cbonesana

# %%
from openai import AsyncOpenAI
from openai.types.chat import ChatCompletion
from dotenv import load_dotenv
from tqdm.asyncio import tqdm as atqdm

import asyncio
import os
import nest_asyncio
import yaml

cbonesana

  version: 1
  disable_existing_loggers: true
  formatters:
    standard_formatter:
      format: "%(asctime)s %(levelname)7s %(name)s: %(message)s"
      datefmt: "%Y-%m-%d %H:%M:%S"
  handlers:
    console:
      formatter: standard_formatter

cbonesana

from numpy.typing import NDArray
from pydantic import BaseModel, BeforeValidator, ConfigDict, PlainSerializer
from typing_extensions import Annotated

import json
import numpy as np


# define serializers and validator
def nd_array_custom_before_validator(x: list[float]) -> NDArray[np.float32]:

cbonesana

FROM python:3.12.3

RUN pip install \
    pandas \
    scikit-learn \
    fastapi \
    aiofiles

cbonesana

from typing import Any
from pydantic import BaseModel, root_validator
import json

# this dictionary will collect all classes constructors
class_registry: dict[str, Any] = dict()


# base class
class Creature(BaseModel):

cbonesana

from sqlalchemy import create_engine

from sqlalchemy import select, insert, func

from tables import Base, Record

from time import time

import ray
import uuid

cbonesana

from multiprocessing import Queue, Process

import random
import time
import signal


stop_sentinel = "STOP"

cbonesana

from pydantic import BaseModel
from enum import Enum

import json


class Season(str, Enum):
    SPRING = 'spring'
    SUMMER = 'summer'
    AUTUMN = 'autumn'

cbonesana

import time 
import multiprocessing
import signal

from multiprocessing import Event

class W(multiprocessing.Process):

    def __init__(self, i: int, event: Event) -> None:
        super().__init__()

cbonesana

#include <iostream>
#include <memory>
#include <string>

// This is just an example object with a text field.
class Content {
    public:
    std::string txt;
    Content(std::string txt) : txt(txt){};
};