"""Pricer agent: suggests fair pricing for handmade craft items."""

from jinja2 import Template

from agents.llm import LLMClient
from agents.models import AgentTrace, CatalogerOutput, PricerOutput

SYSTEM_PROMPT = (
    "You are a pricing expert for handmade crafts sold on Etsy, Instagram, "
    "and at craft fairs.\n\n"
    "PRICING RULES (follow strictly):\n"
    "1. The price MUST ALWAYS be HIGHER than the material cost. Never suggest "
    "a price below material cost.\n"
    "2. Labor rate: $15-25/hour for moderate work, $25-40/hour for complex work.\n"
    "3. Formula: price = material_cost + (hours * labor_rate) + profit_margin.\n"
    "4. Profit margin: add 20-40% on top.\n"
    "5. If material_cost + labor exceeds $100, the price must reflect that.\n\n"
    "Always respond with valid JSON."
)

USER_TEMPLATE = Template(
    "Suggest a fair price range for this {{ craft_type }} item:\n\n"
    "Category: {{ catalog.category }} / {{ catalog.sub_category }}\n"
    "Materials: {{ catalog.materials | join(', ') }}\n"
    "Size: {{ catalog.estimated_size }}\n"
    "Complexity: {{ catalog.complexity }}\n"
    "{% if material_cost %}Material cost: ${{ material_cost }}{% endif %}\n"
    "{% if time_hours %}Time spent: {{ time_hours }} hours{% endif %}\n"
    "{% if material_cost and time_hours %}\n"
    "MINIMUM price calculation:\n"
    "- Materials: ${{ material_cost }}\n"
    "- Labor ({{ time_hours }}h x $20/hr): ${{ (time_hours * 20) | int }}\n"
    "- Subtotal: ${{ (material_cost + time_hours * 20) | int }}\n"
    "- The suggested_price_min MUST be at least ${{ (material_cost + time_hours * 20) | int }}\n"
    "{% endif %}\n"
    "Provide suggested_price_min, suggested_price_max, reasoning, and cost_breakdown."
)


async def run(
    llm: LLMClient,
    craft_type: str,
    catalog: CatalogerOutput,
    material_cost: float | None = None,
    time_hours: float | None = None,
) -> tuple[PricerOutput, AgentTrace]:
    prompt = USER_TEMPLATE.render(
        craft_type=craft_type,
        catalog=catalog,
        material_cost=material_cost,
        time_hours=time_hours,
    )
    result, duration_ms = await llm.agenerate(
        system=SYSTEM_PROMPT,
        prompt=prompt,
        output_schema=PricerOutput,
    )
    trace = AgentTrace(
        agent_name="pricer",
        input_text=prompt,
        output_data=result.model_dump(),
        duration_ms=duration_ms,
        model_id=llm.model_id,
    )
    return result, trace