use axum::{
    extract::{Multipart, State},
    http::StatusCode,
    response::Json,
    routing::{get, post},
    Router,
};
use serde::{Deserialize, Serialize};
use sqlx::Row;
use std::path::PathBuf;

use crate::api::types::AppState;
use crate::core::db::schema;
use crate::core::db::PostgresDb;

pub fn identity_agent_routes() -> Router<AppState> {
    Router::new()
        .route(
            "/api/v1/agents/identity/match-from-photo",
            post(match_from_photo),
        )
        .route(
            "/api/v1/agents/identity/match-from-trace",
            post(match_from_trace),
        )
}

#[derive(Debug, Serialize)]
pub struct IdentityResult {
    pub identity_id: String,
    pub person_ids: Vec<String>,
    pub speaker_ids: Vec<String>,
    pub confidence: f64,
    pub evidence: IdentityEvidence,
    pub reasoning: String,
}

#[derive(Debug, Serialize)]
pub struct IdentityEvidence {
    pub face_similarity: Option<f64>,
    pub speaker_overlap: f64,
    pub time_overlap: f64,
    pub frame_ratio: f64,
}

#[derive(Debug, Serialize)]
struct MatchFromPhotoResponse {
    success: bool,
    identity_uuid: String,
    file_uuid: String,
    matches: usize,
    traces_matched: Vec<i32>,
    message: String,
}

async fn match_from_photo(
    State(state): State<AppState>,
    mut multipart: Multipart,
) -> Result<Json<MatchFromPhotoResponse>, (StatusCode, Json<serde_json::Value>)> {
    let mut identity_uuid = String::new();
    let mut file_uuid = String::new();
    let mut image_data: Option<Vec<u8>> = None;

    while let Ok(Some(field)) = multipart.next_field().await {
        let name = field.name().unwrap_or("").to_string();
        match name.as_str() {
            "identity_uuid" => {
                identity_uuid = field.text().await.unwrap_or_default();
            }
            "file_uuid" => {
                file_uuid = field.text().await.unwrap_or_default();
            }
            "image" => {
                image_data = Some(field.bytes().await.unwrap_or_default().to_vec());
            }
            _ => {}
        }
    }

    let uuid_clean = identity_uuid.replace('-', "");
    if uuid_clean.is_empty() || file_uuid.is_empty() {
        return Err((
            StatusCode::BAD_REQUEST,
            Json(serde_json::json!({
                "success": false, "message": "identity_uuid and file_uuid are required"
            })),
        ));
    }
    let data = image_data.ok_or_else(|| {
        (
            StatusCode::BAD_REQUEST,
            Json(serde_json::json!({
                "success": false, "message": "No image field found. Use field name 'image'."
            })),
        )
    })?;

    // 1. Save uploaded image to temp
    let scripts_dir = std::env::var("MOMENTRY_SCRIPTS_DIR")
        .unwrap_or_else(|_| "/Users/accusys/momentry_core_0.1/scripts".to_string());
    let python_path = std::env::var("MOMENTRY_PYTHON_PATH")
        .unwrap_or_else(|_| "/opt/homebrew/bin/python3.11".to_string());
    let temp_dir = std::env::temp_dir().join("momentry_match_face");
    std::fs::create_dir_all(&temp_dir).map_err(|e| {
        (
            StatusCode::INTERNAL_SERVER_ERROR,
            Json(serde_json::json!({"message": format!("Failed to create temp dir: {}", e)})),
        )
    })?;
    let temp_img = temp_dir.join(format!("{}.jpg", uuid_clean));
    std::fs::write(&temp_img, &data).map_err(|e| {
        (
            StatusCode::INTERNAL_SERVER_ERROR,
            Json(serde_json::json!({"message": format!("Failed to save temp image: {}", e)})),
        )
    })?;

    // 2. Extract face embedding via Python script
    let extract_script = std::path::Path::new(&scripts_dir).join("extract_face_embedding.py");
    let output = tokio::process::Command::new(&*python_path)
        .arg(&extract_script)
        .arg(&temp_img)
        .output()
        .await
        .map_err(|e| {
            (
                StatusCode::INTERNAL_SERVER_ERROR,
                Json(serde_json::json!({"message": format!("Failed to run extractor: {}", e)})),
            )
        })?;

    let _ = std::fs::remove_file(&temp_img);

    if !output.status.success() {
        let stderr = String::from_utf8_lossy(&output.stderr);
        return Err((
            StatusCode::BAD_REQUEST,
            Json(serde_json::json!({
                "success": false, "message": format!("Face extraction failed: {}", stderr)
            })),
        ));
    }

    let stdout = String::from_utf8_lossy(&output.stdout);
    let extract_result: serde_json::Value = serde_json::from_str(&stdout).map_err(|_| {
        (
            StatusCode::INTERNAL_SERVER_ERROR,
            Json(serde_json::json!({"message": "Failed to parse extractor output"})),
        )
    })?;

    let embedding: Vec<f64> = serde_json::from_value(
        extract_result
            .get("embedding")
            .ok_or_else(|| {
                (
                    StatusCode::BAD_REQUEST,
                    Json(serde_json::json!({"message": "No embedding in extractor output"})),
                )
            })?
            .clone(),
    )
    .map_err(|_| {
        (
            StatusCode::INTERNAL_SERVER_ERROR,
            Json(serde_json::json!({"message": "Invalid embedding format"})),
        )
    })?;

    let embedding_f32: Vec<f32> = embedding.into_iter().map(|v| v as f32).collect();

    // 3. Look up identity internal ID
    let id_table = schema::table_name("identities");
    let identity_id_row: Option<(i32,)> = sqlx::query_as(&format!(
        "SELECT id FROM {} WHERE REPLACE(uuid::text, '-', '') = $1",
        id_table
    ))
    .bind(&uuid_clean)
    .fetch_optional(state.db.pool())
    .await
    .map_err(|e| {
        (
            StatusCode::INTERNAL_SERVER_ERROR,
            Json(serde_json::json!({"message": format!("DB error: {}", e)})),
        )
    })?;

    let identity_id = match identity_id_row {
        Some((id,)) => id,
        None => {
            return Err((
                StatusCode::NOT_FOUND,
                Json(serde_json::json!({
                    "success": false, "message": "Identity not found"
                })),
            ))
        }
    };

    // 4. Find best matching trace (highest similarity, no threshold)
    let fd_table = schema::table_name("face_detections");
    let best_match: Option<(i32, i32, f64)> = sqlx::query_as(&format!(
        r#"SELECT id, trace_id,
                   1 - (embedding::vector <=> $1::vector) as similarity
               FROM {}
               WHERE file_uuid = $2 AND embedding IS NOT NULL
               ORDER BY embedding::vector <=> $1::vector
               LIMIT 1"#,
        fd_table
    ))
    .bind(&embedding_f32)
    .bind(&file_uuid)
    .fetch_optional(state.db.pool())
    .await
    .map_err(|e| {
        (
            StatusCode::INTERNAL_SERVER_ERROR,
            Json(serde_json::json!({"message": format!("Search failed: {}", e)})),
        )
    })?;

    // 5. Update best match face_detection
    let mut traces_matched: Vec<i32> = Vec::new();
    if let Some((fb_id, fb_trace, fb_sim)) = best_match {
        let _ = sqlx::query(&format!(
            "UPDATE {} SET identity_id = $1 WHERE id = $2",
            fd_table
        ))
        .bind(identity_id)
        .bind(fb_id)
        .execute(state.db.pool())
        .await;
        traces_matched.push(fb_trace);

        // 6. Save identity file
        let _ = crate::core::identity::storage::save_identity_file(&*state.db, &uuid_clean).await;

        Ok(Json(MatchFromPhotoResponse {
            success: true,
            identity_uuid: uuid_clean,
            file_uuid,
            matches: 1,
            traces_matched,
            message: format!(
                "Best trace: trace_id={}, similarity={:.4}",
                fb_trace, fb_sim
            ),
        }))
    } else {
        Ok(Json(MatchFromPhotoResponse {
            success: true,
            identity_uuid: uuid_clean,
            file_uuid,
            matches: 0,
            traces_matched,
            message: "No matching face found in video".to_string(),
        }))
    }
}

#[derive(Debug, Deserialize)]
struct MatchFromTraceRequest {
    file_uuid: String,
    trace_id: i32,
    identity_uuid: String,
}

async fn match_from_trace(
    State(state): State<AppState>,
    Json(req): Json<MatchFromTraceRequest>,
) -> Result<Json<MatchFromPhotoResponse>, (StatusCode, Json<serde_json::Value>)> {
    let uuid_clean = req.identity_uuid.replace('-', "");

    // 1. Get 3 best face embeddings from this trace at different angles
    //    Divide trace frame range into 3 segments, pick best face from each
    let fd_table = schema::table_name("face_detections");
    let all_faces: Vec<(Vec<f32>, i64)> = sqlx::query_as::<_, (Vec<f32>, i64)>(&format!(
        "SELECT embedding, frame_number FROM {} \
             WHERE file_uuid = $1 AND trace_id = $2 AND embedding IS NOT NULL \
             ORDER BY frame_number ASC",
        fd_table
    ))
    .bind(&req.file_uuid)
    .bind(req.trace_id)
    .fetch_all(state.db.pool())
    .await
    .map_err(|e| {
        (
            StatusCode::INTERNAL_SERVER_ERROR,
            Json(serde_json::json!({"message": format!("DB error: {}", e)})),
        )
    })?;

    if all_faces.is_empty() {
        return Err((
            StatusCode::NOT_FOUND,
            Json(serde_json::json!({
                "success": false, "message": "No embedding found for this trace"
            })),
        ));
    }

    // Pick 3 samples: divide frame range into 3 segments, use face with largest area per segment
    let total = all_faces.len();
    let segments = [
        (0, total / 3),
        (total / 3, total * 2 / 3),
        (total * 2 / 3, total),
    ];

    let mut query_embeddings: Vec<Vec<f32>> = Vec::new();

    // Get width*height info if available (not all pipelines store it)
    let face_sizes: Vec<(i64, i32)> = sqlx::query_as::<_, (i64, i32)>(&format!(
        "SELECT frame_number, COALESCE(width, 0) * COALESCE(height, 0) AS area \
             FROM {} WHERE file_uuid = $1 AND trace_id = $2 AND embedding IS NOT NULL \
             ORDER BY frame_number ASC",
        fd_table
    ))
    .bind(&req.file_uuid)
    .bind(req.trace_id)
    .fetch_all(state.db.pool())
    .await
    .unwrap_or_default();

    let face_sizes_map: std::collections::HashMap<i64, i32> = face_sizes.into_iter().collect();

    for (start, end) in segments {
        let seg_start = start.min(total - 1);
        let seg_end = end.min(total);
        if seg_start >= seg_end {
            continue;
        }
        let seg_slice = &all_faces[seg_start..seg_end];
        // Pick the face with largest area within this segment
        let best_idx = seg_slice
            .iter()
            .enumerate()
            .max_by_key(|(_, f)| face_sizes_map.get(&f.1).copied().unwrap_or(0))
            .map(|(i, _)| i)
            .unwrap_or(0);
        query_embeddings.push(seg_slice[best_idx].0.clone());
    }

    if query_embeddings.is_empty() {
        query_embeddings.push(all_faces[total / 2].0.clone());
    }

    // 2. Three angles each find their best match; union all results
    let mut validated: Vec<(i32, i32, f64)> = Vec::new();
    let mut seen_trace_ids = std::collections::HashSet::new();

    for qemb in &query_embeddings {
        let top = sqlx::query_as::<_, (i32, i32, f64)>(&format!(
            r#"SELECT id, trace_id,
                       1 - (embedding::vector <=> $1::vector) as similarity
                   FROM {}
                   WHERE file_uuid = $2
                     AND trace_id != $3
                     AND embedding IS NOT NULL
                   ORDER BY embedding::vector <=> $1::vector
                   LIMIT 1"#,
            fd_table
        ))
        .bind(qemb)
        .bind(&req.file_uuid)
        .bind(req.trace_id)
        .fetch_optional(state.db.pool())
        .await
        .map_err(|e| {
            (
                StatusCode::INTERNAL_SERVER_ERROR,
                Json(serde_json::json!({"message": format!("Search failed: {}", e)})),
            )
        })?;

        if let Some((cface_id, c_trace_id, c_sim)) = top {
            if seen_trace_ids.insert(c_trace_id) {
                validated.push((cface_id, c_trace_id, c_sim));
            }
        }
    }

    // 3. Look up identity internal ID
    let id_table = schema::table_name("identities");
    let identity_id_row: Option<(i32,)> = sqlx::query_as(&format!(
        "SELECT id FROM {} WHERE REPLACE(uuid::text, '-', '') = $1",
        id_table
    ))
    .bind(&uuid_clean)
    .fetch_optional(state.db.pool())
    .await
    .map_err(|e| {
        (
            StatusCode::INTERNAL_SERVER_ERROR,
            Json(serde_json::json!({"message": format!("DB error: {}", e)})),
        )
    })?;

    let identity_id = match identity_id_row {
        Some((id,)) => id,
        None => {
            return Err((
                StatusCode::NOT_FOUND,
                Json(serde_json::json!({
                    "success": false, "message": "Identity not found"
                })),
            ))
        }
    };

    // 4. Update matched face_detections
    let mut traces_matched: Vec<i32> = Vec::new();
    for (id, trace_id, _similarity) in &validated {
        if let Err(e) = sqlx::query(&format!(
            "UPDATE {} SET identity_id = $1 WHERE id = $2",
            fd_table
        ))
        .bind(identity_id)
        .bind(id)
        .execute(state.db.pool())
        .await
        {
            tracing::warn!(
                "[match-from-trace] Failed to update face_detection {}: {}",
                id,
                e
            );
        } else {
            if !traces_matched.contains(trace_id) {
                traces_matched.push(*trace_id);
            }
        }
    }

    // 5. Also bind the source trace itself
    let _ = sqlx::query(&format!(
        "UPDATE {} SET identity_id = $1 WHERE file_uuid = $2 AND trace_id = $3",
        fd_table
    ))
    .bind(identity_id)
    .bind(&req.file_uuid)
    .bind(req.trace_id)
    .execute(state.db.pool())
    .await;

    if !traces_matched.contains(&req.trace_id) {
        traces_matched.push(req.trace_id);
    }

    // 6. Save identity file
    let _ = crate::core::identity::storage::save_identity_file(&*state.db, &uuid_clean).await;

    let match_count = validated.len() + 1;
    let trace_count = traces_matched.len();
    Ok(Json(MatchFromPhotoResponse {
        success: true,
        identity_uuid: uuid_clean,
        file_uuid: req.file_uuid,
        matches: match_count,
        traces_matched,
        message: format!(
            "Matched {} faces ({} unique traces)",
            match_count, trace_count
        ),
    }))
}

fn extract_persons_from_face_data(face_data: &serde_json::Value) -> Vec<PersonData> {
    let mut persons = Vec::new();
    if let Some(frames) = face_data.get("frames").and_then(|f| f.as_array()) {
        let mut person_frames_map: std::collections::HashMap<String, Vec<i32>> =
            std::collections::HashMap::new();
        for frame in frames {
            if let Some(frame_num) = frame.get("frame").and_then(|f| f.as_i64()) {
                if let Some(person_id) = frame.get("person_id").and_then(|p| p.as_str()) {
                    person_frames_map
                        .entry(person_id.to_string())
                        .or_insert_with(Vec::new)
                        .push(frame_num as i32);
                }
            }
        }
        for (person_id, frames) in person_frames_map {
            persons.push(PersonData {
                person_id,
                frames,
                avg_embedding: None,
            });
        }
    }
    persons
}

fn extract_speakers_from_asrx_data(asrx_data: &Option<serde_json::Value>) -> Vec<SpeakerData> {
    let mut speakers = Vec::new();
    if let Some(data) = asrx_data {
        if let Some(segments) = data.get("segments").and_then(|s| s.as_array()) {
            for seg in segments {
                if let (Some(start), Some(end), Some(speaker_id)) = (
                    seg.get("start_time").and_then(|v| v.as_f64()),
                    seg.get("end_time").and_then(|v| v.as_f64()),
                    seg.get("speaker_id").and_then(|v| v.as_str()),
                ) {
                    speakers.push(SpeakerData {
                        speaker_id: speaker_id.to_string(),
                        segments: vec![(start, end)],
                    });
                }
            }
        }
    }
    speakers
}

fn analyze_person_speaker_overlap(
    persons: &[PersonData],
    speakers: &[SpeakerData],
) -> Vec<IdentityResult> {
    let mut identities: Vec<IdentityResult> = Vec::new();
    let mut visited_persons: std::collections::HashSet<String> = std::collections::HashSet::new();

    for person in persons {
        if visited_persons.contains(&person.person_id) {
            continue;
        }

        let mut matched_persons = vec![person.person_id.clone()];
        let mut matched_speakers: Vec<String> = Vec::new();
        visited_persons.insert(person.person_id.clone());

        for other_person in persons {
            if visited_persons.contains(&other_person.person_id) {
                continue;
            }

            // Check if persons co-occur in time (frame proximity)
            let overlap = person
                .frames
                .iter()
                .any(|f| other_person.frames.contains(f));
            if overlap {
                matched_persons.push(other_person.person_id.clone());
                visited_persons.insert(other_person.person_id.clone());
            }
        }

        // Check speaker overlap
        let person_time_range = (
            person.frames.iter().min().copied().unwrap_or(0) as f64,
            person.frames.iter().max().copied().unwrap_or(0) as f64,
        );
        for speaker in speakers {
            let has_overlap = speaker
                .segments
                .iter()
                .any(|(start, end)| *start <= person_time_range.1 && *end >= person_time_range.0);
            if has_overlap {
                if !matched_speakers.contains(&speaker.speaker_id) {
                    matched_speakers.push(speaker.speaker_id.clone());
                }
            }
        }

        let frame_count = person.frames.len() as f64;
        let speaker_overlap = if matched_speakers.is_empty() {
            0.0
        } else {
            matched_speakers.len() as f64 / speakers.len().max(1) as f64
        };

        identities.push(IdentityResult {
            identity_id: person.person_id.clone(),
            person_ids: matched_persons.clone(),
            speaker_ids: matched_speakers.clone(),
            confidence: 0.5 + (speaker_overlap * 0.3),
            evidence: IdentityEvidence {
                face_similarity: None,
                speaker_overlap,
                time_overlap: 1.0,
                frame_ratio: frame_count / 100.0,
            },
            reasoning: format!(
                "Matched {} persons with {} speakers, overlap={:.2}",
                matched_persons.len(),
                speaker_overlap,
                speaker_overlap
            ),
        });
    }

    identities
}

#[derive(Debug)]
struct PersonData {
    person_id: String,
    frames: Vec<i32>,
    avg_embedding: Option<Vec<f64>>,
}

#[derive(Debug)]
struct SpeakerData {
    speaker_id: String,
    segments: Vec<(f64, f64)>,
}

fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
    if a.len() != b.len() || a.is_empty() {
        return 0.0;
    }
    let dot: f32 = a.iter().zip(b).map(|(x, y)| x * y).sum();
    let na: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
    let nb: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
    if na == 0.0 || nb == 0.0 {
        0.0
    } else {
        dot / (na * nb)
    }
}

/// 迭代多角度 face embedding 比對 + 傳播
/// Round 1: 用 TMDb seed face_embedding 比對 face_detections (threshold 0.50)
/// Round 2+: 用已匹配 trace 的所有 face 作為 seed，傳播到未匹配 trace
async fn match_faces_iterative(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::Result<usize> {
    // Step 1: 載入 TMDb identities (source='tmdb' 且有 face_embedding)
    let identities_table = schema::table_name("identities");
    let tmdb_rows = sqlx::query_as::<_, (i32, String, Vec<f32>)>(
        &format!("SELECT id, name, face_embedding::real[] FROM {} WHERE source='tmdb' AND face_embedding IS NOT NULL", identities_table)
    )
    .fetch_all(pool).await?;

    if tmdb_rows.is_empty() {
        tracing::warn!("[FaceMatch] No TMDb identities with face embeddings");
        return Ok(0);
    }
    tracing::info!(
        "[FaceMatch] Loaded {} TMDb seed identities",
        tmdb_rows.len()
    );

    // Step 2: 載入所有 face_detections（含 frame_number），按 trace_id 分組
    let fd_table = schema::table_name("face_detections");
    let fd_rows = sqlx::query_as::<_, (i32, i32, Vec<f32>)>(&format!(
        "SELECT trace_id, frame_number, embedding FROM {} \
         WHERE file_uuid=$1 AND trace_id IS NOT NULL AND embedding IS NOT NULL \
         ORDER BY trace_id, frame_number",
        fd_table
    ))
    .bind(file_uuid)
    .fetch_all(pool)
    .await?;

    if fd_rows.is_empty() {
        tracing::warn!("[FaceMatch] No face detections with embeddings");
        return Ok(0);
    }

    // 分組：trace_id → (frame_number, embedding)
    use std::collections::HashMap;
    let mut trace_faces_raw: HashMap<i32, Vec<(i32, Vec<f32>)>> = HashMap::new();
    for (tid, frame, emb) in &fd_rows {
        trace_faces_raw
            .entry(*tid)
            .or_insert_with(Vec::new)
            .push((*frame, emb.clone()));
    }

    // 從每個 trace 選取不同角度的 3 個 face embedding
    // 策略：按 frame_number 排序，取前中後各 1 個
    let mut trace_samples: HashMap<i32, Vec<Vec<f32>>> = HashMap::new();
    for (tid, mut faces) in trace_faces_raw {
        faces.sort_by_key(|(frame, _)| *frame);
        let n = faces.len();
        let indices = if n <= 3 {
            (0..n).collect()
        } else {
            let mid = n / 2;
            vec![0, mid, n - 1]
        };
        let samples: Vec<Vec<f32>> = indices.iter().map(|&i| faces[i].1.clone()).collect();
        trace_samples.insert(tid, samples);
    }

    let total_traces = trace_samples.len();
    let sample_count: usize = trace_samples.values().map(|v| v.len()).sum();
    tracing::info!(
        "[FaceMatch] Loaded {} traces, sampled {} embeddings (3-angle)",
        total_traces,
        sample_count
    );

    // Step 3: 建立 TMDb 查找表
    let tmdb_seeds: Vec<(i32, String, Vec<f32>)> = tmdb_rows;

    // Step 4: 迭代匹配
    const TH: f32 = 0.50;
    let mut matched: HashMap<i32, String> = HashMap::new(); // trace_id → identity_name

    // Round 1: 用 3-angle samples 比對 TMDb
    // 每個 trace 選 3 個不同角度 face，取最高 similarity
    for (&tid, samples) in &trace_samples {
        let mut best_name = String::new();
        let mut best_sim = 0.0f32;
        for (_, ref name, ref tmdb_emb) in &tmdb_seeds {
            for face_emb in samples {
                let s = cosine_similarity(face_emb, tmdb_emb);
                if s > best_sim {
                    best_sim = s;
                    best_name = name.clone();
                }
            }
        }
        if best_sim >= TH {
            matched.insert(tid, best_name);
        }
    }
    tracing::info!(
        "[FaceMatch] Round 1: {} matched ({}%) — writing to DB",
        matched.len(),
        matched.len() * 100 / total_traces
    );

    // Step 5: 寫入 DB — Round 1 結果先存
    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();
        for (&tid, name) in &matched {
            if let Some(samples) = trace_samples.get(&tid) {
                seed_pool
                    .entry(name.clone())
                    .or_default()
                    .extend(samples.iter());
            }
        }

        let mut new_matches: Vec<(i32, String)> = Vec::new();
        for (&tid, samples) in &trace_samples {
            if matched.contains_key(&tid) {
                continue;
            }
            let mut best_name = String::new();
            let mut best_sim = 0.0f32;
            if samples.is_empty() {
                continue;
            }
            // 用 3-angle samples 分別比對 seed，取最高 similarity
            for (name, seed_faces) in &seed_pool {
                for face_emb in samples {
                    for seed in seed_faces {
                        let s = cosine_similarity(face_emb, seed);
                        if s > best_sim {
                            best_sim = s;
                            best_name = name.clone();
                        }
                    }
                }
            }
            if best_sim >= TH {
                new_matches.push((tid, best_name));
            }
        }
        for (tid, name) in new_matches {
            matched.insert(tid, name);
        }
        let new = matched.len() - prev;
        tracing::info!(
            "[FaceMatch] Round {}: +{} matched (total {}, {}%)",
            round_n,
            new,
            matched.len(),
            matched.len() * 100 / total_traces
        );
        if new < 5 {
            break;
        }
    }

    // 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(&format!(
        "UPDATE {} SET stranger_id = trace_id \
             WHERE file_uuid = $1 AND trace_id IS NOT NULL AND identity_id IS NULL \
               AND (stranger_id IS NULL OR stranger_id != trace_id)",
        fd_table
    ))
    .bind(file_uuid)
    .execute(pool)
    .await?;
    let stranger_count = stranger_update.rows_affected();

    // Step 7: Save identity files for all affected identities
    let affected = sqlx::query_scalar::<_, uuid::Uuid>(&format!(
        "SELECT DISTINCT i.uuid FROM {} i \
         JOIN {} fd ON fd.identity_id = i.id \
         WHERE fd.file_uuid=$1 AND fd.identity_id IS NOT NULL",
        identities_table, fd_table
    ))
    .bind(file_uuid)
    .fetch_all(pool)
    .await
    .unwrap_or_default();
    for uuid in &affected {
        let us = uuid.to_string().replace('-', "");
        if let Err(e) = crate::core::identity::storage::save_identity_file_by_pool(pool, &us).await
        {
            tracing::warn!("[FaceMatch] Failed to save identity file {}: {}", us, e);
        }
    }
    tracing::info!(
        "[FaceMatch] Done: {}/{} traces matched ({}%), {} strangers, {} identity files",
        matched.len(),
        total_traces,
        matched.len() * 100 / total_traces,
        stranger_count,
        affected.len()
    );
    Ok(updated)
}

/// Bind ASRX speakers to face traces based on temporal overlap.
/// Reads face_detections (trace_id, identity_id, frame_number) and ASRX
/// segments (speaker_id, start_time, end_time), computes overlap,
/// and stores bindings in identity_bindings table.
pub async fn bind_speakers(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::Result<usize> {
    // Load face traces with identity_id and frame numbers
    let fd_table = schema::table_name("face_detections");
    let traces = sqlx::query_as::<_, (i32, Vec<i32>)>(&format!(
        "SELECT trace_id, array_agg(frame_number ORDER BY frame_number) \
         FROM {} WHERE file_uuid=$1 AND trace_id IS NOT NULL AND identity_id IS NOT NULL \
         GROUP BY trace_id",
        fd_table
    ))
    .bind(file_uuid)
    .fetch_all(pool)
    .await?;

    if traces.is_empty() {
        tracing::info!("[SpeakerBind] No face traces with identities");
        return Ok(0);
    }

    // Load ASRX speakers from the output JSON
    let output_dir = std::env::var("MOMENTRY_OUTPUT_DIR")
        .unwrap_or_else(|_| "/Users/accusys/momentry/output".to_string());
    let asrx_path = std::path::Path::new(&output_dir).join(format!("{}.asrx.json", file_uuid));

    let asrx_data: serde_json::Value = match std::fs::read_to_string(&asrx_path) {
        Ok(s) => serde_json::from_str(&s).unwrap_or_default(),
        Err(_) => {
            tracing::info!("[SpeakerBind] No ASRX file found");
            return Ok(0);
        }
    };

    // Extract speaker segments: speaker_id → [(start_time, end_time)]
    use std::collections::HashMap;
    let mut speakers: HashMap<String, Vec<(f64, f64)>> = HashMap::new();
    if let Some(segments) = asrx_data.get("segments").and_then(|s| s.as_array()) {
        for seg in segments {
            let sid = seg
                .get("speaker_id")
                .and_then(|s| s.as_str())
                .or_else(|| seg.get("speaker").and_then(|s| s.as_str()));
            if let Some(sid) = sid {
                let start = seg
                    .get("start_time")
                    .or_else(|| seg.get("start"))
                    .and_then(|v| v.as_f64())
                    .unwrap_or(0.0);
                let end = seg
                    .get("end_time")
                    .or_else(|| seg.get("end"))
                    .and_then(|v| v.as_f64())
                    .unwrap_or(0.0);
                speakers
                    .entry(sid.to_string())
                    .or_default()
                    .push((start, end));
            }
        }
    }

    if speakers.is_empty() {
        tracing::info!("[SpeakerBind] No speakers found in ASRX data");
        return Ok(0);
    }

    // Get fps for frame-to-time conversion
    let fps: f64 = 25.0; // default, could also read from DB

    // For each trace, compute overlap with each speaker
    let mut bindings = 0usize;
    for (trace_id, frames) in &traces {
        if frames.is_empty() {
            continue;
        }

        // Get identity_id for this trace
        let fd_table = schema::table_name("face_detections");
        let identity_id: Option<i32> = sqlx::query_scalar(
            &format!("SELECT identity_id FROM {} WHERE file_uuid=$1 AND trace_id=$2 AND identity_id IS NOT NULL LIMIT 1", fd_table)
        )
        .bind(file_uuid).bind(trace_id)
        .fetch_optional(pool).await?.flatten();

        if identity_id.is_none() {
            continue;
        }
        let identity_id = identity_id.unwrap();

        // Compute overlap with each speaker
        let mut best_speaker = String::new();
        let mut best_overlap = 0usize;

        for (speaker_id, segments) in &speakers {
            let mut overlap = 0usize;
            for &fn_num in frames {
                let frame_time = fn_num as f64 / fps;
                for (start, end) in segments {
                    if frame_time >= *start && frame_time <= *end {
                        overlap += 1;
                        break;
                    }
                }
            }
            if overlap > best_overlap {
                best_overlap = overlap;
                best_speaker = speaker_id.clone();
            }
        }

        // Only bind if meaningful overlap
        let overlap_ratio = best_overlap as f64 / frames.len() as f64;
        if overlap_ratio > 0.3 && !best_speaker.is_empty() {
            let metadata = serde_json::json!({
                "trace_id": trace_id,
                "overlap_frames": best_overlap,
                "total_frames": frames.len(),
                "overlap_ratio": overlap_ratio,
            });

            let ib_table = schema::table_name("identity_bindings");
            let _ = sqlx::query(
                &format!("INSERT INTO {} (identity_id, identity_type, identity_value, confidence, metadata) \
                 VALUES ($1, 'speaker', $2, $3, $4::jsonb) \
                 ON CONFLICT (identity_id, identity_type, identity_value) DO UPDATE SET confidence = EXCLUDED.confidence, metadata = EXCLUDED.metadata", ib_table)
            )
            .bind(identity_id)
            .bind(&best_speaker)
            .bind(overlap_ratio)
            .bind(&metadata)
            .execute(pool).await;

            bindings += 1;
        }
    }

    tracing::info!(
        "[SpeakerBind] Created {}/{} speaker bindings",
        bindings,
        traces.len()
    );
    Ok(bindings)
}

/// Pipeline-triggered entry point: runs the full identity agent for a file.
/// Reads face_clustered.json + asrx.json, extracts persons/speakers, creates identities,
/// runs iterative face matching, and binds speakers.
pub async fn run_identity_agent(db: &PostgresDb, file_uuid: &str) -> anyhow::Result<()> {
    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() {
        face_clustered_path
    } else {
        PathBuf::from(&output_dir).join(format!("{}.face_clustered.json", file_uuid))
    };

    if face_clustered_path.exists() {
        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()?)
        } else {
            None
        };

        let persons = extract_persons_from_face_data(&face_data);
        let speakers = extract_speakers_from_asrx_data(&asrx_data);
        let identities = analyze_person_speaker_overlap(&persons, &speakers);

        for (idx, id_result) in identities.iter().enumerate() {
            let identity_name = format!("stranger_{}", idx);
            let metadata = serde_json::json!({
                "source": "identity_agent",
                "trace_ids": id_result.person_ids,
                "speaker_ids": id_result.speaker_ids,
                "confidence": id_result.confidence,
                "evidence": {
                    "speaker_overlap": id_result.evidence.speaker_overlap,
                    "frame_ratio": id_result.evidence.frame_ratio,
                },
                "reasoning": id_result.reasoning,
            });
            let _ = sqlx::query(
                &format!("INSERT INTO {} (name, identity_type, source, metadata, status) VALUES ($1, 'people', 'auto', $2::jsonb, 'pending') ON CONFLICT DO NOTHING", schema::table_name("identities"))
            )
            .bind(&identity_name)
            .bind(&metadata)
            .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 bound = bind_speakers(pool, file_uuid).await.unwrap_or(0);

    tracing::info!(
        "[IdentityAgent] Done for {}: {} face matches, {} speaker bindings",
        file_uuid,
        matched,
        bound
    );
    Ok(())
}