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

src/config.jl

@@ -10,6 +10,7 @@ Base.@kwdef struct Config
     spool_dir::String   = "data/spool"   # files land here on intake (pending)
     done_dir::String    = "data/done"    # files move here after successful processing
     failed_dir::String  = "data/failed"  # files move here if a worker throws
+    model_path::String  = "model/classifier.jld2"  # committed classifier artifact, loaded at startup
 end
 
 """
@@ -21,11 +22,11 @@ and for `FileServer.run(; port=...)`).
 
 Recognised variables:
   FS_HOST, FS_PORT, FS_WORKERS, FS_QUEUE_CAPACITY,
-  FS_SPOOL_DIR, FS_DONE_DIR, FS_FAILED_DIR
+  FS_SPOOL_DIR, FS_DONE_DIR, FS_FAILED_DIR, FS_MODEL_PATH
 """
 function config_from_env(; host=nothing, port=nothing, worker_count=nothing,
                           queue_capacity=nothing, spool_dir=nothing,
-                          done_dir=nothing, failed_dir=nothing)
+                          done_dir=nothing, failed_dir=nothing, model_path=nothing)
     Config(
         host           = something(host,           get(ENV, "FS_HOST", "127.0.0.1")),
         port           = something(port,           parse(Int, get(ENV, "FS_PORT", "8080"))),
@@ -34,6 +35,7 @@ function config_from_env(; host=nothing, port=nothing, worker_count=nothing,
         spool_dir      = something(spool_dir,      get(ENV, "FS_SPOOL_DIR", "data/spool")),
         done_dir       = something(done_dir,       get(ENV, "FS_DONE_DIR", "data/done")),
         failed_dir     = something(failed_dir,     get(ENV, "FS_FAILED_DIR", "data/failed")),
+        model_path     = something(model_path,     get(ENV, "FS_MODEL_PATH", "model/classifier.jld2")),
     )
 end