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from pathlib import Path
import pandas as pd

DATA_FOLDER = Path("data")

# get all csv files in data and its subfolders
for csv_file in DATA_FOLDER.rglob("*.csv"):

	csv_full_path = csv_file.resolve()

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import time
from functools import wraps
from pathlib import Path
from typing import Callable, Optional, Union
import numpy as np
import pandas as pd
import ray


def pipeline_logger(function: Callable):

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from bounding_box import bounding_box as bb


names = [
    "person",
    "bicycle",
    "car",
    "motorcycle",
    "airplane",
    "bus",

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import torch
import cv2
from tags import label_bbbox


model = torch.hub.load("ultralytics/yolov5", "yolov5s", pretrained=True, classes=80)
model.conf = 0.75

capture = cv2.VideoCapture(0)
assert capture.isOpened(), "Cannot open camera"

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from pytrends.request import TrendReq
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.neighbors import KNeighborsRegressor
from sktime.forecasting.base import ForecastingHorizon
from sktime.forecasting.compose import EnsembleForecaster, ReducedForecaster
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.performance_metrics.forecasting import smape_loss
from sktime.utils.plotting import plot_series

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import torch
import torch.nn as nn
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification


MODEL_NAME = "microsoft/deberta-xlarge-mnli"


class Model:

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from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LogisticRegression

# assumes classification problem data split with numeric, categorical and text features
# [Salary, Bonus, Age, Gender, Descriptions Target]