Specification: Event-Driven API Metrics Updates

Overview

Refactor the API metrics update mechanism to be event-driven. Currently, the UI likely polls or recalculates metrics on every frame. This track will implement a signal/event system where ai_client.py broadcasts updates only when significant API activities (requests, responses, tool calls, or stream chunks) occur.

Functional Requirements

Event System: Implement a robust event/signal mechanism (e.g., using a queue or a simple observer pattern) to communicate API lifecycle events.
Client Instrumentation: Update ai_client.py to emit events at key points:
- Request Start: When a call is sent to the provider.
- Response Received: When a full or final response is received.
- Tool Execution: When a tool call is processed or a result is returned.
- Stream Update: When a chunk of a streaming response is processed.
UI Listener: Update the GUI components (in gui.py or associated panels) to subscribe to these events and update metrics displays only when notified.
Decoupling: Remove any metrics calculation or display logic that is triggered by the UI's main graphical update loop (per-frame).

Non-Functional Requirements

Efficiency: Significant reduction in UI main thread CPU usage related to metrics.
Integrity: Maintain 100% accuracy of token counts and usage data.
Responsiveness: Metrics should update immediately following the corresponding API event.

Acceptance Criteria

UI metrics for token usage, costs, and session state do NOT recalculate on every frame (can be verified by adding logging to the recalculation logic).
Metrics update precisely when API calls are made or responses are received.
Automated tests confirm that events are emitted correctly by the ai_client.
The application remains stable and metrics accuracy is verified against the existing polling implementation.

Out of Scope

Adding new metrics or visual components.
Refactoring the core AI logic beyond the event/metrics hook.

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