FASTAPI与Alembic一起运行，但autogen不会检测模型

Question

我对FASTAPI比较陌生，但决定与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，但由于某些原因，我没有从我的模型中获得任何更新，唉，它们保持空白。我有点迷失了方向。

主要的py公司

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}"}

数据库py公司

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（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()

如果有人有任何想法，我将非常伟大。干杯！

Answer 1

env.py文件找不到模型，因为您还没有导入它们。解决方法之一是，您只需将它们立即导入env.py文件中，如下所示：

从...模型导入*

但是，您需要在模型目录中有一个init.py文件，并在其中包含所有模型。

另一种方法（但不推荐）：如果只有一个模型，则可以将其直接导入为：

从…起模型。交易对手模型导入

Answer 2

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，也无法获取模型的输入和输出。我在案例中使用的代码：

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，将文本：str作为输入，将标题、概率和预测作为实体输出类中的输出，然后通过模型进行识别，并用输出保存200个状态代码