José Manuel Gómez
4848253c49
- airtable_client: restrict leadform cursoid path (path 5) to PMX-only campaigns, preventing triple-attribution to search+pmx+leadform companion - slack_reporter: derive margen from ingreso-coste instead of stale MetricasDiarias field; split monthly rankings from fields to separate section blocks; add _trunc() helper to clamp all text blocks under Slack's 3000-char limit - agent: increase portfolio_daily_analysis max_tokens 500→800 - backfill: extend to June 11 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
98 lines
3.6 KiB
Python
98 lines
3.6 KiB
Python
"""
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Recalcula leads_lake en MetricasDiarias para días 8-10 de junio 2026
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añadiendo la atribución de leadforms via cursoid + UserAgent.
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También actualiza ConvLeadsLakeMes, Leads Lake y ConvLeadsLakeMesFinal.
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"""
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import json
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import re
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from datetime import datetime
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from airtable_client import AirtableClient
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DAYS = ["2026-06-08", "2026-06-09", "2026-06-10", "2026-06-11"]
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def _course_num(name: str) -> str | None:
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m = re.search(r'fco_(?:search|pmx)_(\d+)', name, re.IGNORECASE)
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return m.group(1) if m else None
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def run():
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at = AirtableClient()
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campaigns = at.get_active_gacampaignmes()
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print(f"→ {len(campaigns)} campañas activas este mes\n")
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# Mapping cursoid → PMX campaign_id para leadforms diarios
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cursoid_to_campaign: dict[str, str] = {}
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for c in campaigns:
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num = _course_num(c["curso"])
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if num and "pmx" in c["curso"].lower() and "_leadform" not in c["curso"].lower():
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cursoid_to_campaign[num] = c["google_campaign_id"]
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print(f"→ Mapping cursoid→campaign: {len(cursoid_to_campaign)} entradas")
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# Obtener leads por campaña para cada día (una llamada bulk por día)
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daily_counts: dict[str, dict[str, int]] = {}
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for date_str in DAYS:
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daily_counts[date_str] = at.get_leads_by_campaign_on_date(date_str, cursoid_to_campaign)
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total = sum(daily_counts[date_str].values())
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print(f" {date_str}: {total} leads totales encontrados")
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print()
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# Actualizar MetricasDiarias para cada campaña
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metricas_updates = []
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for campaign in campaigns:
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cid = campaign["google_campaign_id"]
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try:
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md = json.loads(campaign["metricas_diarias"])
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except (json.JSONDecodeError, TypeError):
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md = {}
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changed = False
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for date_str in DAYS:
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day_key = date_str[8:10]
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if day_key not in md:
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continue
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new_count = daily_counts[date_str].get(cid, 0)
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old_count = md[day_key].get("leads_lake", 0)
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if old_count != new_count:
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print(f" {campaign['curso'][:45]} día {day_key}: leads_lake {old_count} → {new_count}")
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md[day_key]["leads_lake"] = new_count
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# Recalcular ingreso_lxp para el día (leads_lake × PPL)
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md[day_key]["ingreso"] = round(new_count * campaign["ppl"], 2)
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changed = True
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if changed:
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metricas_updates.append({
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"airtable_id": campaign["airtable_id"],
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"metricas_json": json.dumps(md, ensure_ascii=False),
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})
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if metricas_updates:
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print(f"\n→ Actualizando MetricasDiarias ({len(metricas_updates)} registros)...")
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at.batch_update_metricas_diarias(metricas_updates)
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print(" ✓ MetricasDiarias actualizado.")
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else:
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print(" ✓ MetricasDiarias sin cambios.")
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# Recalcular totales mensuales con la nueva atribución
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print("\n→ Recalculando totales mensuales (leads_lake acumulado del mes)...")
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final_leads_data = []
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for campaign in campaigns:
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cid = campaign["google_campaign_id"]
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leads, lead_ids = at.get_leads_this_month_gads(cid, campaign["curso"])
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at.update_gacampaignmes_leads_lake(campaign["airtable_id"], lead_ids)
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final_leads_data.append({
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"airtable_id": campaign["airtable_id"],
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"conv_leads_lake_mes": leads,
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"conv_leads_lake_mes_grupo": leads,
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})
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if leads > 0:
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print(f" {campaign['curso'][:45]}: {leads} leads")
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at.batch_update_gacampaignmes_final_leads(final_leads_data)
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print(" ✓ ConvLeadsLakeMesFinal actualizado.")
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if __name__ == "__main__":
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run()
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