99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
from airtable_client import AirtableClient
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from google_ads_client import GoogleAdsClient
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from analyzer import analyze
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from agent import decide
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from optimizer import apply_decision
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import config
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from datetime import datetime
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ICONOS = {
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"PAUSAR": "⛔",
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"SPRINT": "🚀",
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"ACELERAR": "📈",
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"FRENAR": "📉",
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"EN_RITMO": "✅",
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}
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def run():
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print(f"\n{'='*55}")
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print(f" LEADS OPTIMIZER — {datetime.now().strftime('%d/%m/%Y %H:%M')}")
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print(f" Modo: {'DRY RUN (sin cambios)' if config.DRY_RUN else '⚡ PRODUCCIÓN'}")
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print(f"{'='*55}\n")
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at = AirtableClient()
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gads = GoogleAdsClient()
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campaigns = at.get_active_campaigns()
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print(f"→ {len(campaigns)} campañas activas encontradas\n")
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resumen = []
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for campaign in campaigns:
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cid = campaign["google_campaign_id"]
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print(f"{'─'*55}")
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print(f"📚 {campaign['curso']}")
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print(f" Campaign ID: {cid} | PPL: {campaign['ppl']}€ | Cap: {campaign['capping_mensual']} leads")
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# 1. Leads reales desde Airtable
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leads = at.get_leads_this_month(cid)
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# 2. Métricas de Google Ads
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metrics = gads.get_campaign_metrics(cid)
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if not metrics:
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print(f" ⚠️ Sin métricas en Google Ads, omitiendo.\n")
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continue
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# 3. Análisis
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analysis = analyze(campaign, leads, metrics)
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icono = ICONOS.get(analysis["urgencia"], "❓")
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print(f" Leads mes: {leads}/{campaign['capping_mensual']} "
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f"({analysis['ratio_leads']*100:.0f}% cap) | "
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f"Ratio mes: {analysis['ratio_mes']*100:.0f}%")
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print(f" CPA actual: {analysis['cpa_actual']}€ | "
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f"CPA máximo: {analysis['cpa_maximo']}€ | "
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f"Margen: {analysis['margen']*100:.0f}%")
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print(f" Urgencia: {icono} {analysis['urgencia']} | "
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f"Rentable: {'✅' if analysis['rentable'] else '❌'}")
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if analysis["alerta_tracking"]:
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print(f" 🚨 ALERTA TRACKING: {analysis['discrepancia_tracking']} leads de diferencia "
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f"entre Airtable ({leads}) y Google Ads ({int(analysis['conversiones_google'])})")
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# 4. Decisión del agente
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decision = decide(analysis)
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print(f" Decisión: {decision['accion']} "
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f"(confianza: {decision['confianza']*100:.0f}%)")
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print(f" Justificación: {decision['justificacion']}")
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if decision.get("alerta"):
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print(f" 🚨 {decision['alerta']}")
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# 5. Aplicar
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apply_decision(campaign, decision, metrics, gads)
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resumen.append({
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"curso": campaign["curso"],
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"urgencia": analysis["urgencia"],
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"accion": decision["accion"],
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"leads": f"{leads}/{campaign['capping_mensual']}",
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"cpa": analysis["cpa_actual"],
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"margen": f"{analysis['margen']*100:.0f}%",
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})
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print()
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# Resumen final
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print(f"{'='*55}")
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print("RESUMEN FINAL")
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print(f"{'='*55}")
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for r in resumen:
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print(f" {r['curso'][:35]:<35} | {r['urgencia']:<12} | {r['accion']}")
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print()
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if __name__ == "__main__":
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run()
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