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# select_ai
# Crie um Chat com Dashboard utilizando **Oracle Autonomous Database SELECT AI**
## 1. Introdução
Este tutorial cria uma **interface web** simples (chat + cards com **gráficos** e **tabelas**) transformando **perguntas em linguagem natural** (pt-BR) em **SQL** automaticamente, usando o **SELECT AI** do Autonomous Database. É ideal para cenários como **Pronto-Socorro (ER)**, vendas, logística etc., quando usuários de negócio querem **insights imediatos** sem escrever SQL.
### Como funciona:
- O **Flask** serve uma página com um campo de pergunta e um histórico de respostas (cada resposta pode ter **SQL gerado**, **tabela** e **gráfico Chart.js**).
- Ao enviar a pergunta, o backend chama `SELECT AI 'sua pergunta' FROM <tabela/view>` — quem gera e executa o SQL é o **SELECT AI** dentro do banco.
- O app formata os resultados, tenta inferir o **tipo de gráfico** e permite **exportar tudo** para **PDF** (html2canvas + jsPDF, no navegador) e **Excel** (duas opções: no navegador com XlsxPopulate ou **no servidor** com `openpyxl`).
### Tecnologias principais:
- **Oracle Autonomous Database 23ai** com **SELECT AI** e **DBMS_CLOUD_AI** (perfis de LLM)
- **Python** + **Flask** + **python-oracledb (Thin)** com **mTLS** (wallet)
- **Front-end**: Chart.js, html2canvas + jsPDF e XlsxPopulate (via CDN)
---
## 2. Pré-requisitos
**Oracle Cloud**
- Um **Autonomous Database (Serverless)** em **23ai** (ou com SELECT AI disponível).
- **Saída de rede do ADB** para acessar o serviço OCI Generative AI.
- Permissões para usar **DBMS_CLOUD** / **DBMS_CLOUD_AI** e criar **perfis de IA**.
- **Modelo/Região** do OCI Generative AI habilitados (ex.: `cohere.command-r-08-2024` em `us-chicago-1`).
**Uma VM Linux para hospedagem da página Web (app Flask)**
- **Python 3.10+** (recomendado).
- Pacotes: `flask`, `oracledb`, `openpyxl`, `pillow`.
- **Wallet** do ADB (ZIP baixado do console).
---
## 3. Entendendo o código (trecho a trecho)
O código-fonte pode ser baixado aqui: [app_select_ai.py](./files/app_select_ai.py)
### Conexão e sessão com perfil de IA
- Lê **config** (wallet, alias TNS, usuário/senha).
- Define **`TNS_ADMIN`**.
- `session_callback` ativa o **perfil de IA** (garante SELECT AI pronto em cada sessão).
![img_1.png](img_1.png)
### run_select_ai
- Monta a sentença **`SELECT AI 'pergunta' FROM <tabela>`**.
- Executa e retorna **SQL gerado**, **headers/rows** e usuário/perfil ativos.
![img_2.png](img_2.png)
### build_chart e format_table
- `build_chart`: heurísticas para inferir gráfico (pizza, barras, linha).
- `format_table`: limita registros e devolve `{headers, rows}`.
![img_3.png](img_3.png)
### Front-end (PAGE)
- **Chart.js** desenha gráficos; toolbar alterna tipos.
- **Exportar PDF**: usa html2canvas + jsPDF.
- **Exportar Excel**: cliente (XlsxPopulate) ou servidor (openpyxl).
### Histórico (session)
- Mantém últimos 10 cards em `timeline`.
---
## 4. SELECT AI no Autonomous Database
- Permite **NL→SQL** direto no banco.
- Configuração feita via **Perfis de IA** (`DBMS_CLOUD_AI.CREATE_PROFILE`).
- Perfis incluem provider, modelo, region, credential e lista de objetos (tables/views).
- Comandos auxiliares: `showsql`, `explain`, `narrate` etc.
---
## 5. Instalação e implantação
### 5.1. Criar usuário e permissionamento como ADMIN
Execute estes comandos como ADMIN no Autonomous Database:
```sql
CREATE USER MEU_USUARIO IDENTIFIED BY "SenhaForte123";
ALTER USER MEU_USUARIO QUOTA UNLIMITED ON DATA;
GRANT CREATE SESSION TO MEU_USUARIO;
GRANT CREATE TABLE, CREATE VIEW, CREATE SEQUENCE TO MEU_USUARIO;
GRANT SELECT ON ADMIN.DATASET_ED_ADMISSION TO MEU_USUARIO;
GRANT EXECUTE ON DBMS_CLOUD TO MEU_USUARIO;
GRANT EXECUTE ON DBMS_CLOUD_AI TO MEU_USUARIO;
```
### 5.2 Escolher o método de autenticação do SELECT AI
Há duas opções. Escolha uma opção (**A ou B**) conforme necessidade:
**Opção A** — Resource Principal (RP) (recomendada no ADB)
Sem armazenar chave no banco; usa a identidade do serviço ADB.
```sql
BEGIN
DBMS_CLOUD_ADMIN.ENABLE_PRINCIPAL_AUTH(provider => 'OCI'); -- habilita provedor OCI no ADB
END;
/
BEGIN
DBMS_CLOUD_ADMIN.ENABLE_RESOURCE_PRINCIPAL(); -- ativa RP no banco
END;
/
BEGIN
DBMS_CLOUD_ADMIN.ENABLE_PRINCIPAL_AUTH( -- permite o schema usar RP
provider => 'OCI',
username => 'MEU_USUARIO'
);
END;
/
```
**Opção B** — Credencial com API Key
Armazena uma credencial (OCID do usuário IAM + chave privada).
Substitua username e password pelos seus valores (OCID e chave/PEM).
```sql
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OCI_GENAI_CRED',
username => 'ocid1.user.oc1..aaaa...vx', -- OCID do usuário IAM
password => 'SUA_CHAVE_PRIVADA_OU_CONTEUDO' -- chave/PEM
);
END;
/
```
### 5.3 Criar o(s) Perfil(is) de IA (SELECT AI)
O perfil define provedor, região, credencial/RP, quais objetos o LLM pode usar e (opcionalmente) o modelo.
Exemplo 1 — Perfil com Resource Principal
```sql
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
'OCI_GENAI',
'{
"provider": "OCI",
"credential_name": "OCI$RESOURCE_PRINCIPAL",
"object_list": [ { "owner": "ADMIN" } ],
"model": "cohere.command-r-08-2024",
"oci_runtimetype": "COHERE",
"temperature": "0.4"
}'
);
END;
/
```
Exemplo 2 — Perfil principal da aplicação (com Credencial)
Inclui explicitamente os objetos que o SELECT AI pode usar:
```sql
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENERATIVE_AI_PROFILE',
attributes =>
'{
"provider":"OCI",
"region":"us-chicago-1",
"credential_name":"OCI$RESOURCE_PRINCIPAL",
"object_list":[
{"owner":"ADMIN","name":"DATASET_ED_ADMISSION"},
{"owner":"MEU_USUARIO","name":"NLU_ED_ADMISSION"}
],
"model":"cohere.command-r-08-2024"
}'
);
END;
/
```
>**Nota:** 💡 Dica: se preferir, você pode omitir "model" e deixar o ADB usar o modelo padrão da região/provedor.
>**Nota:** 🔒 Segurança: liste somente os objetos necessários em object_list.
### 5.4 Ativar o perfil e torná-lo ativo na sessão
```sql
BEGIN
DBMS_CLOUD_AI.ENABLE_PROFILE('OCI_GENERATIVE_AI_PROFILE');
END;
/
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_GENERATIVE_AI_PROFILE');
SELECT DBMS_CLOUD_AI.GET_PROFILE() AS active_after FROM dual;
```
>**IMPORTANTE:** É necessário executar este comando abaixo antes de cada consulta SELECT AI:
```sql
CALL DBMS_CLOUD_AI.SET_PROFILE('OCI_GENERATIVE_AI_PROFILE');
```
### 5.5 Enriquecer o vocabulário
Enriquecer o vocabulário com COMMENT ON na tabela gerada no tutorial: [Hospital Risk Admission Prediction with Machine Learning](https://github.com/hoshikawa2/hospital_risk_admission)
Comentários ajudam o LLM a entender o domínio (descrições de tabelas/colunas).
```sql
COMMENT ON TABLE DATASET_ED_ADMISSION IS
'Tabela de pacientes do pronto-socorro / ER patients admission table';
COMMENT ON COLUMN DATASET_ED_ADMISSION.subject_id IS
'Patient ID / ID do paciente (unique identifier)';
COMMENT ON COLUMN DATASET_ED_ADMISSION.hadm_id IS
'Hospital admission ID / ID da internação hospitalar (NULL if not admitted)';
COMMENT ON COLUMN DATASET_ED_ADMISSION.stay_id IS
'ER stay ID / ID da estadia no pronto-socorro';
COMMENT ON COLUMN DATASET_ED_ADMISSION.intime IS
'ER entry timestamp / Data-hora de entrada no pronto-socorro (use EXTRACT(MONTH) for month filter)';
COMMENT ON COLUMN DATASET_ED_ADMISSION.outtime IS
'ER discharge timestamp / Data-hora de saída do pronto-socorro';
COMMENT ON COLUMN DATASET_ED_ADMISSION.gender IS
'Gender (M/F) / Sexo (M/F)';
COMMENT ON COLUMN DATASET_ED_ADMISSION.race IS
'Race/Ethnicity / Raça ou etnia do paciente';
COMMENT ON COLUMN DATASET_ED_ADMISSION.arrival_transport IS
'Arrival transport mode (ambulance, walk) / Forma de chegada (ambulância, caminhada)';
COMMENT ON COLUMN DATASET_ED_ADMISSION.disposition IS
'Disposition after ER (ADMITTED, HOME, etc.) / Destino após atendimento';
COMMENT ON COLUMN DATASET_ED_ADMISSION.admitted_from_ed IS
'Hospitalized from ER (1=yes, 0=no) / Internado a partir do pronto-socorro';
COMMENT ON COLUMN DATASET_ED_ADMISSION.temperature IS
'Body temperature (Celsius) / Temperatura corporal';
COMMENT ON COLUMN DATASET_ED_ADMISSION.heartrate IS
'Heart rate (bpm) / Frequência cardíaca';
COMMENT ON COLUMN DATASET_ED_ADMISSION.resprate IS
'Respiratory rate (breaths/min) / Frequência respiratória';
COMMENT ON COLUMN DATASET_ED_ADMISSION.o2sat IS
'Oxygen saturation (SpO2) / Saturação de oxigênio';
COMMENT ON COLUMN DATASET_ED_ADMISSION.sbp IS
'Systolic blood pressure / Pressão arterial sistólica';
COMMENT ON COLUMN DATASET_ED_ADMISSION.dbp IS
'Diastolic blood pressure / Pressão arterial diastólica';
COMMENT ON COLUMN DATASET_ED_ADMISSION.n_diagnosis IS
'Number of diagnoses / Número de diagnósticos registrados';
COMMENT ON COLUMN DATASET_ED_ADMISSION.split IS
'Data split flag (train, val, test) / Particionamento dos dados';
```
### 5.6 Criar uma view para NL com nomes claros
Esta view, mais amigável, facilitará a LLM do banco de dados a entender mais facilmente os campos.
Vista no schema atual (quando estiver em ADMIN):
```sql
CREATE OR REPLACE VIEW MEU_USUARIO.NLU_ED_ADMISSION AS
SELECT
subject_id AS patient_id,
hadm_id AS admission_id,
stay_id AS er_stay_id,
intime AS er_entry_time,
outtime AS er_exit_time,
gender, race, arrival_transport, disposition,
admitted_from_ed AS admitted,
temperature, heartrate, resprate, o2sat, sbp, dbp,
n_diagnosis, split,
EXTRACT(MONTH FROM intime) AS month_num,
EXTRACT(YEAR FROM intime) AS year_num
FROM ADMIN.DATASET_ED_ADMISSION;
```
E uma view equivalente no schema do usuário (recomendado para consumo pelo app):
```sql
-- execute no MESMO schema usado pelo SELECT AI (ex.: MEU_USUARIO)
CREATE OR REPLACE VIEW MEU_USUARIO.NLU_ED_ADMISSION AS
SELECT
subject_id AS patient_id,
hadm_id AS admission_id,
stay_id AS er_stay_id,
intime AS er_entry_time,
outtime AS er_exit_time,
gender, race, arrival_transport, disposition,
admitted_from_ed AS admitted, -- 1/0
temperature, heartrate, resprate, o2sat, sbp, dbp,
n_diagnosis, split,
EXTRACT(MONTH FROM intime) AS month_num,
EXTRACT(YEAR FROM intime) AS year_num
FROM ADMIN.DATASET_ED_ADMISSION;
COMMENT ON TABLE MEU_USUARIO.NLU_ED_ADMISSION IS 'ER admissions with friendly names for NL queries / Tabela para NL';
COMMENT ON COLUMN NLU_ED_ADMISSION.patient_id IS 'Patient ID / ID do paciente';
COMMENT ON COLUMN NLU_ED_ADMISSION.admitted IS 'Hospitalized from ER (1=yes, 0=no) / Internado a partir do PS';
COMMENT ON COLUMN NLU_ED_ADMISSION.er_entry_time IS 'ER entry timestamp / Entrada no PS';
COMMENT ON COLUMN NLU_ED_ADMISSION.month_num IS 'Month number (1..12)';
COMMENT ON COLUMN NLU_ED_ADMISSION.year_num IS 'Year';
```
>**Por que?** Nomes como patient_id, admitted, month_num facilitam a tradução NL→SQL e evitam ambiguidade.
Valide o SELECT AI, executando os comandos abaixo numa mesma sessão:
### Testar direto no SQL
```sql
BEGIN
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_GENERATIVE_AI_PROFILE');
SELECT AI 'quantos pacientes no hospital' FROM MEU_USUARIO.NLU_ED_ADMISSION;
END;
```
### 5.7 Preparar o app Flask
Configure o arquivo [config](./files/config) com os dados da Wallet do Autonomous Database:
```json
{
"WALLET_PATH": "/caminho/Wallet_ADB",
"DB_ALIAS": "oradb_high",
"USERNAME": "MEU_USUARIO",
"PASSWORD": "SenhaForte123"
}
```
Baixar o arquivo [requirements.txt](./files/requirements.txt). Instalar dependências:
```bash
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```
---
## 6. Testar a aplicação
Rodar o app [app_select_ai.py](./files/app_select_ai.py):
```bash
python app_select_ai.py
# abre http://localhost:5001
```
### Acessando a aplicação
Abra `http://localhost:5001` e faça perguntas como:
- “comparar risco de internacao de pacientes”
- “quantos pacientes chegaram no hospital”
- “mostrar pacientes e suas pressoes arteriais acima de 120 80”
### Exportar resultados
- **PDF**: botão “Exportar PDF”.
- **Excel**: botão “Exportar Excel” (via servidor).
---
![img.png](img.png)
## Reference
- [Install OCI-CLI](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/cliinstall.htm)
- [Download Wallet Database Connection File](https://docs.oracle.com/en/cloud/paas/autonomous-database/serverless/adbsb/connect-download-wallet.html#GUID-DED75E69-C303-409D-9128-5E10ADD47A35)
- [About SELECT AI](https://docs.oracle.com/en-us/iaas/autonomous-database-serverless/doc/select-ai.html)
- [Hospital Risk Admission Prediction with Machine Learning](https://github.com/hoshikawa2/hospital_risk_admission)
## Acknowledgments
- **Author** - Cristiano Hoshikawa (Oracle LAD A-Team Solution Engineer)

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# app_select_ai.py
# ---------------------------------------------------------
# Chat + Dashboard (histórico) para SELECT AI no Autonomous Database
# ---------------------------------------------------------
from flask import Flask, request, render_template_string, session
import oracledb, os, json
from flask import send_file, jsonify
from io import BytesIO
import base64
from openpyxl import Workbook
from openpyxl.utils import get_column_letter
from openpyxl.drawing.image import Image as XLImage
from PIL import Image as PILImage
# ======================
# CONFIGURAÇÕES DO BANCO
# ======================
with open("./config", "r") as f:
config_data = json.load(f)
WALLET_PATH = config_data["WALLET_PATH"]
DB_ALIAS = config_data["DB_ALIAS"]
USERNAME = config_data["USERNAME"]
PASSWORD = config_data["PASSWORD"]
os.environ["TNS_ADMIN"] = WALLET_PATH
PROFILE_NAME = "OCI_GENERATIVE_AI_PROFILE"
def set_select_ai_profile(conn, requested_tag):
# Ativa o profile em cada sessão do pool
with conn.cursor() as cur:
cur.execute("BEGIN DBMS_CLOUD_AI.SET_PROFILE(:p); END;", p=PROFILE_NAME)
pool = oracledb.create_pool(
user=USERNAME,
password=PASSWORD,
dsn=DB_ALIAS,
config_dir=WALLET_PATH,
wallet_location=WALLET_PATH,
wallet_password=PASSWORD,
min=1, max=5, increment=1,
session_callback=set_select_ai_profile
)
# ======================
# APP FLASK
# ======================
app = Flask(__name__)
app.secret_key = "troque-esta-chave" # necessário p/ sessão (histórico)
PAGE = """
<!doctype html>
<html lang="pt-br">
<head>
<meta charset="utf-8">
<title>ER Analytics · Select AI</title>
<meta name="viewport" content="width=device-width, initial-scale=1">
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<script>
Chart.defaults.font.size = 11;
Chart.defaults.plugins.legend.labels.boxWidth = 10;
</script>
<!-- Export PDF -->
<script src="https://cdn.jsdelivr.net/npm/jspdf@2.5.1/dist/jspdf.umd.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/html2canvas@1.4.1/dist/html2canvas.min.js"></script>
<!-- Export Excel (browser) -->
<script src="https://cdn.jsdelivr.net/npm/xlsx-populate/browser/xlsx-populate.min.js"></script>
<style>
:root { --bg:#0f1115; --card:#141826; --border:#22283a; --muted:#9aa4af; --text:#e5e7eb; --brand:#4f46e5; }
*{box-sizing:border-box}
body{margin:0; font-family:system-ui,-apple-system,Segoe UI,Roboto,Arial; background:var(--bg); color:var(--text);}
/* Layout de coluna: topo = histórico, base = input */
.page { min-height: 100vh; display:flex; flex-direction:column; }
.wrap { max-width:1200px; width:100%; margin:0 auto; padding:16px; flex:1; display:flex; flex-direction:column; gap:12px; }
.header{ display:flex; align-items:center; gap:10px; }
.logo{ width:34px; height:34px; border-radius:8px; background:linear-gradient(135deg,#4f46e5,#06b6d4); display:flex; align-items:center; justify-content:center; font-weight:800; }
.title{ font-size:18px; font-weight:700 }
.muted{ color:var(--muted); font-size:12px; }
.card{ background:var(--card); border:1px solid var(--border); border-radius:12px; padding:14px; }
.kpi{ display:flex; gap:8px; flex-wrap:wrap; }
.pill{ padding:4px 8px; border:1px solid var(--border); border-radius:999px; font-size:12px; }
/* Histórico vertical com rolagem; cada item contém gráfico e/ou tabela */
#history { flex:1; overflow:auto; display:flex; flex-direction:column; gap:10px; padding-right:4px; max-height: 68vh; }
.item { background:#0f1424; border:1px solid var(--border); border-radius:10px; padding:12px; }
.sql { font-family: ui-monospace, SFMono-Regular, Menlo, Consolas, "Liberation Mono", monospace; font-size:12px; color:#cbd5e1; background:#0b0f19; border:1px solid var(--border); border-radius:8px; padding:8px; overflow:auto; }
.block { margin-top:8px; }
table{ width:100%; border-collapse:collapse; font-size:14px; }
th,td{ padding:8px; border-bottom:1px solid var(--border); text-align:left; }
th{ background:#0f1524; position:sticky; top:0; }
/* Área de input sempre embaixo */
.footer { position: sticky; bottom: 0; background:var(--bg); padding:12px 16px; border-top:1px solid var(--border); }
.ask { display:flex; gap:8px; flex-wrap:wrap; }
input[type="text"]{ flex:1; min-width:280px; padding:10px 12px; border-radius:10px; border:1px solid var(--border); background:#0c0f19; color:var(--text); }
button{ background:var(--brand); color:#fff; border:none; border-radius:10px; padding:10px 14px; font-weight:600; cursor:pointer; }
button:hover{ filter:brightness(1.1) }
.chart-box{
height: 220px; /* altura final do gráfico */
width: 100%;
border: 1px solid var(--border);
border-radius: 10px;
background: #0b0f19;
padding: 8px;
}
@media (max-width: 640px){
.chart-box{ height: 180px; } /* menor em telas pequenas */
}
.item { max-width: 100%; } /* já está */
.hist .item { max-width: 520px; } /* se usar histórico horizontal */
.toolbar{ display:flex; gap:6px; flex-wrap:wrap; margin:6px 0 6px; }
.pill{ background:#0e1420; border:1px solid var(--border); color:#cbd5e1;
padding:6px 10px; border-radius:999px; font-size:12px; cursor:pointer; }
.pill:hover{ border-color:#3a4258; }
.chart-box{
height: 220px; width: 100%;
border:1px solid var(--border); border-radius:10px;
background:#0b0f19; padding:8px;
}
@media (max-width:640px){ .chart-box{ height:180px; } }
</style>
</head>
<body>
<div class="page">
<div class="wrap">
<div class="header">
<div class="logo">ER</div>
<div>
<div class="title">ER Analytics · Select AI</div>
<div class="muted">Respostas e gráficos no topo; pergunta no rodapé. A última resposta fica visível automaticamente.</div>
</div>
</div>
{% if timeline %}
<div class="card">
<div class="kpi">
<span class="pill">Conexão: <b>{{ db_alias }}</b></span>
<span class="pill">Usuário: <b>{{ session_user }}</b></span>
<span class="pill">Profile AI: <b>{{ profile or '—' }}</b></span>
<!-- Botões de export -->
<span class="pill" style="cursor:auto; border:none;">|</span>
<button type="button" class="pill" onclick="exportPDF()" title="Exporta todas as respostas (com gráficos e tabelas) para PDF">Exportar PDF</button>
<button type="button" class="pill" onclick="exportExcelServer()">Exportar Excel</button>
</div>
</div>
<div id="history">
{% for item in timeline %}
<div class="item">
<div><b>Pergunta:</b> {{ item.prompt }}</div>
{% if item.sql %}<div class="block"><div class="sql">{{ item.sql }}</div></div>{% endif %}
{% if item.chart %}
<div class="toolbar">
<span class="pill" onclick="render_{{ loop.index0 }}('bar')">Barras</span>
<span class="pill" onclick="render_{{ loop.index0 }}('line')">Linhas</span>
<span class="pill" onclick="render_{{ loop.index0 }}('pie')">Pizza</span>
{% if item.table %}
<span class="pill" onclick="toggleTable_{{ loop.index0 }}()">Mostrar/ocultar Tabela</span>
{% endif %}
</div>
<div class="block chart-box">
<canvas id="chart_{{ loop.index0 }}"></canvas>
</div>
<script>
(function(){
const DATA_{{ loop.index0 }} = {
type: '{{ item.chart["type"] }}',
labels: {{ item.chart["labels"]|tojson }},
values: {{ item.chart["values"]|tojson }},
seriesLabel: '{{ item.chart["seriesLabel"] }}',
title: '{{ item.chart["title"] }}',
xLabel: '{{ item.chart["xLabel"] }}',
yLabel: '{{ item.chart["yLabel"] }}'
};
let ch_{{ loop.index0 }} = null;
function makeChart_{{ loop.index0 }}(kind){
const ctx = document.getElementById('chart_{{ loop.index0 }}').getContext('2d');
if (ch_{{ loop.index0 }}) ch_{{ loop.index0 }}.destroy();
ch_{{ loop.index0 }} = new Chart(ctx, {
type: kind,
data: {
labels: DATA_{{ loop.index0 }}.labels,
datasets: [{
label: DATA_{{ loop.index0 }}.seriesLabel,
data: DATA_{{ loop.index0 }}.values,
borderWidth: 1,
pointRadius: 2
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
layout: { padding: 4 },
plugins: {
legend: { display: true, labels: { boxWidth: 10, font: { size: 10 } } },
title: { display: true, text: DATA_{{ loop.index0 }}.title, font: { size: 12 } },
tooltip:{ bodyFont: { size: 11 }, titleFont: { size: 11 } }
},
scales: {
x: { title: { display: true, text: DATA_{{ loop.index0 }}.xLabel, font: { size: 11 } },
ticks: { font: { size: 10 }, maxRotation: 0, autoSkip: true } },
y: { title: { display: true, text: DATA_{{ loop.index0 }}.yLabel, font: { size: 11 } },
ticks: { font: { size: 10 } }, beginAtZero: true, grace: "5%" }
},
elements: { line: { tension: 0.2 } }
}
});
// garante que a última resposta fica visível após render
setTimeout(scrollHistoryBottom, 60);
}
// expõe funções por-card
window.render_{{ loop.index0 }} = makeChart_{{ loop.index0 }};
{% if item.table %}
window.toggleTable_{{ loop.index0 }} = function(){
const el = document.getElementById('table_{{ loop.index0 }}');
if (!el) return;
el.style.display = (el.style.display === 'none') ? '' : 'none';
setTimeout(scrollHistoryBottom, 40);
};
{% endif %}
// inicial: usa o tipo sugerido pelo backend
makeChart_{{ loop.index0 }}(DATA_{{ loop.index0 }}.type);
})();
</script>
{% endif %}
{% if item.table %}
<div id="table_{{ loop.index0 }}" class="block" style="border:1px solid var(--border); border-radius:10px; overflow:auto; max-height:40vh;">
<table>
<thead><tr>{% for h in item.table.headers %}<th>{{ h }}</th>{% endfor %}</tr></thead>
<tbody>
{% for r in item.table.rows %}
<tr>{% for c in r %}<td>{{ c }}</td>{% endfor %}</tr>
{% endfor %}
</tbody>
</table>
</div>
{% endif %}
</div>
{% endfor %}
<!-- ⬇️ marcador fixo de fim -->
<div id="history-bottom"></div>
</div>
{% endif %}
</div>
<!-- Rodapé com input SEMPRE embaixo -->
<div class="footer">
<form class="ask" method="post" onsubmit="markScrollToBottom()">
<input type="text" name="frase" placeholder="Faça sua pergunta… (Enter para enviar)" required>
<input type="text" name="tabela" value="{{ default_table }}">
<button type="submit">Enviar</button>
</form>
<div class="muted" style="margin-top:6px">Dica: cite colunas (ex.: <code>admitted</code>, <code>month_num</code>, <code>year_num</code>) para respostas mais precisas.</div>
</div>
<script>
document.addEventListener("DOMContentLoaded", () => {
const frase = document.querySelector('input[name="frase"]');
frase.addEventListener("keydown", (e) => {
if(e.key === "Enter"){
e.preventDefault();
frase.form.requestSubmit(); // envia o form
}
});
});
</script>
</div>
<script>
function scrollHistoryBottom(smooth=true){
const end = document.getElementById('history-bottom');
if (end) end.scrollIntoView({behavior: smooth ? 'smooth' : 'auto', block: 'end'});
}
// Marca a intenção de rolar após o POST (reload da página)
function markScrollToBottom(){
try { localStorage.setItem('__scrollToBottom', '1'); } catch(e){}
}
// Rola quando a página carrega (e re-rola depois que gráficos expandirem)
function settleAndScroll(){
scrollHistoryBottom(false); // imediato
setTimeout(scrollHistoryBottom, 80); // após layout inicial
setTimeout(scrollHistoryBottom, 250);// após Chart.js desenhar
setTimeout(scrollHistoryBottom, 600);// salvaguarda final
}
// Executa no load
window.addEventListener('load', () => {
// se veio de um submit, respeite a marca
let must = false;
try { must = localStorage.getItem('__scrollToBottom') === '1'; } catch(e){}
if (must) {
try { localStorage.removeItem('__scrollToBottom'); } catch(e){}
settleAndScroll();
} else {
// mesmo sem submit, mantenha a última resposta à vista
settleAndScroll();
}
});
</script>
<script>
function _grabTableData(itemEl){
const table = itemEl.querySelector('table');
if(!table) return null;
const headers = Array.from(table.querySelectorAll('thead th')).map(th => th.textContent.trim());
const rows = Array.from(table.querySelectorAll('tbody tr')).map(tr =>
Array.from(tr.querySelectorAll('td')).map(td => td.textContent)
);
return { headers, rows };
}
function _grabQuestion(itemEl){
const b = itemEl.querySelector('div>b');
if(b && b.parentElement){
return b.parentElement.textContent.replace(/^Pergunta:\s*/,'').trim();
}
return "Pergunta";
}
function _grabSQL(itemEl){
const sqlEl = itemEl.querySelector('.sql');
return sqlEl ? sqlEl.textContent : "";
}
async function exportExcelServer(){
const items = Array.from(document.querySelectorAll('#history .item'));
if(items.length === 0){
alert('Não há respostas para exportar.');
return;
}
// monta payload
const payload = { items: [] };
for(const item of items){
const question = _grabQuestion(item);
const sql = _grabSQL(item);
const table = _grabTableData(item);
const canvas = item.querySelector('canvas');
let chartPng = null;
if(canvas){
try{
chartPng = canvas.toDataURL('image/png'); // inclui prefixo data:
}catch(e){
chartPng = null;
}
}
payload.items.push({
question, sql, table, chartPng
});
}
// envia para o servidor
const res = await fetch("/export/xlsx", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify(payload)
});
if(!res.ok){
const t = await res.text();
console.error('Falha no export:', t);
alert('Falha ao gerar Excel no servidor.');
return;
}
// baixa o arquivo
const blob = await res.blob();
const url = URL.createObjectURL(blob);
const a = document.createElement("a");
a.href = url;
a.download = "respostas-select-ai.xlsx";
document.body.appendChild(a);
a.click();
a.remove();
setTimeout(()=>URL.revokeObjectURL(url), 1000);
}
// Utilitário para baixar Blob
function downloadBlob(blob, filename){
const url = URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url; a.download = filename; a.click();
setTimeout(()=>URL.revokeObjectURL(url), 1000);
}
// ============ Exportar PDF (com charts + tabelas como na tela) ============
async function exportPDF(){
const { jsPDF } = window.jspdf;
const pdf = new jsPDF('p','mm','a4');
const pageWidth = pdf.internal.pageSize.getWidth();
const pageHeight = pdf.internal.pageSize.getHeight();
const margin = 10;
const maxW = pageWidth - margin*2;
const items = Array.from(document.querySelectorAll('#history .item'));
if(items.length === 0){ alert('Não há respostas para exportar.'); return; }
for(let i=0;i<items.length;i++){
const el = items[i];
// usa html2canvas para “fotografar” o card, incluindo canvas e tabelas
const canvas = await html2canvas(el, {backgroundColor: '#0f1115', scale: 2, useCORS: true});
const imgData = canvas.toDataURL('image/png');
// dimensiona para caber na página
const imgW = maxW;
const imgH = canvas.height * (imgW / canvas.width);
// se altura maior que a página, escala para caber verticalmente
const effW = imgW;
const effH = Math.min(imgH, pageHeight - margin*2);
const scale = Math.min(imgW / canvas.width, (pageHeight - margin*2) / canvas.height);
const drawW = canvas.width * scale;
const drawH = canvas.height * scale;
if(i>0) pdf.addPage();
pdf.addImage(imgData, 'PNG', margin, margin, drawW, drawH);
}
pdf.save('respostas-select-ai.pdf');
}
// ============ Exportar Excel (uma planilha por resposta; gráfico como imagem) ============
// Helper para baixar Blob
function downloadBlob(blob, filename){
const url = URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url; a.download = filename; a.click();
setTimeout(()=>URL.revokeObjectURL(url), 1000);
}
// Helper: insere imagem tentando múltiplas assinaturas do XlsxPopulate
async function insertImage(wb, sheet, base64Png, topLeftCell, width, height){
// 1) Tentativa com 'base64' + 'anchor'
try{
await sheet.addImage({
base64: base64Png, // sem prefixo data:
name: 'chart',
anchor: topLeftCell, // ex.: 'A12'
width, height
});
return true;
}catch(e1){
// 2) Tentativa com 'image' (dataURL completo) + 'topLeftCell'
try{
const dataUrl = 'data:image/png;base64,' + base64Png;
await sheet.addImage({
image: dataUrl,
topLeftCell: topLeftCell,
width, height
});
return true;
}catch(e2){
// 3) Via workbook.addImage() + sheet.addImage({image})
try{
const img = wb.addImage({ base64: base64Png, extension: 'png' });
await sheet.addImage({
image: img,
topLeftCell: topLeftCell,
width, height
});
return true;
}catch(e3){
console.warn('Falha ao inserir imagem no Excel:', {e1, e2, e3});
return false;
}
}
}
}
// Exporta TODAS as respostas para Excel: dados + gráfico (PNG)
async function exportExcel(){
if (!window.XlsxPopulate) {
alert("Biblioteca XlsxPopulate não carregada.");
return;
}
const items = Array.from(document.querySelectorAll('#history .item'));
if(items.length === 0){
alert('Não há respostas para exportar.');
return;
}
try {
const wb = await XlsxPopulate.fromBlankAsync();
for (let i = 0; i < items.length; i++) {
const item = items[i];
const name = ("Resp " + (i+1)).slice(0, 31);
const sheet = wb.addSheet(name);
// --- Pergunta ---
let question = 'Resposta ' + (i+1);
const qTitle = item.querySelector('div>b');
if (qTitle && qTitle.parentElement) {
question = qTitle.parentElement.textContent.replace(/^Pergunta:\s*/,'').trim();
}
sheet.cell('A1').value('Pergunta:').style({ bold:true });
sheet.cell('B1').value(question);
sheet.row(1).height(22);
// --- SQL (se houver) ---
const sqlEl = item.querySelector('.sql');
if (sqlEl){
sheet.cell('A2').value('SQL:').style({ bold:true });
sheet.cell('B2').value(sqlEl.textContent);
sheet.column('B').width(100);
}
// --- Tabela (se houver) ---
let nextRow = 4;
const tableEl = item.querySelector('table');
if (tableEl){
const headers = Array.from(tableEl.querySelectorAll('thead th')).map(th => th.textContent.trim());
const rows = Array.from(tableEl.querySelectorAll('tbody tr')).map(tr =>
Array.from(tr.querySelectorAll('td')).map(td => td.textContent)
);
if (headers.length){
const lastColLetter = String.fromCharCode(64 + headers.length); // A..Z (ok p/ até 26 colunas)
sheet.range(`A${nextRow}:${lastColLetter}${nextRow}`).value([headers]).style({ bold:true, fill: 'EFEFEF' });
if (rows.length){
sheet.range(`A${nextRow+1}:${lastColLetter}${nextRow+rows.length}`).value(rows);
}
// larguras aproximadas
headers.forEach((h, idx) => sheet.column(idx+1).width(Math.max(12, Math.min(40, h.length + 4))));
nextRow = nextRow + rows.length + 2;
}
}
// --- Gráfico (se houver) ---
const canvasEl = item.querySelector('canvas');
let embedded = false;
if (canvasEl){
try{
const dataUrl = canvasEl.toDataURL('image/png');
const base64 = dataUrl.split(',')[1]; // remove prefixo
embedded = await insertImage(wb, sheet, base64, `A${nextRow}`, 640, 300);
if (embedded) nextRow += 18;
}catch(err){
console.warn('Falha ao capturar canvas:', err);
}
}
// Fallback: se não tinha canvas ou inserção falhou, captura o card inteiro (html2canvas)
if (!embedded){
try{
const snap = await html2canvas(item, { backgroundColor: '#ffffff', scale: 2, useCORS: true });
const base64 = snap.toDataURL('image/png').split(',')[1];
await insertImage(wb, sheet, base64, `A${nextRow}`, 640, 360);
nextRow += 20;
}catch(err2){
console.warn(`Sem gráfico na aba ${name} (seguindo só com dados).`, err2);
}
}
}
// (Opcional) renomeia a primeira planilha vazia
try { wb.sheet(0).name('Resumo'); } catch(e){}
const blob = await wb.outputAsync();
downloadBlob(blob, 'respostas-select-ai.xlsx');
} catch (e) {
console.error('Falha ao gerar Excel:', e);
alert('Falha ao gerar Excel. Veja o console para detalhes.');
}
}
</script>
</body>
</html>
"""
def _auto_width(ws, start_col, end_col, extra=2):
for col_idx in range(start_col, end_col+1):
col_letter = get_column_letter(col_idx)
max_len = 0
for cell in ws[col_letter]:
try:
max_len = max(max_len, len(str(cell.value)) if cell.value is not None else 0)
except:
pass
ws.column_dimensions[col_letter].width = min(50, max(12, max_len + extra))
@app.route("/export/xlsx", methods=["POST"])
def export_xlsx():
"""
Espera JSON com uma lista 'items'. Cada item:
{
"question": str,
"sql": str|None,
"table": { "headers":[...], "rows":[[...], ...] } | None,
"chartPng": "data:image/png;base64,..." | None
}
Retorna um arquivo XLSX com 1 aba por resposta.
"""
try:
payload = request.get_json(force=True)
items = payload.get("items", [])
if not items:
return jsonify({"error":"sem itens"}), 400
wb = Workbook()
# use a folha ativa como “Resumo”
ws0 = wb.active
ws0.title = "Resumo"
ws0["A1"] = "Export gerado pelo ER Analytics · Select AI"
ws0["A2"] = f"Total de respostas: {len(items)}"
for i, it in enumerate(items, start=1):
title = f"Resp {i}"
ws = wb.create_sheet(title[:31])
# Pergunta
ws["A1"] = "Pergunta:"
ws["A1"].font = ws["A1"].font.copy(bold=True)
ws["B1"] = it.get("question") or f"Resposta {i}"
ws.row_dimensions[1].height = 22
# SQL
sql = (it.get("sql") or "").strip()
if sql:
ws["A2"] = "SQL:"
ws["A2"].font = ws["A2"].font.copy(bold=True)
ws["B2"] = sql
row = 4
# Tabela
table = it.get("table")
if table and table.get("headers"):
headers = table["headers"]
rows = table.get("rows", [])
for j, h in enumerate(headers, start=1):
cell = ws.cell(row=row, column=j, value=h)
cell.font = cell.font.copy(bold=True)
cell.fill = cell.fill.copy()
row += 1
for r in rows:
for j, val in enumerate(r, start=1):
ws.cell(row=row, column=j, value=val)
row += 1
# largura de colunas
_auto_width(ws, 1, len(headers))
row += 1
# Gráfico como PNG (se vier)
chart_png = it.get("chartPng")
if chart_png:
try:
# aceita dataURL com prefixo
if "," in chart_png:
chart_png = chart_png.split(",", 1)[1]
img_bytes = base64.b64decode(chart_png)
# normaliza via PIL (corrige metadados)
pil = PILImage.open(BytesIO(img_bytes)).convert("RGBA")
buf = BytesIO()
pil.save(buf, format="PNG")
buf.seek(0)
xl_img = XLImage(buf)
anchor_cell = f"A{row}"
ws.add_image(xl_img, anchor_cell)
# empurra linhas para não sobrepor texto
row += 20
except Exception as e:
# segue sem o gráfico
pass
# remove folha “Sheet” se sobrar (caso libs criem extra)
for sh in list(wb.sheetnames):
if sh.lower().startswith("sheet") and sh != ws0.title:
try:
del wb[sh]
except:
pass
out = BytesIO()
wb.save(out)
out.seek(0)
return send_file(
out,
mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
as_attachment=True,
download_name="respostas-select-ai.xlsx",
)
except Exception as e:
return jsonify({"error": str(e)}), 500
def _to_float(x):
if x is None: return None
s = str(x).strip().replace(',', '.')
if s.endswith('%'): s = s[:-1]
try:
return float(s)
except Exception:
return None
def build_chart(headers, rows):
"""
Gera metadados de gráfico + eixos/legenda:
- 1x1 percentual -> pizza (Internados vs Não)
- 2 colunas (cat, num) -> barras
- 3 colunas com (categoria, count, percentage) -> usa percentage; senão count
- 3 colunas com temporal (col contém MONTH/YEAR/DATE) -> linha
Retorna dict {type, labels, values, seriesLabel, title, xLabel, yLabel} ou None
"""
if not headers or not rows: return None
H = [h.upper() for h in headers]
# 1) 1x1 percentual
if len(headers) == 1 and len(rows) == 1:
v = _to_float(rows[0][0])
if v is None: return None
pct = v*100 if 0 <= v <= 1 else v
if 0 <= pct <= 100:
return {
"type": "pie",
"labels": ["Internados", "Não internados"],
"values": [round(pct,2), round(100-pct,2)],
"seriesLabel": "Percentual",
"title": f"{headers[0]}",
"xLabel": "Status",
"yLabel": "%"
}
# 2) 2 colunas: categoria × valor
if len(headers) == 2:
labels, values = [], []
for r in rows:
labels.append(str(r[0]))
vf = _to_float(r[1])
if vf is None: return None
values.append(vf)
return {
"type": "bar",
"labels": labels,
"values": values,
"seriesLabel": headers[1],
"title": f"{headers[1]} por {headers[0]}",
"xLabel": headers[0],
"yLabel": headers[1]
}
# 3) 3 colunas: temporal ou categoria + (count, percentage)
if len(headers) == 3:
# 3a) temporal
idx_time = next((i for i,h in enumerate(H) if any(k in h for k in ["DATE","DATA","MONTH","YEAR","TIME"])), None)
if idx_time is not None:
idx_val = 2
labels, values = [], []
for r in rows:
labels.append(str(r[idx_time]))
vf = _to_float(r[idx_val])
if vf is None: return None
values.append(vf)
return {
"type": "line",
"labels": labels,
"values": values,
"seriesLabel": headers[idx_val],
"title": f"{headers[idx_val]} por {headers[idx_time]}",
"xLabel": headers[idx_time],
"yLabel": headers[idx_val]
}
# 3b) categoria + count + percentage
idx_pct = next((i for i,h in enumerate(H) if "PERCENT" in h), None)
idx_cnt = next((i for i,h in enumerate(H) if any(k in h for k in ["COUNT","NUMBER"])), None)
idx_cat = 0
idx_val = idx_pct if idx_pct is not None else idx_cnt
if idx_val is not None:
labels, values = [], []
for r in rows:
labels.append(str(r[idx_cat]))
vf = _to_float(r[idx_val])
if vf is None: return None
values.append(vf)
ylab = headers[idx_val] + (" (%)" if idx_val == idx_pct else "")
return {
"type": "bar" if idx_val == idx_cnt else "pie",
"labels": labels,
"values": values,
"seriesLabel": headers[idx_val],
"title": f"{headers[idx_val]} por {headers[idx_cat]}",
"xLabel": headers[idx_cat],
"yLabel": ylab
}
return None
def format_table(headers, rows, limit=500):
if not headers: return None
rows = rows if len(rows) <= limit else rows[:limit]
return {"headers": headers, "rows": rows}
def run_select_ai(nl_prompt, table_name):
frase_segura = nl_prompt.replace("'", "''")
sql = f"SELECT AI '{frase_segura}' FROM {table_name}"
with pool.acquire() as conn:
with conn.cursor() as cur:
cur.execute("select user, dbms_cloud_ai.get_profile() from dual")
user_, profile_ = cur.fetchone()
cur.execute(sql)
rows = cur.fetchall()
headers = [d[0] for d in cur.description] if cur.description else []
return sql, headers, rows, user_, profile_
def ensure_timeline():
if "timeline" not in session:
session["timeline"] = []
return session["timeline"]
@app.route("/", methods=["GET","POST"])
def index():
default_table = "MEU_USUARIO.NLU_ED_ADMISSION"
timeline = ensure_timeline()
if request.method == "POST":
frase = request.form["frase"].strip()
tabela = request.form.get("tabela", default_table).strip()
try:
sql, headers, rows, user_, profile_ = run_select_ai(frase, tabela)
table = format_table(headers, rows)
chart = build_chart(headers, rows)
# empilha no histórico (mantém últimos 10)
timeline.append({
"prompt": frase,
"sql": sql,
"table": table,
"chart": chart
})
if len(timeline) > 10:
timeline[:] = timeline[-10:]
session["timeline"] = timeline
return render_template_string(
PAGE,
timeline=timeline,
default_table=default_table,
db_alias=DB_ALIAS,
session_user=user_,
profile=profile_
)
except Exception as e:
timeline.append({
"prompt": frase,
"sql": None,
"table": {"headers": ["Erro"], "rows": [[str(e)]]},
"chart": None
})
if len(timeline) > 10:
timeline[:] = timeline[-10:]
session["timeline"] = timeline
return render_template_string(
PAGE,
timeline=timeline,
default_table=default_table,
db_alias=DB_ALIAS,
session_user=USERNAME,
profile=None
)
# GET
return render_template_string(
PAGE,
timeline=timeline,
default_table=default_table,
db_alias=DB_ALIAS,
session_user=USERNAME,
profile=None
)
if __name__ == "__main__":
app.run(debug=True, port=5001)

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{
"WALLET_PATH": "./Wallet_ORADB23ai",
"DB_ALIAS": "oradb23ai_high",
"USERNAME": "admin",
"PASSWORD": "*********"
}

4
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flask
oracledb
openpyxl
pillow

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