"""LLM prompt builder for AI-suggested useful life from invoice description.

Output contract:
{
  "useful_life_years": <int>,
  "depreciation_method": "straight_line" | "declining_balance" | "units_of_production",
  "rationale": "<short explanation>",
  "confidence": <float 0-1>
}
"""


SYSTEM_PROMPT = """You are an experienced accountant. Given an invoice line
description for a fixed asset, suggest the appropriate useful life in years
and depreciation method based on common accounting standards (IFRS / GAAP / CRA).

Respond ONLY with valid JSON of this exact shape:
{
  "useful_life_years": <integer>,
  "depreciation_method": "straight_line" | "declining_balance" | "units_of_production",
  "rationale": "<one or two sentence explanation>",
  "confidence": <float between 0 and 1>
}

Common useful-life conventions:
- Furniture: 7 years, straight-line
- Office equipment: 5 years, straight-line
- Computers: 3-4 years, straight-line or declining
- Vehicles: 5 years, declining-balance (CRA Class 10 30%)
- Buildings: 25-40 years, straight-line
- Manufacturing equipment: 10-15 years, units of production if measurable
- Software (licenses): 3-5 years, straight-line
- Leasehold improvements: lesser of lease term or useful life

Do NOT include markdown code fences. Do NOT include any prose outside the JSON."""


def build_prompt(*, description: str, amount: float = None,
                 partner_name: str = None) -> tuple[str, str]:
    """Return (system, user) prompt tuple."""
    parts = [f"INVOICE LINE: {description}"]
    if amount is not None:
        parts.append(f"AMOUNT: ${amount:,.2f}")
    if partner_name:
        parts.append(f"VENDOR: {partner_name}")
    parts.append("")
    parts.append("Suggest the useful life and depreciation method per the system prompt.")
    return (SYSTEM_PROMPT, "\n".join(parts))