feat: AI photo enhancement (Real-ESRGAN container) + restore original

- new photo-ai service: FastAPI + Real-ESRGAN x2plus on CPU, internal only
- async job queue in server (POST enhance-ai / GET status), 5min timeout, sequential processing
- keep original photo backup (entries.photo_original_path, uploads/.originals), restore-original endpoint
- UI: AI button and restore-original button in enhance modal
- db: photo_original_path column (init.sql, migration.sql, runtime ensure)

photo-ai/Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y --no-install-recommends libgl1 libglib2.0-0 && rm -rf /var/lib/apt/lists/*
+
+RUN pip install --no-cache-dir torch torchvision --index-url https://download.pytorch.org/whl/cpu
+
+RUN pip install --no-cache-dir realesrgan==0.3.0 fastapi "uvicorn[standard]" python-multipart opencv-python-headless numpy
+
+RUN python - <<'EOF'
+import basicsr, os
+p = os.path.join(os.path.dirname(basicsr.__file__), 'data', 'degradations.py')
+s = open(p).read()
+s = s.replace('torchvision.transforms.functional_tensor', 'torchvision.transforms.functional')
+open(p, 'w').write(s)
+print('basicsr patch applied')
+EOF
+
+COPY app.py .
+
+ENV MODEL_PATH=/models/RealESRGAN_x2plus.pth
+
+VOLUME /models
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8080"]

photo-ai/app.py

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+import io
+import os
+import asyncio
+import threading
+import urllib.request
+
+from fastapi import FastAPI, File, Form, UploadFile
+from fastapi.responses import Response
+import numpy as np
+import cv2
+import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from realesrgan import RealESRGANer
+
+MODEL_PATH = os.environ.get('MODEL_PATH', '/models/RealESRGAN_x2plus.pth')
+MODEL_URL = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth'
+MAX_PIXELS = int(os.environ.get('MAX_INPUT_PIXELS', str(4_000_000)))
+
+lock = threading.Lock()
+upsampler = None
+app = FastAPI()
+
+
+def ensure_model():
+    if not os.path.exists(MODEL_PATH):
+        os.makedirs(os.path.dirname(MODEL_PATH), exist_ok=True)
+        tmp = MODEL_PATH + '.tmp'
+        urllib.request.urlretrieve(MODEL_URL, tmp)
+        os.replace(tmp, MODEL_PATH)
+
+
+def load_model():
+    global upsampler
+    ensure_model()
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, scale=2, num_feat=64, num_block=23, num_grow_ch=32)
+    upsampler = RealESRGANer(
+        scale=2,
+        model_path=MODEL_PATH,
+        model=model,
+        tile=256,
+        tile_pad=10,
+        pre_pad=0,
+        half=False,
+        device='cpu',
+    )
+
+
+@app.on_event('startup')
+async def startup():
+    await asyncio.to_thread(load_model)
+
+
+@app.get('/health')
+def health():
+    return {'ok': upsampler is not None}
+
+
+@app.post('/enhance')
+async def enhance(image: UploadFile = File(...), scale: int = Form(2)):
+    data = await image.read()
+    img = cv2.imdecode(np.frombuffer(data, np.uint8), cv2.IMREAD_COLOR)
+    if img is None:
+        return Response('bad image', status_code=400)
+    if img.shape[0] * img.shape[1] > MAX_PIXELS:
+        r = (MAX_PIXELS / (img.shape[0] * img.shape[1])) ** 0.5
+        img = cv2.resize(img, (int(img.shape[1] * r), int(img.shape[0] * r)), interpolation=cv2.INTER_AREA)
+    outscale = min(max(int(scale), 2), 4)
+    with lock:
+        out, _ = upsampler.enhance(img, outscale=outscale)
+    ok, enc = cv2.imencode('.jpg', out, [int(cv2.IMWRITE_JPEG_QUALITY), 92])
+    if not ok:
+        return Response('encode failed', status_code=500)
+    return Response(enc.tobytes(), media_type='image/jpeg')