leads-optimizer/run.py

import sys
import io
import os
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", line_buffering=True)

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


class Tee:
    """Escribe simultáneamente en consola y en archivo de log."""
    def __init__(self, filepath):
        os.makedirs(os.path.dirname(filepath), exist_ok=True)
        self._file = open(filepath, "w", encoding="utf-8")
        self._stdout = sys.stdout

    def write(self, data):
        self._stdout.write(data)
        self._file.write(data)

    def flush(self):
        self._stdout.flush()
        if not self._file.closed:
            self._file.flush()

    def close(self):
        self._file.close()


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()

    # Sincronizar catálogo de campañas desde Google Ads → Airtable
    print("→ Sincronizando campañas desde Google Ads...")
    google_campaigns = gads.get_all_campaigns()
    monthly_metrics = gads.get_monthly_metrics_all()
    print("  Calculando PPL desde CENTROCURSO...")
    ppl_lookup = at.build_ppl_lookup()
    sync_result = at.sync_campaigns_from_google_ads(google_campaigns, monthly_metrics, ppl_lookup)
    if sync_result["created"]:
        print(f"  ✅ Campañas nuevas importadas ({len(sync_result['created'])}):")
        for c in sync_result["created"]:
            print(f"     + [{c['id']}] {c['name']}  →  {c['status']}")
    if sync_result["updated"]:
        print(f"  🔄 Campañas actualizadas ({len(sync_result['updated'])}):")
        for c in sync_result["updated"]:
            for field, val in c["changes"].items():
                print(f"     ~ [{c['id']}] {c['name']}  |  {field}: '{val['antes']}' → '{val['ahora']}'")
    if not sync_result["created"] and not sync_result["updated"]:
        print("  ✓ Sin cambios en el catálogo.")
    print()

    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 + vincular en campo Leads Lake
        leads, lead_ids = at.get_leads_this_month(campaign["cursoid_text"])
        at.update_campaign_leads_lake(campaign["airtable_id"], lead_ids)

        # 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("consejo"):
            print(f"   💡 Consejo: {decision['consejo']}")

        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}%",
            "consejo": decision.get("consejo", ""),
        })

        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']:<25} | {r['leads']} leads | {r['margen']} margen")
        if r["consejo"]:
            print(f"  {'':35}   {'💡':>14}  {r['consejo']}")
    print()


if __name__ == "__main__":
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    log_path = os.path.join("logs", f"{timestamp}.log")
    tee = Tee(log_path)
    sys.stdout = tee
    try:
        run()
    finally:
        tee.close()
        print(f"\nLog guardado en: {log_path}", file=tee._stdout)