notes/notes_generator.py

import os
import json
import logging
from datetime import datetime
from typing import Any, Dict, List, Optional
import pandas as pd
from pydantic import BaseModel, ValidationError
from pydantic_settings import BaseSettings

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Settings(BaseSettings):
	"""Application settings loaded from environment variables or .env file."""
	trial_balance_json: str = "data/output1/parsed_trial_balance.json"
	output_json: str = "data/output2/notes_output.json"
	output_md: str = "data/output2/financial_notes_all.md"
	company_name: str = "Company Name"
	financial_year: str = "2024-03-31"

settings = Settings()

class MatchedAccount(BaseModel):
	account: str
	amount: float
	amount_lakhs: float
	group: str

class NoteStructure(BaseModel):
	note_number: str
	note_title: str
	full_title: str
	total_amount: float
	total_amount_lakhs: float
	matched_accounts_count: int
	matched_accounts: List[MatchedAccount]
	breakdown: Dict[str, Any]
	table_data: List[Dict[str, Any]]
	comparative_data: Dict[str, Any]
	notes_and_disclosures: List[str]
	markdown_content: str

def clean_value(value: Any) -> float:
	"""Clean and convert value to float."""
	try:
		if isinstance(value, str):
			value = value.replace(',', '').strip()
		return float(value) if value else 0.0
	except (ValueError, TypeError):
		return 0.0

def to_lakhs(value: float) -> float:
	"""Convert value to lakhs."""
	return round(value / 100000, 2)

def find_account_col(df: pd.DataFrame) -> str:
	"""Find the account column in DataFrame."""
	for col in df.columns:
		if df[col].astype(str).str.contains('account|particulars|name', case=False, na=False).any():
			return col
	return df.columns[0]

def find_balance_col(df: pd.DataFrame) -> Optional[str]:
	"""Find the balance column in DataFrame."""
	for col in df.columns:
		if df[col].dtype in [float, int] and df[col].notna().any():
			return col
	return df.columns[1] if len(df.columns) > 1 else None


def generate_notes(tb_df: pd.DataFrame) -> Dict[str, Any]:
	"""
	Generate notes 16-26 from parsed trial balance data.
	Returns a dict with metadata and notes.
	"""
	# ...full implementation from your old file goes here...
	# (Paste the entire generate_notes function and all its logic from your old file)
	# For brevity, see your previous message for the full function body.

	# After the function, ensure all supporting functions and logic are present.
#
def process_json(json_path: str) -> None:
	"""
	Loads the JSON file, processes it, and writes the output as in your main().
	"""
	if not os.path.exists(json_path):
		logger.error(f"{json_path} not found!")
		raise FileNotFoundError(f"{json_path} not found!")
	with open(json_path, "r", encoding="utf-8") as f:
		parsed_data = json.load(f)
	if isinstance(parsed_data, list):
		tb_df = pd.DataFrame(parsed_data)
	else:
		tb_records = parsed_data.get("trial_balance", parsed_data)
		tb_df = pd.DataFrame(tb_records)
	if 'amount' in tb_df.columns:
		tb_df['amount'] = tb_df['amount'].apply(clean_value)
	notes_data = generate_notes(tb_df)
	os.makedirs(os.path.dirname(settings.output_json), exist_ok=True)
	with open(settings.output_json, "w", encoding="utf-8") as f:
		json.dump(notes_data, f, ensure_ascii=False, indent=2)
	logger.info(f"Notes output written to {settings.output_json}")
import os
import json
import logging
from datetime import datetime
from typing import Any, Dict, List, Optional
import pandas as pd
from pydantic import BaseModel, ValidationError
from pydantic_settings import BaseSettings

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Settings(BaseSettings):
	"""Application settings loaded from environment variables or .env file."""
	trial_balance_json: str = "data/output1/parsed_trial_balance.json"
	output_json: str = "data/output2/notes_output.json"
	output_md: str = "data/output2/financial_notes_all.md"
	company_name: str = "Company Name"
	financial_year: str = "2024-03-31"

settings = Settings()

class MatchedAccount(BaseModel):
	account: str
	amount: float
	amount_lakhs: float
	group: str

class NoteStructure(BaseModel):
	note_number: str
	note_title: str
	full_title: str
	total_amount: float
	total_amount_lakhs: float
	matched_accounts_count: int
	matched_accounts: List[MatchedAccount]
	breakdown: Dict[str, Any]
	table_data: List[Dict[str, Any]]
	comparative_data: Dict[str, Any]
	notes_and_disclosures: List[str]
	markdown_content: str

def clean_value(value: Any) -> float:
	"""Clean and convert value to float."""
	try:
		if isinstance(value, str):
			value = value.replace(',', '').strip()
		return float(value) if value else 0.0
	except (ValueError, TypeError):
		return 0.0

def to_lakhs(value: float) -> float:
	"""Convert value to lakhs."""
	return round(value / 100000, 2)

def find_account_col(df: pd.DataFrame) -> str:
	"""Find the account column in DataFrame."""
	for col in df.columns:
		if df[col].astype(str).str.contains('account|particulars|name', case=False, na=False).any():
			return col
	return df.columns[0]

def find_balance_col(df: pd.DataFrame) -> Optional[str]:
	"""Find the balance column in DataFrame."""
	for col in df.columns:
		if df[col].dtype in [float, int] and df[col].notna().any():
			return col
	return df.columns[1] if len(df.columns) > 1 else None

def calculate_note(
    df: pd.DataFrame,
    note_name: str,
    keywords: List[str],
    exclude: Optional[List[str]] = None
) -> Dict[str, Any]:
    """Calculate total and matched accounts for a note."""
    account_col = 'account_name' if 'account_name' in df.columns else find_account_col(df)
    balance_col = 'amount' if 'amount' in df.columns else find_balance_col(df)
    if not balance_col:
        return {'total': 0, 'matched_accounts': []}
    df = df.fillna(0)
    total = 0
    matched_accounts = []
    for idx, row in df.iterrows():
        account_name = str(row[account_col]).strip().lower()
        if any(kw.lower() in account_name for kw in keywords) and (not exclude or not any(ex.lower() in account_name for ex in exclude)):
            amount = clean_value(row[balance_col])
            total += amount
            matched_accounts.append({
                'account': str(row[account_col]),
                'amount': amount,
                'amount_lakhs': to_lakhs(amount),
                'group': row.get('group', 'Unknown') if 'group' in df.columns else 'Unknown'
            })
    return {'total': total, 'matched_accounts': matched_accounts}

def parse_markdown_table(content: str) -> List[Dict[str, Any]]:
    """Parse markdown table content into list of dicts."""
    lines = [line.strip() for line in content.strip().splitlines() if line.strip()]
    table_lines = [line for line in lines if "|" in line and not line.startswith("|--")]
    if not table_lines:
        return []
    table_data = []
    for line in table_lines:
        cells = [cell.strip() for cell in line.split("|") if cell.strip()]
        if len(cells) >= 2:
            row_data = {
                "particulars": cells[0],
                "current_year": cells[1] if len(cells) > 1 else "",
                "previous_year": cells[2] if len(cells) > 2 else ""
            }
            table_data.append(row_data)
    return table_data

def create_detailed_note_structure(
    note_name: str,
    result: Dict[str, Any],
    content: str,
    special_data: Optional[Dict[str, Any]] = None
) -> Dict[str, Any]:
    """Create detailed note structure for output."""
    note_number = note_name.split('.')[0] if '.' in note_name else note_name
    note_title = note_name.split('.', 1)[1].strip() if '.' in note_name else note_name
    table_data = parse_markdown_table(content)
    matched_accounts = []
    for acc in result.get('matched_accounts', []):
        matched_accounts.append({
            "account": acc['account'],
            "amount": acc['amount'],
            "amount_lakhs": to_lakhs(acc['amount']),
            "group": acc.get('group', 'Unknown')
        })
    note_structure = {
        "note_number": note_number,
        "note_title": note_title,
        "full_title": note_name,
        "total_amount": result['total'],
        "total_amount_lakhs": to_lakhs(result['total']),
        "matched_accounts_count": len(matched_accounts),
        "matched_accounts": matched_accounts,
        "breakdown": special_data.get('breakdown', {}) if special_data else {},
        "table_data": table_data,
        "comparative_data": {
            "current_year": {"year": settings.financial_year, "amount": result['total'], "amount_lakhs": to_lakhs(result['total'])},
            "previous_year": {"year": "2023-03-31", "amount": 0, "amount_lakhs": 0}
        },
        "notes_and_disclosures": [],
        "markdown_content": f"### {note_name}\n\n{content}\n\n**Account-wise breakdown:**\n"
    }
    for acc in matched_accounts:
        note_structure["markdown_content"] += f"- {acc['account']}: ₹{acc['amount']:,.2f} ({acc['amount_lakhs']} Lakhs)\n"
    if special_data:
        note_structure.update(special_data)
    return note_structure

def generate_notes(tb_df: pd.DataFrame) -> Dict[str, Any]:
    """
    Generate notes 16-26 from parsed trial balance data.
    Returns a dict with metadata and notes.
    """
    notes = []
    note_mappings = {
        '2. Share Capital': {'keywords': ['Share Capital', 'share capital', 'equity share', 'paid up']},
        '3. Reserves and Surplus': {'keywords': ['Reserves', 'Surplus', 'reserves', 'surplus', 'retained earnings']},
        '4. Long Term Borrowings': {'keywords': ['loan', 'borrowing', 'term loan'], 'exclude': ['current maturities', 'short term']},
        '5. Deferred Tax Liability': {'keywords': ['Deferred Tax', 'deferred tax']},
        '6. Trade Payables': {'keywords': ['Creditors', 'creditors', 'trade payable', 'suppliers']},
        '7. Other Current Liabilities': {'keywords': ['Expenses Payable', 'Current Maturities', 'payable', 'accrued']},
        '8. Short Term Provisions': {'keywords': ['Provision', 'provision', 'taxation']},
        '9. Fixed Assets': {'keywords': ['Equipment', 'Furniture', 'Building', 'Vehicle', 'Motor', 'Asset', 'plant', 'machinery']},
        '10. Long Term Loans and Advances': {'keywords': ['Long Term', 'Security Deposits', 'advances', 'deposits']},
        '11. Inventories': {'keywords': ['Stock', 'Inventory', 'stock', 'inventory', 'goods']},
        '12. Trade Receivables': {'keywords': ['Receivables', 'receivables', 'debtors', 'trade receivable']},
        '13. Cash and Bank Balances': {'keywords': ['Cash-in-hand', 'Bank accounts', 'Deposits']},
        '14. Short Term Loans and Advances': {'keywords': ['Prepaid Expenses', 'TDS Receivables', 'Loans & Advances', 'TCS RECEIVABLES', 'TDS Advance Tax Paid', 'Advance to Perennail']},
        '15. Other Current Assets': {'keywords': ['Interest accrued', 'accrued', 'current asset']},
        '16. Revenue from Operations': {
            'keywords': ['Revenue', 'Sales', 'Service', 'Income', 'Consultancy', 'Gain / Loss on Sales of Fixed Assets', 'Income Tax',
                         'Servicing of BA/BE PROJECTS', 'Working Standards - Export', 'SERVICING OF BA PROJECTS', 'SERVICING OF ONLY CLINICAL']
        },
        '17. Other Income': {
            'keywords': ['Interest on FD', 'Interest on Income Tax Refund', 'Unadjusted Forex Gain/Loss', 'Forex Gain / Loss', 'Interest']
        },
        '18. Cost of Materials Consumed': {
            'keywords': ['Opening Stock', 'Bio Lab Consumables', 'Non GST', 'Purchase GST', 'Closing Stock']
        },
        '19. Employee Benefit Expense': {
            'keywords': ['Salary', 'Wages', 'Bonus', 'Employee', 'Remuneration', 'Comp Offs', 'Retainership', 
                         'Employees Group Life Insurance', 'Employees Health & Personal Accident Insurance', 
                         'Prepaid - Employees Group Life Insurance', 'Prepaid Insurance - Employees Health & Personal Accident', 
                         'Staff Welfare Expenses', 'Employees Expenses Reimbursement', 'Contribution to PF', 'Contribution to ESI']
        },
        '20. Other Expenses': {
            'keywords': ['BA / BE NOC', 'BA Expenses', 'Payments to Volunteers', 'Other Operating Expenses', 'Laboratory testing', 
                         'Rent', 'Rates & Taxes', 'Fees & licenses', 'Insurance', 'Membership & Subscription', 
                         'Postage & Communication', 'Printing and Stationery', 'CSR Fund', 'Telephone & Internet', 
                         'Travelling and Conveyance', 'Translation Charges', 'Electricity Charges', 'Security Charges', 
                         'Annual Maintenance', 'Repairs and maintenance', 'Business Development', 'Professional & Consultancy', 
                         'Payment to Auditors', 'Bad Debts', 'Fire Extinguishers', 'Food Expenses', 'Diesel Expenses', 
                         'Interest Under 234 C', 'Loan Processing Charges', 'Sitting Fee of Directors', 'Customs Duty', 
                         'Transportation and Unloading', 'Software Equipment', 'Miscellaneous expenses', 'Laptop Accessories', 
                         'Professional Fee', 'Office Rent', 'Security Deposit']
        },
        '21. Depreciation and Amortisation Expense': {
            'keywords': ['Depreciation', 'Amortization', 'Accumulated Depreciation', 'Depreciation And Amortisation']
        },
        '22. Loss on Sale of Assets & Investments': {
            'keywords': ['Short Term Loss', 'Long term loss', 'Loss on Sale of Fixed Assets', 'Loss on Sale of Investments']
        },
        '23. Finance Costs': {
            'keywords': ['Bank Charges', 'Finance Charges', 'Interest', 'Loan Processing', 'Interest and penalty', 'Interest on TDS']
        },
        '24. Payment to Auditor': {
            'keywords': ['Payment to Auditors', 'Audit Fee', 'Tax Audit', 'Certification Fees']
        },
        '25. Earnings in Foreign Currency': {
            'keywords': ['Income from export of services', 'Servicing of BA/BE PROJECTS EXPORT', 'Working Standards - Export']
        },
        '26. Particulars of Un-hedged Foreign Currency Exposure': {
            'keywords': ['Income from export of services', 'Servicing of BA/BE PROJECTS EXPORT', 'Working Standards - Export']
        }
    }

    logger.info("Generating notes 16-26 from parsed trial balance data...")
    logger.info(f"Total records in trial balance: {len(tb_df)}")
    
    for note_name, mapping in note_mappings.items():
        keywords = mapping['keywords']
        result = calculate_note(tb_df, note_name, keywords)

        if result['matched_accounts']:
            logger.info(f"\n{note_name}:")
            logger.info(f"   Total: ₹{result['total']:,.2f} ({to_lakhs(result['total'])} Lakhs)")
            logger.info(f"   Matched {len(result['matched_accounts'])} accounts:")
            for acc in result['matched_accounts'][:3]:
                logger.info(f"      • {acc['account']}: ₹{acc['amount']:,.2f}")
            if len(result['matched_accounts']) > 3:
                logger.info(f"      ... and {len(result['matched_accounts']) - 3} more")
        else:
            logger.warning(f"\n{note_name}: No matching accounts found")

        content = ""
        special_data = {}
        
        if note_name == '2. Share Capital':
            content = """
| Particulars                  | 2024-03-31 | 2023-03-31 |
|------------------------------|------------|------------|
| **Authorised shares**        |            |            |
| 75,70,000 equity shares of ₹ 10/- each | 757.0 | 757.0 |
| **Issued, subscribed and fully paid-up shares** | | |
| 54,25,210 equity shares of ₹ 10/- each | {total_lakhs} | 542.52 |
| **Total issued, subscribed and fully paid-up share capital** | {total_lakhs} | 542.52 |
""".format(total_lakhs=to_lakhs(result['total']))
            special_data = {
                "breakdown": {
                    "authorised_shares": {"description": "75,70,000 equity shares of ₹ 10/- each", "amount": 75700000, "amount_lakhs": 757.0},
                    "issued_subscribed_paid_up": {"description": "54,25,210 equity shares of ₹ 10/- each", "amount": result['total'], "amount_lakhs": to_lakhs(result['total'])}
                }
            }
        
        elif note_name == '3. Reserves and Surplus':
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Reserves and Surplus         | {total_lakhs} | - |
""".format(total_lakhs=to_lakhs(result['total']))
        
        elif note_name == '4. Long Term Borrowings':
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Long Term Borrowings         | {total_lakhs} | - |
""".format(total_lakhs=to_lakhs(result['total']))
        
        elif note_name == '5. Deferred Tax Liability':
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Deferred Tax Liability       | {total_lakhs} | - |
""".format(total_lakhs=to_lakhs(result['total']))
        
        elif note_name == '6. Trade Payables':
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Trade Payables               | {total_lakhs} | - |
""".format(total_lakhs=to_lakhs(result['total']))
        
        elif note_name == '7. Other Current Liabilities':
            expenses_payable = calculate_note(tb_df, note_name, ['Expenses Payable', 'payable', 'accrued'])['total']
            current_maturities = calculate_note(tb_df, note_name, ['Current Maturities', 'current portion'])['total']
            statutory_dues = 7935166.72
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Current Maturities of Long Term Borrowings | {cm_lakhs} | 139.20 |
| Outstanding Liabilities for Expenses | {ep_lakhs} | 156.88 |
| Statutory dues               | {sd_lakhs} | 48.03 |
| **Total**                    | {total_lakhs} | 344.12 |
""".format(cm_lakhs=to_lakhs(current_maturities), ep_lakhs=to_lakhs(expenses_payable), sd_lakhs=to_lakhs(statutory_dues), total_lakhs=to_lakhs(current_maturities + expenses_payable + statutory_dues))
            special_data = {
                "breakdown": {
                    "current_maturities": {"description": "Current Maturities of Long Term Borrowings", "amount": current_maturities, "amount_lakhs": to_lakhs(current_maturities)},
                    "expenses_payable": {"description": "Outstanding Liabilities for Expenses", "amount": expenses_payable, "amount_lakhs": to_lakhs(expenses_payable)},
                    "statutory_dues": {"description": "Statutory dues", "amount": statutory_dues, "amount_lakhs": to_lakhs(statutory_dues)}
                }
            }
        
        elif note_name == '8. Short Term Provisions':
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Short Term Provisions        | {total_lakhs} | - |
""".format(total_lakhs=to_lakhs(result['total']))
        
        elif note_name == '9. Fixed Assets':
            equipments = calculate_note(tb_df, note_name, ['Equipment', 'equipment'])['total']
            furniture = calculate_note(tb_df, note_name, ['Furniture', 'furniture', 'fixture'])['total']
            building = calculate_note(tb_df, note_name, ['Building', 'building'])['total']
            vehicle = calculate_note(tb_df, note_name, ['Vehicle', 'vehicle', 'car'])['total']
            content = """
| Particulars                  | Gross Carrying Value | Accumulated Depreciation | Net Carrying Value |
|------------------------------|----------------------|--------------------------|--------------------|
| As at 1st April 2023 | Additions | Deletion | As at 31st March 2024 | As at 1st April 2023 | For the year | Deletion | As at 31st March 2024 | As at 31st March 2024 | As at 1st April 2023 |
|------------------------------|----------------------|--------------------------|--------------------|
| Tangible Assets              |                      |                          |                    |
| Buildings                    | 312.66 | {bldg_add} | 0 | {bldg_gcv} | 312.65 | 1478.81 | 0 | 1791.46 | {bldg_ncv} | 1.00 |
| Equipments                   | {eq_gcv} | 0 | 0 | {eq_gcv} | 0 | 0 | 0 | 0 | {eq_ncv} | {eq_ncv} |
| Furniture & Fixtures         | {fur_gcv} | 0 | 0 | {fur_gcv} | 0 | 0 | 0 | 0 | {fur_ncv} | {fur_ncv} |
| Motor Vehicle                | {veh_gcv} | 0 | 0 | {veh_gcv} | 0 | 752.98 | 0 | 752.98 | {veh_ncv} | {veh_gcv} |
""".format(bldg_add=to_lakhs(building), bldg_gcv=to_lakhs(312655 + building), bldg_ncv=to_lakhs(building), eq_gcv=to_lakhs(equipments), eq_ncv=to_lakhs(equipments), fur_gcv=to_lakhs(furniture), fur_ncv=to_lakhs(furniture), veh_gcv=to_lakhs(vehicle), veh_ncv=to_lakhs(vehicle - 752982.45))
            special_data = {
                "breakdown": {
                    "buildings": {"gross_value": 312655 + building, "net_value": building, "accumulated_depreciation": 1791462},
                    "equipments": {"gross_value": equipments, "net_value": equipments, "accumulated_depreciation": 0},
                    "furniture_fixtures": {"gross_value": furniture, "net_value": furniture, "accumulated_depreciation": 0},
                    "motor_vehicle": {"gross_value": vehicle, "net_value": vehicle - 752982.45, "accumulated_depreciation": 752982.45}
                }
            }
        
        elif note_name == '10. Long Term Loans and Advances':
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Long Term Loans and Advances | {total_lakhs} | - |
""".format(total_lakhs=to_lakhs(result['total']))
        
        elif note_name == '11. Inventories':
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Consumables                  | {total_lakhs} | - |
""".format(total_lakhs=to_lakhs(result['total']))
        
        elif note_name == '12. Trade Receivables':
            over_6m = result.get('over_6m', 0)
            content = """
| Particulars                  | 2024-03-31 | 2023-03-31 |
|------------------------------|------------|------------|
| Unsecured, considered good   |            |            |
| Outstanding for a period exceeding six months | {over_6m} | 104.65 |
| Total                        | {total_lakhs} | 1037.59 |
""".format(over_6m=to_lakhs(over_6m), total_lakhs=to_lakhs(result['total']))
            special_data = {
                "breakdown": {
                    "over_six_months": {"description": "Outstanding for a period exceeding six months", "amount": over_6m, "amount_lakhs": to_lakhs(over_6m)},
                    "total_receivables": {"description": "Total Trade Receivables", "amount": result['total'], "amount_lakhs": to_lakhs(result['total'])}
                }
            }
        
        elif note_name == '13. Cash and Bank Balances':
            cash_in_hand = calculate_note(tb_df, note_name, ['Cash-in-hand'])['total']
            bank_accounts = calculate_note(tb_df, note_name, ['Bank accounts'])['total']
            fixed_deposit = calculate_note(tb_df, note_name, ['Deposits'])['total']
            total = cash_in_hand + bank_accounts + fixed_deposit
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| **Cash and cash equivalents**|                |                |
| Balances with banks in current accounts | {ba_lakhs} | - |
| Cash in hand                 | {cih_lakhs} | - |
| **Other Bank Balances**      |                |                |
| Fixed Deposit                | {fd_lakhs} | - |
| **Total**                    | {total_lakhs} | - |
""".format(ba_lakhs=to_lakhs(bank_accounts), cih_lakhs=to_lakhs(cash_in_hand), fd_lakhs=to_lakhs(fixed_deposit), total_lakhs=to_lakhs(total))
            result['total'] = total
            special_data = {
                "breakdown": {
                    "cash_in_hand": {"description": "Cash in hand", "amount": cash_in_hand, "amount_lakhs": to_lakhs(cash_in_hand)},
                    "bank_balances": {"description": "Balances with banks in current accounts", "amount": bank_accounts, "amount_lakhs": to_lakhs(bank_accounts)},
                    "fixed_deposits": {"description": "Fixed Deposit", "amount": fixed_deposit, "amount_lakhs": to_lakhs(fixed_deposit)}
                }
            }
        
        elif note_name == '14. Short Term Loans and Advances':
            other_advances = calculate_note(tb_df, note_name, ['Loans & Advances'])['total']
            prepaid_expenses = calculate_note(tb_df, note_name, ['Prepaid Expenses'])['total']
            advance_tax = calculate_note(tb_df, note_name, ['TDS Advance Tax Paid'])['total']
            balances = calculate_note(tb_df, note_name, ['TDS Receivables'])['total']
            total = other_advances + prepaid_expenses + advance_tax + balances
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| **Unsecured, considered good**|                |                |
| Prepaid Expenses             | {pe_lakhs} | - |
| Other Advances               | {oa_lakhs} | - |
| **Other loans and advances** |                |                |
| Advance tax                  | {at_lakhs} | - |
| Balances with statutory/government authorities | {bs_lakhs} | - |
| **Total**                    | {total_lakhs} | - |
""".format(pe_lakhs=to_lakhs(prepaid_expenses), oa_lakhs=to_lakhs(other_advances), at_lakhs=to_lakhs(advance_tax), bs_lakhs=to_lakhs(balances), total_lakhs=to_lakhs(total))
            result['total'] = total
            special_data = {
                "breakdown": {
                    "prepaid_expenses": {"description": "Prepaid Expenses", "amount": prepaid_expenses, "amount_lakhs": to_lakhs(prepaid_expenses)},
                    "other_advances": {"description": "Other Advances", "amount": other_advances, "amount_lakhs": to_lakhs(other_advances)},
                    "advance_tax": {"description": "Advance tax", "amount": advance_tax, "amount_lakhs": to_lakhs(advance_tax)},
                    "statutory_balances": {"description": "Balances with statutory/government authorities", "amount": balances, "amount_lakhs": to_lakhs(balances)}
                }
            }
        
        elif note_name == '15. Other Current Assets':
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Other Current Assets         | {total_lakhs} | - |
""".format(total_lakhs=to_lakhs(result['total']))
            
        elif note_name == '16. Revenue from Operations':
            servicing_babe_export = calculate_note(tb_df, note_name, ['Servicing of BA/BE PROJECTS EXPORT'])['total']
            working_standards_export = calculate_note(tb_df, note_name, ['Working Standards - Export'])['total']
            exports = servicing_babe_export + working_standards_export
            servicing_babe_inter_state = calculate_note(tb_df, note_name, ['Servicing of BA/BE PROJECTS-Inter State'])['total']
            servicing_babe_intra_state = calculate_note(tb_df, note_name, ['Servicing of BA/BE PROJECTS-Intra State'])['total']
            servicing_ba_intra_state = calculate_note(tb_df, note_name, ['SERVICING OF BA PROJECTS-Intra State'])['total']
            servicing_clinical_intra_state = calculate_note(tb_df, note_name, ['SERVICING OF ONLY CLINICAL INTRA STATE'])['total']
            domestic = servicing_babe_inter_state + servicing_babe_intra_state + servicing_ba_intra_state + servicing_clinical_intra_state
            sales_other = calculate_note(tb_df, note_name, ['Sales', 'Gain / Loss on Sales of Fixed Assets', 'Consultancy & Service Fee', 'Income', 'Income Tax'])['total']
            total = result['total']  # Use total from calculate_note
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| **Sale of Services**         |                |                |
| Domestic                     | {dom_lakhs}    | -              |
| Exports                      | {exp_lakhs}    | -              |
| Sales and Other Income       | {sales_lakhs}  | -              |
| **Total**                    | {total_lakhs}  | -              |
""".format(dom_lakhs=to_lakhs(domestic), exp_lakhs=to_lakhs(exports), sales_lakhs=to_lakhs(sales_other), total_lakhs=to_lakhs(total))
            special_data = {
                "breakdown": {
                    "domestic_revenue": {"description": "Domestic Sales", "amount": domestic, "amount_lakhs": to_lakhs(domestic), "components": {
                        "ba_be_interstate": servicing_babe_inter_state,
                        "ba_be_intrastate": servicing_babe_intra_state,
                        "ba_intrastate": servicing_ba_intra_state,
                        "clinical_intrastate": servicing_clinical_intra_state
                    }},
                    "export_revenue": {"description": "Export Sales", "amount": exports, "amount_lakhs": to_lakhs(exports), "components": {
                        "ba_be_export": servicing_babe_export,
                        "working_standards_export": working_standards_export
                    }},
                    "sales_and_other": {"description": "Sales and Other Income", "amount": sales_other, "amount_lakhs": to_lakhs(sales_other)}
                }
            }
        
        elif note_name == '17. Other Income':
            interest_income = calculate_note(tb_df, note_name, ['Interest on FD', 'Interest on Income Tax Refund', 'Interest'])['total']
            forex_gain = calculate_note(tb_df, note_name, ['Unadjusted Forex Gain/Loss', 'Forex Gain / Loss'])['total']
            total = result['total']  # Use total from calculate_note
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Interest income              | {ii_lakhs}     | -              |
| Foreign exchange gain (Net)  | {fg_lakhs}     | -              |
| **Total**                    | {total_lakhs}  | -              |
""".format(ii_lakhs=to_lakhs(interest_income), fg_lakhs=to_lakhs(forex_gain), total_lakhs=to_lakhs(total))
            special_data = {
                "breakdown": {
                    "interest_income": {"description": "Interest income", "amount": interest_income, "amount_lakhs": to_lakhs(interest_income)},
                    "forex_gain": {"description": "Foreign exchange gain (Net)", "amount": forex_gain, "amount_lakhs": to_lakhs(forex_gain)}
                }
            }
        
        elif note_name == '18. Cost of Materials Consumed':
            opening_stock = calculate_note(tb_df, note_name, ['Opening Stock'])['total']
            purchases = calculate_note(tb_df, note_name, ['Bio Lab Consumables', 'Non GST', 'Purchase GST'])['total']
            closing_stock = calculate_note(tb_df, note_name, ['Closing Stock'])['total']
            total = opening_stock + purchases - closing_stock  # As per note structure
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Opening Stock                | {os_lakhs}     | -              |
| Add: Purchases               | {pur_lakhs}    | -              |
|                              | {subtotal_lakhs}| -             |
| Less: Closing Stock          | {cs_lakhs}     | -              |
| Cost of materials consumed   | {total_lakhs}  | -              |
""".format(os_lakhs=to_lakhs(opening_stock), pur_lakhs=to_lakhs(purchases), subtotal_lakhs=to_lakhs(opening_stock + purchases), 
           cs_lakhs=to_lakhs(closing_stock), total_lakhs=to_lakhs(total))
            special_data = {
                "breakdown": {
                    "opening_stock": {"description": "Opening Stock", "amount": opening_stock, "amount_lakhs": to_lakhs(opening_stock)},
                    "purchases": {"description": "Purchases", "amount": purchases, "amount_lakhs": to_lakhs(purchases)},
                    "closing_stock": {"description": "Closing Stock", "amount": closing_stock, "amount_lakhs": to_lakhs(closing_stock)},
                    "cost_consumed": {"description": "Cost of materials consumed", "amount": total, "amount_lakhs": to_lakhs(total)}
                }
            }
        
        elif note_name == '19. Employee Benefit Expense':
            salaries_wages_bonus = calculate_note(tb_df, note_name, ['Salary', 'Wages', 'Bonus', 'Remuneration', 'Comp Offs', 'Retainership'])['total']
            pf_esi = calculate_note(tb_df, note_name, ['Contribution to PF', 'Contribution to ESI'])['total']
            staff_welfare = calculate_note(tb_df, note_name, ['Staff Welfare Expenses', 'Employees Expenses Reimbursement'])['total']
            insurance = calculate_note(tb_df, note_name, ['Employees Group Life Insurance', 'Employees Health & Personal Accident Insurance', 
                                                         'Prepaid - Employees Group Life Insurance', 'Prepaid Insurance - Employees Health & Personal Accident'])['total']
            total = result['total']  # Use total from calculate_note
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Salaries, wages and bonus    | {swb_lakhs}    | -              |
| Contribution to PF & ESI     | {pf_esi_lakhs} | -              |
| Staff welfare expenses       | {sw_lakhs}     | -              |
| **Total**                    | {total_lakhs}  | -              |
""".format(swb_lakhs=to_lakhs(salaries_wages_bonus), pf_esi_lakhs=to_lakhs(pf_esi), sw_lakhs=to_lakhs(staff_welfare), 
           ins_lakhs=to_lakhs(insurance), total_lakhs=to_lakhs(total))
            special_data = {
                "breakdown": {
                    "salaries_wages_bonus": {"description": "Salaries, wages and bonus", "amount": salaries_wages_bonus, "amount_lakhs": to_lakhs(salaries_wages_bonus)},
                    "pf_esi": {"description": "Contribution to PF & ESI", "amount": pf_esi, "amount_lakhs": to_lakhs(pf_esi)},
                    "staff_welfare": {"description": "Staff welfare expenses", "amount": staff_welfare, "amount_lakhs": to_lakhs(staff_welfare)},
                    "insurance": {"description": "Insurance Expenses", "amount": insurance, "amount_lakhs": to_lakhs(insurance)}
                }
            }
        
        elif note_name == '20. Other Expenses':
            ba_be_noc = calculate_note(tb_df, note_name, ['BA / BE NOC Charges'])['total']
            ba_expenses = calculate_note(tb_df, note_name, ['BA Expenses'])['total']
            volunteers = calculate_note(tb_df, note_name, ['Payments to Volunteers'])['total']
            other_operating = calculate_note(tb_df, note_name, ['Other Operating Expenses'])['total']
            lab_testing = calculate_note(tb_df, note_name, ['Laboratory testing charges'])['total']
            rent = calculate_note(tb_df, note_name, ['Rent', 'Office Rent'])['total']
            rates_taxes = calculate_note(tb_df, note_name, ['Rates & Taxes'])['total']
            fees_licenses = calculate_note(tb_df, note_name, ['Fees & licenses'])['total']
            insurance = calculate_note(tb_df, note_name, ['Insurance'])['total']
            membership = calculate_note(tb_df, note_name, ['Membership & Subscription Charges'])['total']
            postage = calculate_note(tb_df, note_name, ['Postage & Communication Cost'])['total']
            printing = calculate_note(tb_df, note_name, ['Printing and Stationery'])['total']
            csr = calculate_note(tb_df, note_name, ['CSR Fund Expenses'])['total']
            telephone = calculate_note(tb_df, note_name, ['Telephone & Internet', 'Telephone Expense'])['total']
            travelling = calculate_note(tb_df, note_name, ['Travelling and Conveyance'])['total']
            translation = calculate_note(tb_df, note_name, ['Translation Charges'])['total']
            electricity = calculate_note(tb_df, note_name, ['Electricity Charges'])['total']
            security = calculate_note(tb_df, note_name, ['Security Charges', 'Security Deposit', 'Security Deposit - ESIC', 
                                                        'Security Deposits - Awfis Space Solutions Private Limited', 
                                                        'Security Deposits - Concept Classic Converge', 'Security Deposit - Hive Space'])['total']
            maintenance = calculate_note(tb_df, note_name, ['Annual Maintenance Charges', 'Laptop Accessories and Maintenance', 
                                                           'Laptop Annual Maintenance Charges'])['total']
            repairs_electrical = calculate_note(tb_df, note_name, ['Repairs and maintenance - Electrical'])['total']
            repairs_office = calculate_note(tb_df, note_name, ['Repairs and maintenance - Office'])['total']
            repairs_machinery = calculate_note(tb_df, note_name, ['Repairs and maintenance - Machinery'])['total']
            repairs_vehicles = calculate_note(tb_df, note_name, ['Repairs and maintenance - Vehicles'])['total']
            repairs_others = calculate_note(tb_df, note_name, ['Repairs and maintenance - Others'])['total']
            business_dev = calculate_note(tb_df, note_name, ['Business Development Expenses'])['total']
            professional = calculate_note(tb_df, note_name, ['Professional & Consultancy', 'Professional Fee', 
                                                            'Provision for Professional Fee', 'Professional Fee (Transfer Pricing)'])['total']
            auditors = calculate_note(tb_df, note_name, ['Payment to Auditors'])['total']
            bad_debts = calculate_note(tb_df, note_name, ['Bad Debts Written Off'])['total']
            fire_extinguishers = calculate_note(tb_df, note_name, ['Fire Extinguishers Refilling Charges'])['total']
            food_guests = calculate_note(tb_df, note_name, ['Food Expenses for Guests'])['total']
            diesel = calculate_note(tb_df, note_name, ['Diesel Expenses'])['total']
            interest_234c = calculate_note(tb_df, note_name, ['Interest Under 234 C'])['total']
            loan_processing = calculate_note(tb_df, note_name, ['Loan Processing Charges'])['total']
            sitting_fee = calculate_note(tb_df, note_name, ['Sitting Fee of Directors'])['total']
            customs_duty = calculate_note(tb_df, note_name, ['Customs Duty Payment'])['total']
            transportation = calculate_note(tb_df, note_name, ['Transportation and Unloading Charges'])['total']
            software = calculate_note(tb_df, note_name, ['Software Equipment'])['total']
            misc = calculate_note(tb_df, note_name, ['Miscellaneous expenses'])['total']
            total = result['total']  # Use total from calculate_note
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| BA / BE NOC Charges          | {ba_be_noc_lakhs} | -           |
| BA Expenses                  | {ba_exp_lakhs} | -             |
| Payments to Volunteers       | {vol_lakhs}    | -             |
| Other Operating Expenses     | {oo_lakhs}     | -             |
| Laboratory testing charges   | {lab_lakhs}    | -             |
| Rent                         | {rent_lakhs}   | -             |
| Rates & Taxes                | {rt_lakhs}     | -             |
| Fees & licenses              | {fl_lakhs}     | -             |
| Insurance                    | {ins_lakhs}    | -             |
| Membership & Subscription Charges | {mem_lakhs}| -             |
| Postage & Communication Cost | {post_lakhs}   | -             |
| Printing and stationery      | {print_lakhs}  | -             |
| CSR Fund Expenses            | {csr_lakhs}    | -             |
| Telephone & Internet         | {tel_lakhs}    | -             |
| Travelling and Conveyance    | {trav_lakhs}   | -             |
| Translation Charges          | {tl_lakhs}     | -             |
| Electricity Charges          | {elec_lakhs}   | -             |
| Security Charges             | {sec_lakhs}    | -             |
| Annual Maintenance Charges   | {maint_lakhs}  | -             |
| Repairs and maintenance      |                |                |
| - Electrical                 | {relec_lakhs}  | -             |
| - Office                     | {roff_lakhs}   | -             |
| - Machinery                  | {rmach_lakhs}  | -             |
| - Vehicles                   | {rveh_lakhs}   | -             |
| - Others                     | {roth_lakhs}   | -             |
| Business Development Expenses| {bd_lakhs}     | -             |
| Professional & Consultancy Fees | {prof_lakhs}| -             |
| Payment to Auditors          | {aud_lakhs}    | -             |
| Bad Debts Written Off        | {bd_debts_lakhs}| -            |
| Fire Extinguishers Refilling Charges | {fire_lakhs} | -          |
| Food Expenses for Guests     | {food_lakhs}   | -             |
| Diesel Expenses              | {diesel_lakhs} | -             |
| Interest Under 234 C Fy 2021-22 | {int234c_lakhs} | -         |
| Loan Processing Charges      | {loan_lakhs}   | -             |
| Sitting Fee of Directors     | {sit_lakhs}    | -             |
| Customs Duty Payment         | {cust_lakhs}   | -             |
| Transportation and Unloading Charges | {trans_lakhs} | -        |
| Software Equipment           | {soft_lakhs}   | -             |
| Miscellaneous expenses       | {misc_lakhs}   | -             |
| **Total**                    | {total_lakhs}  | -             |
""".format(ba_be_noc_lakhs=to_lakhs(ba_be_noc), ba_exp_lakhs=to_lakhs(ba_expenses), vol_lakhs=to_lakhs(volunteers), 
           oo_lakhs=to_lakhs(other_operating), lab_lakhs=to_lakhs(lab_testing), rent_lakhs=to_lakhs(rent), 
           rt_lakhs=to_lakhs(rates_taxes), fl_lakhs=to_lakhs(fees_licenses), ins_lakhs=to_lakhs(insurance), 
           mem_lakhs=to_lakhs(membership), post_lakhs=to_lakhs(postage), print_lakhs=to_lakhs(printing), 
           csr_lakhs=to_lakhs(csr), tel_lakhs=to_lakhs(telephone), trav_lakhs=to_lakhs(travelling), 
           tl_lakhs=to_lakhs(translation), elec_lakhs=to_lakhs(electricity), sec_lakhs=to_lakhs(security), 
           maint_lakhs=to_lakhs(maintenance), relec_lakhs=to_lakhs(repairs_electrical), roff_lakhs=to_lakhs(repairs_office), 
           rmach_lakhs=to_lakhs(repairs_machinery), rveh_lakhs=to_lakhs(repairs_vehicles), roth_lakhs=to_lakhs(repairs_others), 
           bd_lakhs=to_lakhs(business_dev), prof_lakhs=to_lakhs(professional), aud_lakhs=to_lakhs(auditors), 
           bd_debts_lakhs=to_lakhs(bad_debts), fire_lakhs=to_lakhs(fire_extinguishers), food_lakhs=to_lakhs(food_guests), 
           diesel_lakhs=to_lakhs(diesel), int234c_lakhs=to_lakhs(interest_234c), loan_lakhs=to_lakhs(loan_processing), 
           sit_lakhs=to_lakhs(sitting_fee), cust_lakhs=to_lakhs(customs_duty), trans_lakhs=to_lakhs(transportation), 
           soft_lakhs=to_lakhs(software), misc_lakhs=to_lakhs(misc), total_lakhs=to_lakhs(total))
            special_data = {
                "breakdown": {
                    "ba_be_noc": {"description": "BA / BE NOC Charges", "amount": ba_be_noc, "amount_lakhs": to_lakhs(ba_be_noc)},
                    "ba_expenses": {"description": "BA Expenses", "amount": ba_expenses, "amount_lakhs": to_lakhs(ba_expenses)},
                    "volunteers": {"description": "Payments to Volunteers", "amount": volunteers, "amount_lakhs": to_lakhs(volunteers)},
                    "other_operating": {"description": "Other Operating Expenses", "amount": other_operating, "amount_lakhs": to_lakhs(other_operating)},
                    "lab_testing": {"description": "Laboratory testing charges", "amount": lab_testing, "amount_lakhs": to_lakhs(lab_testing)},
                    "rent": {"description": "Rent", "amount": rent, "amount_lakhs": to_lakhs(rent)},
                    "rates_taxes": {"description": "Rates & Taxes", "amount": rates_taxes, "amount_lakhs": to_lakhs(rates_taxes)},
                    "fees_licenses": {"description": "Fees & licenses", "amount": fees_licenses, "amount_lakhs": to_lakhs(fees_licenses)},
                    "insurance": {"description": "Insurance", "amount": insurance, "amount_lakhs": to_lakhs(insurance)},
                    "membership": {"description": "Membership & Subscription Charges", "amount": membership, "amount_lakhs": to_lakhs(membership)},
                    "postage": {"description": "Postage & Communication Cost", "amount": postage, "amount_lakhs": to_lakhs(postage)},
                    "printing": {"description": "Printing and stationery", "amount": printing, "amount_lakhs": to_lakhs(printing)},
                    "csr": {"description": "CSR Fund Expenses", "amount": csr, "amount_lakhs": to_lakhs(csr)},
                    "telephone": {"description": "Telephone & Internet", "amount": telephone, "amount_lakhs": to_lakhs(telephone)},
                    "travelling": {"description": "Travelling and Conveyance", "amount": travelling, "amount_lakhs": to_lakhs(travelling)},
                    "translation": {"description": "Translation Charges", "amount": translation, "amount_lakhs": to_lakhs(translation)},
                    "electricity": {"description": "Electricity Charges", "amount": electricity, "amount_lakhs": to_lakhs(electricity)},
                    "security": {"description": "Security Charges", "amount": security, "amount_lakhs": to_lakhs(security)},
                    "maintenance": {"description": "Annual Maintenance Charges", "amount": maintenance, "amount_lakhs": to_lakhs(maintenance)},
                    "repairs_electrical": {"description": "Repairs and maintenance - Electrical", "amount": repairs_electrical, "amount_lakhs": to_lakhs(repairs_electrical)},
                    "repairs_office": {"description": "Repairs and maintenance - Office", "amount": repairs_office, "amount_lakhs": to_lakhs(repairs_office)},
                    "repairs_machinery": {"description": "Repairs and maintenance - Machinery", "amount": repairs_machinery, "amount_lakhs": to_lakhs(repairs_machinery)},
                    "repairs_vehicles": {"description": "Repairs and maintenance - Vehicles", "amount": repairs_vehicles, "amount_lakhs": to_lakhs(repairs_vehicles)},
                    "repairs_others": {"description": "Repairs and maintenance - Others", "amount": repairs_others, "amount_lakhs": to_lakhs(repairs_others)},
                    "business_dev": {"description": "Business Development Expenses", "amount": business_dev, "amount_lakhs": to_lakhs(business_dev)},
                    "professional": {"description": "Professional & Consultancy Fees", "amount": professional, "amount_lakhs": to_lakhs(professional)},
                    "auditors": {"description": "Payment to Auditors", "amount": auditors, "amount_lakhs": to_lakhs(auditors)},
                    "bad_debts": {"description": "Bad Debts Written Off", "amount": bad_debts, "amount_lakhs": to_lakhs(bad_debts)},
                    "fire_extinguishers": {"description": "Fire Extinguishers Refilling Charges", "amount": fire_extinguishers, "amount_lakhs": to_lakhs(fire_extinguishers)},
                    "food_guests": {"description": "Food Expenses for Guests", "amount": food_guests, "amount_lakhs": to_lakhs(food_guests)},
                    "diesel": {"description": "Diesel Expenses", "amount": diesel, "amount_lakhs": to_lakhs(diesel)},
                    "interest_234c": {"description": "Interest Under 234 C Fy 2021-22", "amount": interest_234c, "amount_lakhs": to_lakhs(interest_234c)},
                    "loan_processing": {"description": "Loan Processing Charges", "amount": loan_processing, "amount_lakhs": to_lakhs(loan_processing)},
                    "sitting_fee": {"description": "Sitting Fee of Directors", "amount": sitting_fee, "amount_lakhs": to_lakhs(sitting_fee)},
                    "customs_duty": {"description": "Customs Duty Payment", "amount": customs_duty, "amount_lakhs": to_lakhs(customs_duty)},
                    "transportation": {"description": "Transportation and Unloading Charges", "amount": transportation, "amount_lakhs": to_lakhs(transportation)},
                    "software": {"description": "Software Equipment", "amount": software, "amount_lakhs": to_lakhs(software)},
                    "misc": {"description": "Miscellaneous expenses", "amount": misc, "amount_lakhs": to_lakhs(misc)}
                }
            }
            content += "\n* Fees is net of GST which is taken as input tax credit."
        
        elif note_name == '21. Depreciation and Amortisation Expense':
            depreciation = calculate_note(tb_df, note_name, ['Depreciation', 'Accumulated Depreciation', 'Depreciation And Amortisation'])['total']
            amortization = calculate_note(tb_df, note_name, ['Amortization'])['total']
            total = result['total']  # Use total from calculate_note
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Depreciation and amortisation  | {total_lakhs}  | -              |
| **Total**                    | {total_lakhs}  | -              |
""".format(total_lakhs=to_lakhs(total))
            special_data = {
                "breakdown": {
                    "depreciation": {"description": "Depreciation", "amount": depreciation, "amount_lakhs": to_lakhs(depreciation)},
                    "amortization": {"description": "Amortization", "amount": amortization, "amount_lakhs": to_lakhs(amortization)}
                }
            }
        
        elif note_name == '22. Loss on Sale of Assets & Investments':
            short_term_loss = calculate_note(tb_df, note_name, ['Short Term Loss on Sale of Investments'])['total']
            long_term_loss = calculate_note(tb_df, note_name, ['Long term loss on sale of investments'])['total']
            fixed_assets_loss = calculate_note(tb_df, note_name, ['Loss on Sale of Fixed Assets'])['total']
            total = result['total']  # Use total from calculate_note
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Short Term Loss on Sale of Investments (Non Derivative Loss) | {stl_lakhs} | - |
| Long term loss on sale of investments | {ltl_lakhs} | -             |
| Loss on Sale of Fixed Assets | {fal_lakhs}   | -              |
| **Total**                    | {total_lakhs} | -              |
""".format(stl_lakhs=to_lakhs(short_term_loss), ltl_lakhs=to_lakhs(long_term_loss), fal_lakhs=to_lakhs(fixed_assets_loss), total_lakhs=to_lakhs(total))
            special_data = {
                "breakdown": {
                    "short_term_loss": {"description": "Short Term Loss on Sale of Investments", "amount": short_term_loss, "amount_lakhs": to_lakhs(short_term_loss)},
                    "long_term_loss": {"description": "Long term loss on sale of investments", "amount": long_term_loss, "amount_lakhs": to_lakhs(long_term_loss)},
                    "fixed_assets_loss": {"description": "Loss on Sale of Fixed Assets", "amount": fixed_assets_loss, "amount_lakhs": to_lakhs(fixed_assets_loss)}
                }
            }
        
        elif note_name == '23. Finance Costs':
            bank_finance = calculate_note(tb_df, note_name, ['Bank Charges', 'Finance Charges', 'Interest', 'Interest and penalty', 'Interest on TDS'])['total']
            loan_processing = calculate_note(tb_df, note_name, ['Loan Processing'])['total']
            total = result['total']  # Use total from calculate_note
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Bank and Finance Charges       | {bf_lakhs}     | -              |
| **Total**                    | {total_lakhs}  | -              |
""".format(bf_lakhs=to_lakhs(bank_finance), lp_lakhs=to_lakhs(loan_processing), total_lakhs=to_lakhs(total))
            special_data = {
                "breakdown": {
                    "bank_finance": {"description": "Bank & Finance Charges", "amount": bank_finance, "amount_lakhs": to_lakhs(bank_finance)},
                    "loan_processing": {"description": "Loan Processing Charges", "amount": loan_processing, "amount_lakhs": to_lakhs(loan_processing)}
                }
            }
        
        elif note_name == '24. Payment to Auditor':
            audit_fee = calculate_note(tb_df, note_name, ['Audit Fee', 'Payment to Auditors'])['total']
            tax_audit = calculate_note(tb_df, note_name, ['Tax Audit', 'Certification Fees'])['total']
            total = result['total']  # Use total from calculate_note
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| - For Audit fee             | {audit_lakhs}  | -              |
| - For Tax Audit / Certification Fees | {tax_lakhs} | -         |
| **Total**                    | {total_lakhs}  | -              |
""".format(audit_lakhs=to_lakhs(audit_fee), tax_lakhs=to_lakhs(tax_audit), total_lakhs=to_lakhs(total))
            special_data = {
                "breakdown": {
                    "audit_fee": {"description": "For Audit fee", "amount": audit_fee, "amount_lakhs": to_lakhs(audit_fee)},
                    "tax_audit": {"description": "For Tax Audit / Certification Fees", "amount": tax_audit, "amount_lakhs": to_lakhs(tax_audit)}
                }
            }
        
        elif note_name == '25. Earnings in Foreign Currency':
            export_income = calculate_note(tb_df, note_name, ['Income from export of services', 'Servicing of BA/BE PROJECTS EXPORT', 'Working Standards - Export'])['total']
            total = result['total']  # Use total from calculate_note
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| **Inflow :**                 |                |                |
| Income from export of services | {exp_lakhs}  | -              |
| **Total**                    | {total_lakhs}  | -              |
""".format(exp_lakhs=to_lakhs(export_income), total_lakhs=to_lakhs(total))
            special_data = {
                "breakdown": {
                    "export_income": {"description": "Income from export of services", "amount": export_income, "amount_lakhs": to_lakhs(export_income)}
                }
            }
        
        elif note_name == '26. Particulars of Un-hedged Foreign Currency Exposure':
            export_income = calculate_note(tb_df, note_name, ['Income from export of services', 'Servicing of BA/BE PROJECTS EXPORT', 'Working Standards - Export'])['total']
            total = result['total']  # Use total from calculate_note
            content = """
"(i) There is no derivate contract outstanding as at the Balance Sheet date.
(ii) Particulars of un-hedged foreign currency exposure as at the Balance Sheet date"

| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| **Inflow :**                 |                |                |
| Income from export of services | {exp_lakhs}  | -              |
| **Total**                    | {total_lakhs}  | -              |
""".format(exp_lakhs=to_lakhs(export_income), total_lakhs=to_lakhs(total))
            special_data = {
                "breakdown": {
                    "export_income": {"description": "Income from export of services", "amount": export_income, "amount_lakhs": to_lakhs(export_income)}
                }
            }
        elif note_name == '28. Earnings per Share':
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Earnings per Share           | {total_lakhs} | - |
""".format(total_lakhs=to_lakhs(result['total']))
        
        elif note_name == '29. Related Party Disclosures':
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| Related Party Disclosures    | {total_lakhs} | - |
""".format(total_lakhs=to_lakhs(result['total']))
        
        elif note_name == '30. Financial Ratios':
            current_assets = sum(calculate_note(tb_df, note_name, [kw])['total'] for kw in ['Stock', 'Cash', 'Bank', 'Receivables', 'Prepaid'])
            current_liabilities = sum(calculate_note(tb_df, note_name, [kw])['total'] for kw in ['Creditors', 'Payable'])
            current_ratio = current_assets / abs(current_liabilities) if current_liabilities != 0 else 0
            content = """
| Particulars                  | 2024-03-31 | 2023-03-31 |
|------------------------------|------------|------------|
| Current Ratio                | {cr} | 2.52 |
| Current Assets               | {ca_lakhs} | - |
| Current Liabilities          | {cl_lakhs} | - |
""".format(cr=round(current_ratio, 2), ca_lakhs=to_lakhs(current_assets), cl_lakhs=to_lakhs(abs(current_liabilities)))
            special_data = {
                "breakdown": {
                    "current_assets": {"description": "Current Assets", "amount": current_assets, "amount_lakhs": to_lakhs(current_assets)},
                    "current_liabilities": {"description": "Current Liabilities", "amount": abs(current_liabilities), "amount_lakhs": to_lakhs(abs(current_liabilities))},
                    "current_ratio": {"description": "Current Ratio", "value": round(current_ratio, 2)}
                }
            }
        
        else:
            content = """
| Particulars                  | March 31, 2024 | March 31, 2023 |
|------------------------------|----------------|----------------|
| {title}                      | {total_lakhs} | - |
""".format(title=note_name.split('.', 1)[1].strip() if '.' in note_name else note_name, total_lakhs=to_lakhs(result['total']))


        detailed_note = create_detailed_note_structure(note_name, result, content, special_data)
        notes.append(detailed_note)
    
    return {
        "metadata": {
            "generated_on": datetime.now().isoformat(),
            "financial_year": settings.financial_year,
            "company_name": settings.company_name,
            "total_notes": len(notes)
        },
        "notes": notes
    }

def process_json(json_path: str) -> None:
    """
    Loads the JSON file, processes it, and writes the output as in your main().
    """
    if not os.path.exists(json_path):
        logger.error(f"{json_path} not found!")
        raise FileNotFoundError(f"{json_path} not found!")
    with open(json_path, "r", encoding="utf-8") as f:
        parsed_data = json.load(f)
    if isinstance(parsed_data, list):
        tb_df = pd.DataFrame(parsed_data)
    else:
        tb_records = parsed_data.get("trial_balance", parsed_data)
        tb_df = pd.DataFrame(tb_records)
    if 'amount' in tb_df.columns:
        tb_df['amount'] = tb_df['amount'].apply(clean_value)
    notes_data = generate_notes(tb_df)
    os.makedirs(os.path.dirname(settings.output_json), exist_ok=True)
    with open(settings.output_json, "w", encoding="utf-8") as f:
        json.dump(notes_data, f, ensure_ascii=False, indent=2)
    logger.info(f"Notes output written to {settings.output_json}")

def main() -> None:
    """Main execution function."""
    try:
        json_file = settings.trial_balance_json
        if not os.path.exists(json_file):
            logger.error(f"{json_file} not found! Please run test_mapping.py first.")
            raise FileNotFoundError(f"{json_file} not found! Please run test_mapping.py first.")
        logger.info(f"Loading data from {json_file}...")
        with open(json_file, "r", encoding="utf-8") as f:
            parsed_data = json.load(f)
        if isinstance(parsed_data, list):
            tb_df = pd.DataFrame(parsed_data)
        else:
            tb_records = parsed_data.get("trial_balance", parsed_data)
            tb_df = pd.DataFrame(tb_records)
        logger.info(f"Loaded {len(tb_df)} records from trial balance")
        logger.info(f"Columns available: {tb_df.columns.tolist()}")
        if 'account_name' not in tb_df.columns or 'amount' not in tb_df.columns:
            logger.error("JSON must have 'account_name' and 'amount' columns")
            raise ValueError("JSON must have 'account_name' and 'amount' columns")
        tb_df['amount'] = tb_df['amount'].apply(clean_value)
        notes_data = generate_notes(tb_df)
        os.makedirs(os.path.dirname(settings.output_md), exist_ok=True)
        output_md = "# Notes to Financial Statements for the Year Ended March 31, 2024\n\n"
        logger.info(f"Generated {len(notes_data['notes'])} notes")
        for note in notes_data['notes']:
            output_md += f"{note['markdown_content']}\n"
            if note['total_amount'] != 0:
                logger.info(f"{note['full_title']}: ₹{note['total_amount']:,.2f} ({note['matched_accounts_count']} accounts)")
            else:
                logger.warning(f"{note['full_title']}: No matching accounts found")
        with open(settings.output_md, "w", encoding="utf-8") as f:
            f.write(output_md)
        with open(settings.output_json, "w", encoding="utf-8") as f:
            json.dump(notes_data, f, ensure_ascii=False, indent=2)
        logger.info("Notes generated successfully!")
        logger.info(f"Markdown: {settings.output_md}")
        logger.info(f"JSON: {settings.output_json}")
    except Exception as e:
        logger.error(f"Error: {str(e)}")
        if 'tb_df' in locals():
            logger.info("Sample trial balance data:")
            logger.info(tb_df.head().to_string())

if __name__ == "__main__":
    main()