utils/gradio_utils.py

import random
import ipaddress
import pandas as pd
import re

### SWAKS ###

def get_subclasses(ip_classes: str):
    ip_classes = ip_classes.split('\n')
    result = []
    for ip_class in ip_classes:
        try:
            network = ipaddress.IPv4Network(ip_class, strict=False)
            for ip in network.subnets(new_prefix=24):
                result.append(str(ip))
        except ValueError:
            result.append(f"Invalid IP class: {ip_class}")
    return result


def update_header(max_ips):
    return f"found=0; max_ips={int(max_ips)};\n"


def str_to_list(ips_string) -> list:
    """
    Converts a string to a list.
    """
    return ips_string.split('\n')


def generate_cmds_from_bulk(big_subclasses_raw: str,
                            domain_raw: str,
                            max_ips: int = 2,
                            sample_size: int = 5):
    """
    big_subclasses_raw: The list of classes
    domain_raw: The list of domains
    max_ips: The maximum number of IPs to pick
    sample_size: The number of sample size to generate IPs from /24 class
    """
    # Gradio fixes
    # big_subclasses_raw = big_subclasses_raw.split('\n')
    domain_raw = domain_raw.split('\n')
    sample_size = int(sample_size)
    max_ips = int(max_ips)
    
    commands = []
    header = f"found=0; max_ips={max_ips};"

    for ip_subclass_24 in get_subclasses(big_subclasses_raw):
        ip_addresses_1_20, ip_addresses_201_255 = generate_ip_addresses(ip_subclass_24)
        commands.append(header)
        
        for ip in random.sample(ip_addresses_1_20, sample_size):
            commands.append(generate_randomized_output_max_ips(ip, domain_raw))
        
        commands.append(header)
        for ip in random.sample(ip_addresses_201_255, sample_size):   
            commands.append(generate_randomized_output_max_ips(ip, domain_raw))
    return '\n'.join(commands)


def generate_ips_per_subclass(ip_subclasses: str, num_of_ips: int) -> str:
    """
    Generates a list of IP addresses for a given list of IP subclasses and the number of IPs.

    :param ip_subclasses: List of non /24 IP subclasses in CIDR notation.
    :param num_of_ips: Number of IP addresses to generate per IP subclass.
    :return: List of generated IP addresses.
    """
    ip_addresses = []

    for ip_subclass_24 in get_subclasses(ip_subclasses):
                ip_addresses.extend(generate_ips_per_slash24(ip_subclass_24, num_of_ips))
    return ip_addresses


def generate_ips_per_slash24(ip_class: str, num_ips: int) -> list:
    """
    Generates a list of IP addresses within a specified class C network (/24).

    :param ip_class: The IP class in CIDR notation, e.g., "192.168.1.1/24".
    :param num_ips: The number of IP addresses to generate.
    :return: A list of generated IP addresses.
    """
    ip_addresses = []
    
    # The IP address ranges for two disjoint domains.
    domain1 = range(1, 20)
    domain2 = range(201, 254)
    num_ips = int(num_ips)

    # The base of the IP address (e.g., "192.168.1." for "192.168.1.1/24").
    base_ip = ip_class.split('/')[0].rsplit('.', 1)[0] + '.'

    for i in range(1, num_ips + 1):
        # If the index is in the first half of the requested IPs, 
        # generate an IP from the first domain.
        if i <= num_ips / 2:
            ip_addresses.append(base_ip +random.choice(domain1).__str__())
        # If the index is in the second half of the requested IPs,
        # generate an IP from the second domain.
        else:
            ip_addresses.append(base_ip +random.choice(domain2).__str__())
    
    return ip_addresses


def generate_ip_addresses(slash_24: str) -> list:
    ip_addresses_1_20 = []
    ip_addresses_201_255 = []

    # The IP address ranges for two disjoint domains.
    domain_1_20 = range(1, 20)
    domain_201_255 = range(201, 255)
    # domain = chain(domain_1_20, domain_201_255)

    # The base of the IP address (e.g., "192.168.1." for "192.168.1.1/24").
    base_ip = slash_24.split('/')[0].rsplit('.', 1)[0] + '.'
    for ip in domain_1_20:        
        ip_addresses_1_20.append(base_ip + ip.__str__())

    for ip in domain_201_255:        
        ip_addresses_201_255.append(base_ip + ip.__str__())

    return [ip_addresses_1_20, ip_addresses_201_255]


def generate_random_string(min_length=2, max_length=7):
    letters = "abcdefghijklmnopqrstuvwxyz"
    length = random.randint(min_length, max_length)
    return ''.join(random.choice(letters) for _ in range(length))


def generate_randomized_output_max_ips(ip_address: str, domain_list: list, sleep_sec: int = 5) -> str:
    part1 = generate_random_string()
    part2 = generate_random_string()
    part3 = generate_random_string()
    part4 = generate_random_string()
    # domain_list = domain_list.split('\n')
    domain = random.choice(domain_list)
    ip_class = ip_address.rsplit('.', 1)[0]
    
    template = f"""if [ $found -lt $max_ips ]; then swaks -t [email protected] -h {part1}-{part2}.{part3}-{part4}.com -f from@{domain} -q from --li {ip_address} |\
tee -a swaks_full.log | \
grep -q 'sender ok'; \
if [ $? -eq 0 ]; \
then \
found=$((found+1)); \
echo {ip_address} >>bune_{ip_class}.txt; \
else echo {ip_address} >>blocate_{ip_class}.txt; \
fi; \
sleep {sleep_sec}; \
fi;"""
    return template


### MIX ###

def mix(domains: str, ip_addresses: str, num_of_ips: int) -> str:
    """
    Mixes the IP addresses with the domains.

    :param ip_addresses: List of IP addresses.
    :param domains: List of domains.
    :return: List of mixed IP addresses and domains.
    """
    domains = domains.split('\n')
    ip_addresses = ip_addresses.split('\n')
    
    mixed = []
  
    # Check if the number of IP addresses is the same than the number of domains.
    if len(ip_addresses) == len(domains):
        for i in range(len(ip_addresses)):
            if i % num_of_ips == 0:
                mixed.append('')
            line = domains[i] + ': ' + ip_addresses[i]
            mixed.append(line)
    else:
        raise ValueError('The number of IP addresses and domains must be the same.')
    
    return "\n".join(mixed)


# def generate_ips_per_subclass(ip_subclasses: str, num_of_ips: int) -> str:
#     """
#     Generates a list of IP addresses for a given list of IP subclasses and the number of IPs.

#     :param ip_subclasses: List of non /24 IP subclasses in CIDR notation.
#     :param num_of_ips: Number of IP addresses to generate per IP subclass.
#     :return: List of generated IP addresses.
#     """
#     ip_addresses = []

#     for ip_subclass_24 in get_subclasses(ip_subclasses):
#                 ip_addresses.extend(generate_ips_per_slash24(ip_subclass_24, num_of_ips))
#     return ip_addresses

def generate_ips_per_subclass(ip_subclasses: str, num_of_ips: int) -> str:
    """
    Generates a list of IP addresses for a given list of IP subclasses and the number of IPs.

    :param ip_subclasses: List of non /24 IP subclasses in CIDR notation.
    :param num_of_ips: Number of IP addresses to generate per IP subclass.
    :return: List of generated IP addresses.
    """
    ip_subclasses = ip_subclasses.split('\n')
    ip_addresses = []
    mask_split_threshold = 24

    for ip_subclass in ip_subclasses:
        ip_base, mask = ip_subclass.rsplit('/', 1) 
        mask = int(mask)
        ip_base = ip_base.rsplit('.', 1)[0]

        if mask == mask_split_threshold:
            ip_addresses.extend(generate_ips_per_slash24(ip_subclass, num_of_ips))
        else:
            for i in range((mask_split_threshold - mask) ** 2):
                split_ip_base = ip_base.split('.')
                third_octet = int(split_ip_base[2]) + i

                # Construct the /24 subnet for the IP subclass
                ip_subclass_24 = split_ip_base[0] + '.' + split_ip_base[1] + '.' + str(third_octet) + '.0/24'

                # Assuming generate_ips_per_slash24 is the same as the previously discussed generate_ips function
                ip_addresses.extend(generate_ips_per_slash24(ip_subclass_24, num_of_ips))
    return "\n".join(ip_addresses)


def _replace_numbers(input_string: str) -> str:
    # Find the numbers before and inside the parentheses
    match = re.search(r'(\d+)\s*\((\d+)\)', input_string)
    if match:
        # Replace the first set of numbers with the second set
        replaced_string = input_string.replace(match.group(1), match.group(2), 1)
        # Remove the parentheses and any surrounding whitespace
        cleaned_string = re.sub(r'\(\d+\)', '', replaced_string).strip()
        return cleaned_string
    else:
        return input_string

def _limit_chars(input_string: str, limit: int = 35) -> str:
    return input_string[:limit]

### GENERATE TOP LISTS ###
def compute_offer(csv_file, days_lookback, min_sent, domain, team, offer_type, x_list, ):
    pd.set_option('display.max_colwidth', 10)

    df_all = pd.read_csv(csv_file.name, parse_dates=['Data'])
    if team == "Team 1":
        team_members = ['Ana Boros', 'Adrian Pop',
                'Liviu Avram', 'Alexandru Popescu', 'Vlad Draghici']
    elif team == "Team 2":
        team_members = ['Cristi Rusu', 'Robert Rachiteanu', 'Adrian Sabau','Gabriel Sabau']
    else:
        team_members = [] # All

    cols = ['Campanie', 'Oferta', 'Nume', 'Server', 'User', 'offer_id',
        'Lista Custom', 'Data', 'HClicks', 'Clicks', 'Unscribers', 'Openers',
        'Click Open', 'Leads', 'CLike', 'Complains', 'Traps', 'Send', 'ECPM', 'Comision', 'Domeniu']

    df_all['offer_id'] = df_all['Nume'].str.extract(r'(\d{3,4}$)')
    
    # Treat Aol as Yahoo
    df_all['Domeniu'].replace('Aol', 'Yahoo', inplace=True)

    if offer_type == "Offers - IDs only" or offer_type == "Offers":
        exclude_list = df_all[(df_all['Data'] > (pd.Timestamp('now') - pd.Timedelta(days=days_lookback))) \
                            & (df_all['Domeniu'] == domain)\
                            & (df_all['User'].isin(team_members))]['offer_id'].unique()
        df_all = df_all[~df_all['offer_id'].isin(exclude_list)]
    elif offer_type == "Newsletters":
        exclude_list = df_all[(df_all['Data'] > (pd.Timestamp('now') - pd.Timedelta(days=days_lookback))) \
                                   & (df_all['Domeniu'] == domain)]['Oferta'].unique()
        df_all = df_all[~df_all['Oferta'].isin(exclude_list)]
        
   
    
    df_all = df_all[df_all['Send'] > int(min_sent)]
    df_all = df_all[cols]
    # fixed a blank line in the csv
    df_all = df_all[df_all["Oferta"] != " "]
    
    df_all['Click Open'] = df_all['Click Open'].str.replace('%', '').astype(float)
    df_all['ECPM'] = df_all['ECPM'].astype(float)
    df_all['Comision'] = df_all['Comision'].astype(float)
    df_all['Send'] = df_all['Send'].astype(int)

    # Limit the characters in the "Nume" column
    # df_all["Nume"] = df_all["Nume"].apply(_limit_chars)

    # Filter for newsletters or offers
    if offer_type == "Newsletters":
        df_all = df_all[
                        df_all['Nume'].str.startswith("Aeon News") & \
                        (~df_all['Nume'].str.contains(r'\(\d{4}\)')) & \
                        (df_all['Nume'].str.contains(r' \d{4}$')) & \
                        (~df_all['Nume'].str.contains('TRIMITE'))
                        ]
    elif offer_type == "Offers" or offer_type == "Offers - IDs only":
        df_all = df_all[~df_all['Nume'].str.startswith("Aeon News")]
        df_all = df_all[~df_all['Nume'].str.contains("NU SE TRIMITE")]
        df_all = df_all[~df_all['Nume'].str.contains("de testat")]
        df_all = df_all[~df_all['Nume'].str.contains("_TEST")]
        df_all = df_all[~df_all['Nume'].str.contains("CPM")]
        df_all = df_all[~df_all['Nume'].str.contains("RESTRICTED")]

    if x_list != "":
        x_list = x_list.split(',')
        df_all = df_all[~df_all['Nume'].str.contains('|'.join(x_list))]
    # Compress the newsletter names
    # df_all = df_all[df_all['Nume'].str.contains(r'\b[A-Z]{3}\b.*\b\d{4}\*?\s*(\(\d{4}\))?\b')]
    # df_all['Nume'] = df_all['Nume'].apply(_replace_numbers)
    # exclude again after the transformation
    # df_all = df_all[~df_all['Oferta'].isin(exclude_list)]

    df_all.reset_index(drop=True, inplace=True)

    if offer_type == "Newsletters":
        final_df = df_all.groupby(["Oferta", "Nume"])\
            .agg( times_sent=('Oferta', 'count'), send_avg=('Send', 'mean'), CO=('Click Open', 'mean'))\
            .sort_values(['CO', 'times_sent'], ascending=False)
        final_df['send_avg'] = final_df['send_avg'].astype(int)
        final_df['CO'] = final_df['CO'].round(2).astype(float)
        final_df.reset_index(inplace=True)
    elif offer_type == "Offers":
        final_df = df_all.groupby(["Oferta", "Nume"])\
            .agg( times_sent=('Oferta', 'count'), send_avg=('Send', 'mean'), ECPM=('ECPM', 'mean'))\
            .sort_values(['ECPM', 'times_sent'], ascending=False)
        final_df['send_avg'] = final_df['send_avg'].astype(int)
        final_df['ECPM'] = final_df['ECPM'].round(2).astype(float)
        final_df.reset_index(inplace=True)
    elif offer_type == "Offers - IDs only":

        final_df = df_all.groupby(["offer_id"])\
            .agg( times_sent=('offer_id', 'count'), send_avg=('Send', 'mean'), total_sent=('Send', 'sum'),\
                USD=('Comision', 'sum'))
            
        final_df['USD'] = final_df['USD'].round(2).astype(float)
        final_df['send_avg'] = final_df['send_avg'].astype(int)
        final_df['ECPM'] = ( ( final_df['USD'] * 33.33 ) / final_df['total_sent'] ) * 1000
        final_df['ECPM'] = final_df['ECPM'].round(2).astype(float)

        final_df.sort_values(by='ECPM', ascending=False, inplace=True)

        final_df.reset_index(inplace=True)
    else:
        final_df = pd.DataFrame()
    return final_df