+ * jRt^RIHt^RIt^RIt^RIt^RIHt]PPRR4t Rs Rt Rt RR ltRR R lltR R ltRRRlltRRltRRltR#)u insights_module.py — Leitura de métricas Meta + Vista Social do Postgres VPS. Schema: six_hype.* no command_center_postgres (127.0.0.1:55433). Workflows n8n alimentam o banco diariamente (meta_insights_pull, vs_snapshots, vs_inbox_sync). Env: INSIGHTS_DB_DSN → ex: postgresql://command_center_app:senha@127.0.0.1:55433/command_center (em prod: leia do .env de produção; em dev: leia do túnel SSH) ) annotationsN)OptionalINSIGHTS_DB_DSNc\'gR#^RIp^RIp\f^RIHpTP^^\4s\P4# \d \ R\ P R7R#i;i)u?Conexão lazy. Usa psycopg2 se disponível; senão, log + None.NuX⚠ psycopg2 não instalado · insights desabilitados. Rode: pip install psycopg2-binary)file)pool) DSNpsycopg2psycopg2.extrasModuleNotFoundErrorprintsysstderr_poolrSimpleConnectionPoolgetconn)r rs '/opt/apps/studio-api/insights_module.py _get_connrsg 3  }!))!Q4 ==?  horoyoyzsA&A;:A;c`\'d"V'd\PV4R#R#R#N)rputconn)conns&r_releaser)s u ducV^8dQhRR/#)returnbool)formats"r __annotate__r!.sDrc \\4#r)rr rrr is_enabledr#.s 9rc$V^8dQhRRRRRR/#r persona_idstrlimitintr list[dict]r)r s"rr!r!2s!#c:rc \4pV'g.#VP4;_uu_4pVPRW34VP4pVPUu.uF qU^,NK ppVUu.uFp\ Wg4NK upuuRRR4\ V4#uupiuupi +'giM;i\ T4R# \ T4i;i)uBTop posts de uma persona nos últimos 30 dias, ordenado por score.a= SELECT id, format, posted_at, caption, permalink, thumbnail_url, impressions, reach, engagement, saves, shares, score FROM six_hype.top_posts_30d WHERE persona_id = %s ORDER BY score DESC LIMIT %s Nrcursorexecutefetchall description _row_to_dictrr&r(rcurrowsdcolsrs&& r top_postsr82s ;D   [[]]c KK# <<>D"%//2/QaDD/D23784aL)48] 38]] @C2B- B#1B-7B( B- C# B-- B= 8C Cc V^8dQhRRRR/#)rr&r'rr*r)r s"rr!r!Ks#*rc \4pV'g.#VP4;_uu_4pVPRV34VP4pVPUu.uF qD^,NK ppVUu.uFp\ WV4NK upuuRRR4\ V4#uupiuupi +'giM;i\ T4R# \ T4i;i)uOPerformance comparativa por formato (reel vs carrossel vs static) últimos 30d.a) SELECT format, n_posts, avg_impressions, avg_reach, avg_engagement, avg_density, last_snapshot FROM six_hype.performance_by_format WHERE persona_id = %s ORDER BY avg_engagement DESC NULLS LAST Nr,)r&rr3r4r5r6r7s& r by_formatr<Ks ;D   [[]]c KK  <<>D"%//2/QaDD/D23784aL)48] 38]] r9c$V^8dQhRRRRRR/#r%r)r s"rr!r!cs!Crc \4pV'g.#VP4;_uu_4pVPRW34VP4pVPUu.uF qU^,NK ppVUu.uFp\ Wg4NK upuuRRR4\ V4#uupiuupi +'giM;i\ T4R# \ T4i;i)uNPalavras-chave/tópicos mais perguntados pelos pacientes nos últimos 30 dias.a SELECT keyword, topic_category, mentions, n_sources, last_mention, channels FROM six_hype.topics_demanded_30d WHERE persona_id = %s ORDER BY mentions DESC LIMIT %s Nr,r2s&& rtopics_demandedr?cs ;D   [[]]c KK# <<>D"%//2/QaDD/D23784aL)48] 38]] r9c V^8dQhRRRR/#)rr&r'rdictr)r s"rr!r!{src\4pV'g/#VP4;_uu_4pVPRV34VP4Uu.uF3pRV^,RV^,RV^,RV^,;'g^/NK5 ppRRR4RX/\ V4#uupi +'giL$;i \ T4i;i)uJHeatmap dia-da-semana × hora-do-dia de melhor performance (últimos 30d).a SELECT EXTRACT(DOW FROM mp.posted_at)::INT AS dow, EXTRACT(HOUR FROM mp.posted_at)::INT AS hour, COUNT(*) AS n, AVG(mi.engagement)::INT AS avg_engagement FROM six_hype.media_posts mp LEFT JOIN LATERAL ( SELECT engagement FROM six_hype.media_insights WHERE post_id = mp.id ORDER BY snapshot_date DESC LIMIT 1 ) mi ON TRUE WHERE mp.persona_id = %s AND mp.posted_at >= NOW() - INTERVAL '30 days' GROUP BY dow, hour dowhournavg_engagementNcells)rr-r.r/r)r&rr3r7rGs& rheatmaprH{s ;D   [[]]c KK   &'A!fadC17G1QRS' %, %]0 s:B>&B+0B&B& B+ B>&B++ B; 6B>> C c$V^8dQhRRRRRR/#)rr6z list[str]rowtuplerrAr)r s"rr!r!s!  y u  rc/p\W4FTwr4\VR4'dVP4W#&K+\V\4'd\ V4W#&KPWBV&KV V#) isoformat)ziphasattrrM isinstancelist)r6rJoutkvs&& rr1r1sW CD 1k " "[[]CF 4 !WCFF  Jr) )__doc__ __future__rjsonosrtypingrenvirongetr rrrr#r8r<r?rHr1rrrr]s^ # jjnn&+ $ 200B r