#!/bin/bash
#
# ML Training Wrapper - Esecuzione Automatica via Systemd
# Carica credenziali da .env in modo sicuro
#

set -e

IDS_ROOT="/opt/ids"
ENV_FILE="$IDS_ROOT/.env"
PYTHON_ML_DIR="$IDS_ROOT/python_ml"
VENV_PYTHON="$PYTHON_ML_DIR/venv/bin/python"
LOG_DIR="/var/log/ids"

# Crea directory log se non esiste
mkdir -p "$LOG_DIR"

# File log dedicato
LOG_FILE="$LOG_DIR/ml-training.log"

# Funzione logging
log() {
    echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1" | tee -a "$LOG_FILE"
}

log "========================================="
log "ML Training - Avvio automatico"
log "========================================="

# Verifica .env
if [ ! -f "$ENV_FILE" ]; then
    log "ERROR: File .env non trovato: $ENV_FILE"
    exit 1
fi

# Carica variabili ambiente
log "Caricamento credenziali database..."
set -a
source "$ENV_FILE"
set +a

# Verifica credenziali
if [ -z "$PGPASSWORD" ]; then
    log "ERROR: PGPASSWORD non trovata in .env"
    exit 1
fi

DB_HOST="${PGHOST:-localhost}"
DB_PORT="${PGPORT:-5432}"
DB_NAME="${PGDATABASE:-ids}"
DB_USER="${PGUSER:-postgres}"

log "Database: $DB_USER@$DB_HOST:$DB_PORT/$DB_NAME"

# Verifica venv
if [ ! -f "$VENV_PYTHON" ]; then
    log "ERROR: Venv Python non trovato: $VENV_PYTHON"
    exit 1
fi

# Parametri training
DAYS="${ML_TRAINING_DAYS:-7}"  # Default 7 giorni, configurabile via env var

log "Training ultimi $DAYS giorni di traffico..."

# Esegui training
cd "$PYTHON_ML_DIR"
"$VENV_PYTHON" train_hybrid.py --train --source database \
  --db-host "$DB_HOST" \
  --db-port "$DB_PORT" \
  --db-name "$DB_NAME" \
  --db-user "$DB_USER" \
  --db-password "$PGPASSWORD" \
  --days "$DAYS" 2>&1 | tee -a "$LOG_FILE"

# Check exit code
if [ ${PIPESTATUS[0]} -eq 0 ]; then
    log "========================================="
    log "✅ Training completato con successo!"
    log "========================================="
    log "Modelli salvati in: $PYTHON_ML_DIR/models/"
    log ""
    log "Il ML backend caricherà automaticamente i nuovi modelli al prossimo riavvio."
    log "Per applicare immediatamente: sudo systemctl restart ids-ml-backend"
    exit 0
else
    log "========================================="
    log "❌ ERRORE durante il training"
    log "========================================="
    log "Controlla log completo: $LOG_FILE"
    exit 1
fi