general.py

import pandas as pd
import schedule
import threading
import socket
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from datetime import datetime

# Function to import CSV file
def import_csv(file_path):
    return pd.read_csv(file_path)

# Function to export DataFrame to CSV file
def export_csv(df, file_path):
    df.to_csv(file_path, index=False)

# Function to analyze data
def analyze_data(df):
    # Placeholder for AI analysis
    print("AI Analysis Placeholder")
    # Add your AI model analysis here

# Function to analyze accounting data
def analyze_accounting_data(df):
    gross_amount = df['Gross Amount'].sum()
    net_amount = df['Net Amount'].sum()
    fees = df['Fees'].sum()
    materials = df['Materials'].sum()
    labor_costs = df['Labor Costs'].sum()
    overheads = df['Overheads'].sum()

    print(f'The total gross amount is: {gross_amount}')
    print(f'The total net amount is: {net_amount}')
    print(f'The total fees are: {fees}')
    print(f'Total materials cost is: {materials}')
    print(f'Total labor costs are: {labor_costs}')
    print(f'Total overheads are: {overheads}')
    print(f'Net amount minus materials, labor, and overheads: {net_amount - materials - labor_costs - overheads}')

# Task scheduling and reminders
def schedule_task(task, time):
    schedule.every().day.at(time).do(task)

def send_reminder(email, task):
    msg = MIMEMultipart()
    msg['From'] = 'your_email@example.com'
    msg['To'] = email
    msg['Subject'] = 'Task Reminder'
    body = f'Reminder to complete: {task}'
    msg.attach(MIMEText(body, 'plain'))
    
    with smtplib.SMTP('smtp.example.com', 587) as server:
        server.starttls()
        server.login(msg['From'], 'your_password')
        server.sendmail(msg['From'], msg['To'], msg.as_string())

def start_scheduler():
    while True:
        schedule.run_pending()

# Function to schedule breaks
def schedule_breaks(email):
    for hour in range(9, 17):  # Working hours from 9 AM to 5 PM
        schedule_task(lambda: send_reminder(email, 'Take a 15-minute break to stretch'), f'{hour}:45')

# Employee efficiency tracking
def log_efficiency(employee_data):
    efficiency_df = pd.DataFrame(employee_data)
    decline_flag = efficiency_df['Efficiency'].pct_change().fillna(0) < -0.1

    if decline_flag.any():
        print("Alert: Significant decline in efficiency detected.")
        # Further actions can be implemented here

# Security monitoring
def monitor_network():
    open_ports = []
    target = 'your_target_ip_or_hostname'
    for port in range(1, 1025):
        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        result = sock.connect_ex((target, port))
        if result == 0:
            open_ports.append(port)
        sock.close()

    print(f'Open ports: {open_ports}')
    if open_ports:
        send_security_alert(open_ports)

def send_security_alert(open_ports):
    alert_message = f'Open ports detected: {open_ports}'
    print(alert_message)
    # Email alert can be sent here similarly as in send_reminder function

# Example usage
if __name__ == "__main__":
    # Import CSV files
    df_main = import_csv('Blank.csv')

    # Analyze accounting data
    analyze_accounting_data(df_main)

    # Schedule tasks with reminders
    user_email = 'your_email@example.com'
    schedule_task(lambda: send_reminder(user_email, 'Complete task A'), '14:00')
    schedule_breaks(user_email)
    threading.Thread(target=start_scheduler).start()

    # Log employee efficiency
    employee_data = [{'Employee': 'John', 'Efficiency': 0.9}, {'Employee': 'Doe', 'Efficiency': 0.8}]
    log_efficiency(employee_data)

    # Monitor network
    threading.Thread(target=monitor_network).start()