learn: wire up /train endpoint for approved candidates

When 's' is pressed on the learn screen, approved candidates are now
sent to the inference server's /train endpoint.

Samples are marked as sent immediately in the UI, and mark_trained()
is called after successful API response to prevent re-scoring.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>

src/subconscious/learn.rs

@@ -648,3 +648,69 @@ pub fn node_timestamp_ms(node: &AstNode) -> Option<i64> {
     }?;
     Some(ts.timestamp_millis())
 }
+
+// ── Training API ────────────────────────────────────────────────
+
+/// Training sample for /train endpoint.
+#[derive(serde::Serialize)]
+struct TrainingSample {
+    context_ids: Vec<u32>,
+    continuation_ids: Vec<u32>,
+}
+
+/// Data needed to send a training sample.
+pub struct TrainData {
+    pub context_ids: Vec<u32>,
+    pub continuation_ids: Vec<u32>,
+    pub timestamp_ms: i64,
+}
+
+/// Send training samples to the server.
+///
+/// Returns job_id on success, marks each sample as trained.
+pub async fn send_to_train(
+    samples: Vec<TrainData>,
+    client: &ApiClient,
+) -> anyhow::Result<String> {
+    if samples.is_empty() {
+        anyhow::bail!("no samples to train");
+    }
+
+    let api_samples: Vec<TrainingSample> = samples.iter()
+        .map(|s| TrainingSample {
+            context_ids: s.context_ids.clone(),
+            continuation_ids: s.continuation_ids.clone(),
+        })
+        .collect();
+
+    let body = serde_json::json!({
+        "training_data": {
+            "samples": api_samples,
+        }
+    });
+
+    let http = http_client();
+    let url = format!("{}/train", client.base_url());
+    let response = http.send_json("POST", &url, &[], &body).await?;
+
+    let status = response.status();
+    let result: serde_json::Value = response.json().await?;
+
+    if !status.is_success() {
+        let msg = result.get("error").and_then(|e| e.as_str()).unwrap_or("unknown error");
+        anyhow::bail!("train API HTTP {}: {}", status, msg);
+    }
+
+    // Mark all samples as trained
+    for s in &samples {
+        mark_trained(s.timestamp_ms);
+    }
+
+    let job_id = result.get("job_id")
+        .and_then(|j| j.as_str())
+        .unwrap_or("unknown")
+        .to_string();
+
+    dbglog!("[finetune] sent {} samples, job_id={}", samples.len(), job_id);
+    Ok(job_id)
+}

src/user/mod.rs

@@ -165,14 +165,39 @@ impl App {
     }
 
     fn finetune_send_approved(&mut self) {
-        // TODO: Send approved candidates to /finetune endpoint
+        // Collect approved candidates
-        // For now, just mark them as sent and record as trained
+        let samples: Vec<crate::subconscious::learn::TrainData> = self.finetune_candidates.iter()
+            .filter(|c| c.status == learn::CandidateStatus::Approved)
+            .map(|c| crate::subconscious::learn::TrainData {
+                context_ids: c.context_ids.clone(),
+                continuation_ids: c.continuation_ids.clone(),
+                timestamp_ms: c.timestamp_ms,
+            })
+            .collect();
+
+        if samples.is_empty() {
+            return;
+        }
+
+        // Mark as sent in UI immediately
         for candidate in &mut self.finetune_candidates {
             if candidate.status == learn::CandidateStatus::Approved {
-                crate::subconscious::learn::mark_trained(candidate.timestamp_ms);
                 candidate.status = learn::CandidateStatus::Sent;
             }
         }
+
+        // Spawn async task to send to training server
+        let client = self.agent.client.clone();
+        tokio::spawn(async move {
+            match crate::subconscious::learn::send_to_train(samples, &client).await {
+                Ok(job_id) => {
+                    dbglog!("[finetune] training started: {}", job_id);
+                }
+                Err(e) => {
+                    dbglog!("[finetune] send failed: {:#}", e);
+                }
+            }
+        });
     }