Add Lux.jl file classifier (known/unknown) with offline trainer

Each uploaded file is scored by a fixed-structure neural net that labels it
known (resembling the training set) or unknown — novelty detection over the
first 16 + last 16 bytes (scaled to [0,1]), Dense(32->64->16->2), argmax.

- src/model.jl: shared architecture + byte->feature mapping (trainer + server)
- src/classify.jl: load committed artifact, classify a file at inference
- bin/train.jl: offline trainer, 1:1 blended negatives (random + grab-bag),
  seeded 80/20 split, writes model/classifier.jld2
- worker: classify (annotate-only) and log classification=known|unknown
- config: FS_MODEL_PATH; server fails fast if the artifact is missing
- deps: Lux, JLD2, Optimisers, Zygote

Project.toml

@@ -5,14 +5,22 @@ authors = ["wardjm@gmail.com"]
 
 [deps]
 HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
+Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
+Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Oxygen = "df9a0d86-3283-4920-82dc-4555fc0d1d8b"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 HTTP = "1.11.0"
+JLD2 = "0.6.4"
 JSON3 = "1.14.3"
 Logging = "1.11.0"
+Lux = "1.31.4"
+Optimisers = "0.4.7"
 Oxygen = "1.10.2"
 UUIDs = "1.11.0"
+Zygote = "0.7.11"