import re
from gliner import GLiNER
import spacy

# Load SpaCy English model
nlp = spacy.blank("en")

# Get SpaCy's stopword list
stop_words = nlp.Defaults.stop_words

# Initialize GLiNER with a pre-trained model
model = GLiNER.from_pretrained("urchade/gliner_medium-v2.1")

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/home/rajaraman/miniconda3 …

import numpy as np
import pandas as pd

df = pd.DataFrame({
    'maths' : [80, 89, 90, 20, 60],
    'science' : [40, 50, 90, 100, 50],
    'language' : [20 …

"""
input sample:
17. random text1
18. random text2

output:
17, random text1
18, random text2
"""

'\ninput sample:\n17. random text1\n18. random text2\n\noutput:\n17, random text1\n18, random text2\n'

input_sample = """
17. random text1
18. random text2
"""

# Process the input sample
output = []
for line in input_sample.strip().split …

import numpy as np
import pandas as pd

df = pd.DataFrame({'a': [7, 1, 5], 'b': ['3','2','1']}, dtype='object')

df

import numpy as np
import pandas as pd

df = pd.DataFrame({'a': [7, 1, 5], 'b': ['3','2','1']}, dtype='object')

df

import numpy as np
import pandas as pd

df = pd.DataFrame({
    'one': [4, 5],
    'two': [10, 20]
})

df

content = "23"

content

'23'

isinstance(content, int)

False

type(content)

str

content.isdigit()

True

text = "one 2 three 4 five 6 seven 8 nine 10"

for x in text.split(" "):
    print(x, "==>", x.isdigit())

one ==> False
2 ==> True …

content = "23"

content

'23'

isinstance(content, int)

False

type(content)

str

content.isdigit()

True

text = "one 2 three 4 five 6 seven 8 nine 10"

for x in text.split(" "):
    print(x, "==>", x.isdigit())

one …

import tensorflow as tf

import os

# Just disables the warning, doesn't enable AVX/FMA
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

init_op = tf.global_variables_initializer()

session = tf.Session()

# initialize variables
session.run(init_op)

import pyutil as pyu
pyu.get_local_pyinfo()

'conda env: ml312-2024; pyv: 3.12.7 | packaged by Anaconda, Inc. | (main, Oct  4 2024, 13:27:36) [GCC 11.2.0]'

# public ip

import random

def generate_public_ip():
    # Exclude private IP ranges
    excluded_ranges = [
        (10, 10),  # 10.0.0.0 - 10.255.255.255 …