Files
smb-online/backoffice/app/db.py
T
mivanchenko 18e346d67b Back office: Postgres source-of-truth + read dashboard
Stand up the DB-first CRM backbone (architecture pivot: Postgres is the
source of truth, Google Sheets becomes a one-way downstream mirror).

- backoffice/ stack: smb-db (Postgres 16) + smb-crm (Flask/waitress service).
- Schema mirrors the six Sheet tabs (clients, leads, projects, activity_log,
  bookings, invoices) with typed columns + updated_at triggers.
- Service-account Sheets client (PyJWT) for the one-time import + future mirror.
- import_from_sheets.py: idempotent seed of Postgres from the live Sheets.
- Read dashboard (Leads & Clients tables) at onboard.mivanchenko.de/crm,
  behind the existing Caddy basic-auth; JSON API reads straight from Postgres.

Deployed + verified: import seeded DB, dashboard/API live, no-auth blocked,
onboarding form unaffected. Add/edit/delete + DB->Sheets sync + n8n ingest
swap are the next steps.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-25 09:12:39 +02:00

149 lines
4.3 KiB
Python

"""Postgres access + the table/column contract shared across the app.
The TABLES map is the single definition of which entities exist, their columns,
and their primary key. Read/CRUD endpoints, the importer and the Sheets mirror
all derive from it so they can never drift apart.
"""
import os
import re
from datetime import datetime, date
import psycopg
from psycopg.rows import dict_row
DATABASE_URL = os.environ["DATABASE_URL"]
# entity -> (sheet tab, primary key, ordered columns)
TABLES = {
"clients": {
"tab": "Clients",
"pk": "client_id",
"cols": ["client_id", "business_name", "owner_name", "email", "phone",
"niche", "tier", "status", "domain", "stack_notes", "vault_ref",
"services", "billing_cycle", "monthly_fee_eur", "start_date",
"renewal_date", "created_at", "notes"],
"dates": ["start_date", "renewal_date"],
"timestamps": ["created_at"],
"numbers": ["monthly_fee_eur"],
"bools": [],
},
"leads": {
"tab": "Leads",
"pk": "lead_id",
"cols": ["lead_id", "received_at", "client_id", "source", "name",
"contact", "service_interest", "message", "status", "notified"],
"dates": [],
"timestamps": ["received_at"],
"numbers": [],
"bools": ["notified"],
},
"projects": {
"tab": "Projects",
"pk": "project_id",
"cols": ["project_id", "client_id", "deliverable", "tier", "checklist",
"go_live_date", "status"],
"dates": ["go_live_date"],
"timestamps": [],
"numbers": [],
"bools": [],
},
"bookings": {
"tab": "Bookings",
"pk": "booking_id",
"cols": ["booking_id", "created_at", "client_id", "customer_name",
"customer_contact", "service", "start_time", "end_time",
"source", "status"],
"dates": [],
"timestamps": ["created_at", "start_time", "end_time"],
"numbers": [],
"bools": [],
},
"invoices": {
"tab": "Invoices",
"pk": "invoice_id",
"cols": ["invoice_id", "client_id", "issued_date", "due_date",
"amount_eur", "period", "status", "paid_date"],
"dates": ["issued_date", "due_date", "paid_date"],
"timestamps": [],
"numbers": ["amount_eur"],
"bools": [],
},
"activity_log": {
"tab": "Activity Log",
"pk": "id",
"cols": ["ts", "workflow", "client_id", "action", "detail", "result"],
"dates": [],
"timestamps": ["ts"],
"numbers": [],
"bools": [],
},
}
def connect():
return psycopg.connect(DATABASE_URL, row_factory=dict_row)
# ---- coercion: Sheet strings / JSON values -> typed Python for Postgres ----
def parse_date(v):
if v in (None, ""):
return None
if isinstance(v, date):
return v
m = re.match(r"(\d{4})-(\d{2})-(\d{2})", str(v))
return date(int(m.group(1)), int(m.group(2)), int(m.group(3))) if m else None
def parse_ts(v):
if v in (None, ""):
return None
if isinstance(v, datetime):
return v
s = str(v).strip()
for fmt in ("%Y-%m-%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y-%m-%dT%H:%M:%S",
"%Y-%m-%dT%H:%M", "%Y-%m-%d"):
try:
return datetime.strptime(s, fmt)
except ValueError:
continue
return None
def parse_num(v):
if v in (None, ""):
return None
s = str(v).replace("€", "").replace(",", ".").strip()
try:
return float(s)
except ValueError:
return None
def parse_bool(v):
if v in (None, ""):
return None
if isinstance(v, bool):
return v
return str(v).strip().lower() in ("true", "1", "yes", "ja", "wahr")
def coerce_row(entity, rec):
"""Return a dict of column -> typed value for the given entity."""
spec = TABLES[entity]
out = {}
for col in spec["cols"]:
v = rec.get(col, None)
if isinstance(v, str):
v = v.strip() or None
if col in spec["dates"]:
v = parse_date(v)
elif col in spec["timestamps"]:
v = parse_ts(v)
elif col in spec["numbers"]:
v = parse_num(v)
elif col in spec["bools"]:
v = parse_bool(v)
out[col] = v
return out