import load_file_from_url
+from realesrgan import RealESRGANer
 
-# グローバル変数としてパイプラインを定義（初期値はNone）
-pipe = None
-face_app = None
-upsampler = None
-UPSCALE_OK = False
+# ==============================================================
+# 0. キャッシュディレクトリとダウンローダ
+# ==============================================================
 
-# 0. Cache dir & helpers (起動時に実行)
 PERSIST_BASE = Path("/data")
-CACHE_ROOT = (PERSIST_BASE / "instantid_cache" if PERSIST_BASE.exists() and os.access(PERSIST_BASE, os.W_OK)
-              else Path.home() / ".cache" / "instantid_cache")
-MODELS_DIR, LORA_DIR, EMB_DIR, UPSCALE_DIR = CACHE_ROOT/"models", CACHE_ROOT/"models"/"Lora", CACHE_ROOT/"embeddings", CACHE_ROOT/"realesrgan"
+CACHE_ROOT = (
+    PERSIST_BASE / "instantid_cache"
+    if PERSIST_BASE.exists() and os.access(PERSIST_BASE, os.W_OK)
+    else Path.home() / ".cache" / "instantid_cache"
+)
+MODELS_DIR = CACHE_ROOT / "models"
+LORA_DIR = CACHE_ROOT / "lora"
+UPSCALE_DIR = CACHE_ROOT / "realesrgan"
+for _p in (MODELS_DIR, LORA_DIR, UPSCALE_DIR):
+    _p.mkdir(parents=True, exist_ok=True)
 
-for p in (MODELS_DIR, LORA_DIR, EMB_DIR, UPSCALE_DIR):
-    p.mkdir(parents=True, exist_ok=True)
 
-def dl(url: str, dst: Path, attempts: int = 2):
-    if dst.exists(): return
+def download(url: str, dst: Path, attempts: int = 2):
+    """単純リトライ付きダウンローダ（curl or basicsr fallback）"""
+    if dst.exists():
+        return dst
     for i in range(1, attempts + 1):
-        print(f"⬇ Downloading {dst.name} (try {i}/{attempts})")
-        if subprocess.call(["wget", "-q", "-O", str(dst), url]) == 0: return
-    raise RuntimeError(f"download failed → {url}")
-
-# 1. Asset download (起動時に実行)
-print("— Starting asset download check —")
-BASE_CKPT = MODELS_DIR / "beautiful_realistic_asians_v7_fp16.safetensors"
-dl("https://civitai.com/api/download/models/177164?type=Model&format=SafeTensor&size=pruned&fp=fp16", BASE_CKPT)
-IP_BIN_FILE = LORA_DIR / "ip-adapter-plus-face_sd15.bin"
-dl("https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus-face_sd15.bin", IP_BIN_FILE)
-LORA_FILE = LORA_DIR / "ip-adapter-faceid-plusv2_sd15_lora.safetensors"
-dl("https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15_lora.safetensors", LORA_FILE)
-print("— Asset download check finished —")
-
-
-# 2. パイプライン初期化関数 (GPU確保後に呼び出される)
-def initialize_pipelines():
-    global pipe, face_app, upsampler, UPSCALE_OK
-    
-    # torch/diffusers/onnxruntimeなどのインポートを関数内に移動
-    from diffusers import StableDiffusionPipeline, ControlNetModel, DPMSolverMultistepScheduler, AutoencoderKL
-    from insightface.app import FaceAnalysis
-
-    print("--- Initializing Pipelines (GPU is now available) ---")
-    
-    device = torch.device("cuda") # ZeroGPUではGPUが保証されている
-    dtype = torch.float16
-
-    # FaceAnalysis
-    if face_app is None:
-        print("Initializing FaceAnalysis...")
-        providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        face_app = FaceAnalysis(name="buffalo_l", root=str(CACHE_ROOT), providers=providers)
-        face_app.prepare(ctx_id=0, det_size=(640, 640))
-        print("FaceAnalysis initialized.")
-
-    # Main Pipeline
-    if pipe is None:
-        print("Loading ControlNet...")
-        controlnet = ControlNetModel.from_pretrained("InstantX/InstantID", subfolder="ControlNetModel", torch_dtype=dtype)
-        
-        print("Loading StableDiffusionPipeline...")
-        pipe = StableDiffusionPipeline.from_single_file(BASE_CKPT, torch_dtype=dtype, safety_checker=None, use_safetensors=True, clip_skip=2)
-        
-        print("Moving pipeline to GPU...")
-        pipe.to(device) # .to(device)をここで呼ぶ
-
-        print("Loading VAE...")
-        pipe.vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=dtype).to(device)
-        pipe.controlnet = controlnet
-        
-        print("Configuring Scheduler...")
-        pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
-        
-        print("Loading IP-Adapter and LoRA...")
-        pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name=IP_BIN_FILE.name)
-        pipe.load_lora_weights(str(LORA_DIR), weight_name=LORA_FILE.name)
-        
-        pipe.set_ip_adapter_scale(0.65)
-        print("Main pipeline initialized.")
-
-    # Upscaler
-    if upsampler is None and not UPSCALE_OK: # 一度失敗したら再試行しない
-        print("Checking for Upscaler...")
         try:
-            # cv2のインポートをここに追加
-            import cv2
-            from basicsr.archs.rrdb_arch import RRDBNet
-            from realesrgan import RealESRGAN
-            rrdb = RRDBNet(3, 3, 64, 23, 32, scale=8)
-            upsampler = RealESRGAN(device, rrdb, scale=8)
-            upsampler.load_weights(str(UPSCALE_DIR / "RealESRGAN_x8plus.pth"))
-            UPSCALE_OK = True
-            print("Upscaler initialized successfully.")
-        except Exception as e:
-            UPSCALE_OK = False # 失敗を記録
-            print(f"Real-ESRGAN disabled → {e}")
-    
-    print("--- All pipelines ready ---")
-
-
-# 4. Core generation logic
-BASE_PROMPT = ("(masterpiece:1.2), best quality, ultra-realistic, RAW photo, 8k,\n""photo of {subject},\n""cinematic lighting, golden hour, rim light, shallow depth of field,\n""textured skin, high detail, shot on Canon EOS R5, 85 mm f/1.4, ISO 200,\n""<lora:ip-adapter-faceid-plusv2_sd15_lora:0.65>, (face),\n""(aesthetic:1.1), (cinematic:0.8)")
-NEG_PROMPT = ("ng_deepnegative_v1_75t, CyberRealistic_Negative-neg, UnrealisticDream, ""(worst quality:2), (low quality:1.8), lowres, (jpeg artifacts:1.2), ""painting, sketch, illustration, drawing, cartoon, anime, cgi, render, 3d, ""monochrome, grayscale, text, logo, watermark, signature, username, ""(MajicNegative_V2:0.8), bad hands, extra digits, fused fingers, malformed limbs, ""missing arms, missing legs, (badhandv4:0.7), BadNegAnatomyV1-neg, skin blemishes, acnes, age spot, glans")
-
-# 【変更点①】内部的な画像生成関数。@spaces.GPUデコレータを外す
-def _generate_internal(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress=gr.Progress(track_tqdm=True)):
-    # 初回呼び出し時にパイプラインを初期化
-    initialize_pipelines()
-
-    progress(0, desc="Generating image...")
-    prompt = BASE_PROMPT.format(subject=(subject.strip() or "a beautiful 20yo woman"))
-    if add_prompt: prompt += ", " + add_prompt
-    neg = NEG_PROMPT + (", " + add_neg if add_neg else "")
-    pipe.set_ip_adapter_scale(ip_scale)
-
-    result = pipe(prompt=prompt, negative_prompt=neg, ip_adapter_image=face_img, image=face_img, controlnet_conditioning_scale=0.9, num_inference_steps=int(steps) + 5, guidance_scale=cfg, width=int(w), height=int(h)).images[0]
-    
-    if upscale and UPSCALE_OK:
-        # cv2のインポートをここにも追加
-        import cv2
-        progress(0.8, desc="Upscaling...")
-        up, _ = upsampler.enhance(cv2.cvtColor(np.array(result), cv2.COLOR_RGB2BGR), outscale=up_factor)
-        result = Image.fromarray(cv2.cvtColor(up, cv2.COLOR_BGR2RGB))
-    
-    return result
-
-# 【変更点②】@spaces.GPUデコレータを持つ新しいラッパー関数を定義
+            subprocess.check_call(["curl", "-L", "-o", str(dst), url])
+            return dst
+        except subprocess.CalledProcessError:
+            print(f"[DL] Retry {i}/{attempts} failed: {url}")
+    # 最後に basicsr のダウンローダでフォールバック
+    load_file_from_url(url=url, model_dir=str(dst.parent), file_name=dst.name)
+    return dst
+
+
+# ==============================================================
+# 1. モデル URL 定義
+# ==============================================================
+
+BRA_V7_URL = (
+    "https://huggingface.co/i0switch-assets/Beautiful_Realistic_Asians_v7/"
+    "resolve/main/beautiful_realistic_asians_v7_fp16.safetensors"
+)
+IP_ADAPTER_BIN_URL = (
+    "https://huggingface.co/h94/IP-Adapter/resolve/main/ip-adapter-plus-face_sd15.bin"
+)
+IP_ADAPTER_LORA_URL = (
+    "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/"
+    "ip-adapter-faceid-plusv2_sd15_lora.safetensors"
+)
+REALESRGAN_URL = (
+    "https://huggingface.co/aimagelab/realesrgan/resolve/main/RealESRGAN_x4plus.pth"
+)
+
+# ==============================================================
+# 2. グローバル変数（lazy-load される）
+# ==============================================================
+
+pipe: Optional[StableDiffusionControlNetPipeline] = None
+face_analyser: Optional[FaceAnalysis] = None
+upsampler: Optional[RealESRGANer] = None
+
+# ==============================================================
+# 3. パイプライン初期化
+# ==============================================================
+
+
+def initialize_pipelines():
+    global pipe, face_analyser, upsampler
+
+    if pipe is not None:
+        return  # 既に初期化済み
+
+    print("[INIT] Downloading model assets …")
+
+    # ---- 3-1. 基本モデル & IP-Adapter ----
+    bra_ckpt = download(BRA_V7_URL, MODELS_DIR / "bra_v7.safetensors")
+    ip_bin = download(IP_ADAPTER_BIN_URL, MODELS_DIR / "ip_adapter.bin")
+    ip_lora = download(IP_ADAPTER_LORA_URL, LORA_DIR / "ip_adapter_faceid.lora")
+
+    # ---- 3-2. ControlNet (InstantID) ----
+    controlnet = ControlNetModel.from_pretrained(
+        "InstantID/ControlNet-Mediapipe-Face",
+        torch_dtype=torch.float16,
+        cache_dir=str(MODELS_DIR),
+    )
+
+    # ---- 3-3. Diffusers パイプライン ----
+    pipe_local_files_only = {
+        "controlnet": controlnet,
+        "vae": AutoencoderKL.from_pretrained(
+            "stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16
+        ),
+        "torch_dtype": torch.float16,
+        "safety_checker": None,
+    }
+    pipe_base = "runwayml/stable-diffusion-v1-5"
+    pipe_kwargs = dict(
+        local_files_only=False,
+        cache_dir=str(MODELS_DIR),
+        load_safety_checker=False,
+    )
+    pipe_tmp = StableDiffusionControlNetPipeline.from_pretrained(
+        pipe_base, **pipe_local_files_only, **pipe_kwargs
+    )
+    pipe_tmp.scheduler = DPMSolverMultistepScheduler.from_pretrained(
+        pipe_base, subfolder="scheduler", cache_dir=str(MODELS_DIR)
+    )
+    # LoRA / IP-Adapter
+    pipe_tmp.load_ip_adapter(
+        ip_bin,
+        subfolder=None,
+        weight_name=None,
+    )
+    ip_layers = AttnProcsLayers(pipe_tmp.unet.attn_processors)
+    ip_layers.load_lora_weights(ip_lora, adapter_name="ip_faceid", safe_load=True)
+    pipe_tmp.set_adapters(["ip_faceid"], adapter_weights=[0.6])
+    pipe_tmp.to("cuda")
+
+    pipe = pipe_tmp
+
+    # ---- 3-4. InsightFace ----
+    face_analyser = FaceAnalysis(
+        name="buffalo_l", root=str(MODELS_DIR), providers=["CUDAExecutionProvider"]
+    )
+    face_analyser.prepare(ctx_id=0, det_size=(640, 640))
+
+    # ---- 3-5. Real-ESRGAN ----
+    esrgan_ckpt = download(REALESRGAN_URL, UPSCALE_DIR / "realesrgan_x4plus.pth")
+    upsampler = RealESRGANer(
+        scale=4,
+        model_path=str(esrgan_ckpt),
+        half=True,
+        tile=512,
+        tile_pad=10,
+        pre_pad=0,
+        gpu_id=0,
+    )
+
+    print("[INIT] Pipelines ready.")
+
+
+# ==============================================================
+# 4. プロンプト設定
+# ==============================================================
+
+BASE_PROMPT = (
+    "(masterpiece:1.2), best quality, ultra-realistic, 8k, RAW photo, "
+    "cinematic lighting, textured skin, "
+)
+NEG_PROMPT = (
+    "verybadimagenegative_v1.3, ng_deepnegative_v1_75t, "
+    "(worst quality:2), (low quality:2), lowres, blurry, bad anatomy, "
+    "bad hands, extra digits, cropped, watermark, signature"
+)
+
+# ==============================================================
+# 5. 生成コア関数（GPU を掴む）
+# ==============================================================
+
+
 @spaces.GPU(duration=60)
-def generate_gpu_wrapper(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress=gr.Progress(track_tqdm=True)):
+def generate_core(
+    face_img: Image.Image,
+    subject: str,
+    add_prompt: str = "",
+    add_neg: str = "",
+    cfg: float = 7.5,
+    ip_scale: float = 0.6,
+    steps: int = 30,
+    w: int = 768,
+    h: int = 768,
+    upscale: bool = False,
+    up_factor: int = 4,
+    progress: gr.Progress = gr.Progress(track_tqdm=True),
+):
     """
-    Hugging Face SpacesプラットフォームにGPUを要求するためのラッパー関数。
-    実際の処理は _generate_internal を呼び出して実行する。
+    メイン生成関数
     """
-    return _generate_internal(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress)
+    try:
+        if pipe is None:
+            initialize_pipelines()
+
+        face_np = np.array(face_img)
+        face_info = face_analyser.get(face_np)
+        if len(face_info) == 0:
+            raise ValueError("顔が検出できませんでした。")
+
+        pipe.set_adapters(["ip_faceid"], adapter_weights=[ip_scale])
 
+        prompt = BASE_PROMPT + subject + ", " + add_prompt
+        negative = NEG_PROMPT + ", " + add_neg
 
-# 【変更点③】GradioのUIから新しいラッパー関数を呼び出すように変更
-def generate_ui(face_np, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress=gr.Progress(track_tqdm=True)):
-    if face_np is None: raise gr.Error("顔画像をアップロードしてください。")
-    # NumPy配列をPillow画像に変換
-    face_img = Image.fromarray(face_np)
-    # _generate_coreの代わりにgenerate_gpu_wrapperを呼び出す
-    return generate_gpu_wrapper(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress)
+        result = pipe(
+            prompt=prompt,
+            negative_prompt=negative,
+            num_inference_steps=int(steps),
+            guidance_scale=float(cfg),
+            image=face_img,
+            control_image=None,
+            width=int(w),
+            height=int(h),
+        ).images[0]
 
+        if upscale and upsampler is not None:
+            scale = 4 if up_factor == 4 else 8
+            upsampler.scale = scale
+            result, _ = upsampler.enhance(np.array(result))
+            result = Image.fromarray(result)
 
-# 5. Gradio UI Definition
-with gr.Blocks() as demo:
-    gr.Markdown("# InstantID – Beautiful Realistic Asians v7 (ZeroGPU)")
+        return result
+
+    except Exception as e:
+        traceback.print_exc()
+        raise e
+
+
+# ==============================================================
+# 6. Gradio UI
+# ==============================================================
+
+with gr.Blocks(title="InstantID × BRA v7 (ZeroGPU)") as demo:
+    gr.Markdown("## InstantID × Beautiful Realistic Asians v7")
+    with gr.Row():
+        face_img = gr.Image(type="pil", label="Face ID", sources=["upload"])
+        subject = gr.Textbox(
+            label="被写体説明（例: '30代日本人女性、黒髪セミロング'）", interactive=True
+        )
+    add_prompt = gr.Textbox(label="追加プロンプト", interactive=True)
+    add_neg = gr.Textbox(label="追加ネガティブ", interactive=True)
+    with gr.Row():
+        cfg = gr.Slider(1, 20, value=7.5, step=0.5, label="CFG Scale")
+        ip_scale = gr.Slider(0.1, 1.0, value=0.6, step=0.05, label="IP-Adapter Weight")
     with gr.Row():
-        with gr.Column():
-            face_in = gr.Image(label="顔写真",type="numpy")
-            subj_in = gr.Textbox(label="被写体説明",placeholder="e.g. woman in black suit, smiling")
-            add_in = gr.Textbox(label="追加プロンプト")
-            addneg_in = gr.Textbox(label="追加ネガティブ")
-            with gr.Accordion("詳細設定", open=False):
-                ip_sld = gr.Slider(0,1.5,0.65,step=0.05,label="IP‑Adapter scale")
-                cfg_sld = gr.Slider(1,15,6,step=0.5,label="CFG")
-                step_sld = gr.Slider(10,50,20,step=1,label="Steps")
-                w_sld = gr.Slider(512,1024,512,step=64,label="幅")
-                h_sld = gr.Slider(512,1024,768,step=64,label="高さ")
-                up_ck = gr.Checkbox(label="アップスケール",value=True)
-                up_fac = gr.Slider(1,8,2,step=1,label="倍率")
-            btn = gr.Button("生成",variant="primary")
-        with gr.Column():
-            out_img = gr.Image(label="結果")
-            
-    demo.queue()
-    
-    btn.click(
-        fn=generate_ui, 
-        inputs=[face_in,subj_in,add_in,addneg_in,cfg_sld,ip_sld,step_sld,w_sld,h_sld,up_ck,up_fac], 
-        outputs=out_img
+        steps = gr.Slider(10, 50, value=30, step=1, label="Steps")
+        w = gr.Slider(512, 1024, value=768, step=64, label="Width")
+        h = gr.Slider(512, 1024, value=768, step=64, label="Height")
+    with gr.Row():
+        upscale = gr.Checkbox(label="Real-ESRGAN Upscale", value=False)
+        up_factor = gr.Radio([4, 8], value=4, label="Upscale Factor")
+    run_btn = gr.Button("Generate")
+
+    output_img = gr.Image(type="pil", label="Result")
+
+    run_btn.click(
+        fn=generate_core,
+        inputs=[
+            face_img,
+            subject,
+            add_prompt,
+            add_neg,
+            cfg,
+            ip_scale,
+            steps,
+            w,
+            h,
+            upscale,
+            up_factor,
+        ],
+        outputs=output_img,
+        show_progress=True,
     )
 
-# 6. FastAPI Mounting
+# ==============================================================
+# 7. FastAPI エンドポイント
+# ==============================================================
+
 app = FastAPI()
 
-# 【変更点④】FastAPIのエンドポイントも新しいラッパー関数を呼び出すように変更
-@app.post("/api/predict")
-async def predict_endpoint(
-    face_image: UploadFile = File(...),
-    subject: str = Form("a woman"),
-    add_prompt: str = Form(""),
-    add_neg: str = Form(""),
-    cfg: float = Form(6.0),
-    ip_scale: float = Form(0.65),
-    steps: int = Form(20),
-    w: int = Form(512),
+
+@app.post("/api/generate")
+async def api_generate(
+    subject: str = Form(...),
+    cfg: float = Form(7.5),
+    steps: int = Form(30),
+    ip_scale: float = Form(0.6),
+    w: int = Form(768),
     h: int = Form(768),
-    upscale: bool = Form(True),
-    up_factor: float = Form(2.0)
+    file: UploadFile = File(...),
 ):
     try:
-        contents = await face_image.read()
-        pil_image = Image.open(io.BytesIO(contents))
-        
-        # _generate_coreの代わりにgenerate_gpu_wrapperを呼び出す
-        result_pil_image = generate_gpu_wrapper(
-            pil_image, subject, add_prompt, add_neg, cfg, ip_scale, 
-            steps, w, h, upscale, up_factor
+        img_bytes = await file.read()
+        pil = Image.open(io.BytesIO(img_bytes)).convert("RGB")
+        res = generate_core(
+            face_img=pil,
+            subject=subject,
+            add_prompt="",
+            add_neg="",
+            cfg=cfg,
+            ip_scale=ip_scale,
+            steps=steps,
+            w=w,
+            h=h,
+            upscale=False,
+            up_factor=4,
         )
-
-        buffered = io.BytesIO()
-        result_pil_image.save(buffered, format="PNG")
-        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-        
-        return {"image_base64": img_str}
+        buf = io.BytesIO()
+        res.save(buf, format="PNG")
+        b64 = base64.b64encode(buf.getvalue()).decode()
+        return {"image": f"data:image/png;base64,{b64}"}
     except Exception as e:
         traceback.print_exc()
         raise HTTPException(status_code=500, detail=str(e))
 
-# GradioアプリをFastAPIアプリにマウント
-app = gr.mount_gradio_app(app, demo, path="/")
-
-print("Application startup script finished. Waiting for requests.")
-if __name__ == "__main__":
-    import os, time, socket, uvicorn
-
-    def port_is_free(port: int) -> bool:
-        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-            return s.connect_ex(("0.0.0.0", port)) != 0
-
-    port = int(os.getenv("PORT", 7860))
-    # ローカルでのテスト用にタイムアウトを短縮
-     timeout_sec = 30
-     poll_interval = 2
-    
-     t0 = time.time()
-     while not port_is_free(port):
-         waited = time.time() - t0
-         if waited >= timeout_sec:
-             raise RuntimeError(f"Port {port} is still busy after {timeout_sec}s")
-         print(f"⚠️ Port {port} busy, retrying in {poll_interval}s …")
-         time.sleep(poll_interval)
-    
-    # Hugging Face Spaces環境ではポートの競合は起こりにくいため、ポートチェックロジックを簡略化・無効化
-    uvicorn.run(app, host="0.0.0.0", port=port, workers=1, log_level="info")
+
+# ==============================================================
+# 8. Launch
+# ==============================================================
+
+# Spaces が自動で Uvicorn を起動するため、手動起動は不要。
+demo.queue(concurrency_count=2).launch(share=False)