FASTAPI 运行结合 Alembic，但自动生成不检测模型

Question

我对 FASTAPI 比较陌生，但决定使用 Postgres 和 Alembic 设置一个项目。每次我使用自动迁移时，我都设法让迁移创建新版本，但由于某种原因，我没有从我的模型中获得任何更新，唉，它们仍然是空白的。我有点不知道出了什么问题。

Main.py

from fastapi import FastAPI
import os
app = FastAPI()


@app.get("/")
async def root():
    return {"message": os.getenv("SQLALCHEMY_DATABASE_URL")}


@app.get("/hello/{name}")
async def say_hello(name: str):
    return {"message": f"Hello {name}"}

Database.py

from sqlalchemy import  create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
import os

SQLALCHEMY_DATABASE_URL = os.getenv("SQLALCHEMY_DATABASE_URL")

engine = create_engine("postgresql://postgres:mysuperpassword@localhost/rodney")
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

Base = declarative_base()

def get_db():
    db = SessionLocal()
    try:
        yield db
    except:
        db.close()

我目前唯一的模型

from sqlalchemy import Integer, String
from sqlalchemy.sql.schema import Column
from ..db.database import  Base


class CounterParty(Base):
    __tablename__ = "Counterparty"

    id = Column(Integer, primary_key=True)
    Name = Column(String, nullable=False)

env.py（蒸馏器）

from logging.config import fileConfig

from sqlalchemy import engine_from_config
from sqlalchemy import pool

from alembic import context

# this is the Alembic Config object, which provides
# access to the values within the .ini file in use.
config = context.config

# Interpret the config file for Python logging.
# This line sets up loggers basically.
fileConfig(config.config_file_name)

# add your model's MetaData object here
# for 'autogenerate' support
from app.db.database import Base
target_metadata = Base.metadata

# other values from the config, defined by the needs of env.py,
# can be acquired:
# my_important_option = config.get_main_option("my_important_option")
# ... etc.


def run_migrations_offline():
    """Run migrations in 'offline' mode.

    This configures the context with just a URL
    and not an Engine, though an Engine is acceptable
    here as well.  By skipping the Engine creation
    we don't even need a DBAPI to be available.

    Calls to context.execute() here emit the given string to the
    script output.

    """
    url = config.get_main_option("sqlalchemy.url")
    context.configure(
        url=url,
        target_metadata=target_metadata,
        literal_binds=True,
        dialect_opts={"paramstyle": "named"},
    )

    with context.begin_transaction():
        context.run_migrations()


def run_migrations_online():
    """Run migrations in 'online' mode.

    In this scenario we need to create an Engine
    and associate a connection with the context.

    """
    connectable = engine_from_config(
        config.get_section(config.config_ini_section),
        prefix="sqlalchemy.",
        poolclass=pool.NullPool,
    )

    with connectable.connect() as connection:
        context.configure(
            connection=connection, target_metadata=target_metadata
        )

        with context.begin_transaction():
            context.run_migrations()


if context.is_offline_mode():
    run_migrations_offline()
else:
    run_migrations_online()

现在当我运行 “alembic revision --autogenerate -m “initial setup””时 Alembic 会创建大量迁移

我的文件夹结构

如果有人有任何想法，我将非常感激。干杯！

Answer 1

在我的例子中，我使用 Transformer BERT 模型部署在 FastApi 上，但 fastapi 无法识别我的模型，也无法接受模型输入和输出。 我用于案例的代码：

from fastapi import FastAPI
from pydantic import BaseModel

class Entities(BaseModel):
    text: str

class EntitesOut(BaseModel):
    headings: str
    Probability: str
    Prediction: str

model_load = load_model('BERT_HATESPEECH')
tokenizer = DistilBertTokenizerFast.from_pretrained('BERT_HATESPEECH_TOKENIZER')
file_to_read = open("label_encoder_bert_hatespeech.pkl", "rb")
label_encoder = pickle.load(file_to_read)

app = FastAPI()

@app.post('/predict', response_model=EntitesOut)
def prep_data(text:Entities):
    text = text.text
    tokens = tokenizer(text, max_length=150, truncation=True, 
                       padding='max_length', 
                       add_special_tokens=True, 
                       return_tensors='tf')
    tokens = {'input_ids': tf.cast(tokens['input_ids'], tf.float64), 'attention_mask': tf.cast(tokens['attention_mask'], tf.float64)}
    headings = '''Non-offensive', 'identity_hate', 'neither', 'obscene','offensive', 'sexism'''
    probs = model_load.predict(tokens)[0]
    pred = label_encoder.inverse_transform([np.argmax(probs)])
    return {"headings":headings,
            "Probability":str(np.round(probs,3)),
            "Prediction":str(pred)}

以上代码使用了 pydantic 的 BaseModel，我为 baseModel 创建了类以获取 text:str as input 和 headings, Probability, and prediction as Outputs in EntitiesOut class 之后它以某种方式被模型识别并保存 200 状态代码并输出

Answer 2

env.py 文件找不到模型，因为您还没有导入它们。一种解决方案是，您只需立即将它们导入 env.py 文件中：

from ..models import *

但是，您需要在模型目录中有一个 init.py 文件，并将所有模型包含在其中。

另一种方式（不过不推荐）：如果你只有一个模型，可以直接导入为：

from ..models.counterPartyModel import

FASTAPI 运行结合 Alembic，但自动生成不检测模型

FASTAPI run in conjunction with Alembic, but autogenerate does not detect the models

python

fastapi

alembic

FASTAPI 运行 结合 Alembic，但自动生成不检测模型

FASTAPI run in conjunction with Alembic, but autogenerate does not detect the models

python

fastapi

alembic

FASTAPI 运行结合 Alembic，但自动生成不检测模型