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Merged
bielvigna merged 2 commits into from
Sep 17, 2026
Merged

Fix/rpa #34

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51d68c6
fix/alteracoes no rpa.py
amandaandrade9410 Sep 17, 2026
b08e6c3
fix/padronizacao do rpa.py
amandaandrade9410 Sep 17, 2026
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154 changes: 117 additions & 37 deletions python/rpa.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,7 @@ def carregar_empresa():
tamanho_map = {"PEQUENA": "PEQUENA", "MEDIA": "MEDIA", "GRANDE": "GRANDE"}

df_destino = pd.DataFrame({
"id": df["empresa_id"],
"id": df["id_empresa"],
"cnpj": df["cnpj"].apply(limpar_digitos).str.zfill(14),
"nome": df["nome"].str.strip(),
"tamanho_empresa": df["tamanho"].map(tamanho_map).fillna("MEDIA"),
Expand All @@ -54,7 +54,13 @@ def carregar_empresa():
"criado_em": pd.Timestamp.now()
})

df_destino.to_sql("empresa", engine_destino, schema="public", if_exists="append", index=False)
ids_existentes = pd.read_sql("SELECT id FROM public.empresa", engine_destino)["id"].tolist()

df_destino = df_destino[~df_destino["id"].isin(ids_existentes)]

if not df_destino.empty:
df_destino.to_sql("empresa", engine_destino, schema="public", if_exists="append", index=False)


# Tabelas usuario_sistema, colaborador, email_colaborador, telefone_colaborador

Expand All @@ -71,22 +77,28 @@ def carregar_usuarios_e_colaboradores():

# Tabela public.usuario_sistema
df_usuario = pd.DataFrame({
"id": df_colab["colaborador_id"],
"id_empresa": df_colab["empresa_id"],
"id": df_colab["id_colaborador"],
"id_empresa": df_colab["id_empresa"],
"nome": (df_colab["nome"].str.strip() + " " + df_colab["sobrenome"].str.strip()),
"email_login": df_colab["email_limpo"],
"firebase_uid": None,
"tipo_usuario": "COLABORADOR",
"status": "PENDENTE",
"criado_em": pd.Timestamp.now()
})
df_usuario.to_sql("usuario_sistema", engine_destino, schema="public", if_exists="append", index=False)

ids_existentes = pd.read_sql("SELECT id FROM public.usuario_sistema", engine_destino)["id"].tolist()

df_usuario = df_usuario[~df_usuario["id"].isin(ids_existentes)]

if not df_usuario.empty:
df_usuario.to_sql("usuario_sistema", engine_destino, schema="public", if_exists="append", index=False)

# Tabela public.colaborador
df_colaborador_detalhe = pd.DataFrame({
"id": df_colab["colaborador_id"],
"id_empresa": df_colab["empresa_id"],
"id_usuario": df_colab["colaborador_id"],
"id": df_colab["id_colaborador"],
"id_empresa": df_colab["id_empresa"],
"id_usuario": df_colab["id_colaborador"],
"cpf": df_colab["cpf_limpo"],
"nome": (df_colab["nome"].str.strip() + " " + df_colab["sobrenome"].str.strip()),
"cargo": df_colab["cargo"].str.strip(),
Expand All @@ -97,28 +109,66 @@ def carregar_usuarios_e_colaboradores():
"status": df_colab["status"].map({"ATIVA": "ATIVO", "INATIVA": "INATIVO"}).fillna("ATIVO"),
"criado_em": pd.Timestamp.now()
})
df_colaborador_detalhe.to_sql("colaborador", engine_destino, schema="public", if_exists="append", index=False)

ids_existentes = pd.read_sql("SELECT id FROM public.colaborador", engine_destino)["id"].tolist()

df_colaborador_detalhe = df_colaborador_detalhe[~df_colaborador_detalhe["id"].isin(ids_existentes)]

if not df_colaborador_detalhe.empty:
df_colaborador_detalhe.to_sql("colaborador", engine_destino, schema="public", if_exists="append", index=False)

# Tabela public.email_colaborador
df_email = pd.DataFrame({
"id_colaborador": df_colab["colaborador_id"],
"id_colaborador": df_colab["id_colaborador"],
"email": df_colab["email_limpo"],
"principal": True,
"criado_em": pd.Timestamp.now()
})
df_email.dropna(subset=["email"]).to_sql("email_colaborador", engine_destino, schema="public", if_exists="append", index=False)

# Tabela public.email_colaborador
df_email = pd.DataFrame({
"id_colaborador": df_colab["id_colaborador"],
"email": df_colab["email_limpo"],
"principal": True,
"criado_em": pd.Timestamp.now()
}).dropna(subset=["email"])

if not df_email.empty:
dados_existentes = pd.read_sql("SELECT id_colaborador, email FROM public.email_colaborador", engine_destino)
emails_existentes = dados_existentes["email"].tolist()
colabs_existentes = dados_existentes["id_colaborador"].tolist()

df_email_filtrado = df_email[
(~df_email["email"].isin(emails_existentes)) &
(~df_email["id_colaborador"].isin(colabs_existentes))
]

if not df_email_filtrado.empty:
df_email_filtrado.to_sql("email_colaborador", engine_destino, schema="public", if_exists="append", index=False)


# Tabela public.telefone_colaborador
df_tel = pd.DataFrame({
"id_colaborador": df_colab["colaborador_id"],
"id_colaborador": df_colab["id_colaborador"],
"numero_telefone": df_colab["telefone_limpo"],
"principal": True,
"criado_em": pd.Timestamp.now()
})


df_tel = df_tel[df_tel["numero_telefone"].str.len().between(10, 15)]
df_tel.to_sql("telefone_colaborador", engine_destino, schema="public", if_exists="append", index=False)

if not df_tel.empty:
dados_tel_existentes = pd.read_sql("SELECT id_colaborador, numero_telefone FROM public.telefone_colaborador", engine_destino)
tels_existentes = dados_tel_existentes["numero_telefone"].tolist()
colabs_tel_existentes = dados_tel_existentes["id_colaborador"].tolist()

df_tel_filtrado = df_tel[
(~df_tel["numero_telefone"].isin(tels_existentes)) &
(~df_tel["id_colaborador"].isin(colabs_tel_existentes))
]

if not df_tel_filtrado.empty:
df_tel_filtrado.to_sql("telefone_colaborador", engine_destino, schema="public", if_exists="append", index=False)


# Tabela pdca.ciclo
Expand All @@ -137,9 +187,9 @@ def carregar_ciclos():
}

df_destino = pd.DataFrame({
"id": df["ciclo_id"],
"id_empresa": df["empresa_id"],
"id_responsavel": df["responsavel_id"],
"id": df["id_ciclo"],
"id_empresa": df["id_empresa"],
"id_responsavel": df["id_responsavel"],
"titulo": df["nome"].str.strip(),
"descricao": df["descricao"].fillna("Sem descrição"),
"status": df["status"].map(status_map).fillna("PLANEJAMENTO"),
Expand All @@ -148,7 +198,12 @@ def carregar_ciclos():
"criado_em": df["criado_em"]
})

df_destino.to_sql("ciclo", engine_destino, schema="pdca", if_exists="append", index=False)
ids_existentes = pd.read_sql("SELECT id FROM pdca.ciclo", engine_destino)["id"].tolist()

df_destino = df_destino[~df_destino["id"].isin(ids_existentes)]

if not df_destino.empty:
df_destino.to_sql("ciclo", engine_destino, schema="pdca", if_exists="append", index=False)

# Tabela pdca.plano_acao

Expand All @@ -172,18 +227,23 @@ def carregar_planos_acao():
}

df_destino = pd.DataFrame({
"id": df["plano_acao_id"],
"id_ciclo": df["ciclo_id"],
"id": df["id_plano_acao"],
"id_ciclo": df["id_ciclo"],
"nome": df["nome"].str.strip(),
"objetivo": df["descricao"],
"prioridade": df["prioridade"].map(prioridade_map).fillna("MEDIA"),
"status": df["status"].map(status_map).fillna("RASCUNHO"),
"origem": "IMPORTACAO",
"criado_por": df["criador_id"],
"criado_por": df["id_criador"],
"criado_em": pd.Timestamp.now()
})

df_destino.to_sql("plano_acao", engine_destino, schema="pdca", if_exists="append", index=False)
ids_existentes = pd.read_sql("SELECT id FROM pdca.plano_acao", engine_destino)["id"].tolist()

df_destino = df_destino[~df_destino["id"].isin(ids_existentes)]

if not df_destino.empty:
df_destino.to_sql("plano_acao", engine_destino, schema="pdca", if_exists="append", index=False)

# Tabela pdca.plano_5w2h

Expand All @@ -195,8 +255,8 @@ def carregar_plano_5w2h():
return

df_destino = pd.DataFrame({
"id": df["plano_acao_5w2h_id"],
"id_plano_acao": df["plano_acao_id"],
"id": df["id_plano_acao_5w2h"],
"id_plano_acao": df["id_plano_acao"],
"id_who_responsavel": df["who"],
"what_acao": df["what"],
"why_justificativa": df["why"].fillna("Sem justificativa definida"),
Expand All @@ -208,7 +268,12 @@ def carregar_plano_5w2h():
"criado_em": pd.Timestamp.now()
})

df_destino.to_sql("plano_5w2h", engine_destino, schema="pdca", if_exists="append", index=False)
ids_existentes = pd.read_sql("SELECT id FROM pdca.plano_5w2h", engine_destino)["id"].tolist()

df_destino = df_destino[~df_destino["id"].isin(ids_existentes)]

if not df_destino.empty:
df_destino.to_sql("plano_5w2h", engine_destino, schema="pdca", if_exists="append", index=False)

# Tabela pdca.meta

Expand All @@ -232,9 +297,9 @@ def carregar_metas():
}

df_destino = pd.DataFrame({
"id": df["meta_id"],
"id_ciclo": df["ciclo_id"],
"id_plano_acao": df["plano_acao_id"],
"id": df["id_meta"],
"id_ciclo": df["id_ciclo"],
"id_plano_acao": df["id_plano_acao"],
"objetivo": (df["descricao_meta"].fillna("") + " - " + df["objetivo"].fillna("")).str.strip(" - "),
"valor_base": 0.00,
"valor_alvo": 0.00,
Expand All @@ -247,7 +312,12 @@ def carregar_metas():
"criado_em": df["criado_em"]
})

df_destino.to_sql("meta", engine_destino, schema="pdca", if_exists="append", index=False)
ids_existentes = pd.read_sql("SELECT id FROM pdca.meta", engine_destino)["id"].tolist()

df_destino = df_destino[~df_destino["id"].isin(ids_existentes)]

if not df_destino.empty:
df_destino.to_sql("meta", engine_destino, schema="pdca", if_exists="append", index=False)

# Tabela pdca.tarefa

Expand All @@ -271,9 +341,9 @@ def carregar_tarefas():
}

df_destino = pd.DataFrame({
"id": df["tarefa_id"],
"id_plano_acao": df["plano_acao_id"],
"id_responsavel": df["colaborador_id"],
"id": df["id_tarefa"],
"id_plano_acao": df["id_plano_acao"],
"id_responsavel": df["id_colaborador"],
"titulo": df["titulo"].str.strip(),
"descricao": df["descricao"].fillna("Sem descrição"),
"prioridade": df["prioridade"].map(prioridade_map).fillna("MEDIA"),
Expand All @@ -283,7 +353,12 @@ def carregar_tarefas():
"criado_em": pd.Timestamp.now()
})

df_destino.to_sql("tarefa", engine_destino, schema="pdca", if_exists="append", index=False)
ids_existentes = pd.read_sql("SELECT id FROM pdca.tarefa", engine_destino)["id"].tolist()

df_destino = df_destino[~df_destino["id"].isin(ids_existentes)]

if not df_destino.empty:
df_destino.to_sql("tarefa", engine_destino, schema="pdca", if_exists="append", index=False)

# Tabela pdca.problema

Expand All @@ -301,10 +376,10 @@ def carregar_problemas():
}

df_destino = pd.DataFrame({
"id": df["problema_id"],
"id_ciclo": df["ciclo_id"],
"id": df["id_problema"],
"id_ciclo": df["id_ciclo"],
"id_problema_pai": None,
"criado_por": df["colaborador_id"],
"criado_por": df["id_colaborador"],
"titulo": df["titulo"].str.strip(),
"descricao": df["descricao"].str.strip(),
"peso": 0.50,
Expand All @@ -314,7 +389,12 @@ def carregar_problemas():
"criado_em": df["encontrado_em"]
})

df_destino.to_sql("problema", engine_destino, schema="pdca", if_exists="append", index=False)
ids_existentes = pd.read_sql("SELECT id FROM pdca.problema", engine_destino)["id"].tolist()

df_destino = df_destino[~df_destino["id"].isin(ids_existentes)]

if not df_destino.empty:
df_destino.to_sql("problema", engine_destino, schema="pdca", if_exists="append", index=False)


# ATUALIZAÇÃO DE SEQUÊNCIAS DO POSTGRES
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