import re

# Pricing per 1M tokens in USD
MODEL_PRICING = [
    (r"gemini-2\.5-flash-lite", {"input_per_mtok": 0.075, "output_per_mtok": 0.30}),
    (r"gemini-2\.5-flash", {"input_per_mtok": 0.15, "output_per_mtok": 0.60}),
    (r"gemini-3-flash-preview", {"input_per_mtok": 0.15, "output_per_mtok": 0.60}),
    (r"gemini-3\.1-pro-preview", {"input_per_mtok": 3.50, "output_per_mtok": 10.50}),
    (r"claude-.*-sonnet", {"input_per_mtok": 3.0, "output_per_mtok": 15.0}),
    (r"claude-.*-opus", {"input_per_mtok": 15.0, "output_per_mtok": 75.0}),
    (r"deepseek-v3", {"input_per_mtok": 0.27, "output_per_mtok": 1.10}),
]

def estimate_cost(model: str, input_tokens: int, output_tokens: int) -> float:
    """
    Estimate the cost of a model call based on input and output tokens.
    Returns the total cost in USD.
    """
    if not model:
        return 0.0
        
    for pattern, rates in MODEL_PRICING:
        if re.search(pattern, model, re.IGNORECASE):
            input_cost = (input_tokens / 1_000_000) * rates["input_per_mtok"]
            output_cost = (output_tokens / 1_000_000) * rates["output_per_mtok"]
            return input_cost + output_cost
            
    return 0.0