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import multiprocessing
import threading
from concurrent import futures
import random

max_workers = multiprocessing.cpu_count() - 2
def compute():
    return sum(
        [random.randint(1, 100) for i in range(1000000)])
        

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 '''
modeling_preparation.py
'''

from .project_imports import *
from .utilties import *
from .model_store import *

def train_val_test_split(trainable_df, train_percent, val_percent,
                         test_percent):

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def intialise_model(weight_path):
    return model_obj

class Inference():
    def __init__(self,weight_path):
        self.model_obj = None
        self.weight_path = weight_path

    def __load_model(self):

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import pandas as pd
import numpy as np
import cv2
import os

from PIL import Image
import glob
from keras.applications.inception_v3 import InceptionV3
from keras.applications.inception_v3 import preprocess_input, decode_predictions

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from concurrent.futures import ProcessPoolExecutor, as_completed
import cv2
import multiprocessing
import os
import sys


def print_progress(iteration, total, prefix='', suffix='', decimals=3, bar_length=100):
    """
    Call in a loop to create standard out progress bar

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import numpy as np
import json
import base64
encode_array = lambda nparray : [str(nparray.dtype.name), nparray.tobytes(), nparray.shape]

def decode_array(encStr):
    #get the encoded json dump
    enc = encStr
    # build the numpy data type
    dataType = np.dtype(enc[0])

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## Plotting the compressed feature

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import colors as mcolors
import seaborn as sns

### 2D

compressed_frame = pd.DataFrame(indexing_features,columns=['x','y','z'])

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from PIL import Image
def check_image_with_pil(path):
     try:
         Image.open(path)
     except IOError:
         return False
     return True