fix: identity agent writes Round 1 matches to DB immediately

src/api/identity_agent_api.rs

@@ -637,13 +637,39 @@ async fn match_faces_iterative(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::
         }
     }
     tracing::info!(
-        "[FaceMatch] Round 1: {} matched ({}%)",
+        "[FaceMatch] Round 1: {} matched ({}%) — writing to DB",
         matched.len(),
         matched.len() * 100 / total_traces
     );
 
-    // Round 2+: 用已匹配的 face 作為 seed 傳播
+    // Step 5: 寫入 DB — Round 1 結果先存
-    for round_n in 2..=10 {
+    let identities_table = schema::table_name("identities");
+    let fd_table = schema::table_name("face_detections");
+    let mut updated = 0usize;
+    for (tid, name) in &matched {
+        let id_opt = sqlx::query_scalar::<_, Option<i32>>(
+            &format!("SELECT id FROM {} WHERE name=$1 AND source='tmdb'", identities_table),
+        )
+        .bind(name)
+        .fetch_optional(pool)
+        .await?;
+        if let Some(identity_id) = id_opt {
+            let _ = sqlx::query(
+                &format!("UPDATE {} SET identity_id=$1 WHERE file_uuid=$2 AND trace_id=$3", fd_table),
+            )
+            .bind(identity_id)
+            .bind(file_uuid)
+            .bind(tid)
+            .execute(pool)
+            .await;
+            updated += 1;
+        }
+    }
+    tracing::info!("[FaceMatch] Round 1: updated {} face_detections", updated);
+
+    // Round 2+: 用已匹配的 face 作為 seed 傳播（剩餘未匹配的 trace）
+    let initial_matched = matched.len();
+    for round_n in 2..=5 {
         let prev = matched.len();
         // 建立 seed pool: name → Vec<embedding>
         let mut seed_pool: HashMap<String, Vec<&Vec<f32>>> = HashMap::new();
@@ -698,30 +724,6 @@ async fn match_faces_iterative(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::
         }
     }
 
-    // Step 5: 寫入 DB — 已匹配的設 identity_id
-    let identities_table = schema::table_name("identities");
-    let fd_table = schema::table_name("face_detections");
-    let mut updated = 0usize;
-    for (tid, name) in &matched {
-        let id_opt = sqlx::query_scalar::<_, Option<i32>>(
-            &format!("SELECT id FROM {} WHERE name=$1 AND source='tmdb'", identities_table),
-        )
-        .bind(name)
-        .fetch_optional(pool)
-        .await?;
-        if let Some(identity_id) = id_opt {
-            let _ = sqlx::query(
-                &format!("UPDATE {} SET identity_id=$1 WHERE file_uuid=$2 AND trace_id=$3", fd_table),
-            )
-            .bind(identity_id)
-            .bind(file_uuid)
-            .bind(tid)
-            .execute(pool)
-            .await;
-            updated += 1;
-        }
-    }
-
     // Step 6: 未匹配的 trace 設 stranger_id = trace_id
     // trace_id 在同一個 file 內是 sequential integer，直接複用為 stranger_id
     let stranger_update = sqlx::query(
@@ -915,6 +917,12 @@ pub async fn run_identity_agent(db: &PostgresDb, file_uuid: &str) -> anyhow::Res
     let output_dir = std::env::var("MOMENTRY_OUTPUT_DIR")
         .unwrap_or_else(|_| "/Users/accusys/momentry/output".to_string());
 
+    let pool = db.pool();
+
+    // Step 1: 先跑 face matching（不需 face_clustered.json）
+    let matched = match_faces_iterative(pool, file_uuid).await.unwrap_or(0);
+
+    // Step 2: 試著載入 face_clustered.json 建立新 identities
     let video_dir = PathBuf::from(&output_dir).join(file_uuid);
     let face_clustered_path = video_dir.join(format!("{}.face_clustered.json", file_uuid));
     let face_clustered_path = if face_clustered_path.exists() {
@@ -923,15 +931,9 @@ pub async fn run_identity_agent(db: &PostgresDb, file_uuid: &str) -> anyhow::Res
         PathBuf::from(&output_dir).join(format!("{}.face_clustered.json", file_uuid))
     };
 
-    if !face_clustered_path.exists() {
+    if face_clustered_path.exists() {
-        tracing::warn!(
+        let face_data: serde_json::Value =
-            "[IdentityAgent] face_clustered.json not found for {}",
+            std::fs::read_to_string(&face_clustered_path)?.parse()?;
-            file_uuid
-        );
-        return Ok(());
-    }
-
-    let face_data: serde_json::Value = std::fs::read_to_string(&face_clustered_path)?.parse()?;
         let asrx_path = video_dir.join(format!("{}.asrx.json", file_uuid));
         let asrx_data: Option<serde_json::Value> = if asrx_path.exists() {
             Some(std::fs::read_to_string(&asrx_path)?.parse()?)
@@ -943,7 +945,6 @@ pub async fn run_identity_agent(db: &PostgresDb, file_uuid: &str) -> anyhow::Res
         let speakers = extract_speakers_from_asrx_data(&asrx_data);
         let identities = analyze_person_speaker_overlap(&persons, &speakers);
 
-    let pool = db.pool();
         let uuid_short = &file_uuid[..8.min(file_uuid.len())];
         for (idx, id_result) in identities.iter().enumerate() {
             let identity_name = format!("stranger_{}_{}", uuid_short, idx);
@@ -966,14 +967,24 @@ pub async fn run_identity_agent(db: &PostgresDb, file_uuid: &str) -> anyhow::Res
             .execute(pool)
             .await;
         }
+        let _created = identities.len();
+        tracing::info!(
+            "[IdentityAgent] Created {} auto identities from face_clustered for {}",
+            _created,
+            file_uuid
+        );
+    } else {
+        tracing::warn!(
+            "[IdentityAgent] face_clustered.json not found for {}, skipping identity creation",
+            file_uuid
+        );
+    }
 
-    let matched = match_faces_iterative(pool, file_uuid).await.unwrap_or(0);
     let bound = bind_speakers(pool, file_uuid).await.unwrap_or(0);
 
     tracing::info!(
-        "[IdentityAgent] Done for {}: {} identities, {} face matches, {} speaker bindings",
+        "[IdentityAgent] Done for {}: {} face matches, {} speaker bindings",
         file_uuid,
-        identities.len(),
         matched,
         bound
     );