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gerardoe
2026-04-09 14:03:06 -07:00
parent 75ecb00a94
commit 092a7048d8
21 changed files with 416 additions and 118 deletions
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111
README.md
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@@ -27,6 +27,115 @@ una vez importado el model, lo lee, lo pasa por la logica en la que se descratan
`notas de Gerardo`
se quedaron implementadas los servicios crud de las funciones que seran publicos, ajustados a el tipo de usuario ('Root', 'Admin')
lo mas dificil de implementar para el back va a ser, implementar las funciones de ajuste "automatico" de cargado de files, con un url, desde las listas, del sat y las publicaciones del diario.
lo mas dificil de implementar para el back va a ser, implementar las funciones de ajuste "automatico" de cargado de file con un url, desde las listas del sat y las publicaciones del diario.
si el tiempo nos cae encima (que es lo mas seguro, voy a concentrarme en trabajar en las listas de el sat(edos, efos y creditos), para tratar de dejar enpoints funcionales).
# Flujo del tomado automatico de las listas del sat
┌─────────────────────────────────────────────────────────────────────────────────────┐
│ PIPELINE DE TRANSFORMACIÓN │
│ (CSV → JSONB → Base de Datos) │
└─────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────┐
│ SAT ORIGEN │
│ archivo.csv │
└────────┬────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────────────────┐
│ LECTOR POR LOTES (chunks) │
│ ┌──────────────────────┐ │
│ │ Bloque 1: filas 1-100│ │
│ │ Bloque 2: filas 101-200│ │
│ │ ... │ │
│ │ Bloque N: últimas │ │
│ └──────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────────────────┐
│ TRANSFORMADOR POR BLOQUE │
├─────────────────────────────────────────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ 1. Agrupar │───▶│ 2. Limpiar │───▶│ 3. Armar │───▶│ 4. Validar │ │
│ │ por RFC │ │ fechas │ │ JSONB │ │ esquema │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────────────────────────────────┐ │
│ │ FUNCIONES REUTILIZABLES │ │
│ │ • normalize_date() → convierte números Excel y strings a ISO │ │
│ │ • get_latest_status() → identifica el estatus más reciente │ │
│ │ • build_jsonb() → construye la estructura final │ │
│ └─────────────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────────────────┐
│ OUTPUT: JSONB POR RFC │
├─────────────────────────────────────────────────────────────────────────────────────┤
│ { │
│ "rfc": "ASA110718HJ5", │
│ "nombre": "ASESORÍA Y SERVICIOS ACANTO, S.A. DE C.V.", │
│ "ultimo_estatus": { │
│ "situacion": "definitivo", │
│ "fecha": "2020-06-24", │
│ "oficio": "500-05-2020-13598" │
│ }, │
│ "historial": [...], │
│ "raw_metadata": {...} │
│ } │
└─────────────────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────────────────┐
│ REPOSITORIO / SERVICE LAYER │
├─────────────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────────────────┐ │
│ │ interface EfosRepository │ │
│ │ ┌─────────────────────┐ ┌─────────────────────┐ │ │
│ │ │ save_batch() │ │ find_by_rfc() │ │ │
│ │ │ update_status() │ │ search_by_name() │ │ │
│ │ │ get_statistics() │ │ count_by_status() │ │ │
│ │ └─────────────────────┘ └─────────────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ┌─────────────────┼─────────────────┐ │
│ ▼ ▼ ▼ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ PostgreSQL │ │ SQLite │ │ MongoDB │ │
│ │ (producción) │ │ (dev/local) │ │ (alternativa│ │
│ └─────────────┘ └─────────────┘ │ JSON nativo)│ │
│ └─────────────┘ │
└─────────────────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────────────────┐
│ FRONT-END API (FastAPI) │
├─────────────────────────────────────────────────────────────────────────────────────┤
│ │
│ GET /contribuyentes/{rfc} → consulta por RFC │
│ GET /contribuyentes?q={nombre} → búsqueda por nombre │
│ GET /contribuyentes?status={x} → filtrar por situación │
│ GET /stats → contar por status (rojo/amarillo/verde) │
│ │
└─────────────────────────────────────────────────────────────────────────────────────┘
# 09/04/2026 - queda pendiente;
- revisar, verificar y tabajar todos los cruds.
- los que trabajan completamente bien, son los de usuarios y los de clients.
- la bd, funciona correctamente y las migraciones estan en su top.
- en este punto el endpoint de tomado automatico de las listas del sat, ya funcionan con el url del CSV. falta ajustar que detecte en que bd escriba, por el momento solo funciona con EDOS.
@ @
* Nota importante *
- !para hacer chunks del cargado de la lista de Efos(articulo69-b) usar una libreria y no hacerle caso al ENFERMO DE HUGO, para hacerlo a mano!
urls importantes:
https://wu1agsprosta001.blob.core.windows.net/agsc-publicaciones/Datos_abiertos/Documents_AGAFF/Listado_completo_69-B.csv
* https://wu1agsprosta001.blob.core.windows.net/agsc-publicaciones/Datos_abiertos/Documents_AGGC/Listado_69_B_Bis_Completo.csv
* https://www.sat.gob.mx/minisitio/DatosAbiertos/contribuyentes_publicados.html#collapseTwo1

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app/helpers/csv_mapper.py
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from typing import List, Dict, Any
#helpers/csv_mapper
from typing import List, Dict, Any, Optional
from app.modules.edos.schema import EdosUpload
from datetime import date, datetime
class CSVMapper:
""" Mapper: filter and mapping only necesary data, based on a schema"""
FIELD_MAPPING = {
'csv_column_name': 'schema_field',
'No.' : 'publicacion_sat',
'RFC' : 'numero',
'Nombre del Contribuyente' : 'razon_social',
'Situación del contribuyente' : 'situacion',
'Número y fecha de oficio global definitivo SAT' : 'numero_definitivo',
'Publicación página SAT definitivo' : 'fecha_definitivo',
'Número y fecha de oficio global definitivo DOF' : 'numero_def_dof',
'Publicación DOF definitivo' : 'fecha_def_dof',
'Número y fecha de oficio global de sentencia favorable SAT' : 'publicacion_dof',
'Publicación página SAT sentencia favorable' : 'numero_fav_sat',
'RFC': 'numero',
'Nombre del Contribuyente': 'razon_social',
'Situación del contribuyente': 'situacion',
'Número y fecha de oficio global definitivo SAT': 'numero_definitivo',
'Publicación página SAT definitivo': 'fecha_definitivo',
'Número y fecha de oficio global definitivo DOF': 'numero_def_dof',
'Publicación DOF definitivo': 'fecha_def_dof',
'Número y fecha de oficio global de sentencia favorable SAT': 'numero_fav_sat',
'Publicación página SAT sentencia favorable': 'publicacion_sat',
'Publicación DOF sentencia favorable': 'publicacion_dof',
}
REQUIRED_FIELD = [ 'publicacion_sat', 'numero', 'razon_social', 'situacion'] #Campos obligatorios
REQUIRED_FIELD = [ 'numero', 'razon_social', 'situacion'] #Campos obligatorios
@staticmethod
def filter_by_schema(raw_rows: List[Dict]) -> List[Dict]:
""" Filter only fields defined on schema"""
filtered = []
def map_rows(csv_rows: List[Dict]) -> List[Dict]:
mapped_rows = []
for row in raw_rows:
mapped_row = {}
for csv_field, schema_field in CSVMapper.FIELD_MAPPING.items():
if csv_field in row and row[csv_field]:
mapped_row[schema_field] = CSVMapper._clean_value(
row[csv_field], schema_field
)
if all(field in mapped_row for field in CSVMapper.REQUIRED_FIELD):
filtered.append(mapped_row)
else:
print(f"omited fields: {mapped_row}")
return filtered
for row in csv_rows:
mapped = {}
for csv_field, bd_field in CSVMapper.FIELD_MAPPING.items():
value = row.get(csv_field, '')
if bd_field == 'razon_social':
value = value.replace('\n', ' ').replace('\r', ' ')
if bd_field == 'situacion':
value = CSVMapper._normalize_situacion(value)
if 'fecha' in bd_field and value:
value = CSVMapper._parse_date(value)
mapped[bd_field] = value if value else None
missing = [f for f in CSVMapper.REQUIRED_FIELD if not mapped.get(f)]
if missing:
print(f"Fila omitida - faltan: {missing}")
continue
mapped_rows.append(mapped)
return mapped_rows
@staticmethod
def _clean_value(value: str, field_type: str) -> Any:
""" Cleanen and typified values"""
if not value or value.strip() == '':
def _normalize_situacion(value: str) -> str:
""" converts text of CVS to format ENUM"""
if not value:
return None
if 'Sentencia Favorable' in value:
return 'sentencia_favorable'
if 'Definitivo' in value:
return 'definitivo'
return value.lower()
@staticmethod
def _parse_date(value: str) -> Optional[date]:
"""Converts DD/MM/YYYY to date"""
if not value:
return None
try:
return datetime.strptime(value.strip(), '%dd%m%Y').date()
except:
return None
if 'date' in field_type:
from datetime import datetime
return datetime.strptime(value.strip(), '%Y-%m-%d')
elif 'amount' in field_type or 'price' in field_type:
return float(value.strip().replace(',','.'))
elif 'int' in field_type.lower():
return int(value.strip())
else:
return value.strip()

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app/helpers/db_adapter.py
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@@ -1,3 +1,5 @@
#helper/db_adapter
from typing import List, Dict
from database import sessionLocal
from app.modules.edos.models import EDOS
@@ -5,11 +7,18 @@ from app.modules.edos.models import EDOS
class DBAdapter:
""" Adapter: insert cleen data on a db"""
def __inti__(self):
def __init__(self):
self.db = sessionLocal()
def insert_filtered_data(self, mapped_rows: List[Dict]) -> Dict:
""" Insert only data filtered and mapped"""
print(f" [DBAdapter] Recibidas {len(mapped_rows)} filas")
if mapped_rows:
print(f" [DBAdapter] Primera fila: {mapped_rows[0]}")
results = {
'total_processed': len(mapped_rows),
'inserted': 0,
@@ -19,8 +28,11 @@ class DBAdapter:
for row in mapped_rows:
try:
#verified dupled data
unique_field_value = row.get('numero')
existing = self.db.query(EDOS).filter_by(
unique_field=row.get('unique_field')
numero=unique_field_value
).first()
if existing:
@@ -28,8 +40,11 @@ class DBAdapter:
continue
instance = EDOS(**row)
self.db.add(instance)
print(f" [DBAdapter] Added instance for {row.get('numero')}")
self.db.commit()
print(f" [DBAdapter] Committed successfully")
results['inserted'] += 1
except Exception as e:
self.db.rollback()
@@ -37,6 +52,6 @@ class DBAdapter:
'row': row,
'error': str(e)
})
self.db.close()
return results
self.db.close()
return results

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app/helpers/extractCsv.py
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@@ -1,3 +1,5 @@
#helper/csv_mapper
import csv
import requests
from io import StringIO
@@ -5,6 +7,10 @@ from io import StringIO
from typing import List, Dict, Any
import os
from dotenv import load_dotenv
#
import pandas as pd
import math
load_dotenv()
@@ -17,6 +23,21 @@ class Settings:
settings = Settings()
def _find_header_row(self, rows, delimiter):
""" Search row with RFC an name"""
for idx, row in enumerate(rows):
#Normalice: Convert to String, clean space, mayus
clean_row = [str(cell).strip().upper() for cell in row]
has_rfc = any('RFC' in cell for cell in clean_row)
has_nombre =any('NOMBRE' in cell for cell in clean_row)
if has_rfc and has_nombre:
return idx
return None
class CSVExtractor:
"""Extract raw data of CSV"""
@@ -36,46 +57,40 @@ class CSVExtractor:
@staticmethod
def read_csv(content: str) -> List[Dict[str, Any]]:
def read_csv(content):
""" Converts CSV to list of dictionary"""
csv_file = StringIO(content)
#detect delimiter auto
sample = csv_file.read(1024)
csv_file.seek(0)
try:
dialect = csv.Sniffer().sniff(sample)
reader =csv.DictReader(csv_file, dialect=dialect)
except:
csv_file.seek(0)
reader = csv.DictReader(csv_file)
for delimiter in[',', ';', '\t']:
rows = [row for row in reader]
try:
lines = content.split('\n')
if rows:
clean_row = {}
header_row_idx = None
for idx, line in enumerate(lines):
if 'RFC' in line.upper():
header_row_idx = idx
break
for key, value in rows[0].items():
if header_row_idx is None:
continue
if key is not None:
clean_key = key.strip().replace('"', '').replace("'", "").replace('\ufeff', '')
else:
clean_key = ''
df = pd.read_csv(
StringIO(content),
delimiter=delimiter,
encoding='utf-8',
skiprows=header_row_idx,
dtype=str,
keep_default_na=False,
na_filter=False)
clean_row[clean_key] = value
df.columns = df.columns.str.strip()
for i, row in enumerate(rows):
new_row = {}
for old_key, value in row.items():
if old_key is not None:
new_key = old_key.strip().replace('"', '').replace("'", "").replace('\ufeff', '')
else:
new_key = ''
#FIX: manage value None for avoid error strip
if value is not None and isinstance(value, str):
new_value = value.strip()
else:
new_value = value
df = df.dropna(how='all')
result = df.to_dict('records')
if result:
return result
except Exception as e:
print(f"Error with delimiter '{delimiter}': {e}")
continue
raise ValueError("Don't read CSV with pandas")
new_row[new_key] = value
rows[i] = new_row
return rows

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app/modules/edos/models.py
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@@ -16,12 +16,12 @@ class EDOS(Base):
__tablename__="edos"
id = Column(Integer, primary_key=True, nullable=False)
numero = Column(Integer, nullable=False)
numero = Column(String(25), nullable=False)
razon_social = Column(String(100), nullable=False)
situacion = Column(SQLEnum(Situacion, name="situcion", create_type=False), nullable=False)
numero_definitivo = Column(String(60), nullable=False)
fecha_definitivo = Column(Date, nullable=True )
publicaccion_sat = Column(Date, nullable=True)
publicacion_sat = Column(Date, nullable=True)
numero_def_dof = Column(String(100), nullable=True)
fecha_def_dof = Column(Date, nullable=True)
publicacion_dof = Column(Date, nullable=True)

3
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@@ -13,7 +13,7 @@ async def import_csv(data: Dict[str, Any] = Body(..., example={"url": "http://ex
csv_url = data.get('url')
dry_run = data.get('dry_run', True)
dry_run = data.get('dry_run', False)
max_rows = data.get('max_rows')
if not csv_url:
@@ -26,6 +26,7 @@ async def import_csv(data: Dict[str, Any] = Body(..., example={"url": "http://ex
)
try:
result = XMACSVService.process_csv_from_url(
csv_url= csv_url,
dry_run=dry_run,

2
app/modules/edos/schema.py
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@@ -16,7 +16,7 @@ class EdosUpload(BaseModel):
situacion : Situacion
numero_definitivo : str
fecha_definitivo : date
publicaccion_sat : date
publicacion_sat : date
numero_def_dof : str
fecha_def_dof : date
publicacion_dof : date

74
app/modules/edos/service.py
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@@ -4,71 +4,95 @@ from typing import Optional
from fastapi import HTTPException
from app.modules.edos.models import EDOS
from app.modules.edos.schema import EdosResponse, EdosUpload, messageResponse
from app.helpers.csv_mapper import CSVMapper
from app.helpers.db_adapter import DBAdapter
from app.helpers.extractCsv import CSVExtractor
class XMACSVService:
""" Orquester"""
"""Orchestrator: extract, map, adapt pipeline"""
@staticmethod
def process_csv_from_url(
csv_url: str,
dry_run: bool = False,
max_rows: int = None
) -> dict:
""" flow download and parsing, filter and adapt"""
) -> dict:
"""Flow: download CSV, parse, map to schema, insert into database"""
try:
# X - EXTRACT
print(f"📥 Descargando CSV: {csv_url}")
# =========================================================
# X - EXTRACT: Download and parse CSV
# =========================================================
print(f"Downloading CSV: {csv_url}")
# ✅ Ahora download_csv retorna (content, encoding)
content, encoding = CSVExtractor.download_csv(csv_url)
print(f" ✅ Descargado {len(content)} bytes, encoding: {encoding}")
print(f" Downloaded {len(content)} bytes, encoding: {encoding}")
# Parsear CSV
# Debug: show first lines
print("First 10 lines of CSV:")
lines = content.split('\n')
for i, line in enumerate(lines[:10]):
print(f"Line {i}: {repr(line[:200])}")
# Parse CSV to raw rows
raw_rows = CSVExtractor.read_csv(content)
print(f" ✅ Parseadas {len(raw_rows)} filas")
print(f" Parsed {len(raw_rows)} rows")
if not raw_rows:
return {
'total_extracted': 0,
'total_mapped': 0,
'inserted': 0,
'errors': ['No se encontraron datos en el CSV']
'dry_run': dry_run,
'errors': ['No data found in CSV']
}
# Mostrar columnas encontradas
print(f" 📋 Columnas: {list(raw_rows[0].keys())}")
print(f" Columns: {list(raw_rows[0].keys())}")
# M - MAP (si tienes mapper)
# Por ahora, usar datos crudos
mapped_rows = raw_rows
# =========================================================
# M - MAP: Transform CSV rows to database schema
# =========================================================
mapped_rows = CSVMapper.map_rows(raw_rows)
print(f" Mapped {len(mapped_rows)} rows to database schema")
if max_rows:
mapped_rows = mapped_rows[:max_rows]
print(f" 🔒 Limitado a {max_rows} filas")
print(f" Limited to {max_rows} rows")
# =========================================================
# A - ADAPT: Insert into database
# =========================================================
inserted_count = 0
errors = []
# A - ADAPT
if not dry_run:
print(f" 💾 Insertando {len(mapped_rows)} filas en BD")
# Aquí iría la inserción en BD
print(f" Inserting {len(mapped_rows)} rows into database")
adapter = DBAdapter()
result = adapter.insert_filtered_data(mapped_rows)
inserted_count = result.get('inserted', 0)
errors = result.get('errors', [])
print(f" Inserted: {inserted_count}")
print(f" Duplicates skipped: {result.get('duplicates_skiped', 0)}")
if errors:
print(f" Errors: {len(errors)}")
else:
print(f" DRY RUN - No data inserted")
# =========================================================
# Return response
# =========================================================
return {
'total_extracted': len(raw_rows),
'total_mapped': len(mapped_rows),
'inserted': len(mapped_rows) if not dry_run else 0,
'inserted': inserted_count,
'dry_run': dry_run,
'columns': list(raw_rows[0].keys()) if raw_rows else [],
'sample': raw_rows[:2] if raw_rows else []
'sample': mapped_rows[:2] if mapped_rows else [],
'errors': errors[:10] if errors else [] # Limit errors in response
}
except Exception as e:
print(f"❌ Error: {str(e)}")
print(f"Error: {str(e)}")
import traceback
traceback.print_exc()
raise

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migrations/versions/876421e4eea2_adjustmen_of_bds.py Normal file
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@@ -0,0 +1,54 @@
"""adjustmen of BDs
Revision ID: 876421e4eea2
Revises: c01f58ebc7d4
Create Date: 2026-04-09 20:28:03.853032
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
# revision identifiers, used by Alembic.
revision: str = '876421e4eea2'
down_revision: Union[str, None] = 'c01f58ebc7d4'
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.alter_column('edos', 'numero',
existing_type=sa.INTEGER(),
type_=sa.String(length=25),
existing_nullable=False)
op.alter_column('edos', 'fecha_definitivo',
existing_type=sa.DATE(),
nullable=True)
op.alter_column('license', 'deleted_at',
existing_type=postgresql.TIMESTAMP(timezone=True),
nullable=True)
op.drop_column('location', 'country_id')
op.drop_column('location', 'city_id')
op.drop_column('location', 'state_id')
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('location', sa.Column('state_id', sa.INTEGER(), autoincrement=False, nullable=False))
op.add_column('location', sa.Column('city_id', sa.INTEGER(), autoincrement=False, nullable=False))
op.add_column('location', sa.Column('country_id', sa.INTEGER(), autoincrement=False, nullable=False))
op.alter_column('license', 'deleted_at',
existing_type=postgresql.TIMESTAMP(timezone=True),
nullable=False)
op.alter_column('edos', 'fecha_definitivo',
existing_type=sa.DATE(),
nullable=False)
op.alter_column('edos', 'numero',
existing_type=sa.String(length=25),
type_=sa.INTEGER(),
existing_nullable=False)
# ### end Alembic commands ###

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"""adjustmen of BDsV2
Revision ID: c242c2733a7f
Revises: 876421e4eea2
Create Date: 2026-04-09 20:37:27.188549
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = 'c242c2733a7f'
down_revision: Union[str, None] = '876421e4eea2'
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('edos', sa.Column('publicacion_sat', sa.Date(), nullable=True))
op.drop_column('edos', 'publicaccion_sat')
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('edos', sa.Column('publicaccion_sat', sa.DATE(), autoincrement=False, nullable=True))
op.drop_column('edos', 'publicacion_sat')
# ### end Alembic commands ###

30
migrations/versions/d91de687dac7_full_bd.py Normal file
View File

@@ -0,0 +1,30 @@
"""full bd
Revision ID: d91de687dac7
Revises: c242c2733a7f
Create Date: 2026-04-09 21:02:22.168790
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = 'd91de687dac7'
down_revision: Union[str, None] = 'c242c2733a7f'
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
pass
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
pass
# ### end Alembic commands ###