konverner

import subprocess
import time

timeout = 40  # Set timeout to 40 seconds 

with open('items.txt', 'r') as file:
    items = file.read().strip().split('\n')

for item in items:
    item = item.strip()  # Clean up any extra whitespace

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import json

def extract_json_from_text(text):
    # Find the first '{' character
    start = text.find('{')
    if start == -1:
        print("No JSON block found in the text.")
        return None

    # Use a stack to track nested braces

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# pip install python-dotenv
import os
from dotenv import find_dotenv, load_dotenv

# load variables from .env
load_dotenv(find_dotenv(usecwd=True))
MY_VAR = os.getenv("MY_VAR")

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"""
A function allows to generate json key file used by google api from environment variables. Example of `.env` file:

```
TYPE="my_service_account"
PROJECT_ID="my_project_id"
PRIVATE_KEY_ID="my_private_key_id"
PRIVATE_KEY="-----BEGIN PRIVATE KEY-----
my_private_key_content
-----END PRIVATE KEY-----

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import json
from typing import Any, Dict, List

import spacy
from spacy.training.iob_utils import biluo_to_iob, doc_to_biluo_tags
from tqdm import tqdm


def spans_to_conll(
        samples: List[Dict[str, Any]],

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def fix_span(text: str, span: dict):
    # let us check that spans are correctly extracted

    fixed_span = span.copy()

    # span starts with a space or a punctuation
    while text[fixed_span["start"]] in [" ", ".", ",", ";", ":", "!", "?"]:
        fixed_span["start"] += 1

    # span is cut in the begging: e.g. "ashington DC"

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mkdir -p ~/.ssh
ssh-keyscan -t rsa github.com >> ~/.ssh/known_hosts
ssh-keygen -t rsa -C <email>
cat ~/.ssh/id_rsa.pub

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import numpy as np
import statsmodels.api as sm


X = np.array([[1, 85, 5],
              [1, 177, 6],
              [1, 100, 9],
              [1, 110, 8],
              [1, 90, 7.5],
              [1, 144, 5.5]])

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!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1
!sudo update-alternatives --config python3
!sudo apt install python3-pip
!python3 --version

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import pandas as pd


df = pd.DataFrame({'name': ['Store A', 'Store B', 'Store C', 'Store D']
                   'district': ['I', "II", "I", "III"],
                   "category": [X, X, Y, Z]
                   }
                  )

district_category_pivot_table = df.pivot_table('name', 'district', 'category', aggfunc='count')