José Manuel Gómez
0016809ece
- analyze_creative_deep was truncating mid-JSON ~50-75% of the time (max_tokens=700 too low once the analysis text runs long), silently falling back to score=0/"Error parseando respuesta." Confirmed via stop_reason=max_tokens on real API calls. Raised to 1400, and bumped analyze_creative/compare_adset_creatives (600→900) as the same risk applies there. Verified 6/6 clean parses after the fix (was failing ~3/4 before). - Dashboard tabs "Campañas", "Familias", and the per-day breakdown in "Por día" still showed a single Meta-only Leads/CPL number; added the Leads/CPL según Airtable (leadform+landing) columns alongside, matching the Slack report so the dashboard never shows a different figure than what's already trusted from the daily report. - Validated both end-to-end with real data: dashboard via Streamlit's AppTest headless runner (0 exceptions across all 5 tabs), creative analyzer via a live run against one campaign (real scores, fatigue detection, comparison, Baserow save, Slack send all confirmed).
719 lines
32 KiB
Python
719 lines
32 KiB
Python
"""Interactive Meta Optimizer Formación dashboard — Streamlit."""
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import streamlit as st
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from datetime import date, timedelta
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import pandas as pd
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import sys
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import os
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sys.path.insert(0, os.path.dirname(__file__))
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from meta_ads_client import MetaAdsClient
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from airtable_client import AirtableClient, extract_cursoid
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from baserow_client import BaserowClient
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import config
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st.set_page_config(
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page_title=f"Meta Optimizer — {config.META_CAMPAIGN_PREFIX}",
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layout="wide",
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initial_sidebar_state="collapsed",
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)
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import streamlit.components.v1 as components
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components.html("""
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<script>
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// Ping cada 30s para mantener el WebSocket activo y evitar el error 401 por inactividad
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setInterval(function() {
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fetch('/_stcore/health').catch(function() {});
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}, 30000);
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</script>
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""", height=0)
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_STRATEGY_LABELS = {
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"LOWEST_COST_WITHOUT_CAP": "Menor coste",
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"LOWEST_COST_WITH_BID_CAP": "Cap. puja",
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"COST_CAP": "Cap. coste",
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"MINIMUM_ROAS": "ROAS mín.",
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}
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_ACTION_COLORS = {
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"INCREASE_BUDGET": "🟢",
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"REDUCE_BUDGET": "🟠",
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"PAUSE": "🔴",
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"MAINTAIN": "⚪",
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}
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_today = date.today()
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_yesterday = _today - timedelta(days=1)
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_default_from = _yesterday - timedelta(days=6)
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def _eur(val: float) -> str:
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return f"{val:.2f}€"
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def _margin(val: float) -> str:
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return f"+{val:.0f}€" if val >= 0 else f"{val:.0f}€"
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def _status(leads: int, spend: float) -> str:
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if leads > 0:
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return "✅"
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if spend > 0:
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return "❌"
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return "—"
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def _familia_of(name: str, familia_lookup: dict) -> str:
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return familia_lookup.get(extract_cursoid(name) or "", "Sin familia")
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def _date_row(key: str, n_extra_cols: int = 0) -> tuple:
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"""Renders [Desde | Hasta | ...extra... | 🔄] columns. Returns (date_from, date_to, *extra_cols)."""
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cols = st.columns([2, 2] + [2] * n_extra_cols + [1])
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d_from = cols[0].date_input("Desde", value=_default_from, max_value=_yesterday, key=f"{key}_from")
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d_to = cols[1].date_input("Hasta", value=_yesterday, min_value=d_from, max_value=_yesterday, key=f"{key}_to")
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if cols[-1].button("🔄", key=f"{key}_ref", use_container_width=True, help="Limpiar caché"):
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st.cache_data.clear()
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st.rerun()
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extra = tuple(cols[2:-1])
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return (d_from, d_to) + extra
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# ── Cached data loaders ───────────────────────────────────────────────────────
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@st.cache_data(ttl=300, show_spinner="Cargando PPL/familia de Airtable...")
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def _load_lookups():
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ppl_lookup, _, familia_lookup = AirtableClient().build_campaign_lookups()
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return ppl_lookup, familia_lookup
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@st.cache_data(ttl=300, show_spinner="Cargando datos de Meta API...")
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def _load_data(date_from: str, date_to: str):
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meta = MetaAdsClient()
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daily_rows = meta.get_daily_campaign_rows(date_from, date_to)
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campaign_metrics = meta.get_campaign_metrics(date_from, date_to)
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return daily_rows, campaign_metrics
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@st.cache_data(ttl=300, show_spinner="Cargando leads de Airtable...")
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def _load_at_leads_by_cursoid(date_from: str, date_to: str) -> dict:
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"""{cursoid: nº leads en Leads Lake (leadform+landing) en el rango}."""
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leads = AirtableClient().get_meta_leads_bulk(date_from, date_to)
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counts: dict = {}
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for l in leads:
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counts[l["cursoid"]] = counts.get(l["cursoid"], 0) + 1
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return counts
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@st.cache_data(ttl=300, show_spinner="Cargando leads de Airtable por día...")
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def _load_at_leads_by_day_cursoid(date_from: str, date_to: str) -> dict:
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"""{(date, cursoid): nº leads en Leads Lake (leadform+landing)}."""
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leads = AirtableClient().get_meta_leads_bulk(date_from, date_to)
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counts: dict = {}
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for l in leads:
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key = (l["date"], l["cursoid"])
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counts[key] = counts.get(key, 0) + 1
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return counts
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@st.cache_data(ttl=300, show_spinner="Cargando métricas diarias (Baserow)...")
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def _load_daily_metrics(date_from: str, date_to: str):
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"""Totales diarios persistidos por run.py (Leads Meta vs Leads Airtable) —
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no depende de volver a pedirle el histórico a Meta."""
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rows = BaserowClient().get_daily_metrics(date_from, date_to)
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return [
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{
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"date": r["date"],
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"spend": float(r.get("spend") or 0),
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"leads_meta": int(r.get("leads_meta") or 0),
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"leads_at": int(r.get("leads_at") or 0),
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"ing_meta": float(r.get("ing_meta") or 0),
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"ing_at": float(r.get("ing_at") or 0),
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"margin": float(r.get("margin") or 0),
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"margin_pct": float(r.get("margin_pct") or 0),
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}
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for r in rows
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]
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@st.cache_data(ttl=300, show_spinner="Cargando detalle de campaña...")
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def _load_detail(campaign_id: str, date_from: str, date_to: str):
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meta = MetaAdsClient()
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adsets = meta.get_adset_metrics(campaign_id, date_from, date_to)
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ads = meta.get_ad_metrics(campaign_id, date_from, date_to)
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bid = meta.get_campaign_bid_config(campaign_id)
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bids = meta.get_adset_bid_configs(campaign_id)
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for adset in adsets:
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b = bids.get(adset["id"], {})
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adset["cost_cap_eur"] = b.get("cost_cap_eur")
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adset["bid_strategy"] = b.get("bid_strategy", "")
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return adsets, ads, bid
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@st.cache_data(ttl=3600, show_spinner=False)
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def _load_campaign_names() -> dict:
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"""Returns {campaign_id: campaign_name} for the last 30 days. Cached 1h."""
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meta = MetaAdsClient()
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end = _yesterday.strftime("%Y-%m-%d")
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start = (_yesterday - timedelta(days=29)).strftime("%Y-%m-%d")
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try:
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metrics = meta.get_campaign_metrics(start, end)
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return {cid: m["name"] for cid, m in metrics.items()}
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except Exception:
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return {}
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@st.cache_data(ttl=120, show_spinner="Cargando fechas disponibles...")
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def _load_snapshot_dates():
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return BaserowClient().get_snapshot_dates()
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@st.cache_data(ttl=120, show_spinner="Cargando análisis del día...")
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def _load_snapshots(run_date: str):
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import json
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rows = BaserowClient().get_snapshots_for_date(run_date)
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result = []
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for r in rows:
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try:
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adsets = json.loads(r.get("adsets_json") or "[]")
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except Exception:
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adsets = []
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try:
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ads = json.loads(r.get("ads_json") or "[]")
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except Exception:
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ads = []
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result.append({
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"campaign_name": r.get("campaign_name", ""),
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"familia": r.get("familia", ""),
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"spend": float(r.get("spend") or 0),
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"leads": int(r.get("leads") or 0),
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"cpl": float(r.get("cpl") or 0),
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"margin": float(r.get("margin") or 0),
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"action_type": r.get("action_type", "MAINTAIN"),
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"justification": r.get("justification", ""),
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"adsets": adsets,
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"ads": ads,
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})
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return sorted(result, key=lambda x: -x["spend"])
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@st.cache_data(ttl=300, show_spinner="Cargando análisis de creatividades...")
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def _load_creatives():
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return BaserowClient().get_all_creative_analyses()
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# ── Header ────────────────────────────────────────────────────────────────────
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st.title(f"Meta Optimizer — {config.META_CAMPAIGN_PREFIX}")
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ppl_lookup, familia_lookup = _load_lookups()
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# ── Tabs ──────────────────────────────────────────────────────────────────────
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tab1, tab2, tab3, tab4, tab5 = st.tabs(
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["📅 Por día", "📊 Campañas", "🏷️ Familias", "🗂️ Histórico", "🎨 Creatividades"]
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)
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# ── Tab 1: Por día ────────────────────────────────────────────────────────────
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with tab1:
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d_from_1, d_to_1 = _date_row("t1")
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if d_from_1 > d_to_1:
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st.error("La fecha inicio debe ser anterior a la fecha fin.")
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else:
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try:
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daily_totals = _load_daily_metrics(d_from_1.strftime("%Y-%m-%d"), d_to_1.strftime("%Y-%m-%d"))
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except Exception as e:
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st.error(f"Error cargando daily_metrics de Baserow: {e}")
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daily_totals = []
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try:
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daily_rows, _cm1 = _load_data(d_from_1.strftime("%Y-%m-%d"), d_to_1.strftime("%Y-%m-%d"))
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except Exception as e:
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st.error(f"Error cargando datos de Meta API: {e}")
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daily_rows = []
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total_spend = sum(d["spend"] for d in daily_totals)
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total_leads_m = sum(d["leads_meta"] for d in daily_totals)
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total_leads_at = sum(d["leads_at"] for d in daily_totals)
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total_ing_m = sum(d["ing_meta"] for d in daily_totals)
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total_margin = total_ing_m - total_spend
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total_pct = round(total_margin / total_ing_m * 100, 1) if total_ing_m > 0 else 0.0
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k1, k2, k3, k4, k5 = st.columns(5)
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k1.metric("Gasto total", _eur(total_spend))
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k2.metric("Leads Meta", f"{total_leads_m:,}")
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k3.metric("Leads Airtable", f"{total_leads_at:,}")
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k4.metric("Margen (Meta)", _margin(total_margin))
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k5.metric("% Margen", f"{total_pct:+.1f}%")
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st.divider()
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if not daily_totals:
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st.info("Sin datos persistidos para el período seleccionado — ejecuta run.py o amplía el rango.")
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else:
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df_daily = pd.DataFrame([
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{
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"Día": d["date"][8:10] + "/" + d["date"][5:7],
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"L. AT": d["leads_at"],
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"L. Meta": d["leads_meta"],
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"Gasto": _eur(d["spend"]),
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"€ AT": _eur(d["ing_at"]),
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"€ Meta": _eur(d["ing_meta"]),
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"Margen": _margin(d["margin"]),
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"% Margen": f"{d['margin_pct']:+.1f}%",
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"Est": _status(d["leads_meta"], d["spend"]),
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}
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for d in daily_totals
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])
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st.dataframe(df_daily, use_container_width=True, hide_index=True)
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st.caption("L. AT = leads Airtable (leadform + landing) · L. Meta = conversión propia de Meta · "
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"€ AT / € Meta = leads × PPL de cada fuente · el margen oficial usa el tracking de Meta.")
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st.subheader("Desglose por campaña")
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day_opts = [d["date"] for d in reversed(daily_totals)]
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selected_day = st.selectbox(
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"Selecciona un día",
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day_opts,
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format_func=lambda s: s[8:10] + "/" + s[5:7] + "/" + s[:4],
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key="t1_day",
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)
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if selected_day:
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try:
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at_leads_day = _load_at_leads_by_day_cursoid(day_opts[-1], day_opts[0])
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except Exception:
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at_leads_day = {}
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day_camp: dict = {}
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for row in daily_rows:
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if row["date"] != selected_day:
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continue
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k = row["campaign_name"]
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if k not in day_camp:
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cursoid = extract_cursoid(k) or ""
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ppl = ppl_lookup.get(cursoid, 0)
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day_camp[k] = {"name": k, "familia": _familia_of(k, familia_lookup),
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"spend": 0.0, "leads": 0, "ppl": ppl, "cursoid": cursoid}
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day_camp[k]["spend"] += row["spend"]
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day_camp[k]["leads"] += row["leads"]
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camp_rows = []
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for c in sorted(day_camp.values(), key=lambda x: -x["spend"]):
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cpl = round(c["spend"] / c["leads"], 2) if c["leads"] > 0 else 0.0
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leads_at = at_leads_day.get((selected_day, c["cursoid"]), 0)
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cpl_at = round(c["spend"] / leads_at, 2) if leads_at > 0 else 0.0
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margin = round(c["leads"] * c["ppl"] - c["spend"], 2)
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camp_rows.append({
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"Campaña": c["name"],
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"Familia": c["familia"],
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"Gasto": _eur(c["spend"]),
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"Leads Meta": c["leads"],
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"Leads AT": leads_at,
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"CPL Meta": _eur(cpl) if c["leads"] > 0 else "—",
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"CPL AT": _eur(cpl_at) if leads_at > 0 else "—",
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"PPL": _eur(c["ppl"]) if c["ppl"] else "—",
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"Margen": _margin(margin),
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})
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if camp_rows:
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st.dataframe(pd.DataFrame(camp_rows), use_container_width=True, hide_index=True)
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else:
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st.info("Sin campañas activas ese día.")
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# ── Tab 2: Campañas ───────────────────────────────────────────────────────────
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with tab2:
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d_from_2, d_to_2, col_fam_2 = _date_row("t2", n_extra_cols=1)
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if d_from_2 > d_to_2:
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st.error("La fecha inicio debe ser anterior a la fecha fin.")
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else:
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try:
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_dr2, campaign_metrics_2 = _load_data(d_from_2.strftime("%Y-%m-%d"), d_to_2.strftime("%Y-%m-%d"))
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except Exception as e:
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st.error(f"Error cargando datos de Meta API: {e}")
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campaign_metrics_2 = {}
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try:
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at_leads_2 = _load_at_leads_by_cursoid(d_from_2.strftime("%Y-%m-%d"), d_to_2.strftime("%Y-%m-%d"))
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except Exception as e:
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st.error(f"Error cargando leads de Airtable: {e}")
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at_leads_2 = {}
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fam_opts_2 = ["Todas"] + sorted({_familia_of(m["name"], familia_lookup) for m in campaign_metrics_2.values()})
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sel_fam_2 = col_fam_2.selectbox("Familia", fam_opts_2, key="t2_fam")
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if sel_fam_2 != "Todas":
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campaign_metrics_2 = {
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cid: m for cid, m in campaign_metrics_2.items()
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if _familia_of(m["name"], familia_lookup) == sel_fam_2
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}
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if not campaign_metrics_2:
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st.info("Sin campañas para el período seleccionado.")
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else:
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camp_rows = []
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for cid, m in sorted(campaign_metrics_2.items(), key=lambda x: -x[1]["spend"]):
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cursoid = extract_cursoid(m["name"]) or ""
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ppl = ppl_lookup.get(cursoid, 0)
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leads_at = at_leads_2.get(cursoid, 0)
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cpl_at = round(m["spend"] / leads_at, 2) if leads_at > 0 else 0.0
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margin = round(m["leads"] * ppl - m["spend"], 2)
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camp_rows.append({
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"Campaña": m["name"],
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"Familia": _familia_of(m["name"], familia_lookup),
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"Gasto": _eur(m["spend"]),
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"Leads Meta": m["leads"],
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"Leads AT": leads_at,
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"CPL Meta": _eur(m["cpl"]) if m["leads"] > 0 else "—",
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"CPL AT": _eur(cpl_at) if leads_at > 0 else "—",
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"PPL": _eur(ppl) if ppl else "—",
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"Margen": _margin(margin),
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"CTR": f"{m['ctr']:.1f}%",
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"_cid": cid,
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})
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df_camps = pd.DataFrame([{k: v for k, v in r.items() if k != "_cid"} for r in camp_rows])
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st.dataframe(df_camps, use_container_width=True, hide_index=True)
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st.caption("Leads AT = leads en Leads Lake (leadform + landing) del curso completo — si el curso "
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"tiene más de una campaña activa, el mismo total aparece en cada una.")
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st.subheader("Detalle de campaña")
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camp_id_map = {r["Campaña"]: r["_cid"] for r in camp_rows}
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selected_camp = st.selectbox("Selecciona una campaña", list(camp_id_map.keys()), key="t2_camp")
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if selected_camp:
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selected_cid = camp_id_map[selected_camp]
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adsets, ads, bid_cfg = _load_detail(
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selected_cid,
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d_from_2.strftime("%Y-%m-%d"),
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d_to_2.strftime("%Y-%m-%d"),
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)
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strategy = bid_cfg.get("bid_strategy", "")
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strat_label = _STRATEGY_LABELS.get(strategy, strategy or "—")
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budget = bid_cfg.get("daily_budget_eur")
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budget_str = f"{budget:.0f}€/día" if budget else "—"
|
||
st.caption(f"Estrategia: **{strat_label}** | Presupuesto: **{budget_str}**")
|
||
|
||
if adsets:
|
||
st.markdown("**Conjuntos de anuncios**")
|
||
df_adsets = pd.DataFrame([
|
||
{
|
||
"Nombre": a["name"],
|
||
"Gasto": _eur(a["spend"]),
|
||
"Leads": a["leads"],
|
||
"CPL": _eur(a["cpl"]) if a["leads"] > 0 else "—",
|
||
"CTR": f"{a['ctr']:.1f}%",
|
||
"Cap": _eur(a["cost_cap_eur"]) if a.get("cost_cap_eur") else "Auto",
|
||
}
|
||
for a in adsets
|
||
])
|
||
st.dataframe(df_adsets, use_container_width=True, hide_index=True)
|
||
else:
|
||
st.info("Sin conjuntos de anuncios con gasto en este período.")
|
||
|
||
if ads:
|
||
st.markdown("**Anuncios**")
|
||
df_ads = pd.DataFrame([
|
||
{
|
||
"Nombre": a["name"],
|
||
"Gasto": _eur(a["spend"]),
|
||
"Leads": a["leads"],
|
||
"CPL": _eur(a["cpl"]) if a["leads"] > 0 else "—",
|
||
"CTR": f"{a['ctr']:.1f}%",
|
||
"CPM": _eur(a["cpm"]),
|
||
}
|
||
for a in ads
|
||
])
|
||
st.dataframe(df_ads, use_container_width=True, hide_index=True)
|
||
else:
|
||
st.info("Sin anuncios con gasto en este período.")
|
||
|
||
|
||
# ── Tab 3: Familias ───────────────────────────────────────────────────────────
|
||
with tab3:
|
||
d_from_3, d_to_3 = _date_row("t3")
|
||
|
||
if d_from_3 > d_to_3:
|
||
st.error("La fecha inicio debe ser anterior a la fecha fin.")
|
||
else:
|
||
try:
|
||
_dr3, campaign_metrics_3 = _load_data(d_from_3.strftime("%Y-%m-%d"), d_to_3.strftime("%Y-%m-%d"))
|
||
except Exception as e:
|
||
st.error(f"Error cargando datos de Meta API: {e}")
|
||
campaign_metrics_3 = {}
|
||
try:
|
||
at_leads_3 = _load_at_leads_by_cursoid(d_from_3.strftime("%Y-%m-%d"), d_to_3.strftime("%Y-%m-%d"))
|
||
except Exception as e:
|
||
st.error(f"Error cargando leads de Airtable: {e}")
|
||
at_leads_3 = {}
|
||
|
||
# Agregar primero por curso (único), luego por familia — si un curso
|
||
# tiene 2 campañas activas no se duplican sus leads de Airtable.
|
||
curso_agg: dict = {}
|
||
for cid, m in campaign_metrics_3.items():
|
||
cursoid = extract_cursoid(m["name"]) or ""
|
||
ca = curso_agg.setdefault(cursoid, {
|
||
"spend": 0.0, "leads_meta": 0,
|
||
"familia": familia_lookup.get(cursoid, "Sin familia"),
|
||
"ppl": ppl_lookup.get(cursoid, 0),
|
||
})
|
||
ca["spend"] += m["spend"]
|
||
ca["leads_meta"] += m["leads"]
|
||
|
||
familias_3: dict = {}
|
||
for cursoid, ca in curso_agg.items():
|
||
leads_at = at_leads_3.get(cursoid, 0)
|
||
margin = ca["leads_meta"] * ca["ppl"] - ca["spend"]
|
||
f = familias_3.setdefault(ca["familia"], {"spend": 0.0, "leads_meta": 0, "leads_at": 0, "margin": 0.0})
|
||
f["spend"] += ca["spend"]
|
||
f["leads_meta"] += ca["leads_meta"]
|
||
f["leads_at"] += leads_at
|
||
f["margin"] += margin
|
||
|
||
if not familias_3:
|
||
st.info("Sin datos de familias.")
|
||
else:
|
||
fam_rows = []
|
||
for fam, data in sorted(familias_3.items(), key=lambda x: -x[1]["margin"]):
|
||
f_spend = data["spend"]
|
||
cpl_meta = round(f_spend / data["leads_meta"], 2) if data["leads_meta"] > 0 else 0.0
|
||
cpl_at = round(f_spend / data["leads_at"], 2) if data["leads_at"] > 0 else 0.0
|
||
fam_rows.append({
|
||
"Familia": fam,
|
||
"Gasto": _eur(f_spend),
|
||
"Leads Meta": data["leads_meta"],
|
||
"Leads AT": data["leads_at"],
|
||
"CPL Meta": _eur(cpl_meta) if data["leads_meta"] > 0 else "—",
|
||
"CPL AT": _eur(cpl_at) if data["leads_at"] > 0 else "—",
|
||
"Margen": _margin(data["margin"]),
|
||
})
|
||
st.dataframe(pd.DataFrame(fam_rows), use_container_width=True, hide_index=True)
|
||
|
||
|
||
# ── Tab 4: Histórico ──────────────────────────────────────────────────────────
|
||
with tab4:
|
||
dates = _load_snapshot_dates()
|
||
|
||
if not dates:
|
||
st.info("Sin análisis guardados aún. Los snapshots se generan al ejecutar run.py.")
|
||
else:
|
||
c1, c2 = st.columns([3, 1])
|
||
fmt_date = lambda s: s[8:10] + "/" + s[5:7] + "/" + s[:4]
|
||
selected_date = c1.selectbox("Fecha del análisis", dates, format_func=fmt_date, key="t4_date")
|
||
if c2.button("🔄 Recargar", key="t4_ref", use_container_width=True):
|
||
st.cache_data.clear()
|
||
st.rerun()
|
||
|
||
snapshots = _load_snapshots(selected_date)
|
||
if not snapshots:
|
||
st.info("Sin datos para esa fecha.")
|
||
else:
|
||
d_spend = sum(s["spend"] for s in snapshots)
|
||
d_leads = sum(s["leads"] for s in snapshots)
|
||
d_cpl = round(d_spend / d_leads, 2) if d_leads > 0 else 0.0
|
||
d_margin = sum(s["margin"] for s in snapshots)
|
||
h1, h2, h3, h4 = st.columns(4)
|
||
h1.metric("Gasto", _eur(d_spend))
|
||
h2.metric("Leads", f"{d_leads:,}")
|
||
h3.metric("CPL", _eur(d_cpl))
|
||
h4.metric("Margen", _margin(d_margin))
|
||
st.divider()
|
||
|
||
df_snap = pd.DataFrame([
|
||
{
|
||
"Acción": _ACTION_COLORS.get(s["action_type"], "⚪") + " " + s["action_type"],
|
||
"Campaña": s["campaign_name"],
|
||
"Familia": s["familia"],
|
||
"Gasto": _eur(s["spend"]),
|
||
"Leads": s["leads"],
|
||
"CPL": _eur(s["cpl"]) if s["leads"] > 0 else "—",
|
||
"Margen": _margin(s["margin"]),
|
||
}
|
||
for s in snapshots
|
||
])
|
||
event = st.dataframe(
|
||
df_snap,
|
||
use_container_width=True,
|
||
hide_index=True,
|
||
on_select="rerun",
|
||
selection_mode="single-row",
|
||
)
|
||
|
||
sel_rows = event.selection.rows
|
||
if sel_rows:
|
||
snap = snapshots[sel_rows[0]]
|
||
st.subheader(snap["campaign_name"])
|
||
st.caption(
|
||
f"Familia: **{snap['familia']}** | "
|
||
f"Decisión: **{snap['action_type']}** | "
|
||
f"Margen: **{_margin(snap['margin'])}**"
|
||
)
|
||
if snap["justification"]:
|
||
st.info(snap["justification"])
|
||
|
||
adsets = snap["adsets"]
|
||
if adsets:
|
||
st.markdown("**Conjuntos de anuncios** _(últimos 3 días)_")
|
||
for a in adsets:
|
||
label = (
|
||
f"{a['name']} — "
|
||
f"{_eur(a['spend'])} · {a['leads']} leads · "
|
||
f"CPL {_eur(a['cpl']) if a['leads'] > 0 else '—'} · "
|
||
f"CTR {a.get('ctr', 0):.1f}%"
|
||
)
|
||
with st.expander(label):
|
||
if a.get("cost_cap_eur"):
|
||
st.caption(f"Cap: {_eur(a['cost_cap_eur'])}")
|
||
if a.get("evaluacion"):
|
||
st.write(f"_{a['evaluacion']}_")
|
||
if a.get("recomendacion"):
|
||
st.write(f"→ {a['recomendacion']}")
|
||
|
||
ads = snap["ads"]
|
||
if ads:
|
||
st.markdown("**Anuncios** _(últimos 7 días)_")
|
||
for a in ads:
|
||
label = (
|
||
f"{a['name']} — "
|
||
f"{_eur(a['spend'])} · {a['leads']} leads · "
|
||
f"CPL {_eur(a['cpl']) if a['leads'] > 0 else '—'} · "
|
||
f"CTR {a.get('ctr', 0):.1f}% · "
|
||
f"CPM {_eur(a.get('cpm', 0))}"
|
||
)
|
||
with st.expander(label):
|
||
if a.get("evaluacion"):
|
||
st.write(f"_{a['evaluacion']}_")
|
||
if a.get("recomendacion"):
|
||
st.write(f"→ {a['recomendacion']}")
|
||
|
||
|
||
# ── Tab 5: Creatividades ──────────────────────────────────────────────────────
|
||
with tab5:
|
||
creatives_raw = _load_creatives()
|
||
|
||
if not creatives_raw:
|
||
st.info("No hay análisis de creatividades. Ejecuta `python analyze_creatives.py` para generar datos.")
|
||
else:
|
||
df_all = pd.DataFrame(creatives_raw)
|
||
df_all["score"] = pd.to_numeric(df_all.get("score", 0), errors="coerce").fillna(0)
|
||
df_all["created_at"] = df_all.get("created_at", pd.Series(dtype=str))
|
||
|
||
# Map campaign_id → name using a dedicated cached call (last 30d)
|
||
camp_id_to_name = _load_campaign_names()
|
||
df_all["campaign_name"] = df_all["campaign_id"].map(
|
||
lambda cid: camp_id_to_name.get(str(cid), str(cid))
|
||
)
|
||
df_all["familia"] = df_all["campaign_name"].map(lambda n: _familia_of(n, familia_lookup))
|
||
|
||
# ── Filters ───────────────────────────────────────────────────────────
|
||
f1, f2, f3, f4 = st.columns([2, 2, 2, 2])
|
||
|
||
dates_available = sorted(df_all["created_at"].dropna().unique(), reverse=True)
|
||
sel_date = f1.selectbox("Fecha análisis", dates_available, key="cr_date")
|
||
|
||
fams_available = sorted(df_all["familia"].dropna().unique().tolist())
|
||
sel_fam_cr = f2.selectbox("Familia", ["Todas"] + fams_available, key="cr_fam")
|
||
|
||
camp_names = sorted(df_all["campaign_name"].dropna().unique().tolist())
|
||
sel_camp = f3.selectbox("Campaña", ["Todas"] + camp_names, key="cr_camp")
|
||
|
||
score_min = f4.slider("Score mínimo", 0.0, 10.0, 0.0, step=0.5, key="cr_score")
|
||
|
||
# Apply filters
|
||
df = df_all.copy()
|
||
if sel_date:
|
||
df = df[df["created_at"] == sel_date]
|
||
if sel_fam_cr != "Todas":
|
||
df = df[df["familia"] == sel_fam_cr]
|
||
if sel_camp != "Todas":
|
||
df = df[df["campaign_name"] == sel_camp]
|
||
if score_min > 0:
|
||
df = df[df["score"] >= score_min]
|
||
|
||
# ── KPIs ──────────────────────────────────────────────────────────────
|
||
scored_df = df[df["score"] > 0]
|
||
avg_sc = round(scored_df["score"].mean(), 1) if not scored_df.empty else 0.0
|
||
fatigue_n = int(df["analysis"].str.contains("FATIGA", na=False).sum()) if "analysis" in df.columns else 0
|
||
last_run = df_all["created_at"].max() if not df_all.empty else "—"
|
||
|
||
k1, k2, k3, k4 = st.columns(4)
|
||
k1.metric("Anuncios", len(df))
|
||
k2.metric("Score medio", f"{avg_sc}/10")
|
||
k3.metric("Con fatiga", fatigue_n)
|
||
k4.metric("Última ejecución", last_run)
|
||
|
||
if fatigue_n:
|
||
fatigued = df[df["analysis"].str.contains("FATIGA", na=False)]
|
||
with st.expander(f"⚠️ {fatigue_n} anuncios con fatiga creativa", expanded=True):
|
||
for _, row in fatigued.iterrows():
|
||
st.warning(f"**{row.get('ad_name', '—')}** — Score {row.get('score', 0):.1f}/10")
|
||
|
||
st.divider()
|
||
|
||
# ── Table + Detail panel ──────────────────────────────────────────────
|
||
rename_map = {
|
||
"campaign_name": "Campaña",
|
||
"familia": "Familia",
|
||
"ad_name": "Anuncio",
|
||
"score": "Score",
|
||
"created_at": "Fecha",
|
||
}
|
||
display_cols = [c for c in rename_map if c in df.columns]
|
||
df_display = df[display_cols].rename(columns=rename_map).reset_index(drop=True)
|
||
|
||
col_table, col_detail = st.columns([3, 2])
|
||
|
||
with col_table:
|
||
st.caption("Haz clic en una fila para ver el detalle →")
|
||
event = st.dataframe(
|
||
df_display,
|
||
use_container_width=True,
|
||
selection_mode="single-row",
|
||
on_select="rerun",
|
||
column_config={
|
||
"Score": st.column_config.ProgressColumn(
|
||
"Score", min_value=0, max_value=10, format="%.1f"
|
||
),
|
||
},
|
||
hide_index=True,
|
||
)
|
||
selected_rows = event.selection.rows if hasattr(event, "selection") else []
|
||
|
||
with col_detail:
|
||
if selected_rows:
|
||
row = df.iloc[selected_rows[0]]
|
||
score = float(row.get("score", 0))
|
||
sc_emoji = "🟢" if score >= 8 else "🟡" if score >= 6 else "🟠" if score >= 4 else "🔴"
|
||
|
||
st.markdown(f"### {row.get('ad_name', '—')}")
|
||
st.markdown(f"{sc_emoji} **Score: {score:.1f} / 10**")
|
||
|
||
img_url = str(row.get("image_url", ""))
|
||
if img_url.startswith("http"):
|
||
try:
|
||
st.image(img_url, use_container_width=True)
|
||
except Exception:
|
||
st.caption("_Imagen no disponible_")
|
||
|
||
analysis = str(row.get("analysis", ""))
|
||
if analysis:
|
||
st.markdown("**Análisis**")
|
||
st.write(analysis)
|
||
|
||
rec = str(row.get("recommendations", ""))
|
||
if rec:
|
||
st.markdown("**Recomendaciones**")
|
||
st.info(rec)
|
||
|
||
ad_id = str(row.get("ad_id", ""))
|
||
if ad_id:
|
||
history = BaserowClient().get_creative_history_by_ad(ad_id)
|
||
if len(history) >= 2:
|
||
st.markdown("**Evolución del score**")
|
||
hist_df = pd.DataFrame(history)[["created_at", "score"]].dropna()
|
||
hist_df["score"] = pd.to_numeric(hist_df["score"], errors="coerce")
|
||
hist_df = hist_df[hist_df["score"] > 0].set_index("created_at")
|
||
st.line_chart(hist_df)
|
||
else:
|
||
st.info("← Selecciona un anuncio en la tabla para ver el detalle.")
|