generated from coulomb/repo-seed
tegwick
0a38def5a5
Add file-based Bubble, Stripe, and OpenRouter importers; usage attribution, cost allocation, pricing simulator, credit wallets, and recommendations in the dashboard API. Document whynot-design UI workflow and archive the finished workplan with all ten tasks marked done.
70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
from __future__ import annotations
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from decimal import Decimal, ROUND_HALF_UP
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from .models import EconomicsSnapshot, PricingModel
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TWOPLACES = Decimal("0.01")
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OVERAGE_RATE = Decimal("0.002") # EUR per token above allowance (observatory estimate)
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def _money(value: Decimal) -> Decimal:
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return value.quantize(TWOPLACES, rounding=ROUND_HALF_UP)
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def _simulate_model(
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model: PricingModel,
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snapshot: EconomicsSnapshot,
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ai_cost_per_member: Decimal,
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included_tokens: int = 100_000,
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actual_tokens: int = 120_000,
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) -> dict:
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members = snapshot.active_members or 1
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subscription_revenue = model.access_fee_amount * members
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overage_revenue = Decimal("0")
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if model.model_type == "hybrid_subscription_usage" and actual_tokens > included_tokens:
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overage_tokens = actual_tokens - included_tokens
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overage_revenue = OVERAGE_RATE * overage_tokens * members
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gross_revenue = subscription_revenue + overage_revenue
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platform_cost = snapshot.monthly_total_platform_cost + (ai_cost_per_member * members)
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margin = gross_revenue - platform_cost
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margin_pct = (margin / gross_revenue * Decimal("100")) if gross_revenue else Decimal("0")
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return {
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"model_id": model.id,
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"model_name": model.name,
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"model_type": model.model_type,
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"status": model.status,
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"access_fee_eur": model.access_fee_amount,
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"projected_revenue_eur": _money(gross_revenue),
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"projected_overage_eur": _money(overage_revenue),
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"projected_platform_cost_eur": _money(platform_cost),
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"projected_margin_eur": _money(margin),
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"projected_margin_pct": _money(margin_pct),
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"assumed_tokens_per_member": actual_tokens,
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"included_tokens": included_tokens if model.model_type != "flat_subscription" else None,
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}
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def build_pricing_simulations(
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snapshot: EconomicsSnapshot,
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models: list[PricingModel],
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ai_cost_per_member: Decimal,
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) -> dict:
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scenarios = [
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_simulate_model(model, snapshot, ai_cost_per_member)
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for model in models
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if model.status in ("active", "candidate")
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]
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active = next((item for item in scenarios if item["status"] == "active"), scenarios[0])
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best_margin = max(scenarios, key=lambda item: item["projected_margin_eur"])
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return {
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"period": snapshot.period,
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"currency": snapshot.currency,
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"active_scenario_id": active["model_id"],
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"best_margin_scenario_id": best_margin["model_id"],
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"scenarios": scenarios,
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"notes": "Projections hold member count and infrastructure cost constant; overage uses observatory token estimate.",
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} |