Show an estimated AI‑credit cost for the next message on the usage gauge

### Feature summary

_No response_

### What problem are you trying to solve?

Cost per turn varies depending on the selected model, the reasoning effort, how much context is loaded, and how the assistant is being run (e.g. a single interactive reply vs. a longer autonomous run). Today users only learn the cost *after* spending it, which makes it hard to make informed choices (switch to a cheaper model, lower reasoning effort, trim context, etc.) before sending.

### Proposed solution

Add a lightweight **"Next message" estimate**: a single, compact prediction of what the upcoming turn will roughly cost, learned only from the user's own recent usage (on‑device, no server calls or extra data collection). Changing the model, reasoning effort, or run mode should visibly move the number.

**UX**
- Visualized in a usage popover, e.g. **"Next message — ~X credits (est.)"**, on a single line alongside "Session" spend.
- Framing: it's an estimate of the *typical* next turn, not a guarantee.

**How the estimate is built**
1. **Learn a typical cost ("anchor") at several granularities.** Maintain a smoothed, geometric (log‑space) moving average of realized per‑turn cost so a few unusually large or small turns don't dominate. Track and blend it at a few levels:
- **Per‑configuration** — keyed by the cost‑relevant choices the user controls: model, reasoning effort, context size tier, and run mode. This is what makes the estimate react when the user switches any of those.
- **Per‑session** — captures the "weight" of the current conversation (a heavy session tends to keep being heavy), ramped in as the session accumulates turns.
- **Global** — a cross‑session fallback used before a given configuration has any history.

2. **Cold start.** Before any history exists, fall back to a context‑proportional approach.

3. **Self‑calibrate.** After each turn, compare what actually happened to what was predicted and fold the realized cost back into the averages, so the estimate improves over time and adapts to the user's habits.

### Workflow impact

_No response_

### Installation context

_No response_

### Additional context

_No response_

まず、使用量ポップオーバーと既存のターンごとのコストデータを見つけます。モデル、推論の強度、コンテキスト、実行モードがどのように表現されているかを追跡し、次にセッションの使用履歴とグローバルな使用履歴をどこで読み取り、更新できるかを確認します。完了条件は、ポップオーバーに次のメッセージの見積もりが明確に示され、それがこれらの選択に応じて変化し、サーバー呼び出しなしで後続のターンからキャリブレーションされることです。

索引モデルが issue の本文から書いたものです。