build: the agent installs one CUDA-13 stack instead of two, the web image drops ML packages it never imported, and Redis moves to 8 (1451, 1452)

Agent:
- The image ran PyPI's CUDA-13 torch 2.14 and onnxruntime-gpu 1.30 on a
  CUDA 12.9 cudnn-runtime base. requirements.txt had silently replaced the
  Dockerfile's torch 2.6+cu124, because ultralytics pulls torchvision, which
  pulls its own torch. That left ~3 GB of base libraries and a ~3 GB torch
  nothing loaded: 10 GB compressed.
- Now: an nvidia/cuda 13.0.3 `base` image, with torch and torchvision
  installed together from cu130. CUDA and cuDNN come from the nvidia-* pip
  packages; onnxruntime-gpu declares its [cuda,cudnn] extras.
- fc_agent/accel.py preloads those libraries for onnxruntime. It then logs,
  and reports in /status, whether torch and the ONNX CUDA provider actually
  got the GPU, since both fall back to the CPU silently.

Web image:
- Drop opencv-python-headless and onnxruntime, plus the opencv-only apt libs.
  Both have been listed since the scaffold and nothing in backend/ imports
  them.
- torch/torchvision move to 2.14/0.29, and the unexplained caps are lifted
  (rule 154).

Redis: 8-alpine in both compose files and both CI service containers. That
gives an AGPLv3 licence option, where 7.4 was RSAL/SSPL only. The client
moves to >=8.1.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01LVjrnpQjRgHdvq95rASoiR

agent/requirements.txt

@@ -1,10 +1,12 @@
 # CCIP + figure detection (ONNX models, auto-downloaded from HuggingFace).
 dghs-imgutils>=0.4
 # GPU inference for the ONNX models. Swap to onnxruntime (CPU) for a slow
-# server-side fallback run.
-onnxruntime-gpu
-# The crop EMBEDDER (concept bag). torch is installed separately in the
-# Dockerfile from the CUDA-12.4 wheel index so the GPU build is deterministic;
+# server-side fallback run. The extras declare the CUDA/cuDNN pip packages its
+# CUDA provider loads (fc_agent/accel.py preloads them) rather than relying on
+# torch happening to install the same ones.
+onnxruntime-gpu[cuda,cudnn]
+# The crop EMBEDDER (concept bag). torch + torchvision are installed separately
+# in the Dockerfile from the cu130 wheel index, so pip never swaps them out;
 # transformers loads whatever SigLIP-family model the server announces.
 transformers>=4.45
 # Crop PROPOSERS — small YOLO detectors (booru_yolo anatomy, COCO person, comic