python FASTAPI与Alembic一起运行,但自动生成不会检测模型

dgsult0t  于 2022-12-21  发布在  Python
关注(0)|答案(3)|浏览(168)

我对FASTAPI比较陌生,但我决定用Postgres和Alembic建立一个项目。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得到任何更新,唉,它们保持空白。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()

目前为止我唯一的模特

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 (alembic)

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“初始设置””x1c 0d1x“时,Alembic会创建ampty迁移
我的文件夹结构

如果有人知道,我会非常感谢的,干杯!

uttx8gqw

uttx8gqw1#

在我的案例中,我使用Transformer BERT模型部署在FastApi上,但fastapi无法识别我的模型,也无法获取模型输入和输出。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)}

上面的代码使用pydantic中的BaseModel,我为baseModel创建了类,以获取text:str as inputheadings, Probability, and prediction as Outputs in EntitiesOut class之后,模型以某种方式识别了它,并保存200状态代码和输出

46scxncf

46scxncf2#

env.py文件找不到模型,因为您还没有导入它们。一个解决方案是,您只需将它们立即导入到env.py文件中,如下所示:
从..模型导入 *
但是,您需要在models目录中有一个init.py文件,并在其中包含所有模型。
另一种方法(但不推荐):如果您只有一个模型,您可以直接将其导入为:
从..模型.交易方模型导入

0dxa2lsx

0dxa2lsx3#

这是一个有点晚的回应,但我刚刚遇到同样的问题,也许我的答案会帮助别人在未来:)在我的情况下,这是由于数据库状态-它已经在一致的状态,这意味着alembic没有试图创建它的修改(它没有看到任何差异)。当我使用sqlite我只是删除sqlite文件(删除表应该工作),并再次运行revision。这一次,它的工作与预期一样,upgradedowngrade函数填充自动生成的代码。

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