Lakebase 연동

command: ['python', 'app.py']
env:
  - name: DB_CONNECTION
    valueFrom: lakebase_db
resources:
  - name: lakebase_db
    type: postgres

# requirements.txt
databricks-sdk
psycopg2-binary    # PostgreSQL 드라이버

import os
import psycopg2

# Lakebase 연결 정보 가져오기
db_connection = os.getenv("DB_CONNECTION")

# PostgreSQL 연결
conn = psycopg2.connect(db_connection)

# CRUD 작업
with conn.cursor() as cur:
    # 테이블 생성
    cur.execute("""
        CREATE TABLE IF NOT EXISTS app_sessions (
            id SERIAL PRIMARY KEY,
            user_id VARCHAR(255),
            data JSONB,
            created_at TIMESTAMP DEFAULT NOW()
        )
    """)

    # 데이터 삽입
    cur.execute(
        "INSERT INTO app_sessions (user_id, data) VALUES (%s, %s)",
        ("user123", '{"page": "dashboard", "action": "view"}')
    )
    conn.commit()

    # 데이터 조회
    cur.execute("SELECT * FROM app_sessions WHERE user_id = %s", ("user123",))
    rows = cur.fetchall()

conn.close()

import os
import streamlit as st
import psycopg2

@st.cache_resource
def get_db_connection():
    """Lakebase 연결을 캐싱하여 재사용합니다."""
    return psycopg2.connect(os.getenv("DB_CONNECTION"))

conn = get_db_connection()

st.title("피드백 수집 앱")

# 피드백 입력 폼
with st.form("feedback_form"):
    name = st.text_input("이름")
    feedback = st.text_area("피드백")
    rating = st.slider("만족도", 1, 5, 3)
    submitted = st.form_submit_button("제출")

    if submitted:
        with conn.cursor() as cur:
            cur.execute(
                "INSERT INTO feedback (name, feedback, rating) VALUES (%s, %s, %s)",
                (name, feedback, rating)
            )
            conn.commit()
        st.success("피드백이 저장되었습니다!")

# 피드백 목록 조회
with conn.cursor() as cur:
    cur.execute("SELECT name, feedback, rating, created_at FROM feedback ORDER BY created_at DESC LIMIT 20")
    rows = cur.fetchall()

if rows:
    import pandas as pd
    df = pd.DataFrame(rows, columns=["이름", "피드백", "만족도", "작성일"])
    st.dataframe(df)