Update app.py

app.py

@@ -97,6 +97,8 @@ def initialize_pipelines():
     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)
@@ -115,10 +117,8 @@ def initialize_pipelines():
 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")
 
-
-# ZeroGPUで実行される本体。durationを60秒に設定。
-@spaces.GPU(duration=60)
-def _generate_core(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress=gr.Progress(track_tqdm=True)):
+# 【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()
 
@@ -131,18 +131,31 @@ def _generate_core(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps,
     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
 
-# GradioのUIから呼び出されるラッパー関数
+# 【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
+@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)):
+    """
+    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。
+    """
+    return _generate_internal(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress)
+
+
+# 【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)
-    return _generate_core(face_img, subject, add_prompt, add_neg, cfg, ip_scale, steps, w, h, upscale, up_factor, progress)
+    # _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)
 
 
 # 5. Gradio UI Definition
@@ -166,7 +179,6 @@ with gr.Blocks() as demo:
         with gr.Column():
             out_img = gr.Image(label="結果")
             
-    # .queue() はGradioの通常機能として必要
     demo.queue()
     
     btn.click(
@@ -178,7 +190,7 @@ with gr.Blocks() as demo:
 # 6. FastAPI Mounting
 app = FastAPI()
 
-# FastAPIのエンドポイントを定義。こちらも内部で_generate_coreを呼ぶ
+# 【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(...),
@@ -197,8 +209,8 @@ async def predict_endpoint(
         contents = await face_image.read()
         pil_image = Image.open(io.BytesIO(contents))
         
-        # FastAPI経由の呼び出しも同じコア関数を利用
-        result_pil_image = _generate_core(
+        # _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
         )
@@ -217,5 +229,24 @@ app = gr.mount_gradio_app(app, demo, path="/")
 
 print("Application startup script finished. Waiting for requests.")
 if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860, workers=1, log_level="info")
+    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")