app_cron.py

from flask import Blueprint, render_template, jsonify, request
from psycopg2.extras import DictCursor
from app_helper import get_db, rq_queue_high, save_task_status, TASK_STATUS_PENDING
import uuid, time, logging
from config import (
    TOP_N_MOODS,
    CLUSTER_ALGORITHM, NUM_CLUSTERS_MIN, NUM_CLUSTERS_MAX,
    DBSCAN_EPS_MIN, DBSCAN_EPS_MAX, DBSCAN_MIN_SAMPLES_MIN, DBSCAN_MIN_SAMPLES_MAX,
    GMM_N_COMPONENTS_MIN, GMM_N_COMPONENTS_MAX,
    SPECTRAL_N_CLUSTERS_MIN, SPECTRAL_N_CLUSTERS_MAX,
    PCA_COMPONENTS_MIN, PCA_COMPONENTS_MAX, CLUSTERING_RUNS, MAX_SONGS_PER_CLUSTER,
    TOP_N_PLAYLISTS, MIN_SONGS_PER_GENRE_FOR_STRATIFICATION, STRATIFIED_SAMPLING_TARGET_PERCENTILE,
    SCORE_WEIGHT_DIVERSITY, SCORE_WEIGHT_SILHOUETTE, SCORE_WEIGHT_DAVIES_BOULDIN, SCORE_WEIGHT_CALINSKI_HARABASZ,
    SCORE_WEIGHT_PURITY, SCORE_WEIGHT_OTHER_FEATURE_DIVERSITY, SCORE_WEIGHT_OTHER_FEATURE_PURITY,
    AI_MODEL_PROVIDER, OLLAMA_SERVER_URL, OLLAMA_MODEL_NAME, GEMINI_API_KEY, GEMINI_MODEL_NAME,
    MISTRAL_API_KEY, MISTRAL_MODEL_NAME, ENABLE_CLUSTERING_EMBEDDINGS
)

cron_bp = Blueprint('cron_bp', __name__)


@cron_bp.route('/cron')
def cron_page():
    return render_template('cron.html')


@cron_bp.route('/api/cron', methods=['GET'])
def get_cron_entries():
    db = get_db()
    cur = db.cursor(cursor_factory=DictCursor)
    cur.execute("SELECT id, name, task_type, cron_expr, enabled, last_run, created_at FROM cron ORDER BY id")
    rows = cur.fetchall()
    cur.close()
    entries = []
    for r in rows:
        entries.append({
            'id': r['id'], 'name': r['name'], 'task_type': r['task_type'], 'cron_expr': r['cron_expr'],
            'enabled': bool(r['enabled']), 'last_run': r['last_run'], 'created_at': str(r['created_at'])
        })
    return jsonify(entries), 200


@cron_bp.route('/api/cron', methods=['POST'])
def save_cron_entry():
    data = request.json or {}
    # Expected fields: id (optional), name, task_type, cron_expr, enabled
    db = get_db()
    cur = db.cursor()
    if data.get('id'):
        cur.execute("UPDATE cron SET name=%s, task_type=%s, cron_expr=%s, enabled=%s WHERE id=%s", (
            data.get('name'), data.get('task_type'), data.get('cron_expr'), bool(data.get('enabled')), data.get('id')
        ))
    else:
        cur.execute("INSERT INTO cron (name, task_type, cron_expr, enabled) VALUES (%s,%s,%s,%s)", (
            data.get('name'), data.get('task_type'), data.get('cron_expr'), bool(data.get('enabled'))
        ))
    db.commit()
    cur.close()
    return jsonify({'message': 'saved'}), 200


def _field_matches(field_expr, value):
    # very small cron field matcher supporting '*', single number, list (comma), and ranges (a-b)
    if field_expr.strip() == '*':
        return True
    parts = field_expr.split(',')
    for p in parts:
        p = p.strip()
        if '-' in p:
            a, b = p.split('-', 1)
            try:
                if int(a) <= value <= int(b):
                    return True
            except ValueError:
                continue
        else:
            try:
                if int(p) == value:
                    return True
            except ValueError:
                continue
    return False


def cron_matches_now(expr, ts=None):
    # expr expected as 'min hour day month dow'
    t = time.localtime(ts) if ts else time.localtime()
    parts = expr.strip().split()
    if len(parts) < 5:
        return False
    minute, hour, dom, month, dow = parts[:5]
    if not _field_matches(minute, t.tm_min):
        return False
    if not _field_matches(hour, t.tm_hour):
        return False
    # day of week: in cron 0=Sun..6=Sat, Python tm_wday 0=Mon..6=Sun -> convert
    py_dow = (t.tm_wday + 1) % 7
    if not _field_matches(dow, py_dow):
        return False
    return True


def run_due_cron_jobs():
    """Read enabled cron rows and enqueue analysis/clustering when cron matches now and not recently run."""
    logger = logging.getLogger(__name__)
    db = get_db()
    cur = db.cursor(cursor_factory=DictCursor)
    cur.execute("SELECT id, name, task_type, cron_expr, enabled, last_run FROM cron WHERE enabled = true")
    rows = cur.fetchall()
    now_ts = time.time()
    for r in rows:
        try:
            last_run = r['last_run'] or 0
            # avoid duplicate runs within 55 seconds
            if now_ts - float(last_run) < 55:
                continue
            if cron_matches_now(r['cron_expr'], now_ts):
                task_type = r['task_type']
                job_id = str(uuid.uuid4())
                # mark queued in task_status
                save_task_status(job_id, f"main_{task_type}", TASK_STATUS_PENDING, details={"message": "Enqueued by cron."})
                if task_type == 'analysis':
                    # enqueue analysis to process everything (0) and use default TOP_N_MOODS from config
                    rq_queue_high.enqueue('tasks.analysis.run_analysis_task', args=(0, TOP_N_MOODS), job_id=job_id, description='Cron Analysis', job_timeout=-1)
                    logger.info(f"Cron: enqueued analysis job {job_id}")
                elif task_type == 'clustering':
                    # Build kwargs with sensible defaults (same defaults used by API endpoint)
                    clustering_kwargs = {
                        "clustering_method": CLUSTER_ALGORITHM,
                        "num_clusters_min": int(NUM_CLUSTERS_MIN),
                        "num_clusters_max": int(NUM_CLUSTERS_MAX),
                        "dbscan_eps_min": float(DBSCAN_EPS_MIN),
                        "dbscan_eps_max": float(DBSCAN_EPS_MAX),
                        "dbscan_min_samples_min": int(DBSCAN_MIN_SAMPLES_MIN),
                        "dbscan_min_samples_max": int(DBSCAN_MIN_SAMPLES_MAX),
                        "gmm_n_components_min": int(GMM_N_COMPONENTS_MIN),
                        "gmm_n_components_max": int(GMM_N_COMPONENTS_MAX),
                        "spectral_n_clusters_min": int(SPECTRAL_N_CLUSTERS_MIN),
                        "spectral_n_clusters_max": int(SPECTRAL_N_CLUSTERS_MAX),
                        "pca_components_min": int(PCA_COMPONENTS_MIN),
                        "pca_components_max": int(PCA_COMPONENTS_MAX),
                        "num_clustering_runs": int(CLUSTERING_RUNS),
                        "max_songs_per_cluster_val": int(MAX_SONGS_PER_CLUSTER),
                        "gmm_n_components_min": int(GMM_N_COMPONENTS_MIN),
                        "gmm_n_components_max": int(GMM_N_COMPONENTS_MAX),
                        "top_n_playlists_param": int(TOP_N_PLAYLISTS),
                        "min_songs_per_genre_for_stratification_param": int(MIN_SONGS_PER_GENRE_FOR_STRATIFICATION),
                        "stratified_sampling_target_percentile_param": int(STRATIFIED_SAMPLING_TARGET_PERCENTILE),
                        "score_weight_diversity_param": float(SCORE_WEIGHT_DIVERSITY),
                        "score_weight_silhouette_param": float(SCORE_WEIGHT_SILHOUETTE),
                        "score_weight_davies_bouldin_param": float(SCORE_WEIGHT_DAVIES_BOULDIN),
                        "score_weight_calinski_harabasz_param": float(SCORE_WEIGHT_CALINSKI_HARABASZ),
                        "score_weight_purity_param": float(SCORE_WEIGHT_PURITY),
                        "score_weight_other_feature_diversity_param": float(SCORE_WEIGHT_OTHER_FEATURE_DIVERSITY),
                        "score_weight_other_feature_purity_param": float(SCORE_WEIGHT_OTHER_FEATURE_PURITY),
                        "ai_model_provider_param": AI_MODEL_PROVIDER,
                        "ollama_server_url_param": OLLAMA_SERVER_URL,
                        "ollama_model_name_param": OLLAMA_MODEL_NAME,
                        "gemini_api_key_param": GEMINI_API_KEY,
                        "gemini_model_name_param": GEMINI_MODEL_NAME,
                        "mistral_api_key_param": MISTRAL_API_KEY,
                        "mistral_model_name_param": MISTRAL_MODEL_NAME,
                        "top_n_moods_for_clustering_param": int(TOP_N_MOODS),
                        "enable_clustering_embeddings_param": bool(ENABLE_CLUSTERING_EMBEDDINGS),
                    }
                    rq_queue_high.enqueue('tasks.clustering.run_clustering_task', kwargs=clustering_kwargs, job_id=job_id, description='Cron Clustering', job_timeout=-1)
                    logger.info(f"Cron: enqueued clustering job {job_id}")
                # update last_run
                cur2 = db.cursor()
                cur2.execute("UPDATE cron SET last_run=%s WHERE id=%s", (now_ts, r['id']))
                db.commit()
                cur2.close()
        except Exception as e:
            logger.exception(f"Error processing cron row {r}: {e}")
    cur.close()