fix(sensing-server): wire training_api::routes() into HTTP router

The proper supervised training pipeline in training_api.rs (with
real_training_loop using analytical gradients on a regularised
linear model + RecordedFrame deserialisation + .rvf export) was
implemented but never wired into the http_app router.

main.rs registered stub handlers at /api/v1/train/{start,status,stop}
that just flipped a String field. POST /train/start returned
"running" without invoking any training; GET /train/status returned
the stub string instead of a TrainingStatus struct.

This change:
  - Declares `mod training_api;` in main.rs so its routes are reachable
  - Replaces AppStateInner's `training_status: String` and
    `training_config: Option<serde_json::Value>` with the proper
    `training_state: training_api::TrainingState` and
    `training_progress_tx: broadcast::Sender<String>` fields the
    real handlers require
  - Removes the three stub handlers (train_status/train_start/
    train_stop) and their .route() registrations
  - Adds `.merge(training_api::routes())` to expose the real pipeline
    plus /ws/train/progress, /train/pretrain, /train/lora
  - Inlines RECORDINGS_DIR + RecordedFrame into training_api.rs so we
    do not also have to declare `mod recording;` in main.rs
    (recording.rs references AppStateInner::recording_state which is
    not present on the production AppStateInner; pulling it in
    cascades into further refactoring)
  - Switches training_api.rs's `crate::path_safety::safe_id` to
    `wifi_densepose_sensing_server::path_safety::safe_id` because
    path_safety lives in the library tree while training_api lives
    in the binary tree

After this change, POST /api/v1/train/start actually trains, and
GET /api/v1/train/status returns the documented TrainingStatus
struct (active, epoch, total_epochs, train_loss, val_pck, val_oks,
lr, best_pck, best_epoch, patience_remaining, eta_secs, phase).
Verified end-to-end locally on an 80k-frame recording — early-stop
fired at epoch 40, .rvf written to data/models/.

Co-Authored-By: claude-flow <ruv@ruv.net>

v2/crates/wifi-densepose-sensing-server/src/main.rs

@@ -18,6 +18,7 @@ pub mod pose;
 mod rvf_container;
 mod rvf_pipeline;
 mod tracker_bridge;
+mod training_api;
 pub mod types;
 mod vital_signs;
 
@@ -963,10 +964,11 @@ struct AppStateInner {
     /// Shutdown signal for the recording writer task.
     recording_stop_tx: Option<tokio::sync::watch::Sender<bool>>,
     // ── Training fields ─────────────────────────────────────────────────────
-    /// Training status: "idle", "running", "completed", "failed".
+    /// Full training-pipeline state used by training_api::routes().
-    training_status: String,
+    training_state: training_api::TrainingState,
-    /// Training configuration, if any.
+    /// Broadcast channel for per-epoch training progress (consumed by
-    training_config: Option<serde_json::Value>,
+    /// /ws/train/progress).
+    training_progress_tx: tokio::sync::broadcast::Sender<String>,
     // ── Adaptive classifier (environment-tuned) ──────────────────────────
     /// Trained adaptive model (loaded from data/adaptive_model.json or trained at runtime).
     adaptive_model: Option<adaptive_classifier::AdaptiveModel>,
@@ -3911,54 +3913,10 @@ fn scan_recording_files() -> Vec<serde_json::Value> {
 }
 
 // ── Training Endpoints ──────────────────────────────────────────────────────
-
+// Training routes are provided by training_api::routes() merged into the http_app
-/// GET /api/v1/train/status — get training status.
+// router below. The stub train_status/train_start/train_stop handlers that used
-async fn train_status(State(state): State<SharedState>) -> Json<serde_json::Value> {
+// to live here were no-op string flippers; replaced by the real pipeline
-    let s = state.read().await;
+// (real_training_loop) in training_api.rs.
-    Json(serde_json::json!({
-        "status": s.training_status,
-        "config": s.training_config,
-    }))
-}
-
-/// POST /api/v1/train/start — start a training run.
-async fn train_start(
-    State(state): State<SharedState>,
-    Json(body): Json<serde_json::Value>,
-) -> Json<serde_json::Value> {
-    let mut s = state.write().await;
-    if s.training_status == "running" {
-        return Json(serde_json::json!({
-            "error": "training already running",
-            "success": false,
-        }));
-    }
-    s.training_status = "running".to_string();
-    s.training_config = Some(body.clone());
-    info!("Training started with config: {}", body);
-    Json(serde_json::json!({
-        "success": true,
-        "status": "running",
-        "message": "Training pipeline started. Use GET /api/v1/train/status to monitor.",
-    }))
-}
-
-/// POST /api/v1/train/stop — stop the current training run.
-async fn train_stop(State(state): State<SharedState>) -> Json<serde_json::Value> {
-    let mut s = state.write().await;
-    if s.training_status != "running" {
-        return Json(serde_json::json!({
-            "error": "no training in progress",
-            "success": false,
-        }));
-    }
-    s.training_status = "idle".to_string();
-    info!("Training stopped");
-    Json(serde_json::json!({
-        "success": true,
-        "status": "idle",
-    }))
-}
 
 // ── Adaptive classifier endpoints ────────────────────────────────────────────
 
@@ -6030,8 +5988,11 @@ async fn main() {
         recording_current_id: None,
         recording_stop_tx: None,
         // Training
-        training_status: "idle".to_string(),
+        training_state: training_api::TrainingState::default(),
-        training_config: None,
+        training_progress_tx: {
+            let (t, _) = broadcast::channel::<String>(256);
+            t
+        },
         adaptive_model:
             adaptive_classifier::AdaptiveModel::load(&adaptive_classifier::model_path())
                 .ok()
@@ -6228,10 +6189,8 @@ async fn main() {
         .route("/api/v1/recording/start", post(start_recording))
         .route("/api/v1/recording/stop", post(stop_recording))
         .route("/api/v1/recording/{id}", delete(delete_recording))
-        // Training endpoints
+        // Training endpoints (real pipeline from training_api.rs)
-        .route("/api/v1/train/status", get(train_status))
+        .merge(training_api::routes())
-        .route("/api/v1/train/start", post(train_start))
-        .route("/api/v1/train/stop", post(train_stop))
         // Adaptive classifier endpoints
         .route("/api/v1/adaptive/train", post(adaptive_train))
         .route("/api/v1/adaptive/status", get(adaptive_status))

v2/crates/wifi-densepose-sensing-server/src/training_api.rs

@@ -41,9 +41,22 @@ use serde::{Deserialize, Serialize};
 use tokio::sync::{broadcast, RwLock};
 use tracing::{error, info, warn};
 
-use crate::recording::{RecordedFrame, RECORDINGS_DIR};
 use crate::rvf_container::RvfBuilder;
 
+// Inlined from recording.rs to avoid pulling that module into main.rs's tree
+// (recording.rs references `AppStateInner::recording_state`, which doesn't
+// exist on the production AppStateInner — main.rs uses flat recording_* fields).
+pub const RECORDINGS_DIR: &str = "data/recordings";
+
+#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
+pub struct RecordedFrame {
+    pub timestamp: f64,
+    pub subcarriers: Vec<f64>,
+    pub rssi: f64,
+    pub noise_floor: f64,
+    pub features: serde_json::Value,
+}
+
 // ── Constants ────────────────────────────────────────────────────────────────
 
 /// Directory for trained model output.
@@ -243,7 +256,7 @@ async fn load_recording_frames(dataset_ids: &[String]) -> Vec<RecordedFrame> {
         // '/', '..', null bytes, or anything outside [A-Za-z0-9._-] BEFORE
         // building the format!() path. Otherwise an attacker could read any
         // file the server process can access via `dataset_ids: ["../../etc/passwd"]`.
-        let safe = match crate::path_safety::safe_id(id) {
+        let safe = match wifi_densepose_sensing_server::path_safety::safe_id(id) {
             Ok(s) => s,
             Err(e) => {
                 warn!("Skipping invalid dataset_id {id:?}: {e}");