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

Question

I am relatively new to FASTAPI but decided to setup a project with Postgres and Alembic. I managed to get the migrations create new versions everytime i use an automigrate, but for some reason I do not get any updates from my models, alas they stay blank. I am kind of lost what is going wrong.

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()

My only model so far

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()

Now Alembic creates ampty migrations when I run "alembic revision --autogenerate -m "initial setup""

My folder structure

If anyone has any idea I would be very greatful. Cheers!

Answer 1

In my case I used Transformer BERT model to deploy on FastApi, but fastapi wasnt able to recognise my model, as well as not taking the Model inputs and outputs. Code I used for my Case:

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)}

Above code is using BaseModel from pydantic and i created classes for baseModel to take text:str as input and headings, Probability, and prediction as Outputs in EntitiesOut class After that somehow it recognised by Model and save 200 status code with output