from airtable_client import AirtableClient
from google_ads_client import GoogleAdsClient
from analyzer import analyze
from agent import decide
from optimizer import apply_decision
import config
from datetime import datetime


ICONOS = {
    "PAUSAR": "⛔",
    "SPRINT": "🚀",
    "ACELERAR": "📈",
    "FRENAR": "📉",
    "EN_RITMO": "✅",
}


def run():
    print(f"\n{'='*55}")
    print(f"  LEADS OPTIMIZER — {datetime.now().strftime('%d/%m/%Y %H:%M')}")
    print(f"  Modo: {'DRY RUN (sin cambios)' if config.DRY_RUN else '⚡ PRODUCCIÓN'}")
    print(f"{'='*55}\n")

    at = AirtableClient()
    gads = GoogleAdsClient()

    campaigns = at.get_active_campaigns()
    print(f"→ {len(campaigns)} campañas activas encontradas\n")

    resumen = []

    for campaign in campaigns:
        cid = campaign["google_campaign_id"]
        print(f"{'─'*55}")
        print(f"📚 {campaign['curso']}")
        print(f"   Campaign ID: {cid} | PPL: {campaign['ppl']}€ | Cap: {campaign['capping_mensual']} leads")

        # 1. Leads reales desde Airtable
        leads = at.get_leads_this_month(cid)

        # 2. Métricas de Google Ads
        metrics = gads.get_campaign_metrics(cid)
        if not metrics:
            print(f"   ⚠️  Sin métricas en Google Ads, omitiendo.\n")
            continue

        # 3. Análisis
        analysis = analyze(campaign, leads, metrics)
        icono = ICONOS.get(analysis["urgencia"], "❓")

        print(f"   Leads mes: {leads}/{campaign['capping_mensual']} "
              f"({analysis['ratio_leads']*100:.0f}% cap) | "
              f"Ratio mes: {analysis['ratio_mes']*100:.0f}%")
        print(f"   CPA actual: {analysis['cpa_actual']}€ | "
              f"CPA máximo: {analysis['cpa_maximo']}€ | "
              f"Margen: {analysis['margen']*100:.0f}%")
        print(f"   Urgencia: {icono} {analysis['urgencia']} | "
              f"Rentable: {'✅' if analysis['rentable'] else '❌'}")

        if analysis["alerta_tracking"]:
            print(f"   🚨 ALERTA TRACKING: {analysis['discrepancia_tracking']} leads de diferencia "
                  f"entre Airtable ({leads}) y Google Ads ({int(analysis['conversiones_google'])})")

        # 4. Decisión del agente
        decision = decide(analysis)
        print(f"   Decisión: {decision['accion']} "
              f"(confianza: {decision['confianza']*100:.0f}%)")
        print(f"   Justificación: {decision['justificacion']}")

        if decision.get("alerta"):
            print(f"   🚨 {decision['alerta']}")

        # 5. Aplicar
        apply_decision(campaign, decision, metrics, gads)

        resumen.append({
            "curso": campaign["curso"],
            "urgencia": analysis["urgencia"],
            "accion": decision["accion"],
            "leads": f"{leads}/{campaign['capping_mensual']}",
            "cpa": analysis["cpa_actual"],
            "margen": f"{analysis['margen']*100:.0f}%",
        })

        print()

    # Resumen final
    print(f"{'='*55}")
    print("RESUMEN FINAL")
    print(f"{'='*55}")
    for r in resumen:
        print(f"  {r['curso'][:35]:<35} | {r['urgencia']:<12} | {r['accion']}")
    print()


if __name__ == "__main__":
    run()