tui: show the currently-served model in the status panel ...

The status panel's Model line was fed the global ollama_default_model, not the
session/profile model, and the server never told the client which model
actually served a call. Now:

- Backends stamp the resolved model onto LLMChunk (first chunk) / LLMResponse.
  The fallback backend reports the model that survived its server+model
  priority list (may differ from the profile's first choice).
- New ModelInfo event ({"type":"model_info","model":...}) emitted once per
  turn from agent._consume_stream, re-emitted only when the model changes
  across iterations. Additive WS event — old clients ignore it.
- TUI: attach_session/switch fetch the profile's configured model (first of
  profile.model) via api.get_profile_model so the panel shows a value before
  the first request; model_info then refines it to the actually-served model.
  Not forwarded to the chat panel. raw CLI prints "[model] ...".

Co-Authored-By: Claude <noreply@anthropic.com>

Step 5-7: Extract async generators from run_stream, unify run() as wrapper ...

- _compression_events_preturn / _compression_events_midturn
- _consume_stream (uses StreamState)
- _execute_tools_with_sink
- run() is now a thin wrapper around run_stream() collecting StreamEnd
- Remove dead imports (json, LLMChunk)
- Mark god-object decomposition complete in architecture_weak_spots.md

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Step 3: Extract AntiStallMonitor from run_stream() ...

- Create navi/core/anti_stall.py with AntiStallMonitor class
- Encapsulates stall detection (todo progress + repeated tool calls)
- Encapsulates adaptive re-plan (failed todo step detection)
- Provides init() / pre_turn() / post_turn() two-phase interface
- Remove ~50 lines of stall/replan logic from agent.py run_stream()
- Remove _todo_status_snapshot and _todo_failed_steps helpers from agent.py
- Update AgentTurnContext: remove stall fields (now live in AntiStallMonitor)
- Add 13 unit tests for pre_turn and post_turn behavior

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Step 2: Extract AgentTurnContext dataclass from run_stream() ...

Move 10 turn-level local variables from run_stream() into AgentTurnContext:
- turn_start, tool_call_count, turn_tokens, subagent_tokens
- stall_no_todo, stall_repeat_tools, prev_tool_sigs
- known_failed, replan_msg, injected_fact_ids

This makes run_stream() readable and prepares the ground for
AntiStallMonitor (Step 3) which will consume this context.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>