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accountant_ai.yaml
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import time
from typing import Dict, List, Union
class FinancialTracker:
"""
A conceptual model for a Financial AI to track earnings and sponsorships.
NOTE: This code is for simulation and educational purposes only.
It does not handle real-world transactions, security, or banking APIs.
Never use simple scripts like this for handling actual money.
"""
def __init__(self):
# Data structures to store transactions
self.earnings: List[Dict[str, Union[str, float, int]]] = []
self.sponsorships: List[Dict[str, Union[str, float, int]]] = []
self.current_balance: float = 0.0
def record_earning(self, source: str, amount: float):
"""Records a new earning transaction (e.g., ad revenue, product sales)."""
if amount > 0:
transaction = {
"type": "EARNING",
"source": source,
"amount": amount,
"timestamp": int(time.time())
}
self.earnings.append(transaction)
self.current_balance += amount
print(f"💰 Recorded Earning: ${amount:.2f} from {source}")
else:
print("🚫 Error: Earning amount must be positive.")
def record_sponsor_fund(self, sponsor_name: str, amount: float):
"""Records a fund received from a sponsor."""
if amount > 0:
transaction = {
"type": "SPONSORSHIP",
"sponsor": sponsor_name,
"amount": amount,
"timestamp": int(time.time())
}
self.sponsorships.append(transaction)
self.current_balance += amount
print(f"🤝 Recorded Sponsorship: ${amount:.2f} from {sponsor_name}")
else:
print("🚫 Error: Sponsorship amount must be positive.")
def get_total_funds(self) -> float:
"""Returns the current calculated total balance."""
return self.current_balance
def get_summary(self):
"""Prints a summary of all financial activities."""
total_earnings = sum(t['amount'] for t in self.earnings)
total_sponsorships = sum(t['amount'] for t in self.sponsorships)
print("\n--- Financial Summary ---")
print(f"Total Earnings: ${total_earnings:.2f}")
print(f"Total Sponsorships: ${total_sponsorships:.2f}")
print(f"--- Current Balance: ${self.current_balance:.2f} ---")
print("-------------------------")
# --- Example Usage ---
tracker = FinancialTracker()
# 1. Collect earnings
tracker.record_earning("Affiliate Sales", 150.75)
tracker.record_earning("Ad Revenue (Platform X)", 420.00)
# 2. Collect sponsor funds
tracker.record_sponsor_fund("Tech Corp Sponsor", 2500.00)
tracker.record_sponsor_fund("Local Business Partner", 500.00)
# 3. View totals
tracker.get_summary()
# 4. Total calculation
print(f"\nFinal Calculated Funds: ${tracker.get_total_funds():.2f}")
import pandas as pd
from datetime import datetime
from typing import Literal
class AdvancedFinancialModel:
"""
A conceptual financial model demonstrating advanced data handling
using the Pandas library for tracking income streams.
"""
def __init__(self):
# Initialize an empty DataFrame to store all transactions
self.transactions = pd.DataFrame(
columns=['Timestamp', 'Category', 'Source', 'Amount']
)
self.transactions['Amount'] = self.transactions['Amount'].astype(float)
def record_transaction(self, category: Literal['EARNING', 'SPONSORSHIP'],
source: str, amount: float):
"""Records a new transaction and adds it to the DataFrame."""
if amount <= 0:
print("🚫 Error: Transaction amount must be positive.")
return
new_data = {
'Timestamp': datetime.now(),
'Category': category,
'Source': source,
'Amount': amount
}
# Add the new data as a row to the DataFrame
self.transactions.loc[len(self.transactions)] = new_data
print(f"✅ Recorded {category}: ${amount:.2f} from {source}")
def get_summary_by_category(self) -> pd.DataFrame:
"""Calculates and returns the total income grouped by category."""
summary = self.transactions.groupby('Category')['Amount'].sum().reset_index()
summary.columns = ['Category', 'Total Income']
return summary
def get_top_sources(self, n: int = 3) -> pd.DataFrame:
"""Identifies and returns the top N income sources."""
top_sources = (self.transactions.groupby('Source')['Amount']
.sum()
.nlargest(n)
.reset_index())
top_sources.columns = ['Source', 'Total Income']
return top_sources
def calculate_monthly_growth(self):
"""Calculates and returns the total income per month."""
if self.transactions.empty:
return "No data to calculate growth."
# Ensure the Timestamp is the index and resample by month
df_monthly = self.transactions.set_index('Timestamp').resample('M')['Amount'].sum()
return df_monthly.to_frame(name='Monthly Total')
# --- Example Usage with Advanced Model ---
model = AdvancedFinancialModel()
# Record sample data
model.record_transaction('EARNING', 'YouTube Ads', 850.50)
model.record_transaction('EARNING', 'Affiliate Sales', 120.00)
model.record_transaction('SPONSORSHIP', 'Gaming Brand X', 5000.00)
model.record_transaction('EARNING', 'YouTube Ads', 910.25)
model.record_transaction('SPONSORSHIP', 'Software Company Y', 1500.00)
print("\n" + "="*40)
print("💰 Categorized Income Summary")
print("="*40)
print(model.get_summary_by_category())
print("\n" + "="*40)
print("🌟 Top 3 Income Sources")
print("="*40)
print(model.get_top_sources(n=3))
# Note: Monthly growth requires transactions spanning multiple dates for meaningful results
# print("\nMonthly Growth (Conceptual):")
# print(model.calculate_monthly_growth())