"""Pricing table and cost per call for the cost page. The backend imports it.

Rates are USD per million tokens. Thinking tokens bill at the output rate. A model
with "long_threshold" switches both rates to its *_long rates when a call's input
tokens go above that threshold.

Replace the table with the rates of the provider and models the project really uses,
copied from the provider's pricing page, and update PRICING_SOURCE and PRICING_AS_OF.
Never guess a rate: a model missing here is an error, never a cost of zero.
"""

PRICING_SOURCE = "https://ai.google.dev/gemini-api/docs/pricing"
PRICING_AS_OF = "2026-08-11"

PRICING = {
    "gemini-2.5-pro": {"input": 1.25, "output": 10.00,
                       "long_threshold": 200_000, "input_long": 2.50, "output_long": 15.00},
    "gemini-2.5-flash": {"input": 0.30, "output": 2.50},
    "gemini-2.5-flash-lite": {"input": 0.10, "output": 0.40},
}


def cost_for_call(model, input_tokens, output_tokens, thinking_tokens=0):
    """USD for one call. Raises ValueError, naming the known models, for a model not in PRICING."""
    if model not in PRICING:
        raise ValueError(f"No pricing for model '{model}'. Add it to PRICING from {PRICING_SOURCE}. "
                         f"Known models: {', '.join(PRICING)}")
    p = PRICING[model]
    in_rate, out_rate = p["input"], p["output"]
    if "long_threshold" in p and input_tokens > p["long_threshold"]:
        in_rate, out_rate = p["input_long"], p["output_long"]
    return (input_tokens * in_rate + (output_tokens + thinking_tokens) * out_rate) / 1_000_000